Title:	Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs
Version:	2.5.0
Description:	Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics (https://www.openintro.org/). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
LazyDataCompression:	xz
RoxygenNote:	7.3.1
Suggests:	broom, dplyr, forcats, knitr, lubridate, scales, testthat (≥ 3.0.0), tidyr, tidytext, stringr, maps
Imports:	ggplot2 (≥ 2.2.1), graphics, readr, rmarkdown, tibble
Depends:	R (≥ 2.10), airports, cherryblossom, usdata
URL:	http://openintrostat.github.io/openintro/, https://github.com/OpenIntroStat/openintro/
BugReports:	https://github.com/OpenIntroStat/openintro/issues
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2024-05-28 12:59:17 UTC; mine
Author:	Mine Çetinkaya-Rundel [aut, cre], David Diez [aut], Andrew Bray [aut], Albert Y. Kim [aut], Ben Baumer [aut], Chester Ismay [aut], Nick Paterno [aut], Christopher Barr [aut]
Maintainer:	Mine Çetinkaya-Rundel <cetinkaya.mine@gmail.com>
Repository:	CRAN
Date/Publication:	2024-05-31 13:40:02 UTC

openintro: Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

Description

Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics (https://www.openintro.org/). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.

Author(s)

Maintainer: Mine Çetinkaya-Rundel cetinkaya.mine@gmail.com (ORCID)

Authors:

• David Diez david.m.diez@gmail.com

• Andrew Bray andrew.bray@gmail.com

• Albert Y. Kim albert.ys.kim@gmail.com (ORCID)

• Ben Baumer ben.baumer@gmail.com

• Chester Ismay chester.ismay@gmail.com

• Nick Paterno nicholas.paterno@gmail.com

• Christopher Barr

See Also

Useful links:

• http://openintrostat.github.io/openintro/

• https://github.com/OpenIntroStat/openintro/

• Report bugs at https://github.com/OpenIntroStat/openintro/issues

Create a Line That may have Arrows on the Ends

Description

Similar to lines, this function will include endpoints that are solid points, open points, or arrows (mix-and-match ready).

Usage

ArrowLines(
  x,
  y,
  lty = 1,
  lwd = 2.5,
  col = 1,
  length = 0.1,
  af = 3,
  cex.pch = 1.2,
  ends = c("a", "a"),
  ...
)

Arguments

Author(s)

David Diez

See Also

lsegments, dlsegments, CCP

Examples

CCP(xlim = c(-6, 6), ylim = c(-6, 6), ticklabs = 2)
x <- c(-2, 0, 2, 4)
y <- c(0, 3, 0, 3)
ArrowLines(x, y, col = COL[1], ends = c("c", "c"))
points(x, y, col = COL[1], pch = 19, cex = 1.2)

CCP(xlim = c(-6, 6), ylim = c(-6, 6), ticklabs = 2)
x <- c(-3, 0, 1, 3)
y <- c(2, 1, -2, 1)
ArrowLines(x, y, col = COL[1], ends = c("c", "c"))
points(x, y, col = COL[1], pch = 19, cex = 1.2)

CCP(xlim = c(-6, 6), ylim = c(-6, 6), ticklabs = 2)
x <- seq(-2, 2, 0.01)
y <- x^2 - 3
ArrowLines(x, y, col = COL[1], ends = c("c", "c"))
x <- seq(-2, 2, 1)
y <- x^2 - 3
points(x, y, col = COL[1], pch = 19, cex = 1.2)

Build Better Looking Axis Labels for US Dollars

Description

Convert and simplify axis labels that are in US Dollars.

Usage

AxisInDollars(side, at, include.symbol = TRUE, simplify = TRUE, ...)

Arguments

Value

The numeric locations on the axis scale at which tick marks were drawn when the plot was first drawn.

Author(s)

David Diez

See Also

buildAxis AxisInDollars AxisInPercent

Examples

x <- sample(50e6, 100)
hist(x, axes = FALSE)
AxisInDollars(1, pretty(x))

Build Better Looking Axis Labels for Percentages

Description

Convert and simplify axis labels that are in percentages.

Usage

AxisInPercent(side, at, include.symbol = TRUE, simplify = TRUE, ...)

Arguments

Value

The numeric locations on the axis scale at which tick marks were drawn when the plot was first drawn.

Author(s)

David Diez

See Also

buildAxis AxisInDollars AxisInDollars

Examples

x <- sample(50e6, 100)
hist(x, axes = FALSE)
AxisInDollars(1, pretty(x))

Add background color to a plot

Description

Overlays a colored rectangle over the entire plotting region.

Usage

BG(col = openintro::COL[5, 9])

Arguments

See Also

COL

Examples

Test <- function(col) {
  plot(1:7,
    col = COL[1:7], pch = 19, cex = 5,
    xlim = c(0, 8),
    ylim = c(0, 9)
  )
  BG(col)
  points(2:8, col = COL[1:7], pch = 19, cex = 5)
  text(2, 6, "Correct Color")
  text(6, 2, "Affected Color")
}

# Works well since black color almost fully transparent
Test(COL[5, 9])

# Works less well since transparency isn't as significant
Test(COL[5, 6])

# Pretty ugly due to overlay
Test(COL[5, 3])

# Basically useless due to heavy color gradient
Test(COL[4, 2])

Plot a Braces Symbol

Description

This function is not yet very flexible.

Usage

Braces(x, y, face.radians = 0, long = 1, short = 0.2, ...)

Arguments

Author(s)

David Diez

See Also

dlsegments

Examples

plot(0:1, 0:1, type = "n")
Braces(0.5, 0.5, face.radians = 3 * pi / 2)

Plot a Cartesian Coordinate Plane

Description

Create a Cartesian Coordinate Plane.

Usage

CCP(
  xlim = c(-4, 4),
  ylim = c(-4, 4),
  mar = rep(0, 4),
  length = 0.1,
  tcl = 0.007,
  xylab = FALSE,
  ticks = 1,
  ticklabs = 1,
  xpos = 1,
  ypos = 2,
  cex.coord = 1,
  cex.xylab = 1.5,
  add = FALSE
)

Arguments

Author(s)

David Diez

See Also

lsegments, dlsegments, ArrowLines

Examples

CCP()

CCP(xylab = TRUE, ylim = c(-3.5, 2), xpos = 3, cex.coord = 1)

CCP(xlim = c(-8, 8), ylim = c(-10, 6), ticklabs = c(2, 2), cex.xylab = 0.8)

OpenIntro Statistics colors

Description

These are the core colors used for the OpenIntro Statistics textbook. The blue, green, yellow, and red colors are also gray-scaled, meaning no changes are required when printing black and white copies.

Usage

COL

Format

A 7-by-13 matrix of 7 colors with thirteen fading scales: blue, green, yellow, red, black, gray, and light gray.

Source

Colors selected by OpenIntro's in-house graphic designer, Meenal Patel.

Examples

plot(1:7, 7:1,
  col = COL, pch = 19, cex = 6, xlab = "", ylab = "",
  xlim = c(0.5, 7.5), ylim = c(-2.5, 8), axes = FALSE
)
text(1:7, 7:1 + 0.7, paste("COL[", 1:7, "]", sep = ""), cex = 0.9)
points(1:7, 7:1 - 0.7, col = COL[, 2], pch = 19, cex = 6)
points(1:7, 7:1 - 1.4, col = COL[, 3], pch = 19, cex = 6)
points(1:7, 7:1 - 2.1, col = COL[, 4], pch = 19, cex = 6)

Contingency Table to Data Frame

Description

Take a 2D contingency table and create a data frame representing the individual cases.

Usage

CT2DF(x, rn = row.names(x), cn = colnames(x), dfn = c("row.var", "col.var"))

Arguments

Value

A data frame with two columns.

Author(s)

David Diez

See Also

MosaicPlot

Examples

a <- matrix(
  c(459, 727, 854, 385, 99, 4198, 6245, 4821, 1634, 578),
  2,
  byrow = TRUE
)
b <-
  CT2DF(
    a,
    c("No", "Yes"),
    c("Excellent", "Very good", "Good", "Fair", "Poor"),
    c("coverage", "health_status")
  )
table(b)

Plot upper tail in chi-square distribution

Description

Plot a chi-square distribution and shade the upper tail.

Usage

ChiSquareTail(
  U,
  df,
  xlim = c(0, 10),
  col = fadeColor("black", "22"),
  axes = TRUE,
  ...
)

Arguments

Value

Nothing is returned from the function.

Author(s)

David Diez

See Also

normTail

Examples

data(COL)
ChiSquareTail(11.7,
  7,
  c(0, 25),
  col = COL[1]
)

Introduction to Modern Statistics (IMS) Colors

Description

These are the core colors used for the Introduction to Modern Statistics textbook. The blue, green, pink, yellow, and red colors are also gray-scaled, meaning no changes are required when printing black and white copies.

Usage

IMSCOL

Format

A 8-by-13 matrix of 7 colors with four fading scales: blue, green, pink, yellow, red, black, gray, and light gray.

Examples

plot(1:7, 7:1,
  col = IMSCOL, pch = 19, cex = 6, xlab = "", ylab = "",
  xlim = c(0.5, 7.5), ylim = c(-2.5, 8), axes = FALSE
)
text(1:7, 7:1 + 0.7, paste("IMSCOL[", 1:7, "]", sep = ""), cex = 0.9)
points(1:7, 7:1 - 0.7, col = IMSCOL[, 2], pch = 19, cex = 6)
points(1:7, 7:1 - 1.4, col = IMSCOL[, 3], pch = 19, cex = 6)
points(1:7, 7:1 - 2.1, col = IMSCOL[, 4], pch = 19, cex = 6)

LAhomes

Description

Data collected by Andrew Bray at Reed College on characteristics of LA Homes in 2010.

Usage

LAhomes

Format

A data frame with 1594 observations on the following 8 variables.

city

City where the home is located.

type

Type of home with levels Condo/Twh - condo or townhouse, SFR - single family residence, and NA

bed

Number of bedrooms in the home.

bath

Number of bathrooms in the home.

garage

Number of cars that can be parked in the garage. Note that a value of 4 refers to 4 or more garage spaces.

sqft

Squarefootage of the home.

pool

Indicates if the home has a pool.

price

Listing price of the home.

Examples

library(ggplot2)

ggplot(LAhomes, aes(sqft, price)) +
  geom_point(alpha = 0.2) +
  theme_minimal() +
  labs(
    title = "Can we predict list price from squarefootage?",
    subtitle = "Homes in the Los Angeles area",
    x = "Square feet",
    y = "List price"
  )

Custom Mosaic Plot

Description

Plot a mosaic plot custom built for a particular figure.

Usage

MosaicPlot(
  formula,
  data,
  col = "#00000022",
  border = 1,
  dir = c("v", "h"),
  off = 0.01,
  cex.axis = 0.7,
  col.dir = "v",
  flip = c("v"),
  ...
)

Arguments

Author(s)

David Diez

Examples

data(email)
data(COL)
email$spam <- ifelse(email$spam == 0, "not\nspam", "spam")
MosaicPlot(number ~ spam, email, col = COL[1:3], off = 0.02)

Plot data and add a regression line

Description

Plot data and add a regression line.

Usage

PlotWLine(
  x,
  y,
  xlab = "",
  ylab = "",
  col = fadeColor(4, "88"),
  cex = 1.2,
  pch = 20,
  n = 4,
  nMax = 4,
  yR = 0.1,
  axes = TRUE,
  ...
)

Arguments

See Also

makeTube

Examples

PlotWLine(1:10, seq(-5, -2, length.out = 10) + rnorm(10))

Absenteeism from school in New South Wales

Description

Researchers interested in the relationship between absenteeism from school and certain demographic characteristics of children collected data from 146 randomly sampled students in rural New South Wales, Australia, in a particular school year.

Usage

absenteeism

Format

A data frame with 146 observations on the following 5 variables.

eth

Ethnicity, representing Aboriginal (A) or not (N).

sex

Gender.

age

Age bucket.

lrn

Learner status, with average learner (AL) and slow learner (SL).

days

Number of days absent.

Source

Venables WN, Ripley BD. 2002. Modern Applied Statistics with S. Fourth Edition. New York: Springer.

Data can also be found in the R MASS package under the dataset name quine.

Examples

library(ggplot2)

ggplot(absenteeism, aes(x = eth, y = days)) +
  geom_boxplot() +
  coord_flip()

American Community Survey, 2012

Description

Results from the US Census American Community Survey, 2012.

Usage

acs12

Format

A data frame with 2000 observations on the following 13 variables.

income

Annual income.

employment

Employment status.

hrs_work

Hours worked per week.

race

Race.

age

Age, in years.

gender

Gender.

citizen

Whether the person is a U.S. citizen.

time_to_work

Travel time to work, in minutes.

lang

Language spoken at home.

married

Whether the person is married.

edu

Education level.

disability

Whether the person is disabled.

birth_qrtr

The quarter of the year that the person was born, e.g. ⁠Jan thru Mar⁠.

Source

https://www.census.gov/programs-surveys/acs

Examples

library(dplyr)
library(ggplot2)
library(broom)

# employed only
acs12_emp <- acs12 |>
  filter(
    age >= 30, age <= 60,
    employment == "employed",
    income > 0
  )

# linear model
ggplot(acs12_emp, mapping = aes(x = age, y = income)) +
  geom_point() +
  geom_smooth(method = "lm")

lm(income ~ age, data = acs12_emp) |>
  tidy()

# log-transormed model
ggplot(acs12_emp, mapping = aes(x = age, y = log(income))) +
  geom_point() +
  geom_smooth(method = "lm")

lm(log(income) ~ age, data = acs12_emp) |>
  tidy()

Age at first marriage of 5,534 US women.

Description

Age at first marriage of 5,534 US women who responded to the National Survey of Family Growth (NSFG) conducted by the CDC in the 2006 and 2010 cycle.

Usage

age_at_mar

Format

A data frame with 5,534 observations and 1 variable.

age

Age a first marriage.

Source

National Survey of Family Growth, 2006-2010 cycle, https://www.cdc.gov/nchs/nsfg/nsfg_2006_2010_puf.htm.

Examples

library(ggplot2)

ggplot(age_at_mar, mapping = aes(x = age)) +
  geom_histogram(binwidth = 3) +
  labs(
    x = "Age", y = "Count", title = "Age at first marriage, US Women",
    subtitle = "Source: National Survey of Family Growth Survey, 2006 - 2010"
  )

Housing prices in Ames, Iowa

Description

Data set contains information from the Ames Assessor's Office used in computing assessed values for individual residential properties sold in Ames, IA from 2006 to 2010. See here for detailed variable descriptions.

Usage

ames

Format

A tbl_df with with 2930 rows and 82 variables:

Order

Observation number.

PID

Parcel identification number - can be used with city web site for parcel review.

area

Above grade (ground) living area square feet.

price

Sale price in USD.

MS.SubClass

Identifies the type of dwelling involved in the sale.

MS.Zoning

Identifies the general zoning classification of the sale.

Lot.Frontage

Linear feet of street connected to property.

Lot.Area

Lot size in square feet.

Street

Type of road access to property.

Alley

Type of alley access to property.

Lot.Shape

General shape of property.

Land.Contour

Flatness of the property.

Utilities

Type of utilities available.

Lot.Config

Lot configuration.

Land.Slope

Slope of property.

Neighborhood

Physical locations within Ames city limits (map available).

Condition.1

Proximity to various conditions.

Condition.2

Proximity to various conditions (if more than one is present).

Bldg.Type

Type of dwelling.

House.Style

Style of dwelling.

Overall.Qual

Rates the overall material and finish of the house.

Overall.Cond

Rates the overall condition of the house.

Year.Built

Original construction date.

Year.Remod.Add

Remodel date (same as construction date if no remodeling or additions).

Roof.Style

Type of roof.

Roof.Matl

Roof material.

Exterior.1st

Exterior covering on house.

Exterior.2nd

Exterior covering on house (if more than one material).

Mas.Vnr.Type

Masonry veneer type.

Mas.Vnr.Area

Masonry veneer area in square feet.

Exter.Qual

Evaluates the quality of the material on the exterior.

Exter.Cond

Evaluates the present condition of the material on the exterior.

Foundation

Type of foundation.

Bsmt.Qual

Evaluates the height of the basement.

Bsmt.Cond

Evaluates the general condition of the basement.

Bsmt.Exposure

Refers to walkout or garden level walls.

BsmtFin.Type.1

Rating of basement finished area.

BsmtFin.SF.1

Type 1 finished square feet.

BsmtFin.Type.2

Rating of basement finished area (if multiple types).

BsmtFin.SF.2

Type 2 finished square feet.

Bsmt.Unf.SF

Unfinished square feet of basement area.

Total.Bsmt.SF

Total square feet of basement area.

Heating

Type of heating.

Heating.QC

Heating quality and condition.

Central.Air

Central air conditioning.

Electrical

Electrical system.

X1st.Flr.SF

First Floor square feet.

X2nd.Flr.SF

Second floor square feet.

Low.Qual.Fin.SF

Low quality finished square feet (all floors).

Bsmt.Full.Bath

Basement full bathrooms.

Bsmt.Half.Bath

Basement half bathrooms.

Full.Bath

Full bathrooms above grade.

Half.Bath

Half baths above grade.

Bedroom.AbvGr

Bedrooms above grade (does NOT include basement bedrooms).

Kitchen.AbvGr

Kitchens above grade.

Kitchen.Qual

Kitchen quality.

TotRms.AbvGrd

Total rooms above grade (does not include bathrooms).

Functional

Home functionality (Assume typical unless deductions are warranted).

Fireplaces

Number of fireplaces.

Fireplace.Qu

Fireplace quality.

Garage.Type

Garage location.

Garage.Yr.Blt

Year garage was built.

Garage.Finish

Interior finish of the garage.

Garage.Cars

Size of garage in car capacity.

Garage.Area

Size of garage in square feet.

Garage.Qual

Garage quality.

Garage.Cond

Garage condition.

Paved.Drive

Paved driveway.

Wood.Deck.SF

Wood deck area in square feet.

Open.Porch.SF

Open porch area in square feet.

Enclosed.Porch

Enclosed porch area in square feet.

X3Ssn.Porch

Three season porch area in square feet.

Screen.Porch

Screen porch area in square feet.

Pool.Area

Pool area in square feet.

Pool.QC

Pool quality.

Fence

Fence quality.

Misc.Feature

Miscellaneous feature not covered in other categories.

Misc.Val

Dollar value of miscellaneous feature.

Mo.Sold

Month Sold (MM).

Yr.Sold

Year Sold (YYYY).

Sale.Type

Type of sale.

Sale.Condition

Condition of sale.

Source

De Cock, Dean. "Ames, Iowa: Alternative to the Boston housing data as an end of semester regression project." Journal of Statistics Education 19.3 (2011).

Acute Myocardial Infarction (Heart Attack) Events

Description

This dataset is simulated but contains realistic occurrences of AMI in NY City.

Usage

ami_occurrences

Format

A data frame with 365 observations on the following variable.

ami

Number of daily occurrences of heart attacks in NY City.

Examples

library(ggplot2)

ggplot(ami_occurrences, mapping = aes(x = ami)) +
  geom_bar() +
  labs(
    x = "Acute Myocardial Infarction events",
    y = "Count",
    title = "Acute Myocardial Infarction events in NYC"
  )

Pre-existing conditions in 92 children

Description

Pre-existing medical conditions of 92 children involved in a study on the optimal duration of antibiotic use in treatment of tracheitis, which is an upper respiratory infection.

Usage

antibiotics

Format

A data frame with 92 observations, each representing a child, on the following variable.

condition

Pre-existing medical condition.

Examples

library(ggplot2)

ggplot(antibiotics, aes(x = condition)) +
  geom_bar() +
  labs(
    x = "Conidition", y = "Count",
    title = "Pre-existing coniditions of children",
    subtitle = "in antibiotic use study"
  ) +
  coord_flip()

Male and female births in London

Description

Arbuthnot's data describes male and female christenings (births) for London from 1629-1710.

Usage

arbuthnot

Format

A tbl_df with with 82 rows and 3 variables:

year

year, ranging from 1629 to 1710

boys

number of male christenings (births)

girls

number of female christenings (births)

Details

John Arbuthnot (1710) used these time series data to carry out the first known significance test. During every one of the 82 years, there were more male christenings than female christenings. As Arbuthnot wondered, we might also wonder if this could be due to chance, or whether it meant the birth ratio was not actually 1:1.

Source

These data are excerpted from the Arbuthnot dataset in the HistData package.

Examples

library(ggplot2)
library(tidyr)

# All births
ggplot(arbuthnot, aes(x = year, y = boys + girls, group = 1)) +
  geom_line()

# Boys and girls
arbuthnot |>
  pivot_longer(cols = -year, names_to = "sex", values_to = "n") |>
  ggplot(aes(x = year, y = n, color = sex, group = sex)) +
  geom_line()

How important is it to ask pointed questions?

Description

In this experiment, each individual was asked to be a seller of an iPod (a product commonly used to store music on before smart phones...). They participant received $10 + 5% of the sale price for participating. The iPod they were selling had frozen twice in the past inexplicably but otherwise worked fine. The prospective buyer starts off and then asks one of three final questions, depending on the seller's treatment group.

Usage

ask

Format

A data frame with 219 observations on the following 3 variables.

question_class

The type of question: general, pos_assumption, and neg_assumption.

question

The question corresponding to the question.class

response

The classified response from the seller, either disclose or hide.

Details

The three possible questions:

• General: What can you tell me about it?

• Positive Assumption: It doesn't have any problems, does it?

• Negative Assumption: What problems does it have?

The outcome variable is whether or not the participant discloses or hides the problem with the iPod.

Source

Minson JA, Ruedy NE, Schweitzer ME. There is such a thing as a stupid question: Question disclosure in strategic communication.

