GCDkit Manual

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Package ‘GCDkit’
February 10, 2016
Version 4.1
Date 2016-02-10
Title Geochemical Data Toolkit for Windows
Author Vojtech Janousek 
Colin Farrow 
Vojtech Erban 
Jean-Francois Moyen 
Maintainer Vojtech Janousek 
Depends R (>= 3.2.0), stats, methods, utils, graphics, MASS, grid, lattice, foreign, RODBC, R2HTML, tcltk, sp
Description A program for recalculation of geochemical data from igneous
and metamorphic rocks. Runs under Windows Vista/7/8/10, complete function/stability under 2000/NT/XP cannot be guaranteed.
License GPL (>= 2)
URL http://www.gcdkit.org

R topics documented:
.claslist . . . . .
about . . . . . .
accessVar . . . .
Add contours . .
addResults . . . .
addResultsIso . .
AFM . . . . . . .
ageEps . . . . . .
Agrawal . . . . .
Ague . . . . . . .
appendSingle . .
apSaturation . . .
assign1col . . . .
assign1symb . . .
assignColLab . .
assignColVar . .
assignSymbGroup
assignSymbLab .
assignSymbLett .

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1
2
2
3
4
4
5
7
9
11
14
14
16
17
17
18
19
20
20

R topics documented:

2
Batchelor . . . . . . .
binary . . . . . . . . .
binaryBoxplot . . . . .
Boolean conditions . .
bpplot2 . . . . . . . .
calc . . . . . . . . . .
calcCore . . . . . . . .
Catanorm . . . . . . .
CIPW . . . . . . . . .
classify . . . . . . . .
cluster . . . . . . . . .
contourGroups . . . .
coplotByGroup . . . .
coplotTri . . . . . . . .
correlationCoefPlot . .
Cox . . . . . . . . . .
crosstab . . . . . . . .
customScript . . . . .
cutMy . . . . . . . . .
Debon . . . . . . . . .
deleteSingle . . . . . .
Edit labels . . . . . . .
Edit numeric data . . .
editLabFactor . . . . .
elemIso . . . . . . . .
epsEps . . . . . . . . .
Export to Access . . .
Export to DBF . . . . .
Export to Excel . . . .
Export to HTML tables
FeMiddlemost . . . . .
figAdd . . . . . . . . .
figaro.identify . . . . .
figCol . . . . . . . . .
figEdit . . . . . . . . .
figGbo . . . . . . . . .
figLoad . . . . . . . .
figMulti . . . . . . . .
figOverplot . . . . . .
figRedraw . . . . . . .
figSave . . . . . . . .
figScale . . . . . . . .
figUser . . . . . . . . .
figZoom . . . . . . . .
filledContourFig . . . .
Frost . . . . . . . . . .
gcdOptions . . . . . .
graphicsOff . . . . . .
groupsByCluster . . .
groupsByDiagram . . .
groupsByLabel . . . .
Harris . . . . . . . . .

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21
23
24
26
27
28
29
31
32
34
35
36
39
41
43
44
47
48
49
50
53
54
54
55
56
57
59
60
60
61
64
65
68
68
70
71
71
72
74
76
77
77
78
79
81
82
85
87
88
89
89
90

R topics documented:
Hastie . . . . . . .
ID . . . . . . . . .
info . . . . . . . .
isochron . . . . . .
isocon . . . . . . .
Jensen . . . . . . .
joinGroups . . . .
Jung . . . . . . . .
Laroche . . . . . .
LaRocheCalc . . .
loadData . . . . . .
Maniar . . . . . . .
mergeData . . . . .
Meschede . . . . .
Mesonorm . . . . .
Middlemost . . . .
millications . . . .
Misc . . . . . . . .
Miyashiro . . . . .
Mode . . . . . . .
Molecular weights
Mullen . . . . . . .
MullerK . . . . . .
Multiple plots . . .
mzSaturation . . .
NaAlK . . . . . . .
Niggli . . . . . . .
OConnor . . . . .
oxide2oxide . . . .
oxide2ppm . . . .
pairsCorr . . . . .
pdfAll . . . . . . .
Pearce 1982 . . . .
Pearce and Cann .
Pearce and Norry .
Pearce et al. 1977 .
Pearce Nb-Th-Yb .
Pearce Nb-Ti-Yb .
Pearce1996 . . . .
PearceGranite . . .
PeceTaylor . . . .
peekDataset . . . .
peterplot . . . . . .
Plate . . . . . . . .
Plate editing . . . .
plateLabelSlots . .
plotPlate . . . . . .
plotWithCircles . .
pokeDataset . . . .
ppm2oxide . . . .
prComp . . . . . .
printSamples . . .

3
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92
94
95
95
97
100
102
103
105
108
109
113
115
116
117
118
120
121
122
124
126
127
129
133
135
136
138
139
142
143
143
145
146
147
149
150
152
154
156
158
160
162
163
165
167
169
170
171
173
174
175
176

R topics documented:

4
printSingle . . . . . . . .
profiler . . . . . . . . . .
psAll . . . . . . . . . . .
purgeDatasets . . . . . .
QAPF . . . . . . . . . .
quitGCDkit . . . . . . .
r2clipboard . . . . . . .
recast . . . . . . . . . .
reciprocalIso . . . . . .
Regular expressions . . .
rtSaturation . . . . . . .
saveData . . . . . . . . .
saveResults . . . . . . .
saveResultsIso . . . . . .
sazava . . . . . . . . . .
Schandl . . . . . . . . .
selectAll . . . . . . . . .
selectByDiagram . . . .
selectByLabel . . . . . .
selectColumnLabel . . .
selectColumnsLabels . .
selectNorm . . . . . . .
selectPalette . . . . . . .
selectSubset . . . . . . .
setCex . . . . . . . . . .
setShutUp . . . . . . . .
setTransparency . . . . .
Shand . . . . . . . . . .
Shervais . . . . . . . . .
showColours . . . . . .
showLegend . . . . . . .
showSymbols . . . . . .
spider . . . . . . . . . .
spider2norm . . . . . . .
spiderBoxplot . . . . . .
spiderByGroupFields . .
spiderByGroupPatterns .
srnd . . . . . . . . . . .
statsByGroup . . . . . .
statsByGroupPlot . . . .
statsIso . . . . . . . . .
strip . . . . . . . . . . .
stripBoxplot . . . . . . .
Subset by range . . . . .
summaryAll . . . . . . .
summaryByGroup . . . .
summarySingle . . . . .
summarySingleByGroup
Sylvester . . . . . . . .
TAS . . . . . . . . . . .
TASMiddlemost . . . . .
ternary . . . . . . . . . .

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.claslist

5
tetrad . . . . . . .
threeD . . . . . . .
tkSelectVariable . .
tk_winDialog . . .
tk_winDialogString
trendTicks . . . . .
Verma . . . . . . .
Villaseca . . . . . .
Wedge . . . . . . .
Whalen . . . . . .
WinFloyd1 . . . .
WinFloyd2 . . . .
Wood . . . . . . .
zrSaturation . . . .

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List of available classification schemes

Description
The function returns a list of classification diagrams available in the system.
Usage
.claslist()
Value
A matrix with two columns:
menu

menu items

function

the attached functions

Author(s)
Vojtech Erban, 

about

About GCDkit

Description
Prints short information about the current version of GCDkit and contact addresses of its authors.
Usage
about()
Arguments
None.

6

accessVar

Author(s)
Vojtech Janousek, 

accessVar

Accessing data in memory of R

Description
Loads data already present in memory of R into GCDkit.
Usage
accessVar(var=NULL,GUI=FALSE)
Arguments
var

a text string specifying the variable to be accessed

GUI

logical; is the function called from GUI (or from the command line)?

Details
This function makes possible to access a variable, already present in R, most importantly the sample
data sets. Firstly these need to be made available using the command data.
Value
WR

numeric matrix: all numeric data

labels

data frame: all at least partly character fields; labels$Symbol contains plotting
symbols and labels$Colour the plotting colours

The function prints a short summary about the attached data. It also loads and executes the Plugins,
i.e. all the R code that is currently stored in the subdirectory ’\Plugin’.
Author(s)
Vojtech Janousek, 
Examples
data(swiss)
accessVar("swiss")
binary("Catholic","Education")
data(sazava)
accessVar("sazava")
binary("SiO2","Ba")

Add contours

Add contours

7

Add contours

Description
Superposes contour lines to a Figaro-compatible plot.
Usage
addContours()
Details
This is, in principle, a front end to the standard R function contour.
Value
None.
Author(s)
Vojtech Erban, 
See Also
’filled.contour’ ’figaro’

addResults

Appending results to data

Description
Appends the most recently calculated results to the data stored in memory.
Usage
addResults(what="results", save=TRUE, GUI=FALSE)
Arguments
what

character; the name of variable to be appended.

save

logical; Append to the data matrix ’WR’?

GUI

logical; Is the function called within the GUI environment?

Details
This function appends the variable ’results’ (a matrix or vector) returned by most of the calculation algorithms to a the numeric data stored in the matrix ’WR’.

8

addResultsIso

Value
Modifies the matrix ’WR’.
Author(s)
Vojtech Janousek, 

addResultsIso

Append Sr-Nd isotopic data

Description
Appends the calculated isotopic parameters stored in the matrix ’init’ to the numeric data already
in the system.
Usage
addResultsIso()
Value
Modifies the numeric data matrix(’WR’) to which it appends the following columns:
Age (Ma)

Age in Ma

87Sr/86Sri

Sr isotopic ratios

143Nd/144Ndi

Initial Nd isotopic ratios

EpsNdi

Initial (N d) values

TDM

Single-stage depleted-mantle Nd model ages (Liew & Hofmann, 1988)

TDM.Gold

Single-stage depleted-mantle Nd model ages (Goldstein et al., 1988)

TDM.2stg

Two-stage depleted-mantle Nd model ages (Liew & Hofmann, 1988)

Plugin
SrNd.r
Author(s)
Vojtech Janousek, 
References
Liew T C & Hofmann A W (1988) Precambrian crustal components, plutonic associations, plate
environment of the Hercynian Fold Belt of Central Europe: indications from a Nd and Sr isotopic
study. Contrib Mineral Petrol 98: 129-138
Goldstein S L, O’Nions R K & Hamilton P J (1984) A Sm-Nd isotopic study of atmospheric dusts
and particulates from major river systems. Earth Planet Sci Lett 70: 221-236
See Also
’addResults’

AFM

9

AFM

AFM diagram (Irvine + Baragar 1971)

Description
Assigns data for AFM ternary diagram into Figaro template (list ’sheet’) and appropriate values
into ’x.data’ and ’y.data’.

Usage
AFM(equ=FALSE)

Arguments
equ

Logical: Should the template use boundary defined by equation?

Details
The AFM diagram is a triangular plot with apices A, F and M defined as follows:
A = (K2 O + N a2 O) wt. %
F = FeOtot wt. %
M = MgO wt. %
A + F + M = 100 %
The classification diagram divides data into ’tholeiite series’ and ’calc-alkaline series’ as proposed
by Irvine & Baragar (1971). For extreme values linear extrapolation of boundary curve is employed.

10

AFM

Value
sheet

list with Figaro Style Sheet data

x.data, y.data A, F, M values (see details) transformed into 2D
Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Irvine T M & Baragar W R (1971) A guide to the chemical classification of common volcanic rocks.
Canad J Earth Sci 8: 523-548 doi: 10.1139/e71-055
See Also
classify figaro plotDiagram

ageEps

11

Examples
#Within GCDkit, AFM is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("AFM")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("AFM", FALSE)

ageEps

Plot Sr or Nd growth lines

Description
Plots Nd or Sr growth curves in the binary diagram age-(N d) or age-Sr isotopic ratio.

12

ageEps

Usage
ageEps()
ageEps2()
ageSr()
Arguments
None.
Details
The Nd growth curves in individual samples can be plotted using either a single- or two-stage (Liew
& Hofmann 1988) models.
In case of Nd are shown growth curves for the two main mantle reservoirs, CHUR and Depleted
Mantle (DM) (the latter in two modifications, after Goldstein et al. (1988) and Liew & Hofmann
(1988).
For Sr only uniform reservoir (UR) development is calculated using parameters of Faure (1986 and
references therein).
The small ticks, or rugs, on x axis correspond to Nd model ages, on y axis to initial (N d) values.
This function is, so far with the exception of rugs, Figaro compatible.
Value
None.
Plugin
SrNd.r
Author(s)
Vojtech Janousek, 
References
Faure G (1986) Principles of Isotope Geology. J.Wiley & Sons, Chichester, 589 pp
Goldstein S L, O’Nions R K & Hamilton P J (1984) A Sm-Nd isotopic study of atmospheric dusts
and particulates from major river systems. Earth Planet Sci Lett 70: 221-236
Liew T C & Hofmann A W (1988) Precambrian crustal components, plutonic associations, plate
environment of the Hercynian Fold Belt of Central Europe: indications from a Nd and Sr isotopic
study. Contrib Mineral Petrol 98: 129-138

Agrawal

Agrawal

13

Trace-element based discrimination plots for (ultra-)basic rocks
(Agrawal et al. 2008)

Description
Plots data stored in ’WR’ into discrimination plots proposed by Agrawal et al. (2008) for (ultra-)
basic rocks (SiO2 < 52 wt. %).
Usage
Agrawal(plot.txt = getOption("gcd.plot.text"))
Arguments
plot.txt

logical, annotate fields by their names?

Details
Suite of five diagrams for discrimination of geotectonic environment of ultrabasic and basic rocks,
proposed by Agrawal et al. (2008). It is based on linear discriminant analysis applied to logtransformed concentration ratios of five trace elements (La, Sm, Yb, Nb, and Th), i.e., using four
ratios ln(La/T h), ln(Sm/T h), ln(Y b/T h), and ln(N b/T h). The two discriminant functions,
DF1 and DF2, are mathematically designed to maximize the separation between the groups and
account for 100 percent of the variance in the data.
Note that only samples with SiO2 < 52 wt. % are plotted.
Also note that each diagram applies only to environments explicitly mentioned. Samples from the
environment not taken into account will be misinterpreted (the CRB + OIB + MORB diagram is not
designed for IAB etc.) See the Agrawal et al (2008) for further details.
Following geotectonic settings may be deduced:
Abbreviation used
IAB
CRB
OIB
MORB

Environment
island arc basic rocks
continental rift basic rocks
ocean-island basic rocks
mid-ocean ridge basic rocks

14

Agrawal

Value
None.
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots.
See Plate, Plate editing and figaro for details.
Author(s)
Vojtech Janousek, 
References
Agrawal S, Guevara M, Verma S (2008) Tectonic discrimination of basic and ultrabasic volcanic
rocks through log-transformed ratios of immobile trace elements. Int Geol Review 50: 1057-1079
doi: 10.2747/0020-6814.50.12.1057
See Also
Verma, Plate, Plate editing, plotPlate, figaro
Examples
#plot the diagrams
plotPlate("Agrawal")

Ague

15

Ague

Concentration ratio diagram (Ague 1994)

Description
Implementation of Concentration ratio diagrams after Ague (1994) used for judging the mobility of
elements or oxides in course of various geochemically open-system processes such as alteration or
partial melting.
Usage
Ague(x = NULL,
whichelems = "SiO2,TiO2,Al2O3,FeOt,MnO,MgO,CaO,Na2O,K2O,P2O5",
immobile = NULL, bars = NULL, plot = TRUE)
Arguments
x

two sample names for analyses of the protolith and altered rock compositions,
respectively.

whichelems

list of elements to be plotted.

immobile

list of (one or more) elements considered as immobile.

bars

optional name of the variable containing 1σ errors for plotting error bars.

plot

logical, should be the diagram plotted or just the results calculated?

Details
The Concentration ratio diagram shows concentration ratio of each geochemical species of interest
(element or oxide) in the ’altered rock’ to that in its presumed ’protolith’. These ratios are plotted
on the y-axis, and the elements are arranged in any convenient order along x.
Following an open-system geological process, any of the perfectly immobile constituents i should
ideally have exactly the same concentration ratio rinv defined as (Ague 2003):
rinv =

c Ai
c 0i

where ci is the concentration of the species i, 0 refers to the ’protolith’ and A to the ’altered rock’.
This ratio, however, would only exceptionally equate unity, when the mass of the whole system
is conserved. Using the presumably immobile species i as the geochemical reference frame, the
change in the rock mass can be defined as Ague (1994):
∆M ass =

c 0i
c Ai

−1

Thus rinv > 1 indicates overall rock mass loss due to removal of mobile constituents; this has the
effect of increasing the concentrations of the immobile species ("residual enrichment"). Conversely,
rinv < 1 shows an overall rock mass gain ("residual dilution").
The mass change of any mobile constituent j can be expressed as (Ague 1994):

16

Ague

∆j =

Mobile species j that have

cA
j
c 0j

A
1 cj
−1
rinv c 0j

ratios greater than rinv have been added to the system, and those

with ratios lower than rinv have been lost.
In the GCDkit’s implementation of the Concentration ratio diagrams, firstly the parental and altered
rock samples can be chosen interactively from a binary plot M gO − SiO2 , if not specified at the
function call. Then the user is prompted for the elements/oxides to be plotted.
If not provided as a comma delimited list among the arguments, the presumably immobile elements
are to be specified. To facilitate this choice, printed and plotted as barplots are ordered ratios of the
cA

elemental concentrations in the ’altered rock’ to that in the ’protolith’ ( c 0j )).
j

Finally the concentration ratio diagram is plotted. If the parameter bars is given, error bars are also
shown corresponding to +/ − 1σ.

Value
Returns a matrix ’results’ with the following columns:
Altered/Protolith
concentration ratios of the given geochemical species in the ’altered rock’ to that
in the ’protolith’ - primary y axis of the plot

appendSingle

17

Gain/loss in % relative gains (positive) or losses (negative) corrected for the rock mass change
- secondary y axis of the plot
Plugin
Isocon.r
Author(s)
Vojtech Janousek, 
References
Ague J J (1994) Mass transfer during Barrovian metamorphism of pelites, south-central Connecticut; I, Evidence for changes in composition and volume. Amer J Sci 294: 989-1057 doi:
10.2475/ajs.294.8.989
Ague J J (2003) Fluid infiltration and transport of major, minor, and trace elements during regional
metamorphism of carbonate rocks, Wepawaug Schist, Connecticut, USA. Amer J Sci 303: 753-816
doi: 10.2475/ajs.303.9.753
Grant J A (1986) The isocon diagram - a simple solution to Gresens equation for metasomatic
alteration. Econ Geol 81: 1976-1982 doi: 10.2113/gsecongeo.81.8.1976
Grant J A (2005) Isocon analysis: a brief review of the method and applications. Phys Chem Earth
(A) 30: 997-1004 doi: 10.1016/j.pce.2004.11.003
Gresens R L (1967) Composition-volume relationships of metasomatism. Chem Geol 2: 47-55 doi:
10.1016/0009-2541(67)90004-6
See Also
Wedge, isocon
Examples
data<-loadData("sazava.data",sep="\t")
Ague(c("Po-4","Po-1"),
"SiO2,TiO2,Al2O3,FeOt,MgO,CaO,Rb,Sr,Ba,Zr,La,Nd,Eu,Gd,Yb,Y",
"TiO2,SiO2,FeOt")

appendSingle

Append empty label or variable

Description
Appends an empty numeric data column or a new label to the current data set.
Usage
appendSingle()
Value
Returns the corrected version of the data frame ’labels’ or numeric matrix ’WR’.

18

apSaturation

Author(s)
Vojtech Janousek, 

apSaturation

Apatite saturation

Description
Calculates apatite saturation temperatures for observed whole-rock major-element compositions.
Prints also phosphorus saturation levels for the given major- element compositions and assumed
magma temperature.
Usage
apSaturation(Si = WR[, "SiO2"], ACNK = WR[, "A/CNK"],
P2O5 = WR[, "P2O5"], T = 0)
Arguments
Si

SiO2 contents in the melt (wt. %)

ACNK

vector with A/CNK (mol %) values

P2O5

vector with P2 O5 concentrations

T

assumed magma temperature in C

Details
* Calculates phosphorus saturation levels following Harrison & Watson (1984):
ln(DP ) =

8400 + 26400(SiO2 − 0.5)
− 3.1 − 12.4(SiO2 − 0.5)
T

P2 O5 .HW =

42
DP

where ’T’ = absolute temperature (K), ’DP ’ = distribution coefficient for phosphorus between
apatite and melt and ’SiO2 ’ is the weight fraction of silica in the melt, SiO2 wt. %/100.
These formulae were shown to be valid only for metaluminous rocks, i.e. A/CNK < 1, and were
modified for peraluminous rocks (A/CNK > 1) by Bea et al. (1992):
P2 O5 .Bea = P2 O5 .HW e

6429(A/CN K−1)
(T −273.15)

and Pichavant et al. (1992):
P2 O5 .P V = P2 O5 .HW + (A/CN K − 1)e

−5900
−3.22SiO2 +9.31
T

Note that the phosphorus saturation concentrations are not returned by the function but printed only.
* Calculates saturation temperatures in C using the observed P2 O5 concentrations (Harrison &
Watson, 1984):

apSaturation

19

T.HW =

8400 + 26400(SiO2 − 0.5)
− 273.15
ln( P242O5 ) + 3.1 + 12.4(SiO2 − 0.5)

for peraluminous rocks (A/CNK > 1) the equation of Bea et al. (1992) needs to be solved for ’T’
(in K) by iterations:
42

P2 O5 .Bea =
e

8400+26400(SiO2 −0.5)
−3.1−12.4(SiO2 −0.5)
T

e

6429(A/CN K−1)
(T −273.15)

as is that of Pichavant et al. (1992):
42

P2 O5 .P V =
e

8400+26400(SiO2 −0.5)
−3.1−12.4(SiO2 −0.5)
T

+ (A/CN K − 1)e

−5900
−3.22SiO2 +9.31
T

Value
Returns a matrix ’results’ with the following columns:
A/CNK

A/CNK values

Tap.sat.C.H+W

saturation T of Harrison & Watson (1984) in C

Tap.sat.C.Bea

saturation T of Bea et al. (1992) in C, peraluminous rocks only

Tap.sat.C.Pich saturation T of Pichavant et al. (1992) in C, peraluminous rocks only
Plugin
Saturation.r
Author(s)
Vojtech Janousek, 
References
Bea F, Fershtater G B & Corretge L G (1992) The geochemistry of phosphorus in granite rocks and
the effects of aluminium. Lithos 29: 43-56 doi: 10.1016/0024-4937(92)90033-U
Harrison T M & Watson E B (1984) The behavior of apatite during crustal anatexis: equilibrium and kinetic considerations. Geochim Cosmochim Acta 48: 1467-1477 doi: 10.1016/00167037(84)90403-4
Pichavant M, Montel J M & Richard L R (1992) Apatite solubility in peraluminous liquids: experimental data and extension of the Harrison-Watson model. Geochim Cosmochim Acta 56: 38553861 doi: 10.1016/0016-7037(92)90178-L

20

assign1symb

assign1col

Uniform colours

Description
Assigns the same plotting colour to all samples.
Usage
assign1col(col=-1)
Arguments
col

numeric; code of the colour.

Details
This function sets the same colour to all of the plotting symbols. If ’col’ = -1 (the default), the user
is prompted to specify its code.
Value
Sets ’labels$Colour’ to code of the selected plotting colour.
Author(s)
Vojtech Janousek, 
See Also
To display the current legend use showLegend. Symbols and colours by a single label can be
assigned by assignSymbLab and assignColLab respectively, symbols and colours by groups simultaneously by assignSymbGroup. Uniform symbols are obtained by assign1symb. Table of
available plotting symbols is displayed by showSymbols and colours by showColours.

assign1symb

Uniform symbols

Description
Assigns the same plotting symbol to all samples.
Usage
assign1symb(pch=-1)
Arguments
pch

numeric; code of the plotting symbol.

assignColLab

21

Details
This function sets the same plotting symbol to all the data points. If ’pch’ = -1 (the default), the
user is prompted to specify its code.
Value
Sets ’labels$Symbol’ to code of the selected plotting symbol.
Author(s)
Vojtech Janousek, 
See Also
To display the current legend use showLegend. Symbols and colours by a single label can be assigned by assignSymbLab and assignColLab respectively, symbols and colours by groups simultaneously by assignSymbGroup. Uniform colours are obtained by assign1col. Table of available
plotting symbols is displayed by showSymbols and colours by showColours.

assignColLab

Colours by label

Description
Assigns plotting colours according to the levels of the chosen label.
Usage
assignColLab()
Arguments
None.
Details
The label is selected using the function ’selectColumnLabel’.
Value
Sets ’leg.col’ to a sequence number of column in ’labels’ that is to be used to build the legend;
’labels$Colour’ contains the codes of desired plotting colours.
Author(s)
Vojtech Janousek, 

22

assignColVar

See Also
To display the current legend use showLegend. Symbols by a single label can be assigned by
assignSymbLab, symbols and colours by groups simultaneously by assignSymbGroup. Uniform
colours and symbols are obtained by assign1symb and assign1col. Table of available plotting
symbols is displayed by showSymbols and colours by showColours.
Selecting a label: selectColumnLabel.

assignColVar

Colours by a variable

Description
Assigns plotting colours according to the values of the variable.
Usage
assignColVar(what=NULL,pal="heat.colours",save=TRUE,n=15)
Arguments
what

variable name or a formula; if NULL a dialogue is displayed

pal

palette

save

should the newly picked colours be assigned to ’labels’?

n

Desired approximate number of colours to be assigned.

Details
For selection of the variable is employed the function ’selectColumnLabel’. The user can specify either existing data column in the ’WR’ or a formula. The colours can be optionally (default
behaviour) assigned globally, so that all the plots will use these from this point on. If not specified upon function call, the palette is picked using selectPalette. The possible values are:
'grays','reds','blues','greens','cyans','violets','yellows','cm.colors',
'heat.colors', 'terrain.colors','topo.colors', 'rainbow' and 'jet.colors'.
The analyses with no data available for the colours assignment will remain black.
Value
A list of two components, col and leg. The former are the plotting colours, the latter information
to build a legend. If save = TRUE, ’labels$Colour’ will acquire the codes of desired plotting
colours.
Author(s)
Vojtech Janousek, 
See Also
Colours by a single variable can be assigned by assignColLab, symbols and colours by groups
simultaneously by assignSymbGroup. Uniform colours are obtained by assign1col. Table of
available plotting colours is obtained by showColours.

assignSymbGroup

23

Examples
assignColVar("Na2O/K2O","greens")
plotDiagram("PeceTaylor",F,F)

assignSymbGroup

Symbols/colours by groups

Description
Lets the user to assign plotting symbols and colours according to the levels of the defined groups.
Usage
assignSymbLab()
Arguments
None.
Value
Sets ’leg.col’ and ’leg.pch’ to zero, ’labels$Symbol’ contains the codes of desired plotting
symbols, ’labels$Colour’ of plotting colours.
Author(s)
Vojtech Janousek, 
See Also
To display the current legend use showLegend. Symbols by a single label can be assigned by
assignSymbLab, colours using assignColLab. Uniform colours and symbols are obtained by
assign1symb and assign1col. Table of available plotting symbols is displayed by showSymbols
and colours by showColours.

assignSymbLab

Symbols by label

Description
Assigns plotting symbols according to the levels of the chosen label.
Usage
assignSymbLab()
Arguments
None.

24

assignSymbLett

Details
The label is selected using the function ’selectColumnLabel’.
Value
Sets ’leg.pch’ to a sequence number of column in ’labels’ that is to be used to build the legend;
’labels$Symbol’ contains the codes for desired plotting symbols.
Author(s)
Vojtech Janousek, 
See Also
To display the current legend use showLegend. Colours by a single label can be assigned by
assignColLab, symbols and colours by groups simultaneously by assignSymbGroup. Uniform
colours and symbols are obtained by assign1symb and assign1col. Table of available plotting
symbols is displayed by showSymbols and colours by showColours.
Selecting a label: selectColumnLabel.

assignSymbLett

Symbols by label - initial letters

Description
Assigns plotting symbols according to the levels of the chosen label.
Usage
assignSymbLett()
Arguments
None.
Details
The label is selected using the function ’selectColumnLabel’.
Value
Sets ’leg.pch’ to a sequence number of column in ’labels’ that is to be used to build the legend;
’labels$Symbol’ contains the plotting symbols, which correspond to initial letters for the levels of
the specified label.
Author(s)
Vojtech Janousek, 

Batchelor

25

See Also
To display the current legend use showLegend. Symbols by a single label can be assigned by
assignSymbLab, colours by assignColLab, symbols and colours by groups simultaneously by
assignSymbGroup. Uniform colours or symbols are achieved by assign1symb and assign1col.
Table of available plotting symbols is displayed by showSymbols and colours by showColours.

Batchelor

Batchelor and Bowden (1985)

Description
Plots data stored in ’WR’ (or its subset) into Batchelor and Bowden’s R1 − R2 diagram.

Usage
Batchelor(ideal=TRUE)

Arguments
ideal

logical, plot ideal minerals composition?

Details
Diagram in R1 − R2 space, proposed by De la Roche et al. (1980), with fields defined by Batchelor
& Bowden (1985) as characteristic for following geotectonic environments:
Mantle Fractionates
Pre-plate Collision
Post-collision Uplift
Late-orogenic
Anorogenic
Syn-collision
Post-orogenic

26

Batchelor

Value
sheet
x.data
y.data

list with Figaro Style Sheet data
R1 = 4 * Si - 11 * (Na + K) - 2 * (Fe[total as bivalent] + Ti), all in millications;
as calculated by the function ’LaRoche’
R2 = 6 * Ca + 2 * Mg + Al, all in millications; as calculated by the function
’LaRoche’

Author(s)
Vojtech Janousek, 
References
Batchelor R A & Bowden P (1985) Petrogenetic interpretation of granitoid rock series using multicationic parameters. Chem Geol 48: 43-55. doi: 10.1016/0009-2541(85)90034-8
De La Roche H, Leterrier J, Grandclaude P, & Marchal M (1980) A classification of volcanic and
plutonic rocks using R1 R2 - diagram and major element analyses - its relationships with current
nomenclature. Chem Geol 29: 183-210. doi: 10.1016/0009-2541(80)90020-0
See Also
LaRoche figaro plotDiagram
Examples
#plot the diagram
plotDiagram("Batchelor", FALSE)

binary

binary

27

Binary plot

Description
These functions display data as a binary plot.
Usage
binary(x=NULL,y=NULL,log="",samples=rownames(WR),
new=TRUE, ...)
plotWithLimits(x.data, y.data,
digits.x=NULL, digits.y=NULL,log = "",new = TRUE,
xmin=.round.min.down(x.data,dec.places=digits.x,expand=TRUE),
xmax=.round.max.up(x.data,dec.places=digits.x,expand=TRUE),
ymin=.round.min.down(y.data,dec.places=digits.y,expand=TRUE),
ymax=.round.max.up(y.data,dec.places=digits.y,expand=TRUE),
xlab = "", ylab = "", fousy = "",
IDlabels=getOption("gcd.ident"), fit = FALSE, main = "",
pch = labels[names(x.data), "Symbol"],
col = labels[names(x.data), "Colour"],
cex=labels[names(x.data),"Size"],title=NULL,interactive=FALSE)
Arguments
x,y
log
samples
new
...
x.data
y.data
digits.x
digits.y
xmin, xmax
ymin, ymax
xlab, ylab
fousy
IDlabels
fit
main
pch
col
cex
title
interactive

character; specification of the plotting variables (formulae OK).
a vector '', 'x', 'y' or 'xy' specifying which of the axes are to be logarithmic
character or numeric vector; specification of the samples to be plotted.
logical; should be opened a new plotting window?
Further parameters to the function ’plotWithLimits’.
a numerical vector with the x data.
a numerical vector with the y data.
Precision to which should be rounded the x axis labels.
Precision to which should be rounded the y axis labels.
limits of the x axis.
limits of the y axis.
labels for the x and y axes, respectively.
numeric vector: if specified, vertical error bars are plotted at each data point.
labels that are to be used to identify the individual data points
logical, should be the data fitted by a least squares line?
main title for the plot.
plotting symbols.
plotting colours.
relative size of the plotting symbols.
title for the plotting window.
logical; for internal use by our French colleagues.

28

binaryBoxplot

Details
The function ’plots.with.limits’ sets up the axes, labels them, plots the data and, if desired,
enables the user to identify the data points interactively.
’binary’ is the user interface to ’plotWithLimits’.
The variables to be plotted are selected using the function ’selectColumnLabel’. In the specification of the variables can be used also arithmetic expressions, see calcCore for the correct syntax.
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSubset for details.
The functions are Figaro-compatible.
Value
None.
Author(s)
Vojtech Janousek, 
See Also
plot
Examples
binary("K2O/Na2O","Rb")
binary("Rb/Sr","Ba/Rb",log="xy",samples=1:10,col="red",pch="+",main="My plot")
plotWithLimits(WR[,"SiO2"]/10,WR[,"Na2O"]+WR[,"K2O"],xlab="SiO2/10",
ylab="alkalis")
plotWithLimits(WR[,"Rb"],WR[,"Sr"],xlab="Rb",ylab="Sr",log="xy")
plotWithLimits(WR[,"SiO2"],WR[,"Rb"],fousy=WR[,"Rb"]*0.05,xlab="SiO2",
ylab="Rb",fit=TRUE)

binaryBoxplot

Binary boxplot

Description
A binary plot combined with boxplots for both variables.
Usage
binaryBoxplot(xaxis="",yaxis="")
Arguments
xaxis, yaxis

specification of the variables. Formulae are OK.

binaryBoxplot

29

Details

Unless specified in the call, the variables to be plotted are selected using the function ’selectColumnLabel.
In the specification of the variables can be used also arithmetic expressions, see calcCore for the
correct syntax.
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSubset for details.
Value
None.
Warning
This function IS NOT Figaro-compatible.
Author(s)
Vojtech Janousek, 
See Also
plot boxplot
Examples
binaryBoxplot("SiO2/10","Na2O+K2O")

30

Boolean conditions

Boolean conditions

Select subset by Boolean condition

Description
Selecting subsets of the current dataset using Boolean conditions that can query both numeric fields
and labels. Regular expressions can be employed to search the labels.
Details
The menu item ’Select subset by Boolean’, connected to the function selectSubset, enables
the user to query by any combination of the numeric columns and labels in the whole dataset. The
current data will be replaced by its newly chosen subset.
First, the user is prompted to enter a search pattern which can contain conditions that may employ
most of the comparison operators common in R, i.e. < (lower than), > (greater than), <= (lower or
equal to), >= (greater or equal to), = or == (equal to), != (not equal to). The character strings should
be quoted. The conditions can be combined together by logical and, or and brackets.
Logical and can be expressed as .and. .AND. &
Logical or can be expressed as .or. .OR. |
Please note that at the moment no extra spaces can be handled (apart from in quoted character
strings).
Value
Overwrites the data frame ’labels’ and numeric matrix ’WR’ by subset that fulfills the search criteria.
Author(s)
Vojtech Janousek, 
See Also
regular.expressions regex
Examples
## Not run:
# Valid search patterns
Intrusion="Rum"
# Finds all analyses from Rum
Intrusion="Rum".and.SiO2>65
Intrusion="Rum".AND.SiO2>65
Intrusion="Rum"&SiO2>65
# All analyses from Rum with silica greater than 65
# (all three expressions are equivalent)
MgO>10&(Locality="Skye"|Locality="Islay")

bpplot2

31

# All analyses from Skye or Islay with MgO greater than 10
MgO>=10&(Locality!="Skye"&Locality!="Islay")
# All analyses from any locality except Skye and Islay with MgO greater
# or equal to 10
Locality="^S"
# All analyses from any locality whose name starts with capital S
## End(Not run)

bpplot2

Box-Percentile Plot

Description
Displays statistical distribution each of the variables in a data frame using a box-percentile plot
(Esty & Banfield 2003).
Usage
bpplot2(x,main="Box-Percentile Plot",sub="",xlab = "",
ylab="",log="y",col="lightgray",horizontal=FALSE,ylim = NULL,axes=TRUE,...)

Arguments
x

data frame with the data to be plotted

main

main title for the plot

sub

sub title for the plot

xlab

label for x axis

ylab

label for y axis

log

which of the axes is to be logarithmic?

col

colour to fill the boxes

horizontal

logical, should be the orientation horizontal?

ylim

optional; limits for the y axis

axes

logical; should be the axis drawn?

...

additional plotting parameters

Details
The box-percentile plot is analogous to a boxplot but the width of the box is variable, mimicking
the distribution of the given variable. As in boxplots, the median and two quartiles are marked by
horizontal lines.

32

calc

Value
None.
Warning
This function IS NOT Figaro-compatible. It means that the set of diagrams cannot be further edited
in GCDkit (e.g. tools in "Plot editing" menu are inactive).
Author(s)
The code represents a modified function 'bpplot' from the package 'Hmisc' by Frank E Harrell Jr. (originally designed by Jeffrey Banfield). Adopted for GCDkit by Vojtech Janousek,
.
References
Esty, W. W. & Banfield, J. D. (2003). The Box-Percentile Plot. Journal of Statistical Software 8
(17)

calc

Calculate a new variable

Description
Calculates a single numeric variable and appends it to the data.
Usage
calc()

calcCore

33

Details
The formula can invoke any combination of names of existing numerical columns, with the constants, brackets, arithmetic operators +-*/^ and R functions. See calcCore for a correct syntax.
If the result is a vector of the length corresponding to the number of the samples in the system, the
user is prompted for the name of the new data column. Unless a column with the specified name
already exists or the given name is empty, the newly calculated column is appended to the data in
memory (’WR’).
Value
results

numerical vector with the results

Modifies, if appropriate, the numeric matrix ’WR’.
Author(s)
Vojtech Janousek, 
See Also
selectColumnLabel.
Examples
## Not run:
# examples of valid formulae....
(Na2O+K2O)/CaO
Rb^2
log10(Sr)
mean(SiO2)/10
# ... but this command is in fact a simple R shell # meaning lots of fun for power users!
summary(Rb,na.rm=T)
cbind(SiO2/2,TiO2,Na2O+K2O)
cbind(major)
hist(SiO2,col="red")
boxplot(Rb~factor(groups))
# possibilities are endless
plot(Rb,Sr,col="blue",pch="+",xlab="Rb (ppm)",ylab="Sr (ppm)",log="xy")
## End(Not run)

calcCore

Calculation of user-defined parameters

Description
Calculates a user-defined parameter specified by the equation.
Usage
calcCore(equation, where = "WR", redo = TRUE)

34

calcCore

Arguments
equation
where
redo

a text string to be evaluated.
which matrix should be used?
logical; should be the routine called again and again?

