Fivethirtyeight Manual

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Package ‘fivethirtyeight’
October 7, 2018
Title Data and Code Behind the Stories and Interactives at
'FiveThirtyEight'
Description Datasets and code published by the data journalism website
'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>.
Note that while we received guidance from editors at 'FiveThirtyEight', this
package is not officially published by 'FiveThirtyEight'.
Version 0.4.0
Maintainer Albert Y. Kim <albert.ys.kim@gmail.com>
Depends R (>= 3.2.4)
License MIT + file LICENSE
Encoding UTF-8
LazyData true
URL https://github.com/rudeboybert/fivethirtyeight
BugReports https://github.com/rudeboybert/fivethirtyeight/issues
RoxygenNote 6.0.1
Suggests fivethirtyeight, tidyverse, lubridate, stringr, magrittr,
knitr, rmarkdown, broom, scales, tidytext, ggthemes, hunspell,
grid, fmsb, wordcloud, gridExtra, corrplot, ggraph, igraph,
highcharter, janitor
VignetteBuilder knitr
NeedsCompilation no
Author Albert Y. Kim [aut, cre],
Chester Ismay [aut],
Jennifer Chunn [aut],
Meredith Manley [ctb],
Maggie Shea [ctb],
Andrew Flowers [ctb],
Jonathan Bouchet [ctb],
G. Elliott Morris [ctb],
Adam Spannbauer [ctb],
Pradeep Adhokshaja [ctb],
Olivia Barrows [ctb],
Jojo Miller [ctb],
Jayla Nakayama [ctb]
Repository CRAN
Date/Publication 2018-02-11 17:34:04 UTC
1
2Rtopics documented:
Rtopics documented:
ahca_polls.......................................... 4
airline_safety ........................................ 5
antiquities_act........................................ 6
avengers........................................... 6
bachelorette......................................... 8
bad_drivers ......................................... 9
bechdel ........................................... 10
biopics............................................ 11
bob_ross........................................... 12
candy_rankings....................................... 14
cand_events_20150114................................... 15
cand_events_20150130................................... 16
cand_state_20150114.................................... 16
cand_state_20150130.................................... 17
chess_transfers ....................................... 18
classic_rock_raw_data ................................... 18
classic_rock_song_list ................................... 19
college_all_ages....................................... 20
college_grad_students.................................... 21
college_recent_grads .................................... 22
comic_characters ...................................... 23
comma_survey ....................................... 24
congress_age ........................................ 25
cousin_marriage....................................... 25
daily_show_guests ..................................... 26
democratic_bench...................................... 27
drinks ............................................ 27
drug_use........................................... 28
elo_blatter.......................................... 29
endorsements ........................................ 30
fandango .......................................... 31
fifa_audience ........................................ 32
fivethirtyeight........................................ 33
flying ............................................ 33
food_world_cup....................................... 35
generic_polllist ....................................... 37
generic_topline ....................................... 38
google_trends........................................ 38
goose ............................................ 39
hate_crimes......................................... 40
hiphop_cand_lyrics..................................... 41
hist_ncaa_bball_casts.................................... 41
hist_senate_preds...................................... 42
librarians .......................................... 43
love_actually_adj ...................................... 43
love_actually_appearance.................................. 44
mad_men .......................................... 45
male_flight_attend ..................................... 46
mayweather_mcgregor_tweets ............................... 47
mediacloud_hurricanes ................................... 48
Rtopics documented: 3
mediacloud_online_news.................................. 48
mediacloud_states...................................... 49
mediacloud_trump ..................................... 50
mlb_as_play_talent..................................... 50
mlb_as_team_talent..................................... 51
mlb_elo ........................................... 52
murder_2015_final ..................................... 54
murder_2016_prelim .................................... 54
nba_carmelo......................................... 55
nba_draft_2015....................................... 56
nba_tattoos ......................................... 57
nfltix_div_avgprice ..................................... 57
nfltix_usa_avg........................................ 58
nflwr_aging_curve ..................................... 58
nflwr_hist .......................................... 59
nfl_elo............................................ 59
nfl_fandom_google ..................................... 60
nfl_fandom_surveymonkey................................. 61
nfl_fav_team ........................................ 63
nfl_suspensions....................................... 64
nutrition_pvalues ...................................... 64
police_deaths ........................................ 65
police_killings........................................ 66
police_locals ........................................ 67
pres_2016_trail ....................................... 68
pres_commencement .................................... 69
pulitzer ........................................... 69
ratings............................................ 70
riddler_castles........................................ 71
riddler_castles2....................................... 72
riddler_pick_lowest..................................... 73
sandy_311.......................................... 74
san_andreas......................................... 75
senate_polls......................................... 76
senators ........................................... 77
spi_global_rankings..................................... 78
spi_matches......................................... 79
steak_survey......................................... 80
tarantino........................................... 81
tennis_events_time ..................................... 81
tennis_players_time..................................... 82
tennis_serve_time...................................... 82
tenth_circuit......................................... 83
trumpworld_issues ..................................... 84
trumpworld_polls...................................... 85
trump_approval_poll .................................... 86
trump_approval_trend.................................... 88
trump_news......................................... 89
trump_twitter ........................................ 89
tv_hurricanes ........................................ 90
tv_hurricanes_by_network ................................. 90
tv_states........................................... 91
4ahca_polls
twitter_presidents...................................... 92
undefeated.......................................... 93
unisex_names........................................ 93
US_births_1994_2003 ................................... 94
US_births_2000_2014 ................................... 94
weather_check ....................................... 95
Index 97
ahca_polls American Health Care Act Polls
Description
The raw data behind the story "Why The GOP Is So Hell-Bent On Passing An Unpopular Health
Care Bill" https://fivethirtyeight.com/features/why-the-gop-is-so-hell-bent-on-passing-an-unpopular-health-care-bill.
Usage
ahca_polls
Format
A data frame with 15 rows representing polls and 7 variables:
start Start date of the poll.
end End date of the poll.
pollster The entity that conducts and collects information from the poll.
favor The number of affirmative responses to the question at the pollster.
oppose The number of negative responses to the question at the pollster.
url The website associated with the polling question.
text The polling question asked at the pollster.
Source
See https://github.com/fivethirtyeight/data/blob/master/ahca-polls/README.md
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
ahca_polls_tidy <- ahca_polls %>%
gather(opinion, count, -c(start, end, pollster, text, url))
airline_safety 5
airline_safety Should Travelers Avoid Flying Airlines That Have Had Crashes in the
Past?
Description
The raw data behind the story "Should Travelers Avoid Flying Airlines That Have Had Crashes in
the Past?" https://fivethirtyeight.com/features/should-travelers-avoid-flying-airlines-that-have-had-crashes-in-the-past/.
Usage
airline_safety
Format
A data frame with 56 rows representing airlines and 9 variables:
airline airline
incl_reg_subsidiaries indicates that regional subsidiaries are included
avail_seat_km_per_week available seat kilometers flown every week
incidents_85_99 Total number of incidents, 1985-1999
fatal_accidents_85_99 Total number of fatal accidents, 1985-1999
fatalities_85_99 Total number of fatalities, 1985-1999
incidents_00_14 Total number of incidents, 2000-2014
fatal_accidents_00_14 Total number of fatal accidents, 2000-2014
fatalities_00_14 Total number of fatalities, 2000-2014
Source
Aviation Safety Network http://aviation-safety.net.
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
airline_safety_tidy <- airline_safety %>%
gather(type, count, -c(airline, incl_reg_subsidiaries, avail_seat_km_per_week)) %>%
mutate(
period = str_sub(type, start=-5),
period = str_replace_all(period, "_", "-"),
type = str_sub(type, end=-7)
)
6avengers
antiquities_act Trump Might Be The First President To Scrap A National Monument
Description
The raw data behind the story "Trump Might Be The First President To Scrap A National Monu-
ment" https://fivethirtyeight.com/features/trump-might-be-the-first-president-to-scrap-a-national-monument/.
Usage
antiquities_act
Format
A data frame with 344 rows representing acts and 9 variables (Note that 7 of the original rows failed
to parse and are omitted here):
current_name Current name of piece of land designated under the Antiquities Act
states State(s) or territory where land is located
original_name If included, original name of piece of land designated under the Antiquities Act
current_agency Current land management agency. NPS = National Parks Service, BLM = Bureau
of Land Management, USFS = US Forest Service, FWS = US Fish and Wildlife Service,
NOAA = National Oceanic and National Oceanic and Atmospheric Administration
action Type of action taken on land
date Date of action
year Year of action
pres_or_congress President or congress that issued action
acres_affected Acres affected by action. Note that total current acreage is not included. National
monuments that cover ocean are listed in square miles.
Source
National Parks Conservation Association https://www.npca.org/ and National Parks Service
Archeology Program https://www.nps.gov/history/archeology/sites/antiquities/MonumentsList.
htm
avengers Joining The Avengers Is As Deadly As Jumping Off A Four-Story Build-
ing
Description
The raw data behind the story "Joining The Avengers Is As Deadly As Jumping Off A Four-Story
Building" https://fivethirtyeight.com/features/avengers-death-comics-age-of-ultron/.
Usage
avengers
avengers 7
Format
A data frame with 173 rows representing characters and 21 variables:
url The URL of the comic character on the Marvel Wikia
name_alias The full name or alias of the character
appearances The number of comic books that character appeared in as of April 30
current Is the member currently active on an avengers affiliated team?
gender The recorded gender of the character
probationary_intro Sometimes the character was given probationary status as an Avenger, this is
the date that happened
full_reserve_avengers_intro The month and year the character was introduced as a full or reserve
member of the Avengers
year The year the character was introduced as a full or reserve member of the Avengers
years_since_joining 2015 minus the year
honorary The status of the avenger, if they were given "Honorary" Avenger status, if they are
simply in the "Academy," or "Full" otherwise
death1 TRUE if the Avenger died, FALSE if not.
return1 TRUE if the Avenger returned from their first death, FALSE if they did not, blank if not
applicable
death2 TRUE if the Avenger died a second time after their revival, FALSE if they did not, blank if
not applicable
return2 TRUE if the Avenger returned from their second death, FALSE if they did not, blank if
not applicable
death3 TRUE if the Avenger died a third time after their second revival, FALSE if they did not,
blank if not applicable
return3 TRUE if the Avenger returned from their third death, FALSE if they did not, blank if not
applicable
death4 TRUE if the Avenger died a fourth time after their third revival, FALSE if they did not,
blank if not applicable
return4 TRUE if the Avenger returned from their fourth death, FALSE if they did not, blank if not
applicable
death5 TRUE if the Avenger died a fifth time after their fourth revival, FALSE if they did not,
blank if not applicable
return5 TRUE if the Avenger returned from their fifth death, FALSE if they did not, blank if not
applicable
notes Descriptions of deaths and resurrections.
Source
Deaths of Marvel comic book characters between the time they joined the Avengers and April 30,
2015, the week before Secret Wars #1.
8bachelorette
bachelorette Bachelorette / Bachelor
Description
The raw data behind the stories: "How To Spot A Front-Runner On The ’Bachelor’ Or ’Bach-
elorette’" https://fivethirtyeight.com/features/the-bachelorette/, "Rachel’s Season Is
Fitting Neatly Into ’Bachelorette’ History" https://fivethirtyeight.com/features/rachels-season-is-fitting-neatly-into-bachelorette-history/,
and "Rachel Lindsay’s ’Bachelorette’ Season, In Three Charts" https://fivethirtyeight.com/
features/rachel-lindsays-bachelorette-season-in-three-charts/.
Usage
bachelorette
Format
A data frame with 887 rows representing the Bachelorette and Bachelor contestants and 23 vari-
ables:
show Bachelor or Bachelorette.
season Which season?
contestant An identifier for the contestant in a given season.
elimination_1 Who was eliminated in week 1.
elimination_2 Who was eliminated in week 2.
elimination_3 Who was eliminated in week 3.
elimination_4 Who was eliminated in week 4.
elimination_5 Who was eliminated in week 5.
elimination_6 Who was eliminated in week 6.
elimination_7 Who was eliminated in week 7.
elimination_8 Who was eliminated in week 8.
elimination_9 Who was eliminated in week 9.
elimination_10 Who was eliminated in week 10.
dates_1 Who was on which date in week 1.
dates_2 Who was on which date in week 2.
dates_3 Who was on which date in week 3.
dates_4 Who was on which date in week 4.
dates_5 Who was on which date in week 5.
dates_6 Who was on which date in week 6.
dates_7 Who was on which date in week 7.
dates_8 Who was on which date in week 8.
dates_9 Who was on which date in week 9.
dates_10 Who was on which date in week 10.
bad_drivers 9
Details
Eliminates connote either an elimination (starts with "E") or a rose (starts with "R"). Eliminations
supersede roses. "E" connotes a standard elimination, typically at a rose ceremony. "EQ" means
the contestant quits. "EF" means the contestant was fired by production. "ED" connotes a date
elimination. "EU" connotes an unscheduled elimination, one that takes place at a time outside of a
date or rose ceremony. "R" means the contestant received a rose. "R1" means the contestant got a
first impression rose. "D1" means a one-on-one date, "D2" means a 2-on-1, "D3" means a 3-on-1
group date, and so on. Weeks of the show are eliminated by rose ceremonies, and may not line up
exactly with episodes.
Source
http://bachelor-nation.wikia.com/wiki/Bachelor_Nation_Wikia and then missing seasons
were filled in by ABC and FiveThirtyEight staffers.
bad_drivers Dear Mona, Which State Has The Worst Drivers?
Description
The raw data behind the story "Dear Mona, Which State Has The Worst Drivers?" https://
fivethirtyeight.com/features/which-state-has-the-worst-drivers/
Usage
bad_drivers
Format
A data frame with 51 rows representing the 50 states + D.C. and 8 variables:
state State
num_drivers Number of drivers involved in fatal collisions per billion miles
perc_speeding Percentage of drivers involved in fatal collisions who were speeding
perc_alcohol Percentage of drivers involved in fatal collisions who were alcohol-impaired
perc_not_distracted Percentage of drivers involved in fatal collisions who were not distracted
perc_no_previous Percentage of drivers involved in fatal collisions who had not been involved in
any previous accidents
insurance_premiums Car insurance premiums ($)
losses Losses incurred by insurance companies for collisions per insured driver ($)
Source
National Highway Traffic Safety Administration 2012, National Highway Traffic Safety Adminis-
tration 2009 & 2012, National Association of Insurance Commissioners 2010 & 2011.
10 bechdel
bechdel The Dollar-And-Cents Case Against Hollywood’s Exclusion of Women
Description
The raw data behind the story "The Dollar-And-Cents Case Against Hollywood’s Exclusion of
Women" https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/.
Usage
bechdel
Format
A data frame with 1794 rows representing movies and 15 variables:
year Year of release
imdb Text to construct IMDB url. Ex: http://www.imdb.com/title/tt1711425
title Movie test
test bechdel test result (detailed, with discrepancies indicated)
clean_test bechdel test result (detailed): ok = passes test, dubious,men = women only talk about
men, notalk = women don’t talk to each other, nowomen = fewer than two women
binary Bechdel Test PASS vs FAIL binary
budget Film budget
domgross Domestic (US) gross
intgross Total International (i.e., worldwide) gross
code Bechdel Code
budget_2013 Budget in 2013 inflation adjusted dollars
domgross_2013 Domestic gross (US) in 2013 inflation adjusted dollars
intgross_2013 Total International (i.e., worldwide) gross in 2013 inflation adjusted dollars
period_code
decade_code
Details
A vignette of an analysis of this dataset using the tidyverse can be found on CRAN or by running:
vignette("bechdel", package = "fivethirtyeight")
Source
www.bechdeltest.com and www.the-numbers.com. The original data can be found at https:
//github.com/fivethirtyeight/data/tree/master/bechdel.
biopics 11
biopics ’Straight Outta Compton’ Is The Rare Biopic Not About White Dudes
Description
The raw data behind the story "’Straight Outta Compton’ Is The Rare Biopic Not About White
Dudes" https://fivethirtyeight.com/features/straight-outta-compton-is-the-rare-biopic-not-about-white-dudes/.
An analysis using this data was contributed by Pradeep Adhokshaja as a package vignette at http:
//fivethirtyeight-r.netlify.com/articles/biopics.html.
Usage
biopics
Format
A data frame with 761 rows representing movies and 14 variables:
title Title of the film.
site Text to construct IMDB url. Ex: http://www.imdb.com/title/tt1711425
country Country of origin.
year_release Year of release.
box_office Gross earnings at U.S. box office.
director Director of film.
number_of_subjects The number of subjects featured in the film.
subject The actual name of the featured subject.
type_of_subject The occupation of subject or reason for recognition.
race_known Indicates whether the subject’s race was discernible based on background of self,
parent, or grandparent.
subject_race Race of the subject.
person_of_color Dummy variable that indicates person of color.
subject_sex Sex of subject.
lead_actor_actress The actor or actress who played the subject.