Examples

library(dplyr)
library(ggplot2)

# Distribution of responses based on question type
ask |>
  count(question_class, response)

# Visualize relative frequencies of responses based on question type
ggplot(ask, aes(x = question_class, fill = response)) +
  geom_bar(position = "fill")

# Perform chi-square test
(test <- chisq.test(table(ask$question_class, ask$response)))

# Check the test's assumption around sufficient expected observations
# per table cell.
test$expected

Simulated data for association plots

Description

Simulated dataset.

Usage

association

Format

A data frame with 121 observations on the following 4 variables.

x1

a numeric vector

x2

a numeric vector

x3

a numeric vector

y1

a numeric vector

y2

a numeric vector

y3

a numeric vector

y4

a numeric vector

y5

a numeric vector

y6

a numeric vector

y7

a numeric vector

y8

a numeric vector

y9

a numeric vector

y10

a numeric vector

y11

a numeric vector

y12

a numeric vector

Examples

library(ggplot2)

ggplot(association, aes(x = x1, y = y1)) +
  geom_point()

ggplot(association, aes(x = x2, y = y4)) +
  geom_point()

ggplot(association, aes(x = x3, y = y7)) +
  geom_point()

Eye color of couples

Description

Colors of the eye colors of male and female partners.

Usage

assortative_mating

Format

A data frame with 204 observations on the following 2 variables.

self_male

a factor with levels blue, brown, and green

partner_female

a factor with blue, brown, and green

Source

B. Laeng et al. Why do blue-eyed men prefer women with the same eye color? In: Behavioral Ecology and Sociobiology 61.3 (2007), pp. 371-384.

Examples

data(assortive_mating)
table(assortive_mating)

Cardiovascular problems for two types of Diabetes medicines

Description

A comparison of cardiovascular problems for Rosiglitazone and Pioglitazone.

Usage

avandia

Format

A data frame with 227571 observations on the following 2 variables.

treatment

a factor with levels Pioglitazone and Rosiglitazone

cardiovascular_problems

a factor with levels no and yes

Source

D.J. Graham et al. Risk of acute myocardial infarction, stroke, heart failure, and death in elderly Medicare patients treated with rosiglitazone or pioglitazone. In: JAMA 304.4 (2010), p. 411. issn: 0098-7484.

Examples

table(avandia)

The Child Health and Development Studies

Description

The Child Health and Development Studies investigate a range of topics. One study, in particular, considered all pregnancies between 1960 and 1967 among women in the Kaiser Foundation Health Plan in the San Francisco East Bay area. We do not have ideal provenance for these data. For a better documented and more recent dataset on a similar topic with similar variables, see births14. Additionally, Gestation dataset in the mosaicData package also contains similar data.

Usage

babies

Format

A data frame with 1236 rows and 8 variables:

case

id number

bwt

birthweight, in ounces

gestation

length of gestation, in days

parity

binary indicator for a first pregnancy (0 = first pregnancy)

age

mother's age in years

height

mother's height in inches

weight

mother's weight in pounds

smoke

binary indicator for whether the mother smokes

Source

These data come from Child Health and Development Studies.

Crawling age

Description

Crawling age of babies along with the average outdoor temperature at 6 months of age.

Usage

babies_crawl

Format

A data frame with 12 observations on the following 5 variables.

birth_month

A factor with levels corresponding to months

avg_crawling_age

a numeric vector

sd

a numeric vector

n

a numeric vector

temperature

a numeric vector

Source

J.B. Benson. Season of birth and onset of locomotion: Theoretical and methodological implications. In: Infant behavior and development 16.1 (1993), pp. 69-81. issn: 0163-6383.

Examples

library(ggplot2)

ggplot(babies_crawl, aes(x = temperature, y = avg_crawling_age)) +
  geom_point() +
  labs(x = "Temperature", y = "Average crawling age")

Beer and blood alcohol content

Description

Here we examine data from sixteen student volunteers at Ohio State University who each drank a randomly assigned number of cans of beer.

Usage

bac

Format

A data frame with 16 observations on the following 3 variables.

student

a numeric vector

beers

a numeric vector

bac

a numeric vector

Source

J. Malkevitch and L.M. Lesser. For All Practical Purposes: Mathematical Literacy in Today's World. WH Freeman & Co, 2008. The data origin is given in the Electronic Encyclopedia of Statistical Examples and Exercises, 1992.

Examples

library(ggplot2)

ggplot(bac, aes(x = beers, y = bac)) +
  geom_point() +
  labs(x = "Number of beers", y = "Blood alcohol content")

Lifespan of ball bearings

Description

A simulated dataset on lifespan of ball bearings.

Usage

ball_bearing

Format

A data frame with 75 observations on the following variable.

life_span

Lifespan of ball bearings (in hours).

Source

Simulated data.

Examples

library(ggplot2)

ggplot(ball_bearing, aes(x = life_span)) +
  geom_histogram(binwidth = 1)

qqnorm(ball_bearing$life_span)

Body measurements of 507 physically active individuals.

Description

Body girth measurements and skeletal diameter measurements, as well as age, weight, height and gender, are given for 507 physically active individuals - 247 men and 260 women. These data can be used to provide statistics students practice in the art of data analysis. Such analyses range from simple descriptive displays to more complicated multivariate analyses such as multiple regression and discriminant analysis.

Usage

bdims

Format

A data frame with 507 observations on the following 25 variables.

bia_di

A numerical vector, respondent's biacromial diameter in centimeters.

bii_di

A numerical vector, respondent's biiliac diameter (pelvic breadth) in centimeters.

bit_di

A numerical vector, respondent's bitrochanteric diameter in centimeters.

che_de

A numerical vector, respondent's chest depth in centimeters, measured between spine and sternum at nipple level, mid-expiration.

che_di

A numerical vector, respondent's chest diameter in centimeters, measured at nipple level, mid-expiration.

elb_di

A numerical vector, respondent's elbow diameter in centimeters, measured as sum of two elbows.

wri_di

A numerical vector, respondent's wrist diameter in centimeters, measured as sum of two wrists.

kne_di

A numerical vector, respondent's knee diameter in centimeters, measured as sum of two knees.

ank_di

A numerical vector, respondent's ankle diameter in centimeters, measured as sum of two ankles.

sho_gi

A numerical vector, respondent's shoulder girth in centimeters, measured over deltoid muscles.

che_gi

A numerical vector, respondent's chest girth in centimeters, measured at nipple line in males and just above breast tissue in females, mid-expiration.

wai_gi

A numerical vector, respondent's waist girth in centimeters, measured at the narrowest part of torso below the rib cage as average of contracted and relaxed position.

nav_gi

A numerical vector, respondent's navel (abdominal) girth in centimeters, measured at umbilicus and iliac crest using iliac crest as a landmark.

hip_gi

A numerical vector, respondent's hip girth in centimeters, measured at at level of bitrochanteric diameter.

thi_gi

A numerical vector, respondent's thigh girth in centimeters, measured below gluteal fold as the average of right and left girths.

bic_gi

A numerical vector, respondent's bicep girth in centimeters, measured when flexed as the average of right and left girths.

for_gi

A numerical vector, respondent's forearm girth in centimeters, measured when extended, palm up as the average of right and left girths.

kne_gi

A numerical vector, respondent's knee diameter in centimeters, measured as sum of two knees.

cal_gi

A numerical vector, respondent's calf maximum girth in centimeters, measured as average of right and left girths.

ank_gi

A numerical vector, respondent's ankle minimum girth in centimeters, measured as average of right and left girths.

wri_gi

A numerical vector, respondent's wrist minimum girth in centimeters, measured as average of right and left girths.

age

A numerical vector, respondent's age in years.

wgt

A numerical vector, respondent's weight in kilograms.

hgt

A numerical vector, respondent's height in centimeters.

sex

A categorical vector, 1 if the respondent is male, 0 if female.

Source

Heinz G, Peterson LJ, Johnson RW, Kerk CJ. 2003. Exploring Relationships in Body Dimensions. Journal of Statistics Education 11(2).

Examples

library(ggplot2)
ggplot(bdims, aes(x = hgt)) +
  geom_histogram(binwidth = 5)

ggplot(bdims, aes(x = hgt, y = wgt)) +
  geom_point() +
  labs(x = "Height", y = "Weight")

ggplot(bdims, aes(x = hgt, y = sho_gi)) +
  geom_point() +
  labs(x = "Height", y = "Shoulder girth")

ggplot(bdims, aes(x = hgt, y = hip_gi)) +
  geom_point() +
  labs(x = "Height", y = "Hip girth")

Efficacy of Pfizer-BioNTech COVID-19 vaccine on adolescents

Description

On March 31, 2021, Pfizer and BioNTech announced that "in a Phase 3 trial in adolescents 12 to 15 years of age with or without prior evidence of SARS-CoV-2 infection, the Pfizer-BioNTech COVID-19 vaccine BNT162b2 demonstrated 100% efficacy and robust antibody responses, exceeding those recorded earlier in vaccinated participants aged 16 to 25 years old, and was well tolerated." These results are from a Phase 3 trial in 2,260 adolescents 12 to 15 years of age in the United States. In the trial, 18 cases of COVID-19 were observed in the placebo group (n = 1,129) versus none in the vaccinated group (n = 1,131).

Usage

biontech_adolescents

Format

A data frame with 2260 observations on the following 2 variables.

group

Study group: vaccine (Pfizer-BioNTech COVID-19 vaccine administered) or placebo.

outcome

Study outcome: COVID-19 or ⁠no COVID-19⁠.

Source

"Pfizer-Biontech Announce Positive Topline Results Of Pivotal Covid-19 Vaccine Study In Adolescents". March 21, 2021. (Retrieved April 25, 2021.)

Examples

library(dplyr)
library(ggplot2)

biontech_adolescents |>
  count(group, outcome)

ggplot(biontech_adolescents, aes(y = group, fill = outcome)) +
  geom_bar()

Aircraft-Wildlife Collisions

Description

A collection of all collisions between aircraft in wildlife that were reported to the US Federal Aviation Administration between 1990 and 1997, with details on the circumstances of the collision.

Usage

birds

Format

A data frame with 19302 observations on the following 17 variables.

opid

Three letter identification code for the operator (carrier) of the aircraft.

operator

Name of the aircraft operator.

atype

Make and model of aircraft.

remarks

Verbal remarks regarding the collision.

phase_of_flt

Phase of the flight during which the collision occurred: Approach, Climb, Descent, En Route, Landing Roll, Parked, Take-off run, Taxi.

ac_mass

Mass of the aircraft classified as 2250 kg or less (1), 2251-5700 kg (2), 5701-27000 kg (3), 27001-272000 kg (4), above 272000 kg (5).

num_engs

Number of engines on the aircraft.

date

Date of the collision (MM/DD/YYYY).

time_of_day

Light conditions: Dawn, Day, Dusk, Night.

state

Two letter abbreviation of the US state in which the collision occurred.

height

Feet above ground level.

speed

Knots (indicated air speed).

effect

Effect on flight: Aborted Take-off, Engine Shut Down, None, Other, Precautionary Landing.

sky

Type of cloud cover, if any: No Cloud, Overcast, Some Cloud.

species

Common name for bird or other wildlife.

birds_seen

Number of birds/wildlife seen by pilot: 1, 2-10, 11-100, Over 100.

birds_struck

Number of birds/wildlife struck: 0, 1, 2-10, 11-100, Over 100.

Details

The FAA National Wildlife Strike Database contains strike reports that are voluntarily reported to the FAA by pilots, airlines, airports and others. Current research indicates that only about 20\ Wildlife strike reporting is not uniform as some organizations have more robust voluntary reporting procedures. Because of variations in reporting, users are cautioned that the comparisons between individual airports or airlines may be misleading.

Source

Aircraft Wildlife Strike Data: Search Tool - FAA Wildlife Strike Database. Available at https://datahub.transportation.gov/Aviation/Aircraft-Wildlife-Strike-Data-Search-Tool-FAA-Wild/jhay-dgxy. Retrieval date: Feb 4, 2012.

Examples

library(dplyr)
library(ggplot2)
library(forcats)
library(tidyr)

# Phase of the flight during which the collision occurred, tabular
birds |>
  count(phase_of_flt, sort = TRUE)

# Phase of the flight during which the collision occurred, barplot
ggplot(birds, aes(y = fct_infreq(phase_of_flt))) +
  geom_bar() +
  labs(x = "Phase of flight")

# Height summary statistics
summary(birds$height)

# Phase of flight vs. effect of crash
birds |>
  drop_na(phase_of_flt, effect) |>
  ggplot(aes(y = phase_of_flt, fill = effect)) +
  geom_bar(position = "fill") +
  labs(x = "Proportion", y = "Phase of flight", fill = "Effect")

North Carolina births, 100 cases

Description

Data on a random sample of 100 births for babies in North Carolina where the mother was not a smoker and another 50 where the mother was a smoker.

Usage

births

Format

A data frame with 150 observations on the following 14 variables.

f_age

Father's age.

m_age

Mother's age.

weeks

Weeks at which the mother gave birth.

premature

Indicates whether the baby was premature or not.

visits

Number of hospital visits.

gained

Weight gained by mother.

weight

Birth weight of the baby.

sex_baby

Gender of the baby.

smoke

Whether or not the mother was a smoker.

Source

Birth records released by North Carolina in 2004.

See Also

We do not have ideal provenance for these data. For a better documented and more recent dataset on a similar topic with similar variables, see births14. Additionally, ncbirths also contains similar data.

Examples

library(ggplot2)

ggplot(births, aes(x = smoke, y = weight)) +
  geom_boxplot()

US births

Description

Every year, the US releases to the public a large dataset containing information on births recorded in the country. This dataset has been of interest to medical researchers who are studying the relation between habits and practices of expectant mothers and the birth of their children. This is a random sample of 1,000 cases from the dataset released in 2014.

Usage

births14

Format

A data frame with 1,000 observations on the following 13 variables.

fage

Father's age in years.

mage

Mother's age in years.

mature

Maturity status of mother.

weeks

Length of pregnancy in weeks.

premie

Whether the birth was classified as premature (premie) or full-term.

visits

Number of hospital visits during pregnancy.

gained

Weight gained by mother during pregnancy in pounds.

weight

Weight of the baby at birth in pounds.

lowbirthweight

Whether baby was classified as low birthweight (low) or not (⁠not low⁠).

sex

Sex of the baby, female or male.

habit

Status of the mother as a nonsmoker or a smoker.

marital

Whether mother is married or ⁠not married⁠ at birth.

whitemom

Whether mom is white or ⁠not white⁠.

Source

United States Department of Health and Human Services. Centers for Disease Control and Prevention. National Center for Health Statistics. Natality Detail File, 2014 United States. Inter-university Consortium for Political and Social Research, 2016-10-07. doi:10.3886/ICPSR36461.v1.

Examples

library(ggplot2)

ggplot(births14, aes(x = habit, y = weight)) +
  geom_boxplot() +
  labs(x = "Smoking status of mother", y = "Birth weight of baby (in lbs)")

ggplot(births14, aes(x = whitemom, y = visits)) +
  geom_boxplot() +
  labs(x = "Mother's race", y = "Number of doctor visits during pregnancy")

ggplot(births14, aes(x = mature, y = gained)) +
  geom_boxplot() +
  labs(x = "Mother's age category", y = "Weight gained during pregnancy")

Blizzard Employee Voluntary Salary Info.

Description

Employee generated anonymous survey of salary information.

Usage

blizzard_salary

Format

A data frame with 466 rows and 9 variables.

timestamp

Time data was entered

status

Specifies employment status.

current_title

Current job title.

current_salary

Current salary (in USD).

salary_type

Frequency with levels year, hour, week.

percent_incr

Raise given July 2020.

other_info

Other information submitted by employee.

location

Current office of employment.

performance_rating

Most recent review performance rating.

Source

Bloomberg - Blizzard workers share salaries in revolt over wage disparities.

Examples

library(ggplot2)
library(dplyr)

plot_data <- blizzard_salary |>
  mutate(annual_salary = case_when(
    salary_type == "week" ~ current_salary * 52,
    salary_type == "hour" ~ current_salary * 40 * 52,
    TRUE ~ current_salary
  ))

ggplot(plot_data, aes(annual_salary)) +
  geom_histogram(binwidth = 25000, color = "white") +
  labs(
    title = "Current Salary of Blizzard Employees",
    x = "Salary",
    y = "Number of Employees"
  )

Sample of books on a shelf

Description

Simulated dataset.

Usage

books

Format

A data frame with 95 observations on the following 2 variables.

type

a factor with levels fiction and nonfiction

format

a factor with levels hardcover and paperback

Examples

table(books)

Box plot

Description

An alternative to boxplot. Equations are not accepted. Instead, the second argument, fact, is used to split the data.

Usage

boxPlot(
  x,
  fact = NULL,
  horiz = FALSE,
  width = 2/3,
  lwd = 1,
  lcol = "black",
  medianLwd = 2,
  pch = 20,
  pchCex = 1.8,
  col = grDevices::rgb(0, 0, 0, 0.25),
  add = FALSE,
  key = NULL,
  axes = TRUE,
  xlab = "",
  ylab = "",
  xlim = NULL,
  ylim = NULL,
  na.rm = TRUE,
  ...
)

Arguments

Author(s)

David Diez

See Also

histPlot, dotPlot, densityPlot

Examples

# univariarate
boxPlot(email$num_char, ylab = "Number of characters in emails")

# bivariate
boxPlot(email$num_char, email$spam,
  xlab = "Spam",
  ylab = "Number of characters in emails"
)

# faded outliers
boxPlot(email$num_char, email$spam,
  xlab = "Spam",
  ylab = "Number of characters in emails",
  col = fadeColor("black", 18)
)

# horizontal plots
boxPlot(email$num_char, email$spam,
  horiz = TRUE,
  xlab = "Spam",
  ylab = "Number of characters in emails",
  col = fadeColor("black", 18)
)

# bivariate relationships where categorical data have more than 2 levels
boxPlot(email$num_char, email$image,
  horiz = TRUE,
  xlab = "Number of attached images",
  ylab = "Number of characters in emails",
  col = fadeColor("black", 18)
)

# key can be used to restrict to only the desired groups
boxPlot(email$num_char, email$image,
  horiz = TRUE, key = c(0, 1, 2),
  xlab = "Number of attached images (limited to 0, 1, 2)",
  ylab = "Number of characters in emails",
  col = fadeColor("black", 18)
)

# combine boxPlot and dotPlot
boxPlot(tips$tip, tips$day,
  horiz = TRUE, key = c("Tuesday", "Friday")
)
dotPlot(tips$tip, tips$day,
  add = TRUE, at = 1:2 + 0.05,
  key = c("Tuesday", "Friday")
)

# adding a box
boxPlot(email$num_char[email$spam == 0], xlim = c(0, 3))
boxPlot(email$num_char[email$spam == 1], add = 2, axes = FALSE)
axis(1, at = 1:2, labels = c(0, 1))
boxPlot(email$num_char[email$spam == 0], ylim = c(0, 3), horiz = TRUE)
boxPlot(email$num_char[email$spam == 1], add = 2, horiz = TRUE, axes = FALSE)
axis(2, at = 1:2, labels = c(0, 1))

Axis function substitute

Description

The function buildAxis is built to provide more control of the number of labels on the axis. This function is still under development.

Usage

buildAxis(side, limits, n, nMin = 2, nMax = 10, extend = 2, eps = 10^-12, ...)

Arguments

Details

The primary reason behind building this function was to allow a plot to be created with similar features but with different datasets. For instance, if a set of code was written for one dataset and the function axis had been utilized with pre-specified values, the axis may not match the plot of a new set of data. The function buildAxis addresses this problem by allowing the number of axis labels to be specified and controlled.

The axis is built by assigning penalties to a variety of potential axis setups, ranking them based on these penalties and then selecting the axis with the best score.

Value

A vector of the axis plotted.

Author(s)

David Diez

See Also

histPlot, dotPlot, boxPlot, densityPlot

Examples

# ===> 0 <===#
limits <- rnorm(100, 605490, 10)
hist(limits, axes = FALSE)
buildAxis(1, limits, 2, nMax = 4)

# ===> 1 <===#
x <- seq(0, 500, 10)
y <- 8 * x + rnorm(length(x), mean = 6000, sd = 200)
plot(x, y, axes = FALSE)
buildAxis(1, limits = x, n = 5)
buildAxis(2, limits = y, n = 3)

# ===> 2 <===#
x <- 9528412 + seq(0, 200, 10)
y <- 8 * x + rnorm(length(x), mean = 6000, sd = 200)
plot(x, y, axes = FALSE)
temp <- buildAxis(1, limits = x, n = 4)
buildAxis(2, y, 3)

# ===> 3 <===#
x <- seq(367, 1251, 10)
y <- 7.5 * x + rnorm(length(x), mean = 6000, sd = 800)
plot(x, y, axes = FALSE)
buildAxis(1, limits = x, n = 4, nMin = 3, nMax = 3)
buildAxis(2, limits = y, n = 4, nMin = 3, nMax = 5)

# ===> 4 <===#
x <- seq(367, 367.1, 0.001)
y <- 7.5 * x + rnorm(length(x), mean = 6000, sd = 0.01)
plot(x, y, axes = FALSE)
buildAxis(1, limits = x, n = 4, nMin = 5, nMax = 6)
buildAxis(2, limits = y, n = 2, nMin = 3, nMax = 4)

# ===> 5 <===#
x <- seq(-0.05, -0.003, 0.0001)
y <- 50 + 20 * x + rnorm(length(x), sd = 0.1)
plot(x, y, axes = FALSE)
buildAxis(1, limits = x, n = 4, nMin = 5, nMax = 6)
buildAxis(2, limits = y, n = 4, nMax = 5)
abline(lm(y ~ x))

# ===> 6 <===#
x <- seq(-0.0097, -0.008, 0.0001)
y <- 50 + 20 * x + rnorm(length(x), sd = 0.1)
plot(x, y, axes = FALSE)
buildAxis(1, limits = x, n = 4, nMin = 2, nMax = 5)
buildAxis(2, limits = y, n = 4, nMax = 5)
abline(lm(y ~ x))

# ===> 7 <===#
x <- seq(0.03, -0.003099, -0.00001)
y <- 50 + 20 * x + rnorm(length(x), sd = 0.1)
plot(x, y, axes = FALSE)
buildAxis(1, limits = x, n = 4, nMin = 2, nMax = 5)
buildAxis(2, limits = y, n = 4, nMax = 6)
abline(lm(y ~ x))

# ===> 8 - repeat <===#
m <- runif(1) / runif(1) +
  rgamma(1, runif(1) / runif(1), runif(1) / runif(1))
s <- rgamma(1, runif(1) / runif(1), runif(1) / runif(1))
x <- rnorm(50, m, s)
hist(x, axes = FALSE)
buildAxis(1, limits = x, n = 5, nMin = 4, nMax = 6, eps = 10^-12)
if (diff(range(x)) < 10^-12) {
  cat("too small\n")
}

Burger preferences

Description

Sample burger place preferences versus gender.