Details
This is a core calculation function.
The expression specified by ’equation’ can involve any combination of names of existing numerical columns in the matrix ’where’, numbers (i.e. constants), arithmetic operators +-*/^ and R
functions.
The most useful of the latter are ’sqrt’ (square root), ’log’ (natural logarithm), ’log10’ (common
logarithm), ’exp’ (exponential function), ’sin’, ’cos’ and ’tan’ (trigonometric functions).
Potentially useful can be also min (minimum), max (maximum), length (number of elements/cases),
’sum’ (sum of the elements), ’mean’ (mean of the elements), and ’prod’ (product of the elements).
However, any user-defined function can be also invoked here.
For most statistical functions, an useful parameter ’na.rm=T’ can be specified. This makes the
function to calculate the result from the available data only, ignoring the not determined value (see
Examples).
The quotation marks in ’equation’ need to preceded by a backslash. Option ’redo’ specifies
whether the routine should be called repeatedly until some meaningful result is obtained. Otherwise
’NA’ is returned.
Value
A list of three items:
equation
results
formula

equation as entered by the user
numeric vector with the results or NA if none can be calculated
the unevaluated expression corresponding to the ’equation’

’
Author(s)
Vojtech Janousek, 
Examples
calcCore("SiO2/10")
calcCore("Na2O+K2O")
calcCore("log10(Na2O+K2O)")
calcCore("SiO2/MW[\"SiO2\"]")
# dividing by the built-in molecularWeight, NB the backslashes
calcCore("length(MgO)")
calcCore("mean(MgO,na.rm=TRUE)")
# na.rm is a safety measure in case some missing values are present
# otherwise the result would be 'NA'

Catanorm

35

Catanorm

Niggli’s Molecular Norm (Catanorm)

Description
Calculates the Niggli’s Molecular Norm (Catanorm) using the algorithm given by Hutchison (1974).
Usage
Catanorm(WR,precision=getOption("gcd.digits"))
Arguments
WR

a numerical matrix; the whole-rock data to be normalized.

precision

precision of the result.

Details
Normative minerals of the Catanorm
Parameter
Q
C
Or
Plag
Ab
An
Lc
Ne
Kp
Ac
Ns
Ks
Hy
Di
Wo
En
Fs
Ol
Fo
Fa
Cs
Mt
Hm
Il
Tn
Pf
Ru
Ap
Fr

Full name
Quartz
Corundum
Orthoclase
Plagioclase
(Albite)
(Anorthite)
Leucite
Nepheline
Kaliophilite
Acmite
Sodium metasilicate
Potassium metasilicate
Hypersthene
Diopside
(Wollastonite)
(Enstatite)
(Ferrosillite)
Olivine
(Forsterite)
(Fayalite)
Calcium orthosilicate
Magnetite
Hematite
Ilmenite
Sphene
Perovskite
Rutile
Apatite
or with no F
Fluorite

Formula
SiO2
AlO1.5
KO0.5 .AlO1.5 .3SiO2
Abx .An100−x
N aO1.5 .AlO1.5 .3SiO2
CaO.2AlO1.5 .2SiO2
KO0.5 .AlO1.5 .2SiO2
N aO0.5 .AlO1.5 .SiO2
KO0.5 .AlO1.5 .SiO2
N aO0.5 .F eO1.5 .2SiO2
2N aO0.5 .SiO2
2KO0.5 .SiO2
Enx .F s100−x
W o50 .Enx .F s50−x
CaO.SiO2
M gO.SiO2
F eO.SiO2
F ox .F a100−x
2M gO.SiO2
2F eO.SiO2
2CaO.SiO2
F eO.2F eO1.5
F eO1.5
F eO.T iO2
CaO.T iO2 .SiO2
CaO.T iO2
T iO2
9CaO.6P O2.5 .CaF2
5CaO.3P O2.5
CaF2

36

CIPW
Py
Cf

Pyrite
Calcite

F eS2
CaO.CO2

Value
A numeric matrix ’results’.
Author(s)
Vojtech Janousek, 
References
Hutchison C S (1974) Laboratory Handbook of Petrographic Techniques. John Wiley & Sons, New
York, p. 1-527

CIPW

CIPW norm

Description
Calculates various modifications of the CIPW norm.
Usage
CIPW(WR, precision = getOption("gcd.digits"), normsum =
FALSE, cancrinite = FALSE, spinel = FALSE, complete.results = FALSE)
CIPWhb(WR, precision = getOption("gcd.digits"), normsum = FALSE,
cancrinite = FALSE, spinel = FALSE, complete.results = FALSE)
Arguments
WR

a numerical matrix; the whole-rock data to be normalized.

precision

precision of the result.

normsum

logical; shall be the normative minerals recast to 100 %?

cancrinite

logical; is cancrinite present/to be calculated?

spinel

logical; is spinel to be calculated (for ultrabasic rocks, i.e. for samples with
SiO2 < 45 % only)?
complete.results
logical; should be returned more extensive list of minerals, including the end
members making up Di, Hy, Ol, Bi and Hbl?

CIPW

37

Details
The method adopted for ’classic’ CIPW norm calculation is that of Hutchison (1974, 1975). The
function ’CIPWHB’ is its modification with biotite and hornblende (Hutchison 1975).
Normative minerals of the standard CIPW norm
Parameter
Q
C
Or
Ab
An
Lc
Ne
Kp
Nc
Ac
Ns
Ks
Di
__(MgDi)
__(FeDi)
Wo
Hy
__(En)
__(Fs)
Ol
__(Fo)
__(Fa)
Dcs
Mt
Il
Hm
Tn
Pf
Ru
Ap
Fr
Py
Sp
__(MgSp)
__(FeSp)
Cc

Full name
Quartz
Corundum
Orthoclase
Albite
Anorthite
Leucite
Nepheline
Kaliophilite
Sodium carbonate
Acmite
Sodium metasilicate
Potassium metasilicate
Diopside
__(Mg-diopside)
__(Fe-diopside)
Wollastonite
Hypersthene
__(Enstatite)
__(Ferrosillite)
Olivine
__(Forsterite)
__(Fayalite)
Dicalcium silicate
Magnetite
Ilmenite
Hematite
Sphene
Perovskite
Rutile
Apatite
Fluorite
Pyrite
Spinel
__(Mg-spinel; spinel s. s.)
__(Fe-spinel; hercynite)
Calcite

Formula
SiO2
Al2 O3
K2 O.Al2 O3 .6SiO2
N a2 O.Al2 O3 .6SiO2
CaO.Al2 O3 .2SiO2
K2 O.Al2 O3 .4SiO2
N a2 O.Al2 O3 .2SiO2
K2 O.Al2 O3 .2SiO2
N a2 O.CO2
N a2 O.F e2 O3 .4SiO2
N a2 O.SiO2
K2 O.SiO2

Molecular weight
60.08
101.96
556.64
524.42
278.20
436.48
284.10
316.32
105.99
461.99
122.06
154.28

CaO.M gO.2SiO2
CaO.F eO.2SiO2
CaO.SiO2

216.55
248.09
116.16

M gO.SiO2
F eO.SiO2

100.39
131.93

2M gO.SiO2
2F eO.2SiO2
2CaO.SiO2
F eO.F e2 O3
F eO.T iO2
F e2 O3
CaO.T iO2 .SiO2
CaO.T iO2
T iO2 .SiO2
3CaO.P2 O5 .1/3CaF2
CaF2
F eS2

140.70
203.78
172.24
231.54
151.75
159.69
196.06
135.98
79.90
336.21
78.08
119.98

CaO.M gO.2SiO2
CaO.F eO.2SiO2
CaO.CO2

142.27
173.81
100.09

Additional minerals of the modification with hornblende and biotite
Parameter
Bi
__(MgBi)
__(FeBi)
Hbl
Act

Full name
Biotite
__(Phlogopite)
__(Annite)
Hornblende
Actinolite

Formula

Molecular weight

KO0.5 .3M gO.AlO1.5 .3SiO2
KO0.5 .3F eO.AlO1.5 .3SiO2

798.50
987.74

38

classify
__(MgAct)
__(FeAct)
Ed
__(MgEd)
__(FeEd)
Ri

__(Tremolite)
__(Ferroactinolite)
Edenite
__(Edenite)
__(Ferroedenite)
Riebeckite

2CaO.5M gO.8SiO2
2CaO.5F eO.8SiO2

794.35
952.05

N aO0.5 .2CaO.5M gO.AlO1.5 .7SiO2
N aO0.5 .2CaO.5F eO.AlO1.5 .7SiO2
2N aO0.5 .2F eO1.5 .3F eO.8SiO2

1632.48
1947.88
917.87

Value
A numeric matrix ’results’.
Author(s)
Vojtech Janousek, 
References
Hutchison C S (1974) Laboratory Handbook of Petrographic Techniques. John Wiley & Sons, New
York, p. 1-527
Hutchison C S (1975) The norm, its variations, their calculation and relationships. Schweiz Mineral
Petrogr Mitt 55: 243-256

classify

Generic Classification Algorithm

Description
Classifies rocks using specified diagram.
Usage
classify(diagram = NULL, grp = TRUE, labs = FALSE,
source.sheet = TRUE, overlap = FALSE, X = x.data,
Y = y.data, silent = FALSE, clas=sheet$d$t, ...)
Arguments
diagram

name of diagram to be used, see details for more info

grp

logical: if TRUE, results are assigned to the variable ’groups’

labs

logical: if TRUE, yes/no dialogue for results assignment into the matrix ’labels’
appears

source.sheet

logical: if TRUE, the sheet for diagram is newly assigned

overlap

logical: if TRUE, possible overlap between polygons of diagram is expected,
and duplicate positive result for one sample is treated as polygon intersection

X

vector of values for abscissa

Y

vector of values for ordinate

silent

logical: if TRUE, informative outputs are reduced to minimum

clas

classification template to be used

...

any additional graphical parameters

cluster

39

Details
Function looks for the name of the polygon within the classification diagram, into which falls the
rock analysis represented by the coordinates [x.data,y.data].
In some cases (TAS diagram, Winchester & Floyd’s diagram) additional computations are performed. The argument ’diagram’ may acquire one of following values:

'AFM', 'PeceTaylor', 'Shand', 'TAS', 'CoxPlut', 'CoxVolc',
'Jensen', 'LarochePlut', 'LarocheVolc', 'WinFloyd1',
'WinFloyd2','TASMiddlemostPlut', 'TASMiddlemostVolc',
'DebonPQ', 'DebonBA', 'MiddlemostPlut', 'QAPFPlut',
'QAPFVolc', 'OConnorPlut', 'QAPFVolc','OConnorVolc',
'Miyashiro', 'Hastie', 'Pearce1996', 'Villaseca', 'NaAlK'.
The function is based on the sp package.
Value
Vector of resulting rock names is stored in a variable ’results’. If ’grp = TRUE’ results are also
assigned to the ’groups’ and ’grouping’ is set to -1 (as if called from the menu 'Data handling').
If rock projection falls on the boundary between two or more fields, rock names in question are
merged together with comment ’boundary between ...’.
Author(s)
The sp package was written by Edzer Pebesma, Roger Bivand and others.
Vojtech Erban, 
See Also
plotDiagram
.claslist
figaro
AFM, PeceTaylor, Shand, NaAlK, TAS, Cox, TASMiddlemost, Jensen, Laroche, WinFloyd1, WinFloyd2,
DebonPQ, DebonBA, Middlemost, QAPF, OConnor Miyashiro Hastie Pearce1996 Villaseca

cluster

Statistics: Hierarchical clustering

Description
Hierarchical cluster analysis on a set of dissimilarities.

40

contourGroups

Usage
cluster(elems = "SiO2,TiO2,Al2O3,FeOt,MnO,MgO,CaO,Na2O,K2O",
method = "average")
Arguments
elems

numerical columns to be used for cluster analysis, typically major elements

method

the agglomeration method to be employed. This should be one of (or an unambiguous abbreviation thereof): 'ward', 'single', 'complete',
'average', 'mcquitty', 'median', 'centroid'.

Details
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
Even though a list of major elements is assumed as a default, different variables can be specified by
the function ’selectColumnsLabels’.
The user is also asked to specify a label for the individual samples, default are their names.
After the dendrogram is drawn, the individual clusters can be identified. For each sample falling
into the given group, specified information (e.g. Locality, Rock Type and/or Author) can be printed.
For further details on the clustering algorithm, see the R manual entry of ’hclust’.
Value
None.
Warning
Names of existing numeric data columns and not formulae involving these can be handled at this
stage. Only complete cases are used for the cluster analysis.
Author(s)
Vojtech Janousek, 
See Also
’hclust’

contourGroups

Outline individual groups in a binary plot

Description
The functions outline the individual clusters of data (groups by default) on a binary plot. Implemented methods are the convex hull or contours. This can be useful for a quick appreciation of the
data distribution, e.g. in classification diagrams.

contourGroups

41

Usage

contourGroups(clusters=groups,border=NULL,fill=FALSE,precision=50, ...)
chullGroups(clusters=groups,border=NULL,fill=FALSE,...)

Arguments

clusters

grouping information for each of the samples.

border

outline colours.

fill

logical; should be the polygons filled by the border colour?

precision

a number indicating how tight the contours should be.

...

additional parameters to the functions contour and polygon, respectively.

Details

If not specified, the colours are selected as the most frequently occurring one defined among samples
within each group.

For the function contourGroups, the shape of the contours drawn can be controlled using the parameter (precision). The higher it is, the smoother contours result.

42

contourGroups

Value
None.
Author(s)
Vojtech Janousek, 
See Also
chull, contour, polygon
Examples
data<-loadData("sazava.data",sep="\t")
groupsByLabel("Intrusion")
plotDiagram("PeceTaylor",FALSE,FALSE)
chullGroups()
chullGroups(fill=TRUE)

coplotByGroup

43

plotDiagram("PeceTaylor",FALSE,FALSE)
contourGroups()

coplotByGroup

Coplot by groups

Description
Plots a series of binary plots, for each of the groups separately.

Usage
coplotByGroup(xaxis = "",yaxis = "",show.leg = "")

Arguments
xaxis

Name of the data column to be used as x axis.

yaxis

Name of the data column to be used as y axis.

show.leg

Logical: are the levels of the conditioning variable (’groups’) to be shown?

Details
For examination of large datasets split into user-defined subsets serves in R function coplot. It
produces a set of binary diagrams with the data filtered out according to the values of the third
(conditioning) variable. In case of the function ’coplotByGroup’ it is done by groups.

44

coplotByGroup

If no parameters 'xlab', 'ylab' and ’show.leg’ are given, the user is prompted to specify them.
The variables to be plotted are selected using the function ’selectColumnLabel.
See manual entry for ’coplot’ for further details.
Value
None.
Warning
Please note that no formulae can be handled at this stage.
This function IS NOT Figaro-compatible.
Author(s)
Vojtech Janousek,  & Vojtech Erban, 

coplotTri

45

See Also
’coplot’

Examples
coplotByGroup("SiO2","Na2O",show.leg=TRUE)

coplotTri

Coplot for three variables

Description
Plots a series of binary plots split into several groups according to the values of the third, so called
conditioning, variable.

Usage
coplotTri(xaxis = "", yaxis = "", zaxis = "", int = "")

Arguments
xaxis

Name of the data column to be used as x axis.

yaxis

Name of the data column to be used as y axis.

zaxis

Name of the data column with the conditioning variable.

int

The specification of the intervals: either ’auto’ or a list of break points separated
by commas.

Details
For examination of large datasets split into user-defined subsets serves in R the function ’coplot’.
It displays a series of binary diagrams with the data filtered out according to the values of the third
(conditioning) variable.

46

coplotTri

If no parameters 'xlab', 'ylab' and 'zlab' are given, the user is prompted to specify them.
The variables to be plotted are selected using the function ’selectColumnLabel.
After this is done, the user is prompted to enter a comma-delimited list of at least one break point
defining the intervals. The default includes the mean, that will be automatically supplemented by
minimum and maximum (i.e. two intervals).
See manual entry for ’coplot’ for further details.
Value
None.
Warning
Please note that no formulae can be handled at this stage.
This function IS NOT Figaro-compatible.
Author(s)
Vojtech Janousek,  & Vojtech Erban, 

correlationCoefPlot

47

See Also
’coplot’

Examples
coplotTri("SiO2","Na2O","MgO","auto")
coplotTri("MgO","Na2O","SiO2","50,60")
# the intervals of the conditioning variable, SiO2,
# will be (min(SiO2) - 50),(50 - 60) and (60 - max(SiO2))

correlationCoefPlot

Statistics: Correlation coefficient patterns

Description
Produces, for each group a separate, set of plots of correlation coefficient patterns.

Usage
correlationCoefPlot(elems = NULL)

Arguments
elems

list of desired elements

Details
The utility of correlation coefficient patterns was discussed by Rollinson (1993 and references
therein). Basically similarity in correlation patterns between two or more elements means their
analogous geochemical behaviour, potentially controlled by the same geochemical process (fractional crystallization, partial melting, weathering, hydrothermal alteration...)

48

Cox

The variables are selected using the function ’selectColumnsLabels’.
Value
None.
Author(s)
Vojtech Janousek, 
References
Rollinson H R (1993) Using Geochemical Data: Evaluation, Presentation, Interpretation. Longman,
London, p. 1-352
Examples
correlationCoefPlot(elems="K,Rb,Sr,Cr,Nb,Ti")

Cox

TAS diagram (Cox et al. 1979)

Description
Assigns data for Cox’s diagram into Figaro template (list ’sheet’) and appropriate values into
’x.data’ and ’y.data’.

Cox

49

Usage
CoxVolc(alkline=TRUE)
CoxPlut(alkline=TRUE)
Arguments
alkline

Logical: Should the boundary between alkaline and subalkaline rocks (Irvine &
Baragar 1971) be drawn?

Details
TAS diagram, as proposed by Cox et al. (1979) for volcanic rocks and adapted by Wilson (1989)
for plutonic rocks contains following fields:
volcanic rocks
basalt
basaltic andesite
andesite
dacite
rhyolite
hawaiite
trachyandesite
basanite/tephrite
mugearite
benmorite
trachyte
nephelinite
phonology nephelinite
phonolitic tephrite
phonolite

plutonic rocks
gabbro
undefined
diorite
quartz diorite (granodiorite)
alkali granite/granite
gabbro
undefined
undefined
syeno-diorite
syenite
syenite
ijolite
undefined
undefined
nepheline syenite

For volcanic rocks, the following diagram is plotted:

50

Cox

And this is the version for plutonic rocks:

crosstab

51

Value
sheet

list with Figaro Style Sheet data

x.data

SiO2 weight percent

y.data

Na2O+K2O weight percent

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Cox K G, Bell J D & Pankhurst (1979) The Interpretation of Igneous Rocks. Allen & Unwin,
London
Wilson M (1989) Igneous Petrogenesis. Chapman & Hall, London
Irvine T M & Baragar W R (1971) A guide to the chemical classification of common volcanic rocks.
Canad J Earth Sci 8: 523-548 doi: 10.1139/e71-055
See Also
classify figaro plotDiagram
Examples
#TAS diagram is called using following auxiliary functions:
#Classifies data stored in WR (Groups by diagram)
classify("CoxVolc")
#or
classify("CoxPlut")
#Plots data stored in WR or its subset (menu Classification)
plotDiagram("CoxVolc", FALSE)
#or
plotDiagram("CoxPlut", FALSE)

crosstab

Cross table of labels

Description
Prints a cross table (contingency table) for 1-3 labels.
Usage
crosstab(plot = TRUE)
Arguments
plot

logical; should be also a barplot plotted?

52

customScript

Details
This command prints a frequency distribution (for a single label) or a contingency table (for 2-3
labels) useful for inspection of the data structure. Optionally a barplot is plotted (for 1-2 labels).
Just press Enter (enter an empty field), when the desired number of variables is reached.
Value
results

the frequency/contingency table

Author(s)
Vojtech Janousek, 

customScript

Add a new variable to a script

Description
Adds a formula to calculate a single numeric variable to the specified *.r file (a R script).
Usage
customScript()
Details
A formula can be entered that can involve any combination of names of existing numerical columns,
with the constants, brackets, arithmetic operators +-*/^ and R functions. See calcCore for a correct
syntax.
Then the user is prompted for the name of the variable an any comments that should appear in the
file.
The filename is chosen interactively, the default suffix for the R programs is .r. If the file exists
already, the script is appended to its end.
If desired, the calculated variable can be, after the script is executed, added automatically to the
numeric data, i.e. the numeric matrix WR. If not, the contents of the calculated variable can be
viewed by simply typing its name in the R Console window.
The script can be run at a later time using the R command File|Source. Alternatively, it can be
placed among the so-called plugins into the subdirectory Plugin. All files placed here with a suffix
*.r are executed each time when the new data file is being loaded into the GCDkit.
Value
None.
Author(s)
Vojtech Janousek, 

cutMy

53

Examples
## Not run:
# examples of valid formulae....
(Na2O+K2O)/CaO
Rb^2
log10(Sr)
mean(SiO2)/10
# ... but this command is in fact a simple R shell # meaning lots of fun for power users!
summary(Rb,na.rm=T)
cbind(SiO2/2,TiO2,Na2O+K2O)
cbind(major)
hist(SiO2,col="red")
boxplot(Rb~factor(groups))
# possibilities are endless
plot(Rb,Sr,col="blue",pch="+",xlab="Rb (ppm)",ylab="Sr (ppm)",log="xy")
## End(Not run)

cutMy

Groups by numerical variable

Description
Grouping the data according to the interval of a single numerical variable it falls into.
Usage
cutMy(where=NULL,int=NULL,int.lab=NULL,na.lab="Unclassified")
Arguments
where

Numeric data column in ’WR’ - the basis of the classification.

int

Boundaries of intervals.

int.lab

Labels for the intervals

na.lab

Labels for samples that cannot be classified

Details
The numeric data column is selected using the function ’selectColumnLabel’.
After this is done, the user is prompted to enter a comma-delimited list or at least one break point
defining the intervals. The default includes the mean, that will be automatically supplemented by
minimum and maximum (i.e. two intervals).
Then the names of the individual groups are to be specified; values out of range are automatically
labeled as 'Unclassified'. The vector containing the information on the current groups can be
appended to the data frame ’labels’.

54

Debon

Value
groups

character vector: the grouping information

grouping

If the new column was appended the data frame labels, sequence number of
this column; if not appended, though, this variable is set to -100.

Author(s)
Vojtech Janousek, 

See Also
cut

Debon

BA and PQ diagrams (Debon + Le Fort 1983)

Description
Assigns data for Debon & Le Fort’s B-A and P-Q diagrams into Figaro template (list ’sheet’) and
appropriate values into ’x.data’ and ’y.data’.

Usage
DebonBA()
DebonPQ()

Details
The B-A diagram as proposed by Debon and Le Fort (1983) defines six sectors (I - VI), reflecting
alumina balance of samples. Following minerals are characteristic for individual sectors:

Debon

55

I
II
III
IV
V
VI

Peraluminous domain

Metaluminous domain

muscovite > biotite
biotite > muscovite
biotite (+- minor amphibole)
biotite, amphibole, +- pyroxene
clinopyroxene, +- amphibole, +-biotite
unusual mineral associations (carbonatites . . . )

Layout of the P-Q diagram of the same authors corresponds to cationic proportions of quartz, Kfeldspar and plagioclase.

56

Debon

Abbreviations used as classification output represent following rocks groups:
label
go
mzgo
mz
s
dq
mzdq
mzq
sq
to
gd
ad
gr

plutonic rocks
gabbro, diorite, anorthosite
monzogabbro, monzodiorite
monzonite
syenite
qtz diorite,qtz gabbro,qtz anorthosite
qtz monzodiorite,qtz monzogabbro
quartz monzonite
quartz syenite
tonalite, trondhjemite
granodiorite, granogabbro
adamellite
granite

volcanic rocks
basalt, andesite, kenningite
latibasalt, latiandesite
latite
trachyte
qtz andesite,qtz basalt
qtz latiandesite,qtz latibasalt
quartz latite
quartz trachyte
dacite
rhyodacite
dellenite
rhyolite

Parameters for the diagram are calculated by the function ’DebonCalc’. All of them are based on
millications (1000 gram-atoms per 100 grams).
P = K - (Na + Ca)
Q = Si / 3 - (K + Na + 2 * Ca / 3)
A = Al - (K + Na + 2 Ca)
B = Fe + Mg + Ti
Note that the diagrams B-A and P-Q are recommended as complementary, i.e. resulting names
should be used in conjunction (granite II etc.). For details, see Debon & Le Fort (1983) or (1988).

deleteSingle

57

Value
sheet

list with Figaro Style Sheet data

x.data

P or B value. See details.

y.data

Q or A value. See details.

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Debon F & Le Fort P (1983) A chemical-mineralogical classification of common plutonic rocks
and associations. Trans Roy Soc Edinb; Earth Sci 73: 135-149
Debon F & Le Fort P (1988) A cationic classification of common plutonic rocks and their magmatic
associations: principles, method, applications. Bull. Mineral 111: 493-511
See Also
classify figaro plotDiagram DebonCalc

deleteSingle

Delete label or variable

Description
Deletes a single numeric variable or a label.
Usage
deleteSingle()
Details
The variables to be deleted is selected using the function ’selectColumnLabel’. In any case, a
confirmation is required before a variable is deleted from the system. Note that some variables are
required by the system and cannot be deleted.
Value
Returns the corrected version of the data frame ’labels’ or numeric matrix ’WR’.
Author(s)
Vojtech Janousek, 

58

Edit numeric data

Edit labels

Edit labels

Description
Simultaneous editing of all labels using a spreadsheet-like interface.
Usage
editLabels()
Arguments
none.
Details
The function invokes a spreadsheet-like interface that enables the user to edit the labels for individual samples. When all the desired changes have been performed, close button is to be clicked.
Value
Returns the corrected version of the data frame ’labels’.
Author(s)
Vojtech Janousek, 
See Also
’data.entry’

Edit numeric data

Edit numeric data

Description
Simultaneous editing of all numeric data using a spreadsheet-like interface.
Usage
editData(x=WR)
Arguments
x

data frame/numeric matrix to be edited; default is ’WR’, i.e. numeric data

editLabFactor

59

Details
The function invokes a spreadsheet-like interface that enables the user to edit the numeric data
for individual samples. When all the desired changes have been performed, close button is to be
clicked.
The system then performs some recalculations as if the data set was loaded from the disc afresh
(calling ’Gcdkit.r’).
Value
Returns the corrected version of the numeric matrix ’WR’.
Author(s)
Vojtech Janousek, 
See Also
’data.entry’

editLabFactor

Edit label as factor

Description
Global replacement each of the discrete values (levels) for a selected label.
Usage
editLabFactor()
Details
The label to be edited is selected using the function ’selectColumnLabel’.
Then the function invokes a spreadsheet-like interface that enables the user to overwrite directly
any of the discrete values for the a given label, in the R jargon called levels. When all the desired
changes have been performed, close button is to be clicked.
Value
Returns the corrected version of the data frame labels.
Author(s)
Vojtech Janousek, 
See Also
’data.entry’

60

elemIso

elemIso

Binary plot of a WR geochemical parameter vs isotopic ratio

Description
Plots a diagram of a selected whole-rock geochemical parameter vs initial Sr isotopic ratios or initial
(N d) for selected samples.

Usage
elemIso()
Arguments
None.
Details
The variable to be plotted as x axis is selected using the function ’selectColumnLabel’. In the
specification of the variable can be used also an arithmetic expression, see calcCore for the correct
syntax.
The plotted isotopic parameters (y axis) can be one of:
Menu item
87Sr/86Sri
143Nd/144Ndi

Explanation
Initial Sr isotopic ratios
Initial Nd isotopic ratios

epsEps

61
EpsNdi
1 stg DM model ages (Goldstein et al. 1988)
1 stg DM model ages (Liew & Hofmann 1988)
2 stg DM model ages (Liew & Hofmann 1988)

Initial (N d) values
Single-stage DM Nd model ages
Single-stage DM Nd model ages
Two-stage DM Nd model ages

The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.

Value
None.

Plugin
SrNd.r

Author(s)
Vojtech Janousek, 

References
Liew T C & Hofmann A W (1988) Precambrian crustal components, plutonic associations, plate
environment of the Hercynian Fold Belt of Central Europe: indications from a Nd and Sr isotopic
study. Contrib Mineral Petrol 98: 129-138
Goldstein S L, O’Nions R K & Hamilton P J (1984) A Sm-Nd isotopic study of atmospheric dusts
and particulates from major river systems. Earth Planet Sci Lett 70: 221-236

epsEps

Binary plot of initial Sr isotopic ratios vs. initial epsilon Nd values

Description
Plots a diagram of initial Sr isotopic ratios vs. initial (N d) values for selected samples.

62

epsEps

Usage
epsEps()
Arguments
None.
Details
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
Value
None.
Plugin
SrNd.r

Export to Access

63

Author(s)
Vojtech Janousek, 
Export to Access

Export to Access

Description
This function serves for exporting the specified data into MDB (MS Access) format (via the ODBC
interface).
Usage
accessExport(what=cbind(labels, WR), tablename=NULL,
transpose=FALSE,dec.places=NULL)
Arguments
what
tablename
transpose
dec.places

a matrix, data frame or a list
name of the data table
logical; transpose the data?
numeric; number of decimal places

Details
The function accessExport outputs the specified data via Microsoft’s ODBC interface, taking an
advantage of the library 'RODBC'. Unlike for the function ’excelExport’, ODBC makes possible
opening a new file.
If the argument 'what' is a matrix or data frame, the name of the table can be specified using the
optional parameter 'tablename'.
For a list, several tables are created, their number and names corresponding to the items present.
Value
None.
Warning
This function is not available on 64-bit systems!
Author(s)
The RODBC package was written by Brian Ripley.
Vojtech Janousek, 
See Also
’excelExport’ ’dbfExport’
Examples
accessExport(results) # Saves the last calculated results

64

Export to Excel

Export to DBF

Export to DBF

Description
This function serves for exporting the specified data into DBF (dBase III) format (using the function
’write.dbf’ of the package ’foreign’).
Usage
dbfExport(what=cbind(labels,WR), transpose=FALSE)
Arguments
what
transpose

a matrix or data frame
logical; transpose the data frame?

Details
The function dbfExport outputs the specified data. Note that it cannot handle lists.
Value
None.
Author(s)
Vojtech Janousek, 
See Also
’write.dbf’ ’excelExport’ ’accessExport’
Examples
dbfExport(results) # Saves the last calculated results

Export to Excel

Export to Excel

Description
This function serves for exporting the specified data into XLS or XLSX (MS Excel) formats (via
the ODBC interface).
Usage
excelExport(what=cbind(labels, WR), tablename =NULL,
transpose=FALSE, dec.places=NULL)
excel2007Export(what=cbind(labels, WR), tablename =NULL,
transpose=FALSE, dec.places=NULL)

Export to HTML tables

65

Arguments
what

a matrix, data frame or a list

tablename

name of the data sheet

transpose

logical; transpose the data?

dec.places

numeric; number of decimal places

Details
The functions excelExport and excel2007Export output the specified data via Microsoft’s ODBC
interface, taking an advantage of the library 'RODBC'.
If the argument 'what' is a matrix or data frame, the name of the sheet can be specified using the
optional parameter 'tablename'.
For a list, several sheets are attached, their number and names corresponding to the items present.
Value
None.
Warning
These functions are not available on 64-bit systems!
Unfortunately the way the ODBC is programmed by Microsoft does not make opening a new Excel
file possible. Thus only adding new sheet(s) to a pre-existing spreadsheet file is feasible.
Author(s)
The RODBC package was written by Brian Ripley.
Vojtech Janousek, 
See Also
’accessExport’ ’dbfExport’
Examples
excelExport(results) # Saves the last calculated results in XLS format
excel2007Export(results) # Saves the last calculated results in XLSX (or XLS) format

Export to HTML tables Export to HTML tables

Description
Outputs the specified data with (optional) labels into HTML. This format is useful for importing
into spreadsheets, word processors or publishing on the WWW.

66

Export to HTML tables

Usage
HTMLTableMain(what,digits=2,desc=NULL,title=" ",sum.up=FALSE,open=TRUE,
close=TRUE,filename=paste(data.dir,"R2HTML/htmltable",sep="/"),rotate=FALSE)
HTMLtableOrdered(what,which=rownames(what),labs=labels,digits=2,desc=NULL,
title=" ",sum.up=FALSE,key1=NULL,key2=NULL,
filename=paste(data.dir,"R2HTML/htmltable",sep="/"),split.by=25,rotate=TRUE)
HTMLTableWR(filename="htmltable")
HTMLTableResults(filename="htmltable")
Arguments
what

numeric matrix; data to be exported

digits

required precision

desc

name of the columns within ’labels’ to be attached to the table

title

main title

sum.up

logical; should be a sum calculated?

open

logical; should be opened a new HTML file?

close

logical; should be the HTML file closed when finished?

filename

optional name for the file produced

rotate

logical, should be the table transposed, with samples in columns and variables
in rows?

which

(optional) sample names in numeric matrix ’what’ for the output

labs

name of variable with textual labels

key1

is a variable in numeric matrix ’what’

key2

is a grouping information (name of a column in ’labs’)

split.by

maximal number of data columns per page

Details
HTMLTableWR and HTMLTableResults are GUI front ends to HTMLTableMain, the former enabling
the user to choose samples (rows) and columns for the output using the searching mechanisms
common in the GCDkit.
HTMLTableWR outputs the numeric data (with optional labels and sum) stored in the data matrix
'WR'.
HTMLtableOrdered also outputs the numeric data stored in the numeric matrix specified by parameter 'what'. Optional argument 'which' gives the list of sample names (rows) in the matrix to
be saved. The data are first sorted based on 'key2', which typically gives a grouping information
(name of a column in 'labs'). Within each of the groups, the data are further sorted based on the
numeric variable 'key1'. See example.
HTMLTableResults outputs the results of the most recent calculation (with optional labels and sum)
as stored in the variable 'results'.
The plugin attempts to format sub- and superscripts in the names of variables.
The created file 'filename' is placed in the subdirectory 'R2HTM' of the current working directory;
when finished, it is previewed in a browser. The style for the table is determined by the cascade
style file R2HTML.css in the subdirectory 'Plugin'.

Export to HTML tables

67

Value
None.
Warning
The plugin uses R2HTML library, which must be downloaded from CRAN and properly installed.
Its presence is checked before the code is executed.
Author(s)
The R2HTML package was written by Eric Lecoutre.
Vojtech Janousek, 
Examples
# Works on the 'sazava' test data set
setwd(paste(gcdx.dir,"Test_data",sep="/"))
loadData("sazava.data")
HTMLTableMain(WR[,c("SiO2","MgO","FeOt")],digits=2,desc="Intrusion",title="Sazava [wt.%]")
HTMLtableOrdered(WR[,LILE],digits=1,key1="SiO2",key2="Intrusion",title="Large Ion Lithophile
Elements (ppm)",split.by=3)

68

FeMiddlemost

FeMiddlemost

Adjustment of Fe oxidation ratio (Middlemost 1989))

Description
Auxiliary function performing adjustment of the iron-oxidation ratio as proposed by Middlemost
(1989).
Usage
FeMiddlemost(anhydrous = TRUE)
Arguments
anhydrous

logical; should be returned major-element analyses recast to anhydrous basis?

Details
This function performs an adjustment of the iron-oxidation ratio for individual volcanic rock types
as proposed by Middlemost (1989).
The classification is based on TAS classification (Le Bas et al. 1986, Le Maitre et al. 1989).
The F e2 O3 /F eO ratios for individual rock types, based on Verma et al. (2002) (Fig. 1), are as
follows:
foidite, N a2 O + K2 O <= 3
foidite, 3 < N a2 O + K2 O <= 7
foidite, 7 < N a2 O + K2 O <= 10
foidite, N a2 O + K2 O > 10
picrobasalt
basalt
basaltic andesite
andesite
dacite
rhyolite
trachybasalt
basaltic trachyandesite
trachyandesite
trachyte/trachydacite
tephrite/basanite, N a2 O + K2 O <= 6
tephrite/basanite, N a2 O + K2 O > 6
phonotephrite
tephriphonolite
phonolite

0.15
0.2
0.3
0.4
0.15
0.2
0.3
0.35
0.4
0.5
0.3
0.35
0.4
0.5
0.2
0.3
0.35
0.4
0.5

If the parameter ’anhydrous’ is set, returned are the major-element data recast to 100 % anhydrous
basis.

figAdd

69

Value
A matrix with adjusted whole-rock chemical data.
No permanent changes to either ’WR’ or ’WRanh’ are made.
Author(s)
Vojtech Janousek, 
References
Le Bas M J, Le Maitre R W, Streckeisen A & Zanettin B (1986) A chemical classification of volcanic rocks based on the total alkali-silica diagram. J Petrology 27: 745-750 doi: 10.1093/petrology/27.3.745
Le Maitre R W et al (1989) Igneous Rocks: A Classification and Glossary of Terms, 1st edition.
Cambridge University Press
Middlemost E A K (1989) Iron oxidation ratios, norms and the classification of volcanic rocks.
Chem Geol 77: 19-26 doi: 10.1016/0009-2541(89)90011-9
Verma S P, Torres-Alvarado I S, Sotelo-Rodriguez Z T (2002) SINCLAS: standard igneous norm
and volcanic rock classification system. Comput and Geosci 28: 711-715 doi: 10.1016/S00983004(01)00087-5
See Also
TAS Verma
figAdd

Plot editing: Add

Description
These functions enable adding new components to Figaro-compatible plots.
Usage
figTicks(major=-0.5, minor=0.25, xmjr=NULL, xmin=NULL, ymjr=NULL, ymin=NULL)
figGrid(lty="dotted", col="gray30")
figLegend()
figAddReservoirs(autoscale=FALSE, var.name=NULL, sample.names=NULL,
reserv.condition=NULL, labs=NULL, pch="*", col="darkred", cex=1.5, type="p",...)
figAddText()
figAddArrow()
figAddBox()
figAddFit()
figAddCurve(equation=NULL)

70

figAdd

Arguments
major

length of the major tick marks.

minor

length of the minor tick marks.

xmjr, ymjr

intervals for the major tick marks.

xmin, ymin

intervals for the minor tick marks.

lty

grid line type.

col

plotting colour.

autoscale

logical; should be the scaling changed so that all the overplotted values are
shown?

var.name

text; either ’reservoirs.data’, ’idealmins.data’or a name of a global variable. See Details.

sample.names
character vector; names of reservoirs, ideal minerals or samples to be plotted.
reserv.condition
text; regular expression specifying reservoirs compositions of which are to be
plotted.
labs

text; optional abbreviated labels for the individual reservoirs

pch

plotting symbol.

cex

numeric; relative size of the plotting symbol.

type

character; plot type; see plot.default.

...

additional parameters to the plotting function. See figOverplot.

equation

text; equation expressed as a function of x; see curve.

