R/datasets.R

In moderndive/moderndive: Tidyverse-Friendly Introductory Linear Regression

#' Early January hourly weather data
#'
#' Hourly meterological data for LGA, JFK and EWR for the month of January 2013. 
#' This is a subset of the `weather` data frame from `nycflights13`.
#'
#' @format A data frame of 358 rows representing hourly measurements and 15 variables
#' \describe{
#' \item{origin}{Weather station. Named `origin` to facilitate merging with
#'   [`nycflights13::flights`] data.}
#' \item{year, month, day, hour}{Time of recording.}
#' \item{temp, dewp}{Temperature and dewpoint in F.}
#' \item{humid}{Relative humidity.}
#' \item{wind_dir, wind_speed, wind_gust}{Wind direction (in degrees), speed
#'   and gust speed (in mph).}
#' \item{precip}{Precipitation, in inches.}
#' \item{pressure}{Sea level pressure in millibars.}
#' \item{visib}{Visibility in miles.}
#' \item{time_hour}{Date and hour of the recording as a `POSIXct` date.}
#' }
#' @seealso [`nycflights13::weather`].
#' @source ASOS download from Iowa Environmental Mesonet,
#'   <https://mesonet.agron.iastate.edu/request/download.phtml>.
"early_january_weather"

#' Early January hourly weather data for 2023
#'
#' Hourly meterological data for LGA, JFK and EWR for the month of January 2023. 
#' This is a subset of the `weather` data frame from `nycflights23`.
#'
#' @format A data frame of 360 rows representing hourly measurements and 15 variables
#' \describe{
#' \item{origin}{Weather station. Named `origin` to facilitate merging with
#'   [`nycflights23::flights`] data.}
#' \item{year, month, day, hour}{Time of recording.}
#' \item{temp, dewp}{Temperature and dewpoint in F.}
#' \item{humid}{Relative humidity.}
#' \item{wind_dir, wind_speed, wind_gust}{Wind direction (in degrees), speed
#'   and gust speed (in mph).}
#' \item{precip}{Precipitation, in inches.}
#' \item{pressure}{Sea level pressure in millibars.}
#' \item{visib}{Visibility in miles.}
#' \item{time_hour}{Date and hour of the recording as a `POSIXct` date.}
#' }
#' @seealso [`nycflights23::weather`].
#' @source ASOS download from Iowa Environmental Mesonet,
#'   <https://mesonet.agron.iastate.edu/request/download.phtml>.
"early_january_weather2023"


#' Alaska flights data
#'
#' 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`.
#'
#' @format A data frame of 714 rows representing Alaska Airlines flights and 19 variables
#' \describe{
#' \item{year, month, day}{Date of departure.}
#' \item{dep_time, arr_time}{Actual departure and arrival times (format HHMM or HMM), local tz.}
#' \item{sched_dep_time, sched_arr_time}{Scheduled departure and arrival times (format HHMM or HMM), local tz.}
#' \item{dep_delay, arr_delay}{Departure and arrival delays, in minutes.
#'   Negative times represent early departures/arrivals.}
#' \item{carrier}{Two letter carrier abbreviation. See [`nycflights13::airlines`]
#'   to get name.}
#' \item{flight}{Flight number.}
#' \item{tailnum}{Plane tail number. See [`nycflights13::planes`] for additional metadata.}
#' \item{origin, dest}{Origin and destination. See [`nycflights13::airports`] for
#'   additional metadata.}
#' \item{air_time}{Amount of time spent in the air, in minutes.}
#' \item{distance}{Distance between airports, in miles.}
#' \item{hour, minute}{Time of scheduled departure broken into hour and minutes.}
#' \item{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.}
#' }
#' @seealso [`nycflights13::flights`].
#' @source RITA, Bureau of transportation statistics
"alaska_flights"