Usage

burger

Format

A data frame with 500 observations on the following 2 variables.

best_burger_place

Burger place.

gender

a factor with levels Female and Male

Source

SurveyUSA, Results of SurveyUSA News Poll #17718, data collected on December 2, 2010.

Examples

table(burger)

Calculate hit streaks

Description

Calculate hit streaks

Usage

calc_streak(x)

Arguments

Value

A data frame with one column, length, containing the length of each hit streak.

Examples

data(kobe_basket)
calc_streak(kobe_basket$shot)

Cancer in dogs

Description

A study in 1994 examined 491 dogs that had developed cancer and 945 dogs as a control group to determine whether there is an increased risk of cancer in dogs that are exposed to the herbicide 2,4-Dichlorophenoxyacetic acid (2,4-D).

Usage

cancer_in_dogs

Format

A data frame with 1436 observations on the following 2 variables.

order

a factor with levels 2,4-D and no 2,4-D

response

a factor with levels cancer and no cancer

Source

Hayes HM, Tarone RE, Cantor KP, Jessen CR, McCurnin DM, and Richardson RC. 1991. Case- Control Study of Canine Malignant Lymphoma: Positive Association With Dog Owner's Use of 2, 4- Dichlorophenoxyacetic Acid Herbicides. Journal of the National Cancer Institute 83(17):1226-1231.

Examples

table(cancer_in_dogs)

Deck of cards

Description

All the cards in a standard deck.

Usage

cards

Format

A data frame with 52 observations on the following 4 variables.

value

a factor with levels 10 2 3 4 5 6 7 8 9 A J K Q

color

a factor with levels black red

suit

a factor with levels Club Diamond Heart Spade

face

a logical vector

Examples

table(cards$value)
table(cards$color)
table(cards$suit)
table(cards$face)
table(cards$suit, cards$face)

cars04

Description

A data frame with 428 rows and 19 columns. This is a record of characteristics on all of the new models of cars for sale in the US in the year 2004.

Usage

cars04

Format

A data frame with 428 observations on the following 19 variables.

name

The name of the vehicle including manufacturer and model.

sports_car

Logical variable indicating if the vehicle is a sports car.

suv

Logical variable indicating if the vehicle is an suv.

wagon

Logical variable indicating if the vehicle is a wagon.

minivan

Logical variable indicating if the vehicle is a minivan.

pickup

Logical variable indicating if the vehicle is a pickup.

all_wheel

Logical variable indicating if the vehicle is all-wheel drive.

rear_wheel

Logical variable indicating if the vehicle is rear-wheel drive.

msrp

Manufacturer suggested retail price of the vehicle.

dealer_cost

Amount of money the dealer paid for the vehicle.

eng_size

Displacement of the engine - the total volume of all the cylinders, measured in liters.

ncyl

Number of cylinders in the engine.

horsepwr

Amount of horsepower produced by the engine.

city_mpg

Gas mileage for city driving, measured in miles per gallon.

hwy_mpg

Gas mileage for highway driving, measured in miles per gallon.

weight

Total weight of the vehicle, measured in pounds.

wheel_base

Distance between the center of the front wheels and the center of the rear wheels, measured in inches.

length

Total length of the vehicle, measured in inches.

width

Total width of the vehicle, measured in inches.

Examples

library(ggplot2)

# Highway gas mileage
ggplot(cars04, aes(x = hwy_mpg)) +
  geom_histogram(
    bins = 15, color = "white",
    fill = openintro::IMSCOL["green", "full"]
  ) +
  theme_minimal() +
  labs(
    title = "Highway gas milage for cars from 2004",
    x = "Gas Mileage (miles per gallon)",
    y = "Number of cars"
  )

cars93

Description

A data frame with 54 rows and 6 columns. This data is a subset of the Cars93 dataset from the MASS package.

Usage

cars93

Format

A data frame with 54 observations on the following 6 variables.

type

The vehicle type with levels large, midsize, and small.

price

Vehicle price (USD).

mpg_city

Vehicle mileage in city (miles per gallon).

drive_train

Vehicle drive train with levels 4WD, front, and rear.

passengers

The vehicle passenger capacity.

weight

Vehicle weight (lbs).

Details

These cars represent a random sample for 1993 models that were in both Consumer Reports and PACE Buying Guide. Only vehicles of type small, midsize, and large were include.

Further description can be found in Lock (1993). Use the URL http://jse.amstat.org/v1n1/datasets.lock.html.

Source

Lock, R. H. (1993) 1993 New Car Data. Journal of Statistics Education 1(1).

Examples

library(ggplot2)

# Vehicle price by type
ggplot(cars93, aes(x = price)) +
  geom_histogram(binwidth = 5) +
  facet_wrap(~type)

# Vehicle price vs. weight
ggplot(cars93, aes(x = weight, y = price)) +
  geom_point()

# Milleage vs. weight
ggplot(cars93, aes(x = weight, y = mpg_city)) +
  geom_point() +
  geom_smooth()

Community college housing (simulated data)

Description

These are simulated data and intended to represent housing prices of students at a community college.

Usage

cchousing

Format

A data frame with 75 observations on the following variable.

price

Monthly housing price, simulated.

Examples

hist(cchousing$price)

Random sample of 2000 U.S. Census Data

Description

A random sample of 500 observations from the 2000 U.S. Census Data.

Usage

census

Format

A data frame with 500 observations on the following 8 variables.

census_year

Census Year.

state_fips_code

Name of state.

total_family_income

Total family income (in U.S. dollars).

age

Age.

sex

Sex with levels Female and Male.

race_general

Race with levels American Indian or Alaska Native, Black, Chinese, Japanese, Other Asian or Pacific Islander, Two major races, White and Other.

marital_status

Marital status with levels Divorced, Married/spouse absent, Married/spouse present, Never married/single, Separated and Widowed.

total_personal_income

Total personal income (in U.S. dollars).

Source

https://data.census.gov/cedsci

Examples

library(dplyr)
library(ggplot2)

census |>
  filter(total_family_income > 0) |>
  ggplot(aes(x = total_family_income)) +
  geom_histogram(binwidth = 25000)

Summary information for 31 cherry trees

Description

Researchers wanting to understand the relationship between these variables for black cherry trees collected data from 31 trees in the Allegheny National Forest, Pennsylvania.

Usage

cherry

Format

A data frame with 31 observations on the following 3 variables.

diam

diameter in inches (at 54 inches above ground)

height

height is measured in feet

volume

volume in cubic feet

Source

D.J. Hand. A handbook of small data sets. Chapman & Hall/CRC, 1994.

Examples

library(ggplot2)
library(broom)

ggplot(cherry, aes(x = diam, y = volume)) +
  geom_point() +
  geom_smooth(method = "lm")

mod <- lm(volume ~ diam + height, cherry)
tidy(mod)

Gender Stereotypes in 5-7 year old Children

Description

Stereotypes are common, but at what age do they start? This study investigates stereotypes in young children aged 5-7 years old. There are four studies reported in the paper, and all four datasets are provided here.

Usage

children_gender_stereo

Format

This data object is more unusual than most. It is a list of 4 data frames. The four data frames correspond to the data used in Studies 1-4 of the referenced paper, and these data frames each have variables (columns) that are among the following:

subject

Subject ID. Note that Subject 1 in the first data frame (dataset) does not correspond to Subject 1 in the second data frame.

gender

Gender of the subject.

age

Age of the subject, in years.

trait

The trait that the children were making a judgement about, which was either nice or smart.

target

The age group of the people the children were making judgements about (as being either nice or smart): children or adults.

stereotype

The proportion of trials where the child picked a gender target that matched the trait that was the same as the gender of the child. For example, suppose we had 18 pictures, where each picture showed 2 men and 2 women (and a different set of people in each photo). Then if we asked a boy to pick the person in each picture who they believed to be really smart, this stereotype variable would report the fraction of pictures where the boy picked a man. When a girl reviews the photos, then this stereotype variable reports the fraction of photos where she picked a woman. That is, this variable differs in meaning depending on the gender of the child. (This variable design is a little confusing, but it is useful when analyzing the data.)

high_achieve_caution

The proportion of trials where the child said that children of their own gender were high-achieving in school.

interest

Average score that measured the interest of the child in the game.

difference

A difference score between the interest of the child in the “smart” game and their interest in the “try-hard” game.

Details

The structure of the data object is a little unusual, so we recommend reviewing the Examples section before starting your analysis.

Thank you to Nicholas Horton for pointing us to this study and the data!

Most of the results in the paper can be reproduced using the data provided here.

% TODO(David) - Add short descriptions of each study.

Source

Bian L, Leslie SJ, Cimpian A. 2017. "Gender stereotypes about intellectual ability emerge early and influence children's interests". Science 355:6323 (389-391). https://www.science.org/doi/10.1126/science.aah6524.

The original data may be found here.

Examples

# This dataset is a little funny to work with.
# If wanting to review the data for a study, we
# recommend first assigning the corresponding
# data frame to a new variable. For instance,
# below we assign the second study's data to an
# object called `d` (d is for data!).
d <- children_gender_stereo[[2]]

Child care hours

Description

The China Health and Nutrition Survey aims to examine the effects of the health, nutrition, and family planning policies and programs implemented by national and local governments.

Usage

china

Format

A data frame with 9788 observations on the following 3 variables.

gender

a numeric vector

edu

a numeric vector

child_care

a numeric vector

Source

UNC Carolina Population Center, China Health and Nutrition Survey, 2006.

Examples

summary(china)

CIA Factbook Details on Countries

Description

Country-level statistics from the US Central Intelligence Agency (CIA).

Usage

cia_factbook

Format

A data frame with 259 observations on the following 11 variables.

country

Country name.

area

Land area, in square kilometers. (1 square kilometer is 0.386 square miles

birth_rate

Birth rate, in births per 1,000 people.

death_rate

Death rate, in deaths per 1,000 people.

infant_mortality_rate

Infant mortality, in deaths per 1,000 live births.

internet_users

Total number of internet users.

life_exp_at_birth

Live expectancy at birth, in years.

maternal_mortality_rate

Number of female deaths per 100,000 live births where the death is related to pregnancy or birth.

net_migration_rate

Net migration rate.

population

Total population.

population_growth_rate

Population growth rate.

Source

CIA Factbook, Country Comparisons, 2014. https://www.cia.gov/the-world-factbook/references/guide-to-country-comparisons/

Examples

library(dplyr)
library(ggplot2)

cia_factbook_iup <- cia_factbook |>
  mutate(internet_users_percent = 100 * internet_users / population)

ggplot(cia_factbook_iup, aes(x = internet_users_percent, y = life_exp_at_birth)) +
  geom_point() +
  labs(x = "Percentage of internet users", y = "Life expectancy at birth")

Simulated class data

Description

This data is simulated and is meant to represent students scores from three different lectures who were all given the same exam.

Usage

classdata

Format

A data frame with 164 observations on the following 2 variables.

m1

Represents a first midterm score.

lecture

Three classes: a, b, and c.

References

OpenIntro Statistics, Chapter 8.

Examples

anova(lm(m1 ~ lecture, classdata))

Cleveland and Sacramento

Description

Data on a sample of 500 people from the Cleveland, OH and Sacramento, CA metro areas.

Usage

cle_sac

Format

A data frame with 500 observations representing people on the following 8 variables.

year

Year the data was collected.

state

State where person resides.

city

City.

age

Age.

sex

Sex.

race

Race.

marital_status

Marital status.

personal_income

Personal income.

Examples

library(ggplot2)

ggplot(cle_sac, aes(x = personal_income)) +
  geom_histogram(binwidth = 20000) +
  facet_wrap(~city)

Temperature Summary Data, Geography Limited

Description

A random set of monitoring locations were taken from NOAA data that had both years of interest (1948 and 2018) as well as data for both summary metrics of interest (dx70 and dx90, which are described below).

Usage

climate70

Format

A data frame with 197 observations on the following 7 variables.

station

Station ID.

latitude

Latitude of the station.

longitude

Longitude of the station.

dx70_1948

Number of days above 70 degrees in 1948.

dx70_2018

Number of days above 70 degrees in 2018.

dx90_1948

Number of days above 90 degrees in 1948.

dx90_2018

Number of days above 90 degrees in 2018.

Details

Please keep in mind that these are two annual snapshots, and a complete analysis would consider much more than two years of data and much additional information for those years.

Source

https://www.ncdc.noaa.gov/cdo-web, retrieved 2019-04-24.

Examples

# Data sampled are from the US, Europe, and Australia.
# This geographic limitation may be due to the particular
# years considered, since locations without both 1948 and
# 2018 were discarded for this (simple) dataset.
plot(climate70$longitude, climate70$latitude)

plot(climate70$dx70_1948, climate70$dx70_2018)
abline(0, 1, lty = 2)
plot(climate70$dx90_1948, climate70$dx90_2018)
abline(0, 1, lty = 2)
hist(climate70$dx70_2018 - climate70$dx70_1948)
hist(climate70$dx90_2018 - climate70$dx90_1948)

t.test(climate70$dx70_2018 - climate70$dx70_1948)
t.test(climate70$dx90_2018 - climate70$dx90_1948)

Climber Drugs Data.

Description

Anonymous data was collected from urine samples at huts along the climb of Mont Blanc. Several types of drugs were tested, and proportions were reported.

Usage

climber_drugs

Format

A data frame with 211 rows and 6 variables.

positive_sample

Idendification number of a specific urine sample.

hut

Location where the sample was taken.

substance

Substance detected to be present in the urine sample.

concentration

Amount of substance found measured in ng/ml.

screening_analysis

Indicates that the concentration was determined by screening analysis.

concomitant

Indicates that this substance was always detected concomitantly with the previous one, within the same urine sample.

Source

PLOS One - Drug Use on Mont Blanc: A Study Using Automated Urine Collection

Examples

library(dplyr)

# Calculate the average concentration of each substance and number of occurrences.
climber_drugs |>
  group_by(substance) |>
  summarize(count = n(), mean_con = mean(concentration))

# Proportion samples in which each substance was detected.
climber_drugs |>
  group_by(substance) |>
  summarize(prop = n() / 154)

Coast Starlight Amtrak train

Description

Travel times and distances.

Usage

coast_starlight

Format

A data frame with 16 observations on the following 3 variables.

station

Station.

dist

Distance.

travel_time

Travel time.

Examples

library(ggplot2)

ggplot(coast_starlight, aes(x = dist, y = travel_time)) +
  geom_point()

comics

Description

A data frame containing information about comic book characters from Marvel Comics and DC Comics.

Usage

comics

Format

A data frame with 21821 observations on the following 11 variables.

name

Name of the character. May include: Real name, hero or villain name, alias(es) and/or which universe they live in (i.e. Earth-616 in Marvel's multiverse).

id

Status of the characters identity with levels Secret, Publie, ⁠No Dual⁠ and Unknown.

align

Character's alignment with levels Good, Bad, Neutral and ⁠Reformed Criminals⁠.

eye

Character's eye color.

hair

Character's hair color.

gender

Character's gender.

gsm

Character's classification as a gender or sexual minority.

alive

Is the character dead or alive?

appearances

Number of comic boooks the character appears in.

first_appear

Date of publication for the comic book the character first appeared in.

publisher

Publisher of the comic with levels Marvel and DC.

Examples

library(ggplot2)
library(dplyr)

# Good v Bad

plot_data <- comics |>
  filter(align == "Good" | align == "Bad")

ggplot(plot_data, aes(x = align, fill = align)) +
  geom_bar() +
  facet_wrap(~publisher) +
  scale_fill_manual(values = c(IMSCOL["red", "full"], IMSCOL["blue", "full"])) +
  theme_minimal() +
  labs(
    title = "Is there a balance of power",
    x = "",
    y = "Number of characters",
    fill = ""
  )

Generate Contingency Tables for LaTeX

Description

Input a data frame or a table, and the LaTeX output will be returned. Options exist for row and column proportions as well as for showing work.

Usage

contTable(
  x,
  prop = c("none", "row", "col"),
  show = FALSE,
  digits = 3,
  caption = NULL,
  label = NULL
)

Arguments

Details

The contTable function makes substantial use of the cat function.

Author(s)

David Diez

See Also

email, cars93, possum, mariokart

Examples

data(email)
table(email[, c("spam", "sent_email")])
contTable(email[, c("spam", "sent_email")])

Sample datasets for correlation problems

Description

Simulated data.

Usage

corr_match

Format

A data frame with 121 observations on the following 9 variables.

x

a numeric vector

y1

a numeric vector

y2

a numeric vector

y3

a numeric vector

y4

a numeric vector

y5

a numeric vector

y6

a numeric vector

y7

a numeric vector

y8

a numeric vector

Source

Simulated dataset.

Examples

library(ggplot2)

ggplot(corr_match, aes(x = x, y = y1)) +
  geom_point()

cor(corr_match$x, corr_match$y1)

Country ISO information

Description

Country International Organization for Standardization (ISO) information.

Usage

country_iso

Format

A data frame with 249 observations on the following 4 variables.

country_code

Two-letter ISO country code.

country_name

Country name.

year

Year the two-letter ISO country code was assigned.

top_level_domain

op-level domain name.

Source

Wikipedia, retrieved 2018-11-18. https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2

Examples

country_iso

CPR dataset

Description

These patients were randomly divided into a treatment group where they received a blood thinner or the control group where they did not receive a blood thinner. The outcome variable of interest was whether the patients survived for at least 24 hours.

Usage

cpr

Format

A data frame with 90 observations on the following 2 variables.

group

a factor with levels control and treatment

outcome

a factor with levels died and survived

Source

Efficacy and safety of thrombolytic therapy after initially unsuccessful cardiopulmonary resuscitation: a prospective clinical trial, by Bottiger et al., The Lancet, 2001.

Examples

table(cpr)

CPU's Released between 2010 and 2020.

Description

Data on computer processors released between 2010 and 2020.

Usage

cpu

Format

A data frame with 875 rows and 12 variables.

company

Manufacturer of the CPU.

name

Model name of the processor.

codename

Name given by manufacturer to all chips with this architecture.

cores

Number of compute cores per processor.

threads

The number of threads represents the number of simultaneous calculations that can be ongoing in the processor.

base_clock

Base speed for the CPU in GHz.

boost_clock

Single-core max speed for the CPU in GHz.

socket

Specifies the type of connection to the motherboard.

process

Size of the process node used in production in nm.

l3_cache

Size of the level 3 cache on the processor in MB.

tdp

Total draw power of the processor.

released

Date which the processor was released to the public.

Source

TechPowerUp CPU Database.

Examples

library(ggplot2)

# CPU base speed
ggplot(cpu, aes(x = company, y = base_clock)) +
  geom_boxplot() +
  labs(
    x = "Company",
    y = "Base Clock (GHz)",
    title = "CPU base speed"
  )

# Process node size vs. boost speed
ggplot(cpu, aes(x = process, y = boost_clock)) +
  geom_point() +
  labs(
    x = "Process node size (nm)",
    y = "Boost Clock (GHz)",
    title = "Process node size vs. boost speed"
  )

College credits.

Description

A simulated dataset of number of credits taken by college students each semester.

Usage

credits

Format

A data frame with 100 observations on the following variable.

credits

Number of credits.

Source

Simulated data.

Examples

library(ggplot2)

ggplot(credits, aes(x = credits)) +
  geom_histogram(binwidth = 1)

Daycare fines

Description

Researchers tested the deterrence hypothesis which predicts that the introduction of a penalty will reduce the occurrence of the behavior subject to the fine, with the condition that the fine leaves everything else unchanged by instituting a fine for late pickup at daycare centers. For this study, they worked with 10 volunteer daycare centers that did not originally impose a fine to parents for picking up their kids late. They randomly selected 6 of these daycare centers and instituted a monetary fine (of a considerable amount) for picking up children late and then removed it. In the remaining 4 daycare centers no fine was introduced. The study period was divided into four: before the fine (weeks 1–4), the first 4 weeks with the fine (weeks 5-8), the entire period with the fine (weeks 5–16), and the after fine period (weeks 17-20). Throughout the study, the number of kids who were picked up late was recorded each week for each daycare. The study found that the number of late-coming parents increased significantly when the fine was introduced, and no reduction occurred after the fine was removed.

Usage

daycare_fines

Format

A data frame with 200 observations on the following 7 variables.

center

Daycare center id.

group

Study group: test (fine instituted) or control (no fine).

children

Number of children at daycare center.

week

Week of study.

late_pickups

Number of late pickups for a given week and daycare center.

study_period_4

Period of study, divided into 4 periods: ⁠before fine⁠, ⁠first 4 weeks with fine⁠, ⁠last 8 weeks with fine⁠, ⁠after fine⁠

study_period_3

Period of study, divided into 4 periods: ⁠before fine⁠, ⁠with fine⁠, ⁠after fine⁠

Source

Gneezy, Uri, and Aldo Rustichini. "A fine is a price." The Journal of Legal Studies 29, no. 1 (2000): 1-17.

