Help for package fivethirtyeight

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:

0.6.2

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:

7.1.1

Suggests:

ggplot2, dplyr, tidyr (≥ 1.0.0), readr, tibble, stringr, broom, knitr, rmarkdown, patchwork, fivethirtyeightdata

Additional_repositories:

https://fivethirtyeightdata.github.io/drat

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2021-10-07 12:58:41 UTC; rudeboybert

Author:

Albert Y. Kim

[aut, cre], Chester Ismay

[aut], Jennifer Chunn [aut], Meredith Manley

[ctb], Maggie Shea [ctb], Starry Yujia Zhou [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], Ben Baumer

[ctb], Rana Gahwagy

[ctb], Natalia Iannucci

[ctb], Marium Tapal

[ctb], Irene Ryan [ctb], Alina Barylsky [ctb], Danica Miguel [ctb], Sunni Raleigh [ctb], Anna Ballou [ctb], Jane Bang [ctb], Jordan Moody [ctb], Kara Van Allen [ctb], Jessica Keast [ctb], Lizette Carpenter [ctb], Fatima Keita [ctb]

Repository:

CRAN

Date/Publication:

2021-10-07 13:40:02 UTC

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

Some People Are Too Superstitious To Have A Baby On Friday The 13th

Description

Usage

US_births_2000_2014

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

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(dplyr)
library(tidyr)
library(stringr)
ahca_polls_tidy <- ahca_polls %>%
  pivot_longer(-c(start, end, pollster, text, url), names_to = "opinion", values_to = "count")

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 https://aviation-safety.net.

Examples

# To convert data frame to tidy data (long) format, run:
library(dplyr)
library(tidyr)
library(stringr)
airline_safety_tidy <- airline_safety %>%
  pivot_longer(-c(airline, incl_reg_subsidiaries, avail_seat_km_per_week), 
    names_to = "type", values_to = "count") %>%
  mutate(
    period = str_sub(type, start=-5),
    period = str_replace_all(period, "_", "-"),
    type = str_sub(type, end=-7)
  )

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 Monument" 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

How Much Trouble Is Ted Cruz Really In?

Description

The raw data behind the story "How Much Trouble Is Ted Cruz Really In?" https://fivethirtyeight.com/features/how-much-trouble-is-ted-cruz-really-in/.

Usage

august_senate_polls

Format

A data frame with 594 rows representing senate polls, and 11 variables:

cycle: the election year
state: the state of the poll
senate_class: the class of the senate
start_date: the start date of the poll
end_date: the end odate of the poll
dem_poll: the percent of support for the Democrat during the poll
rep_poll: the percent of support for the Republican during the poll
dem_result: the result percent of support for the Democrat during the election
rep_result: the result percent of support for the Republican during the election
error: the difference between the percent of support of one party during the poll and the result percent of support for the same party during the election
absolute_error: the absolute value of the error value

Source

Emerson College’s poll of registered voters

Joining The Avengers Is As Deadly As Jumping Off A Four-Story Building

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

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.

Bachelorette / Bachelor

Description

The raw data behind the stories: "How To Spot A Front-Runner On The 'Bachelor' Or 'Bachelorette'" 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 variables:

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.

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

https://bachelor-nation.fandom.com/wiki/Bachelor_Nation_Wiki and then missing seasons were filled in by ABC and FiveThirtyEight staffers.

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 Administration 2009 & 2012, National Association of Insurance Commissioners 2010 & 2011.

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: https://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 = "fivethirtyeightdata")

Source

https://bechdeltest.com/ and https://www.the-numbers.com/. The original data can be found at https://github.com/fivethirtyeight/data/tree/master/bechdel.

'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 https://fivethirtyeightdata.github.io/fivethirtyeightdata/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: https://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 https://www.imdb.com/

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 https://fivethirtyeightdata.github.io/fivethirtyeightdata/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)
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)

Source

See https://github.com/fivethirtyeight/data/tree/master/bob-ross

Examples

# To convert data frame to tidy data (long) format, run:
library(dplyr)
library(tidyr)
library(stringr)
bob_ross_tidy <- bob_ross %>%
  pivot_longer(-c(episode, season, episode_num, title), 
     names_to = "object", values_to = "present") %>%
  mutate(present = as.logical(present)) %>%
  arrange(episode, object)

Two Years In, Turnover In Trump’s Cabinet Is Still Historically High

Description

The raw data behind the story "Two Years In, Turnover In Trump’s Cabinet Is Still Historically High" https://fivethirtyeight.com/features/two-years-in-turnover-in-trumps-cabinet-is-still-historically-high/.

Usage

cabinet_turnover

Format

A data frame with 312 rows representing cabinet members and 8 variables:

president: Surname of of sitting President
position: Cabinet Position
appointee: Appointee's full name
start: Date the appointee was sworn in
end: Date the appointee left office
length: Length of Tenure, in days
days: Days into administration that the appointee left office
combined: Whether or not Cabinet member served in more than one administrations

Source

from Federal Government Websites and News Reports

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

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

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

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

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

Candy Power Ranking

Description

The raw data behind the story "The Ultimate Halloween Candy Power Ranking" https://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

Examples

# To convert data frame to tidy data (long) format, run:
library(dplyr)
library(tidyr)
library(stringr)
candy_rankings_tidy <- candy_rankings %>%
  pivot_longer(-c(competitorname, sugarpercent, pricepercent, winpercent), 
     names_to = "characteristics", values_to = "present") %>%
  mutate(present = as.logical(present)) %>%
  arrange(competitorname)

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

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

Source

See https://github.com/fivethirtyeight/data/tree/master/classic-rock

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

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.

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 variables:

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.

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.

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 Oxford 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 considering 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.

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

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

Format

A data frame with 70 rows representing countries and 2 variables:

country: Country
percent: Percent of marriages that are consanguineous

Source

consang.net

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.

Master list of all datasets

Description

All datasets included in both fivethirtyeight and fivethirtyeightdata packages

Usage

datasets_master

Format

A data frame with 9 variables:

Data Frame Name: Name of lazy-loaded data frame
In fivethirtyeightdata?: Whether the (large) dataset is in the fivethirtyeightdata package
Article Title: Title as it appears on FiveThirtyEight.com
URL: Link to article on FiveThirtyEight.com
Author 1: Main author
Author 2: Second author (if any)
Author 3: Third author (if any)
Date: Date published
Filed Under: Tag for article

Democratic Primary Candidates 2018

Description

The raw data behind the stories: "We Researched Hundreds Of Races. Here’s Who Democrats Are Nominating" https://fivethirtyeight.com/features/democrats-primaries-candidates-demographics/ and "How’s The Progressive Wing Doing In Democratic Primaries So Far?" https://fivethirtyeight.com/features/the-establishment-is-beating-the-progressive-wing-in-democratic-primaries-so-far/.

