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Type:

Package

Title:

Tidyverse-Friendly Introductory Linear Regression

Version:

0.7.0

Maintainer:

Albert Y. Kim <albert.ys.kim@gmail.com>

Description:

Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at https://moderndive.com/.

Depends:

R (≥ 3.4.0)

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

URL:

https://moderndive.github.io/moderndive/, https://github.com/moderndive/moderndive/

BugReports:

https://github.com/moderndive/moderndive/issues

Imports:

magrittr, dplyr, purrr, tidyr, ggplot2, tibble, janitor, broom (≥ 0.4.3), formula.tools, stringr, knitr, infer, rlang (≥ 0.2.0), glue

RoxygenNote:

7.3.2

Suggests:

testthat, covr, rmarkdown, vdiffr, openintro, patchwork, viridis, readr, nycflights13, nycflights23

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2024-09-01 20:28:30 UTC; akim04

Author:

Albert Y. Kim

[aut, cre], Chester Ismay

[aut], Andrew Bray

[ctb], Delaney Moran [ctb], Evgeni Chasnovski

[ctb], Will Hopper

[ctb], Benjamin S. Baumer

[ctb], Marium Tapal

[ctb], Wayne Ndlovu [ctb], Catherine Peppers [ctb], Annah Mutaya [ctb], Anushree Goswami [ctb], Ziyue Yang

[ctb], Clara Li

[ctb], Caroline McKenna [ctb], Catherine Park

[ctb], Abbie Benfield [ctb], Georgia Gans [ctb], Kacey Jean-Jacques [ctb], Swaha Bhattacharya [ctb], Vivian Almaraz [ctb], Elle Jo Whalen [ctb], Jacqueline Chen [ctb], Michelle Flesaker [ctb], Irene Foster [ctb], Aushanae Haller [ctb], Benjamin Bruncati

[ctb], Quinn White

[ctb], Tianshu Zhang

[ctb], Katelyn Diaz

[ctb], Rose Porta [ctb], Renee Wu [ctb], Arris Moise [ctb], Kate Phan [ctb], Grace Hartley [ctb], Silas Weden [ctb], Emma Vejcik [ctb], Nikki Schuldt [ctb], Tess Goldmann [ctb], Hongtong Lin [ctb], Alejandra Munoz [ctb], Elina Gordon-Halpern [ctb], Haley Schmidt

[ctb]

Repository:

CRAN

Date/Publication:

2024-09-01 21:40:05 UTC

moderndive - Tidyverse-Friendly Introductory Linear Regression

Description

Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the tidyverse" available at https://moderndive.com/.

Author(s)

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

Authors:

Chester Ismay chester.ismay@gmail.com (ORCID)

Other contributors:

Andrew Bray abray@reed.edu (ORCID) [contributor]
Delaney Moran delaneyemoran@gmail.com [contributor]
Evgeni Chasnovski evgeni.chasnovski@gmail.com (ORCID) [contributor]
Will Hopper wjhopper510@gmail.com (ORCID) [contributor]
Benjamin S. Baumer ben.baumer@gmail.com (ORCID) [contributor]
Marium Tapal mariumtapal@gmail.com (ORCID) [contributor]
Wayne Ndlovu waynedndlovu5@gmail.com [contributor]
Catherine Peppers cpeppers@smith.edu [contributor]
Annah Mutaya annahmutaya18@gmail.com [contributor]
Anushree Goswami anushreeegoswami@gmail.com [contributor]
Ziyue Yang zyang2k@gmail.com (ORCID) [contributor]
Clara Li clarasepianli@gmail.com (ORCID) [contributor]
Caroline McKenna carolinemckenna101@gmail.com [contributor]
Catherine Park jcathyp@gmail.com (ORCID) [contributor]
Abbie Benfield abbidabbers@gmail.com [contributor]
Georgia Gans georgiagans@live.com [contributor]
Kacey Jean-Jacques kjeanjacques@smith.edu [contributor]
Swaha Bhattacharya itsswahabhattacharya@gmail.com [contributor]
Vivian Almaraz viviansofia101@gmail.com [contributor]
Elle Jo Whalen ewhalen@smith.edu [contributor]
Jacqueline Chen jchen76@smith.edu [contributor]
Michelle Flesaker mflesaker@smith.edu [contributor]
Irene Foster ifoster25@smith.edu [contributor]
Aushanae Haller aushanaenhaller@gmail.com [contributor]
Benjamin Bruncati kbruncati@smith.edu (ORCID) [contributor]
Quinn White quinnarlise@gmail.com (ORCID) [contributor]
Tianshu Zhang tzhang26@smith.edu (ORCID) [contributor]
Katelyn Diaz katndiaz@gmail.com (ORCID) [contributor]
Rose Porta rporta@smith.edu [contributor]
Renee Wu rwu30@smith.edu [contributor]
Arris Moise amoise@smith.edu [contributor]
Kate Phan kphan@smith.edu [contributor]
Grace Hartley grace.hartley@gmail.com [contributor]
Silas Weden silasweden@gmail.com [contributor]
Emma Vejcik evejcik@gmail.com [contributor]
Nikki Schuldt nikkischuldt@gmail.com [contributor]
Tess Goldmann tessgoldmann@aol.com [contributor]
Hongtong Lin cccynthialht@gmail.com [contributor]
Alejandra Munoz amunozgarcia@smith.edu [contributor]
Elina Gordon-Halpern egordonhalpern@smith.edu [contributor]
Haley Schmidt heschmidt00@gmail.com (ORCID) [contributor]

Examples

library(moderndive)

# Fit regression model:
mpg_model <- lm(mpg ~ hp, data = mtcars)

# Regression tables:
get_regression_table(mpg_model)

# Information on each point in a regression:
get_regression_points(mpg_model)

# Regression summaries
get_regression_summaries(mpg_model)

# Plotting parallel slopes models
library(ggplot2)
ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes(se = FALSE)

Pipe operator

Description

See ⁠magrittr::[\%>\%][magrittr::pipe]⁠ for details.

Usage

lhs %>% rhs

Dunkin Donuts vs Starbucks

Description

Number of Dunkin Donuts & Starbucks, median income, and population in 1024 census tracts in eastern Massachusetts in 2016.

Usage

DD_vs_SB

Format

A data frame of 1024 rows representing census tracts and 6 variables

county: County where census tract is located. Either Bristol, Essex, Middlesex, Norfolk, Plymouth, or Suffolk county
FIPS: Federal Information Processing Standards code identifying census tract
median_income: Median income of census tract
population: Population of census tract
shop_type: Coffee shop type: Dunkin Donuts or Starbucks
shops: Number of shops

Source

US Census Bureau. Code used to scrape data available at https://github.com/DelaneyMoran/FinalProject

Massachusetts Public High Schools Data

Description

Data on Massachusetts public high schools in 2017

Usage

MA_schools

Format

A data frame of 332 rows representing Massachusetts high schools and 4 variables

school_name: High school name.
average_sat_math: Average SAT math score. Note 58 of the original 390 values of this variable were missing; these rows were dropped from consideration.
perc_disadvan: Percent of the student body that are considered economically disadvantaged.
size: Size of school enrollment; small 13-341 students, medium 342-541 students, large 542-4264 students.