Examples

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

# The following tables roughly match results presented in Table 2 of the source article
# The results are only off by rounding for some of the weeks
daycare_fines |>
  group_by(center, study_period_4) |>
  summarise(avg_late_pickups = mean(late_pickups), .groups = "drop") |>
  pivot_wider(names_from = study_period_4, values_from = avg_late_pickups)

daycare_fines |>
  group_by(center, study_period_3) |>
  summarise(avg_late_pickups = mean(late_pickups), .groups = "drop") |>
  pivot_wider(names_from = study_period_3, values_from = avg_late_pickups)

# The following plot matches Figure 1 of the source article
daycare_fines |>
  group_by(week, group) |>
  summarise(avg_late_pickups = mean(late_pickups), .groups = "drop") |>
  ggplot(aes(x = week, y = avg_late_pickups, group = group, color = group)) +
  geom_point() +
  geom_line()

Density plot

Description

Compute kernel density plots, written in the same structure as boxPlot. Histograms can be automatically added for teaching purposes.

Usage

densityPlot(
  x,
  fact = NULL,
  bw = "nrd0",
  histo = c("none", "faded", "hollow"),
  breaks = "Sturges",
  fading = "0E",
  fadingBorder = "25",
  lty = NULL,
  lwd = 1,
  col = c("black", "red", "blue"),
  key = NULL,
  add = FALSE,
  adjust = 1,
  kernel = c("gaussian", "epanechnikov", "rectangular", "triangular", "biweight",
    "cosine", "optcosine"),
  weights = NULL,
  n = 512,
  from,
  to,
  na.rm = FALSE,
  xlim = NULL,
  ylim = NULL,
  main = "",
  ...
)

Arguments

Author(s)

David Diez

See Also

histPlot, dotPlot, boxPlot

Examples

# hollow histograms
histPlot(tips$tip[tips$day == "Tuesday"],
  hollow = TRUE, xlim = c(0, 30),
  lty = 1, main = "Tips by day"
)
histPlot(tips$tip[tips$day == "Friday"],
  hollow = TRUE, border = "red",
  add = TRUE, main = "Tips by day"
)
legend("topright",
  col = c("black", "red"),
  lty = 1:2, legend = c("Tuesday", "Friday")
)

# density plots
densityPlot(tips$tip, tips$day,
  col = c("black", "red"), main = "Tips by day"
)
legend("topright",
  col = c("black", "red"),
  lty = 1:2, legend = c("Tuesday", "Friday")
)

densityPlot(tips$tip,
  histo = "faded",
  breaks = 15, main = "Tips by day"
)

densityPlot(tips$tip,
  histo = "hollow",
  breaks = 30, fadingBorder = "66",
  lty = 1, main = "Tips by day"
)

Type 2 Diabetes Clinical Trial for Patients 10-17 Years Old

Description

Three treatments were compared to test their relative efficacy (effectiveness) in treating Type 2 Diabetes in patients aged 10-17 who were being treated with metformin. The primary outcome was lack of glycemic control (or not); lacking glycemic control means the patient still needed insulin, which is not the preferred outcome for a patient.

Usage

diabetes2

Format

A data frame with 699 observations on the following 2 variables.

treatment

The treatment the patient received.

outcome

Whether there patient still needs insulin (failure) or met a basic positive outcome bar (success).

Details

Each of the 699 patients in the experiment were randomized to one of the following treatments: (1) continued treatment with metformin (coded as met), (2) formin combined with rosiglitazone (coded as rosi), or or (3) a lifestyle-intervention program (coded as lifestyle).

Source

Zeitler P, et al. 2012. A Clinical Trial to Maintain Glycemic Control in Youth with Type 2 Diabetes. N Engl J Med.

Examples

lapply(diabetes2, table)
(cont.table <- table(diabetes2))
(m <- chisq.test(cont.table))
m$expected

Create a Double Line Segment Plot

Description

Creae a plot showing two line segments. The union or intersection of those line segments can also be generated by utilizing the type argument.

Usage

dlsegments(
  x1 = c(3, 7),
  x2 = c(5, 9),
  l = c("o", "o"),
  r = c("c", "c"),
  type = c("n", "u", "i"),
  COL = 2,
  lwd = 2.224,
  ylim = c(-0.35, 2),
  mar = rep(0, 4),
  hideOrig = FALSE
)

Arguments

Author(s)

David Diez

See Also

lsegments, CCP, ArrowLines

Examples

dlsegments(c(-3, 3), c(1, 1000),
  r = c("o", "o"), l = c("c", "o"), COL = COL[4]
)

dlsegments(c(-3, 3), c(1, 1000),
  r = c("o", "o"), l = c("c", "o"), type = "un", COL = COL[4]
)

dlsegments(c(-3, 3), c(1, 1000),
  r = c("o", "o"), l = c("c", "o"), type = "in", COL = COL[4]
)

Dot plot

Description

Plot observations as dots.

Usage

dotPlot(
  x,
  fact = NULL,
  vertical = FALSE,
  at = 1,
  key = NULL,
  pch = 20,
  col = fadeColor("black", "66"),
  cex = 1.5,
  add = FALSE,
  axes = TRUE,
  xlim = NULL,
  ylim = NULL,
  ...
)

Arguments

Author(s)

David Diez

See Also

histPlot, densityPlot, boxPlot

Examples

library(dplyr)

# Price by type
dotPlot(cars93$price,
  cars93$type,
  key = c("large", "midsize", "small"),
  cex = 1:3
)

# Hours worked by educational attainment or degree
gss2010_nona <- gss2010 |>
  filter(!is.na(hrs1) & !is.na(degree))

dotPlot(gss2010_nona$hrs1,
  gss2010_nona$degree,
  col = fadeColor("black", "11")
)

# levels reordered
dotPlot(gss2010_nona$hrs1,
  gss2010_nona$degree,
  col = fadeColor("black", "11"),
  key = c("LT HIGH SCHOOL", "HIGH SCHOOL", "BACHELOR", "JUNIOR COLLEGE", "GRADUATE")
)

# with boxPlot() overlaid
dotPlot(mariokart$total_pr,
  mariokart$cond,
  ylim = c(0.5, 2.5), xlim = c(25, 80), cex = 1
)
boxPlot(mariokart$total_pr,
  mariokart$cond,
  add = 1:2 + 0.1,
  key = c("new", "used"), horiz = TRUE, axes = FALSE
)

Add a Stacked Dot Plot to an Existing Plot

Description

Add a stacked dot plot to an existing plot. The locations for the points in the dot plot are returned from the function in a list.

Usage

dotPlotStack(x, radius = 1, seed = 1, addDots = TRUE, ...)

Arguments

Value

Returns a list with a height that can be used as the upper bound of ylim for a plot, then also the x- and y-coordinates of the points in the stacked dot plot.

Author(s)

David Diez

See Also

dotPlot, histPlot

Examples

#

Survey on views of the DREAM Act

Description

A SurveyUSA poll.

Usage

dream

Format

A data frame with 910 observations on the following 2 variables.

ideology

a factor with levels Conservative Liberal Moderate

stance

a factor with levels No Not sure Yes

Source

SurveyUSA, News Poll #18927, data collected Jan 27-29, 2012.

Examples

table(dream)

Quadcopter Drone Blades

Description

Quality control dataset for quadcopter drone blades, where this data has been made up for an example.

Usage

drone_blades

Format

A data frame with 2000 observations on the following 2 variables.

supplier

The supplier for the blade.

inspection

The inspection conclusion.

References

OpenIntro Statistics, Third Edition and Fourth Edition.

Examples

library(dplyr)

drone_blades |>
  count(supplier, inspection)

Drug use of students and parents

Description

Summary of 445 student-parent pairs.

Usage

drug_use

Format

A data frame with 445 observations on the following 2 variables.

student

a factor with levels not uses

parents

a factor with levels not used

Source

Ellis GJ and Stone LH. 1979. Marijuana Use in College: An Evaluation of a Modeling Explanation. Youth and Society 10:323-334.

Examples

table(drug_use)

Sale prices of houses in Duke Forest, Durham, NC

Description

Data on houses that were recently sold in the Duke Forest neighborhood of Durham, NC in November 2020.

Usage

duke_forest

Format

A data frame with 98 rows and 13 variables.

address

Address of house.

price

Sale price, in USD.

bed

Number of bedrooms.

bath

Number of bathrooms.

area

Area of home, in square feet.

type

Type of home (all are Single Family).

year_built

Year the home was built.

heating

Heating sytem.

cooling

Cooling system (other or central).

parking

Type of parking available and number of parking spaces.

lot

Area of the entire property, in acres.

hoa

If the home belongs to an Home Owners Association, the associted fee (NA otherwise).

url

URL of the listing.

Source

Data were collected from Zillow in November 2020.

Examples

library(ggplot2)

# Number of bedrooms and price
ggplot(duke_forest, aes(x = as.factor(bed), y = price)) +
  geom_boxplot() +
  labs(
    x = "Number of bedrooms",
    y = "Sale price (USD)",
    title = "Homes for sale in Duke Forest, Durham, NC",
    subtitle = "Data are from November 2020"
  )

# Area and price
ggplot(duke_forest, aes(x = area, y = price)) +
  geom_point() +
  labs(
    x = "Area (square feet)",
    y = "Sale price (USD)",
    title = "Homes for sale in Duke Forest, Durham, NC",
    subtitle = "Data are from November 2020"
  )

Earthquakes

Description

Select set of notable earthquakes from 1900 to 1999.

Usage

earthquakes

Format

A data frame with 123 rows and 7 variables.

year

Year the earthquake took place.

month

Month the earthquake took place.

day

Day the earthquake took place

richter

Magnitude of earthquake using the Richter Scale.

area

City or geographic location of earthquakes.

region

Country or countries if the earthquake occurred on a border.

deaths

Approximate number of deaths caused by earthquake

Source

World Almanac and Book of Facts: 2011.

Examples

library(ggplot2)

ggplot(earthquakes, aes(x = richter, y = deaths)) +
  geom_point()

ggplot(earthquakes, aes(x = log(deaths))) +
  geom_histogram()

Survey on Ebola quarantine

Description

In New York City on October 23rd, 2014, a doctor who had recently been treating Ebola patients in Guinea went to the hospital with a slight fever and was subsequently diagnosed with Ebola. Soon thereafter, an NBC 4 New York/The Wall Street Journal/Marist Poll asked New Yorkers whether they favored a "mandatory 21-day quarantine for anyone who has come in contact with an Ebola patient". This poll included responses of 1,042 New York adults between October 26th and 28th, 2014.

Usage

ebola_survey

Format

A data frame with 1042 observations on the following variable.

quarantine

Indicates whether the respondent is in favor or against the mandatory quarantine.

Source

Poll ID NY141026 on maristpoll.marist.edu.

Examples

table(ebola_survey)

Exploratory data analysis plot

Description

Explore different plotting methods using a click interface.

Usage

edaPlot(
  dataFrame,
  Col = c("#888888", "#FF0000", "#222222", "#FFFFFF", "#CCCCCC", "#3377AA")
)

Arguments

Author(s)

David Diez

See Also

histPlot, densityPlot, boxPlot, dotPlot

Examples

data(mlbbat10)
bat <- mlbbat10[mlbbat10$at_bat > 200, ]
# edaPlot(bat)

data(mariokart)
mk <- mariokart[mariokart$total_pr < 100, ]
# edaPlot(mk)

Elmhurst College gift aid

Description

A random sample of 50 students gift aid for students at Elmhurst College.

Usage

elmhurst

Format

A data frame with 50 observations on the following 3 variables.

family_income

Family income of the student.

gift_aid

Gift aid, in $1000s.

price_paid

Price paid by the student (tuition - gift aid).

Source

These data were sampled from a table of data for all freshman from the 2011 class at Elmhurst College that accompanied an article titled What Students Really Pay to Go to College published online by The Chronicle of Higher Education: https://www.chronicle.com/article/what-students-really-pay-to-go-to-college/?sra=true.

Examples

library(ggplot2)
library(broom)

ggplot(elmhurst, aes(x = family_income, y = gift_aid)) +
  geom_point() +
  geom_smooth(method = "lm")

mod <- lm(gift_aid ~ family_income, data = elmhurst)
tidy(mod)

Data frame representing information about a collection of emails

Description

These data represent incoming emails for the first three months of 2012 for an email account (see Source).

Usage

email

Format

A email (email_sent) data frame has 3921 (1252) observations on the following 21 variables.

spam

Indicator for whether the email was spam.

to_multiple

Indicator for whether the email was addressed to more than one recipient.

from

Whether the message was listed as from anyone (this is usually set by default for regular outgoing email).

cc

Number of people cc'ed.

sent_email

Indicator for whether the sender had been sent an email in the last 30 days.

time

Time at which email was sent.

image

The number of images attached.

attach

The number of attached files.

dollar

The number of times a dollar sign or the word “dollar” appeared in the email.

winner

Indicates whether “winner” appeared in the email.

inherit

The number of times “inherit” (or an extension, such as “inheritance”) appeared in the email.

viagra

The number of times “viagra” appeared in the email.

password

The number of times “password” appeared in the email.

num_char

The number of characters in the email, in thousands.

line_breaks

The number of line breaks in the email (does not count text wrapping).

format

Indicates whether the email was written using HTML (e.g. may have included bolding or active links).

re_subj

Whether the subject started with “Re:”, “RE:”, “re:”, or “rE:”

exclaim_subj

Whether there was an exclamation point in the subject.

urgent_subj

Whether the word “urgent” was in the email subject.

exclaim_mess

The number of exclamation points in the email message.

number

Factor variable saying whether there was no number, a small number (under 1 million), or a big number.

Source

David Diez's Gmail Account, early months of 2012. All personally identifiable information has been removed.

See Also

email50

Examples

e <- email

# ______ Variables For Logistic Regression ______#
# Variables are modified to match
#   OpenIntro Statistics, Second Edition
# As Is (7): spam, to_multiple, winner, format,
#            re_subj, exclaim_subj
# Omitted (6): from, sent_email, time, image,
#              viagra, urgent_subj, number
# Become Indicators (5): cc, attach, dollar,
#                        inherit, password
e$cc <- ifelse(email$cc > 0, 1, 0)
e$attach <- ifelse(email$attach > 0, 1, 0)
e$dollar <- ifelse(email$dollar > 0, 1, 0)
e$inherit <- ifelse(email$inherit > 0, 1, 0)
e$password <- ifelse(email$password > 0, 1, 0)
# Transform (3): num_char, line_breaks, exclaim_mess
# e$num_char     <- cut(email$num_char, c(0,1,5,10,20,1000))
# e$line_breaks  <- cut(email$line_breaks, c(0,10,100,500,10000))
# e$exclaim_mess <- cut(email$exclaim_mess, c(-1,0,1,5,10000))
g <- glm(
  spam ~ to_multiple + winner + format +
    re_subj + exclaim_subj +
    cc + attach + dollar +
    inherit + password, # +
  # num_char + line_breaks + exclaim_mess,
  data = e, family = binomial
)
summary(g)


# ______ Variable Selection Via AIC ______#
g. <- step(g)
plot(predict(g., type = "response"), e$spam)


# ______ Splitting num_char by html ______#
x <- log(email$num_char)
bw <- 0.004
R <- range(x) + c(-1, 1)
wt <- sum(email$format == 1) / nrow(email)
htmlAll <- density(x, bw = 0.4, from = R[1], to = R[2])
htmlNo <- density(x[email$format != 1],
  bw = 0.4,
  from = R[1], to = R[2]
)
htmlYes <- density(x[email$format == 1],
  bw = 0.4,
  from = R[1], to = R[2]
)
htmlNo$y <- htmlNo$y #* (1-wt)
htmlYes$y <- htmlYes$y #* wt + htmlNo$y
plot(htmlAll, xlim = c(-4, 6), ylim = c(0, 0.4))
lines(htmlNo, col = 4)
lines(htmlYes, lwd = 2, col = 2)

Sample of 50 emails

Description

This is a subsample of the email dataset.

Usage

email50

Format

A data frame with 50 observations on the following 21 variables.

spam

Indicator for whether the email was spam.

to_multiple

Indicator for whether the email was addressed to more than one recipient.

from

Whether the message was listed as from anyone (this is usually set by default for regular outgoing email).

cc

Number of people cc'ed.

sent_email

Indicator for whether the sender had been sent an email in the last 30 days.

time

Time at which email was sent.

image

The number of images attached.

attach

The number of attached files.

dollar

The number of times a dollar sign or the word “dollar” appeared in the email.

winner

Indicates whether “winner” appeared in the email.

inherit

The number of times “inherit” (or an extension, such as “inheritance”) appeared in the email.

viagra

The number of times “viagra” appeared in the email.

password

The number of times “password” appeared in the email.

num_char

The number of characters in the email, in thousands.

line_breaks

The number of line breaks in the email (does not count text wrapping).

format

Indicates whether the email was written using HTML (e.g. may have included bolding or active links).

re_subj

Whether the subject started with “Re:”, “RE:”, “re:”, or “rE:”

exclaim_subj

Whether there was an exclamation point in the subject.

urgent_subj

Whether the word “urgent” was in the email subject.

exclaim_mess

The number of exclamation points in the email message.

number

Factor variable saying whether there was no number, a small number (under 1 million), or a big number.

Source

David Diez's Gmail Account, early months of 2012. All personally identifiable information has been removed.

See Also

email

Examples

index <- c(
  101, 105, 116, 162, 194, 211, 263, 308, 361, 374,
  375, 465, 509, 513, 571, 691, 785, 842, 966, 968,
  1051, 1201, 1251, 1433, 1519, 1727, 1760, 1777, 1899, 1920,
  1943, 2013, 2052, 2252, 2515, 2629, 2634, 2710, 2823, 2835,
  2944, 3098, 3227, 3360, 3452, 3496, 3530, 3665, 3786, 3877
)
order <- c(
  3, 33, 12, 1, 21, 15, 43, 49, 8, 6,
  34, 25, 24, 35, 41, 9, 22, 50, 4, 48,
  7, 14, 46, 10, 38, 32, 26, 18, 23, 45,
  30, 16, 17, 20, 40, 47, 31, 37, 27, 11,
  5, 44, 29, 19, 13, 36, 39, 42, 28, 2
)
d <- email[index, ][order, ]
identical(d, email50)

American Adults on Regulation and Renewable Energy

Description

Pew Research conducted a poll to find whether American adults support regulation or believe the private market will move the American economy towards renewable energy.

Usage

env_regulation

Format

A data frame with 705 observations on the following variable.

statement

There were three possible outcomes for each person: "Regulations necessary", "Private marketplace will ensure", and "Don't know".

Details

The exact statements being selected were: (1) Government regulations are necessary to encourage businesses and consumers to rely more on renewable energy sources. (2) The private marketplace will ensure that businesses and consumers rely more on renewable energy sources, even without government regulations.

The actual sample size was 1012. However, the original data were not from a simple random sample; after accounting for the design, the equivalent sample size was about 705, which was what was used for the dataset here to keep things simpler for intro stat analyses.

Source

https://www.pewresearch.org/science/2017/05/16/public-divides-over-environmental-regulation-and-energy-policy-2/

Examples

table(env_regulation)

Vehicle info from the EPA for 2012

Description

Details from the EPA.

Usage

epa2012

Format

A data frame with 1129 observations on the following 28 variables.

model_yr

a numeric vector

mfr_name

Manufacturer name.

division

Vehicle division.

carline

Vehicle line.

mfr_code

Manufacturer code.

model_type_index

Model type index.

engine_displacement

Engine displacement.

no_cylinders

Number of cylinders.

transmission_speed

Transmission speed.

city_mpg

City mileage.

hwy_mpg

Highway mileage.

comb_mpg

Combined mileage.

guzzler

Whether the car is considered a "guzzler" or not, a factor with levels N and Y.

air_aspir_method

Air aspiration method.

air_aspir_method_desc

Air aspiration method description.

transmission

Transmission type.

transmission_desc

Transmission type description.

no_gears

Number of gears.

trans_lockup

Whether transmission locks up, a factor with levels N and Y.

trans_creeper_gear

A factor with level N only.

drive_sys

Drive system, a factor with levels.

drive_desc

Drive system description.

fuel_usage

Fuel usage, a factor with levels.

fuel_usage_desc

Fuel usage description.

class

Class of car.

car_truck

Car or truck, a factor with levels car, 1, 2.

release_date

Date of vehicle release.

fuel_cell

Whether the car has a fuel cell or not, a factor with levels N, Y.

Source

Fueleconomy.gov, Shared MPG Estimates: Toyota Prius 2012.

See Also

epa2021

Examples

library(ggplot2)
library(dplyr)

# Variable descriptions
distinct(epa2012, air_aspir_method_desc, air_aspir_method)
distinct(epa2012, transmission_desc, transmission)
distinct(epa2012, drive_desc, drive_sys)
distinct(epa2012, fuel_usage_desc, fuel_usage)

# Guzzlers and their mileages
ggplot(epa2012, aes(x = city_mpg, y = hwy_mpg, color = guzzler)) +
  geom_point() +
  facet_wrap(~guzzler, ncol = 1)

Vehicle info from the EPA for 2021

Description

Details from the EPA.

Usage

epa2021

Format

A data frame with 1108 observations on the following 28 variables.

model_yr

a numeric vector

mfr_name

Manufacturer name.

division

Vehicle division.

carline

Vehicle line.

mfr_code

Manufacturer code.

model_type_index

Model type index.

engine_displacement

Engine displacement.

no_cylinders

Number of cylinders.

transmission_speed

Transmission speed.

city_mpg

City mileage.

hwy_mpg

Highway mileage.

comb_mpg

Combined mileage.

guzzler

Whether the car is considered a "guzzler" or not, a factor with levels N and Y.

air_aspir_method

Air aspiration method.

air_aspir_method_desc

Air aspiration method description.

transmission

Transmission type.

transmission_desc

Transmission type description.

no_gears

Number of gears.

trans_lockup

Whether transmission locks up, a factor with levels N and Y.

trans_creeper_gear

A factor with level N only.

drive_sys

Drive system, a factor with levels.

drive_desc

Drive system description.

fuel_usage

Fuel usage, a factor with levels.

fuel_usage_desc

Fuel usage description.

class

Class of car.

car_truck

Car or truck, a factor with levels car, 1, ⁠??⁠, 1.

release_date

Date of vehicle release.

fuel_cell

Whether the car has a fuel cell or not, a factor with levels N, NA.