Details
’figTicks’ adds major and minor tick marks for the x and y axes. Their length is specified as a
fraction of the height of a line of text. Negative numbers imply outward and positive inward pointing
ticks. The user is prompted for four numbers separated by commas, xmjr, xmin, ymjr, ymin. These
specify the intervals of major and minor ticks for x and y axes, respectively. Not implemented to
logarithmic plots and spiderplots yet.
’figGrid’ adds grid lines for x and/or y axes.
’figLegend’ adds legend(s) on specified location.
’figAddReservoirs’ overplots compositions of selected geochemical reservoirs (from the file ’reservoirs.data’,
see selectNorm for the file structure as well as relevant references) or ideal minerals (from the file
’idealmins.data’). Alternatively, if the name of a numeric matrix or dataframe in the global environment is provided via the argument ’var.name’, the selection of data from this object is used
(see Examples). The selection is specified by either ’sample.names’ or by ’reserv.condition’
parameters. Optional parameter ’labs’ can specify alternative, perhaps abbreviated textual labels
to the points plotted. Please note that this function is available so far for spiderplots, binary and
ternary plots only.
’figAddText’ adds text on specified location. The parameters are the text style (’n’ = normal, ’b’ =
bold, ’i’ = italic and ’bi’ = bold italic), colour and relative size.
’figAddArrow’ adds arrow on specified location. The parameters are colour and line style (’solid’,
’dashed’, ’dotted’ and ’dotdash’).
’figAddBox’ adds box on specified location (click bottom left and then top right corner).

figAdd

71

’figAddFit’ adds either a single least-squares fit to all data, or several fit lines, for each of the
groups separately. The parameters are colour and line style (’solid’, ’dashed’, ’dotted’ and
’dotdash’). The equation of each fit line is plotted at the user-defined location.
’figAddCurve’ adds a curve, specified as a function of variable ’x’. The parameters are colour and
line style (’solid’, ’dashed’, ’dotted’ and ’dotdash’).
The colours can be specified both by their code (see table under menu ’Data handling|Show
available colours’) or R name (see Examples).
The additional two menu items, available for binary and ternary plots, allow adding contours or
convex hulls outlining individual groups of data. See contourGroups and chullGroups.

Value
For ’figAddReservoirs’, a numeric matrix with the overplotted analyses from the reference dataset.

Warning
Most of these functions serve to adding some extra components/annotations immediately before the
graph is printed/exported. Note that, except for ’figAddReservoirs’, all user-defined components
added via ’Plot editing: Add’ will be lost upon redrawing, zooming . . . .
Author(s)
Colin M. Farrow, 
and Vojtech Janousek, 
See Also
’par’ ’showColours’ ’colours’ ’figaro’ ’selectNorm’ ’contourGroups’ ’chullGroups’ ’figOverplot’
’curve’

Examples
binary("Zr/Nb","Ba/La")
# Sun & McDonough mantle reservoirs, Taylor & McLennan 1995 Upper and Lower Crust
reserv<-c("MORB|OIB .* McDonough","Upper .* 1995","Lower .* 1995")
reserv.names<-c("NMORB","EMORB","OIB","UCC","LCC")
figAddReservoirs(TRUE,"reservoirs.data",reserv.condition=reserv,labs=reserv.names)
figTicks(major=-0.5, minor=0.25,10,1,10,1)
ternary("SiO2/10","MgO","FeOt")
figAddReservoirs(var.name="idealmins.data",sample.names=c("Or","Bt","Ph"))
spider(WR,"NMORB..Sun",field=TRUE,colour="gray",field.colour=T,ymin=0.1,ymax=100)
figAddReservoirs(var.name="reservoirs.data",reserv.condition="Continental Crust",
autoscale=TRUE,col=c("red","black","darkblue"),pch=1:3)

72

figCol

figaro.identify

Plot editing: Identification of plotted symbols

Description
These functions allow the user to identify points in Figaro-compatible plots.
Usage
figIdentify()
highlightSelection()
Details
’figIdentify’ identifies points closest to a mouse pointer, if a mouse button is pressed. For binary
and ternary plots, sample names are plotted; for spider plots the function prints the sample name,
concentration of the given element (in ppm) and highlights the whole pattern. The identification is
terminated by pressing the right button and selecting 'Stop' from the menu.
’highlightSelection’ allows the selected analyses to be highlighted. The samples can be selected
based on combination of three searching mechanisms (by sample name/label, range or a Boolean
condition) - see selectSubset for details.
If the search results are empty or embrace all samples, the user is given a chance to select the
samples from the list of their names. Press Ctrl+click to select multiple ones.
For binary and ternary plots, Press Esc in the Console window to stop the points blinking. In spider
plots are shown overall ranges of normalized concentrations (by a gray field) with superimposed
patterns for selected samples.
Author(s)
Colin M. Farrow, 
and Vojtech Janousek, 
See Also
identify selectSubset ’figaro’

figCol

Plot editing: Colours

Description
These functions enable altering colours for titles or all plotting symbols in Figaro-compatible plots.

figCol

73

Usage
figCol(col=NULL)
figColMain(col=NULL)
figColSub(col=NULL)
figBw()
Arguments
col

colour specification

Details
The colours can be specified both by their code (see table under Data handling|Show available
colours) or R name (see Examples).
figBw sets the whole plot (main title and subtitle, axes and plotting symbols) in black and white,
making them ready for printing/exporting.
Author(s)
Colin M. Farrow, 
& Vojtech Janousek, 
See Also
’showColours’ ’colours’ ’figaro’
Examples
colours() # prints the list of available colour names
plotDiagram("TAS",FALSE) # example of a classification plot
figSub(txt="My TAS diagram")
figCol(col="green")
figColMain(col="red")
figColSub(col="blue")
figBw()
spider(WR,selectNorm("Boynton"),0.1,1000,pch=labels$Symbol,col=labels$Colour)
figMain(txt="My REE plot")
figSub(txt="Normalized by Boynton (1989)")
figCol(col="green")
figColMain(col="red")
figColSub(col="blue")

74

figEdit

figEdit

Plot editing: Changing titles and axis labels

Description
These functions enable altering titles and axis labels of binary (figXlab, figYlab) and ternary
(figAlab, figBlab, figClab), Figaro-compatible plots.
Usage
figMain(txt=NULL)
figSub(txt=NULL)
figXlab(txt=NULL)
figYlab(txt=NULL)
figAlab(txt=NULL)
figBlab(txt=NULL)
figClab(txt=NULL)
Arguments
txt

text

Details
If specified, the parameter txt will be passed to the function 'annotate' to guess the correct
reformatting to sub- and superscripts for production of "publication quality" plots. Otherwise, the
current value (titles or labels for axes/apices) are edited.
Author(s)
Colin M. Farrow, 
and Vojtech Janousek, 
See Also
’annotate’
’figaro’
Examples
plotDiagram("TAS",FALSE) # example of a classification plot
figMain(txt="My TAS diagram")
figSub(txt="test")
figXlab(txt="Silica")
figYlab(txt="Total alkalis")

figGbo

75

figGbo

Defining groups on Figaro-compatible plots

Description
Interactive definition of groups on any Figaro-compatible plot.
Usage
figGbo(x.tol = 0, y.tol = 0, max.points = 100, max.polygons = 25)
Arguments
x.tol, y.tol

tolerance for the automatic closing of polygons.

max.points

maximum number of vertices for a single polygon.

max.polygons

maximum number of polygons.

Details
Each of the groups is defined by clicking vertices of a polygon with the corresponding data points.
The polygons are closed automatically. To finish, right click anywhere on the plot and select ’Stop’.
The groups are numbered consecutively, points falling into two or more fields form extra groups, as
do unclassified samples.
Author(s)
Vojtech Erban,  & Vojtech Janousek, 
See Also
’figaro’

figLoad

Loading a Figaro plot

Description
Loads a Figaro-compatible plot (both the template and the data) stored in a file.
Usage
figLoad()
Arguments
None.

76

figMulti

Details
The default suffix for the saved diagrams is ’fgr’. Note that only the data needed for the plotting
(’x.data’, ’y.data’) are stored in the ’fgr’ files. Thus the data set currently in memory (e.g.,
variables ’WR’, ’labels’, . . . ) is unaffected by the function ’figLoad’.
Author(s)
Colin Farrow, 
and Vojtech Janousek, 
See Also
figSave figaro

figMulti

Figaro: Multiple plot by groups

Description
Displays multiple plots, for each of the groups one, based on a most recently plotted Figarocompatible template. For spiderplots, the colour field denotes the total variation with the whole
dataset.
Usage
figMulti(x=x.data,y=y.data,nrow=NULL,ncol=NULL,xlab=sheet$demo$call$xlab,
ylab=sheet$demo$call$ylab,pch=NULL,col=NULL,
cex = NULL,plot.symb=NULL,shaded.col="gray",rotate.xlab=TRUE,
offset=TRUE,centered=FALSE,title=NULL,...)
Arguments
x, y

data to be plotted

nrow, ncol

dimensions of the plots’ matrix

xlab, ylab

labels for the axes

pch

plotting symbols

col

plotting colours

cex

relative size of the plotting symbols

plot.symb

logical, spiders. Shall be shown also plotting symbols or just lines?

shaded.col

(spiders) Colour for the field portraying the overall variability in the dataset.

rotate.xlab

logical, spiders. Shall be the element names on x axis rotated?

offset

logical, spiders. Shall be the names for odd and even elements shifted relative
to each other?

centered

logical, spiders. Shall be the element names on x axis plotted in between tick
marks?

title

optional title for the whole plate. If not provided, it is taken from the title of the
Figaro template.

...

any additional graphical parameters

figMulti

77

Note

This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
and Colin M. Farrow, 
See Also
figaro, Plate, Plate editing binary, ternary, spider

78

figOverplot

Examples
# Note that groups should have been defined before running these.
# switch on the field names (default, valid globally for the whole system)
options("gcd.plot.text"=TRUE)
plotDiagram("TAS",FALSE)
figMulti()
# switch off the field names
options("gcd.plot.text"=FALSE)
plotDiagram("LarochePlut",FALSE)
figMulti(col="black",pch="*",cex=2)
options("gcd.plot.text"=TRUE)
spider(WR,selectNorm("Boynton"),0.1,1000,pch=labels$Symbol,col=labels$Colour,cex=labels$cex)
figMulti(plot.symb=TRUE,cex=2)
figMulti(col="red",plot.symb=FALSE,rotate.xlab=FALSE,offset=TRUE)
figMulti(col="red",plot.symb=FALSE,rotate.xlab=FALSE,offset=FALSE,shaded.col="khaki")

figOverplot

Adding data to pre-existing plots

Description
This function allows overplotting new data points onto Figaro-compatible binary or ternary plots,
or patterns onto spiderplots. It is most useful in adding selected data from typical geochemical
reservoirs (e.g., Upper Continental Crust, MORB . . . ), ideal mineral compositions, results of petrogenetic modelling or another dataset used for comparison (hitherto referred to as a reference
dataset).
Usage
figOverplot(var.name, mat=NULL, sample.names=NULL, condition=NULL,
labs=NULL, autoscale=FALSE, pch="*", col="darkred", cex=1, type="p",...)
Arguments
var.name

text; either ’reservoirs.data’, ’idealmins.data’ or a name of a global variable.

mat

numeric matrix or dataframe with reference dataset.

sample.names

character vector; names of reservoirs, ideal minerals or samples in the reference
dataset.

condition

text; regular expression specifying samples in the reference dataset.

labs

text; optional abbreviated labels for the overplotted data from the reference
dataset.

autoscale

logical; should be the scaling changed so that all the plotted analyses are shown?

pch

plotting symbol(s) for the reference dataset.

col

plotting colour(s) for the reference dataset.

figOverplot

79

cex

numeric; relative size of the plotting symbol(s) for the reference dataset.

type

character; plot type; see plot.default. Not implemented for spiderplots.

...

additional parameters to the underlying plotting function(s). See Details.

Details
This function is invoked by ’figAddReservoirs’ to overplot selected compositions from typical geochemical reservoirs (system file ’reservoirs.data’) or chemistries of ideal minerals (system file
’idealmins.data’). These come through a numeric matrix or dataframe ’mat. See figAddReservoirs for details.
If called directly, the function is employed to overplot data from the reference dataset, either realworld data or a numeric matrix spanning from petrogenetic modelling. The data originate from
a two-dimensional variable in the global environment, whose name is provided via the obligatory
argument ’var.name’. Argument ’mat’ does not need to be specified here as the data frame/matrix
is generated by the function ’figOverplot’ itself.
In both cases, the selection from the numeric matrix or dataframe ’mat’ is based on a list of all
’sample.names’ or on a regular expression yielding them (’condition’). Of course, from this
selection, only analyses with data sufficient to be plotted on the current diagram are used.
If neither ’sample.names’ nor ’condition’ is provided, the sample names can be chosen interactively from a drop-down list.
For plotting are used functions ’points’, ’triplotadd’ and ’spider’ for binary plots, ternary plots and
spiderplots, respectively. Argument ’...’ can supply additional parameters to these underlying
functions. NB that thus introduced changes to the plot are likely not to be permanent, i.e. they will
be lost upon redrawing, zooming . . . .
Logical argument ’autoscale’ determines whether the plot should be rescaled to accommodate
both the original data points and the reference dataset. It does not make sense for a ternary plot.
Optional parameter ’labs’ can specify alternative, perhaps abbreviated textual labels to the points
plotted.
Value
A numeric matrix with the overplotted analyses from the reference dataset.
Warning
Please note that this function is so far available solely for spiderplots, binary and ternary plots.
Author(s)
Vojtech Janousek, 
See Also
’figAddReservoirs’
’points’ ’triplotadd’ ’spider’
’figaro’ ’selectNorm’ ’par’

80

figRedraw

Examples
data(sazava)
accessVar("sazava")
# Calculate Rayleigh-type fractionation trend
ff<-seq(1,0.1,-0.1) # F, amount of melt left
x<-80*ff^(1.2-1)
# cL for three elements, arbitrary D of 1.2, 2.0 and 1.3
y<-550*ff^(2.0-1)
z<-1000*ff^(1.3-1)
my.trend<-cbind(x,y,z)
colnames(my.trend)<-c("Rb","Sr","Ba")
rownames(my.trend)<-ff
binary("Rb","Sr",log="xy")
figOverplot(var.name="my.trend",pch="+",col="blue",autoscale=TRUE,type="o",labs=ff)
ternary("10*Rb","2*Sr","Ba/2")
figOverplot(var.name="my.trend",pch="+",col="blue",type="o")

figRedraw

Redrawing/refreshing a Figaro plot

Description
This function redraws/refreshes a Figaro-compatible plot.
Usage
figRedraw(x=x.data,y=y.data,zoom=NULL,bw=FALSE)
refreshFig()
Arguments
x
y
zoom
bw

vector of x coordinates
vector of y coordinates
logical; redraw while zooming?
logical; should be the output black and white?

Warning
Note that all user-defined components added via ’Plot editing: Add’ (legend, lines, text, boxes,
. . . ) - will be lost.
Author(s)
Colin M. Farrow, 
and Vojtech Janousek, 
See Also
figaro

figSave

81

figSave

Saving a Figaro plot

Description
Saves the current Figaro-compatible plot, both the template and the data needed for the plotting
(’x.data’, ’y.data’).
Usage
figSave()
Arguments
None.
Details
The default suffix for the saved diagrams is ’fgr’.
Author(s)
Colin M. Farrow, 
and Vojtech Janousek, 
See Also
figLoad figaro

figScale

Plot editing: Scaling text or plotting symbols

Description
These functions enable changing a size of titles, axis labels or plotting symbols of Figaro-compatible
plots. The size is relative to 1 (the original).
Usage
figCex(x=NULL)
figCexLab(x=NULL)
figCexMain(x=NULL)
figCexSub(x=NULL)
Arguments
x

numeric: scaling factor.

82

figUser

Author(s)
Colin M. Farrow, 
and Vojtech Janousek, 
See Also
’figaro’
Examples
plotDiagram("TAS",FALSE) # example of a classification plot
figSub(txt="My TAS diagram")
figCex(2)
figCexMain(1.5)
figCexSub(0.5)
spider(WR,selectNorm("Boynton"),0.1,1000,pch=labels$Symbol,col=labels$Colour)
figMain(txt="My REE plot")
figSub(txt="Normalized by Boynton (1989)")
figCex(2)
figCexMain(1.5)
figCexSub(0.5)

figUser

Plot editing: User defined parameter

Description
Enables the power users to modify the plot parameters directly.
Usage
figUser(expression=NULL)
Arguments
expression

character; expression to be evaluated

Details
The parameters can be specified at the function call. If not, they are chosen by a dialogue. Several
of the, can entered simultaneously, as a semicolon delimited list. The most useful might be:
main
sub
xlab
ylab
xlim
ylim
bg
pch
col

Main title
Sub title
Label of x axis
Label of y axis
Limits for the x axis
Limits for the y axis
Colour of background
Plotting symbols
Colour of plotting symbols

figZoom

83
cex
log

Relative size of plotting symbols
Which of the axes is logarithmic? ("","x","y" or "xy")

If no parameters are entered, they can be chosen from a list (still experimental!)
Menu
Plot editing: User defined parameter
Warning
If requesting a logarithmic plot, make sure that the axis ranges are positive. See Examples or invoke
menu items ’Plot editing: Scale x axis’ and ’Plot editing: Scale y axis’.
Author(s)
Colin M. Farrow, 
and Vojtech Janousek, 
See Also
par figaro
Examples
plotDiagram("TAS")
figUser()
figUser("pch=1; col=2")
figUser("pch=\"+\"")
figUser("col=\"darkblue\"")
figUser("bg=\"khaki\",cex=1.5") # for camouflage purposes
figUser("main=\"My plot\"; las=2; font.main=4; cex.main=2; col.main=\"blue\"")

figZoom

Plot editing: Zooming

Description
These functions zoom in and out Figaro-compatible plots.

84

figZoom

Usage
figZoom()
figUnzoom()
figXlim(range=NULL)
figYlim(range=NULL)
Arguments
range

numeric: two limits, minimum and maximum, for the given axis.

Details
’figZoom’ zooms the specified rectangular area (click bottom left and then top right corner) in a
new window. The zoomed area is highlighted in the old window.
’figUnzoom’ closes the new window with blown up portion of the plotting window and returns to
the original window.
’figXlim’ and ’figYlim’ allow to change the plotting limits (as a list of two components, separated
by commas).

Warning
If requesting a logarithmic plot, make sure that the axis ranges are positive.

Author(s)
Colin M. Farrow, 
and Vojtech Janousek, 
See Also
’figaro’

Examples
## Not run:
# requires a preexisting Figaro-compatible plot
plot.diagram("Shand",select.samples=FALSE)
figXlim(c(0.6,1.2))
figYlim(c(0.8,3))
## End(Not run)

filledContourFig

filledContourFig

85

Filled contours plot

Description
Generates a frequency plot on the basis of the most recently plotted Figaro template.

Usage
filledContourFig(xlab=sheet$demo$call$xlab,ylab=sheet$demo$call$ylab,
xlim=sheet$demo$call$xlim,ylim=sheet$demo$call$ylim,
annotate.fields=FALSE,...)

Arguments
xlab

character vector; label for the x axis

ylab

character vector; label for the y axis

xlim

limits for the x axis

ylim

limits for the y axis

annotate.fields
logical; should be the plotted fields labeled by their names?
...

additional plotting parameters

Details
This is a somewhat modified version of the R function ’filled.contour’ that produces a frequency
plot on the basis of a Figaro template and superimposes, if desired, selected data points.

86

Frost

First the user is prompted, how many intervals should be each of the axes split into. This corresponds to a density of grid, in which are the individual points classified into. Then a colour scheme
(palette) can be chosen. Lastly, after the frequency plot is generated, selected analyses can be
plotted (’overplot’). In the latter case, standard GCDkit routine is used to selectSamples.
Value
None.
Author(s)
Vojtech Janousek, 
See Also
’addContours’ ’selectSubset’ ’figaro’

Frost

Frost et al. (2001)

Description
Classification of granitic rocks proposed by Frost et al (2001).

Frost

87

Usage
Frost(plot.txt = getOption("gcd.plot.text"),
clssf = FALSE, GUI = FALSE)
Arguments
plot.txt

logical, annotate fields by their names?

clssf

logical, should the samples be classified?

GUI

logical, is the function called from a GUI?

Details
Classification scheme proposed by Frost et al. (2001). It consists of three diagrams:
• F e number vs. SiO2 . Note, that the Fe-number is calculated as weight proportion of
F eO/(F eO + M gO) (or F eOtot /(F eOtot + M gO)), see below). The approach used here
should not be confused with the more common usage of the term "Fe-number" (as well as
"Mg-number") as molecular proportions.
• N a2 O + K2 O − CaO vs. SiO2 (in wt. %).
• A/N K vs. ASI, where A/N K stands for molecular Al2 O3 /(N a2 O + K2 O), and ASI for
molecular Al2 O3 /(N a2 O + K2 O + CaO − 1.67P2 O5 ). In fact, it is the A/CNK parameter
of Shand (1943), corrected for the Ca content in apatite. As approved by one of the authors
(C. Barnes, pers. comm., 2008), the equation for ASI in the original work (Frost et al. 2001)
was stated erroneously in molecular proportions of elements, instead of oxides. In fact, this
diagram was not plotted in the paper, but it replaces the conditions mentioned in the text and
is in our view more instructive.
The classification is designed to work both with analyses distinguishing between ferrous and ferric
iron (preferred) and those with total iron only. The dialogue box lets the user decide, whether to use
the ferrous iron value or the total iron.
Similarly, if some P2 O5 concentration are missing in the dataset, the user is prompted whether
the missing values should be replaced with zero. If not, the problematic analyses are not plotted/classified.
Value
The function returns table of calculated coefficients (Fe-Number, MALI, ASI). There are two values
for the ASI: one labeled 'ASI' is calculated from molecular proportions of oxides, and is used for
plotting and classification. The other one is labeled 'ASI_orig', and is calculated exactly as stated
in the original paper (i.e. Al/(Ca − 1.67P + N a + K).
The following associations are distinguished:
ferroan
magnesian
alkalic
alkali-calcic
calc-alkalic
calcic
peralkaline
metaluminous

88

Frost
peraluminous

The geologically reasonable combinations, together with examples, are listed in the ../doc/FrostTable.
html, modified from the original article.
Note
Due to the specific design of this classification (combination of multiple diagrams), the classification option is not available via the pull-down menus. Currently, the only way to apply Frost’s
classification in GCDkit on individual samples is to call the function manually from the Console
(Frost(clssf = TRUE)).
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate,
Plate editing and figaro for details.
Author(s)
Vojtech Erban, 
& Vojtech Janousek 
References
Frost B R, Barnes C G, Collins W J, Arculus R J, Ellis D J, Frost C D (2001) A geochemical
classification for granitic rocks. J Petrol 42: 2033-2048. doi: 10.1093/petrology/42.11.2033
Shand, S J (1943) Eruptive rocks, 2nd ed. John Wiley, New York, pp 1-444
See Also
classify Shand Plate Plate editing plotPlate figaro
Examples
#plot the diagrams
plotPlate("Frost")
#classify the samples, suppress the graphical output

gcdOptions

89

Frost(clssf = TRUE)

gcdOptions

GCDkit options

Description
A graphical user interface (GUI, programmed in Tcl/Tk) for setting the main options controlling
the behaviour of the GCDkit.
Usage
gcdOptions()
Arguments
None.
Details
The settings are stored permanently in the file ’gcdkit.xxx’ residing in the main GCDkit directory.
They are loaded upon start up. If is missing or damaged, this file is created anew based on the
default values.
The panel connected to the function ’gcdOptions’ serves to change several parameters. Most of
them are passed to a list accessible in a way similar to the standard R options. See the corresponding manual page for details and Examples for their implementation. Only a few are stored in
dedicated variables (see below).
First, the default working directory can be set (and stored in the global variable data.dir).
The parameter ’Minimize output on screen?’ is linked to the option gcd.shut.up. It controls
excessive output to the Console window. Its default value is FALSE, meaning that detailed information is to be printed. This, however, may become not viable on slower systems and/or for extensive
data sets.
The preferred precision of the numeric values that need to be rounded off are controlled by the
parameter ’Precision of results’ (option gcd.digits).
Using the parameter ’Plotting symbols magnification’, linked to the option gcd.cex, one
can define a factor, by which are multiplied the plotting sizes defined for individual analyses upon
startup and stored in the variable ’labels[,"Size"]’. Please note that this is effective for the next
plot if the GUI frontend is used to set this parameter, otherwise it will work for data files loaded
from now on.
In this way, the magnification is maintained proportional to the original sizes. If uniform plotting
symbols sizes are desired, one should use the function setCex invoked from the menu
Plot settings|Set uniform symbol size.
The parameter ’Annotate fields in discrimination plots?’ toggles the labeling of the fields
on and off, typically for classification or geotectonic diagrams. It is stored in a logical variable
gcd.plot.text, whose default is TRUE. The language for the field annotations can be selected
using the list box connected to the option gcd.language.
The next possibility is to alter the colours used, e.g, for texts or field boundaries on diagrams. There
are in total three colours stored in the list plt.col. Alternatively, all the plots can be set to black

90

gcdOptions
and white (check box ’Set to BW?’ linked to the option ’gcd.plot.bw’), excluding the data points.
The default is FALSE (i.e. colour plotting).
The parameter ’Identify points?’ toggles on and off the identification/labelling of individual
data points on plots. In general, the identification can be either interactive (option
gcd.ident.each = TRUE) or all the points can be labeled automatically as soon as the plotting is
finished (option
gcd.ident.each = FALSE). In the former case, the user may click the left mouse button near the
points to be identified, pressing the right mouse button when finished.
The option gcd.ident determines whether identification should take place at all (the default value
is zero, which means no identification). If the identification is on, the option gcd.ident attains
either 1 (identification by sample name), or the sequential number of the column in the data frame
’labels’ increased by one (identification by a label).
The identification by sample name for a current plot can be invoked also from the menu
’Plot editing|Identify points’. There can be also chosen alternative means of points identification (’Plot editing|Highlight multiple points’).

Value
Sets the following options:
gcd.plot.text
gcd.language
gcd.plot.bw
gcd.shut.up
gcd.ident

logical; should be fields on classification diagrams labeled by their names?
language for these labels.
logical; if TRUE, plots are produced as black and white.
logical; determines whether extensive textual output is to be printed.
numeric; if zero, no identification takes place after plotting each diagram. If
greater than zero, indicates the variable used to identify individual data points.
See Details.
gcd.ident.each logical; are the data points to be identified individually?
gcd.digits
preferred number of digits for rounding off the numeric values.
gcd.cex
a factor by which are multiplied all symbol sizes previously defined.

Remaining options changed by GCDkit which cannot be altered via the GUI, though:

graphicsOff

91

prompt
"GCDkit-> "
windowsBuffered
FALSE
locatorBell

FALSE

scipen

20

max.print

99999999

If necessary they can be set directly in the file ’gcdkit.xxx’.
Apart from that the GUI panel sets the variables data.dir (default data directory) and plt.col
(colours for Figaro-compatible plots).
Author(s)
Vojtech Janousek, 
See Also
options identify ID figaro setCex
Examples
bak <- options()
# backup the current options
options("gcd.ident"=1)
# identify by sample names
options("gcd.ident.each"=FALSE) # to label by sample names automatically,
# i.e. without the user interference
plotDiagram("TAS",F,F)
options("gcd.ident"=0)
options("gcd.plot.bw"=TRUE)
plotDiagram("TAS",F,F)

# to turn off the identification completely
# to set the diagram to black and white

options("gcd.cex"=2)

# make the plotting symbols double as big
# (effective for the data files loaded from now on;
# for immediate result use the GUI front end)

getOption("gcd.plot.bw")
options(bak)

# printing the current value of the given option
# restore the previous options

graphicsOff

Close all graphic windows

Description
Closes all graphic windows.
Usage
graphicsOff()
Arguments
None.

92

groupsByCluster

Details
Under Windows 95/98/ME, the R system may become install, failing to redraw graphical windows
if too many of them are being open. It is always a good idea to close the unnecessary ones, for
instance using this function.
See Also
’dev.off’

groupsByCluster

Groups by cluster analysis

Description
Grouping the data using the cluster analysis.
Usage
groupsByCluster(elems=
"SiO2,TiO2,Al2O3,FeOt,MnO,MgO,CaO,Na2O,K2O",
method="ave")
Arguments
elems

numerical columns to be used for cluster analysis, typically major elements

method

the agglomeration method to be employed. This should be one of (or an unambiguous abbreviation thereof): 'ward', 'single', 'complete',
'average', 'mcquitty', 'median', 'centroid'.

Details
After the dendrogram is drawn, the user is asked how many clusters is the dataset to be broken into.
The vector containing the information on the current groups can be appended to the data frame
’labels’.
The groups are initially numbered but this can be changed readily using the function editLabFactor.
For further details on the clustering algorithm, see the R manual entry of ’hclust’.
Value
groups

character vector: the grouping information

grouping

set to zero.

Author(s)
Vojtech Janousek, 
See Also
classify groupsByLabel groupsByDiagram

groupsByDiagram

groupsByDiagram

93

Groups by diagram

Description
Grouping the data on a basis of selected classification diagram.
Usage
groupsByDiagram(silent = TRUE)
Arguments
silent

logical; should be echoed the information about classification each of the samples?

Value
groups

character vector: the grouping information

grouping

set to -1.

Author(s)
Vojtech Erban, 
See Also
classify groupsByLabel groupsByCluster

groupsByLabel

Groups by label

Description
Grouping the data according to the levels of a single label.
Usage
groupsByLabel(lab=NULL)
Arguments
lab

name or sequence number of the label

Details
Sets the groups on the selected column within the data frame ’labels’. If not specified at the
function call, the appropriate label is selected by the function ’selectColumnLabel’.

94

Harris

Value
groups

character vector: the grouping information

grouping

the sequence number of the column in the data frame ’labels’ used for grouping

Author(s)
Vojtech Janousek, 
See Also
classify groupsByCluster groupsByDiagram
Examples
data<-loadData("sazava.data",sep="\t")
groupsByLabel("Intrusion")

Harris

Harris et al. (1986) Hf-Rb/30-Ta*3

Description
Assigns data for the Hf-Rb/30-Ta*3 ternary diagram of Harris et al. (1986) into Figaro template
(list ’sheet’) and appropriate values into ’x.data’ and ’y.data’.
Usage
Harris()
Details
Triangular diagram with apices Hf, Rb/30 and Ta*3, proposed by Harris et al. (1986) for classification of collisional granites.
Following fields are defined:
VA
WP
Group 2
Group 3

Volcanic-Arc granites (Group 1, VA)
Within-Plate granites (Group 4, WP)

Harris

95

Quoting from their abstract:
(i) Group 1 - Pre-collision calc-alkaline (volcanic-arc) intrusions which are mostly derived from
mantle modified by a subduction component and which are characterized by selective enrichments
in LIL elements.
(ii) Group 2 - Syn-collision peraluminous intrusions (leucogranies) which may be derived from the
hydrated bases of continental thrust sheets and which are characterized by high Rb/Zr and Ta/Nb
and low K/Rb ratios.
(iii) Group 3 - Late or post-collision calc-alkaline intrusions which may be derived from a mantle
source but undergo extensive crustal contamination and can only be distinguished from volcanic-arc
intrusions by their higher ratios of Ta/Hf and Ta/Zr.
(iv) Group 4 - Post-collision alkaline intrusions which may be derived from mantle lithosphere
beneath the collision zones and which carry high concentrations of both LIL and HFS elements.
Value
sheet

list with Figaro Style Sheet data

x.data, y.data Th, Hf/3 and Ta in ppm recalculated into two dimensions

96

Hastie

Author(s)
Vojtech Janousek, 
References
Harris N B W, Pearce J A, Tindle A G (1986) Geochemical characteristics of collision-zone magmatism. In: Coward M P, Ries A C (eds) Collision Tectonics. Geological Society London Special
Publication 19, pp 67-81
See Also
figaro plotDiagram
Examples
#plot the diagram
plotDiagram("Harris", FALSE)

Hastie

Co-Th diagram (Hastie et al. 2007)

Description
Assigns data for Co vs. Th (ppm) diagram into Figaro template (list ’sheet’) and appropriate values
into ’x.data’ and ’y.data’
Usage
Hastie()
Details
Diagram in Co vs. Th space, proposed by Hastie et al. (2007) for subdivision of volcanic arc
rocks. This is thought to be a more robust replacement for SiO2 vs. K2 O plot of Peccerillo &
Taylor (1976) for altered/weathered volcanic rocks. The decreasing Co concentrations are used as
an index of fractionation (as a proxy for SiO2 ), the Th contents mimic those of K2 O.
The following fields are defined:
Tholeiite Series
Calc-alkaline Series
High-K Calc-alkaline and Shoshonite Series
Rocks with composition falling beyond defined boundaries are labeled ’undefined’ by the ’classify’
function.
In addition, the diagram discriminates between the following rock types:
Abbreviation
B
BA/A
D/R*

Full name
basalt
basaltic andesite and andesite
dacite and rhyolite*

* latites and trachytes also fall in the D/R fields

Hastie

97

Value
sheet

list with Figaro Style Sheet data

x.data

Co ppm

y.data

Th ppm

Author(s)
Vojtech Janousek, 
References
Hastie AR, Kerr AC, Pearce JA & Mitchell SF (2007) Classification of altered volcanic island arc
rocks using immobile trace elements: development of the Th-Co discrimination diagram. J Pet 48:
2341-2357 doi: 10.1093/petrology/egm062
Peccerillo A & Taylor S R (1976) Geochemistry of Eocene calc-alkaline volcanic rocks from the
Kastamonu area, Northern Turkey. Contrib Mineral Petrol 58: 63-81 doi: 10.1007/BF00384745

98

ID

See Also
classify figaro plotDiagram
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("Hastie")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("Hastie", FALSE)

ID

Sample identification

Description
Identification/labelling of individual data points on plots.
Usage
ID(x, y, labs = getOption("gcd.ident"), offset = 0.4,
col = "gray30", cex = 1)
Arguments
x, y

vector with x-y coordinates of the data points

labs

text to label individual data points, see details

offset

distance (in char widths) between label and identified points.

col

colour of the text

cex

its size

Details
In GCDkit, the option ’ident’ determines whether the user wishes to identify data points on binary
and ternary plots. The default is zero, which means no identification.
If ’ident’ differs from zero, internal function ’ID’ can be invoked. Its parameter labs is either a
single number, or character vector.
In the former case, the variable ’labs’ contains either 1 (identification by sample name), or the
sequential number of the column in the data frame ’labels’ increased by one (identification by a
user- defined label).
Alternatively, a character vector labs can be used to specify the text directly.
Value
None.
Author(s)
Vojtech Janousek, 

info

99

See Also
identify gcdOptions options
Examples
getOption("ident")

info

# yields the current value of the given option

Info on datafile

Description
Prints information about the current dataset (and its selected subset, if applicable).
Usage
info()
Details
This function prints comprehensive information about the current dataset. For each of the labels,
individual levels and their frequencies are given. The number of numeric columns is printed, and
for each of the variables number of available values. Moreover, the information concerning the
total number of samples, the names of the samples in the selected subset (or all samples if none is
defined) and the current grouping are shown.
Value
None
Author(s)
Vojtech Janousek, 

isochron

Rb-Sr and Sm-Nd isochrons

Description
Plots a Rb-Sr or Sm-Nd isochron diagram and calculates a simple linear fit to the selected data.

100

isochron

Usage
isochron()
Arguments
None.
Details
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
If empty list is given, all the samples for which the required isotopic data are available are plotted
and the user can choose their subset interactively. Then the isochron diagram is redrawn only with
those samples.
The data are fitted by simple least-squares linear fit, from which the age and initial ratio are calculated.

isocon

101

Value
Returns a numeric vector with the calculated age and initial ratio.
Plugin
SrNd.r
Author(s)
Vojtech Janousek, 

isocon

Isocon plots (Grant 1986)

Description
Implementation of isocon plot after Grant (1986, 2005) widely used for quantitative estimates of
changes in mass/volume/concentration of elements or oxides in course of various open-system geochemical processes such as alteration or partial melting.
Usage
isocon(x = NULL, whichelems = NULL, immobile = NULL, atomic = FALSE, plot = TRUE)
isoconAtoms()
isoconOxides()

Arguments
x

numeric matrix with the chemical data

whichelems

list of elements for plotting, separated by commas

immobile

list of presumed immobile elements, separated by commas

atomic

logical; should be atomic wt. % used for oxides?

plot

logical; is the graphical output desirable?

Details
Isocon plot (Grant 1986, 2005) spans from the theoretical quantitative treatment of losses or gains
of geochemical species (elements or oxides). It is applicable to balancing mass, volume and/or concentration changes in course of open-system processes such as weathering, hydrothermal alteration,
metasomatic addition/leaching or migmatitization.
According to Grant (2005 and references therein) the equation for composition/volume changes in
open-system process can be written as:
c

A
M0 0
= A (c + ∆ci )
i
M
i

102

isocon
where ci is the concentration of the species i, 0 refers to the original rock and A to the altered rock,
M 0 is the equivalent mass before and M A after alteration.
0

M
For immobile element (∆ci = 0) the ratio M
A reflecting the overall change in mass can be obtained.
This can be done graphically in the plot of the analytical data for presumed protolith (c 0i ) and altered
rock (c Ai ). Such a straight line passing through the origin is termed isocon, the equation of which
is:

cA = (

M0 0
)c
MA

Species plotting above the isocon were gained, whereas those plotting below were lost, and the gain
or loss is according to Grant (2005):
A

∆ci
MA c i
0 = M0 0 − 1
ci
ci
where

cA
i
c 0i

is the slope of the tie line from the origin to the data point.

In the GCDkit’s implementation of the function 'isocon', firstly the parental and altered rock
samples are to be chosen interactively from a binary plot M gO − SiO2 . Then the user is prompted
for the elements/oxides to be used in the isocon analysis. Printed and plotted in the form of barplots
are ordered slopes for each data point in the isocon diagram.
The user can choose the presumably immobile elements. These can be either provided as a comma
delimited list, or, if empty, chosen interactively from the isocon plot. Finally are plotted two isocons,
as well as a blue equiline (a straight line with the slope 1).
Implemented are two methods for assessing the change in mass of the system. Traditionally used
has been the slope of the isocon line, obtained by linear regression of the presumably immobile data
(dark green). However, this depends on the scaling of the isocon plot, which is arbitrary. In particular, the data plotted close to the origin may appear erroneously to lie on an isocon (Baumgartner
& Olsen, 1995).

More objectively, the change in the mass can be estimated by clustering slopes to data points, deciphering the elements/oxides with a similar behaviour and averaging the slopes for the selected
presumably immobile species.
Functions ’isoconAtoms’ and ’isoconOxides’ are frontends to the function 'isocon', providing
different default values. See Arguments above.

isocon

103

Value
Returns a list ’results’ with the following components:
slope.avg

slope of the isocon obtained as an average of the slopes for the individual presumably ’immobile’ species
slope.regression
slope obtained by linear regression
balance

numeric matrix; balance of individual species. This matrix contains the following columns:

XXX=orig.

composition of the parental (unaltered) rock

XXX=alt.
composition of the altered rock
Slope data point
slope of the line connecting the data point with origin
G/L rel.(LQ)

relative mass gain/loss, isocon slope by least-squares fit

G/L rel.(avg) relative mass gain/loss, averaged slopes for immobile elements
G/L wt%/ppm(LQ)
absolute mass gain/loss, isocon slope by least-squares fit
G/L wt%/ppm(avg)
absolute mass gain/loss, averaged slopes for immobile elements
Plugin
Isocon.r
Author(s)
Vojtech Janousek, 
References
Baumgartner L P & Olsen S N (1995). A least-squares approach to mass transport calculations
using the isocon method. Econ Geol 90: 1261-1270 doi: 10.2113/gsecongeo.90.5.1261
Grant J A (1986) The isocon diagram - a simple solution to Gresens equation for metasomatic
alteration. Econ Geol 81: 1976-1982 doi: 10.2113/gsecongeo.81.8.1976
Grant J A (2005) Isocon analysis: A brief review of the method and applications. Phys Chem Earth
(A) 30: 997-1004 doi: 10.1016/j.pce.2004.11.003
Gresens R L (1967) Composition-volume relationships of metasomatism. Chem Geol 2: 47-55
Examples
# Grant (2005) - see Tab. 1, Fig. 1
x<-matrix(c(46.45,1.29,14.30,11.05,0.17,5.28,12.14,2.93,0.49,3.00,3.29,42,327,
313,67,77,100,170,29,80,45.62,1.30,14.74,8.20,0.15,3.89,8.29,2.09,3.12,2.18,
10.96,39,305,282,42,75,72,214,17,140), byrow=TRUE,nrow=2)
y<-"SiO2,TiO2,Al2O3,Fe2O3,MnO,MgO,CaO,Na2O,K2O,H2O,CO2,Sc,V,Cr,Ni,Cu,Zn,Sr,Y,Ba"
colnames(x)<-unlist(strsplit(y,","))
rownames(x)<-c("UA","401")
isocon(x,y,atomic=FALSE,plot=TRUE,immobile="Al2O3,SiO2,TiO2,Cu,Sc")

104

Jensen

isocon(x,y,atomic=TRUE,plot=FALSE)

Jensen

Jensen cation plot (1976)

Description
Assigns data for Jensen’s cation plot into Figaro template (list ’sheet’) and appropriate values into
’x.data’ and ’y.data’.