Source

The original source of the data are Massachusetts Department of Education reports https://profiles.doe.mass.edu/state_report/, however the data was downloaded from Kaggle at https://www.kaggle.com/ndalziel/massachusetts-public-schools-data

Alaska flights data

Description

On-time data for all Alaska Airlines flights that departed NYC (i.e. JFK, LGA or EWR) in 2013. This is a subset of the flights data frame from nycflights13.

Usage

alaska_flights

Format

A data frame of 714 rows representing Alaska Airlines flights and 19 variables

year, month, day: Date of departure.
dep_time, arr_time: Actual departure and arrival times (format HHMM or HMM), local tz.
sched_dep_time, sched_arr_time: Scheduled departure and arrival times (format HHMM or HMM), local tz.
dep_delay, arr_delay: Departure and arrival delays, in minutes. Negative times represent early departures/arrivals.
carrier: Two letter carrier abbreviation. See nycflights13::airlines to get name.
flight: Flight number.
tailnum: Plane tail number. See nycflights13::planes for additional metadata.
origin, dest: Origin and destination. See nycflights13::airports for additional metadata.
air_time: Amount of time spent in the air, in minutes.
distance: Distance between airports, in miles.
hour, minute: Time of scheduled departure broken into hour and minutes.
time_hour: Scheduled date and hour of the flight as a POSIXct date. Along with origin, can be used to join flights data to nycflights13::weather data.

Source

RITA, Bureau of transportation statistics

Chocolate-covered almonds data

Description

5000 chocolate-covered almonds selected from a large batch, weighed in grams.

Usage

almonds_bowl

Format

A data frame with 5000 observations on the following 2 variables

ID: Identification value for a given chocolate-covered almond
weight: Weight of the chocolate-covered almond in grams (to the nearest tenth)

Chocolate-covered almonds data sample of size 25

Description

A sample of 25 chocolate-covered almonds, weighed in grams.

Usage

almonds_sample

Format

A data frame with 25 observations on the following 2 variables

replicate: Replicate number set to 1 since there is only one sample
ID: Identification value for a given chocolate-covered almond
weight: Weight of the chocolate-covered almond in grams (to the nearest tenth)

Chocolate-covered almonds data sample of size 100

Description

A sample of 100 chocolate-covered almonds, weighed in grams.

Usage

almonds_sample_100

Format

A data frame with 100 observations on the following 2 variables

replicate: Replicate number set to 1 since there is only one sample
ID: Identification value for a given chocolate-covered almond
weight: Weight of the chocolate-covered almond in grams (to the nearest tenth)

Sample of Amazon books

Description

A random sample of 325 books from Amazon.com.

Usage

amazon_books

Format

A data frame of 325 rows representing books listed on Amazon and 13 variables.

title: Book title
author: Author who wrote book
list_price: recommended retail price of book
amazon_price: lowest price of book shown on Amazon
hard_paper: book is either hardcover or paperback
num_pages: number of pages in book
publisher: Company that issues the book for sale
pub_year: Year the book was published
isbn_10: 10-character ISBN number
height, width, thick, weight_oz: height, width, weight and thickness of the book

Source

The Data and Story Library (DASL) https://dasl.datadescription.com/datafile/amazon-books

Avocado Prices by US Region

Description

Gathered from https://docs.google.com/spreadsheets/d/1cNuj9V-9Xe8fqV3DQRhvsXJhER3zTkO1dSsQ1Q0j96g/edit#gid=1419070688

Usage

avocados

Format

A data frame of 54 regions over 3 years of weekly results

date: Week of Data Recording
average_price: Average Price of Avocado
total_volume: Total Amount of Avocados
small_hass_sold: Amount of Small Haas Avocados Sold
large_hass_sold: Amount of Large Haas Avocados Sold
xlarge_hass_sold: Amount of Extra Large Haas Avocados Sold
total_bags: Total Amount of Bags of Avocados
small_bags: Total Amount of Bags of Small Haas Avocados
large_bags: Total Amount of Bags of Large Haas Avocados
x_large_bags: Total Amount of Bags of Extra Large Haas Avocados
type: Type of Sale
year: Year of Sale
region: Region Where Sale Took Place

Data on maternal smoking and infant health

Description

Data on maternal smoking and infant health

Usage

babies

Format

A data frame of 1236 rows of individual mothers.

id: Identification number
pluralty: Marked 5 for single fetus, otherwise number of fetuses
outcome: Marked 1 for live birth that survived at least 28 days
date: Birth date where 1096 is January 1st, 1961
birthday: Birth date in mm-dd-yyyy format
gestation: Length of gestation in days, marked 999 if unknown
sex: Infant's sex, where 1 is male, 2 is female, and 9 is unknown
wt: Birth weight in ounces, marked 999 if unknown
parity: Total number of previous pregnancies including fetal deaths and stillbirths, marked 99 if unknown
race: Mother's race where 0-5 is white, 6 is Mexican, 7 is Black, 8 is Asian, 9 is mixed, and 99 is unknown
age: Mother's age in years at termination of pregnancy, 99=unknown
ed: Mother's education 0= less than 8th grade, 1 = 8th -12th grade - did not graduate, 2= HS graduate–no other schooling , 3= HS+trade, 4=HS+some college 5= College graduate, 6&7 Trade school HS unclear, 9=unknown
ht: Mother's height in inches to the last completed inch, 99=unknown
wt_1: Mother prepregnancy wt in pounds, 999=unknown
drace: Father's race, coding same as mother's race
dage: Father's age, coding same as mother's age
ded: Father's education, coding same as mother's education
dht: Father's height, coding same as for mother's height
dwt: Father's weight coding same as for mother's weight
marital: 0= legally separated, 1=married, 2= divorced, 3=widowed, 5=never married
inc: Family yearly income in $2500 increments 0 = under 2500, 1=2500-4999, ..., 8= 12,500-14,999, 9=15000+, 98=unknown, 99=not asked
smoke: Does mother smoke? 0=never, 1= smokes now, 2=until current pregnancy, 3=once did, not now, 9=unknown
time: If mother quit, how long ago? 0=never smoked, 1=still smokes, 2=during current preg, 3=within 1 yr, 4= 1 to 2 years ago, 5= 2 to 3 yr ago, 6= 3 to 4 yrs ago, 7=5 to 9yrs ago, 8=10+yrs ago, 9=quit and don't know, 98=unknown, 99=not asked
number: Number of cigs smoked per day for past and current smokers 0=never, 1=1-4, 2=5-9, 3=10-14, 4=15-19, 5=20-29, 6=30-39, 7=40-60, 8=60+, 9=smoke but don't know, 98=unknown, 99=not asked

Source

Data on maternal smoking and infant health from https://www.stat.berkeley.edu/~statlabs/labs.html

A sampling bowl of red and white balls

Description

A sampling bowl used as the population in a simulated sampling exercise. Also known as the urn sampling framework https://en.wikipedia.org/wiki/Urn_problem.