Usage

dem_candidates

Format

A data frame with 811 rows representing Democratic candidates, and 32 variables:

candidate: All candidates who received votes in 2018’s Democratic primary elections for U.S. Senate, U.S. House and governor in which no incumbent ran. Supplied by Ballotpedia.
state: The state in which the candidate ran. Supplied by Ballotpedia.
body: The body of government for which the candidate ran. Supplied by Ballotpedia.
district_num: If applicable, congressional district number for which the candidate ran. Supplied by Ballotpedia.
office_type: The office for which the candidate ran. Supplied by Ballotpedia.
race_type: Whether it was a “regular” or “special” election. Supplied by Ballotpedia.
race_primary_election_date: The date on which the primary was held. Supplied by Ballotpedia.
primary_status: Whether the candidate lost (“Lost”) the primary or won/advanced to a runoff (“Advanced”). Supplied by Ballotpedia.
primary_runoff_status: “None” if there was no runoff; “On the Ballot” if the candidate advanced to a runoff but it hasn’t been held yet; “Advanced” if the candidate won the runoff; “Lost” if the candidate lost the runoff. Supplied by Ballotpedia.
general_status: “On the Ballot” if the candidate won the primary or runoff and has advanced to November; otherwise, “None.” Supplied by Ballotpedia.
partisan_lean: The FiveThirtyEight partisan lean of the district or state in which the election was held. Partisan leans are calculated by finding the average difference between how a state or district voted in the past two presidential elections and how the country voted overall, with 2016 results weighted 75 percent and 2012 results weighted 25 percent.
primary_percent: The percentage of the vote received by the candidate in his or her primary. In states that hold runoff elections, we looked only at the first round (the regular primary). In states that hold all-party primaries (e.g., California), a candidate’s primary percentage is the percentage of the total Democratic vote they received. Unopposed candidates and candidates nominated by convention (not primary) are given a primary percentage of 100 but were excluded from our analysis involving vote share. Numbers come from official results posted by the secretary of state or local elections authority; if those were unavailable, we used unofficial election results from the New York Times.
won_primary: “Yes” if the candidate won his or her primary and has advanced to November; “No” if he or she lost.
race: “White” if we identified the candidate as non-Hispanic white; “Nonwhite” if we identified the candidate as Hispanic and/or any nonwhite race; blank if we could not identify the candidate’s race or ethnicity. To determine race and ethnicity, we checked each candidate’s website to see if he or she identified as a certain race. If not, we spent no more than two minutes searching online news reports for references to the candidate’s race.
veteran: If the candidate’s website says that he or she served in the armed forces, we put “Yes.” If the website is silent on the subject (or explicitly says he or she didn’t serve), we put “No.” If the field was left blank, no website was available.
lgbtq: If the candidate’s website says that he or she is LGBTQ (including indirect references like to a same-sex partner), we put “Yes.” If the website is silent on the subject (or explicitly says he or she is straight), we put “No.” If the field was left blank, no website was available.
elected_official: We used Ballotpedia, VoteSmart and news reports to research whether the candidate had ever held elected office before, at any level. We put “Yes” if the candidate has held elected office before and “No” if not.
self_funder: We used Federal Election Committee fundraising data (for federal candidates) and state campaign-finance data (for gubernatorial candidates) to look up how much each candidate had invested in his or her own campaign, through either donations or loans. We put “Yes” if the candidate donated or loaned a cumulative $400,000 or more to his or her own campaign before the primary and “No” for all other candidates.
stem: If the candidate identifies on his or her website that he or she has a background in the fields of science, technology, engineering or mathematics, we put “Yes.” If not, we put “No.” If the field was left blank, no website was available.
obama_alum: We put “Yes” if the candidate mentions working for the Obama administration or campaign on his or her website, or if the candidate shows up on this list of Obama administration members and campaign hands running for office. If not, we put “No.”
party_support: “Yes” if the candidate was placed on the DCCC’s Red to Blue list before the primary, was endorsed by the DSCC before the primary, or if the DSCC/DCCC aired pre-primary ads in support of the candidate. (Note: according to the DGA’s press secretary, the DGA does not get involved in primaries.) “No” if the candidate is running against someone for whom one of the above things is true, or if one of those groups specifically anti-endorsed or spent money to attack the candidate. If those groups simply did not weigh in on the race, we left the cell blank.
emily_endorsed: “Yes” if the candidate was endorsed by Emily’s List before the primary. “No” if the candidate is running against an Emily-endorsed candidate or if Emily’s List specifically anti-endorsed or spent money to attack the candidate. If Emily’s List simply did not weigh in on the race, we left the cell blank.
guns_sense_candidate: “Yes” if the candidate received the Gun Sense Candidate Distinction from Moms Demand Action/Everytown for Gun Safety before the primary, according to media reports or the candidate’s website. “No” if the candidate is running against an candidate with the distinction. If Moms Demand Action simply did not weigh in on the race, we left the cell blank.
biden_endorsed: “Yes” if the candidate was endorsed by Joe Biden before the primary. “No” if the candidate is running against a Biden-endorsed candidate or if Biden specifically anti-endorsed the candidate. If Biden simply did not weigh in on the race, we left the cell blank.
warren_endorsed: “Yes” if the candidate was endorsed by Elizabeth Warren before the primary. “No” if the candidate is running against a Warren-endorsed candidate or if Warren specifically anti-endorsed the candidate. If Warren simply did not weigh in on the race, we left the cell blank.
sanders_endorsed: “Yes” if the candidate was endorsed by Bernie Sanders before the primary. “No” if the candidate is running against a Sanders-endorsed candidate or if Sanders specifically anti-endorsed the candidate. If Sanders simply did not weigh in on the race, we left the cell blank.
our_revolution_endorsed: “Yes” if the candidate was endorsed by Our Revolution before the primary, according to the Our Revolution website. “No” if the candidate is running against an Our Revolution-endorsed candidate or if Our Revolution specifically anti-endorsed or spent money to attack the candidate. If Our Revolution simply did not weigh in on the race, we left the cell blank.
justice_dems_endorsed: “Yes” if the candidate was endorsed by Justice Democrats before the primary, according to the Justice Democrats website, candidate website or news reports. “No” if the candidate is running against a Justice Democrats-endorsed candidate or if Justice Democrats specifically anti-endorsed or spent money to attack the candidate. If Justice Democrats simply did not weigh in on the race, we left the cell blank.
pccc_endorsed: “Yes” if the candidate was endorsed by the Progressive Change Campaign Committee before the primary, according to the PCCC website, candidate website or news reports. “No” if the candidate is running against a PCCC-endorsed candidate or if the PCCC specifically anti-endorsed or spent money to attack the candidate. If the PCCC simply did not weigh in on the race, we left the cell blank.
indivisible_endorsed: “Yes” if the candidate was endorsed by Indivisible before the primary, according to the Indivisible website, candidate website or news reports. “No” if the candidate is running against an Indivisible-endorsed candidate or if Indivisible specifically anti-endorsed or spent money to attack the candidate. If Indivisible simply did not weigh in on the race, we left the cell blank.
wfp_endorsed: “Yes” if the candidate was endorsed by the Working Families Party before the primary, according to the WFP website, candidate website or news reports. “No” if the candidate is running against a WFP-endorsed candidate or if the WFP specifically anti-endorsed or spent money to attack the candidate. If the WFP simply did not weigh in on the race, we left the cell blank.
vote_vets_endorsed: “Yes” if the candidate was endorsed by VoteVets before the primary, according to the VoteVets website, candidate website or news reports. “No” if the candidate is running against a VoteVets-endorsed candidate or if VoteVets specifically anti-endorsed or spent money to attack the candidate. If VoteVets simply did not weigh in on the race, we left the cell blank.
no_labels_support: “Yes” if a No Labels-affiliated group (Citizens for a Strong America Inc., Forward Not Back, Govern or Go Home, United for Progress Inc. or United Together) spent money in support of the candidate in the primary. “No” if the candidate is running against an candidate supported by a No Labels-affiliated group or if a No Labels-affiliated group specifically anti-endorsed or spent money to attack the candidate. If No Labels simply did not weigh in on the race, we left the cell blank.