Usage
Jensen()

Details
Jensen’s cation plot, proposed by Jensen (1976) and modified by Jensen & Pyke (1982). The triangular diagram is defined on the basis of millications as follows:
left apex: Al
upper apex: F eT + T i
right apex: Mg

Jensen

105

The diagram defines following rock series and names:
Komatiite series (KOMATIITE)
Tholeiite series (TH)

Calc-alkaline series (CA)

Komatiite
Komatiitic basalt
Rhyolite
Dacite
Andesite
High-Fe tholeiite basalt
High-Mg tholeiite basalt
Rhyolite
Dacite
Andesite
Basalt

Value
x.data, y.data Values for the three apices transformed into 2D space
sheet

list with Figaro Style Sheet data

106

joinGroups

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Grunsky E C (1981) An algorithm for the classification of subalkalic volcanic rocks using the Jensen
cation plot. In: Wood J, White O L, Barlow R B, Colvine A C (eds). Ontario Geological Survey,
Misc Pap 100, pp 61-65
Jensen L S (1976) A new cation plot for classifying subalkalic volcanic rocks. Ont Div Mines, Misc
Pap 66, 1-21
Jensen L S & Pyke D R (1982) Komatiites in the Ontario portion of the Abitibi belt. In: Arndt N T
& Nisbet E G (eds) Komatiites. Allen & Unwin, London
See Also
classify figaro plotDiagram
Examples
#plot the diagram
plotDiagram("Jensen", FALSE)

joinGroups

Merge groups

Description
Enables merging several groups into a single one.
Usage
joinGroups()
Arguments
None.
Details
This function is the most useful to merge several groups, defined e.g. on the basis of a classification
plot. A simple spreadsheet is invoked with two columns, the first ('Old') containing the old levels
of groups and the second, 'New', which can be edited. Finally, groups with identical names will be
merged into a single one.
Optionally, the vector containing the information on the current groups can be appended to the data
frame ’labels’.
Value
groups

character vector: the grouping information

grouping

Sequential number of the column with grouping information in labels (if appended) or simply set to -100.

Jung

107

Author(s)
Vojtech Janousek, 

Jung

Al/Ti thermometer for granitic rocks (Jung + Pfander 2007)

Description
This function estimates the temperature of a granitic magma based on measured Al2 O3 /T iO2 ratio
and experimental constraints. The regression formulae were defined by Jung & Pfander (2007).

Usage
Jung(model = NULL, plot = TRUE)

Arguments
model

specification of the model

plot

logical; should be shown a Al2 O3 /T iO2 vs. CaO/N a2 O plot?

Details
As shown by Sylvester (1998), the Al2 O3 /T iO2 ratio in the granitic magmas is temperature sensitive, decreasing with the increasing temperature of the crustal anatexis. This probably reflects an
increasing instability of Ti-bearing phases with progressive crustal fusion.
Jung & Pfander (2007) compiled the available experimental data and defined a set of regression
formulae (linear, power law and exponential) for several types of protoliths.
Any of the following models can be chosen: pelite melting, psammite melting, igneous rock melting, A-type granite melting, amphibolite melting after Rapp & Watson (1995) and amphibolite
melting after Patino Douce & Beard (1995).
Optionally, also Al2 O3 /T iO2 vs. CaO/N a2 O plot could be displayed with three secondary axes
annotated by the calculated temperatures.

108

Jung

Value
Returns a matrix ’results’ with the following columns:
Al2O3/TiO2

wt. % ratio of Al2 O3 /T iO2

T_Al/Ti.power.C
temperature in C, power law calibration
T_Al/Ti.exp.C

temperature in C, exponential calibration

T_Al/Ti.linear.C
temperature in C, linear calibration
T_Al/Ti.mean.C mean temperature in C, based on the above three models
Plugin
Jung.r

Laroche

109

Erratum
As pointed out by S. Jung (pers. com. 2009), in Table 1 of their original paper were printed wrongly
several of the regression coefficients. These are:
Rock
A-type
amphibolite (Rapp and Watson 1995)

Model
power law
power law

Jung and Pfander (2007)
B = 0.992
A = 2.82x10^3

Corrected
B = 9.921
A = 2.82x10^30

The function implements these corrected values.
Author(s)
Vojtech Janousek, 
References
Jung S, Pfander J A (2007) Source composition and melting temperatures of orogenic granitoids:
constraints from CaO/N a2 O, Al2 O3 /T iO2 and accessory mineral saturation thermometry. Eur J
Mineral 19: 859-870 doi: 10.1127/0935-1221/2007/0019-1774
Patino Douce A E, Beard J S (1995) Dehydration-melting of biotite gneiss and quartz amphibolite
from 3 to 15 kbar. J Petrol 36: 707-738 doi: 10.1093/petrology/36.3.707
Rapp R P, Watson E B (1995) Dehydration melting of metabasalt at 8-32 kbar: implications for continental growth and crust-mantle recycling. J Petrol 36: 891-931 doi: 10.1093/petrology/36.4.891
Sylvester P J (1998) Post-collisional strongly peraluminous granites. Lithos 45: 29-44
doi: 10.1016/S0024-4937(98)00024-3
Examples
Jung()
Jung("A-type")
Jung("psammite",plot=FALSE)

Laroche

R1-R2 diagram (De la Roche et al. 1980)

Description
Assigns data for the R1 − R2 diagram into Figaro template (list ’sheet’) and appropriate values
into ’x.data’ and ’y.data’.
Usage
LarocheVolc()
LarochePlut()

110

Laroche

Details
R1 − R2 plot, as proposed by De La Roche et al. (1980) for volcanic, as well as plutonic rocks.

volcanic rocks
trachyphonolite
phonolite
phono-tephrite
tephrite
alkali rhyolite
qtz.trachyte
trachyte
rhyolite
qtz.latite
rhyodacite
dacite
andesite
lati-andesite
latite
mugearite

plutonic rocks
nepheline syenite
nepheline syenite
essexite
essexite
alkali granite
qtz.syenite
syenite
granite
qtz.monzonite
granodiorite
tonalite
diorite
monzodiorite
monzonite
syenodiorite

Laroche

111
nephelinite
andesi-basalt
lati-basalt
hawaiite
tholeiite
basalt
alkali basalt
basanite
ankaratrite
picritic rock

ijolite
gabbro-diorite
monzogabbro
syenogabbro
gabbronorite
gabbro
alkaligabbro
theralite
melteigite
ultramafic rock

Value
sheet
x.data
y.data

list with Figaro Style Sheet data
R1 = 4 * Si - 11 * (Na + K) - 2 * (Fe[total as bivalent] + Ti), all in millications;
as calculated by the function ’LaRocheCalc()’
R2 = 6 * Ca + 2 * Mg + Al, all in millications; as calculated by the function
’LaRocheCalc()’

112

LaRocheCalc

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
De La Roche H, Leterrier J, Grandclaude P, & Marchal M (1980) A classification of volcanic and
plutonic rocks using R1 R2 - diagram and major element analyses - its relationships with current
nomenclature. Chem Geol 29: 183-210 doi: 10.1016/0009-2541(80)90020-0
See Also
classify figaro LaRocheCalc millications plotDiagram
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To classify data stored in WR (Groups by diagram)
classify("LarocheVolc")
#or
classify("LarochePlut")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("LarocheVolc", FALSE)
#or
plotDiagram("LarochePlut", FALSE)

LaRocheCalc

Calculation: De la Roche

Description
Recalculates whole-rock data into R1 − R2 values of De La Roche et al. (1980).
Usage
LaRocheCalc(rock=WR)
Arguments
rock

a numeric matrix with whole-rock data to be recalculated.

Details
R1 − R2 parameters, as proposed by De La Roche et al. (1980):
R1 = 4 * Si - 11 * (Na + K) - 2 * (Fe[total as bivalent] + Ti), all in millications R2 = 6 * Ca + 2 *
Mg + Al, all in millications
Value
results

numeric matrix with the two above specified parameters

loadData

113

Author(s)
Vojtech Janousek, 
References
De La Roche H, Leterrier J, Grandclaude P, & Marchal M (1980) A classification of volcanic and
plutonic rocks using R1 R2 - diagram and major element analyses - its relationships with current
nomenclature. Chem Geol 29: 183-210 doi: 10.1016/0009-2541(80)90020-0
See Also
LaRoche

loadData

Loading data into GCDkit

Description
Loads data from a file (or, alternatively, a clipboard) into GCDkit. The files may contain plain text,
or, if library RODBC (has been installed, can be in the dBase III/IV (*.dbf), Excel (*.xls), Access
(*.mdb), PetroGraph (*.peg), IgPet or NewPet (*.roc) formats.
Usage
loadData(filename=NULL,separators = c("\t", ",", ";"," "),
na.strings = c("NA","-","bd", "b.d.", "bdl", "b.d.l.", "N.A.","n.d."),
clipboard = FALSE, merging = FALSE);
loadDataOdbc(filename=NULL,na.strings=c("NA","-", "bd",
"b.d.", "bdl", "b.d.l.", "N.A.","n.d."),merging=FALSE,
ODBC.choose=TRUE)
Arguments
filename

fully qualified name of the file to be loaded, including suffix.

separators

strings that should be tested as prospective delimiters separating individual items
in the data file.

na.strings

strings that will be interpreted, together with empty items, zeros and negative
numbers, as missing values (NA).

clipboard

logical; is clipboard to be read instead of a file?

merging

logical; is the function invoked during merging of two data files?

ODBC.choose

logical; if TRUE, ODBC channel can be chosen interactively.

114

loadData

Details
If library RODBC is available, the functions attempt to establish an ODBC connection to the selected file, and open it as dBase III/IV (*.dbf), Excel (*.xls) or Access (*.mdb) format. The DBF
files are used to store data by other popular geochemical packages, such as IgPet (Carr, 1995) or
MinPet (Richard, 1995).
Another format that can be imported is *.csv. It is employed by geochemical database systems
such as GEOROC (http://georoc.mpch-mainz.gwdg.de/georoc/) and PETDB (http://www.
petdb.org/).
The import filter for the *.csv files has been tailored to keep the structure of these databases in mind.
The package PetroGraph (Petrelli et al. 2005) saves data into *.peg files that are also, in principle,
*.csv files compatible with the GCDkit.
Data files *.roc are yet another variant of *.csv files, used by NewPet (Clarke et al. 1994). This is
not to be confused with the *.roc format designed for IgPet (Carr, 1995). This is a text file with a
quite complex structure, whose import is still largely experimental. DBF files are to be preferred
for this purpose.
If not successful, the function ’loadData’ assumes that it is dealing with a simple text file.
On the other hand ’loadDataOdbc’ allows an ODBC channel to be specified interactively if
’ODBC.choose=TRUE’.
Plain text files can be delimited by tabs, commas or semicolons (the delimiter is recognized automatically). Alternative separators list can be specified by the optional ’separators’ parameter.
The Windows clipboard is just taken as a special kind of a tab-delimited text file.
In the text file, the first line contains names for the data columns (except for the first one that is
automatically assumed to contain the sample names); hence the first line may (or may not) have one
item less than the following ones. The data rows start with sample name and do not have to be all
of the same length (the rest of the row is filled by ’NA’ automatically).
Missing values (’NA’) are allowed anywhere in the data file (naturally apart from sample and column
names); any of 'NA', 'N.A.', '-', 'b.d.', 'bd', 'b.d.l.','bdl' or 'n.d.' are also treated
as such, as specified by the parameter na.strings.
While loading, the values ’#WHATEVER!’ (Excel error messages) are also replaced by ’NA’ automatically.
Please note that the function ’loadDataOdbc’, due to the current limitations of the RODBC package,
cannot handle correctly columns of mixed numeric and textual data. In such a column all textual
information is converted to 'NA' and this unfortunately concerns the sample names as well. If
encountering any problems, please use import from text file or via clipboard, which are much more
robust.
The negative numbers and values ’< x’ (used by some authors to indicate items below detection
limit) can be either replaced by their half (i.e. half of the detection limit) or ’NA’. User is prompted
which of these options he prefers.
Alternatively, the negative values can be viewed either as missing (’NA’) or can be imported, as may
be desirable for instance for stable isotope data in the delta notation.
Decimal commas, if present in text file, are converted to decimal points.
The data files can be practically freeform, i.e. no specified oxides/elements are required and no exact
order of these is to be adhered to. Analyses can contain as many numeric columns as necessary, the
names of oxides and trace elements are self-explanatory (e.g. "SiO2", "Fe2O3", "Rb", "Nd".
In the text files (or if pasting from clipboard), any line starting with the hash symbol ('#') is ignored
and can be used to introduce comments or to prevent the given analysis from loading temporarily.

loadData

115

Note that names of variables are case sensitive in R. However, any of the fully upper case names of
the oxides/elements that appear in the following list are translated automatically to the appropriate
capitalization:
SiO2, TiO2, Al2O3, Fe2O3, FeO, MnO, MgO, CaO, Na2O, FeOt, Fe2O3t,
Li2O, mg#, Ac, Ag, Al, As, At, Au, Ba, Be, Bi,
Br, Ca, Cd, Ce, Cl, Co, Cr, Cs, Cu, Dy, Er, Eu,
Fe, Ga, Gd, Ge, Hf, Hg, Ho, In, Ir, La, Li, Lu,
Mg, Mn, Mo, Na, Nb, Nd, Ne, Ni, Np, Os, Pa, Pb,
Pd, Pm, Pr, Pt, Pu, Rb, Re, Rh, Ru, S, Sb, Sc,
Se, Si, Sm, Sn, Sr, Ta, Tb, Te, Th, Ti, Tl, Tm,
Yb, Zn, Zr.
Total iron, if given, should be expressed either as ferrous oxide (’FeOt’, ’FeOT’, ’FeOtot’, ’FeOTOT’
or ’FeO*’) or ferric oxide (’Fe2O3t’, ’Fe2O3T’, ’Fe2O3tot’, ’Fe2O3TOT’ or ’Fe2O3*’).
Structurally bound water can be named 'H2O.PLUS', 'H2O+', 'H2OPLUS','H2OP' or ’H2O_PLUS’.
Upon loading, all the completely empty columns are removed first. Any non-numeric items found
in a data column with one of the names listed in the above dictionary are assumed to be typos and
replaced by ’NA’, after a warning appears. At the next stage all fully numeric data columns are
stored in a numeric data matrix ’WR’.
For any missing major- and minor-element data (SiO2, TiO2, Al2O3, Fe2O3, FeO, MnO, MgO,
CaO, Na2O, K2O, H2O.PLUS, CO2, P2O5, F, S), an empty (NA) column is created automatically.
The remaining, that is all at least partly textual data columns are transferred to the data frame
’labels’. To this are also attached a column whose name starts with ’Symbol’ (if any) that is
taken as containing plotting symbols and a column whose name is ’Colour’ or ’Color’(if any,
capitalization does not matter) that may contain plotting colours specification. The relative size of
the individual plotting symbols may be specified in a column named ’Size’ or ’cex’ that is also to
be attached to the ’labels’.
The plotting symbols can be given either by their code (see showSymbols) or directly as strings of
single characters.
The colours can be specified as codes (1-49) or English names (see showColours or type 'colours()'
into the Console window).
If specifications of the plotting symbols and colours are missing completely, and at least one nonnumeric variable is present, the user is prompted whether he does not want to have the symbols and
colours assigned automatically, from 1 to n, according to the levels of the selected label. Otherwise
default symbols (empty black circles) are used.
The default grouping is set on the basis of plotting symbols ’(labels$Symbol)’ or the data column
used to autoassign the plotting symbols and colours.
Lastly, a backup copy of the data is stored in the list ’WRCube’ using the function ’pokeDataset’.
It is stored either under the name of the file, or, if it already exists, under the file name with a time
stamp attached.

116

loadData

Value
WR

numeric matrix: all numeric data

labels

data frame: all at least partly character fields; labels$Symbol contains plotting
symbols and labels$Colour the plotting colours

The function prints a short summary about the loaded file. It also loads and executes the Plugins,
i.e. all the R code (*.r) that is currently stored in the subdirectory ’\Plugin’. Finally, the system
performs some recalculations (calling ’Gcdkit.r’).

Note
In order to ensure the database functionality, duplicated column (variable) names are not allowed.
This concerns, to a large extent, also the sample names. The only exception are CSV files - if
duplicated samples are found, sequence numbers are assigned instead.
All completely empty rows and columns in both labels and numeric data are ignored.

Author(s)
The RODBC package was written by Brian Ripley.
Vojtech Janousek, 

References
Carr M (1995) Program IgPet. Terra Softa, Somerset, New Jersey, U.S.A.
Clarke D, Mengel F, Coish RA, Kosinowski MHF(1994) NewPet for DOS, version 94.01.07. Department of Earth Sciences, Memorial University of Newfoundland, Canada.
Petrelli M, Poli G, Perugini D, Peccerillo A (2005) PetroGraph: A new software to visualize, model,
and present geochemical data in igneous petrology. Geochemistry Geophysics Geosystems 6: 1-15
Richard LR (1995) MinPet: Mineralogical and Petrological Data Processing System, Version 2.02.
MinPet Geological Software, Quebec, Canada.

See Also
’saveData’ ’mergeData’ ’pokeDataset’ ’showColours’ ’showSymbols’ ’read.table’ ’getwd’
’setwd’

Examples
# Sets the working path and loads the 'sazava' test data set
setwd(paste(gcdx.dir,"Test_data",sep="/"))
loadData("sazava.data")

Maniar

117

Maniar

Maniar and Piccoli (1989)

Description
Plots data stored in ’WR’ (or its subset) into Maniar and Piccoli’s series of diagrams.
Usage
Maniar(plot.txt = getOption("gcd.plot.text"))
Arguments
plot.txt

logical, annotate fields by their names?

Details
Collection of six binary diagrams, based on major elements chemistry, developed by Maniar &
Piccoli (1989) for tectonic discrimination of granitic rocks. Shand’s (1943) diagram is also used.
Diagrams are defined as follows:
x axis

y axis

SiO2

K2 O

SiO2

Al2 O3

SiO2

F eO(T )
(F eO(T )+M gO)

100∗M gO
(Al2 O3 +N a2 O+K2 O+F eO(T )+M gO)

100∗F eO(T )
(Al2 O3 +N a2 O+K2 O+F eO(T )+M gO)

M and F proportion in the AFM system
100∗CaO
(Al2 O3 +N a2 O+K2 O+F eO(T )+M gO+CaO)

100∗(F eO(T )+M gO)
(Al2 O3 +N a2 O+K2 O+F eO(T )+M gO+CaO)

C and F proportion in the ACF system
A/CNK (molar)

A/NK (molar)

Abbreviations used in diagrams represent granitoids from following geotectonic environments:
IAG
CAG
CCG
POG
RRG
CEUG
OP

Island Arc Granitoids
Continental Arc Granitoids
Continental Collision Granitoids
Post-orogenic Granitoids
Rift-related Granitoids
Continental Epeirogenic Uplift Granitoids
Oceanic Plagiogranites

118

Maniar

Peralkaline, Metaluminous and Peraluminous rocks are defined in the last (Shand’s) diagram.
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots.
See Plate, Plate editing and figaro for details.
Author(s)
Vojtech Janousek, 
References
Maniar P D & Piccoli P M (1989) Tectonic discriminations of granitoids. Geol Soc Amer Bull 101:
635-643. doi: 10.1130/0016-7606(1989)101<0635:TDOG>2.3.CO;2
Shand (1943) Eruptive Rocks. John Wiley & Sons.
See Also
Plate, Plate editing, figaro, plotPlate,
Examples
#plot the diagrams
plotPlate("Maniar")

mergeData

mergeData

119

Appending data to a current data set

Description
These functions append new data to the analyses currently stored in the memory of the GCDkit.
Usage
mergeDataRows()
mergeDataCols(all.rows=NULL)
Arguments
all.rows

logical; should be all samples preserved, even those missing in one of the datasets
?

Details
The function ’mergeDataRows’ appends new samples (i.e. new rows). The structures of both
datafiles are, as much as possible, matched against each other, and, if necessary, new empty columns
are introduced to the original data file, if they are missing. If any duplicated sample names are found,
they are replaced by sequence numbers and a new column ’old.ID’ is appended to the labels. Also
appended is a column named ’file’ containing the name of the file the particular sample originated
from.
’mergeDataCols’ adds new data (i.e. new data columns) to the samples stored in the memory. If
desired (’all.rows’ is ’TRUE’), included are also samples that occur solely in one of the files.
For the guidelines on correct formatting of the data files see loadData.
Value
WR

numeric matrix: all numeric data

labels

data frame: all at least partly character fields; labels$Symbol contains plotting
symbols and labels$Colour the plotting colours

The function prints a short summary about the loaded file.
Author(s)
Vojtech Janousek, 
See Also
’loadData’ ’saveData’ ’merge’

120

Meschede

Meschede

Meschede (1986) Zr/4-2Nb-Y

Description
Assigns data for a Meschede’s (1986) triangular diagram into Figaro template (list ’sheet’) and
appropriate values into ’x.data’ and ’y.data’.
Usage
Meschede()
Details
Triangular diagram with apices Zr/4, 2Nb and Y, proposed by Meschede (1986). The plot serves
primarily for tectonic discrimination of tholeiitic basalts.

Abbreviations used in diagram represent following geotectonic settings:

Mesonorm

121
AI-AII
AII-C
B
D
C-D

Within-Plate Alkaline Basalts
Within-Plate Tholeiites
P-type Mid-Ocean Ridge Basalts
N-type Mid-Ocean Ridge Basalts
Volcanic Arc Basalts

Value
sheet

list with Figaro Style Sheet data

x.data, y.data Zr/4, 2Nb and Y values recalculated into two dimensions
Author(s)
Vojtech Janousek, 
References
Meschede M (1986) A method of discriminating between different types of mid-ocean ridge basalts
and continental tholeiites with the Nb-Zr-Y diagram. Chem Geol 56: 207-218 doi: 10.1016/00092541(86)90004-5
See Also
figaro plotDiagram
Examples
#plot the diagram
plotDiagram("Meschede",FALSE)

Mesonorm

Improved Mesonorm for granitoid rocks

Description
Calculates eine bessere Mesonorm for granitoids of Mielke & Winkler (1979).
Usage
Mesonorm(WR, GUI = FALSE, precision = getOption("gcd.digits"))
Streckeisen(x, new = TRUE)
Arguments
WR

a numerical matrix; the whole-rock data to be normalized.

GUI

logical, is the function called from the GUI?

precision

precision of the result.

x

Normative minerals calculated by the function Mesonorm.

new

logical, is a new plotting window to be opened?

122

Middlemost

Details
This method of norm calculation should yield mineral proportions close to the actual mode of
granitoid rocks. The calculated minerals are:
Orthoclase, Albite, Anorthite, Quartz, Apatite, Magnetite,
Hematite, Ilmenite, Biotite, Amphibole, Calcite, Corundum,
Rest
If desired, the function plots Q’-ANOR diagram of Streckeisen & Le Maitre (1979) using the function Streckeisen. The fields in this diagram are labeled as follows:
2
3
4
5
6*
7*
8*
9*
10*
6
7
8
9
10

alkali feldspar granite
granite
granodiorite
tonalite
quartz alkali feldspar syenite
quartz syenite
quartz monzonite
quartz monzodiorite/quartz monzogabbro
quartz diorite/quartz gabbro
alkali feldspar syenite
syenite
monzonite
monzodiorite/monzogabbro
diorite/gabbro

Value
A numeric matrix ’results’.
Author(s)
Vojtech Janousek, 
& Vojtech Erban, 
References
Mielke P & Winkler H G F (1979) Eine bessere Berechnung der Mesonorm fuer granitische Gesteine.
Neu Jb Mineral, Mh 471-480
Streckeisen, A. & Le Maitre, R. W. (1979) A chemical approximation to the modal QAPF classification of the igneous rocks. Neu Jb Mineral, Abh 136, 169-206.

Middlemost

Middlemost’s diagram (1985)

Description
Assigns data for Middlemost’s diagram into Figaro template (list ’sheet’) and appropriate values
into ’x.data’ and ’y.data’.

Middlemost

123

Usage
MiddlemostPlut()
Details
Classification diagram, as proposed by Middlemost (1985) for plutonic rocks.

Value
sheet

list with Figaro Style Sheet data

x.data

SiO2 weight percent

y.data

Na2O+K2O weight percent

results

matrix with classification results

groups

vector with classification results

grouping

set to -1

124

millications

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Middlemost E A K (1985) Magmas and Magmatic Rocks. Longman, London
See Also
classify figaro plotDiagram
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To classify data stored in WR (Groups by diagram)
classify("MiddlemostPlut")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("MiddlemostPlut", FALSE)

millications

Millications

Description
Returns millications.
Usage
millications(x,print=FALSE,save=FALSE)
Arguments
x

matrix or vector with major-element data

print

logical: print the result?

save

logical: should be the results assigned globally?

Details
The millications are used for many plots of the French school, e.g. De la Roche et al. (1980) or
Debon & Le Fort (1983, 1988).
The calculated values are Si, Ti, Al, Fe3, Fe2, Fe, Mn, Mg, Ca, Na, K, P.
Elementi = 1000

Oxidei (wt.%)
∗ x(Elementi )
M W (Oxidei ))

Where: MW = molecularWeight of the Oxide[i], x = number of atoms of Element[i] in its formula
Value
Numeric matrix (or vector) with the millications. If ’save=TRUE’, ’results’ and ’milli’ are assigned globally.

Misc

125

Author(s)
Vojtech Janousek, 
References
De La Roche H, Leterrier J, Grandclaude P, & Marchal M (1980) A classification of volcanic and
plutonic rocks using R1R2- diagram and major element analyses - its relationships with current
nomenclature. Chem Geol 29: 183-210
Debon F & Le Fort P (1988) A cationic classification of common plutonic rocks and their magmatic
associations: principles, method, applications. Bull Mineral 111: 493-510
Debon F & Le Fort P (1983) A chemical-mineralogical classification of common plutonic rocks
and associations. Trans Roy Soc Edinb, Earth Sci 73: 135-149

Misc

Miscellaneous geochemical indexes

Description
Calculates a series of useful geochemical indexes.
Usage
Misc(WR)
Arguments
WR

a numerical matrix; the whole-rock data to be recalculated.

Details
Various petrochemical indexes are calculated, such as:
• total iron as F e2 O3
• F e2 O3 /FeO, N a2 O/K2 O and K2 O/N a2 O ratios
• Larsen’s DI - Differentiation index (Larsen 1938)
• Kuno’s SI - Solidification index (Kuno 1959)
• Agpaitic index (Ussing 1912)
Value
A numeric matrix ’results’.
Author(s)
Vojtech Janousek, 

126

Miyashiro

References
Kuno H (1959) Origin of Cenozoic petrographic provinces of Japan and surrounding
provinces. Bull Volcanol 20: 37-76
Larsen E S (1938) Some new variation diagrams for groups of igneous rocks. J Geol 46: 505-520
Sorensen H (1997) The agpaitic rocks; an overview. Min Mag 61: 485-498
Ussing N V (1912) Geology of the country around Sulianehaab, Greenland. Meddr Grolnland, 38:
1-426

Miyashiro

SiO2-FeOt/MgO diagram (Miyashiro 1974)

Description
Assigns data for SiO2 vs. F eOt /M gO diagram into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’

Usage
Miyashiro()

Details
Diagram in SiO2 vs. F eOt /M gO space, proposed by Miyashiro (1974), defines the following
fields:
Tholeiite Series
Calc-alkaline Series

Miyashiro

127

As the boundary was defined by Akiho Miyashiro as straight line passing through two specific
points, no limits of diagram validity for ultrabasic and high-silica rocks were given. Thus, the
boundary implemented in GCDkit script spreads from F eOt /M gO = 0 to SiO2 = 100%.
Value
sheet

list with Figaro Style Sheet data

x.data

SiO2 weight percent

y.data

FeOt/MgO weight percent

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Miyashiro A (1974) Volcanic rock series in island arcs and active continental margins. Am J Sci
274, 321-355. doi: 10.2475/ajs.274.4.321

128

Mode

See Also
classify figaro plotDiagram
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("Miyashiro")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("Miyashiro", FALSE)

Mode

Approximating the mode by least-squares method

Description
The functions ’Mode’ and ’ModeC’ calculate the best approximations of the mode given majorelement compositions of the rock and its main mineral constituents. Function ’WRComp’ does the
opposite, i.e. yields the whole-rock composition given the chemistry of individual minerals and
their modal proportions.
Usage
ModeMain(WR,sample.id="",select.oxides=TRUE,select.minerals=TRUE)
Mode(rock, mins,sample.id="")
ModeC(rock, mins,sample.id="")
ModeAll(WR)
WRComp(mins, f)
Arguments
WR

a numerical matrix; the whole-rock data to be normalized.

rock

whole-rock composition of the given sample.

sample.id

(optional) sample name.

select.oxides (logical) should be selected oxides used for calculation?
select.minerals
(logical) should be selected minerals used for calculation?
mins

composition of its main rock-forming minerals.

f

their modal proportions.

Details
’Mode’ uses unconstrained least-squares method taking advantage of the standard R function
’lsfit(mins,rock,intercept=F)’. It produces results that generally do not sum up to 100 % due
to the presence of elements not used in calculation (such as water), and, or, analytical noise.
’ModeC’ is the constrained variation whose output ought to sum up to 100 % by definition (Albarede
1995). As such it seems to be more appropriate in most applications.

Mode

129

In both cases, the printed output involves the input data, calculated modal proportions of the individual minerals, the calculated composition of the rock (using the auxiliary function ’WRComp’) and
differences between the approximated and the real data (residuals).
The sum of squared residuals is a measure of fit (as a rough guide it should be less than ca. 1).
The mineral compositions are provided by a tab-delimited ASCII file, whose first row contains the
names of the determined oxides, the following ones start with the mineral abbreviation and the
numeric data (hence the first row has one item less than the following ones).
’ModeMain’ is entry point to both ’Mode’ and ’ModeC’ that enables the user to read the mineral data
file, select the oxides and minerals to be used in the calculation.
The options ’select.oxides=FALSE’ and ’select.minerals=FALSE’ read the mineral file in its
entirety, using all minerals and oxides present.
’ModeAll’ is a front end that performs the constrained least squares calculation for samples specified
by the function selectSamples.
Value
’ModeMain’, ’Mode’ and ’ModeC’ return a list with two items. The first of them (’table’) is a matrix
with the real composition of the rock and its minerals, the calculated whole-rock composition and
the residuals. The second (’(un)constrained’) returns calculated mineral proportions and sum of
squared residuals.
’ModeAll’ returns a simple matrix listing, for each rock sample, calculated proportions of rockforming minerals and the sum of squared residuals.
’WRComp’ yields a vector with the calculated whole-rock composition.
Author(s)
Vojtech Janousek, 
References
Albarede F (1995) Introduction to Geochemical Modeling. Cambridge University Press, Cambridge, p. 1-543
See Also
For example of the mineral data, see file ’Test_data\sazava mins.data’.
Examples
# Albarede (1995) - page 7
# Calculate WRComposition of olivine gabbro containing 40 % olivine,
# 30 % diopside and 30 % plagioclase.
mins<-matrix(c(40.01,0.00,14.35,45.64,0.00,0.00,54.69,0.00,3.27,16.51,
25.52,0.00,48.07,33.37,0.00,0.00,16.31,2.25),3,6,byrow=TRUE)
rownames(mins)<-c("ol","di","plg")
colnames(mins)<-c("SiO2","Al2O3","FeO","MgO","CaO","Na2O")
print(mins)
f<-c(0.4,0.3,0.3)
names(f)<-c("ol","di","plg")
print(f)

130

Molecular weights

rock<-WRComp(mins,f)
print(rock)
# Reverse
mode1<-Mode(rock,mins)
mode2<-ModeC(rock,mins)

Molecular weights

Calculating molecularWeights of oxides

Description
These functions plot multiple binary plots with a common x axis, such as Harker plots.
Usage
molecularWeight(formula)
Arguments
formula

a character vector of length 1, a formula of the oxide.

Details
So far only simple oxide formulae in form of Ax Oy (where x, y are optional indexes) can be
handled. The atomic weights are stored in a file MW.data. The atomic weights come from official
CIAAW web site http://www.ciaaw.org.
Value
A list with items:
MW
x.atoms
x.oxygen

molecularWeight
number of atoms in the formula
number of oxygens

Author(s)
Vojtech Janousek,  Vojtech Erban, 
References
Commission on Isotopic Abundances and Atomic Weights (CIAAW) of the International Union of
Pure and Applied Chemistry. Accessed on January 8, 2016, at http://www.ciaaw.org
Examples
molecularWeight("SiO2")
molecularWeight("SiO2")[[1]]

Mullen

131
oxides<-c("SiO2","TiO2","Al2O3","Fe2O3","FeO")
sapply(oxides,molecularWeight)

Mullen

Mullen (1983) 10MnO-TiO2-10P2O5

Description
Assigns data for the diagram of Mullen (1983) into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’.
Usage
Mullen()
Details
Triangular diagram with apices 10MnO, T iO2 and 10P2 O5 , proposed by Mullen (1983).

132

Mullen
Abbreviations used in diagram represent following geotectonic settings:

MullerK

133
CAB
IAT
MORB
OIA
OIT

Calc-Alkaline Basalts
Island Arc Tholeiites
Mid-Ocean Ridge Basalts
Ocean Island Andesites
Ocean Island Tholeiites

Value
sheet
list with Figaro Style Sheet data
x.data, y.data 10MnO, T iO2 and 10P2 O5 in wt. % recalculated to 2D
Author(s)
Vojtech Janousek, 
References
Mullen E D (1983) MnO/T iO2 /P2 O5 : a minor element discriminant for basaltic rocks of oceanic
environments and its implications for petrogenesis. Earth Planet Sci Lett 62: 53-62 doi: 10.1016/0012821X(83)90070-5
See Also
figaro plotDiagram
Examples
#plot the diagram
plotDiagram("Mullen",FALSE)

MullerK

Muller et al. (1992) potassic igneous rocks discrimination

Description
Assigns Figaro templates to geotectonic diagrams for potassic igneous rocks of Müller et al. (1992)
into the list ’plate’) and appropriate values into the list ’plate.data’ for subsequent plotting.
Usage
MullerKbinary(plot.txt=getOption("gcd.plot.text"))
MullerKternary(plot.txt=getOption("gcd.plot.text"))
Arguments
plot.txt

logical, annotate fields by their names?

Details
Suite of binary and ternary diagrams for discrimination of geotectonic environment of potassic igneous rocks, proposed by Müller et al. (1992) and Müller & Groves (1995). Following geotectonic
settings may be deduced:

134

MullerK
Abbreviation used
CAP
PAP
IOP
LOP
WIP

Environment
Continental Arc
Postcollisonal Arc
Initial Oceanic Arc
Late Oceanic Arc
Within Plate

MullerK

135

136

MullerK

Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.

Author(s)
Vojtech Janousek, 

References
Müller D, Rock NMS, Groves DI (1992) Geochemical discrimination between shoshonitic and
potassic volcanic rocks in different tectonic settings: a pilot study. Mineral Petrol 46: 259-289
doi:10.1007/BF01173568
Müller D, Groves DI (1995) Potassic Igneous Rocks and Associated Gold-Copper Mineralization.
Springer, Berlin, pp 1- 210

See Also
Plate, Plate editing, plotPlate, figaro

Examples
plotPlate("MullerKbinary")
plotPlate("MullerKternary")

Multiple plots

Multiple plots

137

Multiple binary plots

Description
These functions plot multiple binary plots with a common x axis, such as Harker plots.
Usage
multiple(x,y=paste(colnames(WR),sep=","),
samples=rownames(WR),pch=labels$Symbol,
col=labels$Colour,xmin=NULL,xmax=NULL,GUI=FALSE,nrow=NULL,ncol=NULL,...)
multipleMjr(x = "",
y = "SiO2,TiO2,Al2O3,FeOt,MgO,CaO,Na2O,K2O,P2O5",
pch = labels$Symbol, col = labels$Colour, ...)
multipleTrc(x = "",
y = "Rb,Sr,Ba,Cr,Ni,La,Ce,Y,Zr,mg#,A/CNK,K2O/Na2O",
pch = labels$Symbol, col = labels$Colour, ...)
Arguments
x

a character vector, name of the common x axis. Formulae are OK.

y

a character vector, names of oxides/elements to be plotted as y axes separated
by commas. Formulae are OK.

nrow, ncol

dimensions of the plots’ matrix

samples

character or numeric vector; specification of the samples to be plotted.

pch

plotting symbols.

col

plotting colours.

xmin, xmax

minimum and maximum for the x axis.

GUI

logical; is the call being made from within GCDkit GUI or not?

...

further graphical parameters: see ’help(par) for details.

Details
If x axis occurs among the arguments to be plotted as y axes, it is skipped.
Functions ’multipleMjr’ and ’multipleTrc’ are entry points supplying the default lists for majorand trace elements.
Even though as a default is assumed a list of major (SiO2, TiO2, Al2O3, FeOt, MnO, MgO,CaO, Na2O, K2O)
or trace (Rb, Sr, Ba, Cr, Ni, La, Ce, Y,Zr and mg#) elements, the variable(s) to be displayed
can be specified.
The easiest way is to type directly the names of the columns, separated by commas. Alternatively
can be used their sequence numbers or ranges. Also built-in lists can be employed, such as ’LILE’,
’REE’, ’major’ and ’HFSE’ or their combinations with the column names.
These lists are simple character vectors, and additional ones can be built by the user (see Examples). Note that currently only a single, stand-alone, user-defined list can be employed as a search
criterion.