Usage

bowl

Format

A data frame 2400 rows representing different balls in the bowl, of which 900 are red and 1500 are white.

ball_ID: ID variable used to denote all balls. Note this value is not marked on the balls themselves
color: color of ball: red or white

Tactile sample of size 50 from a bowl of balls

Description

A single tactile sample of size n = 50 balls from https://github.com/moderndive/moderndive/blob/master/data-raw/sampling_bowl.jpeg

Usage

bowl_sample_1

Format

A data frame of 50 rows representing different balls and 1 variable.

color: Color of ball sampled

Sampling from a bowl of balls

Description

Counting the number of red balls in 10 samples of size n = 50 balls from https://github.com/moderndive/moderndive/blob/master/data-raw/sampling_bowl.jpeg

Usage

bowl_samples

Format

A data frame 10 rows representing different groups of students' samples of size n = 50 and 5 variables

group: Group name
red: Number of red balls sampled
white: Number of white balls sampled
green: Number of green balls sampled
n: Total number of balls samples

Coffee Quality Dataset

Description

This dataset contains detailed information about coffee quality evaluations from various origins. It includes data on the country and continent of origin, farm name, lot number, and various quality metrics. The dataset also includes attributes related to coffee processing, grading, and specific sensory attributes.

Usage

coffee_quality

Format

A data frame with 207 rows and 30 variables:

country_of_origin: character. The country where the coffee originated.
continent_of_origin: character. The continent where the coffee originated.
farm_name: character. The name of the farm where the coffee was grown.
lot_number: character. The lot number assigned to the batch of coffee.
mill: character. The name of the mill where the coffee was processed.
company: character. The company associated with the coffee batch.
altitude: character. The altitude range (in meters) where the coffee was grown.
region: character. The specific region within the country where the coffee was grown.
producer: character. The name of the coffee producer.
in_country_partner: character. The in-country partner organization associated with the coffee batch.
harvest_year: character. The year or range of years during which the coffee was harvested.
grading_date: date. The date when the coffee was graded.
owner: character. The owner of the coffee batch.
variety: character. The variety of the coffee plant.
processing_method: character. The method used to process the coffee beans.
aroma: numeric. The aroma score of the coffee, on a scale from 0 to 10.
flavor: numeric. The flavor score of the coffee, on a scale from 0 to 10.
aftertaste: numeric. The aftertaste score of the coffee, on a scale from 0 to 10.
acidity: numeric. The acidity score of the coffee, on a scale from 0 to 10.
body: numeric. The body score of the coffee, on a scale from 0 to 10.
balance: numeric. The balance score of the coffee, on a scale from 0 to 10.
uniformity: numeric. The uniformity score of the coffee, on a scale from 0 to 10.
clean_cup: numeric. The clean cup score of the coffee, on a scale from 0 to 10.
sweetness: numeric. The sweetness score of the coffee, on a scale from 0 to 10.
overall: numeric. The overall score of the coffee, on a scale from 0 to 10.
total_cup_points: numeric. The total cup points awarded to the coffee, representing the sum of various quality metrics.
moisture_percentage: numeric. The moisture percentage of the coffee beans.
color: character. The color description of the coffee beans.
expiration: character. The expiration date of the coffee batch.
certification_body: character. The body that certified the coffee batch.

Source

Coffee Quality Institute

Data from the Coffee Quality Institute's review pages in January 2018

Description

1,340 digitized reviews on coffee samples from https://database.coffeeinstitute.org/.

Usage

coffee_ratings

Format

A data frame of 1,340 rows representing each sample of coffee.

total_cup_points: Number of points in final rating (scale of 0-100)
species: Species of coffee bean plant (Arabica or Robusta)
owner: Owner of coffee plant farm
country_of_origin: Coffee bean's country of origin
farm_name: Name of coffee plant farm
lot_number: Lot number for tested coffee beans
mill: Name of coffee bean's processing facility
ico_number: International Coffee Organization number
company: Name of coffee bean's company
altitude: Altitude at which coffee plants were grown
region: Region where coffee plants were grown
producer: Name of coffee bean roaster
number_of_bags: Number of tested bags
bag_weight: Tested bag weight
in_country_partner: Partner for the country
harvest_year: Year the coffee beans were harvested
grading_date: Day the coffee beans were graded
owner_1: Owner of the coffee beans
variety: Variety of the coffee beans
processing_method: Method used for processing the coffee beans
aroma: Coffee aroma rating
flavor: Coffee flavor rating
aftertaste: Coffee aftertaste rating
acidity: Coffee acidity rating
body: Coffee body rating
balance: Coffee balance rating
uniformity: Coffee uniformity rating
clean_cup: Cup cleanliness rating
sweetness: Coffee sweetness rating
cupper_points: Cupper Points, an overall rating for the coffee
moisture: Coffee moisture content
category_one_defects: Number of category one defects for the coffee beans
quakers: Number of coffee beans that don't dark brown when roasted
color: Color of the coffee beans
category_two_defects: Number of category two defects for the coffee beans
expiration: Expiration date of the coffee beans
certification_body: Entity/Institute that certified the coffee beans
certification_address: Body address of certification for coffee beans
certification_contact: Certification contact for coffee beans
unit_of_measurement: Unit of measurement for altitude
altitude_low_meters: Lower altitude level coffee beans grow at
altitude_high_meters: Higher altitude level coffee beans grow at
altitude_mean_meters: Average altitude level coffee beans grow at

Source

Coffee Quality Institute. Access cleaned data available at https://github.com/jldbc/coffee-quality-database

Early January hourly weather data for 2023

Description

Hourly meteorological data for LGA, JFK and EWR for the month of January 2023. This is a subset of the weather data frame from nycflights23.

Usage

early_january_2023_weather

Format

A data frame of 360 rows representing hourly measurements and 15 variables

origin: Weather station. Named origin to facilitate merging with nycflights23::flights data.
year, month, day, hour: Time of recording.
temp, dewp: Temperature and dewpoint in F.
humid: Relative humidity.
wind_dir, wind_speed, wind_gust: Wind direction (in degrees), speed and gust speed (in mph).
precip: Precipitation, in inches.
pressure: Sea level pressure in millibars.
visib: Visibility in miles.
time_hour: Date and hour of the recording as a POSIXct date.

Source

ASOS download from Iowa Environmental Mesonet, https://mesonet.agron.iastate.edu/request/download.phtml.

Early January hourly weather data

Description

Hourly meteorological data for LGA, JFK and EWR for the month of January 2013. This is a subset of the weather data frame from nycflights13.

Usage

early_january_weather

Format

A data frame of 358 rows representing hourly measurements and 15 variables

origin: Weather station. Named origin to facilitate merging with nycflights13::flights data.
year, month, day, hour: Time of recording.
temp, dewp: Temperature and dewpoint in F.
humid: Relative humidity.
wind_dir, wind_speed, wind_gust: Wind direction (in degrees), speed and gust speed (in mph).
precip: Precipitation, in inches.
pressure: Sea level pressure in millibars.
visib: Visibility in miles.
time_hour: Date and hour of the recording as a POSIXct date.

Source

ASOS download from Iowa Environmental Mesonet, https://mesonet.agron.iastate.edu/request/download.phtml.