#' Alaska flights data for 2023
#'
#' On-time data for all Alaska Airlines flights that departed NYC (i.e. JFK, LGA or EWR) 
#' in 2023. This is a subset of the `flights` data frame from `nycflights23`.
#'
#' @format A data frame of 7843 rows representing Alaska Airlines flights and 19 variables
#' \describe{
#' \item{year, month, day}{Date of departure.}
#' \item{dep_time, arr_time}{Actual departure and arrival times (format HHMM or HMM), local tz.}
#' \item{sched_dep_time, sched_arr_time}{Scheduled departure and arrival times (format HHMM or HMM), local tz.}
#' \item{dep_delay, arr_delay}{Departure and arrival delays, in minutes.
#'   Negative times represent early departures/arrivals.}
#' \item{carrier}{Two letter carrier abbreviation. See [`nycflights13::airlines`]
#'   to get name.}
#' \item{flight}{Flight number.}
#' \item{tailnum}{Plane tail number. See [`nycflights13::planes`] for additional metadata.}
#' \item{origin, dest}{Origin and destination. See [`nycflights13::airports`] for
#'   additional metadata.}
#' \item{air_time}{Amount of time spent in the air, in minutes.}
#' \item{distance}{Distance between airports, in miles.}
#' \item{hour, minute}{Time of scheduled departure broken into hour and minutes.}
#' \item{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.}
#' }
#' @seealso [`nycflights23::flights`].
#' @source RITA, Bureau of transportation statistics
"alaska_flights2023"


#' A sample of 50 pennies
#'
#' 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>.
#'
#' @format A data frame of 50 rows representing 50 sampled pennies and 2 variables
#' \describe{
#'   \item{ID}{Variable used to uniquely identify each penny.}
#'   \item{year}{Year of minting.}
#' }
#' @note The original `pennies_sample` has been renamed [orig_pennies_sample()]
#' as of `moderndive` v0.3.0.
"pennies_sample"



#' Bootstrap resamples of a sample of 50 pennies
#'
#' 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()] .
#'
#' @format A data frame of 1750 rows representing 35 students' bootstrap
#' resamples of size 50 and 3 variables
#' \describe{
#'   \item{replicate}{ID variable of replicate/resample number.}
#'   \item{name}{Name of student}
#'   \item{year}{Year on resampled penny}
#' }
#' @seealso [pennies_sample()]
"pennies_resamples"



#' A population of 800 pennies sampled in 2011
#'
#' A dataset of 800 pennies to be treated as a sampling population. Data on
#' these pennies were recorded in 2011.
#'
#' @format A data frame of 800 rows representing different pennies and 2 variables
#' \describe{
#'   \item{year}{Year of minting}
#'   \item{age_in_2011}{Age in 2011}
#' }
#' @source StatCrunch <https://www.statcrunch.com:443/app/index.html?dataid=301596>
"pennies"



#' A random sample of 40 pennies sampled from the `pennies` data frame
#'
#' 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.
#'
#' @format A data frame of 40 rows representing 40 randomly sampled pennies from [pennies()] and 2 variables
#' \describe{
#'   \item{year}{Year of minting}
#'   \item{age_in_2011}{Age in 2011}
#' }
#' @source StatCrunch <https://www.statcrunch.com:443/app/index.html?dataid=301596>
#' @seealso [pennies()]
"orig_pennies_sample"






#' A sampling bowl of red and white balls
#'
#' 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>.
#'
#' @format A data frame 2400 rows representing different balls in the bowl, of which
#' 900 are red and 1500 are white.
#' \describe{
#'   \item{ball_ID}{ID variable used to denote all balls. Note this value is not
#'   marked on the balls themselves}
#'   \item{color}{color of ball: red or white}
#' }
"bowl"



#' Sampling from a bowl of balls
#'
#' 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>
#'
#' @format A data frame 10 rows representing different groups of students'
#' samples of size n = 50 and 5 variables
#' \describe{
#'   \item{group}{Group name}
#'   \item{red}{Number of red balls sampled}
#'   \item{white}{Number of white balls sampled}
#'   \item{green}{Number of green balls sampled}
#'   \item{n}{Total number of balls samples}
#' }
#' @seealso [bowl()]
"bowl_samples"



#' Tactile sampling from a tub of balls
#'
#' 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>
#'
#' @format A data frame of 33 rows representing different groups of students'
#' samples of size n = 50 and 4 variables
#' \describe{
#'   \item{group}{Group members}
#'   \item{replicate}{Replicate number}
#'   \item{red_balls}{Number of red balls sampled out of 50}
#'   \item{prop_red}{Proportion red balls out of 50}
#' }
#' @seealso [bowl()]
"tactile_prop_red"



