Package 'GmooG'

Description

Buolamwini and Gebru used their own database that included more women and more people of colour to evaluate how well commercial gender classification algorithms coped with different shades of skin colour in a gender-balanced test database.

Usage

data(aFacial)

Format

A data frame with 72 observations on the following 5 variables.

Sex

Female or Male

Skin

one of six shades of skin colour from I to VI

Prediction

Correct or Wrong

Freq

number of cases

Software

one of three facial recognition software packages

Details

Summary data tables of percentages and some numerical totals were provided in the paper and the supplementary material. Assuming the results had to be based on integer numbers of cases it was possible to reconstruct summary raw numbers of the dataset. The dataset is analysed in Chapter 22, "Comparing software for facial recognition".

Source

Buolamwini, Joy, and Timnit Gebru. 2018. "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." Proceedings of Machine Learning Research 81: 1-15

Examples

data(aFacial, package="GmooG")
head(aFacial, n=12)

Description

The best times up till mid-2021 are for 17 individual swimming events for men and women and for three relay events.

Usage

data(All200)

Format

A data frame with 7685 observations on the following 10 variables.

full_name_computed

Name of swimmer

team_code

country

sdate

date of swim

bdate

date of birth

SwimTime

performance (in seconds)

Gender

Women or Men

style

one of four swimming strokes or three relay events

distance

length of swim with special coding for relays (e.g. 4x100)

dist

length of swim in metres

Rank_Order

ranking within an event

Details

The dataset is analysed in Chapter 20, "Are swimmers swimming faster?".

Source

https://www.worldaquatics.com/swimming/rankings

Examples

data(All200, package="GmooG")
with(All200, table(style))

Description

Individuals who travelled into space between 1961 and 2019.

Usage

data(astronauts)

Format

A data frame with 1277 observations on the following 24 variables.

id

id number of record

number

id number of individual

nationwide_number

national number of individual

name

individual's name

original_name

name in own language

sex

sex of individual

year_of_birth

year of birth of individual

nationality

nationality

military_civilian

military or civilian

selection

selection group

year_of_selection

selection year

mission_number

mission number of individual

total_number_of_missions

total missions of individual

occupation

role on flight: commander, pilot, flight engineer, ...

year_of_mission

Mission year

mission_title

Mission name

ascend_shuttle

Name of ascent shuttle

in_orbit

Name of spacecraft used in orbit

descend_shuttle

Name of descent shuttle

hours_mission

Duration of mission in hours

total_hrs_sum

Total duration of all missions in hours

field21

Instances of EVA by mission

eva_hrs_mission

Duration of extravehicular activities during the mission

total_eva_hrs

Total duration of all extravehicular activities in hours

Details

This dataset is used in Chapter 10, "Who went up in space for how long?"

Source

https://github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-07-14

Examples

data(astronauts, package="GmooG")
library(tidyverse)
nc <- astronauts %>% count(nationality) %>% arrange(-n)

Description

The number of votes by each state for each candidate on each ballot for the Democratic nomination for president.

Usage

data(DC1912)

Format

A data frame with 3939 observations on the following 4 variables.

State

State or territory name (there were 52)

Candidate

Name of one of the 13 candidates or 'NotVoting'

Ballot

Ballot number (1 to 46)

Votes

Number of votes for the candidate on that ballot from the state

Details

Two other smaller datasets are used in combination with this one for the final plot of Chapter 4 (Figure 4.7), "Voting 46 times to choose a Presidential candidate", the estimated times of the ballots (DC1912ballots) and the adjournment times (DC1912adjourns).

Source

Woodson, Urey. 1912. Official Report of the Proceedings of the Democratic National Convention. Chicago: Peterson linotyping Company

Examples

data(DC1912, package="GmooG")
with(DC1912, table(State))

Description

Times that the six adjournments started and finished, taken from Woodson's convention report.

Usage

data(DC1912adjourns)

Format

A data frame with 6 observations on the following 2 variables.

StartT

Date and time of start of adjournment

EndT

Date and time of end of adjournment

Details

This dataset is used in combination with the datasets DC1912 and DC1912ballots for the final plot of Chapter 4 (Figure 4.7), "Voting 46 times to choose a Presidential candidate".