Envoy Air flights data for 2023

Description

On-time data for all Envoy Air flights that departed NYC (i.e. JFK, LGA or EWR) in 2023. This is a subset of the flights data frame from nycflights23.

Usage

envoy_flights

Format

A data frame of 357 rows representing Alaska Airlines flights and 19 variables

year, month, day: Date of departure.
dep_time, arr_time: Actual departure and arrival times (format HHMM or HMM), local tz.
sched_dep_time, sched_arr_time: Scheduled departure and arrival times (format HHMM or HMM), local tz.
dep_delay, arr_delay: Departure and arrival delays, in minutes. Negative times represent early departures/arrivals.
carrier: Two letter carrier abbreviation. See nycflights23::airlines to get name.
flight: Flight number.
tailnum: Plane tail number. See nycflights23::planes for additional metadata.
origin, dest: Origin and destination. See nycflights23::airports for additional metadata.
air_time: Amount of time spent in the air, in minutes.
distance: Distance between airports, in miles.
hour, minute: Time of scheduled departure broken into hour and minutes.
time_hour: Scheduled date and hour of the flight as a POSIXct date. Along with origin, can be used to join flights data to nycflights23::weather data.

Source

RITA, Bureau of transportation statistics

Electric vehicle charging sessions for a workplace charging program

Description

This dataset consists of information on 3,395 electric vehicle charging sessions across locations for a workplace charging program. The data contains information on multiple charging sessions from 85 electric vehicle drivers across 25 workplace locations, which are located at facilities of various types.

Usage

ev_charging

Format

A data frame of 3,395 rows on 24 variables, where each row is an electric vehicle charging session.

session_id

Unique identifier specifying the electric vehicle charging session

kwh_total

Total energy used at the charging session, in kilowatt hours (kWh)

dollars

Quantity of money paid for the charging session in U.S. dollars

created

Date and time recorded at the beginning of the charging session

ended

Date and time recorded at the end of the charging session

start_time

Hour of the day when the charging session began (1 through 24)

end_time

Hour of the day when the charging session ended (1 through 24)

charge_time_hrs

Length of the charging session in hours

weekday

First three characters of the name of the weekday when the charging session occurred

platform

Digital platform the driver used to record the session (android, ios, web)

distance

Distance from the charging location to the driver's home, expressed in miles NA if the driver did not report their address

user_id

Unique identifier for each driver

station_id

Unique identifier for each charging station

location_id

Unique identifier for each location owned by the company where charging stations were located

manager_vehicle

Binary variable that is 1 when the vehicle is a type commonly used by managers of the firm and 0 otherwise

facility_type

Categorical variable that represents the facility type:

1 = manufacturing
2 = office
3 = research and development
4 = other

mon, tues, wed, thurs, fri, sat, sun

Binary variables; 1 if the charging session took place on that day, 0 otherwise

reported_zip

Binary variable; 1 if the driver did report their zip code, 0 if they did not

Source

Harvard Dataverse doi:10.7910/DVN/NFPQLW. Note data is released under a CC0: Public Domain license.

Teaching evaluations at the UT Austin

Description

The data are gathered from end of semester student evaluations for a sample of 463 courses taught by 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 either the course or the professor https://www.openintro.org/data/index.php?data=evals

Usage

evals

Format

A data frame with 463 observations corresponding to courses on the following 13 variables.

ID: Identification variable for course.
prof_ID: Identification variable for professor. Many professors are included more than once in this dataset.
score: Average professor evaluation score: (1) very unsatisfactory - (5) excellent.
age: Age of professor.
bty_avg: Average beauty rating of professor.
gender: Gender of professor (collected as a binary variable at the time of the study): female, male.
ethnicity: Ethnicity of professor: not minority, minority.
language: Language of school where professor received education: English or non-English.
rank: Rank of professor: teaching, tenure track, tenured.
pic_outfit: Outfit of professor in picture: not formal, formal.
pic_color: Color of professor’s picture: color, black & white.
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.

Source

Çetinkaya-Rundel M, Morgan KL, Stangl D. 2013. Looking Good on Course Evaluations. CHANCE 26(2).

Regression model with one categorical explanatory/predictor variable

Description

geom_categorical_model() fits a regression model using the categorical x axis as the explanatory variable, and visualizes the model's fitted values as piece-wise horizontal line segments. Confidence interval bands can be included in the visualization of the model. Like geom_parallel_slopes(), this function has the same nature as geom_smooth() from the ggplot2 package, but provides functionality that geom_smooth() currently doesn't have. When using a categorical predictor variable, the intercept corresponds to the mean for the baseline group, while coefficients for the non-baseline groups are offsets from this baseline. Thus in the visualization the baseline for comparison group's median is marked with a solid line, whereas all offset groups' medians are marked with dashed lines.

Usage

geom_categorical_model(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  se = TRUE,
  level = 0.95,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

...

Other arguments passed on to layer()'s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through .... Unknown arguments that are not part of the 4 categories below are ignored.

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

se

Display confidence interval around model lines? TRUE by default.

level

Level of confidence interval to use (0.95 by default).

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

Examples

library(dplyr)
library(ggplot2)

p <- ggplot(mpg, aes(x = drv, y = hwy)) +
  geom_point() +
  geom_categorical_model()
p

# In the above visualization, the solid line corresponds to the mean of 19.2
# for the baseline group "4", whereas the dashed lines correspond to the
# means of 28.19 and 21.02 for the non-baseline groups "f" and "r" respectively.
# In the corresponding regression table however the coefficients for "f" and "r"
# are presented as offsets from the mean for "4":
model <- lm(hwy ~ drv, data = mpg)
get_regression_table(model)

# You can use different colors for each categorical level
p %+% aes(color = drv)

# But mapping the color aesthetic doesn't change the model that is fit
p %+% aes(color = class)

Parallel slopes regression model

Description

geom_parallel_slopes() fits parallel slopes model and adds its line output(s) to a ggplot object. Basically, it fits a unified model with intercepts varying between groups (which should be supplied as standard {ggplot2} grouping aesthetics: group, color, fill, etc.). This function has the same nature as geom_smooth() from {ggplot2} package, but provides functionality that geom_smooth() currently doesn't have.

Usage

geom_parallel_slopes(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  se = TRUE,
  formula = y ~ x,
  n = 100,
  fullrange = FALSE,
  level = 0.95,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

...

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

se

Display confidence interval around model lines? TRUE by default.

formula

Formula to use per group in parallel slopes model. Basic linear y ~ x by default.

n

Number of points per group at which to evaluate model.

fullrange

If TRUE, the smoothing line gets expanded to the range of the plot, potentially beyond the data. This does not extend the line into any additional padding created by expansion.

level

Level of confidence interval to use (0.95 by default).