Note

This data was also used in "We Looked At Hundreds Of Endorsements. Here’s Who Democrats Are Listening To" published on 2008-08-14 https://fivethirtyeight.com/features/the-establishment-is-beating-the-progressive-wing-in-democratic-primaries-so-far/

Source

Ballotpedia, New York Times, and candidate websites. See also https://github.com/fivethirtyeight/data/blob/master/primary-candidates-2018/README.md

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.

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

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(dplyr)
library(tidyr)
library(stringr)
drinks_tidy <- drinks %>%
  pivot_longer(cols = ends_with("servings"), names_to = "type", values_to = "servings") %>%
  mutate(
    type = str_sub(type, start=1, end=-10)
  ) %>%
  arrange(country, type)

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
n: Number 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
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 https://www.icpsr.umich.edu/icpsrweb/content/SAMHDA/index.html.

Examples

# To convert data frame to tidy data (long) format, run:
library(dplyr)
library(tidyr)
library(stringr)
use <- drug_use %>%
  select(age, n, ends_with("_use")) %>%
  pivot_longer(-c(age, n), names_to = "drug", values_to = "use") %>%
  mutate(drug = str_sub(drug, start=1, end=-5))
freq <- drug_use %>%
  select(age, n, ends_with("_freq")) %>%
  pivot_longer(-c(age, n), names_to = "drug", values_to = "freq") %>%
  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)

Political Elasticity Scores

Description

This folder contains the data behind the story 'Election Update: The House Districts That Swing The Most (And Least) With The National Mood' https://fivethirtyeight.com/features/election-update-the-house-districts-that-swing-the-most-and-least-with-the-national-mood/

Usage

elasticity_by_district

Format

A dataset with 435 rows representing congressional districts and 2 variables

district: congressional district
pvi_538: pvi

Note

The original dataset only has 2 columns: "district" and "elasticity". I separated the "district" columns into two. For example, in row 1 of the dataset, the original "district" = "MI-5", and I separated it into “state" = "Michigan" and "district_number" = "5". In addition, I used the full names for all states instead of abbreviations.

Source

An elasticity score measures how sensitive a state or district it is to changes in the national political environment.

Political Elasticity Scores

Description

Usage

elasticity_by_state

Format

A dataset with 435 rows representing each state and the District of Columbia and 2 variables

state: state
pvi_538: pvi

Note

I used the full names for all states instead of abbreviations.

Source

An elasticity score measures how sensitive a state or district it is to changes in the national political environment.

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

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.

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 candidate'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

The 2020 Endorsement Primary - Which Democratic candidates are receiving the most support from prominent members of their party?

Description

The raw data behind the story "The 2020 Endorsement Primary - Which Democratic candidates are receiving the most support from prominent members of their party?" https://projects.fivethirtyeight.com/2020-endorsements/democratic-primary/.

Usage

endorsements_2020

Format

A data frame with 1000 rows representing endorsements and 13 variables:

date: date of the endorsement
position: position of the endorser
city: city of the endorser
state: state of the endorser
endorser: name of the endorser
endorsee: name of the endorsee
endorser_party: party of the endorser
source: source link of the endorsement
order: order of the endorsement
category: category of the endorsement
body: body of the endorsement
district: district
points: points the endorsement counts for

Source

2020 endorsement tracker. Methodology: https://fivethirtyeight.com/methodology/how-our-presidential-endorsement-tracker-works/.

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 Fandango_Ratingvalue

Source

The data from Fandango was pulled on Aug. 24, 2015.

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(dplyr)
library(tidyr)
library(stringr)
fifa_audience_tidy <- fifa_audience %>%
  pivot_longer(-c(country, confederation), 
     names_to = "type", values_to = "share") %>%
  mutate(type = str_sub(type, start=1, end=-7)) %>%
  arrange(country)

Our Guide To The Exuberant Nonsense of College Fight Songs

Description

The data behind the story "Our Guide To The Exuberant Nonsense Of College Fight Songs" https://projects.fivethirtyeight.com/college-fight-song-lyrics/.

Usage

fight_songs

Format

A data frame with 65 rows representing college fight songs, and 23 variables:

school: school name
conference: school college football conference
song_name: song title
writers: song author(s)
year: year the song was written; some years are unknown
student_writer: TRUE if song was written by a student, FALSE if not
official_song: TRUE if song is an official fight song according to the university, FALSE if not
contest: TRUE if song was chosen as part of a contest, FALSE if not
bpm: beats per minute
sec_duration: duration of the song in seconds
fight: TRUE if song says 'fight', FALSE if not
num_fights: number of time song says 'fight'
victory: TRUE if song says 'victory', FALSE if not
win_won: TRUE if song says 'win' or 'won', FALSE if not
victory_win_won: TRUE if song says 'victory', 'win', or 'won'
rah: TRUE if song says 'rah', FALSE if not
nonsense: TRUE if song uses nonsense syllables, FALSE if not
colors: TRUE if song mentions school colors, FALSE if not
men: TRUE if song refers to a group of men, boys, sons, etc., FALSE if not
opponents: TRUE if song mentions opponents, FALSE if not
spelling: TRUE if song spells something out, FALSE if not
trope_count: total number of tropes in song
spotify_id: Spotify id for song

Source

Spotify https://www.spotify.com/us/

fivethirtyeight: Data and Code Behind the Stories and Interactives at 'FiveThirtyEight'

Description

An R package 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. You can explore all datasets here: https://fivethirtyeight-r.netlify.app/articles/fivethirtyeight.html

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")

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
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

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
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

How FiveThirtyEight's 2018 Midterm Forecasts Did

Description

The raw data behind the story 'How FiveThirtyEight's 2018 Midterm Forecasts Did' https://fivethirtyeight.com/features/how-fivethirtyeights-2018-midterm-forecasts-did/

Usage

forecast_results_2018

Format

A dataframe with 1518 rows representing forecast results (as of December 3, 2018) and 11 variables:

cycle: cycle of the election
branch: branch of the election
race: election forecast for the gubernatorial race
forecastdate: the date of the forecast
version: version of the election forecast
dem_win_prob: the probability of winning for the Democrat
rep_win_prob: the probability of winning for the Republican
category: the predicted political affiliation of the forecast
democrat_won: whether the Democrat won
republican_won: whether the Republican won
uncalled: if a race was uncalled

Source

FiveThirtyEight's 2018 House Forecast https://projects.fivethirtyeight.com/2018-midterm-election-forecast/house/

We Watched 906 Foul Balls To Find Out Where The Most Dangerous Ones

Description

The raw data behind the story "We Watched 906 Foul Balls To Find Out Where The Most Dangerous Ones" https://fivethirtyeight.com/features/we-watched-906-foul-balls-to-find-out-where-the-most-dangerous-ones-land/.