138

Multiple plots

In the specification of the x axis or any of the y axes can be used also arithmetic expressions, see
calcCore for the correct syntax.
Lastly, the user is asked to enter the limits for the x axis, two numbers separated by a comma.
Note that the scaling takes into account the size of the plotting symbols, i.e. the axes are extended
somewhat.
Value
None.
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.

mzSaturation

139

Author(s)
Vojtech Janousek, 
See Also
figaro, Plate, Plate editing
Examples
multipleMjr("SiO2")
multiple("Na2O+K2O",LILE,xmin=0)
# Plots the LILE against the sum of alkalis
multiple("FeOt/MgO","SiO2,CaO,Na2O+K2O,TiO2",pch="+",col="red",samples=1:10,cex=2.5)
multipleTrc("Zr")
# Plots the default trace-element set against the Zr

mzSaturation

Monazite saturation (Montel 1993)

Description
Calculates monazite saturation temperatures for given major-element compositions and LREE contents of the magma.
Usage
mzSaturation(cats = milli,
REE = filterOut(WR, c("La", "Ce", "Pr","Nd","Sm", "Gd"), 1),
H2O = 3, Xmz = 0)
Arguments
cats
REE
H2O
Xmz

numeric matrix; whole-rock data recast to millications
numeric matrix with LREE concentrations - only complete set of La-Gd
assumed water contents of the magma
mole fractions of the REE-phosphates in monazite

Details
This function uses saturation model of Montel (1993). The formulae are as follows:
P
REEi
( at.weight(REEi)
)
LREE =
Xmz
where REE\_i: La, Ce, Pr, Nd, Sm, Gd.
Dmz = 100

T mz.sat.C =

N a + K + 2Ca
1
.
Al
Al + Si

13318
√
− 273.15
9.5 + 2.34Dmz + 0.3879 H2 O − ln(LREE)

140

NaAlK

Value
Returns a matrix ’results’ with the following components:
Dmz

distribution coefficient

Tmz.sat.C

monazite saturation temperature

Plugin
Saturation.r

Author(s)
Vojtech Janousek, 

References
Montel J M (1993) A model for monazite/melt equilibrium and application to the generation of
granitic magmas. Chem Geol 110: 127-146 doi: 10.1016/0009-2541(93)90250-M

NaAlK

Na2O - Al2O3 - K2O (mol. %) diagram

Description
Assigns data for ternary diagram N a2 O - Al2 O3 - K2 O (mol. %) into Figaro template (list ’sheet’)
and appropriate values into ’x.data’ and ’y.data’. Calculates molar concentrations of alkalis and
alumina, as well as several molar ratios involving these three oxides.

Usage
NaAlK()

Details
Ternary plot N a2 O - Al2 O3 - K2 O (mol. %). Dashed lines define the following compositional
fields (all oxides are expressed in mol. %):

NaAlK

141

peraluminous + metaluminous (Shand 1943)
peralkaline (Shand 1943)
perpotassic
potassic
ultrapotassic

(N a2 O + K2 O)/Al2 O3 < 1
(N a2 O + K2 O)/Al2 O3 > 1
K2 O/Al2 O3 > 1andK2 O/N a2 O > 1
1 < K2 O/N a2 O < 3
K2 O/N a2 O >= 3

The molar ratio of K2 O/N a2 O >= 3, is equivalent to K2 O/N a2 O >= 2 in wt. %, i.e. to the
definition of ultrapotassic igneous rocks by Foley et al. (1987).
Value
sheet

list with Figaro Style Sheet data

x.data, y.data N a2 O, Al2 O3 andK2 Ocontentsinmol.%transf ormedinto2D
Na2O

N a2 Oinmol.%

Al2O3

Al2 O3 inmol.%

K2O

K2 Oinmol.%

142

Niggli
(Na2O+K2O)/Al2O3
molecular ratio (N a2 O + K2 O)/Al2 O3
K2O/Al2O3

molecular ratio K2 O/Al2 O3

K2O/Na2O

molecular ratio K2 O/N a2 O

Author(s)
Vojtech Janousek, 
References
Foley S F, Venturelli G, Green D H, Toscani L (1987) Ultrapotassic rocks: characteristics, classification and constraints for petrogenetic models. Earth Sci Rev 24: 81-134 doi: 10.1016/00128252(87)90001-8
Shand (1943) Eruptive Rocks. John Wiley & Sons
See Also
classify figaro plotDiagram Shand
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("NaAlK")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("NaAlK", FALSE)

Niggli

Niggli’s values

Description
Calculates cationic parameters of Niggli (1948).
Usage
Niggli(WR, precision = getOption("gcd.digits"))
Arguments
WR

a numerical matrix; the whole-rock data to be normalized.

precision

precision of the result.

Details
The calculated parameters are:
si, al, fm, c, alk, k, mg, ti, p, c/fm, qz

OConnor

143

Value
A numeric matrix ’results’.

Author(s)
Vojtech Janousek, 

References
Niggli P (1948) Gesteine und Minerallagerstatten. Birkhauser, Basel, p. 1-540

OConnor

Classification diagram for siliceous igneous rocks, based on Fsp composition (O’Connor 1965)

Description
Assigns data for O’Connor’s triangular diagram into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’.

Usage
OConnorVolc()
OConnorPlut()

Details
The O’Connor’s triangular diagram is based on combination of Albite, Anorthite and K-feldspar
modal or normative data. While the function ’OConnorPlut’ can plot either modal or normative
diagrams for plutonic rocks, ’OConnorVolc’ is to be used exclusively with normative data computed
from chemical compositions of volcanic rocks.

144

OConnor

In fact, the triangle represents projection of the Quartz - K-feldspar - Albite - Anorthite tetrahedron.
All three diagrams are designed for quartz-rich rocks, i.e. those with quartz contents higher than
10 such silica-rich samples, the rock type can be determined purely on the basis of the feldspars’
proportions.

OConnor

145

As the specific version of the normative calculation is not mentioned in the original paper by
’O’Connor (1965)’, the function ’CIPW’, designed after ’Hutchison (1974, 1975)’ was implemented.
Value
sheet

list with Figaro Style Sheet data

x.data, y.data An, Ab and Or data (see details) transformed to orthogonal coordinates
Author(s)
Vojtech Erban, 
References
O’Connor J T (1965) A classification for Quartz-rich igneous rocks based on feldspar ratios. U.S.
Geol. Survey Prof Paper 525-B: B79-B84
Hutchison C S (1974) Laboratory Handbook of Petrographic Techniques. John Wiley & Sons, New
York, p. 1-527
Hutchison C S (1975) The norm, its variations, their calculation and relationships. Schweiz Mineral
Petrogr Mitt 55: 243-256
See Also
classify figaro CIPW plotDiagram

146

oxide2oxide

Examples
plotDiagram("OConnorVolc", FALSE)
classify("OConnorVolc")

oxide2oxide

Recalculation of one oxide to a different one

Description
Returns a factor needed to multiply concentrations of an element given as an oxide (in wt %) to a
different target oxide (of the same element).

Usage
oxide2oxide(formula1, formula2)

Arguments
formula1

character: the oxide which is to be recalculated

formula2

character: the target oxide

Value
A factor for recalculation.

Author(s)
Vojtech Janousek, 

See Also
oxide2ppm, ppm2oxide, molecularWeight

Examples
oxide2oxide("FeO","Fe2O3")
oxide2oxide("Mn2O3","MnO")

oxide2ppm

oxide2ppm

147

Calculation of ppm of atom from wt% of an oxide

Description
Recasts concentrations of an oxide (in wt. %) to that of appropriate cation (in ppm).
Usage
oxide2ppm(formula,where="WR")
Arguments
formula

character: the oxide which is to be recalculated

where

character: a name of matrix or dataframe with teh data to be recalculated

Value
A numeric matrix with one column containing the recalculated concentrations of the given cation
(ppm) for individual samples.
Author(s)
Vojtech Janousek, 
See Also
ppm2oxide, oxide2oxide, molecularWeight
Examples
data(sazava)
accessVar("sazava")
oxide2ppm("K2O")

pairsCorr

Statistics: Correlation

Description
Plots a matrix of scatterplots in the lower panel and one of other pre-defined panel functions in the
upper.
Usage
pairsCorr(elems = major)
pairsMjr()
pairsTrc()

148

pairsCorr

Arguments
elems

list of desired elements

Details
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
Even though a list of major elements is assumed as a default, different variables can be specified by
the function ’selectColumnsLabels’.

The upper panels to choose from are:
’panel.corr’
’panel.cov’
’panel.smooth’
’panel.hist’

Prints correlations, with size proportional to the correlations;
Prints covariances;
Fits smooth trendlines;
Plots frequency histograms.

pdfAll

149

Value
None.
Warning
Names of existing numeric data columns and not formulae involving these can be handled at this
stage.
Author(s)
Vojtech Janousek, 
Examples
pairsCorr(LILE)
pairsMjr()
pairsTrc()
# user-defined list
my.elems<-c("Rb","Sr","Ba")
pairsCorr(my.elems)

pdfAll

Save all graphics to PDF

Description
Saves all graphical windows to a single PDF file.
Usage
pdfAll(filename=NULL)
Arguments
filename

a name of file for saving the output.

Details
The function prompts for filename under which it saves all graphical windows, each on a separate
page. PDF is the most portable format, that should preserve practically the same layout on all
platforms.
Individual diagram can be saved from a menu that appears after clicking on the appropriate graphical
window (’File|Save as|PDF’).
Value
None.

150

Pearce 1982

Author(s)
Vojtech Janousek, 
See Also
’psAll’ ’pdf’

Pearce 1982

Pearce (1982)

Description
Assigns data for the diagram of Pearce (1982) into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’.

Usage
Pearce1982()
Details
Diagram proposed by Pearce (1982) for geotectonic discrimination between lavas from distinct
geotectonic positions:
Within-plate lavas
Island-arc lavas
Mid-ocean Ridge Basalts

Pearce and Cann

151

Value
sheet

list with Figaro Style Sheet data

x.data

Zr ppm

y.data

Ti ppm

Author(s)
Jean-Francois Moyen, 
References
Pearce, J A (1982) Trace element characteristics of lavas from destructive plate boundaries. In: R
S Thorpe (ed) Andesites: Orogenic Andesites and Related Rocks. John Wiley & Sons, Chichester,
pp 525-548, ISBN 0 471 28034 8
See Also
figaro plotDiagram
Examples
#plot the diagram
plotDiagram("Pearce1982",FALSE)

Pearce and Cann

Pearce and Cann (1973)

Description
Plots data stored in ’WR’ (or its subset) into Pearce and Cann’s diagrams.
Usage
Cann(plot.txt = getOption("gcd.plot.text"))
Arguments
plot.txt

logical, annotate fields by their names?

Details
Set of two triangular and one binary diagram, proposed by Pearce & Cann (1973).

152

Pearce and Cann

Following abbreviations are used:
IAT
MORB
CAB
WPB

Low-K Tholeiites
Ocean Floor Basalts
Island Arc Basalts
Within Plate Basalts

Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.

Author(s)
Vojtech Janousek, 

References
Pearce J A & Cann J R (1973) Tectonic setting of basic volcanic rocks determined using trace
element analyses. Earth Planet Sci Lett 19: 290-300. doi: 10.1016/0012-821X(73)90129-5

See Also
Plate, Plate editing, plotPlate, figaro

Examples
#plot the diagrams
plotPlate("Cann")

Pearce and Norry

153

Pearce and Norry

Pearce and Norry (1979)

Description
Assigns data for the diagram of Pearce & Norry (1979) into Figaro template (list ’sheet’) and
appropriate values into ’x.data’ and ’y.data’.
Usage
Norry()
Details
Diagram proposed by Pearce & Norry (1979) for geotectonic discrimination between basaltic rocks
from distinct geotectonic positions:
Within-plate Basalts
Island-arc basalts
Mid-ocean Ridge Basalts

Value
sheet

list with Figaro Style Sheet data

x.data

Zr ppm

y.data

Zr/Y by weight

154

Pearce et al. 1977

Author(s)
Vojtech Janousek, 

References
Pearce J A & Norry M J (1979) Petrogenetic implications of Ti, Zr, Y, and Nb variations in volcanic
rocks. Contrib Mineral Petrol 69: 33-47. doi: 10.1007/BF00375192

See Also
figaro plotDiagram

Examples
#plot the diagram
plotDiagram("Norry",FALSE)

Pearce et al. 1977

Pearce et al. (1977) MgO-FeOt-Al2O3

Description
Assigns data for the MgO-FeOt-Al2 O3 triangle proposed by Pearce et al.(1977) into Figaro template (list ’sheet’) and appropriate values into ’x.data’ and ’y.data’.

Pearce et al. 1977

155

Usage
PearceEtAl()
Details
Triangular diagram with apices MgO, FeOt and Al2 O3 , proposed by Pearce et al.(1977). The
boundaries were defined solely for subalkaline volcanic rocks with SiO2 between 51-56 wt %.
Following geotectonic positions may be identified using the diagram:
Spreading Center Island (or inter-plate island) - oceanic islands adjacent to ocean-ridge spreading,
such as Iceland or Galapagos; the authors ’do not consider this field well established’.
Orogenic
Ocean Ridge and Floor
Ocean Island
Continental
Value
sheet

list with Figaro Style Sheet data

156

Pearce Nb-Th-Yb
x.data, y.data MgO, FeOt and Al2 O3 in wt. % recalculated to two dimensions

Author(s)
Vojtech Janousek, 
References
Pearce T H, Gorman B E & Birkett T C (1977) The relationship between major element geochemistry and tectonic environment of basic and intermediate volcanic rocks. Earth Planet Sci Lett 36:
121-132. doi: 10.1016/0012-821X(77)90193-5
See Also
figaro plotDiagram
Examples
#plot the diagram
plotDiagram("PearceEtAl",FALSE)

Pearce Nb-Th-Yb

Pearce (2008) Nb/Yb-Th/Yb diagram

Description
Assigns data for a Th/Yb vs. Nb/Yb diagram into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’.
Usage
PearceNbThYb(reservoirs=TRUE,xmin=0.1,xmax=1000,ymin=0.01,ymax=100)
Arguments
reservoirs

logical, should be plotted average NMORB, EMORB and OIB?

xmin,xmax

numeric, limits for the x axis.

ymin,ymax

numeric, limits for the y axis.

Details
This diagram (Th/Yb vs. Nb/Yb) has been developed by J. Pearce in the 2000s to characterize (and
discriminate) arc magmatism. The current version is based on paper by Pearce (2008) dealing with
oceanic basalts, though. According to this author, ThNb serves as a ’crustal input proxy’ and hence
for demonstrating an oceanic, non-subduction setting.
The ’MORB-OIB array’ at the bottom extends from N-MORB to OIB (plotted for reference are
average compositions of NMORB, EMORB and OIB taken from Sun and McDonough (1989).
Melting of the metasomatized mantle yields trends parallel to the mantle array.
Arc lavas, formed by fluxed melting of the mantle, are shifted above the mantle array; the same
effects have mantle-derived magma-crust interactions. The top dashed line is the outer limit of
typical arc lavas, but there is a great deal of variation.

Pearce Nb-Th-Yb

157

Value
sheet

list with Figaro Style Sheet data

x.data

Nb/Yb

y.data

Th/Yb

Author(s)
Vojtech Janousek,  and Jean-Francois Moyen, 
References
Pearce JA (2008) Geochemical fingerprinting of oceanic basalts with applications to ophiolite classification and the search for Archean oceanic crust. Lithos 100: 14-48 doi:10.1016/j.lithos.2007.06.016
Sun SS, McDonough WF (1989) Chemical and isotopic systematics of oceanic basalts: implications
for mantle composition and processes. In: Saunders AD, Norry M (eds) Magmatism in Ocean
Basins. Geological Society of London Special Publications 42, pp 313-345

158

Pearce Nb-Ti-Yb

See Also
figaro plotDiagram PearceNbTiYb

Examples
#plot the diagram plotDiagram("PearceNbThYb",FALSE,FALSE,reservoirs=TRUE)
plotDiagram("PearceNbThYb",FALSE,FALSE,reservoirs=FALSE)

Pearce Nb-Ti-Yb

Pearce (2008) Nb/Yb-TiO2/Yb diagram

Description
Assigns data for a T iO2 /Yb vs. Nb/Yb diagram into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’.

Usage
PearceNbTiYb(reservoirs=TRUE,xmin=0.1,xmax=100,ymin=0.1,ymax=10)

Arguments
reservoirs

logical, should be plotted average NMORB, EMORB and OIB?

xmin,xmax

numeric, limits for the x axis.

ymin,ymax

numeric, limits for the y axis.

Details
The diagram T iO2 /Yb vs. Nb/Yb serves as ’melting depth proxy’ and hence for indicating mantle
temperature and thickness of the conductive lithosphere (Pearce 2008). It distinguishes basalts,
which have originated by shallow melting, out of garnet stability field (’MORB array’) from those
spanning from deep melting with garnet in the residue (’OIB array’). Plotted for reference are
average compositions of NMORB, EMORB and OIB taken from Sun and McDonough (1984).

Pearce Nb-Ti-Yb

159

Value
sheet

list with Figaro Style Sheet data

x.data

Nb/Yb

y.data

TiO2/Yb

Author(s)
Vojtech Janousek, 
References
Pearce JA (2008) Geochemical fingerprinting of oceanic basalts with applications to ophiolite classification and the search for Archean oceanic crust. Lithos 100: 14-48 doi:10.1016/j.lithos.2007.06.016
Sun SS, McDonough WF (1989) Chemical and isotopic systematics of oceanic basalts: implications
for mantle composition and processes. In: Saunders AD, Norry M (eds) Magmatism in Ocean
Basins. Geological Society of London Special Publications 42, pp 313-345

160

Pearce1996

See Also
figaro plotDiagram PearceNbThYb

Examples
#plot the diagram
plotDiagram("PearceNbTiYb",FALSE,FALSE,reservoirs=TRUE)
plotDiagram("PearceNbTiYb",FALSE,FALSE,reservoirs=FALSE)

Pearce1996

Nb/Y - Zr/Ti diagram (Winchester + Floyd 1977, modified by Pearce
1996)

Description
Assigns data for Nb/Y vs. Zr/Ti diagram into Figaro template (list ’sheet’) and appropriate values
into ’x.data’ and ’y.data’.

Usage
Pearce1996()

Details
Classification diagram proposed by Winchester & Floyd (1977) using incompatible element ratios
(Nb/Y vs. Zr/Ti). As the original plot has been designed prior to the publication of the TAS diagram
Le Bas et al. 1986, the field definition has been subsequently modified by Pearce (1996).

Pearce1996

161

The following fields are defined:
(Subalkaline) Basalt
Alkali basalt
Foidite
Andesite/Basaltic andesite
Trachyandesite
Tephriphonolite
Rhyolite/Dacite
Trachyte
Phonolite
Alkali Rhyolite
Value
sheet

list with Figaro Style Sheet data

x.data

Nb/Y wt. % ratio

y.data

Zr/Ti wt. % ratio

162

PearceGranite

Author(s)
Vojtech Janousek, 
References
Le Bas M J, Le Maitre R W, Streckeisen A & Zanettin B (1986) A chemical classification of volcanic rocks based on the total alkali-silica diagram. J Petrology 27: 745-750 doi: 10.1093/petrology/27.3.745
Pearce J A (1996) A User’s Guide to Basalt Discrimination Diagrams. In Wyman D A (ed) Trace
Element Geochemistry of Volcanic Rocks: Applications for Massive Sulphide Exploration. Geological Association of Canada, Short Course Notes 12, pp 79-113
Winchester J A & Floyd P A (1977) Geochemical discrimination of different magma series and their
differentiation products using immobile elements. Chem Geol 20: 325-343 doi: 10.1016/00092541(77)90057-2

See Also
WinFloyd1 classify figaro plotDiagram

Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("Pearce1996")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("Pearce1996", FALSE)

PearceGranite

Pearce et al. (1984)

Description
Assigns Figaro templates to Pearce’s geotectonic diagrams for granitoids into the list ’plate’) and
appropriate values into the list ’plate.data’ for subsequent plotting.

Usage
PearceGranite(plot.txt = getOption("gcd.plot.text"))
Arguments
plot.txt

logical, annotate fields by their names?

PearceGranite

163

Details
Suite of four diagrams for discrimination of geotectonic environment of granitoid rocks, proposed
by Pearce et al. (1984). It is based on combination of five trace elements (namely Y, Nb, Rb, Yb
and Ta).

Following geotectonic settings may be deduced:
Abbreviation used
ORG
VAG
WPG
COLG

Environment
Ocean Ridge Granites
Volcanic Arc Granites
Within Plate Granites
Collision Granites

164

PeceTaylor

Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
References
Pearce J A, Harris N W & Tindle A G (1984) Trace element discrimination diagrams for the tectonic
interpretation of granitic rocks. J Petrology 25: 956-983. doi:10.1093/petrology/25.4.956
See Also
Plate, Plate editing, plotPlate, figaro
Examples
plotPlate("PearceGranite")

PeceTaylor

SiO2-K2O diagram (Peccerillo + Taylor 1976)

Description
Assigns data for SiO2 vs. K2 O diagram into Figaro template (list ’sheet’) and appropriate values
into ’x.data’ and ’y.data’
Usage
PeceTaylor()
Details
Diagram in SiO2 vs. K2 O space, proposed by Peccerillo & Taylor (1976), defines the following
fields:
Tholeiite Series
Calc-alkaline Series
High-K Calc-alkaline Series
Shoshonite Series
Field boundaries were linearly extrapolated up to 75% of SiO2 between ’Calc-alkaline Series’ and
’High-K Calc-alkaline Series’, and up to 70% of SiO2 between ’High-K Calc-alkaline Series’ and
’Shoshonite Series’.

PeceTaylor

165

To employ boundaries as originally defined by Peccerillo & Taylor (1976), change the value of
variable ’extrapolated’ to ’FALSE’ in the file ’[R-root] \ library \ GCDkit \ Diagrams \ Classification
\ PeceTaylor.r’. Also note that the second value for the middle boundary (i.e. [52,1.5]) is in the
original paper obviously misquoted as 1.3 .
Rocks with composition falling beyond defined boundaries are labeled ’undefined’ by the ’classify’
function.
For comparison with similar diagrams used by other authors see Rickwood (1989).
Value
sheet

list with Figaro Style Sheet data

x.data

SiO2 weight percent

y.data

K2O weight percent

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 

166

peekDataset

References
Peccerillo A & Taylor S R (1976) Geochemistry of Eocene calc-alkaline volcanic rocks from the
Kastamonu area, Northern Turkey. Contrib Mineral Petrol 58: 63-81 doi: 10.1007/BF00384745
Rickwood P C (1989) Boundary lines within petrologic diagrams which use oxides of major and
minor elements. Lithos 22: 247-263 doi: 10.1016/0024-4937(89)90028-5
See Also
classify figaro plotDiagram
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("PeceTaylor")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("PeceTaylor", FALSE)

peekDataset

Retrieving previous dataset stored in memory

Description
Both functions restore the previously stored dataset and make it current.
Usage
peekDataset(which.dataset=NULL)
selectDataset()
Arguments
which.dataset

character; a name of the stored dataset.

Details
The function ’peekDataset’ restores a dataset saved previously into memory by the function
’pokeDataset’. This means that it assigns all global variables specified by individual items of
the list ’WRCube’.
These typically are: ’WR’, ’WRanh’, ’milli’, ’labels’, ’filename’, ’groups’ and ’grouping’.
The function ’selectDataset’ provides a graphical interface to ’peekDataset’, i.e. shows a list
box filled by the names of datasets currently stored in the memory.
Value
None. But several global variables, among others ’WR’, ’WRanh’, ’milli’ and ’labels’, are affected.
The name of the current dataset is stored in ’dataset.name’.
Author(s)
Vojtech Janousek, 

peterplot

167

See Also
’pokeDataset’ ’purgeDatasets’
Examples
data(sazava)
accessVar("sazava")
# stored as sazava in WRCube
assignColVar("MgO","blues")
assign1symb(15)
# store a new copy in the WRCube
pokeDataset("coloured sazava")
data(swiss)
accessVar("swiss")
# stored as swiss in WRCube
peekDataset("sazava")
binary("SiO2","Ba")
peekDataset("coloured sazava")
binary("SiO2","Ba")
peekDataset("swiss")
binary("Catholic","Education",pch=15,col="darkgreen")

peterplot

Anomaly plot

Description
This function plots a conventional binary diagram but the type and size of the plotting symbols is
assigned according to the distribution of a third, conditioning variable.
Usage
peterplot(xaxis = "", yaxis = "", zaxis = "", ident = FALSE,
scaling.small = labels[1,"Size"], scaling.big = 2 * scaling.small,
assign.symbols = FALSE)
Arguments
xaxis, yaxis

character; specification of the axes

zaxis

character; conditioning variable

ident

logical; identify the individual points?

scaling.small

scaling factor for the smaller plotting symbols

scaling.big

scaling factor for the larger plotting symbols

assign.symbols logical; should be the plotting symbols and their sizes assigned permanently?

168

peterplot

Details
If no parameters xaxis, yaxis and zaxis are specified, the user is prompted to do so interactively.
The plotting symbols are assigned as follows: the values within 25 quartiles) obtain a dot, the
higher ones are denoted by ’+’and lower ones by ’-’. If the given value is an outlier, its plotting size
is doubled.

Optionally, the user can assign the plotting symbols and their sizes permanently, for use in other
diagrams throughout the system.
Value
May modify the variable cex, as well as the codes of plotting symbols stored in the data frame
labels.
Author(s)
Vojtech Janousek, 
References
Reimann C, Filzmoser P, Garrett R G (2002) Factor analysis applied to regional geochemical data:
problems and possibilities. Applied Geochemistry 17: 185-206
Examples
peterplot("SiO2","MgO","K2O")
peterplot("SiO2","MgO","K2O",assign.symbols=TRUE)
plotDiagram("TAS",F)

Plate

169

Plate

Plotting plates of several diagrams

Description
Functions to set up, save or load a so-called ’plate’, i.e. a regular grid of slots to accommodate
(any mixture of) binary or ternary plots, spiderplots or such alike. For instance, Harker plots are
implemented using the plate concept.
Usage
multiplePerPage(which=NULL,nrow=NULL,ncol=NULL,title="Plate",
dummy=TRUE)
Plate(scr=NULL)
plateRedraw(device="windows",filename=NULL,colormodel="rgb")
platePS(colormodel="rgb")
plateSave()
plateLoad()
Arguments
which

total number of slots to be occupied by individual diagrams.

nrow

number of rows in the plots’ matrix.

ncol

number of columns in the plots’ matrix.

title

title for the whole plate.

dummy

logical; if TRUE, dummy plots are shown. See Details.

scr

(optional) number of screen to be selected.

device

output device; either 'windows' or 'postscript'.

filename

name of file if output redirected to Postscript.

colormodel

color mode for Postscript; 'rgb' or 'gray'.

Details
The function 'multiplePerPage' serves to setting up a matrix of slots, each of which could be
taken by a single Figaro-compatible diagram (a binary plot, a ternary plot, a spiderplot,. . . ). If
'which' is NULL, the function asks for their number, and then suggests number of rows ('nrow')
and columns ('ncol') for the matrix arrangement.
If desired, the slots can be filled by the so-called ’dummy plots’, i.e. gray boxes showing the exact
position and the size of each of them.
If 'which' is an integer, specified number of slots is allocated. Alternatively, this argument may
represent a vector containing any mixture of names of diagrams that can be plotted by the function
plotDiagram or even plotting commands themselves used to fill the individual slots directly. See
Examples.

170

Plate
Once set up, a single slot can be selected for further work using the function 'Plate'. The function
can be called directly, with the number of the screen desired. If none is specified, a red boxlike cursor appears in the graphical window, which can be moved around using the cursor keys,
Spacebar or by mouse. The appropriate slot can be chosen by left mouse button or by pressing
Enter. Right-click anywhere on the plate invokes a context menu which enables several actions:

Menu item
Introduce plot
Plot editing
Plate editing

Function
Select a new Figaro-compatible diagram for this slot.
Modify the existing diagram (similarly to the menu Plot editing for stand alone plots).
Functions to modify the overall plate properties or all its diagrams simultaneously.

The function 'plateRedraw' serves for replotting a ’clean! version of the whole plate, eg. for
saving/printing, For this purpose, its output can be redirected to Postscript, either in colour or as
black and white. As a wrapper for the Postscript output serves the function 'platePS'
The functions 'plateSave' and 'plateLoad' are designed to save and retrieve definitions of plates
(Figaro sheets and the relevant data) for later use. The default suffix for the saved plates is ’mgr’.
Note that only the data needed for the plotting (’x.data’, ’y.data’) are stored in the ’mgr’ files.
Thus the data set currently in memory (e.g., variables ’WR’, ’labels’, . . . ) is unaffected by the
function ’plateLoad’.
Starting with GCDkit version 3, the plates concept is used by some built-in functions, such as
’Multiple plots’ (function multiple) or ’Multiple plots by groups’ (function figMulti).
Value
plate

list of Figaro definitions for individual diagrams

plate.data

list containing 'x.data' and 'y.data' for each of them

Author(s)
Vojtech Janousek, 
See Also
Plate editing, plotPlate, multiple, figMulti, plot, binary, ternary, spider, figaro, figLoad, figSave
Examples
data<-loadData("sazava.data",sep="\t")
multiplePerPage(which=c("binary(\"K2O/Na2O\",
\"Rb\",new=FALSE)",
"DebonPQ","AFM","PeceTaylor","Shand"))
Plate()
Plate(3)
plotDiagram("LarochePlut",FALSE,FALSE)

Plate editing

171

Plate editing

Editing the plate properties/all its plots simultaneously

Description
A collection of functions to modify the properties of a plate (or all its diagrams) simultaneously.
Usage
plateXLim(xlim=NULL)
plateYLim(ylim=NULL)
plate0YLim()
plateCex(n=NULL)
plateCexLab(n=NULL)
plateCexMain(n=NULL)
plateAnnotationsRemove()
platePch(pch=NULL)
plateCol(col=NULL)
plateBW()
plateExpand(scr=NULL)
plateExtract(diagram,which=NULL,main=NULL,...)
Arguments
xlim

scaling for the x axis

ylim

scaling for the y axis

n

relative size (use n = 1 for normal one).

pch

plotting symbol specification, either as string or a numeric code (showSymbols).

col

colour specification, either by its English name, or by a numeric code (showColours).

scr

number of screen to be expanded.

diagram

name of the function plotting a plate.

which

sequential number of plot in its definition.

main

optional alternative main title to the diagram.

...

additional parameters to the diagram (plate) plotting function.

172

Plate editing

Details
The functions serve to change properties of all particular diagrams forming the given plate. They can
be used to set up the uniform size of plotting symbols ('plateCex'), main title ('plateCexMain')
or of the axes’ labels ('plateCexLab'), remove the annotation of classification fields ('plateAnnotationsRemove'),
uniform plotting symbol ('platePch') and/or colour
('plateCol') to all plots, or set them into black and white ('plateBW'). If the same variable is
plotted as x or y axis in all diagrams forming the plate (e.g., on Harker plots), it can be scaled by
means of the functions 'plateXLim' and 'plateYLim'. Using the command 'plate0YLim' it is
possible to set the origin of all non-logarithmic y axes to zero.
The function 'plateExpand' displays a zoomed up version of the selected diagram in a separate
window.
The function 'plateExtract' extracts a Figaro definition of a single plot from a plate plotted by
the function 'diagram'.
Value
None.
Author(s)
Vojtech Janousek, 
References
Pearce J A, Harris N W & Tindle A G (1984) Trace element discrimination diagrams for the tectonic
interpretation of granitic rocks. J Petrology 25: 956-983. doi:10.1093/petrology/25.4.956
See Also
Plate, plotPlate, figaro, figScale, figCol, showSymbols, showColours
Examples
data<-loadData("sazava.data",sep="\t")
showSymbols()
showColours()
multiplePerPage(which=c("binary(\"K2O/Na2O\",
\"Rb\",new=FALSE)","DebonPQ","AFM","PeceTaylor","Shand"))
plateCex(0.5)
plateCex(2)
platePch(11)
platePch("+")
plateCol(11)
plateCol("red")
plateBW()
multiple("SiO2",major)

plateLabelSlots

173

plateXLim(c(50,70))
groupsByLabel("Intrusion")
spider(WR,selectNorm("Boynton"),0.1,1000,pch=labels$Symbol,col=labels$Colour)
figMulti(plot.symb=TRUE)
plateYLim(c(1,100))
graphicsOff()
plotDiagram("DebonBA",FALSE,FALSE)
figMulti()
plate0YLim()
plateExpand(2)
plateExtract("PearceGranite",2) # Second plot of Pearce et al. (1984), i.e. Y-Nb

plateLabelSlots

Annotate individual slots by letters or Roman numerals

Description
Annotates individual slots in a plate by letters or Roman numerals. For instance (a), (b), (c)... or (i),
(ii), (iii), (iv), (v)...
Usage
plateLabelSlots(text=letters,style="()",cex=1.5,pos="topright")
Arguments
text

desired type of labels; see Details.

style

optional character strings before and after label, typically brackets.

cex

relative size of the text compared to the current codepar("cex").

pos

character; position of the label relative to the plot.

Details
The argument ’what’ may acquire one of following values:
'letters' 'LETTERS' 'numbers' 'roman' 'ROMAN'

or can be user-defined character string of longer or of the same length as is the number of slots to
be annotated (see the last example).
Possible positions (parameter pos) are:
'bottomright' 'bottom' 'bottomleft' 'left'
'topleft' 'top' 'topright' 'right' 'center'
.

174

plotPlate

Value
none
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
See Also
Plate, Plate editing, figaro
Examples
multipleMjr("SiO2")
plateLabelSlots("letters","",pos="bottomleft")
plateLabelSlots("ROMAN","{}")
my_labs<-c("1st","2nd","3rd","4th","5th","6th","7th","8th","9th")
plateLabelSlots(my_labs)

plotPlate

Plot Plate of Diagrams

Description
Plots a plate of diagrams, based on the Figaro style sheets.
Usage
plotPlate(diagram,where="WR",...)
Arguments
diagram

a valid name of the function that uses the plate concept to plot the given diagram.
See Details.

where

name of the data matrix/data frame, columns of which are to be used for plotting.

...

optional parameters for the diagram function call.

Details
The argument ’diagram’ may acquire one of following values:
'Maniar' 'Frost' 'PearceGranite' 'Schandl'
'Verma' 'Agrawal' 'Cann' 'Wood'

plotWithCircles

175

Value
none
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
See Also
Plate, Plate editing, figaro
Examples
plotPlate("PearceGranite")

plotWithCircles

xyz plotWithCircles

Description
Plots a binary diagram of two specified variables and the whole dataset or its selection. The size
and colours of the plotted circles correspond to the third.
Usage
plotWithCircles(xaxis = "", yaxis = "", zaxis = "",
colour = "heat.colors", scaling.factor = NULL,
bins = NULL, ident = getOption("gcd.ident"))
Arguments
xaxis

Name of the data column to be used as x axis.

yaxis

Name of the data column to be used as y axis.

zaxis

Name of the data column to determine the size/colour of the circles.

colour

colour scheme for the circles.

scaling.factor a factor determine the size of the circles.
bins

number of intervals for the legend.

ident

Logical: should be the individual samples identified?

176

plotWithCircles

Details
If no parameters 'xlab', 'ylab' and 'zlab' are given, the user is prompted to specify them.
The variables are selected using the function ’selectColumnLabel.
In the specification of the apices can be used also arithmetic expressions, see calcCore for the
correct syntax.

The samples to be plotted can be selected based on combination of three searching mechanisms (by
sample name/label, range or a Boolean condition) - see selectSubset for details.
The legal colour schemes are: ’"grays","reds","blues","greens","cyans","violets","yellows"
’"cm.colors","heat.colors","terrain.colors","topo.colors","rainbow", "jet.colors"’.
Value
None.
Warning
This function IS NOT Figaro-compatible.
Author(s)
Vojtech Janousek, 
& Vojtech Erban, 
Examples
plotWithCircles("SiO2","Na2O+K2O","MgO+FeOt",colour="rainbow")
plotWithCircles("SiO2","MgO","K2O",colour="grays",scaling.factor=0.5,ident=TRUE)

pokeDataset

pokeDataset

177

Storing a dataset into memory for later use

Description
Saves the current dataset into memory so that it can be later re-stored.
Usage

pokeDataset(which.dataset = NULL, par.list = "WR,WRanh,milli,labels,filename,groups,grouping")
Arguments
which.dataset

character; a name of the stored dataset.

par.list

list of global variables to be stored.

Details
This function stores the global variables specified by par.list, typically ’WR’, ’WRanh’, ’milli’
’labels’, ’filename’, ’groups’ and ’grouping’ into the list ’WRCube’.
If no which.dataset is provided upon the call, it can be typed in or selected from the list of existing
datasets.
Please note that ’pokeDataset’ is also invoked when a new dataset is loaded into memory using
the functions ’loadData’ or ’accessVar’. In the former case it is stored under the name of the file,
in the latter under the variable name. If such a name already exists in ’WRCube’, a time stamp is
attached.
For restoring the stored variables serve functions ’peekDataset’ and ’selectDataset’. The function ’purgeDatasets’ removes all older datasets, apart from the most recent copy of the current
one.
Value
None.
Warning
If not called from a GUI, no warning is issued upon rewriting the existing dataset.
Author(s)
Vojtech Janousek, 
See Also
’peekDataset’ ’selectDataset’ ’purgeDatasets’ ’loadData’ ’accessVar’

178

ppm2oxide

Examples
data(sazava)
accessVar("sazava")
# stored as sazava in WRCube
assignColVar("MgO","blues")
assign1symb(15)
# store a new copy in the WRCube
pokeDataset("coloured sazava")
data(swiss)
accessVar("swiss")
# stored as swiss in WRCube
peekDataset("sazava")
binary("SiO2","Ba")
peekDataset("coloured sazava")
binary("SiO2","Ba")
peekDataset("swiss")
binary("Catholic","Education",pch=15,col="darkgreen")

ppm2oxide

Calculation of wt% of the given oxide from ppm of atom

Description
Recasts concentrations of a cation (in ppm) to those of the selected oxide (in wt %).
Usage
ppm2oxide(formula,where="WR")
Arguments
formula

character: the oxide which is to be recalculated

where

character: a name of matrix or dataframe with teh data to be recalculated

Value
A numeric matrix with one column containing the recalculated concentrations of the given oxide
(in wt %) for individual samples.
Author(s)
Vojtech Janousek, 
See Also
oxide2ppm, oxide2oxide, molecularWeight

prComp

179

Examples
data(sazava)
accessVar("sazava")
ppm2oxide("K2O")
oxide2ppm("FeOt")
oxide2ppm("FeO")+oxide2ppm("Fe2O3")

prComp

Statistics: Principal components

Description
Performs principal components analysis (scaled variables, covariance or correlation matrix) and
plots a biplot (Gabriel, 1971).
Usage
prComp(elems = "SiO2,TiO2,Al2O3,FeOt,MnO,MgO,CaO,Na2O,K2O",...)
Arguments
elems

numerical columns to be used for principal components analysis, typically major
elements

...

additional parameters

Details
Biplot aims to represent both the observations and variables of a data matrix on a single bivariate
plot (Gabriel, 1971; Buccianti & Peccerillo, 1999).
In the biplots, the length of the individual arrows is proportional to the relative variation of each
variable. A comparable direction of two arrows implies that both variables are positively correlated; the opposite one indicates a strong negative correlation. When two links are perpendicular it
indicates independence of the two variables (Buccianti & Peccerillo, 1999).
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
Even though a list of major elements is assumed as a default, different variables can be specified by
the function ’selectColumnsLabels’.
Value
Vector of the scores of the supplied data on the principal components is stored in a variable ’results’.
Returns invisibly the complete output from the underlying function ’princomp’.
Warning
Names of existing numeric data columns and not formulae involving these can be handled at this
stage. Only complete cases are used for the principal components analysis.