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

inherit.aes

Examples

library(dplyr)
library(ggplot2)

ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes(se = FALSE)

# Basic usage
ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes()
ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes(se = FALSE)

# Supply custom aesthetics
ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes(se = FALSE, size = 4)

# Fit non-linear model
example_df <- house_prices %>%
  slice(1:1000) %>%
  mutate(
    log10_price = log10(price),
    log10_size = log10(sqft_living)
  )
ggplot(example_df, aes(x = log10_size, y = log10_price, color = condition)) +
  geom_point(alpha = 0.1) +
  geom_parallel_slopes(formula = y ~ poly(x, 2))

# Different grouping
ggplot(example_df, aes(x = log10_size, y = log10_price)) +
  geom_point(alpha = 0.1) +
  geom_parallel_slopes(aes(fill = condition))

Get correlation value in a tidy way

Description

Determine the Pearson correlation coefficient between two variables in a data frame using pipeable and formula-friendly syntax

Usage

get_correlation(data, formula, na.rm = FALSE, ...)

Arguments

data

a data frame object

formula

a formula with the response variable name on the left and the explanatory variable name on the right

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

...

further arguments passed to stats::cor()

Value

A 1x1 data frame storing the correlation value

Examples

library(moderndive)

# Compute correlation between mpg and cyl:
mtcars %>%
  get_correlation(formula = mpg ~ cyl)

# Group by one variable:
library(dplyr)
mtcars %>%
  group_by(am) %>%
  get_correlation(formula = mpg ~ cyl)

# Group by two variables:
mtcars %>%
  group_by(am, gear) %>%
  get_correlation(formula = mpg ~ cyl)

Get regression points

Description

Output information on each point/observation used in an lm() regression in "tidy" format. This function is a wrapper function for broom::augment() and renames the variables to have more intuitive names.

Usage

get_regression_points(
  model,
  digits = 3,
  print = FALSE,
  newdata = NULL,
  ID = NULL
)

Arguments

model

an lm() model object

digits

number of digits precision in output table

print

If TRUE, return in print format suitable for R Markdown

newdata

A new data frame of points/observations to apply model to obtain new fitted values and/or predicted values y-hat. Note the format of newdata must match the format of the original data used to fit model.

ID

A string indicating which variable in either the original data used to fit model or newdata should be used as an identification variable to distinguish the observational units in each row. This variable will be the left-most variable in the output data frame. If ID is unspecified, a column ID with values 1 through the number of rows is returned as the identification variable.

Value

A tibble-formatted regression table of outcome/response variable, all explanatory/predictor variables, the fitted/predicted value, and residual.

Examples

library(dplyr)
library(tibble)

# Convert rownames to column
mtcars <- mtcars %>%
  rownames_to_column(var = "automobile")

# Fit lm() regression:
mpg_model <- lm(mpg ~ cyl, data = mtcars)

# Get information on all points in regression:
get_regression_points(mpg_model, ID = "automobile")

# Create training and test set based on mtcars:
training_set <- mtcars %>%
  sample_frac(0.5)
test_set <- mtcars %>%
  anti_join(training_set, by = "automobile")

# Fit model to training set:
mpg_model_train <- lm(mpg ~ cyl, data = training_set)

# Make predictions on test set:
get_regression_points(mpg_model_train, newdata = test_set, ID = "automobile")

Get regression summary values

Description

Output scalar summary statistics for an lm() regression in "tidy" format. This function is a wrapper function for broom::glance().

Usage

get_regression_summaries(model, digits = 3, print = FALSE)

Arguments

model

an lm() model object

digits

number of digits precision in output table

print

If TRUE, return in print format suitable for R Markdown

Value

A single-row tibble with regression summaries. Ex: r_squared and mse.

Examples

library(moderndive)

# Fit lm() regression:
mpg_model <- lm(mpg ~ cyl, data = mtcars)

# Get regression summaries:
get_regression_summaries(mpg_model)

Get regression table

Description

Output regression table for an lm() regression in "tidy" format. This function is a wrapper function for broom::tidy() and includes confidence intervals in the output table by default.

Usage

get_regression_table(
  model,
  conf.level = 0.95,
  digits = 3,
  print = FALSE,
  default_categorical_levels = FALSE
)

Arguments

model

an lm() model object

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

digits

number of digits precision in output table

print

If TRUE, return in print format suitable for R Markdown

default_categorical_levels

If TRUE, do not change the non-baseline categorical variables in the term column. Otherwise non-baseline categorical variables will be displayed in the format "categorical_variable_name: level_name"

Value

A tibble-formatted regression table along with lower and upper end points of all confidence intervals for all parameters lower_ci and upper_ci; the confidence levels default to 95\

Examples

library(moderndive)

# Fit lm() regression:
mpg_model <- lm(mpg ~ cyl, data = mtcars)

# Get regression table:
get_regression_table(mpg_model)

# Vary confidence level of confidence intervals
get_regression_table(mpg_model, conf.level = 0.99)

Plot parallel slopes model

Description

NOTE: This function is deprecated; please use geom_parallel_slopes() instead. Output a visualization of linear regression when you have one numerical and one categorical explanatory/predictor variable: a separate colored regression line for each level of the categorical variable

Usage

gg_parallel_slopes(y, num_x, cat_x, data, alpha = 1)

Arguments

y

Character string of outcome variable in data

num_x

Character string of numerical explanatory/predictor variable in data

cat_x

Character string of categorical explanatory/predictor variable in data

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.

alpha

Transparency of points

Value

A ggplot2::ggplot() object.

Examples

## Not run: 
library(ggplot2)
library(dplyr)
library(moderndive)

# log10() transformations
house_prices <- house_prices %>%
  mutate(
    log10_price = log10(price),
    log10_size = log10(sqft_living)
  )

# Output parallel slopes model plot:
gg_parallel_slopes(
  y = "log10_price", num_x = "log10_size", cat_x = "condition",
  data = house_prices, alpha = 0.1
) +
  labs(
    x = "log10 square feet living space", y = "log10 price in USD",
    title = "House prices in Seattle: Parallel slopes model"
  )

# Compare with interaction model plot:
ggplot(house_prices, aes(x = log10_size, y = log10_price, col = condition)) +
  geom_point(alpha = 0.1) +
  geom_smooth(method = "lm", se = FALSE, size = 1) +
  labs(
    x = "log10 square feet living space", y = "log10 price in USD",
    title = "House prices in Seattle: Interaction model"
  )

## End(Not run)

House Sales in King County, USA

Description

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. This dataset was obtained from Kaggle.com https://www.kaggle.com/harlfoxem/housesalesprediction/data

Usage

house_prices

Format

A data frame with 21613 observations on the following 21 variables.

id: a notation for a house
date: Date house was sold
price: Price is prediction target
bedrooms: Number of Bedrooms/House
bathrooms: Number of bathrooms/bedrooms
sqft_living: square footage of the home
sqft_lot: square footage of the lot
floors: Total floors (levels) in house
waterfront: House which has a view to a waterfront
view: Has been viewed
condition: How good the condition is (Overall)
grade: overall grade given to the housing unit, based on King County grading system
sqft_above: square footage of house apart from basement
sqft_basement: square footage of the basement
yr_built: Built Year
yr_renovated: Year when house was renovated
zipcode: zip code
lat: Latitude coordinate
long: Longitude coordinate
sqft_living15: Living room area in 2015 (implies– some renovations) This might or might not have affected the lotsize area
sqft_lot15: lotSize area in 2015 (implies– some renovations)

Source

Kaggle https://www.kaggle.com/harlfoxem/housesalesprediction. Note data is released under a CC0: Public Domain license.