#' Tactile sample of size 50 from a bowl of balls
#'
#' A single tactile sample of size n = 50 balls from
#' <https://github.com/moderndive/moderndive/blob/master/data-raw/sampling_bowl.jpeg>
#'
#' @format A data frame of 50 rows representing different balls and 1 variable.
#' \describe{
#'   \item{color}{Color of ball sampled}
#' }
#' @seealso [bowl()]
"bowl_sample_1"




#' Bank manager recommendations based on (binary) gender
#'
#' Data from a 1970's study on whether gender influences hiring recommendations.
#' Originally used in OpenIntro.org.
#'
#' @format A data frame with 48 observations on the following 3 variables.
#' \describe{
#'   \item{id}{Identification variable used to distinguish rows.}
#'   \item{gender}{gender (collected as a binary variable at the time of the study): a factor with two levels `male` and `female`}
#'   \item{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.
#' @seealso The data in `promotions` is a slight modification of [openintro::gender_discrimination()].
"promotions"


#' One permutation/shuffle of promotions
#'
#' Shuffled/permuted data from a 1970's study on whether gender influences hiring recommendations.
#'
#' @format A data frame with 48 observations on the following 3 variables.
#' \describe{
#'   \item{id}{Identification variable used to distinguish rows.}
#'   \item{gender}{shuffled/permuted (binary) gender: a factor with two levels `male` and `female`}
#'   \item{decision}{a factor with two levels: `promoted` and `not`}
#' }
#' @seealso [promotions()].
"promotions_shuffled"


#' Massachusetts Public High Schools Data
#'
#' Data on Massachusetts public high schools in 2017
#'
#' @format A data frame of 332 rows representing Massachusetts high schools and 4 variables
#' \describe{
#'   \item{school_name}{High school name.}
#'   \item{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.}
#'   \item{perc_disadvan}{Percent of the student body that are considered economically disadvantaged.}
#'   \item{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>
"MA_schools"



#' Dunkin Donuts vs Starbucks
#'
#' Number of Dunkin Donuts & Starbucks, median income, and population in 1024
#' census tracts in eastern Massachusetts in 2016.
#'
#' @format A data frame of 1024 rows representing census tracts and 6 variables
#' \describe{
#'   \item{county}{County where census tract is located. Either Bristol, Essex, Middlesex, Norfolk, Plymouth, or Suffolk county}
#'   \item{FIPS}{Federal Information Processing Standards code identifying census tract}
#'   \item{median_income}{Median income of census tract}
#'   \item{population}{Population of census tract}
#'   \item{shop_type}{Coffee shop type: Dunkin Donuts or Starbucks}
#'   \item{shops}{Number of shops}
#' }
#' @source US Census Bureau. Code used to scrape data available at <https://github.com/DelaneyMoran/FinalProject>
"DD_vs_SB"



#' House Sales in King County, USA
#'
#' 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>
#'
#' @format A data frame with 21613 observations on the following 21 variables.
#' \describe{
#'   \item{id}{a notation for a house}
#'   \item{date}{Date house was sold}
#'   \item{price}{Price is prediction target}
#'   \item{bedrooms}{Number of Bedrooms/House}
#'   \item{bathrooms}{Number of bathrooms/bedrooms}
#'   \item{sqft_living}{square footage of the home}
#'   \item{sqft_lot}{square footage of the lot}
#'   \item{floors}{Total floors (levels) in house}
#'   \item{waterfront}{House which has a view to a waterfront}
#'   \item{view}{Has been viewed}
#'   \item{condition}{How good the condition is (Overall)}
#'   \item{grade}{overall grade given to the housing unit, based on King County grading system}
#'   \item{sqft_above}{square footage of house apart from basement}
#'   \item{sqft_basement}{square footage of the basement}
#'   \item{yr_built}{Built Year}
#'   \item{yr_renovated}{Year when house was renovated}
#'   \item{zipcode}{zip code}
#'   \item{lat}{Latitude coordinate}
#'   \item{long}{Longitude coordinate}
#'   \item{sqft_living15}{Living room area in 2015 (implies-- some renovations) This might or might not have affected the lotsize area}
#'   \item{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.
"house_prices"