Source
IMDB http://www.imdb.com/
12 bob_ross
bob_ross A Statistical Analysis of the Work of Bob Ross
Description
The raw data behind the story "A Statistical Analysis of the Work of Bob Ross" https://fivethirtyeight.
com/features/a-statistical-analysis-of-the-work-of-bob-ross/. An analysis using this
data was contributed by Jonathan Bouchet as a package vignette at http://fivethirtyeight-r.
netlify.com/articles/bob_ross.html.
Usage
bob_ross
Format
A data frame with 403 rows representing episodes and 71 variables:
episode Episode code
season Season number
episode_num Episode number
title Title of episode
apple_frame Present (1) or not (0)
aurora_borealis Present (1) or not (0)
barn Present (1) or not (0)
beach Present (1) or not (0)
boat Present (1) or not (0)
bridge Present (1) or not (0)
building Present (1) or not (0)
bushes Present (1) or not (0)
cabin Present (1) or not (0)
cactus Present (1) or not (0)
circle_frame Present (1) or not (0)
cirrus Present (1) or not (0)
cliff Present (1) or not (0)
clouds Present (1) or not (0)
conifer Present (1) or not (0)
cumulus Present (1) or not (0)
deciduous Present (1) or not (0)
diane_andre Present (1) or not (0)
dock Present (1) or not (0)
double_oval_frame Present (1) or not (0)
farm Present (1) or not (0)
fence Present (1) or not (0)
bob_ross 13
fire Present (1) or not (0)
florida_frame Present (1) or not (0)
flowers Present (1) or not (0)
fog Present (1) or not (0)
framed Present (1) or not (0)
grass Present (1) or not (0)
guest Present (1) or not (0)
half_circle_frame Present (1) or not (0)
half_oval_frame Present (1) or not (0)
hills Present (1) or not (0)
lake Present (1) or not (0)
lakes Present (1) or not (0)
lighthouse Present (1) or not (0)
mill Present (1) or not (0)
moon Present (1) or not (0)
mountain Present (1) or not (0)
mountains Present (1) or not (0)
night Present (1) or not (0)
ocean Present (1) or not (0)
oval_frame Present (1) or not (0)
palm_trees Present (1) or not (0)
path Present (1) or not (0)
person Present (1) or not (0)
portrait Present (1) or not (0)
rectangle_3d_frame Present (1) or not (0)
rectangular_frame Present (1) or not (0)
river Present (1) or not (0)
rocks Present (1) or not (0)
seashell_frame Present (1) or not (0)
snow Present (1) or not (0)
snowy_mountain Present (1) or not (0)
split_frame Present (1) or not (0)
steve_ross Present (1) or not (0)
structure Present (1) or not (0)
sun Present (1) or not (0)
tomb_frame Present (1) or not (0)
tree Present (1) or not (0)
trees Present (1) or not (0)
triple_frame Present (1) or not (0)
waterfall Present (1) or not (0)
waves Present (1) or not (0)
windmill Present (1) or not (0)
window_frame Present (1) or not (0)
winter Present (1) or not (0)
wood_framed Present (1) or not (0)
14 candy_rankings
Source
See https://github.com/fivethirtyeight/data/tree/master/bob-ross
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
bob_ross_tidy <- bob_ross %>%
gather(object, present, -c(episode, season, episode_num, title)) %>%
mutate(present = as.logical(present)) %>%
arrange(episode, object)
candy_rankings Candy Power Ranking
Description
The raw data behind the story "The Ultimate Halloween Candy Power Ranking" http://fivethirtyeight.
com/features/the-ultimate-halloween-candy-power-ranking/.
Usage
candy_rankings
Format
A data frame with 85 rows representing Halloween candy and 13 variables:
competitorname The name of the Halloween candy.
chocolate Does it contain chocolate?
fruity Is it fruit flavored?
caramel Is there caramel in the candy?
peanutyalmondy Does it contain peanuts, peanut butter or almonds?
nougat Does it contain nougat?
crispedricewafer Does it contain crisped rice, wafers, or a cookie component?
hard Is it a hard candy?
bar Is it a candy bar?
pluribus Is it one of many candies in a bag or box?
sugarpercent The percentile of sugar it falls under within the data set.
pricepercent The unit price percentile compared to the rest of the set.
winpercent The overall win percentage according to 269,000 matchups.
Source
See https://github.com/fivethirtyeight/data/tree/master/candy-power-ranking
cand_events_20150114 15
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
candy_rankings_tidy <- candy_rankings %>%
gather(characteristics, present, -c(competitorname, sugarpercent, pricepercent, winpercent)) %>%
mutate(present = as.logical(present)) %>%
arrange(competitorname)
cand_events_20150114 Looking For Clues: Who Is Going To Run For President In 2016?
Description
The raw data behind the story "Looking For Clues: Who Is Going To Run For President In 2016?"
https://fivethirtyeight.com/features/2016-president-who-is-going-to-run/.
Usage
cand_events_20150114
Format
A data frame with 42 rows representing events attended in Iowa and New Hampshire by potential
presidential primary candidates and 8 variables:
person Potential presidential candidate
party Political party
state State of event
event Name of event
type Type of event
date Date of event
link Link to event
snippet Snippet of event description
Source
See https://github.com/fivethirtyeight/data/tree/master/potential-candidates
See Also
cand_state_20150114,cand_events_20150130, and cand_state_20150130
16 cand_state_20150114
cand_events_20150130 Who Will Run For President: Romney Is Out
Description
The raw data behind the story "Who Will Run For President: Romney Is Out" https://fivethirtyeight.
com/features/romney-not-running-for-president/.
Usage
cand_events_20150130
Format
A data frame with 74 rows representing events attended by potential presidential primary candidates
and 8 variables:
person Potential presidential candidate
party Political party
state State of event
event Name of event
type Type of event
date Date of event
link Link to event
snippet Snippet of event description
Source
See https://github.com/fivethirtyeight/data/tree/master/potential-candidates
See Also
cand_state_20150130,cand_events_20150114, and cand_state_20150114
cand_state_20150114 Looking For Clues: Who Is Going To Run For President In 2016?
Description
The raw data behind the story "Looking For Clues: Who Is Going To Run For President In 2016?"
https://fivethirtyeight.com/features/2016-president-who-is-going-to-run/.
Usage
cand_state_20150114
cand_state_20150130 17
Format
A data frame with 25 rows representing potential presidential primary candidates and 5 variables:
person Potential presidential candidate
party Political party
date Date of event
latest Latest statement
score Likelihood of running score, 1 = Not running, 5 = Definitely running
Source
See https://github.com/fivethirtyeight/data/tree/master/potential-candidates
See Also
cand_events_20150114,cand_events_20150130, and cand_state_20150130
cand_state_20150130 Who Will Run For President: Romney Is Out
Description
The raw data behind the story "Who Will Run For President: Romney Is Out" https://fivethirtyeight.
com/features/romney-not-running-for-president/.
Usage
cand_state_20150130
Format
A data frame with 27 rows representing potential presidential primary candidates and 5 variables:
person Potential presidential candidate
party Political party
date Date of event
latest Latest statement
score Likelihood of running score, 1 = Not running, 5 = Definitely running
Source
See https://github.com/fivethirtyeight/data/tree/master/potential-candidates
See Also
cand_events_20150130,cand_events_20150114, and cand_state_20150114
18 classic_rock_raw_data
chess_transfers Chess Transfers
Description
The raw data behind the story "American Chess Is Great Again" https://fivethirtyeight.com/
features/american-chess-is-great-again/.
Usage
chess_transfers
Format
A data frame with 932 rows representing international player transfers and 5 variables:
url The corresponding website on the World Chess Federation page which details the transfers of
a given year.
id An numeric identifier for the chess player who transferred.
federation The current national federation of the chess player
form_fed The national federation from which the chess player has transferred.
transfer_date The date at which the transfer took place.
Source
World Chess Federation
classic_rock_raw_data Why Classic Rock Isn’t What It Used To Be
Description
The raw data behind the story "Why Classic Rock Isn’t What It Used To Be" https://fivethirtyeight.
com/features/why-classic-rock-isnt-what-it-used-to-be/.
Usage
classic_rock_raw_data
Format
A data frame with 37,673 rows representing song plays and 8 variables:
song Song name
artist Artist name
callsign Station callsign
time Time of song play in seconds elapsed since January 1, 1970
date_time Time of song play in date/time format
unique_id Unique ID for each song play
combined Song and artist name combined
classic_rock_song_list 19
Source
See https://github.com/fivethirtyeight/data/tree/master/classic-rock
See Also
classic_rock_song_list
classic_rock_song_list
Why Classic Rock Isn’t What It Used To Be
Description
The raw data behind the story "Why Classic Rock Isn’t What It Used To Be" https://fivethirtyeight.
com/features/why-classic-rock-isnt-what-it-used-to-be/.
Usage
classic_rock_song_list
Format
A data frame with 2230 rows representing unique songs and 7 variables:
song Song name
artist Artist name
release_year Release year as listed in SongFacts
combined Song and artist name combined
has_year Logical variable of whether release year is included
playcount Number of plays across all stations
playcount_has_year Number of plays across all stations if a year was found
Source
SongFacts and https://github.com/fivethirtyeight/data/tree/master/classic-rock
See Also
classic_rock_raw_data
20 college_all_ages
college_all_ages The Economic Guide To Picking A College Major
Description
The raw data behind the story "The Economic Guide To Picking A College Major" https://
fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/.
Usage
college_all_ages
Format
A data frame with 173 rows representing majors (all ages) and 11 variables:
major_code Major code, FO1DP in ACS PUMS
major Major description
major_category Category of major from Carnevale et al
total Total number of people with major
employed Number employed (ESR == 1 or 2)
employed_fulltime_yearround Employed at least 50 weeks (WKW == 1) and at least 35 hours
(WKHP >= 35)
unemployed Number unemployed (ESR == 3)
unemployment_rate Unemployed / (Unemployed + Employed)
p25th 25th percentile of earnings
median Median earnings of full-time, year-round workers
p75th 75th percentile of earnings
Source
See https://github.com/fivethirtyeight/data/blob/master/college-majors/readme.md.
See Also
college_grad_students,college_recent_grads
college_grad_students 21
college_grad_students The Economic Guide To Picking A College Major
Description
The raw data behind the story "The Economic Guide To Picking A College Major" https://
fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/.
Usage
college_grad_students
Format
A data frame with 173 rows representing majors (graduate vs nongraduate students) and 22 vari-
ables:
major_code Major code, FO1DP in ACS PUMS
major Major description
major_category Category of major from Carnevale et al
grad_total Total number of people with major
grad_sample_size Sample size (unweighted) of full-time, year-round ONLY (used for earnings)
grad_employed Number employed (ESR == 1 or 2)
grad_employed_fulltime_yearround Employed at least 50 weeks (WKW == 1) and at least 35
hours (WKHP >= 35)
grad_unemployed Number unemployed (ESR == 3)
grad_unemployment_rate Unemployed / (Unemployed + Employed)
grad_p25th 25th percentile of earnings
grad_median Median earnings of full-time, year-round workers
grad_p75th 75th percentile of earnings
nongrad_total Total number of people with major
nongrad_employed Number employed (ESR == 1 or 2)
nongrad_employed_fulltime_yearround Employed at least 50 weeks (WKW == 1) and at least
35 hours (WKHP >= 35)
nongrad_unemployed Number unemployed (ESR == 3)
nongrad_unemployment_rate Unemployed / (Unemployed + Employed)
nongrad_p25th 25th percentile of earnings
nongrad_median Median earnings of full-time, year-round workers
nongrad_p75th 75th percentile of earnings
grad_share grad_total / (grad_total + nongrad_total)
grad_premium (grad_median-nongrad_median)/nongrad_median
Source
See https://github.com/fivethirtyeight/data/blob/master/college-majors/readme.md.
See Also
college_all_ages,college_recent_grads
22 college_recent_grads
college_recent_grads The Economic Guide To Picking A College Major
Description
The raw data behind the story "The Economic Guide To Picking A College Major" https://
fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/.
Usage
college_recent_grads
Format
A data frame with 173 rows representing majors (recent graduates) and 21 variables:
rank Rank by median earnings
major_code Major code, FO1DP in ACS PUMS
major Major description
major_category Category of major from Carnevale et al
total Total number of people with major
sample_size Sample size (unweighted) of full-time, year-round ONLY (used for earnings)
men Men with major
women Women with major
sharewomen Proportion women
employed Number employed (ESR == 1 or 2)
employed_fulltime Employed 35 hours or more
employed_parttime Employed less than 35 hours
employed_fulltime_yearround Employed at least 50 weeks (WKW == 1) and at least 35 hours
(WKHP >= 35)
unemployed Number unemployed (ESR == 3)
unemployment_rate Unemployed / (Unemployed + Employed)
p25th 25th percentile of earnings
median Median earnings of full-time, year-round workers
p75th 75th percentile of earnings
college_jobs Number with job requiring a college degree
non_college_jobs Number with job not requiring a college degree
low_wage_jobs Number in low-wage service jobs
Source
See https://github.com/fivethirtyeight/data/blob/master/college-majors/readme.md.
Note that women-stem.csv was a subset of the original recent-grads.csv, so no data frame was
created.
See Also
college_grad_students,college_all_ages
comic_characters 23
comic_characters Comic Books Are Still Made By Men, For Men And About Men
Description
The raw data behind the story "Comic Books Are Still Made By Men, For Men And About Men"
https://fivethirtyeight.com/features/women-in-comic-books/. An analysis using this
data was contributed by Jonathan Bouchet as a package vignette at http://fivethirtyeight-r.
netlify.com/articles/comics_gender.html.
Usage
comic_characters
Format
A data frame with 23272 rows representing characters and 16 variables:
publisher Comic publisher: DC Comics or Marvel
page_id The unique identifier for that characters page within the wikia
name The name of the character
urlslug The unique url within the wikia that takes you to the character
id The identity status of the character (Secret Identity, Public identity, [on marvel only: No Dual
Identity])
align If the character is Good, Bad or Neutral
eye Eye color of the character
hair Hair color of the character
sex Sex of the character (e.g. Male, Female, etc.)
gsm If the character is a gender or sexual minority (e.g. Homosexual characters, bisexual charac-
ters)
alive If the character is alive or deceased
appearances The number of appearances of the character in comic books (as of Sep. 2, 2014.
Number will become increasingly out of date as time goes on.)
first_appearance The month and year of the character’s first appearance in a comic book, if avail-
able
month The month of the character’s first appearance in a comic book, if available
year The year of the character’s first appearance in a comic book, if available
date The date of the character’s first appearance in a comic book, if available
Source
DC Wikia http://dc.wikia.com/wiki/Main_Page and Marvel Wikia http://marvel.wikia.
com/Main_Page. Characters were scraped on August 24, 2014. Appearance counts were scraped
on September 2, 2014. The month and year of the first issue each character appeared in was pulled
on October 6, 2014.
24 comma_survey
comma_survey Elitist, Superfluous, Or Popular? We Polled Americans on the Oxford
Comma
Description
The raw data behind the story "Elitist, Superfluous, Or Popular? We Polled Americans on the Ox-
ford Comma" https://fivethirtyeight.com/features/elitist-superfluous-or-popular-we-polled-americans-on-the-oxford-comma/.
Usage
comma_survey
Format
A data frame with 1129 rows representing respondents and 13 variables:
respondent_id Respondent ID
gender Gender
age Age
household_income Household income bracket
education Education level
location Location (census region)
more_grammar_correct In your opinion, which sentence is more grammatically correct?
heard_oxford_comma Prior to reading about it above, had you heard of the serial (or Oxford)
comma?
care_oxford_comma How much, if at all, do you care about the use (or lack thereof) of the serial
(or Oxford) comma in grammar?
write_following How would you write the following sentence?
data_singular_plural When faced with using the word "data", have you ever spent time consider-
ing if the word was a singular or plural noun?
care_data How much, if at all, do you care about the debate over the use of the word "data" as a
singular or plural noun?
care_proper_grammar In your opinion, how important or unimportant is proper use of grammar?
Source
See https://github.com/fivethirtyeight/data/tree/master/comma-survey.
congress_age 25
congress_age Both Republicans And Democrats Have an Age Problem
Description
The raw data behind the story "Both Republicans And Democrats Have an Age Problem" https://
fivethirtyeight.com/features/both-republicans-and-democrats-have-an-age-problem/.