Source

Woodson, Urey. 1912. Official Report of the Proceedings of the Democratic National Convention. Chicago: Peterson linotyping Company

Examples

data(DC1912adjourns, package="GmooG")
DC1912adjourns

Description

The date and time that each ballot took place have been estimated from Woodson's convention report.

Usage

data(DC1912ballots)

Format

A data frame with 46 observations on the following 2 variables.

Ballot

Ballot number (1 to 46)

DateT

Date and time of the ballot

Details

This dataset is used in combination with the datasets DC1912 and DC1912adjourns for the final plot of Chapter 4 (Figure 4.7), "Voting 46 times to choose a Presidential candidate".

Source

Woodson, Urey. 1912. Official Report of the Proceedings of the Democratic National Convention. Chicago: Peterson linotyping Company

Examples

data(DC1912ballots, package="GmooG")
head(DC1912ballots)

Description

The number of pledged delegates by group at the 2020 Democratic convention.

Usage

data(DC1912dels)

Format

A data frame with 58 observations on the following 3 variables.

State

Name of group (mostly state or territory)

TotP

Number of pledged delegates by group at the 2020 Democratic convention

region

Ordered factor: MidWest, NorthEast, West, South, Territory, NA

Details

This dataset is used in Chapter 4, "Voting 46 times to choose a Presidential candidate".

Source

https://ballotpedia.org/Democratic_delegate_rules,_2020 and https://www.census.gov

Examples

data(DC1912dels, package="GmooG")
head(DC1912dels)

Description

The number of electoral votes for each of the 50 states and D.C. from 1788 till 2020.

Usage

data(DC1912evs)

Format

A data frame with 51 observations on the following 36 variables.

Code

Code for State

State

State name (there were 51 including D.C.)

y1788

Numbers of electoral votes by State in 1788

y1792

Numbers of electoral votes by State in 1792

y17961800

Numbers of electoral votes by State for 1796 and 1800

y18041808

Numbers of electoral votes by State in 1804 and 1808

y1812

Numbers of electoral votes by State in 1812

y1816

Numbers of electoral votes by State in 1816

y1820

Numbers of electoral votes by State in 1820

y18241828

Numbers of electoral votes by State in 1824 and 1828

y1832

Numbers of electoral votes by State in 1832

y18361840

Numbers of electoral votes by State in 1836 and 1840

y1844

Numbers of electoral votes by State in 1844

y1848

Numbers of electoral votes by State in 1848

y18521856

Numbers of electoral votes by State in 1852 and 1856

y1860

Numbers of electoral votes by State in 1860

y1864

Numbers of electoral votes by State in 1864

y1868

Numbers of electoral votes by State in 1868

y1872

Numbers of electoral votes by State in 1872

y18761880

Numbers of electoral votes by State in 1876 and 1880

y18841888

Numbers of electoral votes by State in 1884 and 1888

y1892

Numbers of electoral votes by State in 1892

y18961900

Numbers of electoral votes by State in 1896 and 1900

y1904

Numbers of electoral votes by State in 1904

y1908

Numbers of electoral votes by State in 1908

y19121928

Numbers of electoral votes by State from 1912 to 1928

y19321940

Numbers of electoral votes by State from 1932 to 1940

y19441948

Numbers of electoral votes by State in 1944 and 1948

y19521956

Numbers of electoral votes by State in 1952 and 1956

y1960

Numbers of electoral votes by State in 1960

y19641968

Numbers of electoral votes by State in 1964 and 1968

y19721980

Numbers of electoral votes by State from 1972 to 1980

y19841988

Numbers of electoral votes by State in 1984 and 1988

y19922000

Numbers of electoral votes by State from 1992 to 2000

y20042008

Numbers of electoral votes by State in 2000 and 2008

y20122020

Numbers of electoral votes by State from 2012 to 2020

Details

This dataset is used in Chapter 4, "Voting 46 times to choose a Presidential candidate".

Source

https://en.wikipedia.org/wiki/United_States_Electoral_College

Examples

data(DC1912evs, package="GmooG")
head(DC1912evs[, c("State", "y1788", "y19121928", "y20122020")])

Description

Details of the best performances of the top decathletes

Usage

data(Decath21)

Format

A data frame with 116 observations on the following 15 variables.