#' Teaching evaluations at the UT Austin
#'
#' 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>
#'
#' @format A data frame with 463 observations corresponding to courses on the following 13 variables.
#' \describe{
#'   \item{ID}{Identification variable for course.}
#'   \item{prof_ID}{Identification variable for professor. Many professors are included more than once in this dataset.}
#'   \item{score}{Average professor evaluation score: (1) very unsatisfactory - (5) excellent.}
#'   \item{age}{Age of professor.}
#'   \item{bty_avg}{Average beauty rating of professor.}
#'   \item{gender}{Gender of professor (collected as a binary variable at the time of the study): female, male.}
#'   \item{ethnicity}{Ethnicity of professor: not minority, minority.}
#'   \item{language}{Language of school where professor received education: English or non-English.}
#'   \item{rank}{Rank of professor: teaching, tenure track, tenured.}
#'   \item{pic_outfit}{Outfit of professor in picture: not formal, formal.}
#'   \item{pic_color}{Color of professor’s picture: color, black & white.}
#'   \item{cls_did_eval}{Number of students in class who completed evaluation.}
#'   \item{cls_students}{Total number of students in class.}
#'   \item{cls_level}{Class level: lower, upper.}
#' }
#' @source Çetinkaya-Rundel M, Morgan KL, Stangl D. 2013. Looking Good on Course Evaluations. CHANCE 26(2). 
#' @seealso The data in `evals` is a slight modification of [openintro::evals()].
"evals"



#' Data from Mythbusters' study on contagiousness of yawning
#'
#' 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.
#'
#' @format A data frame of 50 rows representing each of the 50 participants
#' in the study.
#' \describe{
#'   \item{subj}{integer value corresponding to identifier variable of
#'   subject ID}
#'   \item{group}{string of either `"seed"`, participant was shown a
#'   yawner, or `"control"`, participant was not shown a yawner}
#'   \item{yawn}{string of either `"yes"`, the participant yawned, or
#'   `"no"`, the participant did not yawn}
#' }
"mythbusters_yawn"



#' Random sample of 68 action and romance movies
#'
#' A random sample of 32 action movies and 36 romance movies from
#' <https://www.imdb.com/> and their ratings.
#'
#' @format A data frame of 68 rows movies.
#' \describe{
#'   \item{title}{Movie title}
#'   \item{year}{Year released}
#'   \item{rating}{IMDb rating out of 10 stars}
#'   \item{genre}{Action or Romance}
#' }
#' @seealso This data was sampled from the `movies` data frame in the `ggplot2movies` package.
"movies_sample"

#' Data from the Coffee Quality Institute's review pages in January 2018
#'
#' 1,340 digitized reviews on coffee samples from
#' <https://database.coffeeinstitute.org/>.
#'
#' @format A data frame of 1,340 rows representing each sample of coffee.
#' \describe{
#'   \item{total_cup_points}{Number of points in final rating (scale of 0-100)}
#'   \item{species}{Species of coffee bean plant (Arabica or Robusta)}
#'   \item{owner}{Owner of coffee plant farm}
#'   \item{country_of_origin}{Coffee bean's country of origin}
#'   \item{farm_name}{Name of coffee plant farm}
#'   \item{lot_number}{Lot number for tested coffee beans}
#'   \item{mill}{Name of coffee bean's processing facility}
#'   \item{ico_number}{International Coffee Organization number}
#'   \item{company}{Name of coffee bean's company}
#'   \item{altitude}{Altitude at which coffee plants were grown}
#'   \item{region}{Region where coffee plants were grown}
#'   \item{producer}{Name of coffee bean roaster}
#'   \item{number_of_bags}{Number of tested bags}
#'   \item{bag_weight}{Tested bag weight}
#'   \item{in_country_partner}{Partner for the country}
#'   \item{harvest_year}{Year the coffee beans were harvested}
#'   \item{grading_date}{Day the coffee beans were graded}
#'   \item{owner_1}{Owner of the coffee beans}
#'   \item{variety}{Variety of the coffee beans}
#'   \item{processing_method}{Method used for processing the coffee beans}
#'   \item{aroma}{Coffee aroma rating}
#'   \item{flavor}{Coffee flavor rating}
#'   \item{aftertaste}{Coffee aftertaste rating}
#'   \item{acidity}{Coffee acidity rating}
#'   \item{body}{Coffee body rating}
#'   \item{balance}{Coffee balance rating}
#'   \item{uniformity}{Coffee uniformity rating}
#'   \item{clean_cup}{Cup cleanliness rating}
#'   \item{sweetness}{Coffee sweetness rating}
#'   \item{cupper_points}{Cupper Points, an overall rating for the coffee}
#'   \item{moisture}{Coffee moisture content}
#'   \item{category_one_defects}{Number of category one defects for the coffee beans}
#'   \item{quakers}{Number of coffee beans that don't dark brown when roasted}
#'   \item{color}{Color of the coffee beans}
#'   \item{category_two_defects}{Number of category two defects for the coffee beans}
#'   \item{expiration}{Expiration date of the coffee beans}
#'   \item{certification_body}{Entity/Institute that certified the coffee beans}
#'   \item{certification_address}{Body address of certification for coffee beans}
#'   \item{certification_contact}{Certification contact for coffee beans}
#'   \item{unit_of_measurement}{Unit of measurement for altitude}
#'   \item{altitude_low_meters}{Lower altitude level coffee beans grow at}
#'   \item{altitude_high_meters}{Higher altitude level coffee beans grow at}
#'   \item{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>
"coffee_ratings"