Usage
congress_age
Format
A data frame with 18,635 rows representing members of Congress (House and Senate) and 13
variables:
congress Congress number.
chamber Chamber of congress: House of Representatives or Senate.
bioguide bioguide
firstname First name
middlename Middle name
lastname Last name
suffix Suffix
birthday Birthday
state State abbreviation
party Party abbreviation
incumbent Boolean variable of whether member was an incumbent.
termstart Start date of session.
age Age at start of session.
Source
See https://github.com/fivethirtyeight/data/tree/master/congress-age
cousin_marriage How Many Americans Are Married To Their Cousins?
Description
The raw data behind the story "How Many Americans Are Married To Their Cousins?" https://
fivethirtyeight.com/features/how-many-americans-are-married-to-their-cousins/.
Usage
cousin_marriage
26 daily_show_guests
Format
A data frame with 70 rows representing countries and 2 variables:
country Country
percent Percent of marriages that are consanguineous
Source
http://www.consang.net/index.php/Main_Page
daily_show_guests Every Guest Jon Stewart Ever Had On ’The Daily Show’
Description
The raw data behind the story "Every Guest Jon Stewart Ever Had On ’The Daily Show’" https://
fivethirtyeight.com/features/every-guest-jon-stewart-ever-had-on-the-daily-show/.
Usage
daily_show_guests
Format
A data frame with 2693 rows representing guests and 5 variables:
year The year the episode aired
google_knowledge_occupation Their occupation or office, according to Google’s Knowledge Graph
or, if they’re not in there, how Stewart introduced them on the program.
show Air date of episode. Not unique, as some shows had more than one guest
group A larger group designation for the occupation. For instance, us senators, us presidents, and
former presidents are all under "politicians"
raw_guest_list The person or list of people who appeared on the show, according to Wikipedia.
The GoogleKnowledge_Occupation only refers to one of them in a given row.
Source
Google Knowledge Graph, The Daily Show clip library, Wikipedia.
democratic_bench 27
democratic_bench Some Democrats Who Could Step Up If Hillary Isn’t Ready For
Hillary
Description
The raw data behind the story "Some Democrats Who Could Step Up If Hillary Isn’t Ready For
Hillary" https://fivethirtyeight.com/features/some-democrats-who-could-step-up-if-hillary-isnt-ready-for-hillary/.
Usage
democratic_bench
Format
A data frame with 67 rows representing members of the Democratic Party and 3 variables:
candidate Candidate
raised_exp Amount the candidate was expected to raise
raised_act Amount the candidate actually raised
Source
See https://github.com/fivethirtyeight/data/tree/master/democratic-bench.
drinks Dear Mona Followup: Where Do People Drink The Most Beer, Wine
And Spirits?
Description
The raw data behind the story "Dear Mona Followup: Where Do People Drink The Most Beer, Wine
And Spirits?" https://fivethirtyeight.com/features/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/.
Usage
drinks
Format
A data frame with 193 rows representing countries and 5 variables:
country country
beer_servings Servings of beer in average serving sizes per person
spirit_servings Servings of spirits in average serving sizes per person
wine_servings Servings of wine in average serving sizes per person
total_litres_of_pure_alcohol Total litres of pure alcohol per person
28 drug_use
Source
World Health Organization, Global Information System on Alcohol and Health (GISAH), 2010.
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
drinks_tidy <- drinks %>%
gather(type, servings, -c(country, total_litres_of_pure_alcohol)) %>%
mutate(
type = str_sub(type, start=1, end=-10)
) %>%
arrange(country, type)
drug_use How Baby Boomers Get High
Description
The raw data behind the story "How Baby Boomers Get High" https://fivethirtyeight.com/
features/how-baby-boomers-get-high/. It covers usage of 13 drugs in the past 12 months
across 17 age groups.
Usage
drug_use
Format
A data frame with 17 rows representing age groups and 28 variables:
age Age group
nNumber of people surveyed
alcohol_use Percentage who used alcohol
alcohol_freq Median number of times a user used alcohol
marijuana_use Percentage who used marijuana
marijuana_freq Median number of times a user used marijuana
cocaine_use Percentage who used cocaine
cocaine_freq Median number of times a user used cocaine
crack_use Percentage who used crack
crack_freq Median number of times a user used crack
heroin_use Percentage who used heroin
heroin_freq Median number of times a user used heroin
hallucinogen_use Percentage who used hallucinogens
hallucinogen_freq Median number of times a user used hallucinogens
inhalant_use Percentage who used inhalants
elo_blatter 29
inhalant_freq Median number of times a user used inhalants
pain_releiver_use Percentage who used pain relievers
pain_releiver_freq Median number of times a user used pain relievers
oxycontin_use Percentage who used oxycontin
oxycontin_freq Median number of times a user used oxycontin
tranquilizer_use Percentage who used tranquilizer
tranquilizer_freq Median number of times a user used tranquilizer
stimulant_use Percentage who used stimulants
stimulant_freq Median number of times a user used stimulants
meth_use Percentage who used meth
meth_freq Median number of times a user used meth
sedative_use Percentage who used sedatives
sedative_freq Median number of times a user used sedatives
Source
National Survey on Drug Use and Health from the Substance Abuse and Mental Health Data
Archive http://www.icpsr.umich.edu/icpsrweb/content/SAMHDA/index.html.
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
use <- drug_use %>%
select(age, n, ends_with("_use")) %>%
gather(drug, use, -c(age, n)) %>%
mutate(drug = str_sub(drug, start=1, end=-5))
freq <- drug_use %>%
select(age, n, ends_with("_freq")) %>%
gather(drug, freq, -c(age, n)) %>%
mutate(drug = str_sub(drug, start=1, end=-6))
drug_use_tidy <- left_join(x=use, y=freq, by = c("age", "n", "drug")) %>%
arrange(age)
elo_blatter Blatter’s Reign At FIFA Hasn’t Helped Soccer’s Poor
Description
The raw data behind the story "Blatter’s Reign At FIFA Hasn’t Helped Soccer’s Poor" https://
fivethirtyeight.com/features/blatters-reign-at-fifa-hasnt-helped-soccers-poor/.
Usage
elo_blatter
30 endorsements
Format
A data frame with 191 rows representing countries and 5 variables:
country FIFA member country
elo98 The team’s Elo in 1998
elo15 The team’s Elo in 2015
confederation Confederation to which country belongs
gdp06 The country’s purchasing power parity GDP as of 2006
popu06 The country’s 2006 population
gdp_source Source for gdp06
popu_source Source for popu06
Source
See https://github.com/fivethirtyeight/data/tree/master/elo-blatter.
endorsements Pols And Polls Say The Same Thing: Jeb Bush Is A Weak Front-Runner
Description
The raw data behind the story "Pols And Polls Say The Same Thing: Jeb Bush Is A Weak Front-
Runner" https://fivethirtyeight.com/features/pols-and-polls-say-the-same-thing-jeb-bush-is-a-weak-front-runner/.
This data includes something we call "endorsement points," an attempt to quantify the importance
of endorsements by weighting each one according to the position held by the endorser: 10 points
for each governor, 5 points for each senator and 1 point for each representative
Usage
endorsements
Format
A data frame with 109 rows representing candidates and 9 variables:
year Election year
party Political party
candidate Candidate running in primary
endorsement_points Weighted endorsements through June 30th of the year before the primary
percentage_endorsement_points Percentage of total weighted endorsement points for the candi-
date’s political party through June 30th of the year before the primary
money_raised Money raised through June 30th of the year before the primary
percentage_of_money Percentage of total money raised by the candidate’s political party through
June 30th of the year before the primary
primary_vote_percentage Percentage of votes won in the primary
won_primary Did the candidate win the primary?
Source
See https://github.com/fivethirtyeight/data/tree/master/endorsements-june-30
fandango 31
fandango Be Suspicious Of Online Movie Ratings, Especially Fandango’s
Description
The raw data behind the story "Be Suspicious Of Online Movie Ratings, Especially Fandango’s"
https://fivethirtyeight.com/features/fandango-movies-ratings/. contains every film
that has a Rotten Tomatoes rating, a RT User rating, a Metacritic score, a Metacritic User score, and
IMDb score, and at least 30 fan reviews on Fandango.
Usage
fandango
Format
A data frame with 146 rows representing movies and 23 variables:
film The film in question
year Year of film
rottentomatoes The Rotten Tomatoes Tomatometer score for the film
rottentomatoes_user The Rotten Tomatoes user score for the film
metacritic The Metacritic critic score for the film
metacritic_user The Metacritic user score for the film
imdb The IMDb user score for the film
fandango_stars The number of stars the film had on its Fandango movie page
fandango_ratingvalue The Fandango ratingValue for the film, as pulled from the HTML of each
page. This is the actual average score the movie obtained.
rt_norm The Rotten Tomatoes Tomatometer score for the film , normalized to a 0 to 5 point system
rt_user_norm The Rotten Tomatoes user score for the film , normalized to a 0 to 5 point system
metacritic_norm The Metacritic critic score for the film, normalized to a 0 to 5 point system
metacritic_user_nom The Metacritic user score for the film, normalized to a 0 to 5 point system
imdb_norm The IMDb user score for the film, normalized to a 0 to 5 point system
rt_norm_round The Rotten Tomatoes Tomatometer score for the film , normalized to a 0 to 5
point system and rounded to the nearest half-star
rt_user_norm_round The Rotten Tomatoes user score for the film , normalized to a 0 to 5 point
system and rounded to the nearest half-star
metacritic_norm_round The Metacritic critic score for the film, normalized to a 0 to 5 point
system and rounded to the nearest half-star
metacritic_user_norm_round The Metacritic user score for the film, normalized to a 0 to 5 point
system and rounded to the nearest half-star
imdb_norm_round The IMDb user score for the film, normalized to a 0 to 5 point system and
rounded to the nearest half-star
metacritic_user_vote_count The number of user votes the film had on Metacritic
imdb_user_vote_count The number of user votes the film had on IMDb
fandango_votes The number of user votes the film had on Fandango
fandango_difference The difference between the presented Fandango_Stars and the actual Fan-
dango_Ratingvalue
32 fifa_audience
Source
The data from Fandango was pulled on Aug. 24, 2015.
fifa_audience How To Break FIFA
Description
The raw data behind the story "How To Break FIFA" https://fivethirtyeight.com/features/
how-to-break-fifa/.
Usage
fifa_audience
Format
A data frame with 3652 rows representing guests and 6 variables:
country FIFA member country
confederation Confederation to which country belongs
population_share Country’s share of global population (percentage)
tv_audience_share Country’s share of global world cup TV Audience (percentage)
gdp_weighted_share Country’s GDP-weighted audience share (percentage)
Source
See https://github.com/fivethirtyeight/data/tree/master/fifa
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
fifa_audience_tidy <- fifa_audience %>%
gather(type, share, -c(country, confederation)) %>%
mutate(type = str_sub(type, start=1, end=-7)) %>%
arrange(country)
fivethirtyeight 33
fivethirtyeight fivethirtyeight: Data and Code Behind the Stories and Interactives at
’FiveThirtyEight’
Description
An R library that provides access to the code and data sets published by FiveThirtyEight https:
//github.com/fivethirtyeight/data. Note that while we received guidance from editors at
538, this package is not officially published by 538. Contribute to this package at https://github.
com/rudeboybert/fivethirtyeight.
Examples
# Example usage:
library(fivethirtyeight)
head(bechdel)
# All information about any data set can be found in the help file:
?bechdel
# To view a list of all data sets:
data(package = "fivethirtyeight")
# To view a detailed list of all data sets:
vignette("fivethirtyeight", package = "fivethirtyeight")
# Some data sets include vignettes with an example analysis:
vignette("bechdel", package = "fivethirtyeight")
# To browse all vignettes:
browseVignettes(package = "fivethirtyeight")
flying 41 Percent Of Fliers Think You’re Rude If You Recline Your Seat
Description
The raw data behind the story "41 Percent Of Fliers Think You’re Rude If You Recline Your Seat"
https://fivethirtyeight.com/features/airplane-etiquette-recline-seat/.
Usage
flying
Format
A data frame with 1040 rows representing respondents and 27 variables:
respondent_id RespondentID
gender Gender
age Age
34 flying
height Height
children_under_18 Do you have any children under 18?
household_income Household income bracket
education Education Level
location Location (census region)
frequency How often do you travel by plane?
recline_frequency Do you ever recline your seat when you fly?
recline_obligation Under normal circumstances, does a person who reclines their seat during a
flight have any obligation to the person sitting behind them?
recline_rude Is it rude to recline your seat on a plane?
recline_eliminate Given the opportunity, would you eliminate the possibility of reclining seats on
planes entirely?
switch_seats_friends Is it rude to ask someone to switch seats with you in order to be closer to
friends?
switch_seats_family Is it rude to ask someone to switch seats with you in order to be closer to
family?
wake_up_bathroom Is it rude to wake a passenger up if you are trying to go to the bathroom?
wake_up_walk Is it rude to wake a passenger up if you are trying to walk around?
baby In general, is it rude to bring a baby on a plane?
unruly_child In general, is it rude to knowingly bring unruly children on a plane?
two_arm_rests In a row of three seats, who should get to use the two arm rests?
middle_arm_rest In a row of two seats, who should get to use the middle arm rest?
shade Who should have control over the window shade?
unsold_seat Is it rude to move to an unsold seat on a plane?
talk_stranger Generally speaking, is it rude to say more than a few words to the stranger sitting
next to you on a plane?
get_up On a 6 hour flight from NYC to LA, how many times is it acceptable to get up if you’re not
in an aisle seat?
electronics Have you ever used personal electronics during take off or landing in violation of a
flight attendant’s direction?
smoked Have you ever smoked a cigarette in an airplane bathroom when it was against the rules?
Source
SurveyMonkey survey
food_world_cup 35
food_world_cup The FiveThirtyEight International Food Association’s 2014 World Cup
Description
The raw data behind the story "The FiveThirtyEight International Food Association’s 2014 World
Cup" https://fivethirtyeight.com/features/the-fivethirtyeight-international-food-associations-2014-world-cup/.
For all the countries below, the response to the following question is presented: "Please rate how
much you like the traditional cuisine of X"
• 5: I love this country’s traditional cuisine. I think it’s one of the best in the world.
• 4: I like this country’s traditional cuisine. I think it’s considerably above average.
• 3: I’m OK with this county’s traditional cuisine. I think it’s about average.
• 2: I dislike this country’s traditional cuisine. I think it’s considerably below average.
• 1: I hate this country’s traditional cuisine. I think it’s one of the worst in the world.
• N/A: I’m unfamiliar with this country’s traditional cuisine.
Usage
food_world_cup
Format
A data frame with 1373 rows representing respondents and 48 variables:
respondent_id Respondent ID
knowledge Generally speaking, how would you rate your level of knowledge of cuisines from
different parts of the world?
interest How much, if at all, are you interested in cuisines from different parts of the world?
gender Gender
age Age
household_income Household income bracket
education Education Level
location Location (census region)
algeria Cuisine of Algeria
argentina Cuisine of Argentina
australia Cuisine of Australia
belgium Cuisine of Belgium
bosnia_and_herzegovina Cuisine of Bosnia & Herzegovina
brazil Cuisine of Brazil
cameroon Cuisine of Cameroon
chile Cuisine of Chile
china Cuisine of China
colombia Cuisine of Colombia
36 food_world_cup
costa_rica Cuisine of Costa Rica
croatia Cuisine of Croatia
cuba Cuisine of Cuba
ecuador Cuisine of Ecuador
england Cuisine of England
ethiopia Cuisine of Ethiopia
france Cuisine of France
germany Cuisine of Germany
ghana Cuisine of Ghana
greece Cuisine of Greece
honduras Cuisine of Honduras
india Cuisine of India
iran Cuisine of Iran
ireland Cuisine of Ireland
italy Cuisine of Italy
ivory_coast Cuisine of Ivory Coast
japan Cuisine of Japan
mexico Cuisine of Mexico
nigeria Cuisine of Nigeria
portugal Cuisine of Portugal
russia Cuisine of Russia
south_korea Cuisine of South Korea
spain Cuisine of Spain
switzerland Cuisine of Switzerland
thailand Cuisine of Thailand
the_netherlands Cuisine of the Netherlands
turkey Cuisine of Turkey
united_states Cuisine of the United States
uruguay Cuisine of Uruguay
vietnam Cuisine of Vietnam
See Also
See https://github.com/fivethirtyeight/data/tree/master/food-world-cup
generic_polllist 37
generic_polllist Congress Generic Ballot Polls
Description
The raw data behind the story "Are Democrats Winning The Race For Congress?" https://
projects.fivethirtyeight.com/congress-generic-ballot-polls/.