Rank

Rank order

Decathlete

Decathlete's name

Nationality

Decathlete's nationality

Total

the total points achieved over all 10 events

Run100m

Time for the 100 metres (secs)

LongJump

Distance jumped (metres)

ShotPut

Distance putting the shot (metres)

HighJump

Height jumped (metres)

Run400m

Time for the 400 metres (secs)

Hurdle110m

Time for the 110 metres hurdles (secs)

DiscusD

Distance throwing the discus (metres)

PoleVault

Height achieved (metres)

JavelinD

Distance throwing the javelin (metres)

Run1500m

Time for the 1500 metres (secs)

Venue

Location and year of performance

Source

https://www.decathlon2000.com

Examples

data(Decath21, package="GmooG")
with(Decath21, summary(Run1500m))

Description

150 psoriasis patients were randomized to Placebo (Treatment A) and 450 to the active treatment (Treatment B). The treatment effect in terms of Quality of Life was assessed at Week 16.

Usage

data(DLQI)

Format

A data frame with 900 observations on the following 15 variables.

USUBJID

individual ID

TRT

Placebo (A) or Treatment (B)

PASI_BASELINE

Psoriasis Area and Severity Index at Baseline

VISIT

Initial or at Week 16

DLQI101

How Itchy, Sore, Painful, Stinging: 0-3

DLQI102

How Embarrassed, Self Conscious: 0-3

DLQI103

Interfered Shopping, Home, Yard: 0-3

DLQI104

Influenced Clothes You Wear: 0-3

DLQI105

Affected Social, Leisure Activity: 0-3

DLQI106

Made It Difficult to Do Any Sports: 0-3

DLQI107

Prevented Working or Studying: 0-3

DLQI108

Problem Partner, Friends, Relative: 0-3

DLQI109

Caused Any Sexual Difficulties: 0-3

DLQI110

How Much a Problem is Treatment: 0-3

DLQI_SCORE

DLQI Total Score: 0-30

Details

This dataset is used in Chapter 12, "Psoriasis and the Quality of Life".

Source

https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2021/2021-01-13

Examples

data(DLQI, package="GmooG")
with(DLQI, summary(PASI_BASELINE))

Description

Numbers of vehicle accidents with deer every half-hour from the beginning of 2002 till the end of 2011.

Usage

data(DVCdeer)

Format

A data frame with 175296 observations on the following 3 variables.

mins

beginning of half-hour period, from 00:00 to 23:30

day

day

Freq

number of accidents

Details

This dataset and the dataset DVCnot are both used in Chapter 24, "When do road accidents with deer happen in Bavaria?".

Source

https://www.jstatsoft.org/article/view/v092i01

Examples

data(DVCdeer, package="GmooG")
with(DVCdeer, table(Freq))

Description

Numbers of vehicle accidents every half-hour from the beginning of 2002 till the end of 2011.

Usage

data(DVCnot)

Format

A data frame with 175296 observations on the following 3 variables.

mins

beginning of half-hour period, from 00:00 to 23:30

day

day, from 2002-01-01 to 2011-12-31

Freq

number of accidents

Details

This dataset and the dataset DVCnot are both used in Chapter 24, "When do road accidents with deer happen in Bavaria?".

Source

https://www.jstatsoft.org/article/view/v092i01

Examples

data(DVCnot, package="GmooG")
with(DVCnot, table(Freq))

Description

A field experiment on electric vehicle charging

Usage

data(ElecCars)

Format

A data frame with 3395 observations on these 24 variables.

sessionId

charging session

kwhTotal

total energy use of a given EV charging session, measured in kWh

dollars

amount paid by the user in US$ for a given charging session

created

date and time the session began

ended

date and time the session ended

startTime

hour of day began

endTime

hour of day ended

chargeTimeHrs

total length of session

weekday

day of the week of session

platform

digital platform used by driver

distance

distance from home, if reported

userId

user code

stationId

station code

locationId

location code

managerVehicle

binary, 1 if manager car

facilityType

type of facility, manufacturing = 1, office = 2, research and development = 3, other = 4

Mon

binary for day of week of session

Tues

binary for day of week of session

Wed

binary for day of week of session

Thurs

binary for day of week of session

Fri

binary for day of week of session

Sat

binary for day of week of session

Sun

binary for day of week of session

reportedZip

binary, 1 if user reported zip code

Details

This dataset is used in Chapter 13, "Charging electric cars".