International Power Lifting Results A subset of international powerlifting results.

Description

International Power Lifting Results A subset of international powerlifting results.

Usage

ipf_lifts

Format

A data frame with 41,152 entries, one entry for individual lifter

name: Individual lifter name
sex: Binary sex (M/F)
event: The type of competition that the lifter entered
equipment: The equipment category under which the lifts were performed
age: The age of the lifter on the start date of the meet
age_class: The age class in which the filter falls
division: division of competition
bodyweight_kg: The recorded bodyweight of the lifter at the time of competition, to two decimal places
weight_class_kg: The weight class in which the lifter competed, to two decimal places
best3squat_kg: Maximum of the first three successful attempts for the lift
best3bench_kg: Maximum of the first three successful attempts for the lift
best3deadlift_kg: Maximum of the first three successful attempts for the lift
place: The recorded place of the lifter in the given division at the end of the meet
date: Date of the event
federation: The federation that hosted the meet
meet_name: The name of the meet

Source

This data is a subset of the open dataset Open Powerlifting

Massachusetts 2020 vs. 2019 Traffic Data Comparison

Description

This dataset contains information about changes in speed, volume, and accidents of traffic between 2020 and 2019 by community and class of road in Massachusetts.

Usage

ma_traffic_2020_vs_2019

Format

A data frame of 264 rows each representing a different community in Massachusetts.

community: City or Town
functional_class: Class or group the road belongs to
change_in_speed: Average estimated Speed (mph)
change_in_volume: Average traffic
change_in_accidents: Average number of accidents

Source

https://massdot-impact-crashes-vhb.opendata.arcgis.com/datasets/MassDOT::2020-vehicle-level-crash-details/explore https://mhd.public.ms2soft.com/tcds/tsearch.asp?loc=Mhd&mod=

Data from Mario Kart Ebay auctions

Description

Ebay auction data for the Nintendo Wii game Mario Kart.

Usage

mario_kart_auction

Format

A data frame of 143 auctions.

id: Auction ID assigned by Ebay
duration: Auction length in days
n_bids: Number of bids
cond: Game condition, either new or used
start_pr: Price at the start of the auction
ship_pr: Shipping price
total_pr: Total price, equal to auction price plus shipping price
ship_sp: Shipping speed or method
seller_rate: Seller's rating on Ebay, equal to the number of positive ratings minus the number of negative ratings
stock_photo: Whether the auction photo was a stock photo or not, pictures used in many options were considered stock photos
wheels: Number of Wii wheels included in the auction
title: The title of the auctions

Source

This data is from https://www.openintro.org/data/index.php?data=mariokart

2020 road traffic volume and crash level date for 13 Massachusetts counties

Description

2020 road traffic volume and crash level date for 13 Massachusetts counties

Usage

mass_traffic_2020

Format

A data frame of 874 rows representing traffic data at the 874 sites

site_id: Site id
county: County in which the site is located
community: Community in which the site is located
rural_urban: Rural (R) or Urban (U)
dir: Direction for traffic movement. Either 1-WAY, 2-WAY, EB (eastbound), RAMP or WB (westbound)
functional_class: Classification of road. Either Arterial, Collector, Freeway & Expressway, Interstate or Local Road
avg_speed: Average traffic speed
total_volume: Number of vehicles recorded at each site in 2020
crashes: Number of vehicle crashes at each site
nonfatal_injuries: Number of non-fatal injuries for all recorded vehicle crashes
fatal_injuries: Number of fatal injuries for all recorded vehicle crashes

Random sample of 68 action and romance movies

Description

A random sample of 32 action movies and 36 romance movies from https://www.imdb.com/ and their ratings.

Usage

movies_sample

Format

A data frame of 68 rows movies.

title: Movie title
year: Year released
rating: IMDb rating out of 10 stars
genre: Action or Romance

Data from Mythbusters' study on contagiousness of yawning

Description

From a study on whether yawning is contagious https://www.imdb.com/title/tt0768479/. The data here was derived from the final proportions of yawns given in the show.

Usage

mythbusters_yawn

Format

A data frame of 50 rows representing each of the 50 participants in the study.

subj: integer value corresponding to identifier variable of subject ID
group: string of either "seed", participant was shown a yawner, or "control", participant was not shown a yawner
yawn: string of either "yes", the participant yawned, or "no", the participant did not yawn

Old Faithful Eruptions Dataset (2024)

Description

This dataset contains records of eruptions from the Old Faithful geyser in Yellowstone National Park, recorded in 2024. It includes details such as the eruption ID, date and time of eruption, waiting time between eruptions, webcam availability, and the duration of each eruption.

Usage

old_faithful_2024

Format

A data frame with 114 rows and 6 variables:

eruption_id: numeric. A unique identifier for each eruption.
date: date. The date of the eruption.
time: numeric. The time of the eruption in HHMM format (e.g., 538 corresponds to 5:38 AM, 1541 corresponds to 3:41 PM).
waiting: numeric. The waiting time in minutes until the next eruption.
webcam: character. Indicates whether the eruption was captured on webcam ("Yes" or "No").
duration: numeric. The duration of the eruption in seconds.

Source

Volunteer information from https://geysertimes.org/retrieve.php

A random sample of 40 pennies sampled from the `pennies` data frame

Description

A dataset of 40 pennies to be treated as a random sample with pennies() acting as the population. Data on these pennies were recorded in 2011.

Usage

orig_pennies_sample

Format

A data frame of 40 rows representing 40 randomly sampled pennies from pennies() and 2 variables

year: Year of minting
age_in_2011: Age in 2011

Source

StatCrunch https://www.statcrunch.com:443/app/index.html?dataid=301596

A population of 800 pennies sampled in 2011

Description

A dataset of 800 pennies to be treated as a sampling population. Data on these pennies were recorded in 2011.

Usage

pennies

Format

A data frame of 800 rows representing different pennies and 2 variables

year: Year of minting
age_in_2011: Age in 2011

Source

StatCrunch https://www.statcrunch.com:443/app/index.html?dataid=301596

Bootstrap resamples of a sample of 50 pennies

Description

35 bootstrap resamples with replacement of sample of 50 pennies contained in a 50 cent roll from Florence Bank on Friday February 1, 2019 in downtown Northampton, Massachusetts, USA https://goo.gl/maps/AF88fpvVfm12. The original sample of 50 pennies is available in pennies_sample() .

Usage

pennies_resamples

Format

A data frame of 1750 rows representing 35 students' bootstrap resamples of size 50 and 3 variables

replicate: ID variable of replicate/resample number.
name: Name of student
year: Year on resampled penny

A sample of 50 pennies

Description

A sample of 50 pennies contained in a 50 cent roll from Florence Bank on Friday February 1, 2019 in downtown Northampton, Massachusetts, USA https://goo.gl/maps/AF88fpvVfm12.

Usage

pennies_sample

Format

A data frame of 50 rows representing 50 sampled pennies and 2 variables

ID: Variable used to uniquely identify each penny.
year: Year of minting.