Usage
generic_polllist
Format
A data frame with 934 rows representing polls and 21 variables:
subgroup No description provided.
modeldate No description provided.
startdate Start date of the poll.
enddate End date of the poll.
pollster The organization that conducted the poll (rather than the organization that paid for or
sponsored it)
grade No description provided.
samplesize No description provided.
population A = ALL ADULTS, RV = REGISTERED VOTERS, LV = LIKELY VOTERS, V =
VOTERS
weight No description provided.
influence No description provided.
dem No description provided.
rep No description provided.
adjusted_dem No description provided.
adjusted_rep No description provided.
multiversions No description provided.
tracking No description provided.
url No description provided.
poll_id No description provided.
question_id No description provided.
createddate No description provided.
timestamp No description provided.
Source
See https://github.com/fivethirtyeight/data/blob/master/congress-generic-ballot/
README.md
See Also
generic_topline
38 google_trends
generic_topline Congress Generic Ballot Polls
Description
The raw data behind the story "Are Democrats Winning The Race For Congress?" https://
projects.fivethirtyeight.com/congress-generic-ballot-polls/.
Usage
generic_topline
Format
A data frame with 751 rows representing polls and 9 variables:
subgroup No description provided.
modeldate No description provided.
dem_estimate No description provided.
dem_hi No description provided.
dem_lo No description provided.
rep_estimate No description provided.
rep_hi No description provided.
rep_lo No description provided.
timestamp No description provided.
Source
See https://github.com/fivethirtyeight/data/blob/master/congress-generic-ballot/
README.md
See Also
generic_polllist
google_trends The Media Really Started Paying Attention to Puerto Rico When
Trump Did
Description
The raw data behind the story "The Media Really Started Paying Attention to Puerto Rico When
Trump Did" https://fivethirtyeight.com/features/the-media-really-started-paying-attention-to-puerto-rico-when-trump-did/:
Google Trends Data.
Usage
google_trends
goose 39
Format
A data frame with 37 rows representing dates and 5 variables:
date Date
hurricane_harvey_us US Google search interest on the specified date for Hurricane Harvey
hurricane_irma_us US Google search interest on the specified date for Hurricane Irma
hurricane_maria_us US Google search interest on the specified date for Hurricane Maria
hurricane_jose_us US Google search interest on the specified date for Hurricane Jose
Details
Google search interest is measured in search term popularity relative to peak popularity in the given
region and time period (with 100 as peak popularity)
Source
Google Trends https://trends.google.com/trends/
See Also
mediacloud_hurricanes,mediacloud_states,mediacloud_online_news,mediacloud_trump,
tv_hurricanes,tv_hurricanes_by_network,tv_states
goose The Save Ruined Relief Pitching. The Goose Egg Can Fix It.
Description
The raw data behind the story "The Save Ruined Relief Pitching. The Goose Egg Can Fix It."
https://fivethirtyeight.com/features/goose-egg-new-save-stat-relief-pitchers/.
Usage
goose
Format
A data frame with 30,533 rows representing pitchers and 12 variables:
name Pitcher name
year Start year of season
team Retrosheet team code
league NL or AL
goose_eggs Goose eggs
broken_eggs Broken eggs
mehs Mehs
league_average_gpct League-average goose percentage
ppf Pitcher park factor
replacement_gpct Replacement-level goose percentage
gwar Goose Wins Above Replacement
key_retro Retrosheet unique player identifier
40 hate_crimes
Source
Retrosheet http://www.retrosheet.org/
hate_crimes Higher Rates Of Hate Crimes Are Tied To Income Inequality
Description
The raw data behind the story "Higher Rates Of Hate Crimes Are Tied To Income Inequality"
https://fivethirtyeight.com/features/higher-rates-of-hate-crimes-are-tied-to-income-inequality/.
Usage
hate_crimes
Format
A data frame with 51 rows representing US states and DC and 12 variables:
state State name
median_house_inc Median household income, 2016
share_unemp_seas Share of the population that is unemployed (seasonally adjusted), Sept. 2016
share_pop_metro Share of the population that lives in metropolitan areas, 2015
share_pop_hs Share of adults 25 and older with a high-school degree, 2009
share_non_citizen Share of the population that are not U.S. citizens, 2015
share_white_poverty Share of white residents who are living in poverty, 2015
gini_index Gini Index, 2015
share_non_white Share of the population that is not white, 2015
share_vote_trump Share of 2016 U.S. presidential voters who voted for Donald Trump
hate_crimes_per_100k_splc Hate crimes per 100,000 population, Southern Poverty Law Center,
Nov. 9-18, 2016
avg_hatecrimes_per_100k_fbi Average annual hate crimes per 100,000 population, FBI, 2010-
2015
Source
See https://github.com/fivethirtyeight/data/tree/master/hate-crimes
hiphop_cand_lyrics 41
hiphop_cand_lyrics Hip-Hop Is Turning On Donald Trump
Description
The raw data behind the story "Hip-Hop Is Turning On Donald Trump" http://projects.fivethirtyeight.
com/clinton-trump-hip-hop-lyrics/.
Usage
hiphop_cand_lyrics
Format
A data frame with 377 rows representing hip-hop songs referencing POTUS candidates in 2016 and
8 variables:
candidate Candidate referenced
song Song name
artist Artist name
sentiment Positive, negative or neutral
theme Theme of lyric
album_release_date Date of album release
line Lyrics
url Genius link
Source
Genius http://genius.com/
hist_ncaa_bball_casts The NCAA Bracket: Checking Our Work
Description
The raw data behind the story "The NCAA Bracket: Checking Our Work" https://fivethirtyeight.
com/features/the-ncaa-bracket-checking-our-work/.
Usage
hist_ncaa_bball_casts
42 hist_senate_preds
Format
A data frame with 253 rows representing NCAA men’s basketball tournament games and 6 vari-
ables:
year
round
favorite
underdog
favorite_prob
favorite_win
Source
See https://fivethirtyeight.com/features/the-ncaa-bracket-checking-our-work/
hist_senate_preds How The FiveThirtyEight Senate Forecast Model Works
Description
The raw data behind the story "How The FiveThirtyEight Senate Forecast Model Works" https://
fivethirtyeight.com/features/how-the-fivethirtyeight-senate-forecast-model-works/.
Usage
hist_senate_preds
Format
A data frame with 207 rows representing US state elections and 5 variables:
state Election
year Year of election
candidate Last name
forecast_prob Probability of winning election per FiveThirtyEight Election Day forecast
result ‘Win‘ or ‘Loss‘
Source
See https://github.com/fivethirtyeight/data/tree/master/forecast-methodology
librarians 43
librarians Where Are America’s Librarians?
Description
The raw data behind the story "Where Are America’s Librarians?" https://fivethirtyeight.
com/features/where-are-americas-librarians/.
Usage
librarians
Format
A data frame with 371 rows representing areas in the US and 9 variables:
prim_state
area_name
tot_emp
emp_prse
jobs_1000
loc_quotient
mor
high_emp
low_emp
Source
Bureau of Labor Statistics http://www.bls.gov/oes/current/oes254021.htm#(1)
love_actually_adj The Definitive Analysis Of ’Love Actually,’ The Greatest Christmas
Movie Of Our Time
Description
The raw data behind the story "The Definitive Analysis Of ’Love Actually,’ The Greatest Christmas
Movie Of Our Time" https://fivethirtyeight.com/features/some-people-are-too-superstitious-to-have-a-baby-on-friday-the-13th/.
The adjacency matrix of which actors appear in the same scene together.
Usage
love_actually_adj
44 love_actually_appearance
Format
A data frame with 14 rows representing actors and 15 variables:
actors
bill_nighy
keira_knightley
andrew_lincoln
hugh_grant
colin_firth
alan_rickman
heike_makatsch
laura_linney
emma_thompson
liam_neeson
kris_marshall
abdul_salis
martin_freeman
rowan_atkinson
See Also
love_actually_appearance.
love_actually_appearance
The Definitive Analysis Of ’Love Actually,’ The Greatest Christmas
Movie Of Our Time
Description
The raw data behind the story "The Definitive Analysis Of ’Love Actually,’ The Greatest Christmas
Movie Of Our Time" https://fivethirtyeight.com/features/the-definitive-analysis-of-love-actually-the-greatest-christmas-movie-of-our-time/.
A table of the central actors in "Love Actually" and which scenes they appear in.
Usage
love_actually_appearance
Format
A data frame with 71 rows representing scenes and 15 variables:
scenes
bill_nighy
keira_knightley
andrew_lincoln
mad_men 45
hugh_grant
colin_firth
alan_rickman
heike_makatsch
laura_linney
emma_thompson
liam_neeson
kris_marshall
abdul_salis
martin_freeman
rowan_atkinson
See Also
love_actually_adj.
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
love_actually_appearance_tidy <- love_actually_appearance %>%
gather(actor, appears, -c(scenes)) %>%
arrange(scenes)
mad_men "Mad Men" Is Ending. What’s Next For The Cast?
Description
The raw data behind the story ""Mad Men" Is Ending. What’s Next For The Cast?" https://
fivethirtyeight.com/features/mad-men-is-ending-whats-next-for-the-cast/.
Usage
mad_men
Format
A data frame with 248 rows representing performers on TV shows and 15 variables:
performer The name of the actor, according to IMDb. This is not a unique identifier - two per-
formers appeared in more than one program
show The television show where this actor appeared in more than half the episodes
show_start The year the television show began
show_end The year the television show ended, "PRESENT" if the show remains on the air as of
May 10.
46 male_flight_attend
status Why the actor is no longer on the program: "END" if the show has concluded, "LEFT" if
the show remains on the air.
charend The year the character left the show. Equal to "Show End" if the performer stayed on until
the final season.
years_since 2015 minus CharEnd
num_lead The number of leading roles in films the performer has appeared in since and including
"CharEnd", according to OpusData
num_support The number of leading roles in films the performer has appeared in since and in-
cluding "CharEnd", according to OpusData
num_shows The number of seasons of television of which the performer appeared in at least half
the episodes since and including "CharEnd", according to OpusData
score #LEAD + #Shows + 0.25*(#SUPPORT)
score_div_y "Score" divided by "Years Since"
lead_notes The list of films counted in #LEAD
support_notes The list of films counted in #SUPPORT
show_notes The seasons of shows counted in #Shows
Source
IMDB http://imdb.com
male_flight_attend Dear Mona, How Many Flight Attendants Are Men?
Description
The raw data behind the story "Dear Mona, How Many Flight Attendants Are Men?" https://
fivethirtyeight.com/features/dear-mona-how-many-flight-attendants-are-men/.
Usage
male_flight_attend
Format
A data frame with 320 rows representing job categories and 2 variables:
job_category Category of job
percentage_male Percentage of workforce that are male
Source
IPUMS 2012 https://usa.ipums.org/usa/
mayweather_mcgregor_tweets 47
mayweather_mcgregor_tweets
Mayweather Vs McGregor Tweets
Description
The raw data behind the story "The Mayweather-McGregor Fight As Told Through Emojis" https:
//fivethirtyeight.com/?post_type=fte_features&p=161615.
Usage
mayweather_mcgregor_tweets
Format
Because of R package size restrictions, only a preview of the first 10 rows of this dataset is in-
cluded; to obtain the entire dataset (12118 rows) see Examples below. A data frame with 10 rows
representing tweets and 7 variables:
created_at Time and date at which the tweet associated with the Mayweather vs. McGregor fight
was sent.
emojis Whether or not emojis were used in the tweet about the fight.
id A numerical identifier for each individual tweet about the fight.
link The link to the tweet about the fight on Twitter.
retweeted Whether or not the tweet about the fight was retweeted.
screen_name The screen name under which the tweet about the fight was posted.
text The text contained in the tweet about the fight.
Source
This data contains 12,118 tweets that contain one or more emojis and match one or more of the
following hashtags: #MayMac, #MayweatherMcGregor, #MayweatherVMcGregor, #Mayweath-
erVsMcGregor, #McGregor and #Mayweather. Data was collected on August 27, 2017 between
12:05 a.m. and 1:15 a.m. EDT using the Twitter streaming API. https://github.com/fivethirtyeight/
data/tree/master/mayweather-mcgregor
Examples
# To obtain the entire dataset, run the code inside the following if statement:
if(FALSE){
library(tidyverse)
url <-
"https://raw.githubusercontent.com/fivethirtyeight/data/master/mayweather-mcgregor/tweets.csv"
mayweather_mcgregor_tweets <- read_csv(url) %>%
mutate(
emojis = as.logical(emojis),
retweeted = as.logical(retweeted),
id = as.character(id)
)
}
48 mediacloud_online_news
mediacloud_hurricanes The Media Really Started Paying Attention to Puerto Rico When
Trump Did
Description
The raw data behind the story "The Media Really Started Paying Attention to Puerto Rico When
Trump Did" https://fivethirtyeight.com/features/the-media-really-started-paying-attention-to-puerto-rico-when-trump-did/:
Mediacloud Hurricanes Data.
Usage
mediacloud_hurricanes
Format
A data frame with 38 rows representing dates and 5 variables:
date Date
harvey The number of sentences in online news which mention Hurricane Harvey on the specified
date
irma The number of sentences in online news which mention Hurricane Irma
maria The number of sentences in online news which mention Hurricane Maria
jose The number of sentences in online news which mention Hurricane Jose
Source
Mediacloud https://mediacloud.org/
See Also
mediacloud_states,mediacloud_online_news,mediacloud_trump,tv_hurricanes,tv_hurricanes_by_network,
tv_states,google_trends
mediacloud_online_news
The Media Really Started Paying Attention to Puerto Rico When
Trump Did
Description
The raw data behind the story "The Media Really Started Paying Attention to Puerto Rico When
Trump Did" https://fivethirtyeight.com/features/the-media-really-started-paying-attention-to-puerto-rico-when-trump-did/:
Mediacloud Top Online News Data.
Usage
mediacloud_online_news
mediacloud_states 49
Format
A data frame with 49 rows representing media outlets and 2 variables:
name Name of media outlet source included in Media Cloud’s "U.S. Top Online News" collection
url URL of corresponding media outlet source
Source
Mediacloud https://mediacloud.org/
See Also
mediacloud_hurricanes,mediacloud_states,mediacloud_trump,tv_hurricanes,tv_hurricanes_by_network,
tv_states,google_trends
mediacloud_states The Media Really Started Paying Attention to Puerto Rico When
Trump Did
Description
The raw data behind the story "The Media Really Started Paying Attention to Puerto Rico When
Trump Did" https://fivethirtyeight.com/features/the-media-really-started-paying-attention-to-puerto-rico-when-trump-did/:
Mediacloud States Data.
Usage
mediacloud_states
Format
A data frame with 51 rows representing dates and 4 variables:
date Date
texas The number of sentences in online news which mention Texas on the specified date
puerto_rico The number of sentences in online news which mention Puerto Rico
florida The number of sentences in online news which mention Florida
Source
Mediacloud https://mediacloud.org/
See Also
mediacloud_hurricanes,mediacloud_online_news,mediacloud_trump,tv_hurricanes,tv_hurricanes_by_network,
tv_states,google_trends
50 mlb_as_play_talent
mediacloud_trump The Media Really Started Paying Attention to Puerto Rico When
Trump Did
Description
The raw data behind the story "The Media Really Started Paying Attention to Puerto Rico When
Trump Did" https://fivethirtyeight.com/features/the-media-really-started-paying-attention-to-puerto-rico-when-trump-did/:
Mediacloud Trump Data.
Usage
mediacloud_trump
Format
A data frame with 51 rows representing dates and 7 variables:
date Date
puerto_rico The number of headlines that mention Puerto Rico on the given date
puerto_rico_and_trump The number of headlines that mention Puerto Rico and either President
or Trump
florida The number of headlines that mention Florida
florida_and_trump The number of headlines that mention Florida and either President or Trump
texas The number of headlines that mention Texas
texas_and_trump The number of headlines that mention Texas and either President or Trump
Source
Mediacloud https://mediacloud.org/
See Also
mediacloud_hurricanes,mediacloud_states,mediacloud_online_news,tv_hurricanes,tv_hurricanes_by_network,
tv_states,google_trends
mlb_as_play_talent The Best MLB All-Star Teams Ever
Description
The raw data behind the story "The Best MLB All-Star Teams Ever" https://fivethirtyeight.
com/features/the-best-mlb-all-star-teams-ever/.
