Package {socviz}

%nin%

Description

Convenience 'not-in' operator

Usage

x %nin% y

Arguments

Details

Complement of the built-in operator %in%. Returns the elements of x that are not in y.

Value

logical vecotor of items in x not in y

Author(s)

Kieran Healy

Examples

fruit <- c("apples", "oranges", "banana")
"apples" %nin% fruit
"pears" %nin% fruit

US County Poverty Rates by Age Group

Description

A dataset of US poverty rates by selected age groups within counties.

Usage

acs_poverty

Format

A tibble with 9,666 rows and 4 columns.

Details

• fips. County FIPS code.

• age_group. Adults 18-64, Children <18, Seniors 65+.

• age_rate. Poverty rate (percent) for 'age_group' in county.

• total_rate. Poverty rate (percent) for all ages in county.

Source

American Community Survey 2023 ACS 5-year estimates, Table B17018.

US County Poverty Rates by Age Group, Longer Version

Description

A dataset of US poverty rates by selected age groups within counties.

Usage

acs_poverty_lon

Format

A tibble with 12,888 rows and 3 columns.

Details

• geoid. County FIPS code.

• age_group. Adults 18-64, Children <18, Seniors 65+, All Ages.

• prop_poor. Proportion (0-1) of 'age_group' in poverty within county.

Source

American Community Survey 2023 ACS 5-year estimates, Table B17018.

American Sociological Association Section Membership

Description

Membership and some financial information for sections of the American Sociological Association in 2014/15.

Usage

asasec

Format

## 'asasec' A data frame with 52 rows and 9 columns:

Section

Section name.

Sname

Short name.

Beginning

Cash on hand at beginning of year (2015).

Revenues

Membership revenues.

Expenses

Section expenses.

Ending

Cash on hand at end of year (2015).

Journal

Does the Section run a journal?

Year

Membership year.

Members

Number of members (2014).

Details

Data from the American Sociological Association.

Author(s)

Kieran Healy

Source

ASA Annual Report 2016.

center_df

Description

Scale and/or center the numeric columns of a data frame or tibble

Usage

center_df(data, sc = FALSE, cen = TRUE)

Arguments

Details

Takes a data frame or tibble as input and scales and/or centers the numeric columns. By default, centers but doesn't scale

Value

An object of the same class as 'data', with the numeric columns scaled or centered as requested

Author(s)

Kieran Healy

Examples

head(center_df(organdata))

Plot a table of color hex values as a table of colors

Description

Plot a table of color hex values as a table of colors

Usage

color_comp(df)

Arguments

Details

Given a data frame of color values, plot them as swatches

Value

Plot of table of colors

Author(s)

Kieran Healy

Examples

color_table
color_comp(color_table)

Draw a palette of colors

Description

Draw a palette of colors

Usage

color_pal(col, border = "gray70", ...)

Arguments

Details

Borrowed from the colorspace library

Value

Plot of a color palette

Author(s)

colorspace library authors

Examples

color_pal(c("#66C2A5", "#FC8D62", "#8DA0CB"))

A table of hex color values related to types of color blindness

Description

Hex values for five default ggplot colors, with corresponding approximations for three kinds of color blindness. Produced by the 'dichromat' package.

Usage

color_table

Format

A tibble with five rows and four columns.

Source

Kieran Healy

US County geometries and demographic data

Description

US County map data layer with selected Census Bureau demographic variables.

Usage

counties_sf

Format

## 'counties_sf' A simple features object 3,144 rows and 16 columns:

fips

FIPS code.

name

County name.

area_sqmi

Area in square miles.

white

N White population.

black

N Black population.

asian

N Asian population.

nh_white

N Non-Hispanic White population.

hispanic

N Hispanic population.

pop

Total population.

black_disc

Percent Black, discretized.

hisp_disc

Percent Hispanic, discretized.

nhwhite_disc

Percent Non-Hispanic White, discretized.

asian_disc

Percent Asian, discretized.

pop_dens

Population density per square mile.

pop_dens_disc

Population density per square mile, discretized.

su_gun6

Firearm-related suicides per 100,000 population, 1999-2015. Factor variable cut into six categories. Note that the values in this variable contain an inaccurate bottom-quartile coding by construction. Do not present this variable as an accurate measure of the firearm-related suicide rate.

pop_dens6

Population density per square mile, discretized into six categories, 2014 estimates.

geometry

Geometry.