180

printSamples

Author(s)
Vojtech Janousek, 
References
Buccianti A & Peccerillo A (1999) The complex nature of potassic and ultrapotassic magmatism
in Central-Southern Italy: a multivariate analysis of major element data. In: Lippard S J, Naess A,
Sinding-Larsen R (eds) Proceedings of the 5th Annual Conference of the International Association
for Mathematical Geology. Tapir, Trondheim, p. 145-150
Gabriel K R (1971) The biplot graphical display of matrices with application to principal component
analysis. Biometrika 58: 453-467
See Also
For further details on the used principal components algorithm and biplots, see the R manual entries
of ’princomp’ and ’biplot.princomp’.

printSamples

Display samples

Description
Displays specified combination of numeric variable(s) and/or labels for selected range of samples.
Usage
printSamples(elems=NULL,which=NULL,select.samples=FALSE,print=TRUE)
Arguments
elems

list of variables to be printed

which

list of samples, useful only for select.samples=FALSE

select.samples logical: if TRUE, samples can be chosen using the appropriate dialogue
print

logical: should be the result indeed printed or just returned for further evaluation?

Details
This function prints the desired numerical columns, textual labels, or their combinations, for selected samples.
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
The variables to be printed are chosen by the function ’selectColumnsLabels’. In the specification
of the variable can be used also arithmetic expressions, see calcCore for the correct syntax.
Value
results

data matrix with the desired data for the specified samples

printSingle

181

Author(s)
Vojtech Janousek, 
Examples
## Not run:
# Querying names of numeric data columns
Search pattern = SiO2, MgO, CaO
Search pattern = major
SiO2, TiO2, Al2O3, Fe2O3, FeO, MnO, MgO, CaO, Na2O, K2O, P2O5
Search pattern = LILE
Rb, Sr, Ba, K, Cs, Li
Search pattern = HFSE
Nb, Zr, Hf, Ti, Ta, La, Ce, Y, Ga, Sc, Th, U
Search pattern = REE
La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu
Search pattern = Locality,SiO2,LILE,HFSE
Locality, SiO2, Rb, Sr, Ba, K, Cs, Li, Nb, Zr, Hf, Ti,
Ta, La, Ce, Y, Ga, Sc, Th, U
Search pattern = 1:5, 7
Numeric data columns number 1, 2, ...5, 7
# User-defined list
my.elems<-c("Rb","Sr","Ba")
Search pattern = my.elems
Rb, Sr, Ba
## End(Not run)

printSingle

Display a variable

Description
Displays a single numeric variable or a result of a calculation.
Usage
printSingle(default="")
Arguments
default

character: list of default column names, separated by commas.

182

profiler

Details
The variable to be printed is selected using the function ’selectColumnLabel’. In the specification
of the variable can be used also arithmetic expressions, see calcCore for the correct syntax.
In the specification of the variable can be used also arithmetic expressions, see calcCore for the
correct syntax.
Value
results

numerical vector/matrix with the results

Author(s)
Vojtech Janousek, 
Examples
## Not run:
# examples of valid formulae....
(Na2O+K2O)/CaO
Rb^2
log10(Sr)
mean(SiO2)/10
# ... but this command is in fact a simple R shell # meaning lots of fun for power users!
summary(Rb,na.rm=TRUE)
cbind(SiO2/2,TiO2,Na2O+K2O)
cbind(major)
hist(SiO2,col="red")
boxplot(Rb~factor(groups))
# possibilities are endless
plot(Rb,Sr,col="blue",pch="+",xlab="Rb (ppm)",ylab="Sr (ppm)",log="xy")
## End(Not run)

profiler

Profile plotting

Description
Plotting geochemical profiles. As a x axis can be specified an arbitrary variable or an numerical
interval (for equidistant measurements).
Usage
profiler(x = NULL, y = NULL, method = "Variable", legend = FALSE,
pch = 1, col = "black", cex = 1, xaxs = "r", yaxs = "i",
main = "",xmin = NULL, xmax = NULL)

profiler

183

Arguments
x

character; optional name of variable to be plotted as x axis.

y

character; name(s) of variable(s) for individual profiles.

method

character; which of the methods is to be used? Valid are "Variable","Equidistant"
or "From-To".

legend

logical; should be plotted also legend (in a separate window)?

pch

plotting symbols specification.

col

plotting colour(s).

cex

numeric; relative size of the plotting symbols.

xaxs, yaxs

character; type of the axes. See par for details.

main

character; main title for the plot

xmin, xmax

range of the x axis (for methods ’Variable’ and ’From/To’))

Details
The function ’profiler’ serves for plotting three different types of profiles involving a single or
several geochemical parameters.

The first one, ’Variable’ uses any numeric variable as the x axis (e.g., SiO2 contents, depth...). It
is in fact a special type of a binary plot, in which the data points are, for each of the y-axis variables,
joined by a line.
The remaining two methods are very similar to each other. The x axis is in both cases equidistant,
and the order of the individual samples follows from their sequence in the data set.
The method ’Equidistant’ uses simply the sequence number of the individual samples in the data
set. It does not label the x-axis, just prints the number of samples used for plotting.

184

profiler
The method ’From/To’ serves for drawing equidistant profiles, where the x axis can be specified by
an interval.
In the specification of the x axis (for the method ’Variable’) or any of the y variables (all methods)
can be used also arithmetic expressions, see calcCore for the correct syntax.
If not called from the command prompt, the samples can be selected based on combination of three
searching mechanisms (by sample name/label, range or a Boolean condition) - see selectSubset
for details.
The easiest way to specify the variable(s) to be plotted on individual profile(s) is to type directly
the names of the columns, separated by commas. Alternatively can be used their sequence numbers
or ranges. Also built-in lists can be employed, such as ’LILE’, ’REE’, ’major’ and ’HFSE’ or their
combinations with the column names.
These lists are simple character vectors, and additional ones can be built by the user (see Examples). Note that currently only a single, stand-alone, user-defined list can be employed as a search
criterion.
If the function is not called from the command prompt, and it desired so, the symbols and colours
for each of the profiles can be specified separately in a simple spreadsheet-like interface.
If x axis occurs among the arguments to be plotted as y axes, it is skipped.
Likewise the relative scaling of the plotting symbols and the scale of the y axis can be specified.
Lastly, the user is asked to enter the limits for the axes, which are always two numbers separated by
a comma.

Value
results

numeric matrix with the values for individual profiles.

Author(s)
Vojtech Janousek, 
Examples
# Profiles of SiO2 versus (scaled) TiO2, MgO and K2O
# if x is specified, method="Variable" assumed automatically
profiler("Na2O+K2O",c("TiO2","6*MgO","SiO2"),pch=c("+","o","@"),col=c("red","blue","darkgreen"),
xmin=2,xmax=10)
# Equidistant profiles of (scaled) MgO, CaO, and Al2O3 (in sample sequence)
# with default symbols and scaling
profiler(y=c("MgO","3*CaO","2*Al2O3"),method="Equidistant",col=c("red","blue","darkgreen"))
# Equidistant profiles of two calculated variables in custom colour
# and user-defined plotting symbols; range of the x axis will be specified
# interactively
profiler(y=c("2*MgO","10*(Na2O+K2O)"),method="From-To",pch=1:10,
col=c("blue","red"),cex=1.5,main="My plot",xmin=10,xmax=30)

psAll

185

psAll

Save all graphics to PS

Description
Saves all graphical windows to Postscript files.
Usage
psAll(filename=NULL)
Arguments
filename

a name of file for saving the output.

Details
The function prompts for a common root of the filenames and then saves all graphical windows,
each in a separate file, numbering them sequentially. Postscript is the best export format from R,
preserving the necessary quality as well as the possibility to be imported by most graphical editors
(such as Corel Draw!) for retouching.
Otherwise individual diagram can be saved from a menu that appears after clicking on the appropriate graphical window (’File|Save as|Postscript’).
Value
None.
Author(s)
Vojtech Janousek, 
See Also
’pdfAll’ ’postscript’

purgeDatasets

Removing stored datasets from the memory

Description
Removes all the stored datasets (apart from the current one) in order to save memory.
Usage
purgeDatasets(GUI=FALSE)
Arguments
GUI

logical; is the function called from GUI?

186

QAPF

Details
This function removes all older datasets, regardless whether stored automatically by the functions
’loadData’ or ’accessVar’, as well as on demand by ’pokeDataset’.
Only the most recent copy of the current dataset is preserved (i.e. the last item within the list
’WRCube’).

Value
None.

Warning
If not called from a GUI, no warning is issued and all but the current dataset are deleted immediately.

Author(s)
Vojtech Janousek, 

See Also
’pokeDataset’ ’peekDataset’ ’selectDataset’

QAPF

QAPF diagram (Streckeisen 1974, 1978)

Description
Assigns data for Streckeisen’s diagram into Figaro template (list ’sheet’) and appropriate values
into ’x.data’ and ’y.data’. The Q, A, P and F coordinates are assigned into matrix ’results’.

Usage
QAPFVolc()
QAPFPlut()

Details
Following the IUGS recommendation (Le Maitre et al 2002), the QAPF diagram should be the
prime classification scheme for holocrystalline plutonic and volcanic rocks containing at least 10%
of felsic minerals.

QAPF

187

The apices are defined as follows:
Q = Quartz modal %
A = Alkali feldspar modal %
P = Plagioclase modal %
F = feldspathoid modal %
Q + A + P + F = 100 %
As the whole QAPF diagram is rather complicated, GCDkit plots just the appropriate triangle if
the dataset contains only Si-oversaturated or only Si-undersaturated rock samples. If both kinds
of rock samples are present, the whole double triangle is shown. This behaviour may be changed
in the source code of the diagram (in file ’QAPFPlut.r’ or ’QAPFVolc.r’, stored in the subdirectory GCDkit\Diagrams\Classification, change the ’triangle<-"auto"’ to ’triangle<-"both"’
and complete double triangle will be always plotted).

188

QAPF

Value
sheet

list with Figaro Style Sheet data

x.data, y.data Q, A, P and F data (see details) transformed to orthogonal coordinates
Author(s)
Vojtech Erban, 
References
Streckeisen A (1974) Classification and nomenclature of plutonic rocks. Geol Rundsch 63: 773-786
doi: 10.1007/BF01820841
Streckeisen A (1978) IUGS Subcommission on the Systematics of Igneous Rocks: Classification
and nomenclature of volcanic rocks, lamprophyres, carbonatites and melilitic rocks; recommendation and suggestions. Neu Jb Min, Abh 134: 1-14.
Le Maitre R. W. et al. (2002) Igneous Rocks. A Classification and Glossary of Terms. 2nd edition.
Cambridge University Press.

quitGCDkit

189

See Also
classify figaro plotDiagram
Examples
#plots the QAPF diagram for current dataset
plotDiagram("QAPFVolc", FALSE)
plotDiagram("QAPFPlut", FALSE)
#classifies the current dataset using the QAPF diagram
classify("QAPFVolc")
classify("QAPFPlut")

quitGCDkit

Exit GCDkit

Description
Exits GCDkit (nicely).
Usage
quitGCDkit()
Arguments
None.
Details
By invoking this command the user is not prompted whether he wants to save his unfinished work
in the ’Workspace image’, i.e. file ’.RData’ in the main GCDkit directory.
Menu
GCDkit: Exit GCDkit
See Also
’quit’

190

recast

r2clipboard

Copy results to clipboard

Description
Copies the most recently calculated results to a clipboard.
Usage
r2clipboard(what=results)
Arguments
what

a variable to be copied, can be either a vector, a matrix, a list or a table.

Details
Copies the variable ’results’ returned by most of the calculation algorithms to the Windows clipboard.
Value
None.
Author(s)
Vojtech Janousek, 

recast

Recast to given sum

Description
Recasts the selected data to a fixed sum.
Usage
recast(total = 100)
normalize2total(what = NULL, total = 100)
Arguments
what

numeric matrix or character vector with a list of column names to be normalized,
separated by commas.

total

a sum the data should be normalized to.

reciprocalIso

191

Details
Both functions return the selected elements/oxides (columns in the data matrix ’WR’) normalized
to the required sum. The function ’recast’ is front-end to ’normalize2total’. If ’what’ is
a comma delimited list, the corresponding columns from the data matrix ’WR’ are selected. If
’what’ is empty, the user is prompted to supply the list of required column names via the function ’selectColumnsLabels’.
Value
results

numerical vector/matrix with the results

Author(s)
Vojtech Janousek, 
Examples
normalize2total(major,1)
recast() # to select the sum and elements interactively

reciprocalIso

Binary plots of reciprocal element concentration vs initial isotopic
composition

Description
Plots a diagram 1/Sr vs initial Sr isotopic ratios or 1/Nd vs initial (N d) for selected samples.

192

Regular expressions

Usage
reciprocalIso()
Arguments
None.
Details
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
Value
None.
Plugin
SrNd.r
Author(s)
Vojtech Janousek, 

Regular expressions

Implementation of regular expressions in GCDkit

Description
Implementation of regular expressions in the searching patterns.
Details
Many enquiries in the GCDkit employ regular expressions. This is a quite powerful searching mechanism more familiar to people working in Unix. Put in simple terms, most characters, including all
letters and digits, are regular expressions that match themselves. However, metacharacters with a
special meaning (’?’ ’+’ ’{’ ’}’ ’|’ ’(’ ’)’) must be preceded by a backslash.
Regular expression
.
^
\$
[]
[m-n]

Matches
Any character
Beginning of the expression
End of the expression
Any of the characters given in square brackets
Any character in the range given by m and n

A subexpression is a regular expression enclosed in ’\(’ and ’\)’. Two such subexpressions may
be joined by the infix operator ’|’ (logical or); the resulting regular expression matches any string
matching either of them. For instance:
\(South\)|\(North\)Uist
yields both

Regular expressions

193

South Uist and North Uist.
A regular expression may be followed by one of several repetition operators:
Repetition operator
?
*
+
{n}
{n,}
{n,m}

The preceding item will be matched
At most once (i.e. is optional)
Zero or more times
One or more times
Exactly n times
At least n times
At least n times, but not more than m times

Author(s)
Vojtech Janousek, 
See Also
regex
Examples
## Not run:
# Subset by label
The searched field corresponds to localities with the following levels:
Mull, Rum, Skye, Coll, Colonsay, Hoy, Westray, Sanday,
Stronsay, Tiree, Islay
Search pattern = ol
Coll, Colonsay
Search pattern = n.a
Colonsay, Sanday, Stronsay
Search pattern = ^S
Skye, Sanday, Stronsay
Search pattern = e$
Skye, Tiree
Search pattern = [ds]ay
Colonsay, Sanday, Stronsay
Search pattern = [p-s]ay
Colonsay, Westray, Stronsay
Search pattern = ol|oy
Coll, Colonsay, Hoy
Search pattern = l{2}
Mull, Coll
# Subset by sample name
The sample names are: Bl-1, Bl-3, Koz-1, Koz-2, Koz-5, Koz-11,
KozD-1, Ri-1.

194

rtSaturation

Search pattern = oz-[1-3]
Koz-1, Koz-2, Koz-11
Search pattern = oz-|BlBl-1, Bl-2, Bl-3, Koz-1, Koz-2, Koz-5, Koz-11
## End(Not run)

rtSaturation

Rutile saturation (Hayden + Watson 2007)

Description
Calculates rutile saturation temperatures for the observed major-element data and Ti concentrations.
Also returns Ti saturation levels for the given major-element compositions and assumed magma
temperature.
Usage
rtSaturation(cats=milli,T=0,P=0,Ti=filterOut(WR,"Ti",1))
Arguments
cats

numeric matrix; whole-rock data recast to millications

T

assumed temperature of the magma in C

P

assumed pressure in kbar, Ryerson & Watson (1987) model only

Ti

numeric vector with Ti concentrations in ppm

Details
Ryerson & Watson (1987) have first formulated rutile saturation model for melts ranging in composition from basalt to rhyodacite. The distribution of T iO2 between rutile and liquid was given
as:
DT iO2 = e(−3.16+

9373
T +0.026P −0.152F M )

where ’T’ is the absolute temperature (K) of the magma, ’P’ pressure (kbar) and ’FM’ is a melt
composition parameter:
FM =

1 N a + K + 2(Ca + M g + F e)
Si
Al

.
The Ti saturation level then would be:
T i.sat.RW =

599342.9
(ppm)
DT iO2

In turn, when the rutile saturation was reached, the magma temperature (in C) can be calculated as:

rtSaturation

195

T Rt.sat.C.RW =

9373
− 273.15
(3.16 + ln(100/T iO2) − 0.026P + 0.152F M )

The Ti solubility in rutile-saturated hydrous siliceous melts was revisited by Hayden & Watson
(2007). According to these authors, it can be expressed as:
T i.sat.HW = 10(7.95−

5305
T +0.124F M )

(ppm)

where ’T’ is the absolute temperature (K) of the magma, and ’FM’ is the melt composition parameter
defined above.
The temperature (in C) for rutile-saturated magma can be calculated as:
T Rt.sat.C.HW =

5305
− 273.15
7.95 − log(T i) + 0.124F M

Using these formulae, the function ’rtSaturation’ calculates the rutile saturation levels, Ti activities and rutile saturation temperatures following both models.
The formulation of Ryerson & Watson (1987) may be more suitable for basic rocks, whereas the
more recent model of Hayden & Watson (2007) seems to be appropriate for siliceous magmas.
Please note also that the latter does not take into account effects of pressure (having been calibrated
at 1 GPa; Hayden & Watson 2007).
Value
Returns a matrix ’results’ with the following columns:
FM

melt composition parameter

Ti

observed Ti concentrations

Ti.sat.RW

saturation levels of Ti for assumed temperature, Ryerson & Watson (1987)

aTi.RW

activity of Ti (ratio of Ti/Ti.sat), Ryerson & Watson (1987)

TRt.sat.C.RW

rutile saturation temperatures in C, Ryerson & Watson (1987)

Ti.sat.HW

saturation levels of Ti for assumed temperature, Hayden & Watson (2007)

aTi.HW

activity of Ti (ratio of Ti/Ti.sat), Hayden & Watson (2007)

TRt.sat.C.HW

rutile saturation temperatures in C, Hayden & Watson (2007)

Plugin
Saturation.r
Author(s)
Vojtech Janousek, 
References
Ryerson F J, Watson E B (1987) Rutile saturation in magmas; implications for Ti-Nb-Ta depletion
in island-arc basalts. Earth Planet Sci Lett 86: 225-239 doi: 10.1016/0012-821X(87)90223-8
Hayden L A, Watson E B (2007) Rutile saturation in hydrous siliceous melts and its bearing on Tithermometry of quartz and zircon. Earth Planet Sci Lett 258: 561-568 doi: doi:10.1016/j.epsl.2007.04.020

196

saveResults

saveData

Save data file

Description
Saves modified data set into a specified datafile.
Usage
saveData(sep="\t")
Arguments
sep

delimiter separating individual items in the data file.

Details
Labels (stored in data frame ’labels’) and numeric data (in numeric matrix ’WR’) for the currently
selected subset are glued together and saved under the specified filename. The format is such that the
data can be retrieved again into GCDkit using the loadData command. Note that no mg numbers
are currently saved.
Value
None.
Author(s)
Vojtech Janousek, 
See Also
’loadData’ ’mergeData’ ’showColours’ ’colours’ ’showSymbols’ ’read.table’

saveResults

Save results

Description
Saves the most recently calculated results to a text file.
Usage
saveResults(what = results, sep = "\t", digits = 2)
Arguments
what

a variable to be saved, can be either a vector, a matrix or a list.

sep

separator; default is a tab-delimited file.

digits

precision of the results to be saved.

saveResultsIso

197

Details
Saves the variable ’results’ returned by most of the calculation algorithms to a tab-delimited
ASCII file.
Value
None.
Author(s)
Vojtech Janousek, 

saveResultsIso

Save Sr-Nd isotopic data

Description
Saves the calculated isotopic parameters stored in the matrix ’init’ to a text file.
Usage
saveResultsIso(digits = 6)
Arguments
digits

precision of the results to be saved.

Details
Saves the data matrix init with the following columns:
Age (Ma)
87Sr/86Sri
143Nd/144Ndi
EpsNdi
TDM
TDM.Gold
TDM.2stg

Age in Ma
Initial Sr isotopic ratios
Initial Nd isotopic ratios
Initial (N d) values
Single-stage depleted-mantle Nd model ages (Liew & Hofmann, 1988)
Single-stage depleted-mantle Nd model ages (Goldstein et al., 1988)
Two-stage depleted-mantle Nd model ages (Liew & Hofmann, 1988)

Value
None.
Plugin
SrNd.r
Author(s)
Vojtech Janousek, 

198

sazava

References
Liew T C & Hofmann A W (1988) Precambrian crustal components, plutonic associations, plate
environment of the Hercynian Fold Belt of Central Europe: indications from a Nd and Sr isotopic
study. Contrib Mineral Petrol 98: 129-138
Goldstein S L, O’Nions R K & Hamilton P J (1984) A Sm-Nd isotopic study of atmospheric dusts
and particulates from major river systems. Earth Planet Sci Lett 70: 221-236
See Also
’saveResults’

sazava

Whole-rock composition of the Sazava suite, Central Bohemian Plutonic Complex

Description
This data set gives the whole-rock major- and trace-element contents in selected samples (gabbros,
quartz diorites, tonalites and trondhjemites) of the c. 355 My old calc-alkaline Sazava suite of the
Variscan Central Bohemian Plutonic Complex (Bohemian Massif, Czech Republic).
Usage
data(sazava)
Format
A data frame containing 14 observations.
Source
Vojtech Janousek, 
References
Janousek V, Rogers G, Bowes DR (1995) Sr-Nd isotopic constraints on the petrogenesis of the
Central Bohemian Pluton, Czech Republic. Geol Rundsch 84: 520-534 doi: 10.1007/BF00284518
Janousek V, Bowes DR, Rogers G, Farrow CM, Jelinek E (2000) Modelling diverse processes in the
petrogenesis of a composite batholith: the Central Bohemian Pluton, Central European Hercynides.
J Petrol 41: 511-543 doi: 10.1093/petrology/41.4.511
Janousek V, Braithwaite CJR, Bowes DR, Gerdes A (2004) Magma-mixing in the genesis of Hercynian calc-alkaline granitoids: an integrated petrographic and geochemical study of the Sazava intrusion, Central Bohemian Pluton, Czech Republic. Lithos 78: 67-99 doi: 10.1016/j.lithos.2004.04.046
Examples
data(sazava)
accessVar("sazava")
binary("SiO2","Ba")

Schandl

199

Schandl

Schandl and Gorton (2002)

Description
Plots data stored in ’WR’ (or its subset) into the classification diagrams after Schandl and Gorton
(2002).

Usage
Schandl(plot.txt = getOption("gcd.plot.text"))

Arguments
plot.txt

logical, annotate fields by their names?

Details
Suite of four diagrams for geotectonic environment discrimination of felsic volcanic rocks (rhyolites), proposed by Schandl and Gorton (2002). It is based on combination of four presumably little
immobile trace elements (namely Ta, Yb, Th, and Hf). Diagrams were designed to decipher the geotectonic setting of felsic volcanic suites, specifically those associated with the volcanogenic massive sulphide (VMS) deposits. a) Ta/Yb versus Th/Yb diagram from Gorton and Schandl (2000) is
divided into three fields: Oceanic Arcs, Active Continental Margins (ACM) and Within-Plate Volcanic Zones (WPVZ). The Within-Plate Basalts (WPB) and Mid-Ocean Ridge Basalts (MORB)
represent compositions previously determined by Pearce (1982, 1983). b) Ta vs. Th diagram
demonstrates the Th enrichment of felsic volcanic rocks at post-Archaean VMS deposits (and of
some unmineralized Archaean rhyolites) with respect to Ta. c) Graph of Ta/Hf vs Th/Hf ratios
shows the similar incompatibility between Th and Ta in two different tectonic environments: Active Continental Margins and Within-Plate Volcanic Zones. d) Yb vs. Th/Ta diagram with fields
for associations of Oceanic Arcs, Active Continental Margins, Within Plate Volcanic Zones and
MORB.

200

Schandl

Taken together, the following geotectonic settings may be deduced:
Rock Association
Oceanic Arcs
Active Continental Margins
Within-Plate Volcanic Zones

Abbreviation
ACM
WPVZ

Further abbreviations used on the plots:
Rock Association
Mid-Oceanic Ridge Basalts
Within-Plate Basalts

Abbreviation
MORB
WPB

selectAll

201

Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
References
Gorton M P & Schandl E S (2000) From continents to island arcs: A geochemical index of tectonic
setting for arc-related and within-plate felsic to intermediate volcanic rocks. Can Min 38: 10651073. doi: 10.2113/gscanmin.38.5.1065
Pearce J A (1982) Trace element characteristics of lavas from destructive plate boundaries. In
Thorpe R S (ed) Andesites: Orogenic Andesites and Related Rocks. John Wiley, Chichester, pp
525-548.
Pearce J A (1983) Role of the sub-continental lithosphere in magma genesis at active continental
margins. In Hawkesworth C J & Norry M J (eds) Continental Basalts and Mantle Xenoliths. Shiva,
Nantwich. pp 230-249
Schandl E S & Gorton M P (2002) Application of high field strength elements to discriminate
tectonic settings in VMS environments. Economic Geology 97: 629-642. doi: 10.2113/97.3.629
See Also
Plate, Plate editing, plotPlate, figaro
Examples
#plot the diagrams
plotPlate("Schandl")

selectAll

Select whole dataset

Description
Restores data for all samples as they were loaded from a data file.
Usage
selectAll(GUI=FALSE)
Arguments
GUI

logical; was the function called from the GUI?.

202

selectByDiagram

Details
When a datafile is loaded into GCDkit using the loadData function, the data and their backup copy
are stored in the memory.
The subsets of the current dataset can be chosen using the functions selectByLabel and selectSubset
(menus 'Select subset by sample name or label', 'Select subset by range'
, 'Select subset by Boolean') and the current data will be replaced by their newly chosen
subset.
The backup copy is kept intact ever since the loadData function has been invoked and can be
uploaded any time in place of the current data set using the function ’selectAll’. Note that all
changes made e.g. to plotting symbols, grouping, newly calculated variables etc. will be lost.
Value
None.
Author(s)
Vojtech Janousek, 

selectByDiagram

Selecting subset by diagram

Description
This function enables selecting samples that plot into certain field(s) of the given classification
diagram.
Usage
selectByDiagram(diagram =

select.list(claslist[, "menu"]))

Arguments
diagram

one of the valid diagram names that appear in ’.claslist()’

Details
The diagram can be chosen from a list (the default) or specified directly as an argument. Clicking
onto a field toggles its inclusion/exclusion - the currently selected fields are cyan.
Value
None.
Author(s)
Vojtech Janousek,  & Vojtech Erban, 
See Also
’selectByLabel’, ’selectSubset’, ’selectAll’ and ’classify’.

selectByLabel

203

Examples
.claslist() # names of existing diagrams
selectByDiagram("TAS")

selectByLabel

Select subset by sample name or label

Description
Selecting subsets of the data stored in memory by searching sample names or a single label.
Usage
selectByLabel()
Details
This function enables the user to query a single textual column, a label, chosen using the function ’selectColumnLabel’. The current data will be replaced by its newly chosen subset. These
enquiries employ regular expressions.
Value
Overwrites the data frame ’labels’ and numeric matrix ’WR’ by subset that fulfills the search criteria.
Author(s)
Vojtech Janousek, 
Examples
## Not run:
# Subset by label
The searched field corresponds to localities with the following levels:
Mull, Rum, Skye, Coll, Colonsay, Hoy, Westray,
Sanday, Stronsay, Tiree, Islay
Search pattern = ol
Coll, Colonsay
Search pattern = n.a
Colonsay, Sanday, Stronsay
Search pattern = ^S
Skye, Sanday, Stronsay
Search pattern = e$
Skye, Tiree
Search pattern = [ds]ay
Colonsay, Sanday, Stronsay

204

selectColumnLabel
Search pattern = [p-s]ay
Colonsay, Westray, Stronsay
Search pattern = ol|oy
Coll, Colonsay, Hoy
Search pattern = l{2}
Mull, Coll
# Subset by sample name
The sample names are: Bl-1, Bl-3, Koz-1, Koz-2,
Koz-5, Koz-11, KozD-1, Ri-1.
Search pattern = oz-[1-3]
Koz-1, Koz-2, Koz-11
Search pattern = oz-|BlBl-1, Bl-2, Bl-3, Koz-1, Koz-2, Koz-5, Koz-11
## End(Not run)

selectColumnLabel

Selecting a single variable in GCDkit

Description
This is an auxiliary function invoked by many others to select a single variable.
Usage
selectColumnLabel(where = colnames(labels),
message = "Select the variable\nor press ENTER to pick from a list",
default = "", sample.names = FALSE, silent = FALSE, print = TRUE,
empty.ok = TRUE)
Arguments
where

names of data columns to choose from

message

prompt

default

comma delimited list of default names

sample.names

logical; should be the sample names listed

silent

logical, echo on/off

print

logical, echo on/off

empty.ok

is empty selection ok?

selectColumnsLabels

205

Details
The easiest way for specification of the variable is to type directly the name of the numerical column
in the data matrix ’WR’ (e.g., ’SiO2’) or its sequence number (2 for the second column). However,
it is not necessary to enter the name in its entirety. Only a substring that appears somewhere in the
column name or other forms of regular.expressions can be specified.
If the result is ambiguous, the correct variable has to be selected by mouse from the list of the
multiple matches. Ultimately, empty response invokes list of all variables available in the memory.

Value
A numeric index of the selected column.

Author(s)
Vojtech Janousek, 
See Also
selectColumnsLabels

selectColumnsLabels

Selecting several data columns

Description
An auxiliary function invoked by many others to select several variables simultaneously.

Usage
selectColumnsLabels(where = colnames(WR),
message = "Select variable(s), e.g. 'SiO2,TiO2,MgO'
or press ENTER to pick from a list", default = "", print = TRUE,
exact.only = TRUE)
Arguments
where

vector of names for data columns to choose from

message

prompt

default

comma delimited list of default names

print

logical, echo on/off

exact.only

logical, should be the input checked for correctness?

206

selectColumnsLabels

Details
The variable(s) can be specified in several ways. The easiest is to type directly the name(s) of the
column(s), separated by commas. Alternatively can be used their sequence numbers or ranges. Also
built-in lists can be employed, such as 'LILE', 'REE', 'major' and 'HFSE' or their combinations
with the column names.
These lists are simple character vectors, and additional ones can be built by the user (see Examples). Note that currently only a single, stand-alone, user-defined list can be employed as a search
criterion.
Empty response invokes list of all variables available. The correct variables have to be selected by
mouse + SHIFT from this list.
If exact.only=TRUE, the individual items in the input line are checked against the list of existing
column/variable names (i.e. components in the vector 'where').
Value
Vector with the selected column names.
Author(s)
Vojtech Janousek, 
Examples
## Not run:
# Querying names of numeric data columns
Search pattern = SiO2, MgO, CaO
Search pattern = major
SiO2, TiO2, Al2O3, Fe2O3, FeO, MnO, MgO, CaO, Na2O, K2O, P2O5
Search pattern = LILE
Rb, Sr, Ba, K, Cs, Li
Search pattern = HFSE
Nb, Zr, Hf, Ti, Ta, La, Ce, Y, Ga, Sc, Th, U
Search pattern = REE
La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu
Search pattern = Locality,SiO2,LILE,HFSE
Locality, SiO2, Rb, Sr, Ba, K, Cs, Li, Nb,
Zr, Hf, Ti, Ta, La, Ce, Y, Ga, Sc, Th, U
Search pattern = 1:5, 7
Numeric data columns number 1, 2, ...5, 7
# User-defined list
my.elems<-c("Rb","Sr","Ba")
Search pattern = my.elems
Rb, Sr, Ba
## End(Not run)

selectNorm

selectNorm

207

Selecting the normalization data for spiderplots

Description
Displays available normalization schemes and lets the user to choose one interactively.
Usage
selectNorm(ref=NULL,elems = "Rb,Sr,Ba,Cr,Ni,La,Ce,Y,Zr",REE.only=FALSE,multiple=FALSE)
Arguments
ref

character: a specification of the normalizing model.

elems

character: a default list of elements.

REE.only

logical: should be only listed normalization schemes for REE?

multiple

logical: is a result with several normalizing schemes allowed?

Details
There are two ways of using this function. Firstly, a search pattern can be specified for a query of
the available normalizing model names. The corresponding parameter ’ref ’ can contain a substring
or even a regular expression. The function fails if no matches are found or the search is ambiguous.
The second possibility is to choose from the list of available normalizing schemes. The first option
offers normalization by a single sample. Its name can be typed in or, after pressing the Enter key,
picked from a list. Then the user is prompted to specify the list and order of elements/oxides that
should appear on the plot. The easiest way is to type directly the names of the columns, separated
by commas. Alternatively can be used their sequence numbers or ranges. Also built-in lists can
be employed, such as ’LILE’, ’REE’, ’major’ and ’HFSE’ or their combinations with the column
names. These lists are simple character vectors, and additional ones can be built by the user (see
Examples). Note that currently only a single, stand-alone, user-defined list can be employed as a
search criterion.
The samples to be plotted can be selected based on combination of three searching mechanisms (by
sample name/label, range or a Boolean condition) - see selectSubset for details.
The second option is similar but it allows to normalize by average concentrations in a group of
samples specified by one of the three searching mechanisms as above (see selectSubset).
The composition of various standards available for normalization and subsequent plotting of spider
diagrams is stored in the file ’spider.data’ in the main GCDkit directory. It is a comma delimited
file such as:
Normalization data used for spiderplots
MORB (Pearce 1983)
Sr,K,Rb,Ba,Th,Ta,Nb,Ce,P,Zr,Hf,Sm,Ti,Y,Yb
120,1245,2,20,.2,.18,3.5,10,534,90,2.4,3.3,8992,30,3.4
REE chondrite (Boynton 1984)
La,Ce,Pr,Nd,Pm,Sm,Eu,Gd,Tb,Dy,Ho,Er,Tm,Yb,Lu
.31,.808,.122,.6,1,.195,.0735,.2590,.0474,

208

selectNorm
.322,.0718,.21,0.0324,.209,.0322
ORG (PearceEtAl.1984)
K2O,Rb,Ba,Th,Ta,Nb,Ce,Hf,Zr,Sm,Y,Yb
0.4,4,50,0.8,0.7,10,35,9,340,9,70,8.0
The first row is always skipped and can contain any comments. The following ones have a fixed
structure. For each normalization scheme, the first row contains the title and reference. If title
starts with ’REE’, the normalization is supposed to be for REE only and special parameters, such as
’Eu/Eu*’, are calculated. The second line gives a comma delimited list of elements in the order they
should appear on the plot. The last line is a comma delimited list of normalization values. There
are empty lines left between the normalization schemes.
As the file ’spider.data’ is read every time ’selectNorm is called, the user can add or delete
normalization schemes on his will using a text editor.

Value
A numeric matrix with one row, containing the normalizing values. The row name contains the
name of the model and reference.
Author(s)
Vojtech Janousek, 
References
Implemented spiderplots:
Becker H, Horan M F, Walker R J, Gao S, Lorand J-P, Rudnick R L (2006) Highly siderophile
element composition of the Earth’s primitive upper mantle: constraints from new data on peridotite
massifs and xenoliths. Geochim Cosmochim Acta 70: 4528-4550 doi: 10.1016/j.gca.2006.06.004
Boynton W V (1984) Cosmochemistry of the rare earth elements: meteorite studies. In: Henderson
P (eds) Rare Earth Element Geochemistry. Elsevier, Amsterdam, pp 63-114
Jochum K P (1996) Rhodium and other platinum-group elements in carbonaceous chondrites.
Geochim Cosmochim Acta 60: 3353-3357 doi: 10.1016/0016-7037(96)00186-X
McDonough W, Sun S S (1995) The composition of the Earth. Chem Geol 120: 223-253 doi:
10.1016/0009-2541(94)00140-4
Nakamura N (1974) Determination of REE, Ba, Fe, Mg, Na and K in carbonaceous and ordinary
chondrites. Geochim Cosmochim Acta 38: 757-775 doi: 10.1016/0016-7037(74)90149-5
Pearce J A (1983) Role of sub-continental lithosphere in magma genesis at active continental margins. Continental Basalts and Mantle Xenoliths. Shiva, Nantwich, pp 230-249
Pearce J A (1996) A user’s guide to basalt discrimination diagrams. In: Wyman D A (eds) Trace
Element Geochemistry of Volcanic Rocks: Applications for Massive Sulphide Exploration. Geological Association of Canada, Short Course Notes 12, pp 79-113
Pearce J A (2014) Immobile element fingerprinting of ophiolites. Elements 10: 101-108 doi:
10.2113/gselements.10.2.101
Pearce J A, Harris N W, Tindle A G (1984) Trace element discrimination diagrams for the tectonic
interpretation of granitic rocks. J Petrology 25: 956-983 doi:10.1093/petrology/25.4.956
Sun S S, McDonough W F (1989) Chemical and isotopic systematics of oceanic basalts: implications for mantle composition and processes. In: Saunders A D, Norry M (eds) Magmatism in Ocean
Basins. Geological Society of London Special Publications 42, pp 313-345

selectPalette

209

Sun S S, Bailey D K, Tarney J, Dunham K (1980) Lead isotopic study of young volcanic rocks from
mid-ocean ridges, ocean islands and island arcs. Philos Trans R Soc London A297: 409-445 doi:
10.1098/rsta.1980.022410.1029/95RG00262
Taylor S R, McLennan S M (1985) The Continental Crust: Its Composition and Evolution. Blackwell, Oxford, pp 1-312
Taylor S R, McLennan S M (1995) The geochemical evolution of the continental crust. Reviews in
Geophysics 33: 241-265 doi: 10.1029/95RG00262
Thompson R N (1982) British Tertiary province. Scott J Geol 18: 49-107
Weaver B L, Tarney J (1984) Empirical approach to estimating the composition of the continental
crust. Nature 310: 575-577 doi: 10.1038/310575a0
Wood D A, Joron J L, Treuil M, Norry M, Tarney J (1979) Elemental and Sr isotope variations
in basic lavas from Iceland and the surrounding ocean floor; the nature of mantle source inhomogeneities. Contrib Mineral Petrol 70: 319-339 doi: 10.1007/BF00375360
Examples
selectNorm()
selectNorm("Boynton")
# Regular expressions in action, we take the string from beginning
# and then replace space and left bracket by dots
selectNorm("^Primitive Mantle..McDonough 1995")

selectPalette

selectPalette

Description
Picks given number of colours from one of the available palettes.
Usage
selectPalette(n,colour.palette=NULL,GUI=TRUE)
Arguments
n

desired number of colours

colour.palette one of the colour palette names, see Details
GUI

logical; is the function called from GUI?