Source

Fuel Economy Data from fueleconomy.gov. Retrieved 6 May, 2021.

See Also

epa2012

Examples

library(ggplot2)
library(dplyr)

# Variable descriptions
distinct(epa2021, air_aspir_method_desc, air_aspir_method)
distinct(epa2021, transmission_desc, transmission)
distinct(epa2021, drive_desc, drive_sys)
distinct(epa2021, fuel_usage_desc, fuel_usage)

# Guzzlers and their mileages
ggplot(epa2021, aes(x = city_mpg, y = hwy_mpg, color = guzzler)) +
  geom_point() +
  facet_wrap(~guzzler, ncol = 1)

# Compare to 2012
epa2021 |>
  bind_rows(epa2012) |>
  group_by(model_yr) |>
  summarise(
    mean_city = mean(city_mpg),
    mean_hwy  = mean(hwy_mpg)
  )

Environmental Sustainability Index 2005

Description

This dataset comes from the 2005 Environmental Sustainability Index: Benchmarking National Environmental Stewardship. Countries are given an overall sustainability score as well as scores in each of several different environmental areas.

Usage

esi

Format

A data frame with 146 observations on the following 29 variables.

code

ISO3 country code.

country

Country.

esi

Environmental Sustainability Index.

system

ESI core component: systems

stress

ESI core component: stresses

vulner

ESI core component: vulnerability

cap

ESI core component: capacity

global

ESI core component: global stewardship

sys_air

Air quality.

sys_bio

Biodiversity.

sys_lan

Land.

sys_wql

Water quality.

sys_wqn

Water quantity.

str_air

Reducing air pollution.

str_eco

Reducing ecosystem stress.

str_pop

Reducing population pressure.

str_was

Reducing waste and consumption pressures.

str_wat

Reducing water stress.

str_nrm

Natural resource management.

vul_hea

Environmental health.

vul_sus

Basic human sustenance.

vul_dis

Exposure to natural disasters.

cap_gov

Environmental governance.

cap_eff

Eco-efficiency.

cap_pri

Private sector responsiveness.

cap_st

Science and technology.

glo_col

Participation in international collaboration efforts.

glo_ghg

Greenhouse gas emissions.

glo_tbp

Reducing transboundary environmental pressures.

Details

ESI and Component scores are presented as standard normal percentiles. Indicator scores are in the form of z-scores. See Appendix A of the report for information on the methodology and Appendix C for more detail on original data sources.

For more information on how each of the indices were calculated, see the documentation linked below.

Source

ESI Component Indicators. 2005 Environmental Sustainability Index: Benchmarking National Environmental Stewardship, Yale Center for Environmental Law and Policy, Yale University & Center for International Earth Science Information Network (CIESIN), Columbia University

In collaboration with: World Economic Forum, Geneva, Switzerland Joint Research Centre of the European Commission, Ispra, Italy.

Available at https://www.earth.columbia.edu/news/2005/images/ESI2005_policysummary.pdf.

References

Esty, Daniel C., Marc Levy, Tanja Srebotnjak, and Alexander de Sherbinin (2005). 2005 Environmental Sustainability Index: Benchmarking National Environmental Stewardship. New Haven: Yale Center for Environmental Law and Policy

Examples

library(ggplot2)

ggplot(esi, aes(x = cap_st, y = glo_col)) +
  geom_point(color = ifelse(esi$code == "USA", "red", "black")) +
  geom_text(
    aes(label = ifelse(code == "USA", as.character(code), "")),
    hjust = 1.2, color = "red"
  ) +
  labs(x = "Science and technology", y = "Participation in international collaboration efforts")

ggplot(esi, aes(x = vulner, y = cap)) +
  geom_point(color = ifelse(esi$code == "USA", "red", "black")) +
  geom_text(
    aes(label = ifelse(code == "USA", as.character(code), "")),
    hjust = 1.2, color = "red"
  ) +
  labs(x = "Vulnerability", y = "Capacity")

Ethanol Treatment for Tumors Experiment

Description

Experiment where 3 different treatments of ethanol were tested on the treatment of oral cancer tumors in hamsters.

Usage

ethanol

Format

A data frame with 24 observations, each representing one hamster, on the following 2 variables.

treatment

Treatment the hamster received.

regress

a factor with levels no yes

Details

The ethyl_cellulose and pure_ethanol treatments consisted of about a quarter of the volume of the tumors, while the pure_ethanol_16x treatment was 16x that, so about 4 times the size of the tumors.

Source

Morhard R, et al. 2017. Development of enhanced ethanol ablation as an alternative to surgery in treatment of superficial solid tumors. Scientific Reports 7:8750.

Examples

table(ethanol)
fisher.test(table(ethanol))

Professor evaluations and beauty

Description

The data are gathered from end of semester student evaluations for 463 courses taught by a sample of 94 professors from the University of Texas at Austin. In addition, six students rate the professors' physical appearance. The result is a data frame where each row contains a different course and each column has information on the course and the professor who taught that course.

Usage

evals

Format

A data frame with 463 observations on the following 23 variables.

course_id

Variable identifying the course (out of 463 courses).

prof_id

Variable identifying the professor who taught the course (out of 94 professors).

score

Average professor evaluation score: (1) very unsatisfactory - (5) excellent.

rank

Rank of professor: teaching, tenure track, tenured.

ethnicity

Ethnicity of professor: not minority, minority.

gender

Gender of professor: female, male.

language

Language of school where professor received education: English or non-English.

age

Age of professor.

cls_perc_eval

Percent of students in class who completed evaluation.

cls_did_eval

Number of students in class who completed evaluation.

cls_students

Total number of students in class.

cls_level

Class level: lower, upper.

cls_profs

Number of professors teaching sections in course in sample: single, multiple.

cls_credits

Number of credits of class: one credit (lab, PE, etc.), multi credit.

bty_f1lower

Beauty rating of professor from lower level female: (1) lowest - (10) highest.

bty_f1upper

Beauty rating of professor from upper level female: (1) lowest - (10) highest.

bty_f2upper

Beauty rating of professor from second level female: (1) lowest - (10) highest.

bty_m1lower

Beauty rating of professor from lower level male: (1) lowest - (10) highest.

bty_m1upper

Beauty rating of professor from upper level male: (1) lowest - (10) highest.

bty_m2upper

Beauty rating of professor from second upper level male: (1) lowest - (10) highest.

bty_avg

Average beauty rating of professor.

pic_outfit

Outfit of professor in picture: not formal, formal.

pic_color

Color of professor's picture: color, black & white.

Source

Daniel S. Hamermesh, Amy Parker, Beauty in the classroom: instructors’ pulchritude and putative pedagogical productivity, Economics of Education Review, Volume 24, Issue 4, 2005. doi:10.1016/j.econedurev.2004.07.013.

Examples

evals

Exam and course grades for statistics students

Description

Grades on three exams and overall course grade for 233 students during several years for a statistics course at a university.

Usage

exam_grades

Format

A data frame with 233 observations, each representing a student.

semester

Semester when grades were recorded.

sex

Sex of the student as recorded on the university registration system: Man or Woman.

exam1

Exam 1 grade.

exam2

Exam 2 grade.

exam3

Exam 3 grade.

course_grade

Overall course grade.

Examples

library(ggplot2)
library(dplyr)

# Course grade vs. each exam
ggplot(exam_grades, aes(x = exam1, y = course_grade)) +
  geom_point()

ggplot(exam_grades, aes(x = exam2, y = course_grade)) +
  geom_point()

ggplot(exam_grades, aes(x = exam2, y = course_grade)) +
  geom_point()

# Semester averages
exam_grades |>
  group_by(semester) |>
  summarise(across(exam1:course_grade, mean, na.rm = TRUE))

Exam scores

Description

Exam scores from a class of 19 students.

Usage

exams

Format

A data frame with 19 observations on the following variable.

scores

a numeric vector

Examples

hist(exams$scores)

Number of Exclusive Relationships

Description

A survey conducted on a reasonably random sample of 203 undergraduates asked, among many other questions, about the number of exclusive relationships these students have been in.

Usage

exclusive_relationship

Format

A data frame with 218 observations on the following variable.

num

Number of exclusive relationships.

Examples

summary(exclusive_relationship$num)
table(exclusive_relationship$num)
hist(exclusive_relationship$num)

Can Americans categorize facts and opinions?

Description

Pew Research Center conducted a survey in 2018, asking a sample of U.S. adults to categorize five factual and five opinion statements. This dataset provides data from this survey, with information on the age group of the participant as well as the number of factual and opinion statements they classified correctly (out of 5).

Usage

fact_opinion

Format

A data frame with 5,035 rows and 3 variables.

age_group

Age group of survey participant.

fact_correct

Number of factual statements classified correctly (out of 5).

opinion_correct

Number of opinion statements classified correctly (out of 5).

Source

Younger Americans are better than older Americans at telling factual news statements from opinions, Pew Research Center, October 23, 2018.

Examples

library(ggplot2)
library(dplyr)
library(tidyr)
library(forcats)

# Distribution of fact_correct by age group
ggplot(fact_opinion, aes(x = age_group, y = fact_correct)) +
  geom_boxplot() +
  labs(
    x = "Age group",
    y = "Number correct (factual)",
    title = "Number of factual statements classified correctly by age group"
  )

# Distribution of opinion_correct by age group
ggplot(fact_opinion, aes(x = age_group, y = opinion_correct)) +
  geom_boxplot() +
  labs(
    x = "Age group",
    y = "Number correct (opinion)",
    title = "Number of opinion statements classified correctly by age group"
  )

# Replicating the figure from Pew report (see source for link)
fact_opinion |>
  mutate(
    facts = case_when(
      fact_correct <= 2 ~ "Two or fewer",
      fact_correct %in% c(3, 4) ~ "Three or four",
      fact_correct == 5 ~ "All five"
    ),
    facts = fct_relevel(facts, "Two or fewer", "Three or four", "All five"),
    opinions = case_when(
      opinion_correct <= 2 ~ "Two or fewer",
      opinion_correct %in% c(3, 4) ~ "Three or four",
      opinion_correct == 5 ~ "All five"
    ),
    opinions = fct_relevel(opinions, "Two or fewer", "Three or four", "All five")
  ) |>
  select(-fact_correct, -opinion_correct) |>
  pivot_longer(cols = -age_group, names_to = "question_type", values_to = "n_correct") |>
  ggplot(aes(y = fct_rev(age_group), fill = n_correct)) +
  geom_bar(position = "fill") +
  facet_wrap(~question_type, ncol = 1) +
  scale_fill_viridis_d(guide = guide_legend(reverse = TRUE)) +
  labs(
    x = "Proportion",
    y = "Age group",
    fill = "Number of\ncorrect\nclassifications"
  )

Fade colors

Description

Fade colors so they are transparent.

Usage

fadeColor(col, fade = "FF")

Arguments

Author(s)

David Diez

Examples

data(mariokart)
new <- mariokart$cond == "new"
used <- mariokart$cond == "used"

# ===> color numbers <===#
dotPlot(mariokart$total_pr[new],
  ylim = c(0, 3), xlim = c(25, 80), pch = 20,
  col = 2, cex = 2, main = "using regular colors"
)
dotPlot(mariokart$total_pr[used], at = 2, add = TRUE, col = 4, pch = 20, cex = 2)
dotPlot(mariokart$total_pr[new],
  ylim = c(0, 3), xlim = c(25, 80),
  col = fadeColor(2, "22"), pch = 20, cex = 2,
  main = "fading the colors first"
)
dotPlot(mariokart$total_pr[used],
  at = 2, add = TRUE,
  col = fadeColor(4, "22"), pch = 20, cex = 2
)

# ===> color names <===#
dotPlot(mariokart$total_pr[new],
  ylim = c(0, 3), xlim = c(25, 80), pch = 20,
  col = "red", cex = 2, main = "using regular colors"
)
dotPlot(mariokart$total_pr[used], at = 2, add = TRUE, col = "blue", pch = 20, cex = 2)
dotPlot(mariokart$total_pr[new],
  ylim = c(0, 3), xlim = c(25, 80),
  col = fadeColor("red", "22"), pch = 20, cex = 2,
  main = "fading the colors first"
)
dotPlot(mariokart$total_pr[used],
  at = 2, add = TRUE,
  col = fadeColor("blue", "22"), pch = 20, cex = 2
)

# ===> hexadecimal <===#
dotPlot(mariokart$total_pr[new],
  ylim = c(0, 3), xlim = c(25, 80), pch = 20,
  col = "#FF0000", cex = 2, main = "using regular colors"
)
dotPlot(mariokart$total_pr[used],
  at = 2, add = TRUE, col = "#0000FF", pch = 20,
  cex = 2
)
dotPlot(mariokart$total_pr[new],
  ylim = c(0, 3), xlim = c(25, 80),
  col = fadeColor("#FF0000", "22"), pch = 20, cex = 2,
  main = "fading the colors first"
)
dotPlot(mariokart$total_pr[used],
  at = 2, add = TRUE,
  col = fadeColor("#0000FF", "22"), pch = 20, cex = 2
)

# ===> alternative: rgb function <===#
dotPlot(mariokart$total_pr[new],
  ylim = c(0, 3), xlim = c(25, 80), pch = 20,
  col = rgb(1, 0, 0), cex = 2, main = "using regular colors"
)
dotPlot(mariokart$total_pr[used],
  at = 2, add = TRUE, col = rgb(0, 0, 1),
  pch = 20, cex = 2
)
dotPlot(mariokart$total_pr[new],
  ylim = c(0, 3), xlim = c(25, 80),
  col = rgb(1, 0, 0, 1 / 8), pch = 20, cex = 2,
  main = "fading the colors first"
)
dotPlot(mariokart$total_pr[used],
  at = 2, add = TRUE,
  col = rgb(0, 0, 1, 1 / 8), pch = 20, cex = 2
)

Simulated sample of parent / teen college attendance

Description

A simulated dataset based on real population summaries.

Usage

family_college

Format

A data frame with 792 observations on the following 2 variables.

teen

Whether the teen goes to college or not.

parents

Whether the parent holds a college degree or not.

Source

Simulation based off of summary information provided at https://eric.ed.gov/?id=ED460660.

Examples

library(dplyr)

family_college |>
  count(teen, parents)

Nutrition in fast food

Description

Nutrition amounts in 515 fast food items. The author of the data scraped only entrees (not sides, drinks, desserts, etc.).

Usage

fastfood

Format

A data frame with 515 observations on the following 17 variables.

restaurant

Name of restaurant

item

Name of item

calories

Number of calories

cal_fat

Calories from fat

total_fat

Total fat

sat_fat

Saturated fat

trans_fat

Trans fat

cholesterol

Cholesterol

sodium

Sodium

total_carb

Total carbs

fiber

Fiber

sugar

Suger

protein

Protein

vit_a

Vitamin A

vit_c

Vitamin C

calcium

Calcium

salad

Salad or not

Source

Retrieved from Tidy Tuesday Fast food entree data.

Summary of male heights from USDA Food Commodity Intake Database

Description

Sample of heights based on the weighted sample in the survey.

Usage

fcid

Format

A data frame with 100 observations on the following 2 variables.

height

a numeric vector

num_of_adults

a numeric vector

Examples

fcid

Female college student heights, in inches

Description

24 sample observations.

Usage

fheights

Format

A data frame with 24 observations on the following variable.

heights

height, in inches

Examples

hist(fheights$heights)

Young fish in the North Sea.

Description

Samples of 50 Tobis fish, or Sand Eels, were collected at three different locations in the North Sea and the number of one-year-old fish were counted.

Usage

fish_age

Format

A data frame with 300 rows and 3 variables:

year

Year the fish was caught with levels 1997 and 1998.

location

Site the fish was caught with levels A, B and C.

one_year_old

Is the fish one-year-old, yes or no?

Source

Henrik Madsen, Paul Thyregod. 2011. Introduction to General and Generalized Linear Models CRC Press. Boca Raton, FL. ISBN: 978-1-4200-9155-7 Website

Examples

library(dplyr)
library(tidyr)

# Count the number of one-year-old fish at each location.

fish_age |>
  filter(one_year_old == "yes") |>
  count(year, location) |>
  pivot_wider(names_from = location, values_from = n)

Findings on n-3 Fatty Acid Supplement Health Benefits

Description

The results summarize each of the health outcomes for an experiment where 12,933 subjects received a 1g fish oil supplement daily and 12,938 received a placebo daily. The experiment's duration was 5-years.

Usage

fish_oil_18

Format

The format is a list of 24 matrices. Each matrix is a 2x2 table, and below are the named items in the list, which also represent the outcomes.

major_cardio_event

Major cardiovascular event. (Primary end point.)

cardio_event_expanded

Cardiovascular event in expanded composite endpoint.

myocardioal_infarction

Total myocardial infarction. (Heart attack.)

stroke

Total stroke.

cardio_death

Death from cardiovascular causes.

PCI

Percutaneous coronary intervention.

CABG

Coronary artery bypass graft.

total_coronary_heart_disease

Total coronary heart disease.

ischemic_stroke

Ischemic stroke.

hemorrhagic_stroke

Hemorrhagic stroke.

chd_death

Death from coronary heart disease.

myocardial_infarction_death

Death from myocardial infraction.

stroke_death

Death from stroke.

invasive_cancer

Invasive cancer of any type. (Primary end point.)

breast_cancer

Breast cancer.

prostate_cancer

Prostate cancer.

colorectal_cancer

Colorectal cancer.

cancer_death

Death from cancer.

death

Death from any cause.

major_cardio_event_after_2y

Major cardiovascular event, excluding the first 2 years of follow-up.

myocardial_infarction_after_2y

Total myocardial infarction, excluding the first 2 years of follow-up.

invasive_cancer_after_2y

Invasive cancer of any type, excluding the first 2 years of follow-up.

cancer_death_after_2y

Death from cancer, excluding the first 2 years of follow-up.

death_after_2y

Death from any cause, excluding the first 2 years of follow-up.

Source

Manson JE, et al. 2018. Marine n-3 Fatty Acids and Prevention of Cardiovascular Disease and Cancer. NEJMoa1811403. doi:10.1056/NEJMoa1811403.

Examples

names(fish_oil_18)
(tab <- fish_oil_18[["major_cardio_event"]])
chisq.test(tab)
fisher.test(tab)

(tab <- fish_oil_18[["myocardioal_infarction"]])
chisq.test(tab)
fisher.test(tab)

River flow data

Description

Flow rates (mesured in cubic feet per second) of Clarks Creek, Leach Creek, Silver Creek, and Wildwood Creek Spring collected by volunteers of the Pierce Conservation District in the State of Washington in the US.

Usage

flow_rates

Format

A data frame with 31 rows and 3 variables.

site

Location where measurements were taken.

date

Date measurements were taken.

flow

Flow rate of the river in cubic feet per second.

Source

Pierce County Water Data Viewer.

Examples

library(ggplot2)

# River flow rates by site
ggplot(flow_rates, aes(x = site, y = flow)) +
  geom_boxplot() +
  labs(
    title = "River flow rates by site",
    x = "Site",
    y = expression(paste("Flow (ft"^3 * "/s)"))
  )

# River flow rates over time
ggplot(flow_rates, aes(x = date, y = flow, color = site, shape = site)) +
  geom_point(size = 2) +
  labs(
    title = "River flow rates over time",
    x = "Date",
    y = expression(paste("Flow (ft"^3 * "/s)")),
    color = "Site", shape = "Site"
  )

Friday the 13th

Description

This dataset addresses issues of how superstitions regarding Friday the 13th affect human behavior, and whether Friday the 13th is an unlucky day. Scanlon, et al. collected data on traffic and shopping patterns and accident frequency for Fridays the 6th and 13th between October of 1989 and November of 1992.

Usage

friday

Format

A data frame with 61 observations and 6 variables.

type

Type of observation, traffic, shopping, or accident.

date

Year and month of observation.

sixth

Counts on the 6th of the month.

thirteenth

Counts on the 13th of the month.

diff

Difference between the sixth and the thirteenth.

location

Location where data is collected.

Details

There are three types of observations: traffic, shopping, and accident. For traffic, the researchers obtained information from the British Department of Transport regarding the traffic flows between junctions 7 to 8 and junctions 9 to 10 of the M25 motorway. For shopping, they collected the numbers of shoppers in nine different supermarkets in southeast England. For accidents, they collected numbers of emergency admissions to hospitals due to transport accidents.

Source

Scanlon, T.J., Luben, R.N., Scanlon, F.L., Singleton, N. (1993), "Is Friday the 13th Bad For Your Health?," BMJ, 307, 1584-1586. https://dasl.datadescription.com/datafile/friday-the-13th-traffic and https://dasl.datadescription.com/datafile/friday-the-13th-accidents.

Examples

library(dplyr)
library(ggplot2)

friday |>
  filter(type == "traffic") |>
  ggplot(aes(x = sixth)) +
  geom_histogram(binwidth = 2000) +
  xlim(110000, 140000)

friday |>
  filter(type == "traffic") |>
  ggplot(aes(x = thirteenth)) +
  geom_histogram(binwidth = 2000) +
  xlim(110000, 140000)

Poll about use of full-body airport scanners

Description

Poll about use of full-body airport scanners, where about 4-in-5 people supported the use of the scanners.

Usage

full_body_scan

Format

A data frame with 1137 observations on the following 2 variables.

answer

a factor with levels do not know / no answer should should not

party.affiliation

a factor with levels Democrat Independent Republican

Source

S. Condon. Poll: 4 in 5 Support Full-Body Airport Scanners. In: CBS News (2010).

Examples

full_body_scan

GDP Countries Data.