Details

A simple features object. Load the ‘sf' package before using. Alaska and Hawaii have had their geometries scaled and shifted to the bottom left of the map area. Alaska’s Aleutian islands are not included. Except where noted, population counts and other demographic information are from the 2024 5-year ACS estimates.

Author(s)

Kieran Healy

Source

US Census Bureau.

US County Components of Population Change

Description

A dataset of components of population change (rates only) for US Counties in 2023.

Usage

county_comp

Format

A tibble with 3,144 rows and 8 columns.

Details

• fips. County FIPS code.

• county. County name.

• state. State abbreviation.

• rbirth. Birth rate.

• rdeath. Death rate.

• rnatchg. Natural change rate.

• rintl. International migration rate.

• rdom. Domestic migration rate.

• rnetmig. Net migration rate.

Source

US Census Bureau Components of Population Change 2023 estimates.

Census Data on US Counties

Description

Selected county data (including US and state-level observations on some variables). Preserved for use with the first edition of the book only.

Usage

county_data

Format

A data frame with 3195 rows and 13 columns.

Details

The variables are as follows:

• id. FIPS State and County code (character)

• name. State or County Name

• state. State abbreviation

• census_region. Census region

• pop_dens. Population density per square mile, 2014 estimate (seven categories).

• pct_black. Percent black population, 2014 estimate (seven category factor)

• pop_dens6. Population density per square mile, 2014 estimate (six categories)

• su_gun6. Firearm-related suicides per 100,000 population, 1999-2015. Factor variable cut into six categories. Note that the values in this variable contain an inaccurate bottom-quartile coding by construction. Do not present this variable as an accurate measure of the firearm-related suicide rate.

Source

US Census Bureau, Centers for Disease Control

US County map file

Description

US county map data

Usage

county_map

Format

A data frame with 191,372 rows and 7 columns.

Details

• long. Longitude

• lat. Latitude

• order. Order

• hole. Hole (true/false)

• piece. Piece

• group. Group

• id. FIPS code

Source

Eric Celeste

Years of school completed by people 25 years and over in the US.

Description

Counts of educational attainment (in thousands) from 1940 to 2016

Usage

edu

Format

A tibble with 366 rows and 11 columns.

Details

The variables are as follows:

• age Character. Cut into 25-34, 35-54, 55>

• sex Character. Male, Female.

• year Integer.

• total Integer. Total in thousands.

• elem4 Double. 0 to 4 years of Elementary School completed.

• elem8 Double. 5 to 8 years of Elementary School completed.

• hs3 Double. 1 to 3 years of High School completed.

• hs4 Double. 4 years of High School completed.

• coll3 Double. 1 to 3 years of College completed.

• coll4 Double. 4 or more years of College completed.

• median Double. Median years of education.

Source

US Census Bureau

US Presidential Election 2016, State-level results

Description

State-level vote totals and shares for the 2016 US Presidential election. The variables are as follows:

• state. State name.

• st. State abbreviation.

• fips. State FIPS code

• total_vote. Total votes cast.

• vote_margin. Winner's vote margin

• winner. Winning candidate.

• party. Winning party.

• pct_margin. Winner's percentage margin (proportion of total vote)

• r_points. Percentage point difference between Trump share and Clinton

• d_points. Percentage point difference between Clinton share and Trump

• pct_clinton. Clinton vote share (proportion)

• pct_trump. Trump vote share (proportion)

• pct_johnson. Johnson vote share (proportion)

• pct_other. Other vote share (proportion)

• clinton_vote. Clinton vote total

• trump_vote. Trump vote total

• johnson_vote. Johnson vote total

• other_vote. Other vote total

• ev_dem. Electoral votes for Clinton

• ev_rep. Electoral votes for Trump

• ev_oth. Electoral votes for Other

• census. Census region.

Usage

election

Format

A (tibble) data frame with 51 rows and 22 columns.

Source

Vote data from Dave Leip, US Election Atlas, http://uselectionatlas.org.

US Presidential Election 2024, State-level results

Description

State-level vote totals and shares for the 2024 US Presidential election.