Details
The desired number of colours has to be given in any case.
The possible palettes are: 'grays','reds','blues','greens','cyans','violets','yellows',
'cm.colors','heat.colors','terrain.colors','topo.colors','rainbow' and 'jet.colors'.
If not specified upon function call, the colour palette can be picked from list of available ones.
Optionally (if GUI = TRUE) it plots a chart with their preview.

210

selectPalette

Value
Returns a matrix with a single row of hexadecimal codes. Its rownames represent the name of the
palette selected.
Author(s)
Vojtech Janousek, 
See Also
Colours by label can be assigned by assignColLab, colours by variable using assignColVar.
Uniform colours are obtained by assign1col. Table of available plotting colours is obtained by
showColours.
Examples
ee<-selectPalette(5,"heat.colours")
ee<-selectPalette(5)
ee<-selectPalette(5,GUI=FALSE)

selectSubset

selectSubset

211

Select subset

Description
Selects samples corresponding to given criteria.
Usage

selectSubset(what=NULL,where=cbind(labels,WR),save=TRUE,multiple=TRUE,
text="Press ENTER for all samples, or specify search pattern \n by sample name, range or Boolean co
range=FALSE,GUI=FALSE, all.nomatch=TRUE)
selectSamples(what=NULL, print=TRUE, multiple=TRUE, text=NULL)
Arguments
what
where
save
multiple
text
range
GUI
all.nomatch
print

search pattern
data to be searched
should the newly selected subset replace the data in memory, i.e. ’labels’ and
’WR’
logical, can be multiple items selected?
text prompt
logical: is the search pattern to be interpreted as a range of samples?
logical: is the function called from within GUI?
logical: return all samples when there is no match?
logical: should be the chosen samples ID printed?

Details
The function ’selectSubset’ has two purposes.
1. If ’save=TRUE’, it is a core function used in selecting subsets of the current data set by ranges
(see subsetRange) or Boolean conditions (see subsetBoolean).
2. If save=FALSE, no permanent subsetting takes place. This is useful for temporary selections of
the data, e.g. in determining which samples are to be plotted on a diagram.
In this case, the samples can be selected based on combination of three searching mechanisms. The
search pattern is first tested whether it obeys a syntax of a valid regular expression that could
be interpreted as a query directed to the sample name(s).
If not, the syntax of the search pattern is assumed to correspond to a selection of sample sequence
numbers.
At the last resort, the search pattern is interpreted as a Boolean condition that may employ most of
the comparison operators common in R, i.e. < (lower than), > (greater than), <= (lower or equal
to), >= (greater or equal to), = or == (equal to), != (not equal to). The character strings should be
quoted. Regular expressions can be employed to search the textual labels.
The conditions can be combined together by logical and, or and brackets.
Logical and can be expressed as .and. .AND. &
Logical or can be expressed as .or. .OR. |
The function ’selectSamples’ is a front-end to ’selectSubset’.

212

selectSubset

Value
If ’save=TRUE’, the function overwrites the data frame ’labels’ and numeric matrix ’WR’ by subset
that fulfills the search criteria. Otherwise names of samples fulfilling the given criteria are returned.
Warning
So far only names of existing numeric data columns and not formulae involving these can be handled.
Author(s)
Vojtech Janousek, 
See Also
regex, selectByLabel and selectAll
Examples
# permanent selection, the variables 'WR' and 'labels' affected
selectSubset("SiO2>70")
# back to the complete, originally loaded dataset
selectAll()
# both expressions below return only sample names of analyses fulfilling
# the given criteria, variables 'WR' and 'labels' NOT affected
selectSamples("SiO2<70&MgO>5")
selectSubset("SiO2<70&MgO>5",save=FALSE)
## Not run:
#EXAMPLES OF SEARCHING PATTERNS
# Searching by sample name
The sample names are: Bl-1, Bl-3, Koz-1, Koz-2,
Koz-5, Koz-11, KozD-1, Ri-1.
oz-[1-3]
# Samples Koz-1, Koz-2, Koz-11
oz-|Bl# Samples Bl-1, Bl-2, Bl-3, Koz-1, Koz-2, Koz-5, Koz-11
# Searching by range
1:5
# First to fifth samples in the data set
1,10
# First and tenth samples
1:5, 10:11, 25
# Samples number 1,

2, ...5, 10, 11, 25

# Searching by Boolean
######################

setCex

213

Intrusion="Rum"
# Finds all analyses from Rum
Intrusion="Rum".and.SiO2>65
Intrusion="Rum".AND.SiO2>65
Intrusion="Rum"&SiO2>65
# All analyses from Rum with silica greater than 65
# (all three expressions are equivalent)
MgO>10&(Locality="Skye"|Locality="Islay")
# All analyses from Skye or Islay with MgO greater than 10
Locality="^S"
# All analyses from any locality whose name starts with capital S
## End(Not run)

setCex

Set uniform symbols size

Description
Defines the default relative size of plotting symbols.
Usage
setCex(x)
Arguments
x

numeric; scaling for the plotting symbols.

Details
The coefficient determining the plotting symbols expansion is stored in a variable ’labels[,"Size"]’,
the default is 1.
Author(s)
Vojtech Janousek, 
See Also
gcdOptions
Examples
setCex(2) # double size
plotDiagram("TAS",FALSE)
setCex(0.5) # half the size
plotDiagram("TAS",FALSE)

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setTransparency

setShutUp

Quiet mode?

Description
Determines whether extensive textual output is to be printed.
Usage
setShutUp()
Arguments
None.
Details
The control option is shut.up, whose default is FALSE, meaning that detailed information is to be
printed. This, however, may become not viable on slower systems and/or for extensive data sets.
This can be set from the menu 'GCDkit|Options' by setting the checkbox 'Minimize output on screen?'
or directly, from the command line (see Examples).
Author(s)
Vojtech Janousek, 
See Also
'gcdOptions' 'options'
Examples
getOption("shut.up")
# query the current value of the given option
options("shut.up"=TRUE) # reduce the printed output to a minimum

setTransparency

Setting transparency of plotting symbols

Description
Sets transparency of plotting colours for selected samples.
Usage
setTransparency(which.samples=NULL,transp=NULL,alpha=NULL,GUI=FALSE)

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215

Arguments
which.samples

list of samples; if NULL a dialogue is displayed

transp

numeric; transparency to be set

alpha

character; alpha value to be set (opacity)

GUI

logical; is the function called form within GUI?

Details
The transparency value has to fall between 1 (completely transparent) to 0 (opaque).
Alternatively, the so-called alpha channel can be specified, which can attain any hexadecimal number between 0 (completely transparent) to ff (opaque).
if GUI = TRUE, the samples can be selected based on combination of three searching mechanisms
(by sample name/label, range or a Boolean condition) - see selectSamples for details.
Value
Assigns ’labels$Colour’.
Warning
As a side product, plotting colours are converted to hexadecimal values, which are not easy to
translate back to symbolic names.
Author(s)
Vojtech Janousek, 
See Also
Colours by a single variable can be assigned by assignColLab, symbols and colours by groups
simultaneously by assignSymbGroup. Uniform colours are obtained by assign1col. Table of
available plotting colours is obtained by showColours.
Examples
setTransparency(transp=0)
setTransparency(transp=0.5)
setTransparency(which.samples=c("Sa-1","Sa-2","Sa-3"),transp=0.5)
setTransparency(which.samples=c("Sa-1","Sa-2","Sa-3"),alpha="6a")

Shand

A/CNK-A/NK diagram (Shand 1943)

Description
Assigns data for Shand’s diagram into Figaro template (list ’sheet’) and appropriate values into
’x.data’ and ’y.data’
Usage
Shand()

216

Shand

Details
Classic Shand’s diagram (1943). Three rock types are defined in the A/CNK vs A/NK plot:
Peralkaline
Metaluminous
Peraluminous

Value
sheet

list with Figaro Style Sheet data

x.data

molecular ratio A/CNK=Al2 O3 /(CaO + N a2 O + K2 O)

y.data

molecular ratio A/NK=Al2 O3 /(N a2 O + K2 O)

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Shand (1943) Eruptive Rocks. John Wiley & Sons
See Also
classify figaro plotDiagram NaAlK

Shervais

217

Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("Shand")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("Shand", FALSE)

Shervais

Shervais (1982)

Description
Assigns data for the diagram of Shervais (1982) into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’.
Usage
Shervais()
Details
Discrimination diagram for basalts, as proposed by Shervais (1982) is based on variability of the
Ti/V ratio under different oxygen fugacity.

Following environments may be distinguished:
ARC

Arc Tholeiites

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showColours
OFB

Ocean Floor Basalts

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Shervais J W (1982) Ti-V plots and the petrogenesis of modern and ophiolitic lavas. Earth Planet
Sci Lett 59: 101-118. doi: 10.1016/0012-821X(82)90120-0

See Also
figaro plotDiagram

Examples
#plot the diagram
plotDiagram("Shervais",FALSE)

showColours

Show available colours

Description
Display colours available for plotting.

Usage
showColours(n=49)
showColours2(n=64)
Arguments
n

numeric: number of colours to display

Details
The function ’showColours’ displays a palette of plotting colours which can be specified by their
numeric codes (1-49). On the other hand, ’showColours2’ demonstrates the colours which can be
given by their English names (there are some 657 of them).

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219

Author(s)
Vojtech Janousek, 
See Also
’colours’

showLegend

Display legend

Description
Displays a graphical legend(s) with assignment of plotting symbols and colours used by majority
of the diagrams.
Usage
showLegend(pch = labels$Symbol, col = labels$Colour,
new.plot = TRUE)
Arguments
pch

numeric or character: plotting symbols.

col

numeric: code for their colour.

new.plot

logical: shall be opened a new plotting window for the legend?

220

showSymbols

Details
The internal variables ’leg.col’ and ’leg.pch’ are set to zero, if the current assignment is on the
basis of ’groups’. Otherwise they contain the sequential number(s) of column(s) in the data frame
’labels’ whose levels are to be used to build the legend(s). If both variables differ, two legends
are created, for plotting symbols and colours separately. If both equal zero, the current grouping
information is used.

Value
None.

Author(s)
Vojtech Janousek, 

See Also
Symbols and colours by a single label can be assigned by functions assignSymbLab and
assignColLab respectively, symbols and colours by groups simultaneously by
assignSymbGroup. Uniform symbols are obtained by assign1symb, uniform colours by assign1col.
Table of available plotting symbols is displayed by showSymbols and colours by showColours.

Examples
showLegend()

showSymbols

Show available symbols

Description
Shows symbols available for plotting.

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221

Usage
showSymbols()
Author(s)
Vojtech Janousek, 

spider

Spider plot(s): Selected samples

Description
Normalization of trace-element data by the given standard and spiderplot plotting.
Usage
spider.individual(new=TRUE)
spider.contour(chondrit = selectNorm(),what=NULL,
colour.palette = "heat.colors", ymin = 0, ymax = 0,
cex = 1,join = TRUE,pch = 15,

222

spider
main = "",sub = "",offset = TRUE,centered = FALSE,
xrotate = FALSE, xaxs = "r", new = TRUE, legend = TRUE)
spider(rock, chondrit = selectNorm(), ymin = 0,
ymax = 0, cex = 1, plot = TRUE, join = TRUE,
field = FALSE, legend = FALSE, add = FALSE,
pch = 0, col = "black", shaded.col = "gray",
density = 0.02, angle = 0, main = "", sub = "",
offset = FALSE, centered = FALSE, xrotate = FALSE,
xaxs = "r", fill.col = TRUE, log = "y", new = TRUE, ...)

Arguments
new

logical; if true, new plotting window is opened.

chondrit

a numeric matrix with one row; the normalizing values.

what

variable name or formula.

colour.palette variable name or formula.
rock

a numeric matrix; the whole-rock data from which will be filtered out those to
be normalized.

ymin, ymax

y range of the diagram.

cex

magnification of the plotting symbols.

plot

logical; if set to FALSE, individual patterns are not plotted.

join

logical; if TRUE, the NAs are extrapolated so that the patterns are unbroken.

field

logical; if TRUE, a shaded field denoting the overall data span is plotted

legend

logical; if TRUE, room for legend is reserved.

add

logical; if FALSE, a new plot is started (otherwise overplot).

pch

a vector specifying the plotting symbols.

col

a numeric vector; colour of the plotting symbols and connecting lines.

fill.col

logical; should be the field of overall variability filled by solid colour?

shaded.col

numeric: colour for the cross-hatched or solid fill.

density

numeric: density of the fill pattern (fraction of the whole plotting range).

angle

numeric: angle of the fill pattern (in degrees).

main

character: the main title for the plot.

sub

character: the subtitle for the plot.

xrotate

logical; shall be the element names on x axis rotated?

offset

logical; shall be the names for odd and even elements shifted relative to each
other?

centered

logical; shall be the element names on x axis plotted in between tick marks?

xaxs

style of the xaxis: see ’help(par) for details.

log

which of the axes should be logarithmic?

...

further graphical parameters: see ’help(par) for details.

spider

223

Details

This is a quite flexible function, a true Mother of All Spiderplots, that can be used in a number of
ways. It is employed by functions of the GCDkit system for normalization and plotting individual
patterns for selected samples (’spiderplot.r’) or each of the groups
(’spider by group individual.r’). In ’spiderplot.r’ is stored a user interface to ’spider’
for plotting individual patterns.

Function ’spider’ can also serve for plotting the overall compositional ranges (shown as crosshatched fields or, optionally, semitransparent filled polygons) in a manner similar to function ’spider by group.r’.

224

spider

In ’spiderplot_contour.r’ is stored a user interface to ’spider’ for plotting individual patterns,
in which the plotting symbols is uniform and colour reflects distribution of an independent variable,
such as silica contents. The variable (or formula) can be specified using the parameter ’what’, the
colour scheme by ’colour.palette’. The legal colour schemes are: ’"grays","reds","blues",
"greens","cyans","violets","yellows","cm.colors","heat.colors","terrain.colors",
"topo.colors","rainbow", "jet.colors"’.

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225

The samples to be plotted can be selected based on combination of three searching mechanisms
(by sample name/label, range or a Boolean condition) - see selectSamples for details. For choosing the correct normalization values serves the auxiliary function selectNorm. Then the user is
prompted whether to use the currently assigned plotting symbols. If desired so, the symbols and
colours can be specified in a simple spreadsheet- like interface. Likewise the scale of the y axis can
be specified. The exact appearance of the labels to the x axis can be fine tuned by the arguments
’rotate.xlab’, ’offset’ and ’centered’. See examples.

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spider

If ’plot=FALSE’, not plotting is done, and only the normalized values are returned.
Value
results

numeric matrix with normalized concentrations.

Author(s)
Vojtech Janousek, ;
Vojtech Erban, , contributed the algorithm hatching closed polygons
See Also
For the syntax of the setup file with normalizing values and adding new normalization schemes
see selectNorm; for further applications of ’spider’ see spider2norm, spiderByGroupPatterns
and spiderByGroupFields.
Examples
ee<-spider.contour("Boynton","SiO2","reds",pch="*",cex=2,ymin=0.01,ymax=1000)
ee<-spider(WR,"Boynton",0.1,1000,pch="*",col="red",cex=2)
# the ee<- construction redirects the textual output
ee<-spider(WR[1:14,],"Boynton",1,500,pch=1:14,col=1:14,legend=TRUE)
ee<-spider(WR,"Boynton",field=TRUE,density=0.02,angle=60,col="darkred",fill.col=FALSE,0.1,1000)
ee<-spider(WR,"Boynton",field=TRUE,fill.col=TRUE,shaded.col="khaki",0.1,1000)
# Shade the background field portraying the overall variation
# Shade the background field portraying the overall variation
ee<-spider(WR,"Boynton",0.1,1000,pch=labels$Symbol,col=labels$Colour,cex=labels$Size)

spider2norm

227

ee<-spider(WR,"Boynton",field=TRUE,fill.col=TRUE,shaded.col="gray",add=TRUE)
ee<-spider(WR,"Boynton",0.1,1000,pch=labels$Symbol,col=labels$Colour,cex=labels$Size)
ee<-spider(WR,"Boynton",field=TRUE,density=0.02,angle=45,col="gray",fill.col=FALSE,add=TRUE)
# Custom normalization scheme
chon<-c(0.4,4,50,0.8,0.7,10,35,9,340,9,70,8.0)
chon<-matrix(chon,nrow=1)
colnames(chon)<-c("K2O","Rb","Ba","Th","Ta","Nb","Ce","Hf","Zr","Sm","Y","Yb")
rownames(chon)<-"ORG (Pearce et al. 1984)"
spider(WR,chon,ymin=0.01,col="navy",ymax=1000)
# Possible styles for x axis
multiplePerPage(8,nrow=2,ncol=4,"Possible x axis styles", dummy=FALSE)
ee<-spider(WR, "Boynton", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=F, xrotate=F, centered=F,
main="offset=F, xrotate=F, centered=F",new=F)
ee<-spider(WR, "Boynton", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=F, xrotate=T, centered=F,
main="offset=F, xrotate=T, centered=F",new=F)
ee<-spider(WR, "Boynton", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=F, xrotate=F, centered=T,
main="offset=F, xrotate=F, centered=T",new=F)
ee<-spider(WR, "Boynton", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=F, xrotate=T, centered=T,
main="offset=F, xrotate=T, centered=T",new=F)
ee<-spider(WR, "Boynton", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=T, xrotate=F, centered=F,
main="offset=T, xrotate=F, centered=F",new=F)
ee<-spider(WR, "Boynton", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=T, xrotate=T, centered=F,
main="offset=T, xrotate=T, centered=F",new=F)
ee<-spider(WR, "Boynton", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=T, xrotate=F, centered=T,
main="offset=T, xrotate=F, centered=T",new=F)
ee<-spider(WR, "Boynton", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=T, xrotate=T, centered=T,
main="offset=T, xrotate=T, centered=T",new=F)
spider(WR,"Boynton",plot=FALSE) # Calculation only

spider2norm

Spider plot(s): Selected samples, double normalized

Description
Plots a double normalized spiderplot. Trace-element data are first normalized by the given standard,
as usual (see spider). Then the normalized concentrations are multiplied by a factor needed to
adjust the normalized content of the selected element in each analysis to a desired value (such as
unity). The goal is to eliminate effects of fractional crystallization (Thompson et al. 1983, Pearce
et al. 2005, Pearce and Stern 2006).

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spider2norm

Usage
spider2norm(rock=WR,norm=NULL,norm2=NULL,ymin=0,ymax=0,which=rep(TRUE,nrow(rock)),
legend=FALSE,pch=labels$Symbol,col=labels$Colour,plot=TRUE,join=TRUE,shaded.col="gray",
density=-1,angle=0,xaxs="r",fill.col=FALSE,field=FALSE,add=FALSE,...)
Arguments
rock

a numeric matrix; the whole-rock data from which will be filtered out those to
be normalized.

norm

a character string specifying the model.

norm2

name of the variable for the second normalization.

ymin, ymax

y range of the diagram.

which

specification of the samples to be plotted.

legend

logical; if TRUE, room for legend is reserved.

pch

a vector specifying the plotting symbols.

col

a numeric vector; colour of the plotting symbols and connecting lines.

plot

logical; if set to FALSE, individual patterns are not plotted.

join

logical; if TRUE, the NAs are extrapolated so that the patterns are unbroken.

shaded.col

numeric: colour for the cross-hatched fill.

density

numeric: density of the fill pattern (fraction of the whole plotting range).

angle

numeric: angle of the fill pattern (in degrees).

xaxs

style of the xaxis: see ’help(par) for details.

fill.col

colour for solid fill

field

logical; if TRUE, a shaded field denoting the overall data span is plotted

add

logical; if TRUE, a new plot is started (otherwise overplot).

...

further graphical parameters: see ’help(par) for details.

Details
The parameter ’norm’ is an optional search pattern to query the available normalizing model names.
It can contain a substring or even a regular expression. For choosing the correct normalization
values serves the auxiliary function selectNorm.The function fails if no matches are found or the
search is ambiguous. See selectNorm for details.
The samples to be plotted can be selected based on combination of three searching mechanisms (by
sample name/label, range or a Boolean condition) - see selectSamples for details.
Then the user is prompted whether to use the currently assigned plotting symbols. If desired so, the
symbols and colours can be specified in a simple spreadsheet- like interface.
Likewise the scale of the y axis can be specified interactively.
Value
results

numeric matrix with normalized concentrations

.
Author(s)
Vojtech Janousek, 

spider2norm

229

References
Pearce J A, Stern R J (2006) Origin of back-arc basin magmas: Trace element and isotope perspectives. Back-Arc Spreading Systems: Geological, Biological, Chemical, and Physical Interactions.
Geophysical Monograph Series 166. American Geophysical Union, pp 63-86
Pearce J A, Stern R J, Bloomer S H, Fryer P (2005) Geochemical mapping of the Mariana arc-basin
system: implications for the nature and distribution of subduction components. Geochem Geophys
Geosyst 6: doi: 10.1029/2004GC000895 doi: 10.1029/2004GC000895
Thompson R N, Morrison M A, Dickin A P, Hendry G L (1983) Continental flood basalts... Arachnids rule OK? In: Hawkesworth C J, Norry M J (eds) Continental Basalts and Mantle Xenoliths.
Shiva, Nantwich, pp 158-185
See Also
For the syntax of the setup file with normalizing values and adding new normalization schemes
see selectNorm; for further variants of spiderplots, see spider, spiderByGroupPatterns and
spiderByGroupFields.
Examples
ee<-spider2norm(WR,"Boynton","Yb",0.1,1000,pch="*",col="red",cex=2)
# the ee<- construction redirects the textual output
ee<-spider2norm(WR,"Boynton","Yb",field=TRUE,density=0.05,angle=60,col="red",0.1,1000)
ee<-spider2norm(WR,"Boynton","Yb",field=TRUE,fill.col=TRUE,shaded.col="khaki",0.1,1000)
# Shade the background field portraying the overall variation
ee<-spider2norm(WR,"Boynton","Lu",0.1,1000,pch=labels$Symbol,col=labels$Colour,cex=labels$Size)
ee<-spider2norm(WR,"Boynton","Lu",field=TRUE,density=0.02,angle=45,col="gray",add=TRUE)
# Shade the background field portraying the overall variation
ee<-spider2norm(WR,"Boynton","Lu",0.1,1000,pch=labels$Symbol,col=labels$Colour,cex=labels$Size)
ee<-spider2norm(WR,"Boynton","Lu",field=TRUE,fill.col=TRUE,shaded.col="gray",add=TRUE)
# Possible styles for x axis
multiplePerPage(8,nrow=2,ncol=4,"Possible x axis styles", dummy=FALSE)
ee<-spider2norm(WR, "Boynton","Yb", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=F, xrotate=F, centered=F,new=F)
ee<-spider2norm(WR, "Boynton","Yb", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=F, xrotate=T, centered=F,new=F)
ee<-spider2norm(WR, "Boynton","Yb", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=F, xrotate=F, centered=T,new=F)
ee<-spider2norm(WR, "Boynton","Yb", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=F, xrotate=T, centered=T,new=F)
ee<-spider2norm(WR, "Boynton","Yb", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=T, xrotate=F, centered=F,new=F)
ee<-spider2norm(WR, "Boynton","Yb", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=T, xrotate=T, centered=F,new=F)
ee<-spider2norm(WR, "Boynton","Yb", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=T, xrotate=F, centered=T,new=F)
ee<-spider2norm(WR, "Boynton","Yb", 0.1, 1000, pch=labels$Symbol,
col=labels$Colour, cex=labels$Size, offset=T, xrotate=T, centered=T,new=F)
spider2norm(WR,"Boynton","Yb",plot=FALSE) # Calculation only

230

spiderBoxplot

spiderBoxplot

Spider plot(s): Selected samples - summary boxplot

Description
Normalization of geochemical data by the given standard (optionally also one of the samples) and
spiderplot plotting. No individual patterns are drawn; instead, the statistical distribution of each
element is portrayed by a boxplot.
Usage
spiderBoxplot(norm = NULL, which = rep(TRUE,nrow(WR)),
doublenorm = FALSE, norm2 = "",
ymin = NULL, ymax = NULL, bpplot = TRUE,
col = "lightgray", log = TRUE)
Arguments
norm

a character string specifying the model.

which

specification of the samples to be plotted.

doublenorm

logical; should be the normalization employed? See details.

norm2

name of the variable for the second normalization.

ymin, ymax

y range of the diagram.

bpplot

logical; if FALSE, boxplot box (instead of box and percentile plot) is shown.

col

fill colour.

log

logical; should be the y axis scaled logarithmically?

Details
The parameter ’norm’ is an optional search pattern to query the available normalizing model names.
It can contain a substring or even a regular expression. The function fails if no matches are found
or the search is ambiguous. See selectNorm for details.
The samples to be plotted can be selected based on combination of three searching mechanisms (by
sample name/label, range or a Boolean condition) - see selectSamples for details.
For choosing the correct normalization values serves the auxiliary function selectNorm, which is
the same as in ordinary spiderplots. If the user desires so, the data can be normalized by a sample
present in the dataset. Then the elements to be plotted and their order is to be specified, as well.
Optionally, double normalization can be used. Trace-element data are first normalized by the given
standard, then by the normalized content of the selected element in each analysis to eliminate effects
of fractional crystallization (Thompson et al. 2003, Pearce et al. 2005, Pearce and Stern 2006). See
spider2norm for details.
Distributions of individual normalized elements are plotted in the form of boxplot or box and percentile plot (Esty and Banfield 2003).

spiderBoxplot

231

In both cases the box denotes 50% of the population (both quartiles), the horizontal line in the
middle is a median and the whiskers denote the overall range. For boxplot this is without outliers.
See manual entry for ’boxplot’ and ’bpplot.my’ for further details.
Printed are number of observations, missing values, mean, standard deviation, minimum, 25% quartile, median (=50% quartile), 75% quartile and maximum.
Value
results

numeric matrix with statistical data for individual elements.

Warning
This function IS NOT Figaro-compatible. It means that the set of diagrams cannot be further edited
in GCDkit (e.g. tools in "Plot editing" menu are inactive).
Author(s)
Vojtech Janousek, 
References
Esty, W. W. & Banfield, J. D. (2003). The Box-Percentile Plot. Journal of Statistical Software 8
(17)
Pearce J A, Stern R J (2006) Origin of back-arc basin magmas: Trace element and isotope perspectives. Back-Arc Spreading Systems: Geological, Biological, Chemical, and Physical Interactions.
Geophysical Monograph Series 166. American Geophysical Union, pp 63-86
Pearce J A, Stern R J, Bloomer S H, Fryer P (2005) Geochemical mapping of the Mariana arc-basin
system: implications for the nature and distribution of subduction components. Geochem Geophys
Geosyst 6: doi: 10.1029/2004GC000895

232

spiderByGroupFields
Thompson R N, Morrison M A, Dickin A P, Hendry G L (1983) Continental flood basalts... Arachnids rule OK? In: Hawkesworth C J, Norry M J (eds) Continental Basalts and Mantle Xenoliths.
Shiva, Nantwich, pp 158-185

See Also
For the syntax of the setup file with normalizing values and adding new normalization schemes
see selectNorm; for further applications of ’spider’ see spiderByGroupPatterns,
spider2norm and spiderByGroupFields.
Examples
spiderBoxplot("Boynton",col="yellow",bpplot=FALSE)
spiderBoxplot("Primordial Wood",doublenorm=TRUE,norm2="Y",
col="khaki",ymin=0.05,ymax=1000,bpplot=TRUE)

spiderByGroupFields

Spider plot(s) - by group fields

Description
Plots a series of spiderplots, for each group one, outlining the overall distribution as a field.
Usage
spiderByGroupFields(rock = WR, norm = NULL,
bw = FALSE, fill = FALSE, ymin = 0, ymax = 0,
xrotate = FALSE, offset = TRUE, centered = FALSE)
Arguments
rock

a numeric matrix; the whole-rock data from which will be filtered out those to
be normalized.

norm

a character string specifying the model.

bw

logical; should be the plot black and white?

fill

logical; should be the fields filled by solid colour (and not hatched)?

ymin, ymax

y range of the diagram.

xrotate

logical; shall be the element names on x axis rotated?

offset

logical; shall be the names for odd and even elements shifted relative to each
other?

centered

logical; shall be the element names on x axis plotted in between tick marks?

Details
The parameter ’norm’ is an optional search pattern to query the available normalizing model names.
It can contain a substring or even a regular expression. For choosing the correct normalization
values serves the auxiliary function selectNorm.The function fails if no matches are found or the
search is ambiguous. See selectNorm for details.
A series of spiderplots is plotted, for each group one, in which the whole variation range is outlined
as filled/cross-hatched fields.

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233

Value
None.
Author(s)
Vojtech Janousek, ; Vojtech Erban, ,
contributed the algorithm hatching closed polygons
See Also
For the syntax of the setup file with normalizing values and adding new normalization schemes see
selectNorm. This function is based on spider.
Examples
## Not run:
data<-loadData("sazava.data",sep="\t")
groupsByLabel("Intrusion")
spiderByGroupFields(norm="Boynton",ymin=1,ymax=1000)
spiderByGroupFields(norm="Boynton",bw=TRUE,ymin=1,ymax=1000,xrotate=TRUE,offset=FALSE)
spiderByGroupFields(norm="Boynton",fill=TRUE,ymin=1,ymax=1000)
## End(Not run)

spiderByGroupPatterns Spider plot(s) - by group patterns

Description
Plots a series of spiderplots, for each group one, in which individual patterns are shown.
Usage
spiderByGroupPatterns(rock = WR, norm = NULL, bw = FALSE,
ymin = 0, ymax = 0, xrotate = FALSE, offset = TRUE, centered = FALSE)
Arguments
rock

a numeric matrix; the whole-rock data from which will be filtered out those to
be normalized.

norm

a character string specifying the model.

bw

logical; should be the plot black and white?

ymin, ymax

y range of the diagram.

xrotate

logical; shall be the element names on x axis rotated?

offset

logical; shall be the names for odd and even elements shifted relative to each
other?

centered

logical; shall be the element names on x axis plotted in between tick marks?

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srnd

Details
Firstly, the normalization scheme is chosen and scaling for all the plots specified. Then, a series of
spiderplots is plotted, for each group one, in which patterns for individual samples are shown.
Value
Returns a list ’results’ with the normalized values, and, in case of REE, some extra parameters.
Author(s)
Vojtech Janousek, 
See Also
For the syntax of the setup file with normalizing values and adding new normalization schemes see
selectNorm. This function is based on spider.
Examples
# Get the data ready
data(sazava)
accessVar("sazava")
groupsByLabel("Intrusion")
#Plot
spiderByGroupPatterns(norm="Boynton",ymin=1,ymax=1000)
spiderByGroupPatterns(norm="Boynton",bw=TRUE,ymin=1,ymax=1000,xrotate=TRUE,offset=FALSE)

srnd

Recalculations of the Sr-Nd isotopic data

Description
Age-corrects the Sr-Nd isotopic data to a given age; calculates initial (N d) values and Nd model
ages.
Usage
srnd(age="")
initial(x,age,system="Nd")
epsilon(WR,age)
DMage(WR)
DMGage(WR)
DMLHage(WR, age)
Arguments
age

age in Ma: if empty, the user is prompted to enter a value

x, WR

isotopic data to be recalculated

system

character; which isotopic system Sr or Nd?

statsByGroup

235

Details
Recalculates the Sr-Nd isotopic data and returns them in the numeric matrix init with the following
columns:
Age (Ma)
87Sr/86Sri
143Nd/144Ndi
EpsNdi
TDM
TDM.Gold
TDM.2stg

Age in Ma
Initial Sr isotopic ratios
Initial Nd isotopic ratios
Initial (N d) values
Single-stage depleted-mantle Nd model ages (Liew & Hofmann, 1988), function Image
Single-stage depleted-mantle Nd model ages (Goldstein et al., 1988), function DMGage
Two-stage depleted-mantle Nd model ages (Liew & Hofmann, 1988), function DMLHage

Value
init

numeric matrix with the results

Plugin
SrNd.r
Author(s)
Vojtech Janousek, 
References
Liew T C & Hofmann A W (1988) Precambrian crustal components, plutonic associations, plate
environment of the Hercynian Fold Belt of Central Europe: indications from a Nd and Sr isotopic
study. Contrib Mineral Petrol 98: 129-138
Goldstein S L, O’Nions R K & Hamilton P J (1984) A Sm-Nd isotopic study of atmospheric dusts
and particulates from major river systems. Earth Planet Sci Lett 70: 221-236
Examples
# recalculation to 500 Ma
srnd(500)
# print the isotopic parameters currently in the memory
init

statsByGroup

Statistics by groups

Description
Calculates simple descriptive statistics for individual columns of the given data matrix; optionally
this can be done for each of the groups separately.
Usage
statsByGroup(data = WR, groups = groups)

236

statsByGroupPlot

Arguments
data

numeric data matrix.

groups

a vector, in which is specified, for each sample, a group it belongs to.

Details
The function returns a list containing the calculated statistical parameters respecting the desired
grouping. The statistical summary involves number of observations, missing values, mean, standard
deviation, minimum, 25% quartile, median (= 50% quartile), 75% quartile and maximum. This is a
core function invoked both by summarySingle and summarySingleByGroup.
Value
results

a list with the results for individual groups

Author(s)
Vojtech Janousek, 
See Also
summarySingle
statistics
summaryAll
summaryByGroup
Examples
statsByGroup(WR)
statsByGroup(WR[,LILE])

statsByGroupPlot

Statistics: Plot summary by element and group

Description
Plots crosses in a binary diagram denoting means and standard deviations for individual groups.
Usage
statsByGroupPlot()
Details
Displays a binary diagram of two elements/oxides in which are plotted averages for the individual
groups with whiskers corresponding to their standard deviations.
The variables are entered via the function ’selectColumnLabel’. In the specification of the variables can be used also arithmetic expressions, see calcCore for the correct syntax.

statsIso

237

Value
results

a matrix with the results for individual groups and selected two elements/oxides

Author(s)
Vojtech Janousek, 

statsIso

Statistical plots of isotopic ratios/model ages

Description
Plots a boxplot or stripplot for a given isotopic parameter, respecting groups.

Usage
boxplotIso()
stripplotIso()

Arguments
None.

Details
The boxplot portrays realistically a statistical distribution of the data. The box represents, for each
of the groups, the two quartiles, the line inside is a median, the whiskers show the whole range
without outliers.

238

statsIso

Stripplot shows 1D scatter plots for each of the groups, with some artificial noise (jitter) added to
make the individual points better visible. Stripplots are a good alternative to boxplots when sample
sizes are small.

statsIso

239

The variables to choose from are:
Menu item
87Sr/86Sri
143Nd/144Ndi
EpsNdi
1 stg DM model ages (Goldstein et al. 1988)
1 stg DM model ages (Liew & Hofmann 1988)
2 stg DM model ages (Liew & Hofmann 1988)

Value
None.

Plugin
SrNd.r

Explanation
Initial Sr isotopic ratios
Initial Nd isotopic ratios
Initial (N d) values
Single-stage DM Nd model ages
Single-stage DM Nd model ages
Two-stage DM Nd model ages

240

strip

Author(s)
Vojtech Janousek, 
References
Liew T C & Hofmann A W (1988) Precambrian crustal components, plutonic associations, plate
environment of the Hercynian Fold Belt of Central Europe: indications from a Nd and Sr isotopic
study. Contrib Mineral Petrol 98: 129-138
Goldstein S L, O’Nions R K & Hamilton P J (1984) A Sm-Nd isotopic study of atmospheric dusts
and particulates from major river systems. Earth Planet Sci Lett 70: 221-236

strip

Statistics: Stripplot by groups

Description
Stripplot for selected samples and variable, respecting the grouping.
Usage
strip(xlab = "", ...)
Arguments
xlab

variable name

...

additional parameters to stripplot

Details
Stripplot shows 1D scatter plots for each of the groups, with some artificial noise (jitter) added to
make the individual points better visible. Stripplots are a good alternative to boxplots when sample
sizes are small.
If no variable is specified as an argument ’xlab’, the user can enter it using the function ’selectColumnLabel’.
In the specification of the variable can be used also arithmetic expressions, see calcCore for the
correct syntax.
Value
None.
Author(s)
Vojtech Janousek, 
See Also
stripplot, stripBoxplot
Examples
strip("(Na2O+K2O)/Al2O3")

stripBoxplot

stripBoxplot

241

Statistics: Stripplot by groups - with boxplots

Description
Stripplot for selected variable, respecting the grouping. Each of the stripplots for the individual
groups are underlain by a boxplot, so that the median, quartiles and range are immediately apparent. Optionally, the data points can be replaced by variously sized/coloured circles, depicting a
distribution of a second variable.

Usage
stripBoxplot(yaxis = "", zaxis = "0", ymin = NULL, ymax = NULL,
pal = "heat.colors", ident = FALSE, silent=TRUE)

Arguments
yaxis

specification of the variable used for stripplots/boxplots

zaxis

(optional) specification of the variable depicted by the circles

ymin, ymax

minimum and maximum of the y axis

pal

name of predefined palette

ident

logical; should be the samples identified interactively after plotting?

silent

logical, should be the above chosen by the appropriate dialogues?

Details
Stripplot shows 1D scatter plots for each of the groups, with some artificial noise (jitter) added to
make the individual points better visible. Stripplots are a good alternative to boxplots when sample
sizes are small.
If no variable is specified as an argument ’yaxis’, the user can enter it using the function ’selectColumnLabel’.
If ’zaxis’ is zero, assigned plotting symbols, colours and symbol sizes are used.
If ’zaxis’ refers to a valid variable name, the data points are shown as circles, the size and colours
of which correspond to this second variable.

242

Subset by range

In the specification of the variable(s) can be used also arithmetic expressions, see calcCore for the
correct syntax.
The colour scheme can be specified by ’pal’. The legal colour schemes are: "grays","reds","blues",
"greens","cyans","violets","yellows","cm.colors","heat.colors","terrain.colors",
"topo.colors","rainbow" and "jet.colors".
Value
None.
Author(s)
Vojtech Janousek, 
See Also
stripplot, boxplot, strip, plotWithCircles
Examples
stripBoxplot("(Na2O+K2O)/Al2O3")

Subset by range

Select subset by range

Description
Selecting subsets of the data stored in memory by their range.

summaryAll

243

Details
The menu item ’Select subset by range’ is connected to the function selectSubset. The
search pattern is treated as a selection of sample sequence numbers (effectively a list separated by
commas that may also contain ranges expressed by colons). The current data will be replaced by its
newly chosen subset.
Value
Overwrites the data frame ’labels’ and numeric matrix ’WR’ by subset that fulfills the search criteria.
Author(s)
Vojtech Janousek, 
Examples
## Not run:
Search pattern = 1:5
# First to fifth samples in the data set
Search pattern = 1,10
# First and tenth samples
Search pattern = 1:5, 10:11, 25
# Samples number 1, 2, ...5, 10, 11, 25
## End(Not run)

summaryAll

Statistics: Statistical summaries for the whole data set or its subset

Description
The function ’summaryAll’ prints statistical summary for selected list of elements (majors as a
default) and the current dataset (or its part). Functions ’summaryMajor’ and ’summaryTrace’ are
entry points supplying the default lists for major- and trace elements.
Usage
summaryAll(elems = major, where = NULL, show.boxplot = FALSE,
show.hist = FALSE, silent=TRUE)
summaryMajor()
summaryTrace()
Arguments
elems

list of desired elements

where

list of desired samples to be processed

show.boxplot

logical, should be plotted the boxplots?

show.hist

logical, should be plotted the histograms?

silent

logical, should be the above chosen by the appropriate dialogues?