Description

From World Bank, GDP in current U.S. dollars 1960-2020 by decade

Usage

gdp_countries

Format

A data frame with 659 rows and 9 variables.

country

Name of country.

description

description of data: GDP (in current US$), GDP growth (annual %), GDP per capita (in current US$)

year_1960

value in 1960

year_1970

value in 1970

year_1980

value in 1980

year_1990

value in 1990

year_2000

value in 2000

year_2010

value in 2010

year_2020

value in 2020

Source

World Bank

Examples

library(dplyr)
# don't use scientific notation
options(scipen = 999)
# List the top 10 countries by GDP (There is a row for World)
gdp_countries |>
  filter(description == "GDP") |>
  mutate(year2020 = format(year_2020, big.mark = ",")) |>
  select(country, year2020) |>
  arrange(desc(year2020)) |>
  top_n(n = 11)

# List the 10 countries with the biggest GDP per capita change from 1960 to 2020
gdp_countries |>
  filter(description == "GDP per capita") |>
  mutate(change = format(round(year_2020 - year_1960, 0), big.mark = ",")) |>
  select(country, change, year_1960, year_2020) |>
  na.omit() |>
  arrange(desc(change)) |>
  top_n(n = 10)

Fake data for a gear company example

Description

Made-up data for whether a sample of two gear companies' parts pass inspection.

Usage

gear_company

Format

A data frame with 2000 observations on the following 2 variables.

company

a factor with levels current prospective

outcome

a factor with levels not pass

Examples

gear_company

Bank manager recommendations based on gender

Description

Study from the 1970s about whether gender influences hiring recommendations.

Usage

gender_discrimination

Format

A data frame with 48 observations on the following 2 variables.

gender

a factor with levels female and male

decision

a factor with levels not promoted and promoted

Source

Rosen B and Jerdee T. 1974. Influence of sex role stereotypes on personnel decisions. Journal of Applied Psychology 59(1):9-14.

Examples

library(ggplot2)

table(gender_discrimination)

ggplot(gender_discrimination, aes(y = gender, fill = decision)) +
  geom_bar(position = "fill")

Get it Dunn Run, Race Times

Description

Get it Dunn is a small regional run that got extra attention when a runner, Nichole Porath, made the Guiness Book of World Records for the fastest time pushing a double stroller in a half marathon. This dataset contains results from the 2017 and 2018 races.

Usage

get_it_dunn_run

Format

A data frame with 978 observations on the following 10 variables.

date

Date of the run.

race

Run distance.

bib_num

Bib number of the runner.

first_name

First name of the runner.

last_initial

Initial of the runner's last name.

sex

Sex of the runner.

age

Age of the runner.

city

City of residence.

state

State of residence.

run_time_minutes

Run time, in minutes.

Source

Data were collected from GSE Timing: 2018 data, 2017 race data.

Examples

d <- subset(
  get_it_dunn_run,
  race == "5k" & date == "2018-05-12" &
    !is.na(age) & state %in% c("MN", "WI")
)
head(d)
m <- lm(run_time_minutes ~ sex + age + state, d)
summary(m)
plot(m$fitted, m$residuals)
boxplot(m$residuals ~ d$sex)
plot(m$residuals ~ d$age)
hist(m$residuals)

Analytical skills of young gifted children

Description

An investigator is interested in understanding the relationship, if any, between the analytical skills of young gifted children and the following variables: father's IQ, mother's IQ, age in month when the child first said "mummy" or "daddy", age in month when the child first counted to 10 successfully, average number of hours per week the child's mother or father reads to the child, average number of hours per week the child watched an educational program on TV during the past three months, average number of hours per week the child watched cartoons on TV during the past three months. The analytical skills are evaluated using a standard testing procedure, and the score on this test is used as the response variable.

Usage

gifted

Format

A data frame with 36 observations and 8 variables.

score

Score in test of analytical skills.

fatheriq

Father's IQ.

motheriq

Mother's IQ.

speak

Age in months when the child first said "mummy" or "daddy".

count

Age in months when the child first counted to 10 successfully.

read

Average number of hours per week the child's mother or father reads to the child.

edutv

Average number of hours per week the child watched an educational program on TV during the past three months.

cartoons

Average number of hours per week the child watched cartoons on TV during the past three months.

Details

Data were collected from schools in a large city on a set of thirty-six children who were identified as gifted children soon after they reached the age of four.

Source

Graybill, F.A. & Iyer, H.K., (1994) Regression Analysis: Concepts and Applications, Duxbury, p. 511-6.

Examples

gifted

Pew survey on global warming

Description

A 2010 Pew Research poll asked 1,306 Americans, "From what you've read and heard, is there solid evidence that the average temperature on earth has been getting warmer over the past few decades, or not?"

Usage

global_warming_pew

Format

A data frame with 2253 observations on the following 2 variables.

party_or_ideology

a factor with levels Conservative Republican Liberal Democrat Mod/Cons Democrat Mod/Lib Republican

response

Response.

Source

Pew Research Center, Majority of Republicans No Longer See Evidence of Global Warming, data collected on October 27, 2010.

Examples

global_warming_pew

Google stock data

Description

Google stock data from 2006 to early 2014, where data from the first day each month was collected.

Usage

goog

Format

A data frame with 98 observations on the following 7 variables.

date

a factor with levels 2006-01-03, 2006-02-01, and so on

open

a numeric vector

high

a numeric vector

low

a numeric vector

close

a numeric vector

volume

a numeric vector

adj_close

a numeric vector

Source

Yahoo! Finance.

Examples

goog

Pew Research poll on government approval ratings

Description

The poll's focus is on Obama and then Democrats and Republicans in Congress.

Usage

gov_poll

Format

A data frame with 4223 observations on the following 2 variables.

poll

a factor with levels approve disapprove

eval

a factor with levels Democrats Obama Republicans

Source

See the Pew Research website: www.people-press.org/2012/03/14/romney-leads-gop-contest-trails-in- matchup-with-obama. The counts in Table 6.19 are approximate.

Examples

gov_poll

Survey of Duke students on GPA, studying, and more

Description

A survey of 55 Duke University students asked about their GPA, number of hours they study at night, number of nights they go out, and their gender.

Usage

gpa

Format

A data frame with 55 observations on the following 5 variables.

gpa

a numeric vector

studyweek

a numeric vector

sleepnight

a numeric vector

out

a numeric vector

gender

a factor with levels female male

Examples

gpa

Sample of students and their GPA and IQ

Description

Data on 78 students including GPA, IQ, and gender.

Usage

gpa_iq

Format

A data frame with 78 observations representing students on the following 5 variables.

obs

a numeric vector

gpa

Grade point average (GPA).

iq

IQ.

gender

Gender.

concept

a numeric vector

Examples

gpa_iq

gpa_study_hours

Description

A data frame with 193 rows and 2 columns. The columns represent the variables gpa and study_hours for a sample of 193 undergraduate students who took an introductory statistics course in 2012 at a private US university.

Usage

gpa_study_hours

Format

A data frame with 193 observations on the following 2 variables.

gpa

Grade point average (GPA) of student.

study_hours

Number of hours students study per week.

Details

GPA ranges from 0 to 4 points, however one student reported a GPA > 4. This is a data error but this observation has been left in the dataset as it is used to illustrate issues with real survey data. Both variables are self reported, hence may not be accurate.

Source

Collected at a private US university as part of an anonymous survey in an introductory statistics course.

Examples

library(ggplot2)

ggplot(gpa_study_hours, aes(x = study_hours, y = gpa)) +
  geom_point(alpha = 0.5) +
  labs(x = "Study hours/week", y = "GPA")

Simulated data for analyzing the relationship between watching TV and grades

Description

This is a simulated dataset to be used to estimate the relationship between number of hours per week students watch TV and the grade they got in a statistics class.

Usage

gradestv

Format

A data frame with 25 observations on the following 2 variables.

tv

Number of hours per week students watch TV.

grades

Grades students got in a statistics class (out of 100).

Details

There are a few potential outliers in this dataset. When analyzing the data one should consider how (if at all) these outliers may affect the estimates of correlation coefficient and regression parameters.

Source

Simulated data

Examples

library(ggplot2)

ggplot(gradestv, aes(x = tv, y = grades)) +
  geom_point() +
  geom_smooth(method = "lm")

Simulated Google search experiment

Description

The data were simulated to look like sample results from a Google search experiment.

Usage

gsearch

Format

A data frame with 10000 observations on the following 2 variables.

type

a factor with levels new search no new search

outcome

a factor with levels current test 1 test 2

Examples

library(ggplot2)

table(gsearch$type, gsearch$outcome)

ggplot(gsearch, aes(x = type, fill = outcome)) +
  geom_bar(position = "fill") +
  labs(y = "proportion")

2010 General Social Survey

Description

Data from the 2010 General Social Survey.

Usage

gss2010

Format

A data frame with 2044 observations on the following 5 variables.

hrsrelax

After an average work day, about how many hours do you have to relax or pursue activities that you enjoy

mntlhlth

For how many days during the past 30 days was your mental health, which includes stress, depression, and problems with emotions, not good?

hrs1

Hours worked each week.

degree

Educational attainment or degree.

grass

Do you think the use of marijuana should be made legal, or not?

Source

US 2010 General Social Survey.

Examples

gss2010

gss_wordsum_class

Description

A data frame containing data from the General Social Survey.

Usage

gss_wordsum_class

Format

A data frame with 795 observations on the following 2 variables.

wordsum

A vocabulary score calculated based on a ten question vocabulary test, where a higher score means better vocabulary. Scores range from 1 to 10.

class

Self-identified social class has 4 levels: lower, working, middle, and upper class.

Examples

library(dplyr)

gss_wordsum_class |>
  group_by(class) |>
  summarize(mean_wordsum = mean(wordsum))

Health Coverage and Health Status

Description

Survey responses for 20,000 responses to the Behavioral Risk Factor Surveillance System.

Usage

health_coverage

Format

A data frame with 20000 observations on the following 2 variables.

coverage

Whether the person had health coverage or not.

health_status

The person's health status.

Source

Office of Surveillance, Epidemiology, and Laboratory Services Behavioral Risk Factor Surveillance System, BRFSS 2010 Survey Data.

Examples

table(health_coverage)

Pew Research Center poll on health care, including question variants

Description

For example, Pew Research Center conducted a survey with the following question: "As you may know, by 2014 nearly all Americans will be required to have health insurance. People who do not buy insurance will pay a penalty while people who cannot afford it will receive financial help from the government. Do you approve or disapprove of this policy?" For each randomly sampled respondent, the statements in brackets were randomized: either they were kept in the order given above, or the two statements were reversed.

Usage

healthcare_law_survey

Format

A data frame with 1503 observations on the following 2 variables.

order

a factor with levels cannot_afford_second penalty_second

response

a factor with levels approve disapprove other

Source

www.people-press.org/2012/03/26/public-remains-split-on-health-care-bill-opposed-to-mandate/. Sample sizes for each polling group are approximate.

Examples

healthcare_law_survey

Heart Transplant Data

Description

The Stanford University Heart Transplant Study was conducted to determine whether an experimental heart transplant program increased lifespan. Each patient entering the program was designated officially a heart transplant candidate, meaning that he was gravely ill and would most likely benefit from a new heart. Then the actual heart transplant occurs between a few weeks to several months depending on the availability of a donor. Very few candidates during this waiting period show improvement and get deselected as a heart transplant candidate, but for the purposes of this experiment those patients were kept in the data as continuing candidates.

Usage

heart_transplant

Format

A data frame with 103 observations on the following 8 variables.

id

ID number of the patient.

acceptyear

Year of acceptance as a heart transplant candidate.

age

Age of the patient at the beginning of the study.

survived

Survival status with levels alive and dead.

survtime

Number of days patients were alive after the date they were determined to be a candidate for a heart transplant until the termination date of the study

prior

Whether or not the patient had prior surgery with levels yes and no.

transplant

Transplant status with levels control (did not receive a transplant) and treatment (received a transplant).

wait

Waiting Time for Transplant

Source

http://www.stat.ucla.edu/~jsanchez/data/stanford.txt

References

Turnbull B, Brown B, and Hu M (1974). "Survivorship of heart transplant data." Journal of the American Statistical Association, vol. 69, pp. 74-80.

Examples

library(ggplot2)

ggplot(heart_transplant, aes(x = transplant, y = survtime)) +
  geom_boxplot() +
  labs(x = "Transplant", y = "Survival time (days)")

ggplot(heart_transplant, aes(x = transplant, fill = survived)) +
  geom_bar(position = "fill") +
  labs(x = "Transplant", y = "Proportion", fill = "Outcome")

Helium football

Description

At the 1976 Pro Bowl, Ray Guy, a punter for the Oakland Raiders, punted a ball that hung mid-air long enough for officials to question whether the pigskin was filled with helium. The ball was found to be filled with air, but since then many have tossed around the idea that a helium-filled football would outdistance an air-filled one. Students at Ohio State University conducted an experiment to test this myth. They used two identical footballs, one air filled with air and one filled with helium. Each football was kicked 39 times and the two footballs were alternated with each kick.

Usage

helium

Format

A data frame with 39 observations on the following 3 variables.

trial

Trial number.

air

Distance in years for air-filled football.

helium

Distance in years for helium-filled football.

Details

Lafferty, M. B. (1993), "OSU scientists get a kick out of sports controversy, "The Columbus Dispatch (November, 21, 1993), B7.

Source

Previously part of the Data and Story Library, https://dasl.datadescription.com. Removed as of 2020.

Examples

boxPlot(helium$air, xlab = "air")
boxPlot(helium$helium, xlab = "helium")

Socioeconomic status and reduced-fee school lunches

Description

Examining the relationship between socioeconomic status measured as the percentage of children in a neighborhood receiving reduced-fee lunches at school (lunch) and the percentage of bike riders in the neighborhood wearing helmets (helmet).

Usage

helmet

Format

A data frame with 12 observations representing neighborhoods on the following 2 variables.

lunch

Percent of students receiving reduced-fee school lunches.

helmet

Percent of bike riders wearing helmets.

Examples

library(ggplot2)

ggplot(helmet, aes(x = lunch, y = helmet)) +
  geom_point()

Human Freedom Index

Description

The Human Freedom Index is a report that attempts to summarize the idea of "freedom" through a bunch of different variables for many countries around the globe. It serves as a rough objective measure for the relationships between the different types of freedom - whether it's political, religious, economical or personal freedom - and other social and economic circumstances. The Human Freedom Index is an annually co-published report by the Cato Institute, the Fraser Institute, and the Liberales Institut at the Friedrich Naumann Foundation for Freedom.

Usage

hfi

Format

A data frame with 1458 observations on the following 123 variables.

year

Year

ISO_code

ISO code of country

countries

Name of country

region

Region where country is located

pf_rol_procedural

Procedural justice

pf_rol_civil

Civil justice

pf_rol_criminal

Criminal justice

pf_rol

Rule of law

pf_ss_homicide

Homicide

pf_ss_disappearances_disap

Disappearances

pf_ss_disappearances_violent

Violent conflicts

pf_ss_disappearances_organized

Violent conflicts

pf_ss_disappearances_fatalities

Terrorism fatalities

pf_ss_disappearances_injuries

Terrorism injuries

pf_ss_disappearances

Disappearances, conflict, and terrorism

pf_ss_women_fgm

Female genital mutilation

pf_ss_women_missing

Missing women

pf_ss_women_inheritance_widows

Inheritance rights for widows

pf_ss_women_inheritance_daughters

Inheritance rights for daughters

pf_ss_women_inheritance

Inheritance

pf_ss_women

Women's security

pf_ss

Security and safety

pf_movement_domestic

Freedom of domestic movement

pf_movement_foreign

Freedom of foreign movement

pf_movement_women

Women's movement

pf_movement

Freedom of movement

pf_religion_estop_establish

Freedom to establish religious organizations

pf_religion_estop_operate

Freedom to operate religious organizations

pf_religion_estop

Freedom to establish and operate religious organizations

pf_religion_harassment

Harassment and physical hostilities

pf_religion_restrictions

Legal and regulatory restrictions

pf_religion

Religious freedom

pf_association_association

Freedom of association

pf_association_assembly

Freedom of assembly

pf_association_political_establish

Freedom to establish political parties

pf_association_political_operate

Freedom to operate political parties

pf_association_political

Freedom to establish and operate political parties

pf_association_prof_establish

Freedom to establish professional organizations

pf_association_prof_operate

Freedom to operate professional organizations

pf_association_prof

Freedom to establish and operate professional organizations

pf_association_sport_establish

Freedom to establish educational, sporting, and cultural organizations

pf_association_sport_operate

Freedom to operate educational, sporting, and cultural organizations

pf_association_sport

Freedom to establish and operate educational, sporting, and cultural organizations

pf_association

Freedom to associate and assemble with peaceful individuals or organizations

pf_expression_killed

Press killed

pf_expression_jailed

Press jailed

pf_expression_influence

Laws and regulations that influence media content

pf_expression_control

Political pressures and controls on media content

pf_expression_cable

Access to cable/satellite

pf_expression_newspapers

Access to foreign newspapers

pf_expression_internet

State control over internet access

pf_expression

Freedom of expression

pf_identity_legal

Legal gender

pf_identity_parental_marriage

Parental rights in marriage

pf_identity_parental_divorce

Parental rights after divorce

pf_identity_parental

Parental rights

pf_identity_sex_male

Male-to-male relationships

pf_identity_sex_female

Female-to-female relationships

pf_identity_sex

Same-sex relationships

pf_identity_divorce

Divor

pf_identity

Identity and relationships

pf_score

Personal Freedom (score)

pf_rank

Personal Freedom (rank)

ef_government_consumption

Government consumption

ef_government_transfers

Transfers and subsidies

ef_government_enterprises

Government enterprises and investments

ef_government_tax_income

Top marginal income tax rate - Top marginal income tax rates

ef_government_tax_payroll

Top marginal income tax rate - Top marginal income and payroll tax rate

ef_government_tax

Top marginal tax rate

ef_government

Size of government

ef_legal_judicial

Judicial independence

ef_legal_courts

Impartial courts

ef_legal_protection

Protection of property rights

ef_legal_military

Military interference in rule of law and politics

ef_legal_integrity

Integrity of the legal system

ef_legal_enforcement

Legal enforcement of contracts

ef_legal_restrictions

Regulatory restrictions on the sale of real property

ef_legal_police

Reliability of police

ef_legal_crime

Business costs of crime

ef_legal_gender

Gender adjustment

ef_legal

Legal system and property rights

ef_money_growth

Money growth

ef_money_sd

Standard deviation of inflation

ef_money_inflation

Inflation - most recent year

ef_money_currency

Freedom to own foreign currency bank account

ef_money

Sound money

ef_trade_tariffs_revenue

Tariffs - Revenue from trade taxes (percentage of trade sector)

ef_trade_tariffs_mean

Tariffs - Mean tariff rate

ef_trade_tariffs_sd

Tariffs - Standard deviation of tariffs rates

ef_trade_tariffs

Tariffs

ef_trade_regulatory_nontariff

Regulatory trade barriers - Nontariff trade barriers

ef_trade_regulatory_compliance

Regulatory trade barriers - Compliance costs of importing and exporting

ef_trade_regulatory

Regulatory trade barriers

ef_trade_black

Black-market exchange rates

ef_trade_movement_foreign

Controls of the movement of capital and people - Foreign ownership/investment restrictions

ef_trade_movement_capital

Controls of the movement of capital and people - Capital controls

ef_trade_movement_visit

Controls of the movement of capital and people - Freedom of foreigners to visit

ef_trade_movement

Controls of the movement of capital and people

ef_trade

Freedom to trade internationally

ef_regulation_credit_ownership

Credit market regulations - Ownership of banks

ef_regulation_credit_private

Credit market regulations - Private sector credit

ef_regulation_credit_interest

Credit market regulations - Interest rate controls/negative real interest rates

ef_regulation_credit

Credit market regulation

ef_regulation_labor_minwage

Labor market regulations - Hiring regulations and minimum wage

ef_regulation_labor_firing

Labor market regulations - Hiring and firing regulations

ef_regulation_labor_bargain

Labor market regulations - Centralized collective bargaining

ef_regulation_labor_hours

Labor market regulations - Hours regulations

ef_regulation_labor_dismissal

Labor market regulations - Dismissal regulations

ef_regulation_labor_conscription

Labor market regulations - Conscription

ef_regulation_labor

Labor market regulation

ef_regulation_business_adm

Business regulations - Administrative requirements

ef_regulation_business_bureaucracy

Business regulations - Bureaucracy costs

ef_regulation_business_start

Business regulations - Starting a business

ef_regulation_business_bribes

Business regulations - Extra payments/bribes/favoritism

ef_regulation_business_licensing

Business regulations - Licensing restrictions

ef_regulation_business_compliance

Business regulations - Cost of tax compliance

ef_regulation_business

Business regulation

ef_regulation

Economic freedom regulation score

ef_score

Economic freedom score

ef_rank

Economic freedom rank

hf_score

Human freedom score

hf_rank

Human freedom rank

hf_quartile

Human freedom quartile

Details

This dataset contains information from Human Freedom Index reports from 2008-2016.

Source

Ian Vasquez and Tanja Porcnik, The Human Freedom Index 2018: A Global Measurement of Personal, Civil, and Economic Freedom (Washington: Cato Institute, Fraser Institute, and the Friedrich Naumann Foundation for Freedom, 2018). https://www.cato.org/sites/cato.org/files/human-freedom-index-files/human-freedom-index-2016.pdf. https://www.kaggle.com/gsutters/the-human-freedom-index.

Histogram or hollow histogram

Description

Create histograms and hollow histograms. This function permits easy color and appearance customization.