Source

doi:10.7910/DVN/NFPQLW

Examples

data(ElecCars, package="GmooG")
with(ElecCars, table(weekday))

Description

Colours for displaying teams

Usage

data(eu20col)

Format

A data frame with 39 observations on these 6 variables.

team_alpha3

three letter short form for country

url_team

webpage for country

kit_shirt

shirt colour in hex format

kit_away

away shirt colour in hex format

kit_shorts

shorts colour in hex format

kit_socks

socks colour in hex format

Details

This dataset and the dataset eu20p are both used in Chapter 15, "Home or away: where do soccer players play?"

Source

https://github.com/guyabel/chord-uefa-ec/

Examples

data(eu20col, package="GmooG")
head(eu20col)

Description

Colours for displaying teams

Usage

data(eu20p)

Format

A data frame with 4012 observations on these 21 variables.

year

year of competition

squad

country

no

player's squad number (from 1968 on)

pos

position, GK=Goalkeeper, DF=Defender, MF=midfield, FW=Forward

player

player name

date_of_birth_age

date of birth and age at competition

caps

number of international caps

club

club team of player

player_url

webpage for player

club_fa_url

webpage for Country Football Association of club

club_fa

Country Football Association of club

club_2

Second name for club

club_country

Country of club

club_country_flag

Image of country's flag

goals

number of goals scored for country

captain

logical TRUE (captain) or FALSE

player_original

player name and whether they were captain

nat_team

International team

club_country_harm

Country of club

nat_team_alpha3

abbreviation for international team

club_alpha3

abbreviation for country of club

Details

This dataset and the dataset eu20col are both used in Chapter 15, "Home or away: where do soccer players play?"

Source

https://github.com/guyabel/chord-uefa-ec/

Examples

data(eu20p, package="GmooG")
with(eu20p, table(pos))

Description

Numbers working in three sectors in each department of France in 1954.

Usage

data(F1954)

Format

A data frame with 90 observations on the following 8 variables.

ID

ID code for the department

Dept

Department name

I.Agriculture

Number in thousands of workers in agriculture

II.Industry

Number in thousands of workers in industry

III.Commerce

Number in thousands of workers in commerce

BertinTotal

Total of the three sectors reported by Bertin

Area

Area of department in sq kms

NOM_DEPT

Alternative name for department

Details

The sector data is from Bertin, while area data has been taken from the Guerry package and Wikipedia. The alternative department name was used for merging with a shape file of France (France54Map). The dataset is analysed in Chapter 7, "Re-viewing Bertin's main example".

Source

Bertin, Jaques. 1973. Semiologie Graphique. 2nd ed. The Hague: Mouton-Gautier

Examples

data(F1954, package="GmooG")
with(F1954, summary(I.Agriculture))

Description

A polygon map of the French departments

Usage

data(France54Map)

Format

An sf object with 90 observations on the following 2 variables

Dept

Department name

geometry

list of department polygons

Details

This shape file is used in Chapter 7, "Re-viewing Bertin's main example", and combined with the data in the file F1954. Combining the six new departments of 1967 into the two former departments of Seine and Seine-et-Oise is approximately right.

Source

http://coulmont.com/cartes/rcarto.pdf Derived from GEOFLADept_FR_Corse_AV_L93/DEPARTEMENT.SHP

Description

Life expectancy at birth for almost 200 countries from 1800 to 2016 and forecasts for 2017 to 2100

Usage

data(GapLifeE)

Format

A data frame with 187 observations on 302 variables. The first variable is the name of the country. Every other variable is named as a year from 1800 to 2100 and the values are the historical life expectancy figures up to 2016 and forecasts of life expectancy from 2017 on.

Details

This dataset and the datasets GapRegions and GapPop are all used in Chapter 2, "Graphics and Gapminder".

Source

https://www.gapminder.org

Examples

data(GapLifeE, package="GmooG")
library(tidyverse)
ggplot(GapLifeE, aes(`1900`, `2000`)) + geom_point()

Description

Population data for almost 200 countries from 1800 to 2016 and forecasts for 2017 to 2100

Usage

data(GapPop)

Format

A data frame with 195 observations on 302 variables. The first variable is the name of the country. Every other variable is named as a year from 1800 to 2100 and the values are the historical population figures up to 2016 and forecasts of population from 2017 on.

Details

This dataset and the datasets GapLifeE and GapRegions are all used in Chapter 2, "Graphics and Gapminder".