Usage

election24

Format

## 'election24' A data frame with 51 rows and 20 columns:

state

State name.

st

State abbreviation.

fips

State FIPS code (character).

total_vote

Total votes case.

vote_margin

Vote margin (Trump positive values; Harris negative.)

winner

Winning candidate.

party

Winning party.

pct_margin

Winner's percentage margin (proportion of total vote)

r_points

Percentage point difference between Trump vote percent and Harris vote percent

d_points

Percentage point difference between Harris vote percent and Trump vote percent

pct_harris

Harris vote share (proportion)

pct_trump

Trump vote share (proportion)

pct_other

Other vote share (proportion)

harris_vote

Harris vote total

trump_vote

Trump vote total

other_vote

Other vote total

ev_dem

Electoral votes for Harris

ev_rep

Electoral votes for Trump

ev_other

Electoral votes for Others

census

Census region

Author(s)

Kieran Healy

Source

Vote data from Wikipedia, https://en.wikipedia.org/wiki/2024_United_States_presidential_election

US County-level Presidential Election data, 2024

Description

A tibble with US presidential election data

Usage

election24_county_df

Format

## 'election24_county_df' A tibble object with 3,153 rows and 7 columns:

fips

County FIPS code.

st

State name abbreviation

votes_dem

Votes for Harris/Walz ticket.

votes_gop

Votes for Trump/Vance ticket.

total_votes

Total votes cast.

winner

Winning party.

flipped

Did the party winner change from the winner in 2020? (Yes/No)

Details

A tibble.

Author(s)

Kieran Healy

Source

Election data derived from https://doi.org/10.7910/DVN/VOQCHQ

US Presidential Election vote shares

Description

A dataset of US presidential elections from 1824 to 2024, with information on the winner, runner up, and various measures of vote share. The variables are as follows:

Usage

elections_historic

Format

A (tibble) data frame with 51 rows and 19 columns.

Details

• election. Number of the election counting from the first US presidential election. 1824 is the 10th election.

• year. Year.

• winner. Full name of winner.

• win_party. Party affiliation of winner.

• ec_votes. Electoral college votes for winner.

• ec_denom. Number of votes in the electoral college.

• ec_pct. Winner's share of electoral college vote. (A proportion. Range is 0 to 1.)

• popular_pct. Winner's share of popular vote. (A proportion. Range is 0 to 1.)

• popular_margin. Winner's margin of the popular vote, expressed as a proportion. Can be positive or negative.

• votes. Total votes cast in the election.

• margin. Winner's vote margin in the popular vote.

• runner_up. Runner up candidate.

• ru_part. Party affiliation of runner up candidate.

• turnout_pct. Voter turnout as a proportion of eligible voters. (A proportion. Range is 0 to 1.)

• winner_lname Last name of winner.

• winner_label Winner's last name and election year.

• ru_lastname. Runner up's last name.

• ru_label. Runner up's last name and election year.

• two_term. Is this a two term presidency? (TRUE/FALSE.) Note that F.D. Roosevelt was elected four times.

Source

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

Child Pedestrians involved in Fatal Motor Vehicle Crashes, 2009-2023

Description

Daily data on child pedestrians (aged 0-17 years) involved in a motor vehicle crash that resulted in a fatality.

Usage

farsinvolved

Format

## 'farsinvolved' A data frame with 5,490 rows and 4 columns:

month

Month (character)

day

Day of the month (character)

year

Year (character)

n

Number of pedestrians

Details

Each row is a day of the year between January 1st 2009 and December 31st 2023. The 'n' column is the number of pedestrians in the United States who were involved in a motor vehicle crash that day, where the event resulted in a fatality and where the pedestrian was aged between 0 and 17 years old. The person killed is not necessarily the pedestrian.

Author(s)

Kieran Healy

Source

National Highway Traffic Safety Administration (NHTSA) Motor Vehicle Crash Data Querying and Reporting

Monetary Base and S&P 500 series

Description

Two time series of financial data from FRED, the _i means indexed to 100 in the base observation.

Usage

fredts

Format

A data frame with 5 columns and 357 rows.

Source

FRED data.

General Social Survey data, 1972-2024

Description

A dataset containing an extract from the General Social Survey. See http://gss.norc.org/Get-Documentation for full documentation of the variables. This data contains many of the same variables as 'gss_sm', but for all available years from 1972-2024.

Usage

gss_lon

Format

A data frame with 75,699 rows and 25 columns.

Details

• year. GSS year for this respondent.

• id. Respondent id number.

• ballot. Ballot used for interview.

• age. Age of respondent.

• degree. R's highest degree.

• race. Race of respondent.

• sex. Respondent's sex.

• siblings. Number of brothers and sisters (recoded from SIBS).

• kids. Number of children (recoded from CHILDS).