Usage
mlb_as_play_talent
mlb_as_team_talent 51
Format
A data frame with 3930 rows representing Major League Baseball players in given seasons and 15
variables:
bbref_id Player’s ID at Baseball-Reference.com
yearid The season in question
gamenum Order of All-Star Game for the season (in years w/ multiple ASGs; set to 0 when only
1 per year)
gameid Game ID at Baseball-Reference.com
lgid League of All-Star team
startingpos Position (according to baseball convention; 1=pitcher, 2=catcher, etc.) if starter
off600 Estimate of offensive talent, in runs above league average per 600 plate appearances
def600 Estimate of fielding talent, in runs above league average per 600 plate appearances
pitch200 Estimate of pitching talent, in runs above league average per 200 innings pitched
asg_pa Number of plate appearances in the All-Star Game itself
asg_ip Number of innings pitched in the All-Star Game itself
offper9innasg Expected offensive runs added above average (from talent) based on PA in ASG,
scaled to a 9-inning game
defper9innasg Expected defensive runs added above average (from talent) based on PA in ASG,
scaled to a 9-inning game
pitper9innasg Expected pitching runs added above average (from talent) based on IP in ASG,
scaled to a 9-inning game
totper9innasg Expected runs added above average (from talent) based on PA/IP in ASG, scaled to
a 9-inning game
Source
http://baseball-reference.com ,http://chadwick-bureau.com, Fangraphs
mlb_as_team_talent The Best MLB All-Star Teams Ever
Description
The raw data behind the story "The Best MLB All-Star Teams Ever" https://fivethirtyeight.
com/features/the-best-mlb-all-star-teams-ever/.
Usage
mlb_as_team_talent
52 mlb_elo
Format
A data frame with 172 rows representing Major League Baseball seasons and 16 variables:
yearid The season in question
gamenum Order of All-Star Game for the season (in years w/ multiple ASGs; set to 0 when only
1 per year)
gameid Game ID at Baseball-Reference.com
lgid League of All-Star team
tm_off_talent Total runs of offensive talent above average per game (36 plate appearances)
tm_def_talent Total runs of fielding talent above average per game (36 plate appearances)
tm_pit_talent Total runs of pitching talent above average per game (9 innings)
mlb_avg_rpg MLB average runs scored/game that season
talent_rspg Expected runs scored per game based on talent (MLB R/G + team OFF talent)
talent_rapg Expected runs allowed per game based on talent (MLB R/G - team DEF talent- team
PIT talent)
unadj_pyth Unadjusted pythagorean talent rating; PYTH =(RSPG^1.83)/(RSPG^1.83+RAPG^1.83)
timeline_adj Estimate of relative league quality where 2015 MLB = 1.00
sos Strength of schedule faced; adjusts an assumed .500 SOS downward based on timeline adjust-
ment
adj_pyth Adjusted pythagorean record; =(SOS*unadj_Pyth)/((2*unadj_Pyth*SOS)-SOS-unadj_Pyth+1)
no_1_player Best player according to combo of actual PA/IP and talent
no_2_player 2nd-best player according to combo of actual PA/IP and talent
Source
http://baseball-reference.com ,http://chadwick-bureau.com, Fangraphs
mlb_elo MLB Elo
Description
The raw data behind the stories: "The Complete History Of MLB" https://projects.fivethirtyeight.
com/complete-history-of-mlb/ and "MLB Predictions" https://projects.fivethirtyeight.
com/2017-mlb-predictions/.
Usage
mlb_elo
mlb_elo 53
Format
Because of R package size restrictions, only a preview of the first 10 rows of this dataset is included;
to obtain the entire dataset (1871 to 2017 games) see Examples below. A data frame with 10 rows
representing Elo ratings and 26 variables:
date The date of the game.
season The season within which the game was played.
neutral No description provided.
playoff No description provided.
team1 One team that participated in the game.
team2 The other team that participated in the match.
elo1_pre The Elo rating for team1 prior to the game.
elo2_pre The Elo rating for team2 prior to the game.
elo_prob1 No description provided.
elo_prob2 No description provided.
elo1_post The Elo rating for team1 after the game.
elo2_post The Elo rating for team2 after the game.
rating1_pre No description provided.
rating2_pre No description provided.
pitcher1 An identifier of the pitcher
pitcher2 No description provided.
pitcher1_rating No description provided.
pitcher2_rating No description provided.
pitcher1_adj No description provided.
pitcher2_adj No description provided.
rating_prob1 No description provided.
rating_prob2 No description provided.
rating1_post No description provided.
rating2_post No description provided.
score1 The number of runs scored by team1.
score2 The number of runs scored by team2.
Source
See https://github.com/fivethirtyeight/data/blob/master/mlb-elo/README.md
Examples
# To obtain the entire dataset, run the code inside the following if statement:
if(FALSE){
library(tidyverse)
mlb_elo <- read_csv("https://projects.fivethirtyeight.com/mlb-api/mlb_elo.csv") %>%
mutate(
playoff = as.factor(playoff),
playoff = ifelse(playoff == "<NA>", NA, playoff),
neutral = as.logical(neutral)
)
}
54 murder_2016_prelim
murder_2015_final A Handful Of Cities Are Driving 2016’s Rise In Murder
Description
The raw data behind the story "A Handful Of Cities Are Driving 2016’s Rise In Murder" https://
fivethirtyeight.com/features/a-handful-of-cities-are-driving-2016s-rise-in-murders/.
Usage
murder_2015_final
Format
A data frame with 83 rows representing large US cities and 5 variables:
city Name of city
state Name of state
murders_2014 Total murders in 2014
murders_2015 Total murders in 2015
change 2015 - 2014
Source
Unknown
murder_2016_prelim A Handful Of Cities Are Driving 2016’s Rise In Murder
Description
The raw data behind the story "A Handful Of Cities Are Driving 2016’s Rise In Murder" https://
fivethirtyeight.com/features/a-handful-of-cities-are-driving-2016s-rise-in-murders/.
Usage
murder_2016_prelim
Format
A data frame with 79 rows representing large US cities and 7 variables:
city Name of city
state Name of state
murders_2015 Number of murders in 2015
murders_2016 Number of murder in 2016 (as of as_of date)
change 2016 - 2015
source Source of data
as_of 2016 murders up to this date
nba_carmelo 55
Source
Listed as source variable in dataset
nba_carmelo The Complete History Of The NBA 2017-18 NBA Predictions
Description
The raw data behind the story "The Complete History Of The NBA" https://projects.fivethirtyeight.
com/complete-history-of-the-nba/ and our "2017-18 NBA Predictions" https://projects.
fivethirtyeight.com/2018-nba-predictions/
Usage
nba_carmelo
Format
Because of R package size restrictions, only a preview of the first 10 rows of this dataset is included;
to obtain the entire dataset (1871 to 2017 games) see Examples below. A data frame with 10 rows
representing games and 20 variables:
date Date
season Season year, 1947-2018
neutral TRUE if the game was played on neutral territory, FALSE if not
playoff TRUE if the game was a playoff game, FALSE if not
team1 The name of one participating team
team2 The name of the other participating team
elo1_pre Team 1’s Elo rating before the game
elo2_pre Team 2’s Elo rating before the game
elo_prob1 Team 1’s probability of winning based on Elo rating
elo_prob2 Team 2’s probability of winning based on Elo rating
elo1_post Team 1’s Elo rating after the game
elo2_post Team 2’s Elo rating after the game
carmelo1_pre Team 1’s CARMELO rating before the game
carmelo2_pre Team 2’s CARMELO rating before the game
carmelo1_post Team 1’s CARMELO rating after the game
carmelo2_post Team 2’s CARMELO rating after the game
carmelo_prob1 Team 1’s probability winning based on CARMELO rating
carmelo_prob2 Team 2’s probability of winning based on CARMELO rating
score1 Points scored by Team 1
score2 Points scored by Team 2
Source
See https://projects.fivethirtyeight.com/nba-model/nba_elo.csv
56 nba_draft_2015
Examples
# To obtain the entire dataset, run the following code:
library(tidyverse)
library(janitor)
nba_carmelo <- read_csv("https://projects.fivethirtyeight.com/nba-model/nba_elo.csv") %>%
clean_names() %>%
mutate(
team1 = as.factor(team1),
team2 = as.factor(team2),
playoff = ifelse(playoff == "t", TRUE, FALSE),
playoff = ifelse(is.na(playoff), FALSE, TRUE),
neutral = ifelse(neutral == 1, TRUE, FALSE)
)
nba_draft_2015 Projecting The Top 50 Players In The 2015 NBA Draft Class
Description
The raw data behind the story "Projecting The Top 50 Players In The 2015 NBA Draft Class"
https://fivethirtyeight.com/features/projecting-the-top-50-players-in-the-2015-nba-draft-class/.
An analysis using this data was contributed by G. Elliott Morris as a package vignette at http:
//fivethirtyeight-r.netlify.com/articles/nba.html.
Usage
nba_draft_2015
Format
A data frame with 1090 rows representing National Basketball Association players/prospects and 9
variables:
player Player name
position The player’s position going into the draft
id The player’s identification code
draft_year The year the player was eligible for the NBA draft
projected_spm The model’s projected statistical plus/minus over years 2-5 of the player’s NBA
career
superstar Probability of becoming a superstar player (1 per draft, SPM >= +3.3)
starter Probability of becoming a starting-caliber player (10 per draft, SPM >= +0.5)
role_player Probability of becoming a role player (25 per draft, SPM >= -1.4)
bust Probability of becoming a bust (everyone else, SPM < -1.4)
Source
See https://fivethirtyeight.com/features/projecting-the-top-50-players-in-the-2015-nba-draft-class/
nba_tattoos 57
nba_tattoos Accurately Counting NBA Tattoos Isn’t Easy, Even If You’re Up Close
Description
The raw data behind the story "Accurately Counting NBA Tattoos Isn’t Easy, Even If You’re Up
Close" https://fivethirtyeight.com/features/accurately-counting-nba-tattoos-isnt-easy-even-if-youre-up-close/.
Usage
nba_tattoos
Format
A data frame with 636 rows representing National Basketball Association players and 2 variables:
player_name Name of player
tattoos TRUE corresponds to player having tattoos, FALSE corresponds to not
Source
Ethan Swan http://nbatattoos.tumblr.com/
nfltix_div_avgprice Who Goes To Meaningless NFL Games And Why?
Description
The raw data behind the story "Who Goes To Meaningless NFL Games And Why?" https://
fivethirtyeight.com/features/who-goes-to-meaningless-nfl-games-and-why/.
Usage
nfltix_div_avgprice
Format
A data frame with 108 rows representing National Football League games and 3 variables:
event NFL divisional game info
division NFL division
avg_tix_price Average ticket price
Source
StubHub
58 nflwr_aging_curve
nfltix_usa_avg Who Goes To Meaningless NFL Games And Why?
Description
The raw data behind the story "Who Goes To Meaningless NFL Games And Why?" https://
fivethirtyeight.com/features/who-goes-to-meaningless-nfl-games-and-why/.
Usage
nfltix_usa_avg
Format
A data frame with 32 rows representing National Football League teams and 2 variables:
team Name of NFL team
avg_tix_price Average ticket price
Source
StubHub
nflwr_aging_curve The Football Hall Of Fame Has A Receiver Problem
Description
The raw data behind the story "The Football Hall Of Fame Has A Receiver Problem" https://
fivethirtyeight.com/features/the-football-hall-of-fame-has-a-receiver-problem/.
Usage
nflwr_aging_curve
Format
A data frame with 24 rows representing National Football League wide receiver ages and 3 vari-
ables:
age_from Beginning age
age_to Ending age
trypg_change Change in TRY per game from one age-year to next
Source
Unknown
nflwr_hist 59
nflwr_hist The Football Hall Of Fame Has A Receiver Problem
Description
The raw data behind the story "The Football Hall Of Fame Has A Receiver Problem" https://
fivethirtyeight.com/features/the-football-hall-of-fame-has-a-receiver-problem/.
Usage
nflwr_hist
Format
A data frame with 6496 rows representing National Football League wide receivers and 6 variables:
pfr_player_id Player identification code at Pro-Football-Reference.com
player_name The player’s name
career_try Career True Receiving Yards
career_ranypa Adjusted Net Yards Per Attempt (relative to average) of player’s career teams,
weighted by TRY w/ each team
career_wowy The amount by which career_ranypa exceeds what would be expected from his
QBs’ (age-adjusted) performance without the receiver
bcs_rating The number of yards per game by which a player would outgain an average receiver
on the same team, after adjusting for teammate quality and age (update of http://www.
sabernomics.com/sabernomics/index.php/2005/02/ranking-the-all-time-great-wide-receivers/)
Source
See https://fivethirtyeight.com/features/the-football-hall-of-fame-has-a-receiver-problem/
nfl_elo The Complete History Of The NFL 2017 NFL Predictions
Description
The raw data behind the story "The Complete History of the NFL" https://projects.fivethirtyeight.
com/complete-history-of-the-nfl/ And our "2017 NFL Predictions" https://projects.
fivethirtyeight.com/2017-nfl-predictions/
Usage
nfl_elo
60 nfl_fandom_google
Format
Because of R package size restrictions, only a preview of the first 10 rows of this dataset is included;
to obtain the entire dataset (1920 to 2018 games) see Examples below. A data frame with 10 rows
representing games and 14 variables:
date Date
season Season year, 1920-2018
neutral TRUE if the game was played on neutral territory, FALSE if not
playoff No description provided
team1 The name of one participating team
team2 The name of the other participating team
elo1_pre Team 1’s Elo rating before the game
elo2_pre Team 2’s Elo rating before the game
elo_prob1 Team 1’s probability of winning based on Elo rating
elo_prob2 Team 2’s probability of winning based on Elo rating
elo1_post Team 1’s Elo rating after the game
elo2_post Team 2’s Elo rating after the game
score1 Points scored by Team 1
score2 Points scored by Team 2
Source
See https://projects.fivethirtyeight.com/nfl-api/nfl_elo.csv # To obtain the entire
dataset, run the following code: library(tidyverse) library(janitor) nfl_elo <- read_csv("https://projects.fivethirtyeight.com/nfl-
api/nfl_elo.csv") clean_names() mutate( team1 = as.factor(team1), team2 = as.factor(team2), neu-
tral = ifelse(neutral == 1, TRUE, FALSE))
nfl_fandom_google How Every NFL Team’s Fans Lean Politically
Description
The raw data behind the story "How Every NFL Team’s Fans Lean Politically" https://fivethirtyeight.
com/features/how-every-nfl-teams-fans-lean-politically: Google Trends Data.
Usage
nfl_fandom_google
Format
a data frame with 207 rows representing designated market areas and 9 variables:
dma Designated Market Area
nfl The percentage of search traffic in the media market region related to the NFL over the past 5
years
nba The percentage of search traffic in the region related to the NBA over the past 5 years
nfl_fandom_surveymonkey 61
mlb The percentage of search traffic in the region related to the MLB over the past 5 years
nascar The percentage of search traffic in the region related to NASCAR over the past 5 years
cbb The percentage of search traffic in the region related to the CBB over the past 5 years
cfb The percentage of search traffic in the region related to the CFB over the past 5 years
trump_2016_vote The percentage of voters in the region who voted for Trump in the 2016 Presi-
dential Election
Source
Google Trends https://g.co/trends/5P8aa
See Also
nfl_fandom_surveymonkey
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
nfl_fandom_google_tidy <- nfl_fandom_google %>%
gather(sport, search_traffic, -c("dma", "trump_2016_vote")) %>%
arrange(dma)
nfl_fandom_surveymonkey
How Every NFL Team’s Fans Lean Politically
Description
The raw data behind the story "How Every NFL Team’s Fans Lean Politically" https://fivethirtyeight.
com/features/how-every-nfl-teams-fans-lean-politically: Surveymonkey Data.