Source

https://www.gapminder.org

Examples

data(GapPop, package="GmooG")
library(tidyverse)
ggplot(GapPop, aes(`1900`, `2000`)) + geom_point()

Description

Gapminder offers several different divisions into regions of the almost 200 countries of the world.

Usage

data(GapRegions)

Format

A data frame with 197 observations on 16 variables.

geo

country abbreviation

name

country name

four_regions

world split into four regions

eight_regions

world split into eight regions

six_regions

world split into six regions

members_oecd_g77

group membership: oecd, g77, other

Latitude

latitude of country

Longitude

longitude of country

UN member since

date of joining UN

World bank region

world split into seven regions by World bank

World bank, 4 income groups 2017

world split into four income groups by World bank

World bank, 3 income groups 2017

world split into three income groups by World bank, all NA

Details

This dataset and the datasets GapLifeE and GapPop are all used in Chapter 2, "Graphics and Gapminder".

Source

https://www.gapminder.org

Examples

data(GapRegions, package="GmooG")
with(GapRegions, table(four_regions, six_regions))

Description

Demographic and cconomic data for the 299 German parliamentary constituencies in 2021

Usage

data(GermanDemographics)

Format

A data frame with 299 observations on the following 17 variables

WkrNr

Constituency (Wahlkreis) number

WkrName

Constituency name

Communities

Number of communities

Area

Area in square kms

Population

Population

Germans

Number of Germans in the population

Foreigners

Percentage of foreigners in the population

PopDensity

Population density, numbers per square km

Under18

Percentage population under 18

Age1824

Percentage population between 18 and 24

Age2534

Percentage population between 25 and 34

Age3559

Percentage population between 35 and 59

Age6074

Percentage population between 60 and 74

Age75up

Percentage population 75 and older

CarsPerP

Cars per 1000 people

Hochschulreife

Percentage qualified for university

Unemployed

Unemployment rate

Details

This dataset and the datasets GermanElection21 and GermanExtraSeats are all used in Chapter 26, "German Election 2021–what happened?"

Source

https://www.bundeswahlleiterin.de Derived from btw21_strukturdaten.csv

Examples

data(GermanDemographics, package="GmooG")
with(GermanDemographics, summary(Under18))

Description

Detailed results by constituency for the German election of 2021 (and for the previous election in 2017)

Usage

data(GermanElection21)

Format

A data frame with 16024 observations on the following 9 variables

WkNr

Constituency (Wahlkreis) number

WkName

Constituency name

Land

Bundesland number

Partei

Party

Stimme

First (personal) or second (party) vote

Anzahl

Number of votes in 2021 election

VorpAnzahl

Number of votes in 2017 election

Bundesland

Bundesland name

Region

Region: West, Berlin, East

Details

This dataset and the datasets GermanDemographics and GermanExtraSeats are all used in Chapter 26, "German Election 2021–what happened?"

Source

https://www.bundeswahlleiterin.de Derived from btw21_kerg2.csv

Examples

library(tidyverse)
data(GermanElection21, package="GmooG")
btw1vP <- GermanElection21 %>% count(Partei) %>% arrange(-n)

Description

Numbers of extra seats (Ueberhangmandate and Ausgleichsmandate) needed to satisfy the German election rules

Usage

data(GermanExtraSeats)

Format

A data frame with 20 observations on these 2 variables.

Year

Election year

Number

Number of extra seats needed

Details

This dataset is used in Chapter 26, "German Election 2021–what happened?".

Source

German election results from https://www.bundeswahlleiter.de

Examples

data(GermanExtraSeats, package="GmooG")
library(tidyverse)
ggplot(GermanExtraSeats, aes(Year, Number)) + geom_line()

Description

A polygon map of the German constituencies

Usage

data(GermanyMap)

Format

An sf object with 299 observations on the following 5 variables

WKR_NR

Constituency (Wahlkreis) number

WKR_NAME

Constituency name

LAND_NR

Bundesland number

LAND_NAME

Bundesland name

geometry

list of constituency polygons

Details

This map file is used in Chapter 26, "German Election 2021–what happened?"

Source

https://www.bundeswahlleiterin.de Derived from Geometrie_Wahlkreise_20DBT_geo.shp

Description

There are 25 chapters of graphical data analyses in the book. Datasets that are not readily available are mainly provided in this package.