Usage

foul_balls

Format

A data frame with 906 rows representing foul balls and 7 variables:

matchup: the two teams that played
game_date: date of the most foul heavy day at each stadium
type_of_hit: fly, grounder, line drive, popup, batter hits self
exit_velocity: recorded velocity of each hit
predicted_zone: zone predicted the foul ball would land in gauging angles
camera_zone: actual zone the ball landed it confirmed by camera angles
used_zone: zone used for analysis

Details

Information on the Zones from the 538 original article: Zones 1, 2 and 3 are the areas behind home plate and the dugouts. Zones 4 and 5 make up most of the foul territory outside the baselines up until the foul pole. Zones 6 and 7 include the areas beyond the foul poles.

Source

Baseball Savant https://baseballsavant.mlb.com/.

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

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

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

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/

2018 Governors Forecast

Description

The raw data behind the story 'Forecasting the races for governor' https://projects.fivethirtyeight.com/2018-midterm-election-forecast/governor/

Usage

governor_national_forecast

Format

A dataframe with 150 rows representing national-level results of the classic, lite, and deluxe gubernatorial forecasts since Oct. 11, 2018. and 11 variables

forecastdate: date of the forecast
party: the party of the forecast
model: the model of the forecast
win_probability: the probability of the corresponding party winning
mean_seats: the mean of the number of seats
median_seats: the median number of seats
p10_seats: the top 10 percentile of number of seats
p90_seats: the top 90 percentile of number of seats
margin: unknown
p10_margin: the margin of p10_seats
p90_margin: the margin of p90_seats

Note

The original dataset included a meaningless column called "state", and all variables under this column was "US". So this column was removed.

Source

FiveThirtyEight’s House, Senate And Governor Models Methodology: https://fivethirtyeight.com/methodology/how-fivethirtyeights-house-and-senate-models-work/

2018 Governors Forecast

Description

The raw data behind the story 'Forecasting the races for governor' https://projects.fivethirtyeight.com/2018-midterm-election-forecast/governor/

Usage

governor_state_forecast

Format

A dataframe with 7743 rows representing state-level results of the classic, lite, and deluxe gubernatorial forecasts since Oct. 11, 2018. and 10 variables

forecastdate: date of the forecast
state: state of the forecast
candidate: name of the candidate
party: party of the candidate
incumbent: whether the candidate is incumbent
model: the model of the forecast
win_probability: the probability of the corresponding party winning
voteshare: the voteshare of the corresponding party
p10_voteshare: the top 10 percentile of the voteshare
p90_voteshare: the top 00 percentile of the voteshare

Note

The original dataset included two empty column "district" and "special",which were removed.

Source

FiveThirtyEight’s House, Senate And Governor Models Methodology: https://fivethirtyeight.com/methodology/how-fivethirtyeights-house-and-senate-models-work/

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 13 variables:

state: State name
state_abbrev: State abbreviation
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

Examples

library(ggplot2)
ggplot(hate_crimes, aes(x = share_vote_trump, y = hate_crimes_per_100k_splc)) +
  geom_text(aes(label = state_abbrev)) +
  geom_smooth(se = FALSE, method = "lm") +
  labs(x = "Proportion of votes for Donald Trump",
       y = "Hate crimes per 100k during Nov 9-18, 2016 (SPLC)",
       title = "Relationship between Trump support & hate crimes")

Hip-Hop Is Turning On Donald Trump

Description

The raw data behind the story "Hip-Hop Is Turning On Donald Trump" https://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 https://genius.com/

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

Format

A data frame with 253 rows representing NCAA men's basketball tournament games and 6 variables:

year
round
favorite
underdog
favorite_prob
favorite_win

Source

See https://fivethirtyeight.com/features/the-ncaa-bracket-checking-our-work/

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

2018 House Forecast

Description

The raw data behind the story 'Forecasting the race for the House' https://projects.fivethirtyeight.com/2018-midterm-election-forecast/house/

Usage

house_national_forecast

Format

A dataframe with 588 rows representing district-level results of the classic, lite, and deluxe house forecasts since 2018/08/01 and 11 variables.

forecastdate: date of the forecast
party: the party of the forecast
model: the model of the forecast
win_probability: the probability of the corresponding party winning
mean_seats: the mean of the number of seats
median_seats: the median number of seats
p10_seats: the top 10 percentile of number of seats
p90_seats: the top 90 percentile of number of seats
margin: unknown
p10_margin: the margin of p10_seats
p90_margin: the margin of p90_seats

Note

The original dataset included a meaningless column called "state", and all variables under this column was "US". So this column was removed.

Source

FiveThirtyEight’s House, Senate And Governor Models Methodology: https://fivethirtyeight.com/methodology/how-fivethirtyeights-house-and-senate-models-work/

Do Americans Support Impeaching Trump?

Description

Raw data behind this story "Do Americans Support Impeaching Trump?" https://projects.fivethirtyeight.com/impeachment-polls/

Usage

impeachment_polls

Format

A data frame with 388 rows of polling data and 24 variables:

start: Poll start date, the first date responses were collected
end: Poll end date, the last date responses were collected
pollster: entity/organization that created poll, collected and published data
sponsor: sponsor of pollster
sample_size: number of respondents for each
pop: categorical variable with 3 categories: a, rv, lv – value unknown
tracking: true/false logical – value unknown
text: poll question
category: category of poll question with 5 categories: impeach and remove, begin proceedings, begin inquiry, reasons, impeach
include: yes/no logical – value unknown
yes: Percent of respondents in sample who answered "Yes" to the poll question
no: Percent of respondents in sample who answered "No" to the poll question
unsure: Percent of respondents in sample who did not answer "Yes" or "No" to the poll question
rep_sample: number of Republican respondents in sample
rep_yes: Percent of Republican respondents who answered "yes"
rep_no: Percent of Republican respondents who answered "no"
dem_sample: number of Democrat respondents in sample
dem_yes: Percent of Democrat respondents who answered "yes"
dem_no: Percent of Democrat respondents who answered "no"
ind_sample: number of Independent respondents in sample
ind_yes: Percent of Independent respondents who answered "yes"
ind_no: Percent of Independent respondents who answered "no"
url: URL links to poll websites
notes: any notes relating to polls in sample

Source

data from https://github.com/fivethirtyeight/data/tree/master/impeachment-polls.