Usage

histPlot(
  x,
  col = fadeColor("black", "22"),
  border = "black",
  breaks = "default",
  probability = FALSE,
  hollow = FALSE,
  add = FALSE,
  lty = 2,
  lwd = 1,
  freqTable = FALSE,
  right = TRUE,
  axes = TRUE,
  xlab = NULL,
  ylab = NULL,
  xlim = NULL,
  ylim = NULL,
  ...
)

Arguments

Author(s)

David Diez

See Also

boxPlot, dotPlot, densityPlot

Examples

histPlot(tips$tip, main = "Tips")

# overlaid hollow histograms
histPlot(tips$tip[tips$day == "Tuesday"],
  probability = TRUE,
  hollow = TRUE,
  main = "Tips by day"
)
histPlot(tips$tip[tips$day == "Friday"],
  probability = TRUE,
  hollow = TRUE,
  add = TRUE,
  lty = 3,
  border = "red"
)
legend("topright",
  col = c("black", "red"),
  lty = 1:2,
  legend = c("Tuesday", "Friday")
)

# breaks and colors
histPlot(tips$tip,
  col = fadeColor("yellow", "33"),
  border = "darkblue",
  probability = TRUE,
  breaks = 30,
  lwd = 3
)

# custom breaks
brks <- c(-1, 0, 1, 2, 3, 4, seq(5, 20, 5), 22, 24, 26)
histPlot(tips$tip,
  probability = TRUE,
  breaks = brks,
  col = fadeColor("darkgoldenrod4", "33"),
  xlim = c(0, 26)
)

United States House of Representatives historical make-up

Description

The make-up of the United States House of Representatives every two years since 1789. The last Congress included is the 112th Congress, which completed its term in 2013.

Usage

house

Format

A data frame with 112 observations on the following 12 variables.

congress

The number of that year's Congress

year_start

Starting year

year_end

Ending year

seats

Total number of seats

p1

Name of the first political party

np1

Number of seats held by the first political party

p2

Name of the second political party

np2

Number of seats held by the second political party

other

Other

vac

Vacancy

del

Delegate

res

Resident commissioner

Source

Party Divisions of the House of Representatives, 1789 to Present. https://history.house.gov/Institution/Party-Divisions/Party-Divisions.

Examples

library(dplyr)
library(ggplot2)
library(forcats)

# Examine two-party relationship since 1855
house_since_1855 <- house |>
  filter(year_start >= 1855) |>
  mutate(
    p1_perc = 100 * np1 / seats,
    p2_perc = 100 * np2 / seats,
    era = case_when(
      between(year_start, 1861, 1865) ~ "Civil War",
      between(year_start, 1914, 1918) ~ "World War I",
      between(year_start, 1929, 1939) ~ "Great Depression",
      between(year_start, 1940, 1945) ~ "World War II",
      between(year_start, 1960, 1965) ~ "Vietnam War Start",
      between(year_start, 1965, 1975) ~ "Vietnam War Escalated",
      TRUE ~ NA_character_
    ),
    era = fct_relevel(
      era, "Civil War", "World War I",
      "Great Depression", "World War II",
      "Vietnam War Start", "Vietnam War Escalated"
    )
  )

ggplot(house_since_1855, aes(x = year_start)) +
  geom_rect(aes(
    xmin = year_start, xmax = lead(year_start),
    ymin = -Inf, ymax = Inf, fill = era
  )) +
  geom_line(aes(y = p1_perc, color = "Democrats")) + # Democrats
  geom_line(aes(y = p2_perc, color = "Republicans")) + # Republicans
  scale_fill_brewer(palette = "Pastel1", na.translate = FALSE) +
  scale_color_manual(
    name   = "Party",
    values = c("Democrats" = "blue", "Republicans" = "red"),
    labels = c("Democrats", "Republicans")
  ) +
  theme_minimal() +
  ylim(0, 100) +
  labs(x = "Year", y = "Percentage of seats", fill = "Era")

Simulated dataset on student housing

Description

Each observation represents a simulated rent price for a student.

Usage

housing

Format

A data frame with 75 observations on the following variable.

cost

a numeric vector

Examples

housing

High School and Beyond survey

Description

Two hundred observations were randomly sampled from the High School and Beyond survey, a survey conducted on high school seniors by the National Center of Education Statistics.

Usage

hsb2

Format

A data frame with 200 observations and 11 variables.

id

Student ID.

gender

Student's gender, with levels female and male.

race

Student's race, with levels african american, asian, hispanic, and white.

ses

Socio economic status of student's family, with levels low, middle, and high.

schtyp

Type of school, with levels public and private.

prog

Type of program, with levels general, academic, and vocational.

read

Standardized reading score.

write

Standardized writing score.

math

Standardized math score.

science

Standardized science score.

socst

Standardized social studies score.

Source

UCLA Institute for Digital Research & Education - Statistical Consulting.

Examples

library(ggplot2)

ggplot(hsb2, aes(x = read - write, y = ses)) +
  geom_boxplot() +
  labs(
    x = "Difference between reading and writing scores",
    y = "Socio-economic status"
  )

Great Britain: husband and wife pairs

Description

The Great Britain Office of Population Census and Surveys once collected data on a random sample of 170 married couples in Britain, recording the age (in years) and heights of the husbands and wives.

Usage

husbands_wives

Format

A data frame with 199 observations on the following 8 variables.

age_husband

Age of husband.

age_wife

Age of wife.

ht_husband

Height of husband (mm).

ht_wife

Height of wife (mm).

age_husb_at_marriage

Age of husband at the time they married.

age_wife_at_marriage

Age of wife at the time they married.

years_married

Number of years married.

Source

Hand DJ. 1994. A handbook of small data sets. Chapman & Hall/CRC.

Examples

library(ggplot2)

ggplot(husbands_wives, aes(x = ht_husband, y = ht_wife)) +
  geom_point()

Poll on illegal workers in the US

Description

910 randomly sampled registered voters in Tampa, FL were asked if they thought workers who have illegally entered the US should be (i) allowed to keep their jobs and apply for US citizenship, (ii) allowed to keep their jobs as temporary guest workers but not allowed to apply for US citizenship, or (iii) lose their jobs and have to leave the country as well as their political ideology.

Usage

immigration

Format

A data frame with 910 observations on the following 2 variables.

response

a factor with levels Apply for citizenship Guest worker Leave the country Not sure

political

a factor with levels conservative liberal moderate

Source

SurveyUSA, News Poll #18927, data collected Jan 27-29, 2012.

Examples

immigration

Infant Mortality Rates, 2012

Description

This entry gives the number of deaths of infants under one year old in 2012 per 1,000 live births in the same year. This rate is often used as an indicator of the level of health in a country.

Usage

infmortrate

Format

A data frame with 222 observations on the following 2 variables.

country

Name of country.

inf_mort_rate

Infant mortality rate per 1,000 live births.

Details

The data is given in decreasing order of infant mortality rates. There are a few potential outliers.

Source

CIA World Factbook, https://www.cia.gov/the-world-factbook/field/infant-mortality-rate/country-comparison.

Examples

library(ggplot2)

ggplot(infmortrate, aes(x = inf_mort_rate)) +
  geom_histogram(binwidth = 10)

ggplot(infmortrate, aes(x = inf_mort_rate)) +
  geom_density()

iowa

Description

A data frame containing information about the 2016 US Presidential Election for the state of Iowa.

Usage

iowa

Format

A data frame with 1386 observations on the following 5 variables.

office

The office that the candidates were running for.

candidate

President/Vice President pairs who were running for office.

party

Political part of the candidate.

county

County in Iowa where the votes were cast.

votes

Number of votes received by the candidate.

Examples

library(ggplot2)
library(dplyr)

plot_data <- iowa |>
  filter(candidate != "Total") |>
  group_by(candidate) |>
  summarize(total_votes = sum(votes) / 1000)

ggplot(plot_data, aes(total_votes, candidate)) +
  geom_col() +
  theme_minimal() +
  labs(
    title = "2016 Presidential Election in Iowa",
    subtitle = "Popular vote",
    y = "",
    x = "Number of Votes (in thousands)
    "
  )

Facebook, Google, and LinkedIn IPO filings

Description

On Feb 1st, 2011, Facebook Inc. filed an S-1 form with the Securities and Exchange Commission as part of their initial public offering (IPO). This dataset includes the text of that document as well as text from the IPOs of two competing companies: Google and LinkedIn.

Usage

ipo

Format

The format is a list of three character vectors. Each vector contains the line-by-line text of the IPO Prospectus of Facebook, Google, and LinkedIn, respectively.

Details

Each of the three prospectuses is encoded in UTF-8 format and contains some non-word characters related to the layout of the original documents. For analysis on the words, it is recommended that the data be processed with packages such as tidytext. See examples below.

Source

All IPO prospectuses are available from the U.S. Securities and Exchange Commission: Facebook, Google, LinkedIn.

References

Zweig, J., 2020. Mark Zuckerberg: CEO For Life?. WSJ.

Examples

library(tidytext)
library(tibble)
library(dplyr)
library(ggplot2)
library(forcats)

# Analyzing Facebook IPO text

facebook <- tibble(text = ipo$facebook, company = "Facebook")

facebook |>
  unnest_tokens(word, text) |>
  anti_join(stop_words) |>
  count(word, sort = TRUE) |>
  slice_head(n = 20) |>
  ggplot(aes(y = fct_reorder(word, n), x = n, fill = n)) +
  geom_col() +
  labs(
    title = "Top 20 most common words in Facebook IPO",
    x = "Frequency",
    y = "Word"
  )

# Comparisons to Google and LinkedIn IPO texts

google <- tibble(text = ipo$google, company = "Google")
linkedin <- tibble(text = ipo$linkedin, company = "LinkedIn")

ipo_texts <- bind_rows(facebook, google, linkedin)

ipo_texts |>
  unnest_tokens(word, text) |>
  count(company, word, sort = TRUE) |>
  bind_tf_idf(word, company, n) |>
  arrange(desc(tf_idf)) |>
  group_by(company) |>
  slice_max(tf_idf, n = 15) |>
  ungroup() |>
  ggplot(aes(tf_idf, fct_reorder(word, tf_idf), fill = company)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~company, ncol = 3, scales = "free") +
  labs(x = "tf-idf", y = NULL)

Length of songs on an iPod

Description

A simulated dataset on lengths of songs on an iPod.

Usage

ipod

Format

A data frame with 3000 observations on the following variable.

song_length

Length of song (in minutes).

Source

Simulated data.

Examples

library(ggplot2)

ggplot(ipod, aes(x = song_length)) +
  geom_histogram(binwidth = 0.5)

iran

Description

A data frame containing information about the 2009 Presidential Election in Iran. There were widespread claims of election fraud in this election both internationally and within Iran.

Usage

iran

Format

A data frame with 366 observations on the following 9 variables.

province

Iranian province where votes were cast.

city

City within province where votes were cast.

ahmadinejad

Number of votes received by Ahmadinejad.

rezai

Number of votes received by Rezai.

karrubi

Number of votes received by Karrubi.

mousavi

Number of votes received by Mousavi.

total_votes_cast

Total number of votes cast.

voided_votes

Number of votes that were not counted.

legitimate_votes

Number of votes that were counted.

Examples

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

plot_data <- iran |>
  summarize(
    ahmadinejad = sum(ahmadinejad) / 1000,
    rezai = sum(rezai) / 1000,
    karrubi = sum(karrubi) / 1000,
    mousavi = sum(mousavi) / 1000
  ) |>
  pivot_longer(
    cols = c(ahmadinejad, rezai, karrubi, mousavi),
    names_to = "candidate",
    values_to = "votes"
  ) |>
  mutate(candidate = str_to_title(candidate))

ggplot(plot_data, aes(votes, candidate)) +
  geom_col() +
  theme_minimal() +
  labs(
    title = "2009 Iranian Presidential Election",
    x = "Number of votes (in thousands)",
    y = ""
  )

Simulated juror dataset

Description

Simulated dataset of registered voters proportions and representation on juries.

Usage

jury

Format

A data frame with 275 observations on the following variable.

race

a factor with levels black hispanic other white

Examples

jury

Kobe Bryant basketball performance

Description

Data from the five games the Los Angeles Lakers played against the Orlando Magic in the 2009 NBA finals.

Usage

kobe_basket

Format

A data frame with 133 rows and 6 variables:

vs

A categorical vector, ORL if the Los Angeles Lakers played against Orlando

game

A numerical vector, game in the 2009 NBA finals

quarter

A categorical vector, quarter in the game, OT stands for overtime

time

A character vector, time at which Kobe took a shot

description

A character vector, description of the shot

shot

A categorical vector, H if the shot was a hit, M if the shot was a miss

Details

Each row represents a shot Kobe Bryant took during the five games of the 2009 NBA finals. Kobe Bryant's performance earned him the title of Most Valuable Player and many spectators commented on how he appeared to show a hot hand.

lab_report

Description

Acts as a simplified template to common parameters passed to rmarkdown::html_document().

Usage

lab_report(
  highlight = "pygments",
  theme = "spacelab",
  toc = TRUE,
  toc_float = TRUE,
  code_download = TRUE,
  code_folding = "show"
)

Arguments

Are Emily and Greg More Employable Than Lakisha and Jamal?

Description

Original data from the experiment run by Bertrand and Mullainathan (2004).

Usage

labor_market_discrimination

Format

A tibble with 4870 observations of 63 variables.

education

Highest education, with levels of 0 = not reported; 1 = high school diploma; 2 = high school graduate; 3 = some college; 4 = college or more.

n_jobs

Number of jobs listed on resume.

years_exp

Number of years of work experience on the resume.

honors

Indicator variable for which 1 = resume mentions some honors.

volunteer

Indicator variable for which 1 = resume mentions some volunteering experience.

military

Indicator variable for which 1 = resume mentions some military experience.

emp_holes

Indicator variable for which 1 = resume mentions some employment holes.

occup_specific

1990 Census Occupation Code. See sources for a key.

occup_broad

Occupation broad with levels 1 = executives and managerial occupations, 2 = administrative supervisors, 3 = sales representatives, 4 = sales workers, 5 = secretaries and legal assistants, 6 = clerical occupations

work_in_school

Indicator variable for which 1 = resume mentions some work experience while at school

email

Indicator variable for which 1 = email address on applicant's resume.

computer_skills

Indicator variable for which 1 = resume mentions some computer skills.

special_skills

Indicator variable for which 1 = resume mentions some special skills.

first_name

Applicant's first name.

sex

Sex, with levels of 'f' = female; 'm' = male.

race

Race, with levels of 'b' = black; 'w' = white.

h

Indicator variable for which 1 = high quality resume.

l

Indicator variable for which 1 = low quality resume.

call

Indicator variable for which 1 = applicant was called back.

city

City, with levels of 'c' = chicago; 'b' = boston.

kind

Kind, with levels of 'a' = administrative; 's' = sales.

ad_id

Employment ad identifier.

frac_black

Fraction of blacks in applicant's zip.

frac_white

Fraction of whites in applicant's zip.

l_med_hh_inc

Log median household income in applicant's zip.

frac_dropout

Fraction of high-school dropouts in applicant's zip.

frac_colp

Fraction of college degree or more in applicant's zip

l_inc

Log per capita income in applicant's zip.

col

Indicator variable for which 1 = applicant has college degree or more.

expminreq

Minimum experience required, if any (in years when numeric).

school_req

Specific education requirement, if any. 'hsg' = high school graduate, 'somcol' = some college, 'colp' = four year degree or higher

eoe

Indicator variable for which 1 = ad mentions employer is 'Equal Opportunity Employer'.

parent_sales

Sales of parent company (in millions of US $).

parent_emp

Number of parent company employees.

branch_sales

Sales of branch (in millions of US $).

branch_emp

Number of branch employees.

fed

Indicator variable for which 1 = employer is a federal contractor.

frac_black_emp_zip

Fraction of blacks in employers's zipcode.

frac_white_emp_zip

Fraction of whites in employer's zipcode.

l_med_hh_inc_emp_zip

Log median household income in employer's zipcode.

frac_dropout_emp_zip

Fraction of high-school dropouts in employer's zipcode.

frac_colp_emp_zip

Fraction of college degree or more in employer's zipcode.

l_inc_emp_zip

Log per capita income in employer's zipcode.

manager

Indicator variable for which 1 = executives or managers wanted.

supervisor

Indicator variable for which 1 = administrative supervisors wanted.

secretary

Indicator variable for which 1 = secretaries or legal assistants wanted.

off_support

Indicator variable for which 1 = clerical workers wanted.

sales_rep

Indicator variable for which 1 = sales representative wanted.

retail_sales

Indicator variable for which 1 = retail sales worker wanted.

req

Indicator variable for which 1 = ad mentions any requirement for job.

exp_req

Indicator variable for which 1 = ad mentions some experience requirement.

com_req

Indicator variable for which 1 = ad mentions some communication skills requirement.

educ_req

Indicator variable for which 1 = ad mentions some educational requirement.

comp_req

Indicator variable for which 1 = ad mentions some computer skill requirement.

org_req

Indicator variable for which 1 = ad mentions some organizational skills requirement.

manuf

Indicator variable for which 1 = employer industry is manufacturing.

trans_com

Indicator variable for which 1 = employer industry is transport or communication.

bank_real

Indicator variable for which 1 = employer industry is finance, insurance or real estate.

trade

Indicator variable for which 1 = employer industry is wholesale or retail trade.

bus_service

Indicator variable for which 1 = employer industry is business or personal services.

oth_service

Indicator variable for which 1 = employer industry is health, education or social services.

miss_ind

Indicator variable for which 1 = employer industry is other or unknown.

ownership

Ownership status of employer, with levels of 'non-profit'; 'private'; 'public'

Details

From the summary: "We study race in the labor market by sending fictitious resumes to help-wanted ads in Boston and Chicago newspapers. To manipulate perceived race, resumes are randomly assigned African-American- or White-sounding names. White names receive 50 percent more callbacks for interviews. Callbacks are also more responsive to resume quality for White names than for African-American ones. The racial gap is uniform across occupation, industry, and employer size. We also find little evidence that employers are inferring social class from the names. Differential treatment by race still appears to be prominent in the U. S. labor market."

Source

Bertrand, Marianne, and Mullainathan, Sendhil. Replication data for: Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. Nashville, TN: American Economic Association [publisher], 2004. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-06. doi:10.3886/E116023V1.

NBER Working Papers

1990 Census Occupation Codes

Note: The description of the variables follows closely the labels provided in the original dataset, with small edits for clarity.

Examples

library(dplyr)

# Percent callback for typical White names and typical African-American names (table 1, p. 997)

labor_market_discrimination |>
  group_by(race) |>
  summarise(call_back = mean(call))

Gender, Socioeconomic Class, and Interview Invites

Description

Resumes were sent out to 316 top law firms in the United States, and there were two randomized characteristics of each resume. First, the gender associated with the resume was randomized by assigning a first name of either James or Julia. Second, the socioeconomic class of the candidate was randomly assigned and represented through five minor changes associated with personal interests and other other minor details (e.g. an extracurricular activity of sailing team vs track and field). The outcome variable was whether the candidate was received an interview.

Usage

law_resume

Format

A data frame with 316 observations on the following 3 variables. Each row represents a resume sent a top law firm for this experiment.

class

The resume represented irrelevant details suggesting either "low" or "high" socioeconomic class.

gender

The resume implied the candidate was either "male" or "female".

outcome

If the candidate received an invitation for an "interview" or "not".

Source

For a casual overview, see https://hbr.org/2016/12/research-how-subtle-class-cues-can-backfire-on-your-resume.

For the academic paper, see Tilcsik A, Rivera LA. 2016. Class Advantage, Commitment Penalty. The Gendered Effect of Social Class Signals in an Elite Labor Market. American Sociological Review 81:6 p1097-1131. doi:10.1177/0003122416668154.

Examples

tapply(law_resume$outcome == "interview", law_resume[, c("class", "gender")], mean)
m <- glm(I(outcome == "interview") ~ gender * class, data = law_resume, family = binomial)
summary(m)
predict(m, type = "response")

Lecture Delivery Method and Learning Outcomes

Description

Data was collected from 276 students in a university psychology course to determine the effect of lecture delivery method on learning. Students were presented a live lecture by the professor on one day and a pre-recorded lecture on a different topic by the same professor on a different day. Survey data was collected during the lectures to determine mind wandering, interest, and motivation. Students were also ultimately asked about the preferred lecture delivery method. Finally, students completed an assessment at the end of the lecture to determine memory recall.

Usage

lecture_learning

Format

A data frame with 552 rows and 8 variables.

student

Identification number of a specific student. Each identification appears twice because same student heard both lecture delivery methods.

gender

Gender of student. Recored a binary variable with levels Male and Female in the study.

method

Delivery method of lecture was either in-person(Live) or pre-recorded(Video).

mindwander

An indicator of distraction during the lecture. It is a proportion of six mind wandering probes during the lecture when a student answered yes that mind wandering had just occurred.

memory

An indicator of recall of information provided during the lecture. It is the proportion of correct answers in a six question assessment given at the end of the lecture presentation.

interest

A Likert scale that gauged student interest level concerning the lecture.

motivation_both

After experiencing both lecture delivery methods, students were asked about which method they were most motivated to remain attentive.

motivation_single

After a single lecture delivery experience, this Likert scale was used to gauge motivation to remain attentive during the lecture.

Source

PLOS One

Examples

library(dplyr)
library(ggplot2)

# Calculate the average memory test proportion by lecture delivery method
# and gender.
lecture_learning |>
  group_by(method, gender) |>
  summarize(average_memory = mean(memory), count = n(), .groups = "drop")

# Compare visually the differences in memory test proportions by delivery
# method and gender.
ggplot(lecture_learning, aes(x = method, y = memory, fill = gender)) +
  geom_boxplot() +
  theme_minimal() +
  labs(
    title = "Difference in memory test proportions",
    x = "Method",
    y = "Memory",
    fill = "Gender"
  )

# Use a paired t-test to determine whether memory test proportion score
# differed by delivery method. Note that paired t-tests are identical
# to one sample t-test on the difference between the Live and Video methods.
learning_diff <- lecture_learning |>
  tidyr::pivot_wider(id_cols = student, names_from = method, values_from = memory) |>
  mutate(time_diff = Live - Video)
t.test(time_diff ~ 1, data = learning_diff)

# Calculating the proportion of students who were most motivated to remain
# attentive in each delivery method.
lecture_learning |>
  count(motivation_both) |>
  mutate(proportion = n / sum(n))

Legalization of Marijuana Support in 2010 California Survey

Description

In a 2010 Survey USA poll, 70% of the 119 respondents between the ages of 18 and 34 said they would vote in the 2010 general election for Prop 19, which would change California law to legalize marijuana and allow it to be regulated and taxed.