244

summaryByGroup

Details
The statistical summary involves number of observations, missing values, mean, standard deviation,
minimum, 25% quartile, median (= 50% quartile), 75% quartile and maximum. The function also
plots summary boxplots and histograms, if desired so.
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
Even though as a default are assumed majors (SiO2, TiO2, Al2O3,FeOt, MnO, MgO, CaO, Na2O, K2O
for ’summaryMajor’) or selected trace (Rb, Sr, Ba, Cr, Ni, La, Eu, Y, Zr for ’summaryTrace’)
elements, the variable(s) to be displayed can be modified/specified in all cases. To this purpose
serves the function ’selectColumnsLabels’.
In the specification of the variable can be used also arithmetic expressions, see calcCore for the
correct syntax.
Value
results

numeric matrix with the results

Author(s)
Vojtech Janousek, 
See Also
statistics summarySingle summarySingleByGroup summaryByGroup
Examples
summaryAll(LILE)
summaryAll(LILE,show.hist=TRUE)
summaryAll(LILE,show.boxplot=TRUE)
# user-defined list
my.elems<-c("Rb","Sr","Ba")
summaryAll(my.elems)
## Not run:
summaryMajor()
summaryTrace()
## End(Not run)

summaryByGroup

Statistics: Statistical summaries by groups

Description
The function ’summaryByGroup’ prints a statistical summary for selected list of elements (majors
as a default) and the whole dataset or its selection, respecting the current grouping. Functions
’summaryByGroupMjr’ and ’summaryByGroupTrc’ are entry points supplying the default lists for
major- and trace elements. The function ’summaryByGroupTrc’ returns only ranges of the given
parameter(s).

summaryByGroup

245

Usage
summaryByGroup(elems = major, where = NULL, show.boxplot = FALSE,
show.hist = FALSE, silent = TRUE)
summaryByGroupMjr()
summaryByGroupTrc()
summaryRangesByGroup(elems=major, where=NULL, silent=TRUE)
Arguments
elems

list of desired elements

where

list of desired samples to be processed

show.boxplot

logical, should be plotted the boxplots?

show.hist

logical, should be plotted the histograms?

silent

logical, should be the above chosen by the appropriate dialogues?

Details
The statistical summary involves number of observations, missing values, mean, standard deviation,
minimum, 25% quartile, median (= 50% quartile), 75% quartile and maximum. The function also
plots a summary boxplots and histograms, if desired so.
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
The defaults are lists of major (SiO2, TiO2, Al2O3, FeOt, MnO, MgO,CaO, Na2O,
K2O) or trace (Rb, Sr, Ba, Cr, Ni, La, Eu, Y, Zr) elements, respectively.
The desired variables are selected using the function ’selectColumnsLabels’.
In the specification of the variable can be used also arithmetic expressions, see calcCore for the
correct syntax.
Value
results

a list with the results for individual groups

Author(s)
Vojtech Janousek, 
Examples
summaryByGroup(LILE)
summaryByGroup(LILE,show.hist=TRUE)
summaryByGroup(LILE,show.boxplot=TRUE)
# user-defined list
my.elems<-c("Rb","Sr","Ba/Sr")
summaryByGroup(my.elems)

246

summarySingle
## Not run:
summaryByGroupTrc()
summaryByGroupMjr()
summaryRangesByGroup(elems="Rb/Sr,Na2O+K2O")
## End(Not run)

summarySingle

Statistics: Single variable all/selection

Description
Prints statistical summary for a single variable and the current dataset (or its part).

Usage
summarySingle(xlab="")

Arguments
xlab

variable name

Details
The statistical summary involves number of observations, missing values, mean, standard deviation,
minimum, 25% quartile, median (=50% quartile), 75% quartile and maximum. The function also
plots a summary boxplot and histogram.
In addition the statistical distribution of the given variable is shown as a boxplot, a box-percentile
plot and two variants of histograms.

summarySingle

247

If no variable is specified as an argument ’xlab’, the user can enter it using the function ’selectColumnLabel’.
In the specification of the variable can be used also arithmetic expressions, see calcCore for the
correct syntax.
The samples can be selected based on combination of three searching mechanisms (by sample
name/label, range or a Boolean condition) - see selectSamples for details.
Value
results

numeric matrix/vector with the results

Author(s)
Vojtech Janousek, 
See Also
boxplot
bpplot2
statistics

248

summarySingleByGroup
summarySingleByGroup summaryAll summaryByGroup

Examples
summarySingle("(Na2O+K2O)/Al2O3")

summarySingleByGroup

Statistics: Single variable by groups

Description
Prints statistical summary for a single variable and the whole dataset, divided by groups.
Usage
summarySingleByGroup(xlab="")
Arguments
xlab

variable name

Details
The statistical summary involves number of observations, missing values, mean, standard deviation,
minimum, 25% quartile, median (= 50% quartile), 75% quartile and maximum. The function also
plots a summary boxplot and histogram.
If no variable is specified as an argument ’xlab’, the user can enter it using the function
’selectColumnLabel’. In the specification of the variable can be used also arithmetic expressions,
see calcCore for the correct syntax.
Value
results

numeric matrix with the results

Author(s)
Vojtech Janousek, 
See Also
boxplot summarySingle statistics summaryAll summaryByGroup
Examples
summarySingleByGroup("(Na2O+K2O)/Al2O3")

Sylvester

249

Sylvester

Sylvester (1989)

Description
Assigns data for a binary plot (Al2 O3 +CaO)/(F eOt+N a2 O +K2 O) vs. 100∗(M gO +F eOt+
T iO2 )/SiO2 , proposed by Sylvester (1989) to distinguish the alkaline collision-related alkaline
granites into Figaro template (list ’sheet’) and appropriate values into ’x.data’ and ’y.data’.
Usage
Sylvester()
Details
In the plot (Al2 O3 + CaO)/(F eOt + N a2 O + K2 O) vs. 100 ∗ (M gO + F eOt + T iO2 )/SiO2
of Sylvester (1989) can be distinguished ’Alkaline’collision-related granites, from ’Calc-alkaline
& Strongly peraluminous’ types (solid line). The strongly fractionated calc-alkaline varieties are
separated by the dashed line.

Note that only samples with SiO2 > 68 wt. % are plotted.
Value
sheet

list with Figaro Style Sheet data

x.data

(Al2O3+CaO)/(FeOt+Na2O+K2O) [wt. %]

y.data

100*(MgO+FeOt+TiO2)/SiO2 [wt. %]

250

TAS

Author(s)
Vojtech Janousek, 

References
Sylvester P J (1989) Post-collisional alkaline granites. J Geol 97: 261-280. doi: 10.1086/629302

See Also
figaro plotDiagram

Examples
#plot the diagram
plotDiagram("Sylvester", FALSE)

TAS

IUGS recommended TAS (Le Bas et al. 1986)

Description
Assigns data for IUGS recommended TAS diagram into Figaro template (list ’sheet’) and appropriate values into ’x.data’ and ’y.data’

Usage
TAS(cutoff=95)

Arguments
cutoff

numeric; the minimal sum of the analysis to be considered for classification

Details
TAS diagram, as proposed by Le Bas et al. (1986), codified by Le Maitre et al. (1989) and slightly
modified by Le Bas (2000).

TAS

251

The diagram (in its basic form) defines following fields:
foidite
picrobasalt
basalt
basaltic andesite
andesite
dacite
rhyolite
trachybasalt
basaltic trachyandesite
trachyandesite
trachyte/trachydacite
tephrite/basanite
phonotephrite
tephriphonolite
phonolite
This primary division is further enhanced by the ’TASadd’ routine (called automatically by ’classify’).
Following actions are carried out:

252

TAS
• Analyses with H2 O > 2 and CO2 > 0.5 (weight percent) are filtered out
• Trachybasalt is subdivided into hawaiite and potassic trachybasalt
• Basaltic trachyandesite is subdivided into mugearite and shoshonite
• Trachyandesite is subdivided into benmoreite and latite
• High-Mg rocks are split into picrite, komatiite, meimechite and boninite
Note that systematics of high-Mg rocks follows revised IUGS Recommendations (Le Bas et al.,
2000; Le Maitre et al. 2002) which differ from their 1st edition (Le Maitre et al, 1989). Further
subdivisions recommended by Le Maitre et al. (1989) are not implemented in GCDkit, mainly for
poorly defined CIPW version used by the Subcommission.

Value
x.data

SiO2 data recast to anhydrous sum (matrix ’WRanh’)

y.data

Na2O+K2O data recast to anhydrous sum (matrix ’WRanh’)

sheet

list with Figaro Style Sheet data

results

matrix with classification results

groups

vector with classification results

grouping

set to -1

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Le Bas M J, Le Maitre R W, Streckeisen A & Zanettin B (1986) A chemical classification of volcanic rocks based on the total alkali-silica diagram. J Petrology 27: 745-750 doi: 10.1093/petrology/27.3.745
Le Bas M J (2000) IUGS Reclassification of the High-Mg and Picritic Volcanic Rocks. J Petrology
41: 1467-1470 doi: 10.1093/petrology/41.10.1467
Le Maitre R W et al (1989) Igneous Rocks: A Classification and Glossary of Terms, 1st edition.
Cambridge University Press
Le Maitre R W et al (2002) A Classification and Glossary of Terms, 1st edition. Cambridge University Press
See Also
classify figaro plotDiagram
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("TAS")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("TAS", FALSE)

TASMiddlemost

TASMiddlemost

253

Middlemost’s modification of TAS diagram

Description

Assigns data for Middlemost’s modification of the TAS diagram into Figaro template (list ’sheet’)
and appropriate values into ’x.data’ and ’y.data’.

Usage

TASMiddlemostVolc()
TASMiddlemostPlut()

Details

Middlemost’s variation of classic IUGS-recommended TAS diagram, originally proposed by Le Bas
et al. (1986). Boundaries of foidite, phonolite, trachyte, trachydacite and rhyolite fields are defined,
as inferred from the phase relations in the TAS system. Moreover, the trachyte + trachydacite field
is split into trachyte and trachydacite fields, silexite and sodalitite + nephelinolith + leucitolith fields
are defined.

254

TASMiddlemost

The same diagram layout is applied also to plutonic rocks as follows:
plutonic rocks
Peridotgabbro
Gabbro
Gabbroic Diorite
Diorite
Granodiorite
Granite
Quartzolite
Monzogabbro
Monzodiorite
Monzonite
Quartzmonzonite
Syenite
Foid Gabbro
Foid Monzodiorite
Foid Monzosyenite
Foid Syenite

volcanic rocks
Picrobasalt
Basalt
Basaltic Andesite
Andesite
Dacite
Rhyolite
Silexite
Trachybasalt
basaltic Trachyandesite
Trachyandesite
Trachydacite
Trachyte
Tephrite
Phonotephrite
Tephriphonolite
Phonolite

TASMiddlemost

255
Foidolite
Tawite/Urtite/Italite

Foidite
sodalitite/nephelinolith/leucitolith

Value
sheet

list with Figaro Style Sheet data

x.data

SiO2 weight percent

y.data

Na2O+K2O weight percent

Author(s)
Vojtech Erban, 
References
Le Bas M J, Le Maitre R W, Streckeisen A & Zanettin B (1986) A chemical classification of volcanic
rocks based on the total alkali-silica diagram. J Petrology 27: 745-750

256

ternary
Middlemost E A K (1994) Naming materials in the magma/igneous rock system. Earth Sci Rev 37:
215-224 doi: 10.1016/0012-8252(94)90029-9

See Also
classify TAS Cox figaro plotDiagram
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("TASMiddlemostVolc")
# or
classify("TASMiddlemostPlut")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("TASMiddlemostVolc", FALSE)
# or
plotDiagram("TASMiddlemostPlut", FALSE)

ternary

Ternary plot

Description
These functions plot/add data to a ternary plot.
Usage
ternary(x = NULL, y = NULL, z = NULL, samples = rownames(WR),
new = TRUE, grid = FALSE, ticks = TRUE, ...)
triplot(aa, bb, cc, alab, blab, clab, title = "", grid.int = 0,
tick.int = 0, label.axes = FALSE, line = FALSE,
pch = labels[names(aa), "Symbol"],
col = labels[names(aa), "Colour"],
cex = labels[names(aa),"Size"],
identify = getOption("gcd.ident"),
new = TRUE,...)
triplotadd(aa, bb, cc,
pch=labels[names(aa),"Symbol"],
col=labels[names(aa),"Colour"],
cex = labels[names(aa),"Size"],
labs=NULL, identify = FALSE, lines = FALSE, lty = "solid", type="p")
Arguments
x

character; specification of the plotting variable for the bottom left apex (formulae OK).

y

character; specification of the plotting variable for the top apex (formulae OK).

ternary

257

z

character; specification of the plotting variables for the bottom right apex (formulae OK).

grid

logical; should be grid plotted?

ticks

logical; should be ticks plotted?

samples

character or numeric vector; specification of the samples to be plotted.

new

logical; should be opened a new plotting window?

...

Further parameters to the functions ’ternary’ and ’triplot’.

aa

a numerical vector, bottom left apex.

bb

a numerical vector, top apex.

cc

a numerical vector, bottom right apex.

alab,blab,clab labels for the apices.
title

title for the whole diagram.

grid.int

interval of grid lines (0-1); if set to zero (default value), no grid is drawn.

tick.int

interval of ticks on axes (0-1); if set to zero (default value), no ticks are drawn.

label.axes

logical; if set to TRUE, axes are labeled by percentages of the components.

line, lines

logical; if set to TRUE, lines are drawn instead of plotting points.

lty

line type.

pch

plotting symbols.

col

plotting colours.

cex

relative size of plotting symbols.

identify

logical; should be samples identified?

labs

character; optional text to label the points.

type

character; plot type; see plot.default.

Details
The function ’ternary’ is the user interface to ’triplot’. The latter sets up the axes, labels the
apices, plots the data and, if desired, enables the user to identify the data points interactively.
If ’new=TRUE’, new plot window is opened.

258

ternary

The values for ’label.axes’ are chosen according to ’tick.int’ or ’grid.int’; if these are not
available, labels are drawn by 10%.
’triplotadd ’adds data points/lines to pre-existing ternary plot.
The variables to be plotted are selected using the function ’selectColumnLabel.
In the specification of the apices can be used also arithmetic expressions, see calcCore for the
correct syntax.
The functions are Figaro-compatible.
Value
A numeric matrix with coordinates of the data points recast to a sum of 1.
Author(s)
Jakub Smid  & Vojtech Janousek, 
See Also
plot

tetrad

259

Examples
ternary("Ba","Rb*10","Sr",col="red",pch="+")
ternary("SiO2/10","2*FeOt","K2O*5",samples=1:10,grid=TRUE)
triplot(WR[,"SiO2"]/10,WR[,"Na2O"]+WR[,"K2O"],WR[,"MgO"],"SiO2","A","MgO",
tick.int=0.1)
triplot(WR[,"Rb"]*10,WR[,"Sr"],WR[,"Ba"],"Rb","Sr","Ba",tick.int=0.05,
grid.int=0.1,pch="+",col="darkblue",label.axes=TRUE)

tetrad

Lanthanide tetrad effect

Description
Calculates lanthanide tetrad effect following the method of Irber (1999).
Usage
tetrad(method=NULL)
Arguments
method

Normalization scheme.

Details
The method indicates which normalization scheme is to be used. It can be either ’Boynton’ or
’Nakamura’. If not specified, the user is prompted to choose it interactively by the function spider.
The anomalies of individual elements are calculated as follows for the first tetrad:
Ce/Cet =

CeN
2
3

1

3
LaN ∗ N dN

P r/P rt =

P rN
1
3

2

3
LaN ∗ N dN

t1 =

p
Ce/Cet ∗ P r/P rt

By analogy, one can define for the third tetrad:
T b/T bt =

T bN
2
3

1

3
GdN ∗ HoN

Dy/Dyt =

DyN
1
3

2

3
GdN ∗ HoN

t3 =

p

T b/T bt ∗ Dy/Dyt

The magnitude of the tetrad effect is then calculated as a geometric mean:
√
t3 = t1 ∗ t3

260

threeD

Value
Returns a matrix ’results’ with the following columns:
Ce/Cet

Ce anomaly

Pr/Prt

Pr anomaly

t1

first tetrad

Tb/Tbt

Tb anomaly

Dy/Dyt

Dy anomaly

t3

third tetrad

TE1-3

degree of lanthanide tetrad effect, geometric mean of t1 and t3

Plugin
tetrad.r
Author(s)
Vojtech Janousek, 
References
Irber W (1999) The lanthanide tetrad effect and its correlation with K/Rb, Eu/Eu*, Sr/Eu, Y/Ho,
and Zr/Hf of evolving peraluminous granite suites. Geochim Cosmochim Acta 63: 489-508
See Also
spider
Examples
tetrad("Boynton")

threeD

3D plot

Description
Plots a 3-D plot of three specified variables.
Usage
threeD(xlab="",ylab="",zlab="")
Arguments
xlab

Name of the data column to be used as x axis.

ylab

Name of the data column to be used as y axis.

zlab

Name of the data column to be used as z axis.

threeD

261

Details
This function displays three variables in a form of 3D plot. The plot can be rotated interactively, if
required so.
The samples to be plotted can be selected based on combination of three searching mechanisms (by
sample name/label, range or a Boolean condition) - see selectSubset for details.
If no parameters 'xlab', 'ylab' and 'zlab' are given, the user is prompted to specify them.
The variables are selected using the function ’selectColumnLabel.

In the specification of the apices can be used also arithmetic expressions, see calcCore for the
correct syntax.
See manual entry for ’cloud’ for further details.
Value
None.
Warning
This function IS NOT Figaro-compatible.

262

tkSelectVariable

Author(s)
Vojtech Janousek,  & Vojtech Erban, 
Examples
threeD("SiO2","Na2O+K2O","MgO+FeOt")

tkSelectVariable

TclTk GUI: Select a single variable

Description
Function to select a single variable using the Tcl/Tk-based Graphical User Interface (GUI).
Usage
tkSelectVariable(top.frame = NULL, where = colnames(WR), preselect = 2,
pack = FALSE, message = "Select a variable", background = "wheat",
variable = "x", on.leave = function() {}, row = 0, column = 0, height = 15,
width = 50, buttons = FALSE, state = "normal")
Arguments
top.frame

name of the parental frame

where

character; names of variables to be chosen from

preselect

numeric; which item is to be preselected

pack

logical; pack the frame?

message

character; textual prompt

background

colour for the frame background

variable

character; variable name with the output

on.leave

function to be invoked upon leave

row, column

coordinates within the parental frame

height, width

size of the frame

buttons

logical; should the frame have also buttons?

state

???

Details
The buttons are: Reset, SortUp, SortDown, OK, Cancel.
Author(s)
Vojtech Janousek, 
See Also
tcltk-package

tk_winDialog

263

tk_winDialog

tk_winDialog

Description
Tcl/Tk replacement for the MS Windows-specific function ’winDialog’.
Usage
tk_winDialog(type="ok",message="")
Arguments
type

Character; the type of the dialogue box.

message

Character. The information field of the dialogue box.

Details
This is a platform-independent implementation of the MS Windows-specific function ’winDialog’,
written using the Tcl/Tk. Possible types of the dialogue box are: ok, okcancel, yesno and yesnocancel.
Value
A character string giving the name of the button pressed (in capitals).
Author(s)
Vojtech Janousek, 
See Also
winDialog tkmessageBox tk_winDialogString tcltk-package
Examples
tk_winDialog(type="yesnocancel",message="Are you sure?")

tk_winDialogString

tk_winDialogString

Description
Tcl/Tk replacement for the MS Windows-specific function ’winDialogString’.
Usage
tk_winDialogString(message="Enter variable",default="",returnValOnCancel=NULL)

264

trendTicks

Arguments
message

Character. The information field of the dialog box.

default
Character; the default string.
returnValOnCancel
Character; a value to be returned when the dialogue is canceled.
Details
This is a platform-independent implementation of the MS Windows-specific function ’winDialogString’,
written using the Tcl/Tk.
Value
A character string giving the contents of the text box when Ok was pressed, or value specified by
’returnValOnCancel’ if Cancel was pressed.
Author(s)
Vojtech Janousek, 
See Also
winDialogString tkentry tk_winDialog tcltk-package
Examples
tk_winDialogString(message="Enter x value",default="15.7")

trendTicks

Petrogenetic trends

Description
Adding a trend with arrow and tick marks to a pre-existing GCDkit plot.
Usage
trendTicks(equation, x, xmin = par("usr")[1], xmax = par("usr")[2],
text = FALSE, col = "blue", lty = "solid", lwd = 1, arrow = FALSE,
autoscale = TRUE)
Arguments
equation

character or expression; a valid formula expressed as a function of x.

x

numeric; x values where the ticks are to be drawn.

xmin

numeric; beginning of the trend.

xmax

numeric; end of the trend.

text

logical; should be the tick marks annotated by text?

col

text or numeric; plotting colour specification.

trendTicks
lty
lwd
arrow
autoscale

265
text or numeric; the line type.
numeric; the line width, a positive number, defaulting to 1.
logical; should be also an arrow head shown?
logical; should the plot be autosized in order to accommodate the whole trend
as well as all data points?

Details
Using the function curve, the function trendTicks adds to an existing GCDkit plot a linear or
curved trend with tick marks and (optionally) arrow head. It is required that the trend is defined as
a function of x. The slope of the individual tick marks is then determined using a derivation of the
main function at the respective points.

Value
a list with two components, x and y, with coordinates of the tick marks.
Warning
Autoscaling will work only with Figaro compatible plots!

266

Verma

Author(s)
Vojtech Janousek, 
See Also
par
Examples
binary("Ba","Sr",xmin=200,xmax=2000,ymin=10,ymax=400)
equation<-"x/8+200"
x<-seq(2000,500,by=-100)
trendTicks(equation,x,min(x),max(x),col="darkred",lty="solid",lwd=2,arrow=T,text=F)
plot(1,1,type="n",xlim=c(0.01,1),ylim=c(0,1),xlab="Rb",ylab="Sr",log="x")
equation<-"6*x/8"
x<-seq(0.01,1,by=0.1)
trendTicks(equation,x,min(x),max(x),col=2,lwd=2,arrow=F,text=F,autoscale=F)

Verma

Major-element based discrimination plots for (ultra-)basic rocks
(Verma et al. 2006)

Description
Plots data stored in ’WR’ (or its subset) into discrimination plots proposed by Verma et al. (2006) for
(ultra-) basic rocks (SiO2 < 52 wt. %).
Usage
Verma(FeMiddlemost=NULL)
Arguments
FeMiddlemost

logical, should be iron adjusted according to Middlemost (1989)?

Details
Suite of five diagrams for discrimination of geotectonic environment of ultrabasic and basic rocks
(SiO2 < 52 wt. %), proposed by Verma et al. (2006). It is based on log-transformed concentration
ratios of major-element oxides. Note that prior to the transformation, the analyses are recast to 100%
anhydrous basis. Each diagram is a plot of two discriminant functions, DF1 and DF2, respectively in
x- and y-axes. Only samples with SiO2 < 52 wt. % are plotted. To work properly, the major element
analysis should be complete (SiO2 , T iO2 , Al2 O3 , F e2 O3 , F eO, M nO, M gO, CaO, N a2 O, K2 O, P2 O5 ).
Following the recommendation by Verma et al. (2006), prior to the plotting can be performed an
adjustment of the iron-oxidation ratio as proposed by Middlemost (1989) (see ’FeMiddlemost’).
For the F e2 O3 /F eO ratios implemented for individual rock types (based on TAS classification),
see Verma et al. (2002) (Fig. 1).

Verma

267

Following geotectonic settings may be deduced:
Abbreviation used
IAB
CRB
OIB
MORB

Environment
island arc basic rocks
continental rift basic rocks
ocean-island basic rocks
mid-ocean ridge basic rocks

Value
None.
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
References
Middlemost E A K (1989) Iron oxidation ratios, norms and the classification of volcanic rocks.
Chem Geol 77: 19-26. doi: 10.1016/0009-2541(89)90011-9

268

Villaseca
Verma S P, Torres-Alvarado I S, Sotelo-Rodriguez Z T (2002) SINCLAS: standard igneous norm
and volcanic rock classification system. Comput and Geosci 28: 711-715. doi: 10.1016/S00983004(01)00087-5
Verma S P, Guevara M, Agrawal S (2006) Discriminating four tectonic settings: Five new geochemical diagrams for basic and ultrabasic volcanic rocks based on log-ratio transformation of
major-element data. Journal of Earth System Science 115: 485-528. doi: 10.1007/BF02702907

See Also
FeMiddlemost Agrawal Plate Plate editing plotPlate figaro

Examples
#plot the diagrams
plotPlate("Verma")

Villaseca

B-A plot (modified by Villaseca et al. 1998)

Description
The B-A diagram as proposed by Debon and Le Fort (1983) with classification fields for various
types of peraluminous rocks designed by Villaseca et al. (1998).

Usage
Villaseca()

Details
Plots modified B-A diagram (designed originally by Debon and Le Fort 1983) with fields for various
peraluminous rock types after Villaseca et al. (1998). Assigns data for the B-A diagram into Figaro
template (list ’sheet’) and appropriate values into ’x.data’ and ’y.data’.

Villaseca

269

The following fields are defined:
l-P
m-P
h-P
f-P
metaluminous

low peraluminous
moderately peraluminous
highly peraluminous
felsic peraluminous

Rocks with composition falling beyond defined boundaries are labeled ’undefined’ by the ’classify’
function.
Parameters for the diagram are calculated by the function ’DebonCalc’. All of them are based on
millications (1000 gram-atoms per 100 grams).
A = Al - (K + Na + 2 Ca)
B = Fe + Mg + Ti
For details, see Debon & Le Fort (1983) or (1988).

270

Wedge

Value
sheet

list with Figaro Style Sheet data

x.data

B value. See details.

y.data

A value. See details.

Author(s)
Vojtech Janousek, 
References
Debon F & Le Fort P (1983) A chemical-mineralogical classification of common plutonic rocks
and associations. Trans Roy Soc Edinb; Earth Sci 73: 135-149
Debon F & Le Fort P (1988) A cationic classification of common plutonic rocks and their magmatic
associations: principles, method, applications. Bull. Mineral 111: 493-511
Villaseca C, Barbero L, Herreros V (1998) A re-examination of the typology of peraluminous granite types in intracontinental orogenic belts. Trans Roy Soc Edinb, Earth Sci 89: 113-119
See Also
classify figaro plotDiagram DebonCalc Debon
Examples
#plot the diagram
plotDiagram("Villaseca",FALSE)

Wedge

Wedge diagrams (Ague 1994)

Description
Implementation of Wedge diagrams after Ague (1994) and Bucholz and Ague (2010) used for judging the mobility of elements or oxides in course of various geochemically open-system processes
such as alteration or partial melting.
Usage
Wedge(x = "Ti", y = NULL, protolith = NULL,
outline = "chull", precision = 10, plotAltered = TRUE,
xmin = 0, ymin = 0, xmax = NULL, ymax = NULL, fun = NULL)
Arguments
x

a single geochemical species presumably immobile during the given rock transformation.

y

list of elements/oxides for plotting, separated by commas.

protolith

Boolean search pattern to specify the protolith samples in the data file.

outline

method for contouring the clusters of protolith and product compositions, see
Details.

Wedge

271

precision

precision of contours drawn, if 'outline'="contour", see Details.

plotAltered

logical; should be the altered analyses plotted or just contoured?

xmin, xmax

(optional) limits for shared x axes of the individual plots.

ymin

(optional) minimum for all of the y axes of the plots.

ymax

(optional) upper limits for each of the y axes of the plots.

fun

panel function to be applied to each of the individual plots.

Details

Wedge diagrams (Ague 1994) enable qualitative treatment of losses/gains of geochemical species
(elements or oxides) during open-system geological processes, such as alteration, metamorphism or
partial melting. As such they represent a viable alternative to the isocon plots (Grant 1986, 2005)
or concentration ratio diagrams (Ague 1994). However, the Wedge diagrams have an advantage in
that they take into account the overall variability of the whole dataset (both of the putative protolith
and the altered product) and not just a selected whole-rock pair.

Wedge diagrams are simple binary plots of a potentially mobile element j versus a reference (immobile) element i. The compositionally heterogeneous protolith samples yield a cloud of points.
The outer edges of this cloud define a wedge-shaped region that converges towards the origin.

As shown by Bucholz and Ague (2010), the altered samples that plot above and to the left of this
wedge are thought to have gained the mobile species j, whereas those falling below and to the right
suffered its loss. The samples that remain in the wedge but moved upwards are thought to record
residual enrichment, and those shifted downwards to have underwent a residual dilution.

The samples defining the protolith variation can be selected based on combination of three searching
mechanisms (by sample name/label, range or a Boolean condition) - see selectSamples for details.

Implemented are two methods for outlining the clusters of the protolith and altered compositions
(as specified by the argument 'outline'), convex hull (chull) and contour (contour). For the latter,
the shape of the contours drawn can be controlled using the parameter (precision). The higher it is,
the smoother contours result. See contourGroups and chullGroups for further details.

272

Wedge

Optionally, the individual data points for the altered samples may be replaced by contours portraying their density, if plotAltered = FALSE.
Parameters xmin, xmax, ymin and ymax are passed to the function plotWithLimits used for the
actual data plotting.
Optionally, panel function specified by fun with two arguments, xlab and ylab, is applied to each of
the plots.

Value
Returns a matrix ’results’ of slopes of tie-lines from individual protolith samples to the origin
(with a component for each diagram, i.e. for each species evaluated). Lines of maximum and
minimum slopes are those which are plotted as dashed lines, thus defining the wedge of the protolith
variation (see Details).

Wedge

273

Plugin
Isocon.r

Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
References
Ague J J (1994) Mass transfer during Barrovian metamorphism of pelites, south-central Connecticut; I, Evidence for changes in composition and volume. Amer J Sci 294: 989-1057 doi:
10.2475/ajs.294.8.989
Bucholz C E, Ague J J (2010) Fluid flow and Al transport during quartz-kyanite vein formation,
Unst, Shetland Islands, Scotland. J Metamorph Geol 28: 19-39 doi: 10.1016/0009-2541(67)900046
Grant J A (1986) The isocon diagram - a simple solution to Gresens equation for metasomatic
alteration. Econ Geol 81: 1976-1982 doi: doi:10.2113/gsecongeo.81.8.1976
Grant J A (2005) Isocon analysis: a brief review of the method and applications. Phys Chem Earth
(A) 30: 997-1004 doi: 10.1016/j.pce.2004.11.003
Gresens R L (1967) Composition-volume relationships of metasomatism. Chem Geol 2: 47-55 doi:
10.1016/0009-2541(67)90004-6

See Also
Ague, isocon, Plate, Plate editing, chull, contour contourGroups chullGroups, plotWithLimits

Examples
data<-loadData("sazava.data",sep="\t")
Wedge("Ti","SiO2,FeOt,MgO,CaO,Na2O,K2O",
protolith="Intrusion=\"Sazava\"","chull")
# Using the default precision of 10
Wedge("Ti","Zr,Nb,Sr,Rb,Ba",protolith="Intrusion=\"Sazava\"","contour")
Wedge("Ti","Zr,Nb,Sr,Rb,Ba",protolith="Intrusion=\"Sazava\"","contour",precision=100)

274

Whalen

Whalen

A type granitoids (Whalen et al. 1987)

Description
Set of discrimination plots to distinguish A-type granitoids as defined by Whalen et al.(1987).
Usage
Whalen(plot.txt = getOption("gcd.plot.text"))
Arguments
plot.txt

logical, annotate fields by their names?

Details
Set of binary plots proposed by Whalen et al.(1987) to distinguish A-type granitoids on the one
hand from ordinary/fractionated I- and S-types on the other.
In total 12 diagrams are plotted split into two pages. Apart from fields for I and S type granites
(’I & S’), sometimes split into ordinary (’OGT’) and fractionated (’'FG'’)domains, average composition of the A type granites (labeled ’A’) are shown. See Figs 1, 2 and 5 in the original paper
(Whalen et al.1987) for comparison.

WinFloyd1

275

The following diagrams are plotted: Zr+N b+Ce+Y vs. F eOt/M gO and (K2 O+N a2 O)/CaO;
10000Ga/Al vs. K2 O+N a2 O, (K2 O+N a2 O)/CaO, K2 O/M gO and F eOt/M gO; 10000Ga/Al
vs. Zr, N b, Ce, Y, Zn and Agpaitic Index.
Value
To the matrix ’WR’ are appended two columns, with Ga/Al ratios and values of the Agpaitic Index
(labeled ’A.I.’).
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
References
Whalen J B, Currie K L, Chappell B W (1987) A-type granites: geochemical characteristics,discrimination
and petrogenesis. Contrib Mineral Petrol 95: 407-419. doi: 10.1007/BF00402202
See Also
Plate Plate editing plotPlate figaro
Examples
#plot the diagrams
plotPlate("Whalen")

WinFloyd1

Nb/Y - Zr/TiO2 diagram (Winchester + Floyd 1977)

Description
Assigns data for Nb/Y vs. Zr/T iO2 diagram into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’.
Usage
WinFloyd1()

276

WinFloyd1

Details
Classification diagram proposed by Winchester & Floyd (1977).

Using incompatible element ratios (Nb/Y vs. Zr/T iO2 ), following fields are defined:
Trachyandesite
Alkali basalt
Basanite/Nephelinite
Trachyte
Phonolite
Comendite/Pantellerite
Rhyolite
Rhyodacite/Dacite
Andesite
Andesite/Basalt
Subalkaline basalt
Value
sheet

list with Figaro Style Sheet data

WinFloyd2

277

x.data

Nb/Y wt. % ratio

y.data

(Zr/TiO2)*0.0001 wt. % ratio

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 

References
Winchester J A & Floyd P A (1977) Geochemical discrimination of different magma series and their
differentiation products using immobile elements. Chem Geol 20: 325-343 doi: 10.1016/00092541(77)90057-2

See Also
classify figaro plotDiagram

Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("WinFloyd1")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("WinFloyd1", FALSE)

WinFloyd2

Zr/TiO2 - SiO2 (Winchester + Floyd 1977)

Description
Assigns data for Zr/T iO2 vs. SiO2 diagram into Figaro template (list ’sheet’) and appropriate
values into ’x.data’ and ’y.data’

Usage
WinFloyd2()

Details
Classification diagram proposed by Winchester & Floyd (1977).

278

WinFloyd2

Using incompatible element ratio and silica (Zr/T iO2 vs. SiO2 ), following fields are defined:
Trachyandesite
Basanite/Trachyte/Nephelinite
Phonolite
Trachyte
Comendite/Pantellerite
Rhyolite/Dacite
Rhyodacite/Dacite
Andesite
Subalkaline basalt
Alkaline basalt
Value
sheet

list with Figaro Style Sheet data

y.data

SiO2 wt. %

x.data

(Zr/TiO2)*0.001 wt. % ratio

Wood

279

Author(s)
Vojtech Erban, 
& Vojtech Janousek, 
References
Winchester J A & Floyd P A (1977) Geochemical discrimination of different magma series and their
differentiation products using immobile elements. Chem Geol 20: 325-343 doi: 10.1016/00092541(77)90057-2
See Also
classify figaro plotDiagram
Examples
#Within GCDkit, the plot is called using following auxiliary functions:
#To Classify data stored in WR (Groups by diagram)
classify("WinFloyd2")
#To plot data stored in WR or its subset (menu Classification)
plotDiagram("WinFloyd2", FALSE)

Wood

Wood (1980)

Description
Assigns Figaro templates to Wood’s geotectonic diagrams for basaltoids into the list ’plate’ and
appropriate values into the list ’plate.data’ for subsequent plotting.
Usage
Wood(ident = getOption("gcd.ident"),
plot.txt = getOption("gcd.plot.text"))
Arguments
ident

logical, identify?

plot.txt

logical, annotate fields by their names?

Details
A series of triangular diagrams with apices Th-Hf/3-Ta, Th-Hf/3-Ta and Th-Zr/117-Nb/16, proposed by Wood (1980).

280

Wood

Following fields are defined:
IAT
CAB
N-MORB
E-MORB
WPT
WPA

Island-arc Tholeiites
Calc-alkaline Basalts
N-type Mid-ocean Ridge Basalts
E-type Mid-ocean Ridge Basalts
Within-plate Tholeiites
Alkaline Within-plate Basalts

Value
sheet

list with Figaro Style Sheet data

x.data, y.data Th, Hf/3 and Ta in ppm recalculated into two dimensions
Note
This function uses the plates concept. The individual plots can be selected and their properties/appearance changed as if they were stand alone Figaro-compatible plots. See Plate, Plate editing
and figaro for details.
Author(s)
Vojtech Janousek, 
References
Pearce J A (1996) A User’s Guide to Basalt Discrimination Diagrams. In Wyman D A (ed) Trace
Element Geochemistry of Volcanic Rocks: Applications for Massive Sulphide Exploration. Geological Association of Canada, Short Course Notes 12, pp 79-113
Wood D A (1980) The application of a Th-Hf-Ta diagram to problems of tectonomagmatic classification and to establishing the nature of crustal contamination of basaltic lavas of the British Tertiary
volcanic province. Earth Planet Sci Lett 50: 11-30 doi:10.1016/0012-821X(80)90116-8
See Also
Plate, Plate editing, plotPlate, figaro

zrSaturation

281

Examples
#plot the diagrams
plotPlate("Wood")

zrSaturation

Zircon saturation (Watson + Harrison 1983)

Description
Calculates zircon saturation temperatures for the observed major-element data and Zr concentrations. Returns also Zr saturation levels for the given major-element compositions and assumed
magma temperature.
Usage
zrSaturation(cats = milli, T = 0, Zr = filterOut(WR, "Zr", 1))
Arguments
cats

numeric matrix; whole-rock data recast to millications

T

assumed temperature of the magma in C

Zr

numeric vector with Zr concentrations

Details
Calculates Zr saturation concentration at a given temperature. Given ’T’ is the estimated absolute
temperature (K) of the magma and ’M’ is a cationic ratio:
M = 100

N a + K + 2Ca
Al.Si

it can be written Watson & Harrison 1983):
DZr = e(−3.8−0.85(M −1)+

12900
)
T

The Zr saturation level is then given by:
Zr.sat =

497644
DZr

On the other hand, the saturation temperature can be obtained from the observed Zr concentration
and magma composition (assuming no zircon inheritance)
DZr =

T Zr.sat.C =

497644
Zr

12900
− 273.15
ln(DZr ) + 3.8 + 0.85(M − 1)

282

zrSaturation

Value
Returns a matrix ’results’ with the following columns:
M

cationic ratios

Zr

observed Zr concentrations

Zr.sat

saturation levels of Zr for assumed temperature

TZr.sat.C

zircon saturation temperatures in C

Plugin
Saturation.r
Author(s)
Vojtech Janousek, 
References
Watson E B & Harrison M (1983) Zircon saturation revisited: temperature and composition effects in a variety of crustal magma types. Earth Planet Sci Lett 64: 295-304 doi: 10.1016/0012821X(83)90211-X



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