Usage
nfl_fandom_surveymonkey
Format
a data frame with 33 rows representing teams and 25 variables:
team NFL team
total_respondents Total number of poll respondents who ranked the given team in their top 3
favorites
asian_dem Number of Asian, democrat poll respondents who ranked the given team in their top 3
favorites
black_dem Number of Black, democrat poll respondents who ranked the given team in their top 3
favorites
hispanic_dem Number of Hispanic, democrat poll respondents who ranked the given team in their
top 3 favorites
62 nfl_fandom_surveymonkey
other_dem Number of democrat poll respondents who identified their race as "other" (not Asian,
Black, Hispanic, or White) and ranked the given team in their top 3 favorites
white_dem Number of White, democrat poll respondents who ranked the given team in their top 3
favorites
total_dem Total number of democrat poll respondents who ranked the given team in their top 3
favorites
asian_ind Number of Asian, independent poll respondents who ranked the given team in their top
3 favorites
black_ind Number of Black, independent poll respondents who ranked the given team in their top
3 favorites
hispanic_ind Number of Hispanic, independent poll respondents who ranked the given team in
their top 3 favorites
other_ind Number of independent poll respondents who identified their race as "other" (not Asian,
Black, Hispanic, or White) and ranked the given team in their top 3 favorites
white_ind Number of White, independent poll respondents who ranked the given team in their top
3 favorites
total_ind Total number of independent poll respondents who ranked the given team in their top 3
favorites
asian_gop Number of Asian, republican poll respondents who ranked the given team in their top 3
favorites
black_gop Number of Black, republican poll respondents who ranked the given team in their top
3 favorites
hispanic_gop Number of Hispanic, republican poll respondents who ranked the given team in their
top 3 favorites
other_gop Number of republican poll respondents who identified their race as "other" (not Asian,
Black, Hispanic, or White) and ranked the given team in their top 3 favorites
white_gop Number of White, republican poll respondents who ranked the given team in their top
3 favorites
total_gop Total number of republican poll respondents who ranked the given team in their top 3
favorites
gop_percent Percent of fans (who ranked the team in their top 3 favorite NFL teams) who are
republicans
dem_percent Percent of fans who are democrats
ind_percent Percent of fans who are independent
white_percent Percent of fans who are White
nonwhite_percent Percent of fans who are not White
Source
See https://github.com/fivethirtyeight/data/tree/master/nfl-fandom/NFL_fandom_data-surveymonkey.
csv
See Also
nfl_fandom_google
nfl_fav_team 63
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
nfl_fandom_surveymonkey_tidy <- nfl_fandom_surveymonkey %>%
gather(key = race_party, value = percent,
-c("team", "total_respondents", "gop_percent", "dem_percent",
"ind_percent", "white_percent", "nonwhite_percent")) %>%
arrange(team)
nfl_fav_team The Rams Are Dead To Me, So I Answered 3,352 Questions To Find A
New NFL Team
Description
The raw data behind the story "The Rams Are Dead To Me, So I Answered 3,352 Questions To Find
A New NFL Team" https://fivethirtyeight.com/features/the-rams-are-dead-to-me-so-i-answered-3352-questions-to-find-a-new-team/.
Usage
nfl_fav_team
Format
A data frame with 32 rows representing National Football League teams and 17 variables:
team Name of NFL team
fan_relations Fan relations - Courtesy by players, coaches and front offices toward fans, and how
well a team uses technology to reach them
ownership Ownership - Honesty; loyalty to core players and the community
players Players - Effort on the field, likability off it
future_wins Future wins - Projected wins over next 5 seasons
bandwagon Bandwagon Factor - Are the team’s next 5 years likely to be better than their previous
5?
tradition Tradition - Championships/division titles/wins in team’s entire history
bang_buck Bang for the buck - Wins per fan dollars spent
behavior Behavior - Suspensions by players on team since 2007, with extra weight to transgres-
sions vs. women
nyc_prox Proximity to New York City
stlouis_prox Proximity to St. Louis
afford Affordability - Price of tickets, parking and concessions
small_market Small Market - Size of market in terms of population, where smaller is better
stadium_exp Stadium experience - Quality of venue; fan-friendliness of environment; frequency
of game-day promotions
coaching Coaching - Strength of on-field leadership
uniform Uniform - Stylishness of uniform design, according to Uni Watch’s Paul Lukas
big_market Big Market - Size of market in terms of population, where bigger is better
64 nutrition_pvalues
Source
http://www.espn.com/sportsnation/teamrankings,http://www.allourideas.org/nflteampickingsample
nfl_suspensions The NFL’s Uneven History Of Punishing Domestic Violence
Description
The raw data behind the story "The NFL’s Uneven History Of Punishing Domestic Violence"
https://fivethirtyeight.com/features/nfl-domestic-violence-policy-suspensions/.
Usage
nfl_suspensions
Format
A data frame with 269 rows representing National Football League players and 7 variables:
name first initial.last name
team team at time of suspension
games number of games suspended (one regular season = 16 games)
category personal conduct, substance abuse, performance enhancing drugs or in-game violence
description description of suspension
year year of suspension
source news source
Source
http://en.wikipedia.org/wiki/List_of_players_and_coaches_suspended_by_the_NFL,http:
//www.spotrac.com/fines-tracker/nfl/suspensions/
nutrition_pvalues You Can’t Trust What You Read About Nutrition
Description
The raw data behind the story "You Can’t Trust What You Read About Nutrition" https://fivethirtyeight.
com/features/you-cant-trust-what-you-read-about-nutrition.
Usage
nutrition_pvalues
police_deaths 65
Format
A data frame with 27716 rows representing Regression fits for p-hacking and 3 variables:
food Name of food (response/dependent variable)
characteristic Name of characteristic (predictor/independent variable)
p_values P-value from regression fit
Source
See https://fivethirtyeight.com/features/you-cant-trust-what-you-read-about-nutrition
police_deaths The Dallas Shooting Was Among The Deadliest For Police In U.S.
History
Description
The raw data behind the story "The Dallas Shooting Was Among The Deadliest For Police In U.S.
History" https://fivethirtyeight.com/features/the-dallas-shooting-was-among-the-deadliest-for-police-in-u-s-history/.
Usage
police_deaths
Format
A data frame with 22800 rows representing Police officers/dogs who lost their lives and 7 variables:
person Name of person/canine who died
cause_of_death Cause of death
date Date of event
year Year of event
canine TRUE if canine, FALSE if human
dept_name Name of police department
state State of police department
Source
Officer Down Memorial Page https://www.odmp.org/
66 police_killings
police_killings Where Police Have Killed Americans In 2015
Description
The raw data behind the story "Where Police Have Killed Americans In 2015" https://fivethirtyeight.
com/features/where-police-have-killed-americans-in-2015.
Usage
police_killings
Format
A data frame with 467 rows representing People who died from interactions with police and 34
variables:
name Name of deceased
age Age of deceased
gender Gender of deceased
raceethnicity Race/ethnicity of deceased
month Month of killing
day Day of incident
year Year of incident
streetaddress Address/intersection where incident occurred
city City where incident occurred
state State where incident occurred
latitude Latitude, geocoded from address
longitude Longitude, geocoded from address
state_fp State FIPS code
county_fp County FIPS code
tract_ce Tract ID code
geo_id Combined tract ID code
county_id Combined county ID code
namelsad Tract description
lawenforcementagency Agency involved in incident
cause Cause of death
armed How/whether deceased was armed
pop Tract population
share_white Share of pop that is non-Hispanic white
share_black Share of pop that is black (alone, not in combination)
share_hispanic Share of pop that is Hispanic/Latino (any race)
p_income Tract-level median personal income
police_locals 67
h_income Tract-level median household income
county_income County-level median household income
comp_income ‘h_income‘ / ‘county_income‘
county_bucket Household income, quintile within county
nat_bucket Household income, quintile nationally
pov Tract-level poverty rate (official)
urate Tract-level unemployment rate
college Share of 25+ pop with BA or higher
Source
See https://github.com/fivethirtyeight/data/tree/master/police-killings
police_locals Most Police Don’t Live In The Cities They Serve
Description
The raw data behind the story "Most Police Don’t Live In The Cities They Serve" https://
fivethirtyeight.com/features/most-police-dont-live-in-the-cities-they-serve/.
Usage
police_locals
Format
A data frame with 75 rows representing cities and 8 variables:
city U.S. city
force_size Number of police officers serving that city
all Percentage of the total police force that lives in the city
white Percentage of white (non-Hispanic) police officers who live in the city
non_white Percentage of non-white police officers who live in the city
black Percentage of black police officers who live in the city
hispanic Percentage of Hispanic police officers who live in the city
asian Percentage of Asian police officers who live in the city
Details
The dataset includes the cities with the 75 largest police forces, with the exception of Honolulu for
which data is not available. All calculations are based on data from the U.S. Census.
The Census Bureau numbers are potentially going to differ from other counts for three reasons:
1. The census category for police officers also includes sheriffs, transit police and others who
might not be under the same jurisdiction as a city’s police department proper. The census
category won’t include private security officers.
68 pres_2016_trail
2. The census data is estimated from 2006 to 2010; police forces may have changed in size since
then.
3. There is always a margin of error in census numbers; they are estimates, not complete counts.
Note: Missing values means that there are fewer than 100 police officers of that race serving that
city.
Source
See https://github.com/fivethirtyeight/data/tree/master/police-locals
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
police_locals_tidy <- police_locals %>%
gather(key = "race", value = "perc_in", all:asian)
pres_2016_trail The Last 10 Weeks Of 2016 Campaign Stops In One Handy Gif
Description
The raw data behind the story "The Last 10 Weeks Of 2016 Campaign Stops In One Handy Gif"
https://fivethirtyeight.com/features/the-last-10-weeks-of-2016-campaign-stops-in-one-handy-gif/.
Usage
pres_2016_trail
Format
A data frame with 177 rows representing 2016 Republican and Democratic candidate campaign
trail stops and 5 variables:
candidate Clinton or Trump
date The date of the event
location The location of the event
lat Latitude of the event location
lng Longitude of the event location
Source
https://hillaryspeeches.com/,http://www.conservativedailynews.com/
pres_commencement 69
pres_commencement Sitting Presidents Give Way More Commencement Speeches Than
They Used To
Description
The raw data behind the story "Sitting Presidents Give Way More Commencement Speeches Than
They Used To" https://fivethirtyeight.com/features/sitting-presidents-give-way-more-commencement-speeches-than-they-used-to/.
Usage
pres_commencement
Format
A data frame with 154 rows representing speeches and 8 variables:
pres Number of president (33 is Harry Truman, the 33rd president; 44 is Barack Obama, the 44th
president)
pres_name Name of president
title Description of commencement speech
date Date speech was delivered
city City where speech was delivered
state State where speech was delivered
building Name of building in which speech was delivered
room Room in which speech was delivered
Source
American Presidency Project, Gerhard Peters and John T. Woolley http://www.presidency.
ucsb.edu
pulitzer Do Pulitzers Help Newspapers Keep Readers?
Description
The raw data behind the story "Do Pulitzers Help Newspapers Keep Readers?" https://fivethirtyeight.
com/features/do-pulitzers-help-newspapers-keep-readers/.
Usage
pulitzer
70 ratings
Format
A data frame with 50 rows representing newspapers and 7 variables:
newspaper Newspaper
circ2004 Daily Circulation in 2004
circ2013 Daily Circulation in 2013
pctchg_circ Percent change in Daily Circulation from 2004 to 2013
num_finals1990_2003 Number of Pulitzer Prize winners and finalists from 1990 to 2003
num_finals2004_2014 Number of Pulitzer Prize winners and finalists from 2004 to 2014
num_finals1990_2014 Number of Pulitzer Prize winners and finalists from 1990 to 2014
Source
See https://fivethirtyeight.com/features/do-pulitzers-help-newspapers-keep-readers/
ratings An Inconvenient Sequel
Description
The raw data behind the story "Al Gore’s New Movie Exposes The Big Flaw In Online Movie Rat-
ings" https://fivethirtyeight.com/features/al-gores-new-movie-exposes-the-big-flaw-in-online-movie-ratings/.
Usage
ratings
Format
A data frame with 80053 rows representing movie ratings and 27 variables:
timestamp The date at which the rating was recorded.
respondents The number of respondents in a category associated with a given timestamp.
category The subgroups of respondents differentiated by demographics like gender, age, and na-
tionality.
link The website associated with a given category’s responses.
average The average rating reported by a given category.
mean The mean rating reported by a given category.
median The median rating reported by a given category.
votes_1 The count of votes denoting a rating of one that respondents gave.
votes_2 The count of votes denoting a rating of two that respondents gave.
votes_3 The count of votes denoting a rating of three that respondents gave.
votes_4 The count of votes denoting a rating of four that respondents gave.
votes_5 The count of votes denoting a rating of five that respondents gave.
votes_6 The count of votes denoting a rating of six that respondents gave.
votes_7 The count of votes denoting a rating of seven that respondents gave.
riddler_castles 71
votes_8 The count of votes denoting a rating of eight that respondents gave.
votes_9 The count of votes denoting a rating of nine that respondents gave.
votes_10 The count of votes denoting a rating of ten that respondents gave.
pct_1 The percentage of votes denoting a rating of one that respondents gave.
pct_2 The percentage of votes denoting a rating of two that respondents gave.
pct_3 The percentage of votes denoting a rating of three that respondents gave.
pct_4 The percentage of votes denoting a rating of four that respondents gave.
pct_5 The percentage of votes denoting a rating of five that respondents gave.
pct_6 The percentage of votes denoting a rating of six that respondents gave.
pct_7 The percentage of votes denoting a rating of seven that respondents gave.
pct_8 The percentage of votes denoting a rating of eight that respondents gave.
pct_9 The percentage of votes denoting a rating of nine that respondents gave.
pct_10 The percentage of votes denoting a rating of ten that respondents gave.
Source
IMBD http://www.imdb.com/title/tt6322922/ratings and see https://github.com/fivethirtyeight/
data/tree/master/inconvenient-sequel
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
library(stringr)
ratings_tidy <- ratings %>%
gather(votes, count, -c(timestamp, respondents, category, link, average, mean, median)) %>%
arrange(timestamp)
riddler_castles Can You Rule Riddler Nation?
Description
The raw data behind the story "Can You Rule Riddler Nation?" https://fivethirtyeight.com/
features/can-you-rule-riddler-nation/. Analysis of the submitted solutions can be found
at: https://fivethirtyeight.com/features/can-you-save-the-drowning-swimmer/
Usage
riddler_castles
72 riddler_castles2
Format
A data frame with 1387 rows representing submissions and 11 variables:
castle1 Number of troops out of 100 send to castle 1
castle2 Number of troops out of 100 send to castle 2
castle3 Number of troops out of 100 send to castle 3
castle4 Number of troops out of 100 send to castle 4
castle5 Number of troops out of 100 send to castle 5
castle6 Number of troops out of 100 send to castle 6
castle7 Number of troops out of 100 send to castle 7
castle8 Number of troops out of 100 send to castle 8
castle9 Number of troops out of 100 send to castle 9
castle10 Number of troops out of 100 send to castle 10
reason Why did you choose your troop deployment?
Source
See https://github.com/fivethirtyeight/data/tree/master/riddler-castles
See Also
riddler_castles2
Examples
# To convert data frame to tidy data (long) format, run
library(tidyverse)
library(stringr)
riddler_castles_tidy<-riddler_castles %>%
gather(key = castle , value = soldiers, castle1:castle10) %>%
mutate(castle = as.numeric(str_replace(castle, "castle","")))
riddler_castles2 The Battle For Riddler Nation, Round 2
Description
The raw data behind the story "The Battle For Riddler Nation, Round 2" https://fivethirtyeight.
com/features/the-battle-for-riddler-nation-round-2/. Analysis of the submitted solu-
tions can be found at: https://fivethirtyeight.com/features/how-much-should-you-bid-for-that-painting/
Usage
riddler_castles2
riddler_pick_lowest 73
Format
A data frame with 932 rows representing submissions and 11 variables:
castle1 Number of troops out of 100 send to castle 1
castle2 Number of troops out of 100 send to castle 2
castle3 Number of troops out of 100 send to castle 3
castle4 Number of troops out of 100 send to castle 4
castle5 Number of troops out of 100 send to castle 5
castle6 Number of troops out of 100 send to castle 6
castle7 Number of troops out of 100 send to castle 7
castle8 Number of troops out of 100 send to castle 8
castle9 Number of troops out of 100 send to castle 9
castle10 Number of troops out of 100 send to castle 10
reason Why did you choose your troop deployment?