• bigregion. Region of interview (identical with REGION).

• region. Region of interview.

• income16. Total family income.

• religion. R's religious preference (recoded from RELIGION)

• marital. Marital status.

• padeg. Father's highest degree.

• madeg. Mother's highest degree.

• partyid. Political party affiliation.

• polviews. Think of self as liberal or conservative.

• happy. General happiness.

• partners_rc. How many sex partners r had in last year. (Recoded from PARTNERS)

• grass. Should marijuana be made legal.

• zodiac. Respondent's astrological sign.

• wtssall. Person weight variable (1972-2018).

• wtssps. Person weight variable (1972-2024).

• vpsu. Sampling unit

• vstrat. Stratification unit

Source

National Opinion Research Center, http://gss.norc.org.

General Social Survey data, 2016

Description

A dataset containing an extract from the 2016 General Social Survey. See http://gss.norc.org/Get-Documentation for full documentation of the variables.

Usage

gss_sm

Format

A data frame with 2538 rows and 26 columns.

Details

• year. gss year for this respondent.

• id. respondent id number.

• ballot. ballot used for interview.

• age. age of respondent.

• childs. number of children.

• sibs. number of brothers and sisters.

• degree. Rs highest degree.

• race. race of respondent.

• sex. respondent's sex.

• region. region of interview.

• income16. total family income.

• relig. rs religious preference.

• marital. marital status.

• padeg. fathers highest degree.

• madeg. mothers highest degree.

• partyid. political party affiliation.

• polviews. think of self as liberal or conservative.

• happy. general happiness.

• partners. how many sex partners r had in last year.

• grass. should marijuana be made legal.

• zodiac. respondents astrological sign.

• pres12. raw variable for whether the Respondent voted for Obama. Recoded to obama in this dataset.

• wtssall. weight variable.

• income_rc. Recoded income variable.

• agegrp. Age variable recoded into age categories

• ageq. Age recoded into quartiles.

• siblings. Top-coded sibs variable.

• kids. Top-coded childs variable.

• bigregion. Region variable (Census divisions) recoded to four Census regions.

• religion. relig variable recoded to six categories.

• partners_rc. partners variable recoded to five categories.

• obama. Respondent says the voted for Obama in 2012. 1 = yes; 0 = all other non-design options (Romney, other candidate, did not vote, refused, etc.)

Source

National Opinion Research Center, http://gss.norc.org.

int_to_year

Description

Convert an integer to a date.

Usage

int_to_year(x, month = "06", day = "15")

Arguments

Value

A vector of dates where the input integer forms the year component. The day and month components added will by default be the 15th of June, so that tick marks will appear in the middle of the series on plots. For input, only years 0:9999 are accepted.

Author(s)

Kieran Healy

Examples

int_to_year(1960)
class(int_to_year(1960))
int_to_year(1960:1965)
int_to_year(1990, month = "01", day = "30")

US Law School Enrollments 1963-2015

Description

Annual enrollments in US Law Schools.

Usage

lawschools

Format

A tibble with 53 rows and 11 columns.

Details

The variables are as follows:

• ay. Academic year. character.

• year. Year. integer.

• n_schools. Number of law schools. integer.

• fy_enrollment. First year enrollment. integer.

• fy_male. First year enrollment, men. integer.

• fy_female. First year enrollment, women. integer.

• jd_total. Total JD enrollment. integer.

• jd_male. Total JD enrollment, men. integer.

• jd_female. Total JD enrollment, women. integer.

• tot_enrolled. Total enrolled. integer.

• jd_llb_awarded. JD/LLB degrees awarded. integer.

Source

American Bar Association

Mauna Loa Atmospheric CO2 Concentration

Description

A subset of the co2 data in base R's [datasets] package, in a ggplot2-friendly format.

Usage

maunaloa

Format

A data frame with 4 columns and 271 rows.

Source

R base datasets; Cleveland (1993).

Life Expectancy in the OECD, 1960-2023.

Description

Life expectancy data for individual countries.

Usage

oecd_le

Format

A tibble with 2,203 rows and 4 columns.

Details

The variables are as follows:

• country. Country. (Character)

• year. Year. (Integer.)

• lifeexp. Life Expectancy at Birth, measured in years.

• is_usa. Indicator for USA or Other country.

Source

OECD

Life Expectancy in the OECD, 1960-2023

Description

Life expectancy data summary table.

Usage

oecd_sum

Format

A tibble with 64 rows and 5 columns.