Details

Other datasets are analysed in the book as well. They are available in various R packages. Some can be downloaded and updated from the web.

Author(s)

Antony Unwin [email protected]

Description

Studying magneto-optical diagnosis of symptomatic malaria in Papua New Guinea.

Usage

data(malaria)

Format

A data frame with 956 observations on the following 24 variables.

ID

Patient ID

Collect_Date

Date blood sample collected

Age

Patient age

Weight

Patient weight

Sex

Patient sex

Temperature

ancillary temperature in degrees Centigrade

Hb

Patient hemoglobin level in g/dL

illMalaria

Malaria in last two weeks

RDT1

HRP2 line positive

RDT2

LDH line positive

RDTb

HRP and LDH lines positive

Pf

qPCR copy number for P. falciparum per microL of blood

Pv

qPCR copy number for P. vivax in copies per microL of blood

LM_Pf

final expert light microscopy result for P. falciparum in parasites per microL of blood

LM_Pfg

final expert light microscopy result for P. falciparum gametocytes in parasites per microL of blood

LM_Pv

final expert light microscopy result for P. vivax in parasites per microL of blood

LM_Pvg

final expert light microscopy result for P. vivax gametocytes in parasites per microL of blood

LM_Pm

final expert light microscopy result for P. malariae in parasites per microL of blood

LM_Po

final expert light microscopy result for P. ovale in parasites per microL of blood

AveMO

Average magneto-optical signalof blood aliquots #1,2,3 in mV/V

sdMO

Standard deviation of the magneto-optical signals of blood aliquots #1,2,3 in mV/V

MO1

Magneto-optical signal of blood aliquot #1 in mV/V

MO2

Magneto-optical signal of blood aliquot #2 in mV/V

MO3

Magneto-optical signal of blood aliquot #3 in mV/V

Details

This dataset is used in Chapter 19, "Comparing tests for malaria".

Source

doi:10.6084/m9.figshare.13078181.v1

Examples

data(malaria, package="GmooG")
with(malaria, summary(AveMO))

Description

Michelson included more details of each experiment in the table of results in his report.

Usage

data(Mich1879)

Format

A data frame with 100 observations on the following 4 variables.

Date

Day of the experiment (from 5 June to 2 July 1879)

Time

AM, PM or Elec (under electric light)

Value

estimate of the speed of light minus 299000, uncorrected for temperature and refraction

Temperature

temperature in degrees Fahrenheit, from 58 to 90

Details

This dataset and the dataset newcomb are both used in Chapter 5, "Measuring the speed of light".

Source

Michelson, Albert. 1880. "Experimental Determination of the Velocity of Light Made at the U.S. Naval Academy, Annapolis." Astronomical Papers 1: 109-45. https://books.google.de/books? id=343nAAAAMAAJ

Examples

data(Mich1879, package="GmooG")
with(Mich1879, summary(Temperature))

Description

Newcomb reported three series of measurements and regarded the third series used here as the best.

Usage

data(newcomb)

Format

A data frame with 66 observations on the following 6 variables.

Date

Day of the experiment (from 24 July to 5 September 1882)

Observer

Newcomb or Holcombe (who assisted Newcombe in these experiments)

Wt1

a weight given by Newcomb for the quality of the image observed

Wt2

a second weight for the quality of the image

Time

time taken in millionths of a second for light to travel a distance of 7.44242 kilometres in air

Wt

overall weight given by Newcomb to the observation

Details

This dataset and the dataset Mich1879 are both used in Chapter 5, "Measuring the speed of light".

Source

Newcomb, Simon. 1891. "Measures of the Velocity of Light Made Under the Direction of the Secretary of the Navy During the Years 1880-1882." Astronomical Papers 2: 107-230

Examples

data(newcomb, package="GmooG")
with(newcomb, summary(Time))

Description

Individuals who competed at the Olympic Games from 1896 to 2016.

Usage

data(OlympicPeople)

Format

A data frame with 219434 observations on the following 4 variables.

Sex

Sex of athlete

NOC

Abbreviation for national team

Year

Year of Games

City

Location of Games

Details

This dataset and the dataset OlympicPerfs are both used in Chapter 6, "The modern Olympic Games in numbers".

Source

Derived from https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results

Examples

data(OlympicPeople, package="GmooG")
with(OlympicPeople, table(Year))

Description

Performances at the Summer Olympic Games from 1896 to 2016.