#' 2020 road traffic volume and crash level date for 13 Massachusetts counties
#' 
#' @format A data frame of 874 rows representing traffic data at the 874 sites 
#' \describe{
#'   \item{site_id}{Site id}
#'   \item{county}{County in which the site is located}
#'   \item{community}{Community in which the site is located}
#'   \item{rural_urban}{Rural (R) or Urban (U)}
#'   \item{dir}{Direction for traffic movement. Either 1-WAY, 2-WAY, EB (eastbound), RAMP or WB (westbound)}
#'   \item{functional_class}{Classification of road. Either Arterial, Collector, Freeway & Expressway, Interstate or Local Road}
#'   \item{avg_speed}{Average traffic speed}
#'   \item{total_volume}{Number of vehicles recorded at each site in 2020}
#'   \item{crashes}{Number of vehicle crashes at each site}
#'   \item{nonfatal_injuries}{Number of non-fatal injuries for all recorded vehicle crashes}
#'   \item{fatal_injuries}{Number of fatal injuries for all recorded vehicle crashes}
#' }
"mass_traffic_2020"

#' Sample of Amazon books 
#' 
#' A random sample of 325 books from Amazon.com. 
#'
#' @format A data frame of 325 rows representing books listed on Amazon and 13 variables.
#' \describe{
#'   \item{title}{Book title}
#'   \item{author}{Author who wrote book}
#'   \item{list_price}{recommended retail price of book}
#'   \item{amazon_price}{lowest price of book shown on Amazon}
#'   \item{hard_paper}{book is either hardcover or paperback}
#'   \item{num_pages}{number of pages in book}
#'   \item{publisher}{Company that issues the book for sale}
#'   \item{pub_year}{Year the book was published}
#'   \item{isbn_10}{10-character ISBN number}
#'   \item{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>
"amazon_books"

#' International Power Lifting Results
#' A subset of international powerlifting results.
#' 
#' @format A data frame with 41,152 entries, one entry for individual lifter
#' \describe{
#'   \item{name}{Individual lifter name}
#'   \item{sex}{Binary sex (M/F)}
#'   \item{event}{The type of competition that the lifter entered}
#'   \item{equipment}{The equipment category under which the lifts were performed}
#'   \item{age}{The age of the lifter on the start date of the meet}
#'   \item{age_class}{The age class in which the filter falls}
#'   \item{division}{division of competition}
#'   \item{bodyweight_kg}{The recorded bodyweight of the lifter at the time of competition, to two decimal places}
#'   \item{weight_class_kg}{The weight class in which the lifter competed, to two decimal places}
#'   \item{best3squat_kg}{Maximum of the first three successful attempts for the lift}
#'   \item{best3bench_kg}{Maximum of the first three successful attempts for the lift}
#'   \item{best3deadlift_kg}{Maximum of the first three successful attempts for the lift}
#'   \item{place}{The recorded place of the lifter in the given division at the end of the meet}
#'   \item{date}{Date of the event}
#'   \item{federation}{The federation that hosted the meet}
#'   \item{meet_name}{The name of the meet}
#' }
#' @source This data is a subset of the open dataset [Open Powerlifting](https://www.openpowerlifting.org/)
"ipf_lifts"