Source
See https://github.com/fivethirtyeight/data/tree/master/riddler-castles
See Also
riddler_castles
Examples
# To convert data frame to tidy data (long) format, run
library(tidyverse)
library(stringr)
riddler_castles_tidy<-riddler_castles2 %>%
gather(key = castle , value = soldiers, castle1:castle10) %>%
mutate(castle = as.numeric(str_replace(castle, "castle","")))
riddler_pick_lowest Pick A Number, Any Number
Description
The raw data behind the story "Pick A Number, Any Number" https://fivethirtyeight.com/
features/pick-a-number-any-number/
Usage
riddler_pick_lowest
Format
A data frame with 3660 rows representing dates and 1 variable:
your_number Guessed number
show_your_work People showing their work
74 sandy_311
sandy_311 The (Very) Long Tail Of Hurricane Recovery
Description
The raw data behind the story "The (Very) Long Tail Of Hurricane Recovery" https://projects.
fivethirtyeight.com/sandy-311/
Usage
sandy_311
Format
A data frame with 1783 rows representing dates and 25 variables:
date Date
nyc_311 No description provided.
acs The number of emergency hotline (311) calls made to the Administration for Children’s Ser-
vices related to Hurricane Sandy on the given date
bpsi The number of emergency hotline (311) calls made to Building Protection Systems, Inc related
to Hurricane Sandy
cau The number of emergency hotline (311) calls made to the Community Affairs Unit related to
Hurricane Sandy
chall The number of emergency hotline (311) calls made to the City Hall related to Hurricane
Sandy
dep The number of emergency hotline (311) calls made to the Department of Environmental Pro-
tection related to Hurricane Sandy
dob The number of emergency hotline (311) calls made to the Department of Buildings related to
Hurricane Sandy
doe The number of emergency hotline (311) calls made to the Department of Education related to
Hurricane Sandy
dof The number of emergency hotline (311) calls made to the Department of Finance related to
Hurricane Sandy
dohmh The number of emergency hotline (311) calls made to the Department of Health and Mental
Hygiene related to Hurricane Sandy
dpr The number of emergency hotline (311) calls made to the Department of Parks and Recreation
related to Hurricane Sandy
fema The number of emergency hotline (311) calls made to the Federal Emergency Management
Agency related to Hurricane Sandy
hpd The number of emergency hotline (311) calls made to the Department of Housing Preservation
and Development related to Hurricane Sandy
hra The number of emergency hotline (311) calls made to the Human Resources Administration
related to Hurricane Sandy
mfanyc The number of emergency hotline (311) calls made to the Mayor’s Fund to Advance NYC
related to Hurricane Sandy
san_andreas 75
mose The number of emergency hotline (311) calls made to the Mayor’s Office of Special Enforce-
ment related to Hurricane Sandy
nycem The number of emergency hotline (311) calls made to Emergency Management related to
Hurricane Sandy
nycha The number of emergency hotline (311) calls made to the New York City Housing Authority
related to Hurricane Sandy
nyc_service The number of emergency hotline (311) calls made to NYC Service related to Hurri-
cane Sandy
nypd The number of emergency hotline (311) calls made to the New York Police Department
related to Hurricane Sandy
nysdol The number of emergency hotline (311) calls made to the NYC Department of Labor related
to Hurricane Sandy
sbs The number of emergency hotline (311) calls made to Small Business Services related to Hur-
ricane Sandy
nys_emergency_mg The number of emergency hotline (311) calls made to NYS Emergency Man-
agement related to Hurricane Sandy
total The total number of emergency hotline (311) calls made related to Hurricane Sandy
Source
Data from NYC Open Data https://data.cityofnewyork.us/City-Government/311-Call-Center-Inquiry/
tdd6-3ysr, Agency acronyms from the Data Dictionary. See also https://github.com/fivethirtyeight/
data/tree/master/sandy-311-calls
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
sandy_311_tidy <- sandy_311 %>%
gather(agency, num_calls, -c("date", "total")) %>%
arrange(date) %>%
select(date, agency, num_calls, total) %>%
rename(total_calls = total) %>%
mutate(agency = as.factor(agency))
san_andreas The Rock Isn’t Alone: Lots Of People Are Worried About ’The Big
One’
Description
The raw data behind the story "The Rock Isn’t Alone: Lots Of People Are Worried About ’The Big
One’" https://fivethirtyeight.com/features/the-rock-isnt-alone-lots-of-people-are-worried-about-the-big-one/.
Usage
san_andreas
76 senate_polls
Format
A data frame with 1013 rows representing respondents and 11 variables:
worry_general In general, how worried are you about earthquakes?
worry_bigone How worried are you about the "Big One," a massive, catastrophic earthquake?
will_occur Do you think the "Big One" will occur in your lifetime?
experience Have you ever experienced an earthquake?
prepared Have you or anyone in your household taken any precautions for an earthquake (packed
an earthquake survival kit, prepared an evacuation plan, etc.)?
fam_san_andreas How familiar are you with the San Andreas Fault line?
fam_yellowstone How familiar are you with the Yellowstone Supervolcano?
age Age
female Gender
hhold_income How much total combined money did all members of your HOUSEHOLD earn last
year?
region US Region
Source
See https://github.com/fivethirtyeight/data/tree/master/san-andreas
senate_polls Early Senate Polls Have Plenty to Tell Us About November
Description
The raw data behind the story "Early Senate Polls Have Plenty to Tell Us About November" https:
//fivethirtyeight.com/features/early-senate-polls-have-plenty-to-tell-us-about-november/.
Usage
senate_polls
Format
A data frame with 107 rows representing a poll and 4 variables:
year Year
election_result Final poll margin
presidential_approval Early presidential approval rating
poll_average Early poll margin
Source
See https://github.com/fivethirtyeight/data/tree/master/early-senate-polls
senators 77
senators Senator Dataset
Description
Senator Dataset
Usage
senators
Format
Because of R package size restrictions, only a preview of the first 10 rows of this dataset is included;
to obtain the entire dataset see Examples below. A data frame with 10 rows representing tweets and
10 variables:
created_at The date and time the tweet was posted
user The user posting the tweet
text The text of the tweet
url The link to the tweet
replies The number of replies to the tweet
retweets The number of retweets
favorites The number of favorites
bioguide_id The poster’s member ID from the "Biographical Directory of the United States Congress"
party The poster’s political party affiliation
state The state the poster represents in Congress
Details
Data collected on Oct 19 and 20
Source
Twitter
See Also
twitter_presidents
Examples
# To obtain the entire dataset, run the code inside the following if statement:
if(FALSE){
library(tidyverse)
url <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/twitter-ratio/senators.csv"
senators <- read_csv(url) %>%
mutate(
party = as.factor(party),
state = as.factor(state),
78 spi_global_rankings
created_at = as.POSIXct(created_at, tz = "GMT", format = "%m/%d/%Y %H:%M"),
text = gsub("[^\x01-\x7F]", "", text)
) %>%
select(created_at, user, everything())
}
spi_global_rankings Current SPI ratings and rankings for men’s club teams
Description
The raw data behind the stories "Club Soccer Predictions" https://projects.fivethirtyeight.
com/soccer-predictions/ and "Global Club Soccer Rankings" https://projects.fivethirtyeight.
com/global-club-soccer-rankings/.
Usage
spi_global_rankings
Format
A data frame with 453 rows representing soccer rankings and 7 variables:
name The name of the soccer club.
league The name of the league to which the club belongs.
rank A club’s current global ranking.
prev_rank A club’s previous global ranking
off Offensive rating for a given team (the higher the value the stronger the team’s offense).
def Defensive rating for a given team (the lower the value the stronger the team’s defense).
spi A club’s SPI score.
Source
See https://github.com/fivethirtyeight/data/blob/master/soccer-spi/README.md
See Also
spi_matches
spi_matches 79
spi_matches Match-by-match SPI ratings and forecasts back to 2016
Description
The raw data behind the stories "Club Soccer Predictions" https://projects.fivethirtyeight.
com/soccer-predictions/ and "Global Club Soccer Rankings" https://projects.fivethirtyeight.
com/global-club-soccer-rankings/.
Usage
spi_matches
Format
A data frame with 10182 rows representing soccer matches and 13 variables:
date The date that the match took place.
league_id A numerical identifier of the league within which the match was played.
team1 One team that participated in the match.
team2 The other team that participated in the match.
spi1 The SPI score of team1.
spi2 The SPI score of team2.
prob1 The probability that team1 would have won the match.
prob2 The probability that team2 would have won the match.
probtie The probability that the match would have resulted in a tie.
proj_score1 The predicted number of goals that team1 would have scored.
proj_score2 The predicted number of goals that team2 would have scored.
score1 The number of goals that team1 scored.
score2 The number of goals that team2 scored.
xg1
xg2
nsxg1
nsxg2
adj_score1
adj_score2
Source
See https://github.com/fivethirtyeight/data/blob/master/soccer-spi/README.md
See Also
spi_global_rankings
80 steak_survey
steak_survey How Americans Like Their Steak
Description
The raw data behind the story "How Americans Like Their Steak" https://fivethirtyeight.
com/features/how-americans-like-their-steak/.
Usage
steak_survey
Format
A data frame with 550 rows representing respondents and 15 variables:
respondent_id Respondent ID
lottery_a not sure
smoke Is respondent a smoker?
alcohol Is respondent a drinker?
gamble Is respondent a gambler?
skydiving Is respondent a skydiver?
speed not sure
cheated not sure
steak not sure
steak_prep Preferred steak preparation
female Is respondent female?
age Age
hhold_income Household income
educ Education level
region Region of US
Source
See https://fivethirtyeight.com/features/how-americans-like-their-steak/
tarantino 81
tarantino A Complete Catalog Of Every Time Someone Cursed Or Bled Out In
A Quentin Tarantino Movie
Description
The raw data behind the story "A Complete Catalog Of Every Time Someone Cursed Or Bled Out In
A Quentin Tarantino Movie" https://fivethirtyeight.com/features/complete-catalog-curses-deaths-quentin-tarantino-films/.
An analysis using this data was contributed by Olivia Barrows, Jojo Miller, and Jayla Nakayama as a
package vignette at http://fivethirtyeight-r.netlify.com/articles/tarantino_swears.
html.
Usage
tarantino
Format
A data frame with 1894 rows representing curse/death instances and 4 variables:
movie Film title
profane Whether the event was a profane word (TRUE) or a death (FALSE)
word The specific profane word, if the event was a word
minutes_in The number of minutes into the film the event occurred
Source
See https://github.com/fivethirtyeight/data/tree/master/tarantino
tennis_events_time Why Some Tennis Matches Take Forever
Description
The raw data behind the story "Why Some Tennis Matches Take Forever" https://fivethirtyeight.
com/features/why-some-tennis-matches-take-forever/.
Usage
tennis_events_time
Format
A data frame with 205 rows representing tournaments and 5 variables:
tournament Name of event
surface Court surface used at the event
sec_added Seconds added per point for this event on this surface in years shown, from regression
model controlling for players, year and other factors
year_start Start year for data used from this tournament in regression
year_end End year for data used from this tournament in regression
82 tennis_serve_time
Source
See https://github.com/fivethirtyeight/data/tree/master/tennis-time
See Also
tennis_players_time and tennis_serve_time
tennis_players_time Why Some Tennis Matches Take Forever
Description
The raw data behind the story "Why Some Tennis Matches Take Forever" https://fivethirtyeight.
com/features/why-some-tennis-matches-take-forever/.
Usage
tennis_players_time
Format
A data frame with 218 rows representing players and 2 variables:
player Player Name
sec_added Weighted average of seconds added per point as loser and winner of matches, 1991-
2015, from regression model controlling for tournament, surface, year and other factors
Source
See https://github.com/fivethirtyeight/data/tree/master/tennis-time
See Also
tennis_events_time and tennis_serve_time
tennis_serve_time Why Some Tennis Matches Take Forever
Description
The raw data behind the story "Why Some Tennis Matches Take Forever" https://fivethirtyeight.
com/features/why-some-tennis-matches-take-forever/.
Usage
tennis_serve_time
tenth_circuit 83
Format
A data frame with 120 rows representing serves and 7 variables:
server Name of player serving at 2015 French Open
sec_between Time in seconds between end of marked point and next serve, timed by stopwatch
app
opponent Opponent, receiving serve
game_score Score in the current game during the timed interval between points
set Set number, out of five
game Score in games within the set
date Date
Source
See https://github.com/fivethirtyeight/data/tree/master/tennis-time
See Also
tennis_events_time and tennis_players_time
tenth_circuit For A Trump Nominee, Neil Gorsuch’s Record Is Surprisingly Moder-
ate On Immigration
Description
The raw data behind the story "For A Trump Nominee, Neil Gorsuch’s Record Is Surprisingly Mod-
erate On Immigration" https://fivethirtyeight.com/features/for-a-trump-nominee-neil-gorsuchs-record-is-surprisingly-moderate-on-immigration/.
Usage
tenth_circuit
Format
A data frame with 954 rows representing cases and 13 variables:
title Name of the case
date Date of decision
federalreporter_cit Case citation, as listed in the Federal Reporter Series
westlaw_cit Case citation, Westlaw format
issue Issue number, in cases divided into multiple issues
weight Weight per issue (total weight per case equals one)
judge1 Name of first judge
judge2 Name of second judge
judge3 Name of third judge
vote1_liberal Vote of first judge. 1 = liberal, 0 = conservative.
vote2_liberal Vote of second judge. 1 = liberal, 0 = conservative.
vote3_liberal Vote of third judge. 1 = liberal, 0 = conservative.
category Category of case, immigration or discrimination
84 trumpworld_issues
Note
In immigration cases, partial relief to immigration petitioner is coded as liberal because the peti-
tioner typically seeks just one core remedy (e.g., withholding of removal, adjustment of status, or
asylum); in discrimination cases, partial relief is coded as multiple issues because the plaintiff of-
ten seeks separate remedies under multiple claims (e.g., disparate treatment, retaliation, etc.) and
different sources of law.
Source
See https://github.com/fivethirtyeight/data/tree/master/tenth-circuit
trumpworld_issues What the World Thinks of Trump
Description
The raw data behind the story "What the World Thinks of Trump" https://fivethirtyeight.
com/features/what-the-world-thinks-of-trump/: Trump World Issues Dataset
Usage
trumpworld_issues
Format
A data frame with 185 rows representing countries and 6 variables:
country The country whose population is being polled
net_approval The difference in the number of respondents from the given country who approve
and who disapprove of the issue (Trump proposal) in question (approve-disapprove)
approve The number of respondents from the given country who approve of the issue (Trump
proposal)
disapprove The number of respondents who disapprove of the issue
dk_refused undefined
issue The specific trump policy proposal being posed. Specifically: 1: Withdraw support for in-
ternational climate change agreements 2: Build a wall on the border between the U. S. and
Mexico 3: Withdraw U.S. support from the Iran nuclear weapons agreement 4: Withdraw U.S.
support for major trade agreements 5: Introduce tighter restrictions on those entering the U.S.
from some majority-Muslim countries
Source
Pew Research Center http://www.pewresearch.org/fact-tank/2017/07/17/9-charts-on-how-the-world-sees-trump/
See Also
trumpworld_polls
trumpworld_polls 85
trumpworld_polls What the World Thinks of Trump
Description
The raw data behind the story "What the World Thinks of Trump" https://fivethirtyeight.
com/features/what-the-world-thinks-of-trump/: Trump World Polls Dataset.
Usage
trumpworld_polls
Format
A data frame with 32 rows representing years and 40 variables:
year Year the poll was conducted
avg The average percentage people who answered the poll question positively (support the presi-
dent or have a favorable view of the U.S.)
canada The percentage of people from Canada who answered the poll question positively
france The percentage of people from France who answered the poll question positively
germany The percentage of people from Germany who answered the poll question positively
greece The percentage of people from Greece who answered the poll question positively
hungary The percentage of people from Hungary who answered the poll question positively
italy The percentage of people from Italy who answered the poll question positively
netherlands The percentage of people from Netherlands who answered the poll question positively
poland The percentage of people from Poland who answered the poll question positively
spain The percentage of people from Spain who answered the poll question positively
sweden The percentage of people from Sweden who answered the poll question positively
uk The percentage of people from the U.K. who answered the poll question positively
russia The percentage of people from Russia who answered the poll question positively
australia The percentage of people from Australia who answered the poll question positively
india The percentage of people from India who answered the poll question positively
indonesia The percentage of people from Indonesia who answered the poll question positively
japan The percentage of people from Japan who answered the poll question positively
philippines The percentage of people from the Philippines who answered the poll question posi-
tively
south_korea The percentage of people from South Korea who answered the poll question posi-
tively
vietnam The percentage of people from Vietnam who answered the poll question positively
israel The percentage of people from Israel who answered the poll question positively
jordan The percentage of people from Jordan who answered the poll question positively
lebanon The percentage of people from Lebanon who answered the poll question positively
tunisia The percentage of people from Tunisia who answered the poll question positively
86 trump_approval_poll
turkey The percentage of people from Turkey who answered the poll question positively
ghana The percentage of people from Ghana who answered the poll question positively
kenya The percentage of people from Kenya who answered the poll question positively
nigeria The percentage of people from Nigeria who answered the poll question positively
senegal The percentage of people from Senegal who answered the poll question positively
south_africa The percentage of people from South Africa who answered the poll question posi-
tively
tanzania The percentage of people from Tanzania who answered the poll question positively
argentina The percentage of people from Argentina who answered the poll question positively
brazil The percentage of people from Brazil who answered the poll question positively
chile The percentage of people from Chile who answered the poll question positively
colombia The percentage of people from Colombia who answered the poll question positively
mexico The percentage of people from Mexico who answered the poll question positively
peru The percentage of people from Peru who answered the poll question positively
venezuela The percentage of people from Venezuela who answered the poll question positively
question The item being polled. Specifically, whether respondents: 1) Have a favorable view of
the U.S. or 2) Trust the U.S. President when it comes to foreign affairs
Source
Pew Research Center http://www.pewresearch.org/fact-tank/2017/07/17/9-charts-on-how-the-world-sees-trump/
See Also
trumpworld_issues
Examples
# To convert data frame to tidy data (long) format, run:
library(tidyverse)
trumpworld_polls_tidy <- trumpworld_polls %>%
gather(country, percent_positive, -c("year", "avg", "question"))
trump_approval_poll How Popular is Donald Trump
Description
The raw data behind the story: "How Popular is Donald Trump" https://projects.fivethirtyeight.
com/trump-approval-ratings/: Approval Poll Dataset
Usage
trump_approval_poll
trump_approval_poll 87
Format
A data frame with 3051 rows representing individual polls and 20 variables:
subgroup The subgroup the poll falls into as defined by the type of people being polled (all polls,
voters, adults)
start_date The date the polling began
end_date The date the polling concluded
pollster The polling group which produced the poll
grade The grade for President Trump that the respondents’ approval ratings correspond to
sample_size The sample size of the poll
population The type of people being polled (a for adults, lv for likely voters, rv for registered
voters)
weight The weight fivethirtyeight gives the poll when determining approval ratings based on his-
torical accuracy of the pollster
approve The percentage of respondents who approve of the president
disapprove The percentage of respondents who disapprove of the president
adjusted_approve The percentage of respondents who approve of the president adjusted for sys-
tematic tendencies of the polling firm
adjusted_disapprove The percentage of respondents who approve of the president adjusted for
systematic tendencies of the polling firm
multiversions True if there are multiple versions of the poll, False if there are not
tracking TRUE if the poll was tracked, FALSE if not
url Poll result URL
poll_id Poll ID number
question_id ID number for the question being polled
created_date Date the poll was created
timestamp Date and time the poll was compiled
Details
Variables "model_date", "influence", and "president" were deleted because each observation con-
tained the same value for these variables: January 5, 2018; 0; and Donald Trump respectively.