Usage

data(OlympicPerfs)

Format

A data frame with 108789 observations on the following 8 variables.

rank

rank in event

medalType

medal won: one of Gold, Silver, Bronze, NA

games

location and year

discipline

discipline of event

event

name of event

result_value

result reported

result_type

type of result: distance, time, points, weight, and four others

country

country

Details

This dataset and the dataset OlympicPeople are both used in Chapter 6, "The modern Olympic Games in numbers".

Source

Derived from a dataset scraped from the web and provided to the maintainer.

Examples

data(OlympicPerfs, package="GmooG")
library(tidyverse)
OlyD <- OlympicPerfs %>% count(discipline)

Description

Plumage and morphological characteristics of three species of shearwaters.

Usage

data(SeaBirds)

Format

A data frame with 153 observations on the following 6 variables.

collar

one of five categories

eyebrows

four levels from none to very pronounced

undertail

four levels: White, Black, Black & White, Black & WHITE

border

none, few or many

sex

male or female

species

one of Audubon, Galapagos, Tropical

Details

This dataset is used in Chapter 23, "Distinguishing shearwaters".

Source

Derived from the R package CoModes (numerial categories have been converted to text and common names rather than scientific names are used for species)

Examples

data(SeaBirds, package="GmooG")
with(SeaBirds, table(species))

Description

Responses on questions about gay rights at State level and Federal level

Usage

data(SurvGR)

Format

A data frame with 81422 observations on 11 variables.

ID

ID number

cDATE

Date of interview

State

Respondent's state of residence

age

Respondent's age

gender

Respondent's gender

race

Respondent's race

urbanity

Urban, Suburban, or Rural

QuF

Question answered about Federal gay rights

valF

Answer to Federal question

valS

Answer to State question

QuS

Question answered about State gay rights

Details

This dataset is used in Chapter 9, "Results from surveys on gay rights".

Source

The Annenberg Public Policy Center of the University of Pennsylvania

Examples

data(SurvGR, package="GmooG")
with(SurvGR, table(urbanity))

Description

Some information on those who sailed on the Titanic

Usage

data(TitanicPassCrew)

Format

A data frame with 2208 observations on 7 variables.

Age

Age of individual

Gender

Gender of individual

Group

Class of passenger or section of crew

Area

abbreviated version of Group

Joined

Port where individual boarded:Belfast, Southampton, Cherbourg or Queenstown

Nationality

Individual's nationality

survived

Whether the individual survived:yes or no

Details

This dataset is used in Chapter 26, "The Titanic Disaster".

Source

Derived from a fuller dataset available from Encyclopedia Titanica

Examples

data(TitanicPassCrew, package="GmooG")
with(TitanicPassCrew, table(Joined))

Description

Map of the contiguous US States including information on the regional classification by the Census Bureau

Usage

data(USregions)

Format

A data frame with 49 observations on 4 variables.

NAME

name of state

State

2-letter code for state

Region

one of four Census Bureau regions: NorthEast, South, MidWest, West

geometry

map polygons for state

Details

This dataset is used in Chapter 9, "Results from surveys on gay rights".

Source

The polygon map data is from the spData package

Examples

data(USregions, package="GmooG")

Description

Fuel economy data for individual models of cars and trucks provided by the US Department of Energy.

Usage

data(VehEffUS)

Format

A data frame with 43516 observations on the following 16 variables.

year

model year, from 1984 to 2022)

make

make of car

model

model of car

VClass

class of vehicle

cylinders

number of cylinders, from 2 to 16

atvType

type of alternative fuel or advanced technology vehicle

displ

engine displacement in liters

drive

drive axle type

trany

transmission

city

city MPG for fuelType1

highway

highway MPG for fuelType1

combined

combined MPG for fuelType1

fuelCostA08

annual fuel cost for fuelType1 ($)

fuelType1

main fuel type

barrels08

annual petroleum consumption in barrels for fuelType1

co2TailpipeGpm

tailpipe CO2 in grams/mile for fuelType1

Details

This dataset is used in Chapter 17, "Fuel efficiency of cars in the USA".

Source

Selection of variables from https://www.fueleconomy.gov/feg/epadata/vehicles.csv.zip

Examples

data(VehEffUS, package="GmooG")
with(VehEffUS, table(drive))