Details

The variables are as follows:

• year. Year. (Integer.)

• other. Life Expectancy at birth in OECD countries excluding the USA. Measured in years.

• usa. Life Expectancy at birth in the USA. Measured in years.

• diff. Difference between usa and other.

• hi_lo. Is usa above or below the oecd average?

Source

OECD

Monthly Births in the U.S., 1933-2015

Description

Births by month, 1933-2015, with decomposition components.

Usage

okboomer

Format

## 'okboomer' A data frame with 996 rows and 11 columns:

date

Date in date format

year_fct

Year as ordered factor

month_fct

Month as ordered factor

n_days

N of days in this month

births

Total births in this month

total_pop

Population

births_pct

Births as a proportion of total population

births_pct_day

Average daily births per million population

seasonal

Seasonal component from an STL decomposition of 'births_pct_day'

trend

Trend component from an STL decomposition of 'births_pct_day'

remainder

Remainder component from an STL decomposition of 'births_pct_day'

Details

Dataset originally constructed to reproduce a visualization exercise by Aaron Penne.

Author(s)

Kieran Healy

Source

U.S. Census Bureau.

Opiate-Related Deaths in the United States, 1999-2020

Description

State-level data on opiate related deaths in the US, from the CDC WONDER database.

Usage

opiates

Format

## 'opiates' A tibble frame with 1,122 rows and 8 columns:

fips

State FIPS code.

st

State abbreviation.

year

Year.

deaths

N opiate-related deaths.

crude

Crude death rate per 100,000 population.

adjusted

Adjusted death rate.

region

Census region.

division_name

Census division.

Details

Dataset is Multiple Cause of Death, 1999-2020. Standard Population: 2000 U.S. Std. Population. Rates per 100,000. Default intercensal populations for years 2001-2009. MCD ICD-10 Codes selected: T40.0 (Opium), T40.1 (Heroin), T40.2 (Other opioids), T40.3 (Methadone), T40.4 (Other synthetic narcotics), T40.6 (Other and unspecified narcotics). UCD ICD-10 Codes selected: X40-X44, X60-X64, X85, Y10-Y14.

Author(s)

Kieran Healy

Source

CDC WONDER, http://wonder.cdc.gov/mcd-icd10.html

Organ donation in the OECD

Description

A dataset containing data on rates of organ donation for seventeen OECD countries between 1991 and 2002. The variables are as follows:

Usage

organdata

Format

A (tibble) data frame with 237 rows and 21 columns.

Details

• country. Country name.

• year. Year.

• donors. Organ Donation rate per million population.

• pop. Population in thousands.

• pop_dens. Population density per square mile.

• gdp. Gross Domestic Product in thousands of PPP dollars.

• gdp_lag. Lagged Gross Domestic Product in thousands of PPP dollars.

• health. Health spending, thousands of PPP dollars per capita.

• health_lag Lagged health spending, thousands of PPP dollars per capita.

• pubhealth. Public health spending as a percentage of total expenditure.

• roads. Road accident fatalities per 100,000 population.

• cerebvas. Cerebrovascular deaths per 100,000 population (rounded).

• assault. Assault deaths per 100,000 population (rounded).

• external. Deaths due to external causes per 100,000 population.

• txp_pop. Transplant programs per million population.

• world. Welfare state world (Esping Andersen.)

• opt. Opt-in policy or Opt-out policy.

• consent_law. Consent law, informed or presumed.

• consent_practice. Consent practice, informed or presumed.

• consistent. Law consistent with practice, yes or no.

• ccode. Abbreviated country code.

Source

Macro-economic and spending data: OECD. Other data: Kieran Healy.

prefix_replace

Description

Replace series of characters (usually variable names) at the beginning of a character vector.

Usage

prefix_replace(var_names, prefixes, replacements, toTitle = TRUE, ...)

Arguments

Details

Takes a character vector (usually vector of variable names from a summarized or tidied model object), along with a vector of character terms (usually the prefix of a dummy or categorical variable added by R when creating model terms) and strips the latter away from the former. Useful for quickly cleaning variable names for a plot.

Value

A character vector with 'prefixes' terms in 'var_names' replaced with the content of the 'replacement' terms.

Author(s)

Kieran Healy

Examples

prefix_replace(iris$Species, c("set", "ver", "vir"), c("sat",
    "ber", "bar"))

prefix_strip

Description

Strip a series of characters from the beginning of a character vector.