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

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

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

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
hugh_grant
colin_firth
alan_rickman
heike_makatsch
laura_linney
emma_thompson
liam_neeson
kris_marshall
abdul_salis
martin_freeman
rowan_atkinson

Examples

# To convert data frame to tidy data (long) format, run:
library(dplyr)
library(tidyr)
library(stringr)
love_actually_appearance_tidy <- love_actually_appearance %>%
  pivot_longer(-scenes, names_to = "actor", values_to = "appears") %>%
  arrange(scenes)

"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 performers 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.
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 including "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 https://imdb.com

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/

Masculinity Survey

Description

This folder contains the data behind the story: "What Do Men Think It Means To Be A Man?" https://fivethirtyeight.com/features/what-do-men-think-it-means-to-be-a-man/

Usage

masculinity_survey

Format

A dataset with 189 rows representing answers and 12 variables:

question: the survey question
response: the survey response
overall: the ratio of overall participants who selected this response
age_18_34: the ratio of participants age 18 to 34 who selected this response
age_35_64: the ratio of participants age 35 to 64 who selected this response
age_65_over: the ratio of participants age 65 or over who selected this response
white_yes: the ratio of overall white participants who selected this response
white_no: the ratio of overall non-white participants who selected this response
children_yes: the ratio of participants who have child(ren) who selected this response
children_no: the ratio of participants who do not have children who selected this response
straight_yes: the ratio of straight participants who selected this response
straight_no: the ratio of non-straight participants who selected this response

Note

The original 'masculinity-survey.csv' contains the results of a survey of 1,615 adult men conducted by SurveyMonkey in partnership with FiveThirtyEight and WNYC Studios from May 10-22, 2018. The modeled error estimate for this survey is plus or minus 2.5 percentage points. The percentages have been weighted for age, race, education, and geography using the Census Bureau’s American Community Survey to reflect the demographic composition of the United States age 18 and over. Crosstabs with less than 100 respondents have been left blank because responses would not be statistically significant. I made heavy editions in Excel to make the dataset easily usable in R.

Source

The original survey responses and original datasets can be found here: https://github.com/fivethirtyeight/data/tree/master/masculinity-survey

Examples

library(dplyr)
library(ggplot2)
library(tidyr)
library(stringr)

# Data wrangling
masculinity_tidy <- masculinity_survey %>%
  # Narrow down rows to those pertaining to first question of survey:
  filter(question == 'In general, how masculine or "manly" do you feel?') %>%
  # Eliminate columns not relating to sexual orientaiton:
  select(-c(age_18_34, age_35_64, age_65_over, white_yes, white_no, children_yes,
            children_no, overall)) %>%
  # Convert data frame to tidy data (long) format:
  pivot_longer(-c(question, response), names_to = "sexuality", values_to = "ratio_by_sexuality")

# Visualize results
ggplot(data = masculinity_tidy, aes(x = response, y = ratio_by_sexuality, fill = sexuality)) +
  geom_bar(stat="identity", position = 'dodge') +
  labs(x = "Response", y = "Proportion", labs = "Sexuality",
       title = "In general, how masculine or 'manly' do you feel?")

2020 Presidential Candidates Media Mentions

Description

The raw data behind the story "Beto O'Rourke Ignored Cable News - And It Ignored Him" https://fivethirtyeight.com/features/beto-orourke-ignored-cable-news-and-it-ignored-him/

Usage

media_mentions_cable

media_mentions_online

Format

2 dataframes about 2020 presidential candidate media mentions

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 954 rows and 6 columns.

media_mentions_cable

A data frame with 972 rows representing weeks of cable coverage and 7 variables:

date: start date for the week of coverage
name: candidate's name
matched_clips: number of 15-second clips in that week that mention the specified candidate
all_candidate_clips: number of 15-second clips in that week that mention any candidates
total_clips: total number of 15-second clips that week across the three networks
pct_of_all_candidate_clips: percentage of clips in which that specific candidate is mentioned out of all clips mentioning any candidate for that week (matched_clips / all_candidate_clips)
query: query used for the GDELT Television API

media_mentions_online

A data frame with 954 rows representing weeks and 6 variables:

date: start date for the week of coverage
name: candidate's name
matched_stories: number of stories in that week that mention the specified candidate
all_candidate_stories: number of stories in that week that mention any candidate
pct_of_all_candidate_stories: percentage of stories in which that specific candidate is mentioned out of all stories mentioning any candidate for that week (matched_stories / all_candidate_stories)
query: query for Media Cloud

Source

The GDELT Television API https://blog.gdeltproject.org/gdelt-2-0-television-api-debuts/, which processes the data from the TV News Archive https://archive.org/details/tv.

Two collections in the Media Cloud https://mediacloud.org/ database U.S. Top Online News https://sources.mediacloud.org/#/collections/58722749 and U.S. Top Digital Native News https://sources.mediacloud.org/#/collections/57078150

The Media Really Started Paying Attention to Puerto Rico When Trump Did

Description

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/

The Media Really Started Paying Attention to Puerto Rico When Trump Did

Description

Usage

mediacloud_online_news

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/

The Media Really Started Paying Attention to Puerto Rico When Trump Did

Description

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/

The Media Really Started Paying Attention to Puerto Rico When Trump Did

Description

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/

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

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

https://www.baseball-reference.com/ , http://chadwick-bureau.com, Fangraphs

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

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 adjustment
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

https://www.baseball-reference.com/ , http://chadwick-bureau.com, Fangraphs

Both Parties Think The Mueller Report Was Fair. They Just Completely Disagree On What It Says.

Description

The raw data behind the story 'Both Parties Think The Mueller Report Was Fair. They Just Completely Disagree On What It Says.' https://fivethirtyeight.com/features/both-parties-think-the-mueller-report-was-fair-they-just-disagree-on-what-it-says/

Usage

mueller_approval_polls

Format

A dataset with 65 rows representing every job approval poll of Robert Mueller that we could find from when Mueller was appointed as special council on May 17, 2017 through May 3, 2019 and 12 variables

start: the start date of the poll
end: the end date of the poll
pollster: the name of the pollster
sample_size: the size of the poll sample
population: unknown
text: the text of the poll question
approve: the number of approval in the poll
disapprove: the number of disapproval in the poll
unsure: the number of unsure in the poll
approve_(republican): the number of approval from Republican
approve_(democrat): the number of approval from Democrat
url: the url of the poll

Source

Polls, Washington Post / ABC and Washington Post / Schar School Polls

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

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

Source

Listed as source variable in dataset

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 https://fivethirtyeightdata.github.io/fivethirtyeightdata/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/

A Better Way to Evaluate NBA Defense

Description

The raw data behind the story "A Better Way to Evaluate NBA Defense" https://fivethirtyeight.com/features/a-better-way-to-evaluate-nba-defense/.