Source
https://projects.fivethirtyeight.com/trump-approval-data/approval_polllist.csv and
https://projects.fivethirtyeight.com/trump-approval-data/approval_topline.csv
See Also
trump_approval_trend
88 trump_approval_trend
trump_approval_trend How Popular is Donald Trump
Description
The raw data behind the story: "How Popular is Donald Trump" https://projects.fivethirtyeight.
com/trump-approval-ratings/: Approval Trend Dataset.
Usage
trump_approval_trend
Format
A data frame with 1044 rows representing poll trends and 11 variables:
subgroup The subgroup the poll falls into as defined by the type of people being polled (all polls,
voters, adults)
modeldate The date the model was created
approve_estimate Estimated approval ratings
approve_high Higher bound of the estimated approval percentage
approve_low Lower bound of the estimated approval percentage
disapprove_estimate Estimated disapproval percentage
disapprove_high Higher bound of the estimated disapproval percentage
disapprove_low Lower bound of the estimated disapproval percentage
timestamp Date and time the model was compiled
Details
The Variable "president" was removed because all values were "Donald Trump"
Source
https://projects.fivethirtyeight.com/trump-approval-data/approval_topline.csv
See Also
trump_approval_poll
trump_news 89
trump_news How Trump Hacked The Media
Description
The raw data behind the story "How Trump Hacked The Media" https://fivethirtyeight.com/
features/how-donald-trump-hacked-the-media/.
Usage
trump_news
Format
A data frame with 286 rows representing lead stories and 3 variables:
date Date of lead story about Donald Trump.
major_cat Story classification
detail
Source
Memeorandum http://www.memeorandum.com/.
trump_twitter The World’s Favorite Donald Trump Tweets
Description
The raw data behind the story "The World’s Favorite Donald Trump Tweets" https://fivethirtyeight.
com/features/the-worlds-favorite-donald-trump-tweets/. Tweets posted on twitter by
Donald Trump (@realDonaldTrump). An analysis using this data was contributed by Adam Spannbauer
as a package vignette at http://fivethirtyeight-r.netlify.com/articles/trump_twitter.
html.
Usage
trump_twitter
Format
A data frame with 448 rows representing tweets and 3 variables:
id
created_at
text
Source
Twitter https://twitter.com/realdonaldtrump
90 tv_hurricanes_by_network
tv_hurricanes The Media Really Started Paying Attention to Puerto Rico When
Trump Did
Description
The raw data behind the story "The Media Really Started Paying Attention to Puerto Rico When
Trump Did" https://fivethirtyeight.com/features/the-media-really-started-paying-attention-to-puerto-rico-when-trump-did/:
TV Hurricanes Data.
Usage
tv_hurricanes
Format
A data frame with 37 rows representing dates and 5 variables:
date Date
harvey The percent of sentences in TV news that mention Hurricane Harvey on the given date
irma The percent of sentences in TV news that mention Hurricane Irma
maria The percent of sentences in TV news that mention Hurricane Maria
jose The percent of sentences in TV news that mention Hurricane Irma
Source
Internet TV News Archive https://archive.org/details/tv and Television Explorer https:
//television.gdeltproject.org/cgi-bin/iatv_ftxtsearch/iatv_ftxtsearch
See Also
mediacloud_hurricanes,mediacloud_states,mediacloud_online_news,mediacloud_trump,
tv_hurricanes_by_network,tv_states,google_trends
tv_hurricanes_by_network
The Media Really Started Paying Attention to Puerto Rico When
Trump Did
Description
The raw data behind the story "The Media Really Started Paying Attention to Puerto Rico When
Trump Did" https://fivethirtyeight.com/features/the-media-really-started-paying-attention-to-puerto-rico-when-trump-did/:
TV Hurricanes by Network Data.
Usage
tv_hurricanes_by_network
tv_states 91
Format
A data frame with 84 rows representing dates and 6 variables:
date Date
query The hurricane in question
bbc_news The percent of sentences on the BBC News TV channel on the given date that mention
the hurricane in question
cnn The percent of sentences on CNN News that mention the hurricane in question
fox_news The percent of sentences on Fox News that mention the hurricane in question
msnbc The percent of sentences on MSNBC News that mention the hurricane in question
Source
Internet TV News Archive https://archive.org/details/tv and Television Explorer https:
//television.gdeltproject.org/cgi-bin/iatv_ftxtsearch/iatv_ftxtsearch
See Also
mediacloud_hurricanes,mediacloud_states,mediacloud_online_news,mediacloud_trump,
tv_hurricanes,tv_states,google_trends
tv_states The Media Really Started Paying Attention to Puerto Rico When
Trump Did
Description
The raw data behind the story "The Media Really Started Paying Attention to Puerto Rico When
Trump Did" https://fivethirtyeight.com/features/the-media-really-started-paying-attention-to-puerto-rico-when-trump-did/:
TV States Data.
Usage
tv_states
Format
A data frame with 52 rows representing dates and 4 variables:
date Date
florida The percent of sentences in TV News on the given day that mention Florida
texas The percent of sentences in TV News on the given day that mention Texas
puerto_rico The percent of sentences in TV News on the given day that mention Puerto Rico
Source
Internet TV News Archive https://archive.org/details/tv and Television Explorer https:
//television.gdeltproject.org/cgi-bin/iatv_ftxtsearch/iatv_ftxtsearch
92 twitter_presidents
See Also
mediacloud_hurricanes,mediacloud_states,mediacloud_online_news,mediacloud_trump,
tv_hurricanes,tv_hurricanes_by_network,google_trends
twitter_presidents The Worst Tweeter in Politics Isn’t Trump
Description
The raw data behind: "The Worst Tweeter in Politics Isn’t Trump" https://fivethirtyeight.
com/features/the-worst-tweeter-in-politics-isnt-trump/
Usage
twitter_presidents
Format
A data frame with 6439 rows describing individual tweets and 8 variables:
created_at The date and time the tweet was posted
user The user posting the tweet
text The text of the tweet
url The link to the tweet
replies The number of replies to the tweet
retweets The number of retweets
favorites The number of favorites
Details
Presidents Dataset:
Data on President Obama’s tweets collected on Oct 20, President Trump’s tweets collected on Oct
23.
Source
Twitter https://twitter.com/BarackObama and https://twitter.com/realDonaldTrump
See Also
senators
undefeated 93
undefeated Mayweather Is Defined By The Zero Next To His Name
Description
The raw data behind: "Mayweather Is Defined By The Zero Next To His Name" https://fivethirtyeight.
com/features/mayweather-is-defined-by-the-zero-next-to-his-name/
Usage
undefeated
Format
A data frame with 2125 rows representing boxing matches and 4 variables:
name Name of boxer
url URL with the boxer’s record
date Date of the match
wins Number of cumulative wins for the boxer including the match at the specified date
Source
Box Rec http://boxrec.com/
unisex_names The Most Common Unisex Names In America: Is Yours One Of Them?
Description
The raw data behind the story "The Most Common Unisex Names In America: Is Yours One Of
Them?" https://fivethirtyeight.com/features/there-are-922-unisex-names-in-america-is-yours-one-of-them/.
Usage
unisex_names
Format
A data frame with 919 rows representing names and 5 variables:
name First names from the Social Security Administration
total Total number of living Americans with the name
male_share Percentage of people with the name who are male
female_share Percentage of people with the name who are female
gap Gap between male_share and female_share
Source
Social Security Administration https://www.ssa.gov/oact/babynames/limits.html. See https:
//github.com/fivethirtyeight/data/tree/master/unisex-names.
94 US_births_2000_2014
US_births_1994_2003 Some People Are Too Superstitious To Have A Baby On Friday The
13th
Description
The raw data behind the story "Some People Are Too Superstitious To Have A Baby On Friday The
13th" https://fivethirtyeight.com/features/some-people-are-too-superstitious-to-have-a-baby-on-friday-the-13th/.
Usage
US_births_1994_2003
Format
A data frame with 3652 rows representing dates and 6 variables:
year Year
month Month
date_of_month Day
date POSIX date
day_of_week Abbreviation of day of week
births Number of births
Source
Centers for Disease Control and Prevention’s National Center for Health Statistics
See Also
US_births_2000_2014
US_births_2000_2014 Some People Are Too Superstitious To Have A Baby On Friday The
13th
Description
The raw data behind the story "Some People Are Too Superstitious To Have A Baby On Friday The
13th" https://fivethirtyeight.com/features/some-people-are-too-superstitious-to-have-a-baby-on-friday-the-13th/.
Usage
US_births_2000_2014
weather_check 95
Format
A data frame with 5479 rows representing dates and 6 variables:
year Year
month Month
date_of_month Day
date POSIX date
day_of_week Abbreviation of day of week
births Number of births
Source
Social Security Administration
See Also
US_births_1994_2003.
weather_check Where People Go To Check The Weather
Description
The raw data behind the story "Where People Go To Check The Weather" https://fivethirtyeight.
com/features/weather-forecast-news-app-habits/.
Usage
weather_check
Format
A data frame with 928 rows representing respondents and 9 variables:
respondent_id Respondent ID
ck_weather Do you typically check a daily weather report?
weather_source How do you typically check the weather?
weather_source_site If they responded "A specific website or app" when asked how they typically
check the weather, they were asked to write-in the app or website they used.
ck_weather_watch If you had a smartwatch (like the soon to be released Apple Watch), how likely
or unlikely would you be to check the weather on that device?
age Age
female Gender
hhold_income How much total combined money did all members of your HOUSEHOLD earn last
year?
region US Region
96 weather_check
Source
The source of the data is a Survey Monkey Audience poll commissioned by FiveThirtyEight and
conducted from April 6 to April 10, 2015. See https://github.com/fivethirtyeight/data/
tree/master/weather-check
Index
∗Topic datasets
ahca_polls,4
airline_safety,5
antiquities_act,6
avengers,6
bachelorette,8
bad_drivers,9
bechdel,10
biopics,11
bob_ross,12
cand_events_20150114,15
cand_events_20150130,16
cand_state_20150114,16
cand_state_20150130,17
candy_rankings,14
chess_transfers,18
classic_rock_raw_data,18
classic_rock_song_list,19
college_all_ages,20
college_grad_students,21
college_recent_grads,22
comic_characters,23
comma_survey,24
congress_age,25
cousin_marriage,25
daily_show_guests,26
democratic_bench,27
drinks,27
drug_use,28
elo_blatter,29
endorsements,30
fandango,31
fifa_audience,32
flying,33
food_world_cup,35
generic_polllist,37
generic_topline,38
google_trends,38
goose,39
hate_crimes,40
hiphop_cand_lyrics,41
hist_ncaa_bball_casts,41
hist_senate_preds,42
librarians,43
love_actually_adj,43
love_actually_appearance,44
mad_men,45
male_flight_attend,46
mayweather_mcgregor_tweets,47
mediacloud_hurricanes,48
mediacloud_online_news,48
mediacloud_states,49
mediacloud_trump,50
mlb_as_play_talent,50
mlb_as_team_talent,51
mlb_elo,52
murder_2015_final,54
murder_2016_prelim,54
nba_carmelo,55
nba_draft_2015,56
nba_tattoos,57
nfl_elo,59
nfl_fandom_google,60
nfl_fandom_surveymonkey,61
nfl_fav_team,63
nfl_suspensions,64
nfltix_div_avgprice,57
nfltix_usa_avg,58
nflwr_aging_curve,58
nflwr_hist,59
nutrition_pvalues,64
police_deaths,65
police_killings,66
police_locals,67
pres_2016_trail,68
pres_commencement,69
pulitzer,69
ratings,70
riddler_castles,71
riddler_castles2,72
riddler_pick_lowest,73
san_andreas,75
sandy_311,74
senate_polls,76
senators,77
spi_global_rankings,78
97
98 INDEX
spi_matches,79
steak_survey,80
tarantino,81
tennis_events_time,81
tennis_players_time,82
tennis_serve_time,82
tenth_circuit,83
trump_approval_poll,86
trump_approval_trend,88
trump_news,89
trump_twitter,89
trumpworld_issues,84
trumpworld_polls,85
tv_hurricanes,90
tv_hurricanes_by_network,90
tv_states,91
twitter_presidents,92
undefeated,93
unisex_names,93
US_births_1994_2003,94
US_births_2000_2014,94
weather_check,95
ahca_polls,4
airline_safety,5
antiquities_act,6
avengers,6
bachelorette,8
bad_drivers,9
bechdel,10
biopics,11
bob_ross,12
cand_events_20150114,15,16,17
cand_events_20150130,15,16,17
cand_state_20150114,15,16,16,17
cand_state_20150130,15–17,17
candy_rankings,14
chess_transfers,18
classic_rock_raw_data,18,19
classic_rock_song_list,19,19
college_all_ages,20,21,22
college_grad_students,20,21,22
college_recent_grads,20,21,22
comic_characters,23
comma_survey,24
congress_age,25
cousin_marriage,25
daily_show_guests,26
democratic_bench,27
drinks,27
drug_use,28
elo_blatter,29
endorsements,30
fandango,31
fifa_audience,32
fivethirtyeight,33
fivethirtyeight-package
(fivethirtyeight),33
flying,33
food_world_cup,35
generic_polllist,37,38
generic_topline,37,38
google_trends,38,48–50,90–92
goose,39
hate_crimes,40
hiphop_cand_lyrics,41
hist_ncaa_bball_casts,41
hist_senate_preds,42
librarians,43
love_actually_adj,43,45
love_actually_appearance,44,44
mad_men,45
male_flight_attend,46
mayweather_mcgregor_tweets,47
mediacloud_hurricanes,39,48,49,50,
90–92
mediacloud_online_news,39,48,48,49,50,
90–92
mediacloud_states,39,48,49,49,50,90–92
mediacloud_trump,39,48,49,50,90–92
mlb_as_play_talent,50
mlb_as_team_talent,51
mlb_elo,52
murder_2015_final,54
murder_2016_prelim,54
nba_carmelo,55
nba_draft_2015,56
nba_tattoos,57
nfl_elo,59
nfl_fandom_google,60,62
nfl_fandom_surveymonkey,61,61
nfl_fav_team,63
nfl_suspensions,64
nfltix_div_avgprice,57
nfltix_usa_avg,58
nflwr_aging_curve,58
nflwr_hist,59
INDEX 99
nutrition_pvalues,64
police_deaths,65
police_killings,66
police_locals,67
pres_2016_trail,68
pres_commencement,69
pulitzer,69
ratings,70
riddler_castles,71,73
riddler_castles2,72,72
riddler_pick_lowest,73
san_andreas,75
sandy_311,74
senate_polls,76
senators,77,92
spi_global_rankings,78,79
spi_matches,78,79
steak_survey,80
tarantino,81
tennis_events_time,81,82,83
tennis_players_time,82,82,83
tennis_serve_time,82,82
tenth_circuit,83
trump_approval_poll,86,88
trump_approval_trend,87,88
trump_news,89
trump_twitter,89
trumpworld_issues,84,86
trumpworld_polls,84,85
tv_hurricanes,39,48–50,90,91,92
tv_hurricanes_by_network,39,48–50,90,
90,92
tv_states,39,48–50,90,91,91
twitter_presidents,77,92
undefeated,93
unisex_names,93
US_births_1994_2003,94,95
US_births_2000_2014,94,94
weather_check,95

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