Note

The original pennies_sample has been renamed orig_pennies_sample() as of moderndive v0.3.0.

Calculate Population Standard Deviation

Description

This function calculates the population standard deviation for a numeric vector.

Usage

pop_sd(x)

Arguments

x

A numeric vector for which the population standard deviation should be calculated.

Value

A numeric value representing the population standard deviation of the vector.

Examples

# Example usage:
library(dplyr)
df <- data.frame(weight = c(2, 4, 6, 8, 10))
df |> 
  summarize(population_mean = mean(weight), 
            population_sd = pop_sd(weight))

Bank manager recommendations based on (binary) gender

Description

Data from a 1970's study on whether gender influences hiring recommendations. Originally used in OpenIntro.org.

Usage

promotions

Format

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

id: Identification variable used to distinguish rows.
gender: gender (collected as a binary variable at the time of the study): a factor with two levels male and female
decision: a factor with two levels: promoted and not

Source

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

One permutation/shuffle of promotions

Description

Shuffled/permuted data from a 1970's study on whether gender influences hiring recommendations.

Usage

promotions_shuffled

Format

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

id: Identification variable used to distinguish rows.
gender: shuffled/permuted (binary) gender: a factor with two levels male and female
decision: a factor with two levels: promoted and not

Objects exported from other packages

Description

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

infer: rep_sample_n, rep_slice_sample

House Prices and Properties in Saratoga, New York

Description

Random sample of 1057 houses taken from full Saratoga Housing Data (De Veaux)

Usage

saratoga_houses

Format

A data frame with 1057 observations on the following 8 variables

price: price (US dollars)
living_area: Living Area (square feet)
bathrooms: Number of Bathroom (half bathrooms have no shower or tub)
bedrooms: Number of Bedrooms
fireplaces: Number of Fireplaces
lot_size: Size of Lot (acres)
age: Age of House (years)
fireplace: Whether the house has a Fireplace

Source

Gathered from https://docs.google.com/spreadsheets/d/1AY5eECqNIggKpYF3kYzJQBIuuOdkiclFhbjAmY3Yc8E/edit#gid=622599674

Spotify 52-Track Sample Dataset

Description

This dataset contains a sample of 52 tracks from Spotify, focusing on two genres: deep-house and metal. It includes metadata about the tracks, the artists, and an indicator of whether each track is considered popular. This dataset is useful for comparative analysis between genres and for studying the characteristics of popular versus non-popular tracks within these genres.

Usage

spotify_52_original

Format

A data frame with 52 rows and 6 columns:

track_id: character. Spotify ID for the track. See: https://developer.spotify.com/documentation/web-api/
track_genre: character. Genre of the track, either "deep-house" or "metal".
artists: character. Names of the artists associated with the track.
track_name: character. Name of the track.
popularity: numeric. Popularity score of the track (0-100). See: https://developer.spotify.com/documentation/web-api/reference/#/operations/get-track
popular_or_not: character. Indicates whether the track is considered popular ("popular") or not ("not popular"). Popularity is defined as a score of 50 or higher which corresponds to the 75th percentile of the popularity column.

Source

https://developer.spotify.com/documentation/web-api/

Examples

data(spotify_52_original)
head(spotify_52_original)

Spotify 52-Track Sample Dataset with 'popular or not' shuffled

Description

This dataset contains a sample of 52 tracks from Spotify, focusing on two genres: deep-house and metal. It includes metadata about the tracks, the artists, and a shuffled indicator of whether each track is considered popular.

Usage

spotify_52_shuffled

Format

A data frame with 52 rows and 6 columns:

track_id: character. Spotify ID for the track. See: https://developer.spotify.com/documentation/web-api/
track_genre: character. Genre of the track, either "deep-house" or "metal".
artists: character. Names of the artists associated with the track.
track_name: character. Name of the track.
popularity: numeric. Popularity score of the track (0-100). See: https://developer.spotify.com/documentation/web-api/reference/#/operations/get-track
popular_or_not: character. A shuffled version of the column of the same name in the spotify_52_original data frame.

Source

https://developer.spotify.com/documentation/web-api/

Examples

data(spotify_52_shuffled)
head(spotify_52_shuffled)

Spotify by Genre Dataset

Description

This dataset contains information on 6,000 tracks from Spotify, categorized by one of six genres. It includes various audio features, metadata about the tracks, and an indicator of popularity. The dataset is useful for analysis of music trends, popularity prediction, and genre-specific characteristics.

Usage

spotify_by_genre

Format

A data frame with 6,000 rows and 21 columns:

track_id: character. Spotify ID for the track. See: https://developer.spotify.com/documentation/web-api/
artists: character. Names of the artists associated with the track.
album_name: character. Name of the album on which the track appears.
track_name: character. Name of the track.
popularity: numeric. Popularity score of the track (0-100). See: https://developer.spotify.com/documentation/web-api/reference/#/operations/get-track
duration_ms: numeric. Duration of the track in milliseconds.
explicit: logical. Whether the track has explicit content.
danceability: numeric. Danceability score of the track (0-1). See: https://developer.spotify.com/documentation/web-api/reference/#/operations/get-audio-features
energy: numeric. Energy score of the track (0-1).
key: numeric. The key the track is in (0-11 where 0 = C, 1 = C#/Db, etc.).
loudness: numeric. The loudness of the track in decibels (dB).
mode: numeric. Modality of the track (0 = minor, 1 = major).
speechiness: numeric. Speechiness score of the track (0-1).
acousticness: numeric. Acousticness score of the track (0-1).
instrumentalness: numeric. Instrumentalness score of the track (0-1).
liveness: numeric. Liveness score of the track (0-1).
valence: numeric. Valence score of the track (0-1), indicating the musical positiveness.
tempo: numeric. Tempo of the track in beats per minute (BPM).
time_signature: numeric. Time signature of the track (typically 3, 4, or 5).
track_genre: character. Genre of the track (country, deep-house, dubstep, hip-hop, metal, and rock).
popular_or_not: character. Indicates whether the track is considered popular ("popular") or not ("not popular"). Popularity is defined as a score of 50 or higher which corresponds to the 75th percentile of the popularity column.

Source

https://developer.spotify.com/documentation/web-api/

Examples

data(spotify_by_genre)
head(spotify_by_genre)

Tactile sampling from a tub of balls

Description

Counting the number of red balls in 33 tactile samples of size n = 50 balls from https://github.com/moderndive/moderndive/blob/master/data-raw/sampling_bowl.jpeg

Usage

tactile_prop_red

Format

A data frame of 33 rows representing different groups of students' samples of size n = 50 and 4 variables

group: Group members
replicate: Replicate number
red_balls: Number of red balls sampled out of 50
prop_red: Proportion red balls out of 50

This function calculates the five-number summary (minimum, first quartile, median, third quartile, maximum) for specified numeric columns in a data frame and returns the results in a long format. It also handles categorical, factor, and logical columns by counting the occurrences of each level or value, and includes the results in the summary. The `type` column indicates whether the data is numeric, character, factor, or logical.