Usage

leg_mari

Format

A data frame with 119 observations on the following variable.

response

One of two values: oppose or support.

Source

Survey USA, Election Poll #16804, data collected July 8-11, 2010.

Examples

table(leg_mari)

Population of Lego Sets for Sale between Jan. 1, 2018 and Sept. 11, 2020.

Description

Data about Lego Sets for sale. Based on JSDSE article by Anna Peterson and Laura Ziegler Data from their article was scrapped from multiple sources including brickset.com

Usage

lego_population

Format

A data frame with 1304 rows and 14 variables.

item_number

Set Item number

set_name

Name of the set.

theme

Set theme: Duplo, City or Friends.

pieces

Number of pieces in the set.

price

Recommended retail price from LEGO.

amazon_price

Price of the set at Amazon.

year

Year that it was produced.

ages

LEGO's recommended ages of children for the set

pages

Pages in the instruction booklet.

minifigures

Number of LEGO people in the data, if unknown "NA" was recorded.

packaging

Type of packaging: bag, box, etc.

weight

Weight of the set of LEGOS in pounds and kilograms.

unique_pieces

Number of pieces classified as unique in the instruction manual.

size

Size of the lego pieces: Large if safe for small children and Small for older children.

Source

Peterson, A. D., & Ziegler, L. (2021). Building a multiple linear regression model with LEGO brick data. Journal of Statistics and Data Science Education, 29(3),1-7. doi:10.1080/26939169.2021.1946450

BrickInstructions.com. (n.d.). Retrieved February 2, 2021 from

Brickset. (n.d.). BRICKSET: Your LEGO® set guide. Retrieved February 2, 2021 from

Examples

library(ggplot2)
library(dplyr)

lego_population |>
  filter(theme == "Friends" | theme == "City") |>
  ggplot(aes(x = pieces, y = amazon_price)) +
  geom_point(alpha = 0.3) +
  labs(
    x = "Pieces in the Set",
    y = "Amazon Price",
    title = "Amazon Price vs Number of Pieces in Lego Sets",
    subtitle = "Friends and City Themes"
  )

Sample of Lego Sets

Description

Data about Lego Sets for sale. Based on JSDSE article by Anna Peterson and Laura Ziegler Data from their article was scrapped from multiple sources including brickset.com

Usage

lego_sample

Format

A data frame with 75 rows and 15 variables.

item_number

Set Item number

set_name

Name of the set.

theme

Set theme: Duplo, City or Friends.

pieces

Number of pieces in the set.

price

Recommended retail price from LEGO.

amazon_price

Price of the set at Amazon.

year

Year that it was produced.

ages

LEGO's recommended ages of children for the set

pages

Pages in the instruction booklet.

minifigures

Number of LEGO people in the data, if unknown "NA" was recorded.

packaging

Type of packaging: bag, box, etc.

weight

Weight of the set of LEGOS in pounds and kilograms.

unique_pieces

Number of pieces classified as unique in the instruction manual.

size

Size of the lego pieces: Large if safe for small children and Small for older children.

Source

Peterson, A. D., & Ziegler, L. (2021). Building a multiple linear regression model with LEGO brick data. Journal of Statistics and Data Science Education, 29(3),1-7. doi:10.1080/26939169.2021.1946450

BrickInstructions.com. (n.d.). Retrieved February 2, 2021 from

Brickset. (n.d.). BRICKSET: Your LEGO® set guide. Retrieved February 2, 2021 from

Examples

library(ggplot2)
library(dplyr)

lego_sample |>
  filter(theme == "Friends" | theme == "City") |>
  ggplot(aes(x = pieces, y = amazon_price)) +
  geom_point(alpha = 0.3) +
  labs(
    x = "Pieces in the Set",
    y = "Amazon Price",
    title = "Amazon Price vs Number of Pieces in Lego Sets",
    subtitle = "Friends and City Themes"
  )

life_exp

Description

A data frame with 3142 rows and 4 columns. County level data for life expectancy and median income in the United States.

Usage

life_exp

Format

A data frame with 3142 observations on the following 4 variables.

state

Name of the state.

county

Name of the county.

expectancy

Life expectancy in the county.

income

Median income in the county, measured in US $.

Examples

library(ggplot2)

# Income V Expectancy
ggplot(life_exp, aes(x = income, y = expectancy)) +
  geom_point(color = openintro::IMSCOL["green", "full"], alpha = 0.2) +
  theme_minimal() +
  labs(
    title = "Is there a relationship between median income and life expectancy?",
    x = "Median income (US $)",
    y = "Life Expectancy (year)"
  )

Create simple regression plot with residual plot

Description

Create a simple regression plot with residual plot.

Usage

linResPlot(
  x,
  y,
  axes = FALSE,
  wBox = TRUE,
  wLine = TRUE,
  lCol = "#00000088",
  lty = 1,
  lwd = 1,
  main = "",
  xlab = "",
  ylab = "",
  marRes = NULL,
  col = fadeColor(4, "88"),
  pch = 20,
  cex = 1.5,
  yR = 0.1,
  ylim = NULL,
  subset = NULL,
  ...
)

Arguments

See Also

makeTube

Examples

# Currently seems broken for this example.
n <- 25
x <- runif(n)
y <- 5 * x + rnorm(n)
myMat <- rbind(matrix(1:2, 2))
myW <- 1
myH <- c(1, 0.45)
par(mar = c(0.35, 0.654, 0.35, 0.654))
layout(myMat, myW, myH)
linResPlot(x, y, col = COL[1, 2])

Field data on lizards observed in their natural habitat

Description

Data on here lizard was observed and the level of sunlight. The data are collected on Sceloporus occidentalis (western fence lizards) by Stephen C. Adolph in 1983 (in desert and mountain sites) and by Dee Asbury in 2002-3 (in valley site).

Usage

lizard_habitat

Format

A data frame with 332 observations on the following 2 variables.

site

Site of lizard observation: desert, mountain, or valley.

sunlight

Sunlight level at time of observation: sun (lizard was observed perching in full sunlight), partial (lizard was observed perching with part of its body in the sun, part in the shade), shade(lizard was observed perching in the shade).

Source

Adolph, S. C. 1990. Influence of behavioral thermoregulation on microhabitat use by two Sceloporus lizards. Ecology 71: 315-327. Asbury, D.A., and S. C. Adolph. 2007. Behavioral plasticity in an ecological generalist: microhabitat use by western fence lizards. Evolutionary Ecology Research 9:801-815.

Examples

library(ggplot2)

# Frequencies
table(lizard_habitat)

# Stacked bar plots
ggplot(lizard_habitat, aes(y = site, fill = sunlight)) +
  geom_bar(position = "fill") +
  labs(x = "Proportion")

Lizard speeds

Description

Data on top speeds measured on a laboratory race track for two species of lizards: Western fence lizard (Sceloporus occidentalis) and Sagebrush lizard (Sceloporus graciosus).

Usage

lizard_run

Format

A data frame with 48 observations on the following 3 variables.

top_speed

Top speed of lizard, meters per second.

common_name

Common name: ⁠Western fence lizard⁠ and ⁠Sagebrush lizard⁠.

scientific_name

Scientific name (Genus and species): ⁠Sceloporus occidentalis⁠ and ⁠Sceloporus graciosus⁠.

Source

Adolph, S. C. 1987. Physiological and behavioral ecology of the lizards Sceloporus occidentalis and Sceloporus graciosus. Dissertation. University of Washington, Seattle, Washington, USA.

Examples

library(ggplot2)
library(dplyr)

# Top speed by species
ggplot(lizard_run, aes(x = top_speed, color = common_name, fill = common_name)) +
  geom_density(alpha = 0.5)

# Top speed summary statistics by species
lizard_run |>
  group_by(common_name) |>
  summarise(
    n    = n(),
    mean = mean(top_speed),
    sd   = sd(top_speed)
  )

Linear regression plot with residual plot

Description

Plot data, the linear model, and a residual plot simultaneously.

Usage

lmPlot(
  x,
  y,
  xAxis = 0,
  yAxis = 4,
  resAxis = 3,
  resSymm = TRUE,
  wBox = TRUE,
  wLine = TRUE,
  lCol = "#00000088",
  lty = 1,
  lwd = 1,
  xlab = "",
  ylab = "",
  marRes = NULL,
  col = "#22558888",
  pch = 20,
  cex = 1.5,
  xR = 0.02,
  yR = 0.1,
  xlim = NULL,
  ylim = NULL,
  subset = NULL,
  parCustom = FALSE,
  myHeight = c(1, 0.45),
  plots = c("both", "mainOnly", "resOnly"),
  highlight = NULL,
  hlCol = NULL,
  hlCex = 1.5,
  hlPch = 20,
  na.rm = TRUE,
  ...
)

Arguments

Author(s)

David Diez

See Also

makeTube

Examples

lmPlot(satgpa$sat_sum, satgpa$fy_gpa)

lmPlot(gradestv$tv, gradestv$grades,
  xAxis = 4,
  xlab = "time watching TV", yR = 0.2, highlight = c(1, 15, 20)
)

Loan data from Lending Club

Description

This dataset represents thousands of loans made through the Lending Club platform, which is a platform that allows individuals to lend to other individuals. Of course, not all loans are created equal. Someone who is a essentially a sure bet to pay back a loan will have an easier time getting a loan with a low interest rate than someone who appears to be riskier. And for people who are very risky? They may not even get a loan offer, or they may not have accepted the loan offer due to a high interest rate. It is important to keep that last part in mind, since this dataset only represents loans actually made, i.e. do not mistake this data for loan applications!

Usage

loans_full_schema

Format

A data frame with 10,000 observations on the following 55 variables.

emp_title

Job title.

emp_length

Number of years in the job, rounded down. If longer than 10 years, then this is represented by the value 10.

state

Two-letter state code.

homeownership

The ownership status of the applicant's residence.

annual_income

Annual income.

verified_income

Type of verification of the applicant's income.

debt_to_income

Debt-to-income ratio.

annual_income_joint

If this is a joint application, then the annual income of the two parties applying.

verification_income_joint

Type of verification of the joint income.

debt_to_income_joint

Debt-to-income ratio for the two parties.

delinq_2y

Delinquencies on lines of credit in the last 2 years.

months_since_last_delinq

Months since the last delinquency.

earliest_credit_line

Year of the applicant's earliest line of credit

inquiries_last_12m

Inquiries into the applicant's credit during the last 12 months.

total_credit_lines

Total number of credit lines in this applicant's credit history.

open_credit_lines

Number of currently open lines of credit.

total_credit_limit

Total available credit, e.g. if only credit cards, then the total of all the credit limits. This excludes a mortgage.

total_credit_utilized

Total credit balance, excluding a mortgage.

num_collections_last_12m

Number of collections in the last 12 months. This excludes medical collections.

num_historical_failed_to_pay

The number of derogatory public records, which roughly means the number of times the applicant failed to pay.

months_since_90d_late

Months since the last time the applicant was 90 days late on a payment.

current_accounts_delinq

Number of accounts where the applicant is currently delinquent.

total_collection_amount_ever

The total amount that the applicant has had against them in collections.

current_installment_accounts

Number of installment accounts, which are (roughly) accounts with a fixed payment amount and period. A typical example might be a 36-month car loan.

accounts_opened_24m

Number of new lines of credit opened in the last 24 months.

months_since_last_credit_inquiry

Number of months since the last credit inquiry on this applicant.

num_satisfactory_accounts

Number of satisfactory accounts.

num_accounts_120d_past_due

Number of current accounts that are 120 days past due.

num_accounts_30d_past_due

Number of current accounts that are 30 days past due.

num_active_debit_accounts

Number of currently active bank cards.

total_debit_limit

Total of all bank card limits.

num_total_cc_accounts

Total number of credit card accounts in the applicant's history.

num_open_cc_accounts

Total number of currently open credit card accounts.

num_cc_carrying_balance

Number of credit cards that are carrying a balance.

num_mort_accounts

Number of mortgage accounts.

account_never_delinq_percent

Percent of all lines of credit where the applicant was never delinquent.

tax_liens

a numeric vector

public_record_bankrupt

Number of bankruptcies listed in the public record for this applicant.

loan_purpose

The category for the purpose of the loan.

application_type

The type of application: either individual or joint.

loan_amount

The amount of the loan the applicant received.

term

The number of months of the loan the applicant received.

interest_rate

Interest rate of the loan the applicant received.

installment

Monthly payment for the loan the applicant received.

grade

Grade associated with the loan.

sub_grade

Detailed grade associated with the loan.

issue_month

Month the loan was issued.

loan_status

Status of the loan.

initial_listing_status

Initial listing status of the loan. (I think this has to do with whether the lender provided the entire loan or if the loan is across multiple lenders.)

disbursement_method

Dispersement method of the loan.

balance

Current balance on the loan.

paid_total

Total that has been paid on the loan by the applicant.

paid_principal

The difference between the original loan amount and the current balance on the loan.

paid_interest

The amount of interest paid so far by the applicant.

paid_late_fees

Late fees paid by the applicant.

Source

This data comes from Lending Club (https://www.lendingclub.com/info/statistics.action), which provides a very large, open set of data on the people who received loans through their platform.

Examples

loans_full_schema

London Borough Boundaries

Description

This dataset contains the coordinates of the boundaries of all 32 boroughs of the Greater London area.

Usage

london_boroughs

Format

A data frame with 45341 observations on the following 3 variables.

borough

Name of the borough.

x

The "easting" component of the coordinate, see details.

y

The "northing" component of the coordinate, see details.

Details

Map data was made available through the Ordnance Survey Open Data initiative. The data use the National Grid coordinate system, based upon eastings (x) and northings (y) instead of longitude and latitude.

The name variable covers all 32 boroughs in Greater London: Barking & Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith & Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington & Chelsea, Kingston, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster

Source

https://data.london.gov.uk/dataset/ordnance-survey-code-point

Contains Ordinance Survey data released under the Open Government License, OGL v2.

See Also

london_murders

Examples

library(dplyr)
library(ggplot2)

# Calculate number of murders by borough
london_murders_counts <- london_murders |>
  group_by(borough) |>
  add_tally()

london_murders_counts
## Not run: 
# Add number of murders to geographic boundary data
london_boroughs_murders <- inner_join(london_boroughs, london_murders_counts, by = "borough")

# Map murders
ggplot(london_boroughs_murders) +
  geom_polygon(aes(x = x, y = y, group = borough, fill = n), colour = "white") +
  scale_fill_distiller(direction = 1) +
  labs(x = "Easting", y = "Northing", fill = "Number of murders")

## End(Not run)

London Murders, 2006-2011

Description

This dataset contains the victim name, age, and location of every murder recorded in the Greater London area by the Metropolitan Police from January 1, 2006 to September 7, 2011.

Usage

london_murders

Format

A data frame with 838 observations on the following 5 variables.

forename

First name(s) of the victim.

age

Age of the victim.

date

Date of the murder (YYYY-MM-DD).

year

Year of the murder.

borough

The London borough in which the murder took place. See the Details section for a list of all the boroughs.

Details

To visualize this dataset using a map, see the london_boroughs dataset, which contains the latitude and longitude of polygons that define the boundaries of the 32 boroughs of Greater London.

The borough variable covers all 32 boroughs in Greater London: Barking & Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith & Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington & Chelsea, Kingston, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster

Source

https://www.theguardian.com/news/datablog/2011/oct/05/murder-london-list#data

References

Inspired by The Guardian Datablog.

Examples

library(dplyr)
library(ggplot2)
library(lubridate)

london_murders |>
  mutate(
    day_count = as.numeric(date - ymd("2006-01-01")),
    date_cut = cut(day_count, seq(0, 2160, 90))
  ) |>
  group_by(date_cut) |>
  add_tally() |>
  ggplot(aes(x = date_cut, y = n)) +
  geom_col() +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
  labs(x = "Date from 01/2006 - 09/2011", y = "Number of deaths per 90 days")

Output a message while inside a loop

Description

NOTE: utils::txtProgressBar() and utils::setTxtProgressBar() are better. Output a message while inside a for loop to update the user on progress. This function is useful in tracking progress when the number of iterations is large or the procedures in each iteration take a long time.

Usage

loop(i, n = NULL, every = 1, extra = NULL)

Arguments

Author(s)

David Diez

See Also

myPDF

Examples

for (i in 1:160) {
  loop(i, 160, 20, paste("iter", i))
}

Create a Line Segment Plot

Description

Creae a simple plot showing a line segment.

Usage

lsegments(
  x = c(3, 7),
  l = "o",
  r = "c",
  ticks = TRUE,
  labs = 1,
  add = 0,
  ylim = c(-0.75, 0.25)
)

Arguments

Author(s)

David Diez

See Also

dlsegments, CCP, ArrowLines

Examples

lsegments(c(2, 7), "o", "c", ylim = c(-0.3, 0.2))

lsegments(c(5, 7), "c", "c", ylim = c(-0.3, 0.2))

lsegments(c(4, 1000), "o", "o", ylim = c(-0.3, 0.2))

Influence of a Good Mood on Helpfulness

Description

This study investigated whether finding a coin influenced a person's likelihood of mailing a sealed but addressed letter that appeared to have been accidentally left in a conspicuous place. Several variables were collected during the experiment, including two randomized variables of whether there was a coin to be found and whether the letter already had a stamp on it.

Usage

mail_me

Format

A data frame with 42 observations on the following 4 variables.

stamped

a factor with levels no yes

found_coin

a factor with levels coin no_coin

gender

a factor with levels female male

mailed_letter

a factor with levels no yes

Details

The precise context was in a phone booth (this study is from the 1970s!), where a person who entered a phone booth would find a dime in the phone tray, which would be sufficient to pay for their phone call. There was also a letter next to the phone, which sometimes had a stamp on it.

Source

Levin PF, Isen AM. 1975. Studies on the Effect of Feeling Good on Helping. Sociometry 31(1), p141-147.

Examples

table(mail_me)
(x <- table(mail_me[, c("mailed_letter", "found_coin")]))
chisq.test(x)

(x <- table(mail_me[, c("mailed_letter", "stamped")]))
chisq.test(x)

m <- glm(mailed_letter ~ stamped + found_coin + gender,
  data = mail_me,
  family = binomial
)
summary(m)

Survey of Duke students and the area of their major

Description

Survey of 218 students, collecting information on their GPAs and their academic major.

Usage

major_survey

Format

A data frame with 218 observations on the following 2 variables.

gpa

Grade point average (GPA).

major

Area of academic major.

Examples

library(ggplot2)

ggplot(major_survey, aes(x = major, y = gpa)) +
  geom_boxplot()

Regression tube

Description

Produce a linear, quadratic, or nonparametric tube for regression data.

Usage

makeTube(
  x,
  y,
  Z = 2,
  R = 1,
  col = "#00000022",
  border = "#00000000",
  type = c("lin", "quad", "robust"),
  stDev = c("constant", "linear", "other"),
  length.out = 99,
  bw = "default",
  plotTube = TRUE,
  addLine = TRUE,
  ...
)

Arguments

Value

Author(s)

David Diez

See Also

lmPlot

Examples

# possum example
plot(possum$total_l, possum$head_l)
makeTube(possum$total_l, possum$head_l, 1)
makeTube(possum$total_l, possum$head_l, 2)
makeTube(possum$total_l, possum$head_l, 3)

# grades and TV example
plot(gradestv)
makeTube(gradestv$tv, gradestv$grades, 1.5)
plot(gradestv)
makeTube(gradestv$tv, gradestv$grades, 1.5, stDev = "o")
plot(gradestv)
makeTube(gradestv$tv, gradestv$grades, 1.5, type = "robust")
plot(gradestv)
makeTube(gradestv$tv, gradestv$grades, 1.5, type = "robust", stDev = "o")

# what can go wrong with a basic least squares model
# 1
x <- runif(100)
y <- 25 * x - 20 * x^2 + rnorm(length(x), sd = 1.5)
plot(x, y)
makeTube(x, y, type = "q")
# 2
x <- c(-0.6, -0.46, -0.091, runif(97))
y <- 25 * x + rnorm(length(x))
y[2] <- y[2] + 8
y[1] <- y[1] + 1
plot(x, y, ylim = range(y) + c(-10, 5))
makeTube(x, y)
# 3
x <- runif(100)
y <- 5 * x + rnorm(length(x), sd = x)
plot(x, y)
makeTube(x, y, stDev = "l", bw = 0.03)

Malaria Vaccine Trial

Description

Volunteer patients were randomized into one of two experiment groups where they would receive an experimental vaccine or a placebo. They were subsequently exposed to a drug-sensitive strain of malaria and observed to see whether they came down with an infection.

Usage

malaria

Format

A data frame with 20 observations on the following 2 variables.

treatment

Whether a person was given the experimental vaccine or a placebo.

outcome

Whether the person got an infection or ⁠no infection⁠.

Details

In this study, volunteer patients were randomized into one of two experiment groups: 14 patients received an experimental vaccine or 6 patients received a placebo vaccine. Nineteen weeks later, all 20 patients were exposed to a drug-sensitive malaria virus strain; the motivation of using a drug-sensitive strain of virus here is for ethical considerations, allowing any infections to be treated effectively.

Source

Lyke et al. 2017. PfSPZ vaccine induces strain-transcending T cells and durable protection against heterologous controlled human malaria infection. PNAS 114(10):2711-2716. doi:10.1073/pnas.1615324114.

Examples

library(dplyr)

# Calculate conditional probabilities of infection after vaccine/placebo
malaria |>
  count(treatment, outcome) |>
  group_by(treatment) |>
  mutate(prop = n / sum(n))

# Fisher's exact text
fisher.test(table(malaria))

Sample of 100 male heights

Description

Random sample based on Food Commodity Intake Database distribution

Usage

male_heights

Format

A data frame with 100 observations on the following variable.

heights

a numeric vector

References

What We Eat In America - Food Commodity Intake Database. Available at https://fcid.foodrisk.org/.

Examples

male_heights