Usage

nba_draymond

Format

A data frame with 3009 rows representing DRAYMOND ratings (Defensive Rating Accounting for Yielding Minimal Openness by Nearest Defender) for every player since the 2013-14 season with 4 variables:

season: The second year of the season; for example, 2018-2019 season would be listed as 2019
player: Name of the player
possessions: Number of possessions a player during the season
draymond: Defensive Rating Accounting for Yielding Minimal Openness by Nearest Defender

Source

see https://github.com/fivethirtyeight/data/tree/master/nba-draymond

NBA Elo Ratings

Description

The raw data behind all nba predictions, including the story "The Complete History of the NBA" https://projects.fivethirtyeight.com/complete-history-of-the-nba

Usage

nba_elo_latest

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 1230 rows and 24 columns.

nba_elo_latest

A data frame with 1230 rows representing game played during the most current season of the NBA, and 24 variables:

date: Date
season: the season in which the game was played
neutral: True if the game was played on neutral territory, False if not
playoff: True if the game was played in a playoff, False if not
team1: name of first team
team2: name of second team
elo1_pre: Team 1 Elo rating before game
elo2_pre: Team 2 Elo rating before 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 Elo rating after the game
elo2_post: Team 2 Elo rating after the game
score1: the score of team 1
score2: the score of team 2

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 https://nbatattoos.tumblr.com/

The Rise And Fall Of Women's NCAA Tournament Dynasties

Description

The raw data behind the story 'The Rise And Fall Of Women's NCAA Tournament Dynasties' https://fivethirtyeight.com/features/louisiana-tech-was-the-uconn-of-the-80s/

Usage

ncaa_w_bball_tourney

Format

A dataset with 2092 rows representing every team that has participated in the NCAA Division I Women's Basketball Tournament since it began in 1982 and 19 variables

year: the year of the game which the team participated in
school: the school of the participating team
seed: The '(OR)' seeding designation in 1983 notes the eight teams that played an opening-round game to become the No. 8 seed in each region.
conference: the conference record of the team (if available)
conf_w: number of winning in conference record
conf_l: number of losses in conference record
conf_percent: percent of winning in conference record
reg_w: number of winning in regular-season record
reg_l: number of losses in regular-season record
reg_percent: percent of winning in regular-season record
how_qual: Whether the school qualified with an automatic bid (by winning its conference or conference tournament) or an at-large bid.
first_home_game: Whether the school played its first-round tournament games on its home court.
tourney_w: number of winning in tournament record
tourney_l: number of losses in tournament record
tourney_finish: The round of the final game for each team. OR=opening-round loss (1983 only); 1st=first-round loss; 2nd=second-round loss; RSF=loss in the Sweet 16; RF=loss in the Elite Eight; NSF=loss in the national semifinals; N2nd=national runner-up; Champ=national champions
full_w: number of winning in full record
full_l: number of losses in full record
full_percent: percent of winning in full record

Source

NCAA

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
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 Presidential Election

Source

Google Trends https://trends.google.com/trends/.

Examples

# To convert data frame to tidy data (long) format, run:
library(dplyr)
library(tidyr)
nfl_fandom_google_tidy <- nfl_fandom_google %>%
  pivot_longer(-c("dma", "trump_2016_vote"), 
    names_to = "sport", values_to = "search_traffic") %>%
  arrange(dma)

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
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/blob/master/nfl-fandom/NFL_fandom_data-surveymonkey.csv

Examples

# To convert data frame to tidy data (long) format, run:
library(dplyr)
library(tidyr)
nfl_fandom_surveymonkey_tidy <- nfl_fandom_surveymonkey %>%
  pivot_longer(-c("team", "total_respondents", "gop_percent", "dem_percent",
            "ind_percent", "white_percent", "nonwhite_percent"),
            names_to = "race_party", values_to = "percent") %>%
  arrange(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 transgressions 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

Source

https://www.allourideas.org/nflteampickingsample

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

https://en.wikipedia.org/wiki/List_of_players_and_coaches_suspended_by_the_NFL, https://www.spotrac.com/fines-tracker/nfl/suspensions/

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

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

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 variables:

age_from: Beginning age
age_to: Ending age
trypg_change: Change in TRY per game from one age-year to next

Source

Unknown

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 https://www.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

Source

See https://fivethirtyeight.com/features/the-football-hall-of-fame-has-a-receiver-problem/

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

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/

FiveThirtyEight's Partisan Lean

Description

This directory contains the data for FiveThirtyEight's partisan lean, which is used in our [House] https://projects.fivethirtyeight.com/2018-midterm-election-forecast/house [Senate] https://projects.fivethirtyeight.com/2018-midterm-election-forecast/senate and [Governor] https://projects.fivethirtyeight.com/2018-midterm-election-forecast/governor/ forecasts.

Usage

partisan_lean_district

Format

A dataset with 435 rows representing votes and 4 variables

state: the state of the vote
district_number: the district_number of the vote
pvi_party: the party of the vote
pvi_amount: the Cook Partisan Voting Index of the vote

Note

The original dataset only has 2 columns: "district" and "pvi_538". I separated each of the 2 columns into two. For example, in row 1 of the dataset, the original "district" = "AK-1", and I separated it into "state" = "Arkansas" and "district_number" = "1"; the original "pvi_538" = "R+15.21", and I separated it into “pvi_party" = "R" and "pvi_amount" = "15.21". In addition, I used the full names for all states instead of abbreviations.

Source

Partisan lean is the average difference between how a state or district votes and how the country votes overall, with 2016 presidential election results weighted 50 percent, 2012 presidential election results weighted 25 percent and results from elections for the state legislature weighted 25 percent.

FiveThirtyEight's Partisan Lean

Description

Usage

partisan_lean_state

Format

A dataset with 50 rows representing states and 3 variables

state: the state
pvi_party: the party of the vote
pvi_amount: the Cook Partisan Voting Index of the vote

Note

The original dataset only has 2 columns: "state" and "pvi_538". I separated the "pvi_538" columns into two. For example, in row 1 of the dataset, the original "pvi_538" = "R+27", and I separated it into “pvi_party" = "R" and "pvi_amount" = "27".

Source

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/

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
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

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:

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.
The census data is estimated from 2006 to 2010; police forces may have changed in size since then.
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(dplyr)
library(tidyr)
police_locals_tidy <- police_locals %>%
   pivot_longer(all:asian, names_to = "race", values_to = "perc_in")

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/, https://www.conservativedailynews.com/

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 https://www.presidency.ucsb.edu

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

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/

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

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

Examples

# To convert data frame to tidy data (long) format, run
library(dplyr)
library(tidyr)
library(stringr)
riddler_castles_tidy<-riddler_castles %>%
   pivot_longer(castle1:castle10, names_to = "castle" , values_to = "soldiers") %>%
   mutate(castle = as.numeric(str_replace(castle, "castle","")))

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 solutions can be found at: https://fivethirtyeight.com/features/how-much-should-you-bid-for-that-painting/

Usage

riddler_castles2

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

Examples

# To convert data frame to tidy data (long) format, run
library(dplyr)
library(tidyr)
library(stringr)
riddler_castles_tidy<-riddler_castles2 %>%
   pivot_longer(castle1:castle10, names_to = "castle" , values_to = "soldiers") %>%
   mutate(castle = as.numeric(str_replace(castle, "castle","")))

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

Russia Investigation

Description

This folder contains data behind the story 'Is The Russia Investigation Really Another Watergate?' https://projects.fivethirtyeight.com/russia-investigation/

Usage

russia_investigation

Format

A dataset with 194 rows representing every special investigation since the Watergate probe began in 1973 and 13 variables

investigation: Unique id for each investigation
investigation_start: Start date of the investigation
investigation_end: End date of the investigation
investigation_days: Length, in days, of the investigation. Days will be negative if the charge occurred before the investigation began.
name: Name of the person charged (if applicable). Will be blank if there were no charges.
indictment_days: Length, in days, from the start of the investigation to the date the person was charged (if applicable). Days will be negative if the charge occurred before the investigation began.
type: Result of charge (if applicable)
cp_date: Date the person plead guilty or was convicted (if applicable)
cp_days: Length, in days, from the start of the investigation to the date the person plead guilty or was convicted (if applicable)
overturned: Whether or not the relevant person's conviction was overturned
pardoned: Whether or not the relevant person's charge was pardoned
american: Whether or not the relevant person's charge was a U.S. resident
president: President at the center of the investigation

Source

Information for this story is drawn from an original data set of special counsel, independent counsel and special prosecutor investigations from 1973 to 2019. The data set was created by consulting historical sources, including final reports generated by independent counsels, special counsels and special prosecutors; reports in Congressional Quarterly; and contemporaneous news stories. Secondary historical sources were also consulted, including a 2006 Congressional Research Service report about independent counsel investigations and a history of the Watergate investigation by Stanley Kutler. Data about pardons was obtained from the Office of the Pardon Attorney. Indicted organizations were excluded from our analysis. The data set, which is available on Github, includes the names of all people charged as part of these investigations, as well as the outcome of their cases and the dates of major actions in their cases.