#' Data on maternal smoking and infant health
#' 
#' @format A data frame of 1236 rows of individual mothers.
#' \describe{
#' \item{id}{Identification number}
#' \item{pluralty}{Marked 5 for single fetus, otherwise number of fetuses}
#' \item{outcome}{Marked 1 for live birth that survived at least 28 days}
#' \item{date}{Birth date where 1096 is January 1st, 1961}
#' \item{birthday}{Birth date in mm-dd-yyyy format}
#' \item{gestation}{Length of gestation in days, marked 999 if unknown}
#' \item{sex}{Infant's sex, where 1 is male, 2 is female, and 9 is unknown}
#' \item{wt}{Birth weight in ounces, marked 999 if unknown}
#' \item{parity}{Total number of previous pregnancies including fetal deaths and stillbirths, marked 99 if unknown}
#' \item{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}
#' \item{age}{Mother's age in years at termination of pregnancy, 99=unknown}
#' \item{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}
#' \item{ht}{Mother's height in inches to the last completed inch, 99=unknown}
#' \item{wt_1}{Mother prepregnancy wt in pounds, 999=unknown}
#' \item{drace}{Father's race, coding same as mother's race}
#' \item{dage}{Father's age, coding same as mother's age}
#' \item{ded}{Father's education, coding same as mother's education}
#' \item{dht}{Father's height, coding same as for mother's height}
#' \item{dwt}{Father's weight coding same as for mother's weight}
#' \item{marital}{0= legally separated, 1=married, 2= divorced, 3=widowed, 5=never married}
#' \item{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}
#' \item{smoke}{Does mother smoke? 0=never, 1= smokes now, 2=until current pregnancy, 3=once did, not now, 9=unknown}
#' \item{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}
#' \item{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>
"babies"

#' Electric vehicle charging sessions for a workplace charging program
#' 
#' 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.
#' 
#' @format A data frame of 3,395 rows on 24 variables, where each row is an electric vehicle
#' charging session.
#' \describe{
#'    \item{session_id}{Unique identifier specifying the electric vehicle charging session}
#'    \item{kwh_total}{Total energy used at the charging session, in kilowatt hours (kWh)}
#'    \item{dollars}{Quantity of money paid for the charging session in U.S. dollars}
#'    \item{created}{Date and time recorded at the beginning of the charging session}
#'    \item{ended}{Date and time recorded at the end of the charging session}
#'    \item{start_time}{Hour of the day when the charging session began (1 through 24)}
#'    \item{end_time}{Hour of the day when the charging session ended (1 through 24)}
#'    \item{charge_time_hrs}{Length of the charging session in hours}
#'    \item{weekday}{First three characters of the name of the weekday when the charging session occurred}
#'    \item{platform}{Digital platform the driver used to record the session (android, ios, web)} 
#'    \item{distance}{Distance from the charging location to the driver's home, expressed in miles
#'    NA if the driver did not report their address}
#'    \item{user_id}{Unique identifier for each driver}
#'    \item{station_id}{Unique identifier for each charging station}
#'    \item{location_id}{Unique identifier for each location owned by the company where charging stations
#'    were located}
#'    \item{manager_vehicle}{Binary variable that is 1 when the vehicle is a type commonly used
#'    by managers of the firm and 0 otherwise}
#'    \item{facility_type}{Categorical variable that represents the facility type:
#'    \itemize{
#'    \item 1 = manufacturing
#'    \item 2 = office
#'    \item 3 = research and development
#'    \item 4 = other} }
#'    \item{mon, tues, wed, thurs, fri, sat, sun}{Binary variables; 1 if the charging session took place on that day,
#'    0 otherwise}
#'    \item{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.
"ev_charging"

#' Massachusetts 2020 vs. 2019 Traffic Data Comparison
#'
#' 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.
#' 
#' @format A data frame of 264 rows each representing a different community in Massachusetts.
#' \describe{
#'   \item{community}{City or Town}
#'   \item{functional_class}{Class or group the road belongs to}
#'   \item{change_in_speed}{Average estimated Speed (mph)}
#'   \item{change_in_volume}{Average traffic}
#'   \item{change_in_accidents}{Average number of accidents}
#' }
#' @source 
#' \url{https://massdot-impact-crashes-vhb.opendata.arcgis.com/datasets/MassDOT::2020-vehicle-level-crash-details/explore}
#' \url{https://mhd.public.ms2soft.com/tcds/tsearch.asp?loc=Mhd&mod=}
"ma_traffic_2020_vs_2019"