Usage

prefix_strip(var_string, prefixes, toTitle = TRUE, ...)

Arguments

Details

Takes a character vector (usually vector of variable names from a summarized or tidied model object), along with a vector of character terms (usually the prefix of a dummy or categorical variable added by R when creating model terms) and strips the latter away from the former. Useful for quickly cleaning variable names for a plot.

Value

A character vector with 'prefixes' terms stripped from the beginning of 'var_name' terms.

Author(s)

Kieran Healy

Examples

prefix_strip(iris$Species, c("set", "v"))

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

magrittr

%>%

round_df

Description

Round numeric columns of a data frame or tibble

Usage

round_df(data, dig = 2)

Arguments

Details

Takes a data frame or tibble as input, rounds the numeric columns to the specified number of digits.

Value

An object of the same class as 'data', with the numeric columns rounded off to 'dig'

Author(s)

Kieran Healy

Examples

head(round_df(iris, 0))

US State geometries

Description

US State map data layer

Usage

states_sf

Format

## 'states_sf' A simple features object with 51 rows and 5 columns:

fips

State FIPS code

st

State name abbreviation

state

State name

census

Census region

geometry

Geometry

Details

A simple features object. Load the ‘sf' package before using. Alaska and Hawaii have had their geometries scaled and shifted to the bottom left of the map area. Alaska’s Aleutian islands are not included.

Author(s)

Kieran Healy

Source

US Census Bureau.

Student debt data

Description

Outstanding student debts in 2016 across 8 income categories, by percent of all borrowers and percent of all balances.

Usage

studebt

Format

## 'studebt' A data frame with 16 rows and 4 columns:

Debt

Debt categories (character)

type

Pct in terms of Borrowers or Balances

pct

Percentage of all type

Debtrc

Debt categories (ordered factor)

Source

Federal Reserve Bank of New York.

A ggplot2 theme for socviz

Description

A ggplot theme with defaults for axis styling, legends, panels, strips, and plot chrome. Requires ggplot2 >= 4.0.0.

Usage

theme_socviz(
  base_size = 12,
  base_family = "Source Sans 3",
  header_family = "Source Sans 3",
  base_line_size = base_size/24,
  base_rect_size = base_size/24,
  ink = "black",
  paper = "white",
  accent = "#0072B2"
)

Arguments

Details

The theme uses Source Sans 3 (regular weight) as the base font family and Source Sans 3 Semibold as the header family. If the fonts are not installed, they will be downloaded automatically from Google Fonts via [systemfonts::require_font].

Value

A ggplot2 theme object.

Examples

## Not run: 
library(ggplot2)
ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  theme_socviz()

## End(Not run)

A map theme for socviz

Description

A theme based on [theme_socviz] with all axes, grids, and borders removed, suitable for plotting maps.

Usage

theme_socviz_map(
  base_size = 12,
  base_family = "Source Sans 3",
  header_family = "Source Sans 3",
  base_line_size = base_size/24,
  base_rect_size = base_size/24,
  ink = "black",
  paper = "white",
  accent = "#0072B2"
)

Arguments

Value

A ggplot2 theme object.

Examples

## Not run: 
library(ggplot2)
ggplot(map_data("state"), aes(long, lat, group = group)) +
  geom_polygon(fill = "gray90", colour = "white") +
  coord_map() +
  theme_socviz_map()

## End(Not run)

A table of survival rates from the Titanic

Description

A small table of survival rates from the Titanic, by sex

Usage

titanic

Format

A data frame with four rows and four columns.

Source

Titanic data

Quickly make a two-way table of proportions (percentages)

Description

Quickly make a two-way table of proportions (percentages)

Usage

tw_tab(x, y, margin = NULL, digs = 1, dnn = NULL, ...)

Arguments

Details

A wrapper for 'table' and 'prop.table' with the margin labels set by default to NULL and the cells rounded to percents at 1 decimal place.

Value

A contingency table of percentage values.

Author(s)

Kieran Healy

Examples

with(gss_sm, tw_tab(bigregion, religion, useNA = "ifany", digs = 1))

with(gss_sm, tw_tab(bigregion, religion, margin = 2, useNA =
    "ifany", digs = 1))

Yahoo Revenue and Employees

Description

Data on Revenue and Employees at Yahoo before and during Marissa Mayer's tenure as CEO.

Usage

yahoo

Format

A tibble with 4 columns and 12 rows.

Source

QZ.com