2006 Congressional Research Service report: https://digital.library.unt.edu/ark:/67531/metadc815038/m2/1/high_res_d/98-19_2006Jun08.pdf

dataset in GitHub: https://github.com/fivethirtyeight/data/tree/master/russia-investigation

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

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

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 Services 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 Protection 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
mose: The number of emergency hotline (311) calls made to the Mayor's Office of Special Enforcement 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 Hurricane 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 Hurricane Sandy
nys_emergency_mg: The number of emergency hotline (311) calls made to NYS Emergency Management 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(dplyr)
library(tidyr)
sandy_311_tidy <- sandy_311 %>%
  pivot_longer(-c("date", "total"), names_to = "agency", values_to = "num_calls") %>%
  arrange(date) %>%
  select(date, agency, num_calls, total) %>%
  rename(total_calls = total) %>%
  mutate(agency = as.factor(agency))

Senate Forecast 2018

Description

This file contains links to the data behind FiveThirtyEight's 'Senate forecasts' https://projects.fivethirtyeight.com/2018-midterm-election-forecast/senate/

Usage

senate_national_forecast

Format

A dataframe with 450 rows representing national-level results of the classic, lite, and deluxe Senate forecasts since Aug. 1, 2018 and 11 variables

forecastdate: date of the forecast
party: the party of the forecast
model: the model of the forecast
win_probability: the probability of the corresponding party winning
mean_seats: the mean of the number of seats
median_seats: the median number of seats
p10_seats: the top 10 percentile of number of seats
p90_seats: the top 90 percentile of number of seats
margin: unknown
p10_margin: the margin of p10_seats
p90_margin: the margin of p90_seats

Note

The original dataset included a meaningless column called "state", and all variables under this column was "US". So this column was removed.

Source

FiveThirtyEight’s House, Senate And Governor Models Methodology: https://fivethirtyeight.com/methodology/how-fivethirtyeights-house-and-senate-models-work/

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

Senate Forecast 2018

Description

This file contains links to the data behind FiveThirtyEight's 'Senate forecasts' https://projects.fivethirtyeight.com/2018-midterm-election-forecast/senate/

Usage

senate_seat_forecast

Format

A dataframe with 28353 rows representing seat-level results of the classic, lite, and deluxe Senate forecasts since Aug. 1, 2018 and 12 variables

forecastdate: date of the forecast
state: state of the forecast
class: class of the forecast
special: unknown
candidate: name of the candidate
party: party of the candidate
incumbent: whether the candidate is incumbent
model: the model of the forecast
win_probability: the probability of the corresponding party winning
voteshare: the voteshare of the corresponding party
p10_voteshare: the top 10 percentile of the voteshare
p90_voteshare: the top 00 percentile of the voteshare

Source

FiveThirtyEight’s House, Senate And Governor Models Methodology: https://fivethirtyeight.com/methodology/how-fivethirtyeights-house-and-senate-models-work/

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/soccer-predictions/global-club-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

Information on each state

Description

State name, abbreviation, US Census designated division & region.

Usage

state_info

Format

A data frame with 51 rows representing airlines and 4 variables:

state: State name
state_abbrev: State abbreviation
division: US Census designated division. Values for division are nested within region
region: US Census designated region

Source

US Census Bureau https://en.wikipedia.org/wiki/List_of_regions_of_the_United_States#Interstate_regions.

Examples

library(dplyr)
# Number of states in each division
state_info %>%
  count(division)
# Number of states in each region
state_info %>%
  count(region)

What America’s Governors Are Talking About

Description

The raw data behind the story "What America’s Governors Are Talking About" https://fivethirtyeight.com/features/what-americas-governors-are-talking-about/

Usage

state_index

state_words

Format

2 data frames about the 50 U.S Governors' Speeches

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 2223 rows and 9 columns.

state_index

A data frame with 50 rows representing the 50 U.S. states and 5 variables:

state: the state
governor: the name of the state's governor
party: the party of the state's governor
filename: the filename of the speech in the speeches folder at https://github.com/rudeboybert/fivethirtyeight/tree/master/data-raw/state-of-the-state/speeches
url: a link to an official/media source for the speech

state_words

A data frame with 2,223 rows representing phrases and 9 variables:

phrase: one-, two-, and three-word phrases spoken repeatedly
category: thematic categories for the phrases
d_speeches: number of Democratic speeches containing the phrase
r_speeches: number of Republican speeches containing the phrase
total: total number of speeches containing the phrase
percent_of_d_speeches: percent of the 23 Democratic speeches containing the phrase
percent_of_r_speeches: percent of the 27 Republican speeches containing the phrase
chi2: the chi-square test statistic for statistical significance
pval: p-value for chi^2 test

Source

The chi-square test statistic https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html#sklearn.feature_selection.chi2

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/

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 https://fivethirtyeightdata.github.io/fivethirtyeightdata/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

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

Source

See https://github.com/fivethirtyeight/data/tree/master/tennis-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

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

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

For A Trump Nominee, Neil Gorsuch’s Record Is Surprisingly Moderate On Immigration

Description

The raw data behind the story "For A Trump Nominee, Neil Gorsuch’s Record Is Surprisingly Moderate 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

Note

In immigration cases, partial relief to immigration petitioner is coded as liberal because the petitioner 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 often 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

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

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 historical 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 systematic 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 contained 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

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

Trump Lawsuits

Description

This folder contains the data behind the stories: 'What Trump's Legal Battles Tell Us About Presidential Power' https://fivethirtyeight.com/features/what-trumps-legal-battles-tell-us-about-presidential-power/; 'Why It Might Be Impossible To Overturn A Presidential Pardon' https://fivethirtyeight.com/features/why-it-might-be-impossible-to-overturn-a-presidential-pardon/; 'Will The Supreme Court Fast-Track Cases Involving Trump?' https://fivethirtyeight.com/features/will-the-supreme-court-fast-track-cases-involving-trump/; 'Why One of Trump’s Biggest Legal Threats Is New York’s Attorney General' https://fivethirtyeight.com/features/why-one-of-trumps-biggest-legal-threats-is-new-yorks-attorney-general/; 'Should Judges Pay Attention To Trump’s Tweets?' https://fivethirtyeight.com/features/should-judges-pay-attention-to-trumps-tweets/; 'Trump Is Losing The Legal Fight Against Sanctuary Cities, But It May Still Pay Off Politically' https://fivethirtyeight.com/features/trump-is-losing-the-legal-fight-against-sanctuary-cities-but-it-may-still-pay-off-politically/; 'Will Trump’s Latest Lawsuits Keep Congress From Investigating Future Presidents?' https://fivethirtyeight.com/features/will-trumps-latest-lawsuits-keep-congress-from-investigating-future-presidents/;