#' Data from Mario Kart Ebay auctions 
#' 
#' Ebay auction data for the Nintendo Wii game Mario Kart.
#' 
#' @format A data frame of 143 auctions.
#' \describe{
#'   \item{id}{Auction ID assigned by Ebay}
#'   \item{duration}{Auction length in days}
#'   \item{n_bids}{Number of bids}
#'   \item{cond}{Game condition, either `new` or `used`}
#'   \item{start_pr}{Price at the start of the auction}
#'   \item{ship_pr}{Shipping price}
#'   \item{total_pr}{Total price, equal to auction price plus shipping price}
#'   \item{ship_sp}{Shipping speed or method}
#'   \item{seller_rate}{Seller's rating on Ebay, equal to the number of positive ratings minus the number of negative ratings}
#'   \item{stock_photo}{Whether the auction photo was a stock photo or not, pictures used in many options were considered stock photos}
#'   \item{wheels}{Number of Wii wheels included in the auction}
#'   \item{title}{The title of the auctions}
#' }
#' @source This data is from <https://www.openintro.org/data/index.php?data=mariokart>
"mario_kart_auction"

#' Avocado Prices by US Region
#' 
#' Gathered from <https://docs.google.com/spreadsheets/d/1cNuj9V-9Xe8fqV3DQRhvsXJhER3zTkO1dSsQ1Q0j96g/edit#gid=1419070688>
#' 
#' @format A data frame of 54 regions over 3 years of weekly results
#' \describe{
#' \item{date}{Week of Data Recording}
#' \item{average_price}{Average Price of Avocado}
#' \item{total_volume}{Total Amount of Avocados}
#' \item{small_hass_sold}{Amount of Small Haas Avocados Sold}
#' \item{large_hass_sold}{Amount of Large Haas Avocados Sold}
#' \item{xlarge_hass_sold}{Amount of Extra Large Haas Avocados Sold}
#' \item{total_bags}{Total Amount of Bags of Avocados}
#' \item{small_bags}{Total Amount of Bags of Small Haas Avocados}
#' \item{large_bags}{Total Amount of Bags of Large Haas Avocados}
#' \item{x_large_bags}{Total Amount of Bags of Extra Large Haas Avocados}
#' \item{type}{Type of Sale}
#' \item{year}{Year of Sale}
#' \item{region}{Region Where Sale Took Place}
#' }
"avocados"


#' House Prices and Properties in Saratoga, New York
#' 
#' Random sample of 1057 houses taken from full Saratoga Housing Data (De Veaux)
#' @format A data frame with 1057 observations on the following 8 variables
#' \describe{
#' \item{price}{price (US dollars)} 
#' \item{living_area}{Living Area (square feet)}
#' \item{bathrooms}{Number of Bathroom (half bathrooms have no shower or tub)}
#' \item{bedrooms}{Number of Bedrooms}
#' \item{fireplaces}{Number of Fireplaces}
#' \item{lot_size}{Size of Lot (acres)}
#' \item{age}{Age of House (years)}
#' \item{fireplace}{Whether the house has a Fireplace}
#' }
#' @source Gathered from <https://docs.google.com/spreadsheets/d/1AY5eECqNIggKpYF3kYzJQBIuuOdkiclFhbjAmY3Yc8E/edit#gid=622599674>
"saratoga_houses" 

#' Chocolate-covered almonds data
#' 
#' 5000 chocolate-covered almonds selected from a large batch, weighed in grams.
#' @format A data frame with 5000 observations on the following 2 variables
#' \describe{
#' \item{ID}{Identification value for a given chocolate-covered almond} 
#' \item{weight}{Weight of the chocolate-covered almond in grams (to the nearest tenth)}
#' }
"almonds_bowl" 

#' Chocolate-covered almonds data sample
#' 
#' A sample of 100 chocolate-covered almonds, weighed in grams.
#' @format A data frame with 100 observations on the following 2 variables
#' \describe{
#' \item{ID}{Identification value for a given chocolate-covered almond} 
#' \item{weight}{Weight of the chocolate-covered almond in grams (to the nearest tenth)}
#' }
"almonds_sample_100"