Description

This function calculates the five-number summary (minimum, first quartile, median, third quartile, maximum) for specified numeric columns in a data frame and returns the results in a long format. It also handles categorical, factor, and logical columns by counting the occurrences of each level or value, and includes the results in the summary. The type column indicates whether the data is numeric, character, factor, or logical.

Usage

tidy_summary(df, columns = names(df), ...)

Arguments

df

A data frame containing the data. The data frame must have at least one row.

columns

Unquoted column names or tidyselect helpers specifying the columns for which to calculate the summary. Defaults to call columns in the inputted data frame.

...

Additional arguments passed to the min, quantile, median, and max functions, such as na.rm.

Value

A tibble in long format with columns:

column: The name of the column.
n: The number of non-missing values in the column for numeric variables and the number of non-missing values in the group for categorical, factor, and logical columns.
group: The group level or value for categorical, factor, and logical columns.
type: The type of data in the column (numeric, character, factor, or logical).
min: The minimum value (for numeric columns).
Q1: The first quartile (for numeric columns).
mean: The mean value (for numeric columns).
median: The median value (for numeric columns).
Q3: The third quartile (for numeric columns).
max: The maximum value (for numeric columns).
sd: The standard deviation (for numeric columns).

Examples

# Example usage with a simple data frame
df <- tibble::tibble(
  category = factor(c("A", "B", "A", "C")),
  int_values = c(10, 15, 7, 8),
  num_values = c(8.2, 0.3, -2.1, 5.5),
  one_missing_value = c(NA, 1, 2, 3),
  flag = c(TRUE, FALSE, TRUE, TRUE)
)

# Specify columns
tidy_summary(df, columns = c(category, int_values, num_values, flag))

# Defaults to full data frame (note an error will be given without
# specifying `na.rm = TRUE` since `one_missing_value` has an `NA`)
tidy_summary(df, na.rm = TRUE)

# Example with additional arguments for quantile functions
tidy_summary(df, columns = c(one_missing_value), na.rm = TRUE)

UN Member States 2024 Dataset

Description

This dataset contains information on 193 United Nations member states as of 2024. It includes various attributes such as country names, ISO codes, official state names, geographic and demographic data, economic indicators, and participation in the Olympic Games. The data is designed for use in statistical analysis, data visualization, and educational purposes.

Usage

un_member_states_2024

Format

A data frame with 193 rows and 39 columns:

country: character. Name of the country.
iso: character. ISO 3166-1 alpha-3 country code. See: https://en.wikipedia.org/wiki/ISO_3166-1_alpha-3
official_state_name: character. Official name of the country. See: https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_and_their_capitals_in_native_languages
continent: factor. Continent where the country is located. See: https://en.wikipedia.org/wiki/Continent
region: character. Specific region within the continent.
capital_city: character. Name of the capital city. See: https://en.wikipedia.org/wiki/List_of_national_capitals_by_population
capital_population: numeric. Population of the capital city.
capital_perc_of_country: numeric. Percentage of the country’s population living in the capital.
capital_data_year: integer. Year the capital population data was collected.
gdp_per_capita: numeric. GDP per capita in USD. See: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD
gdp_per_capita_year: numeric. Year the GDP per capita data was collected.
summers_competed_in: numeric. Number of times the country has competed in the Summer Olympics
summer_golds: integer. Number of gold medals won in the Summer Olympics.
summer_silvers: integer. Number of silver medals won in the Summer Olympics.
summer_bronzes: integer. Number of bronze medals won in the Summer Olympics.
summer_total: integer. Total number of medals won in the Summer Olympics.
winters_competed_in: integer. Number of times the country has competed in the Winter Olympics
winter_golds: integer. Number of gold medals won in the Winter Olympics.
winter_silvers: integer. Number of silver medals won in the Winter Olympics.
winter_bronzes: integer. Number of bronze medals won in the Winter Olympics.
winter_total: integer. Total number of medals won in the Winter Olympics.
combined_competed_ins: integer. Total number of times the country has competed in both Summer and Winter Olympics. See: https://en.wikipedia.org/wiki/All-time_Olympic_Games_medal_table
combined_golds: integer. Total number of gold medals won in both Summer and Winter Olympics.
combined_silvers: integer. Total number of silver medals won in both Summer and Winter Olympics.
combined_bronzes: integer. Total number of bronze medals won in both Summer and Winter Olympics.
combined_total: integer. Total number of medals won in both Summer and Winter Olympics.
driving_side: character. Indicates whether the country drives on the left or right side of the road. See: https://en.wikipedia.org/wiki/Left-_and_right-hand_traffic
obesity_rate_2024: numeric. Percentage of the population classified as obese in 2024. See: https://en.wikipedia.org/wiki/List_of_countries_by_obesity_rate
obesity_rate_2016: numeric. Percentage of the population classified as obese in 2016.
has_nuclear_weapons_2024: logical. Indicates whether the country has nuclear weapons as of 2024. See: https://en.wikipedia.org/wiki/List_of_states_with_nuclear_weapons
population_2024: numeric. Population of the country in 2024. See: https://data.worldbank.org/indicator/SP.POP.TOTL
area_in_square_km: numeric. Area of the country in square kilometers. See: https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area
area_in_square_miles: numeric. Area of the country in square miles.
population_density_in_square_km: numeric. Population density in square kilometers.
population_density_in_square_miles: numeric. Population density in square miles.
income_group_2024: factor. World Bank income group classification in 2024. See: https://data.worldbank.org/indicator/NY.GNP.PCAP.CD
life_expectancy_2022: numeric. Life expectancy at birth in 2022. See: https://en.wikipedia.org/wiki/List_of_countries_by_life_expectancy
fertility_rate_2022: numeric. Fertility rate in 2022 (average number of children per woman). See: https://en.wikipedia.org/wiki/List_of_countries_by_total_fertility_rate
hdi_2022: numeric. Human Development Index in 2022. See: https://en.wikipedia.org/wiki/List_of_countries_by_Human_Development_Index

Examples

data(un_member_states_2024)
head(un_member_states_2024)

moderndive - Tidyverse-Friendly Introductory Linear Regression

Description

Author(s)

See Also

Examples

Pipe operator

Description

Usage

Dunkin Donuts vs Starbucks

Description

Usage

Format

Source

Massachusetts Public High Schools Data

Description

Usage

Format

Source

Alaska flights data

Description

Usage

Format

Source

See Also

Chocolate-covered almonds data

Description

Usage

Format

Chocolate-covered almonds data sample of size 25

Description

Usage

Format

Chocolate-covered almonds data sample of size 100

Description

Usage

Format

Sample of Amazon books

Description

Usage

Format

Source

Avocado Prices by US Region

Description

Usage

Format

Data on maternal smoking and infant health

Description

Usage

Format

Source

A sampling bowl of red and white balls

Description

Usage

Format

Tactile sample of size 50 from a bowl of balls

Description

Usage

Format

See Also

Sampling from a bowl of balls

Description

Usage

Format

See Also

Coffee Quality Dataset

Description

Usage

Format

Source

Data from the Coffee Quality Institute's review pages in January 2018

Description

Usage

Format

Source

Early January hourly weather data for 2023

Description

Usage

Format

Source

See Also