Usage

trump_lawsuits

Format

A dataset with 57 rows representing lawsuits and 16 variables

docket_number: Current docket number
date_filed: Date lawsuit was originally filed
case_name: Case name (current)
plaintiff: Names of plaintiffs (if more than five, "et al" for plaintiffs who are not in case name)
defendant: Names of defendants (if more than five, "et al" for defendants who are not in case name)
current_location: Court the lawsuit is currently in front of
previous_location: Other courts the case has appeared before
jurisdiction: Where the case is being heard | 1 = Federal; 2 = State
judge: Names of the judges the case is currently before
nature: PACER code for nature of lawsuit (Not relevant for criminal cases) https://pacer.uscourts.gov/help/faqs/what-nature-suit-code
trump_category: Whether the case is related to action before Trump was president, his personal conduct as president, or a policy action as president | 1 = Case directed at pre-presidency action; 2 = Case directed at personal action of Trump as president; 3 = Case directed at policy action of Trump as president
capacity: The capacity in which Trump is implicated | 1 = Case directed at Trump personally; 2 = Case directed at action of Trump administration; 3 = Trump as plaintiff; 4 = Trump administration as plaintiff; 5 = Case directed at Trump associate; 6 = Other
type: Criminal vs. civil | 1 = Criminal; 2 = Civil
issue: Key topic area raised in the case (i.e. emoluments, First Amendment, DACA, etc). Categories created based on key policy topic area or legal issue. Calls are subjective and based on reporting and may change.
docket_orig: Original docket number, if case has been appealed or changed jurisdiction
status: Whether the case, or the part of the case connected to Trump, is ongoing. | 1 = Case is ongoing; 2 = Case or part of case connected to Trump is closed

Source

Approval Polls

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 https://www.memeorandum.com/.

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 https://fivethirtyeightdata.github.io/fivethirtyeightdata/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

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 international 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 https://www.pewresearch.org/fact-tank/2017/07/17/9-charts-on-how-the-world-sees-trump/

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 president 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 positively
south_korea: The percentage of people from South Korea who answered the poll question positively
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
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 positively
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 https://www.pewresearch.org/fact-tank/2017/07/17/9-charts-on-how-the-world-sees-trump/

Examples

# To convert data frame to tidy data (long) format, run:
library(dplyr)
library(tidyr)
trumpworld_polls_tidy <- trumpworld_polls %>%
  pivot_longer(-c("year", "avg", "question"), 
    names_to = "country", values_to = "percent_positive")

The Media Really Started Paying Attention to Puerto Rico When Trump Did

Description

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

The Media Really Started Paying Attention to Puerto Rico When Trump Did

Description

Usage

tv_hurricanes_by_network

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

The Media Really Started Paying Attention to Puerto Rico When Trump Did

Description

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

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

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.

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

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

2019 Women's World Cup Predictions

Description

The raw data behind the story "2019 Women’s World Cup Predictions" https://projects.fivethirtyeight.com/2019-womens-world-cup-predictions/

Usage

wwc_2019_forecasts

wwc_2019_matches

Format

2 dataframes about the 2019 Women's World Cup matches and teams

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 52 rows and 18 columns.

wwc_2019_forecasts

A data frame with 192 rows representing 2019 Women's World Cup team match-by-match projections, and 21 variables:

date: Date match was played
team: Team
group: Assigned group for the group stage
spi: Soccer power index
global_o: SPI offensive rating
global_d: SPI defensive rating
sim_wins: Simulated number of wins
sim_ties: Simulated number of ties
sim_losses: Simulated number of losses
sim_goal_diff: Simulated difference between goals_scored and goals_against
goals_scored: The number of goals that a team is expected to score against an average team on a neutral field
goals_against: The number of goals that a team is expected to concede against an average team on a neutral field
group_1: Chance of winning group stage game 1
group_2: Chance of winning group stage game 2
group_3: Chance of winning group stage game 3
group_4: Chance of winning group stage game 4
make_round_of_16: Chance of playing in the round of 16
make_quarters: Chance of playing in the quarter-finals
make_semis: Chance of playing in the semi-finals
make_final: Chance of playing in the finals
win_league: Chance of winning the tournament

wwc_2019_matches

2019 Women's World Cup Predictions A data frame with 52 rows representing Women's World Cup matches, and 18 variables:

date: Date match was played
team1: Team 1
team2: Team 2
spi1: Soccer power index of team 1
spi2: Soccer power index of team 2
prob1: Probability that team 1 will win match
prob2: Probability that team 2 will win match
prob_tie: Probability that the teams will tie the match
proj_score1: Projected number of goals scored by team 1
proj_score2: Projected number of goals scored by team 2
score1: Actual number of goals scored by team 1
score2: Actual number of goals scored by team 2
xg1: Shot-based expected goals for team 1
xg2: Shot-based expected goals for team 2
nsxg1: Non-shot expected goals for team 1
nsxg2: Non-shot expected goals for team 2
adj_score1: Goals scored by team 1 accounting for the conditions under which each goal was scored
adj_score2: Goals scored by team 2 accounting for the conditions under which each goal was scored

Source

https://projects.fivethirtyeight.com/soccer-api/international/2019/wwc_forecasts.csv

https://projects.fivethirtyeight.com/soccer-api/international/2019/wwc_matches.csv

Some People Are Too Superstitious To Have A Baby On Friday The 13th

Description

Usage

Format

Source

See Also

Some People Are Too Superstitious To Have A Baby On Friday The 13th

Description

Usage

Format

Source

See Also

American Health Care Act Polls

Description

Usage

Format

Source

Examples

Should Travelers Avoid Flying Airlines That Have Had Crashes in the Past?

Description

Usage

Format

Source

Examples

Trump Might Be The First President To Scrap A National Monument

Description

Usage

Format

Source

How Much Trouble Is Ted Cruz Really In?

Description

Usage

Format

Source

Joining The Avengers Is As Deadly As Jumping Off A Four-Story Building

Description

Usage

Format

Source

Bachelorette / Bachelor

Description

Usage

Format

Details

Source

Dear Mona, Which State Has The Worst Drivers?

Description

Usage

Format

Source

The Dollar-And-Cents Case Against Hollywood's Exclusion of Women

Description

Usage

Format

Details

Source

'Straight Outta Compton' Is The Rare Biopic Not About White Dudes

Description

Usage

Format

Source

A Statistical Analysis of the Work of Bob Ross

Description

Usage

Format

Source

Examples

Two Years In, Turnover In Trump’s Cabinet Is Still Historically High

Description

Usage

Format

Source

Looking For Clues: Who Is Going To Run For President In 2016?

Description

Usage

Format

Source

See Also

Who Will Run For President: Romney Is Out

Description