R/data.R
In mdsr-book/mdsr: Complement to 'Modern Data Science with R'
#' Data about recent major league baseball teams
#'
#' A dataset containing information about Major League Baseball teams
#' from 2008-2014.
#'
#' @docType data
#' @format A \code{\link{tbl_df}} object.
#' \describe{
#' \item{yearID}{season in which the team played}
#' \item{teamID}{the team's three character identifier}
#' \item{lgID}{the league in which the team played}
#' \item{W}{number of wins}
#' \item{L}{number of losses}
#' \item{WPct}{winning percentage}
#' \item{attendance}{number of fans in attendance}
#' \item{normAttend}{number of fans in attendance, relative to the team with
#' the highest attendance in this sample (the 2008 New York Yankees)}
#' \item{payroll}{the sum of the salaries of the players on each team. Note
#' that this number is only an estimate of the actual team payroll -- and may
#' not even be a very good one. Salaries are accumulated from \code{\link[Lahman]{Salaries}}}
#' \item{metroPop}{the size of the team's home city's metropolitan population, according
#' to Wikipedia and the 2010 US Census}
#' \item{name}{the full name of the team}
#' }
#'
#' @seealso \code{\link[Lahman]{Teams}}
#'
#' @source The \code{\link[Lahman]{Teams}} table from \code{\link[Lahman]{Lahman-package}}
#' and \url{https://en.wikipedia.org/wiki/List_of_Metropolitan_Statistical_Areas}
#'
"MLB_teams"
#' Several variables on countries from the CIA Factbook, 2014.
#'
#' The CIA Factbook has geographic, demographic, and economic data on a
#' country-by-country basis.
#' In the description of the variables, the 4-digit
#' number indicates the code used to specify that variable on the data and
#' documentation web site.
#'
#' @name CIACountries
#' @docType data
#' @format A data frame with the following variables for each of the Countries
#' in the World. (236 countries are given.)
#' \describe{
#' \item{country}{Name of the country}
#' \item{pop}{number of people, 2119}
#' \item{area}{area (sq km), 2147}
#' \item{oil_prod}{Crude oil - production (bbl/day), 2241}
#' \item{gdp}{Gross Domestic Product per capita ($/person), 2001}
#' \item{educ}{education spending (\% of GDP), 2206}
#' \item{roadways}{Roadways per unit area (km/sq km), 2085}
#' \item{net_users}{Fraction of Internet users (\% of population), 2153}
#' }
#' @source From the CIA World Factbook,
#' \url{https://www.cia.gov/the-world-factbook/}
#' @seealso \code{\link[mosaic]{CIAdata}}
#' @references \url{https://github.com/factbook/factbook/blob/master/CATEGORIES.md}
#' @keywords datasets
#' @examples
#' str(CIACountries)
#'
"CIACountries"
#' Election Statistics
#' @docType data
#' @format An object of class \\code{tbl_df} (inherits from \\code{tbl}, \\code{data.frame})
#' with 117 rows and 13 columns.
#' \describe{
#' \item{Ward}{Name of the country}
#' \item{Precinct}{number of people, 2119}
#' \item{Registered.Voters.at.7am}{area (sq km), 2147}
#' \item{Voters.Registering.at.Polls}{Crude oil - production (bbl/day), 2241}
#' \item{gdp}{Gross Domestic Product per capita ($/person), 2001}
#' \item{educ}{education spending (\% of GDP), 2206}
#' \item{roadways}{Roadways per unit area (km/sq km), 2085}
#' \item{net_users}{Fraction of Internet users (\% of population), 2153}
#' }
"Elections"
#' Cherry Blossom runs
#' @docType data
#' @format An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame})
#' with 41,248 rows and 8 columns. Each row refers to an individual runner in one race of the
#' Cherry Blossom Ten Miler. The data cover the years 1999 to 2008. All of the runners listed ran
#' at least two of the races in that period, some ran many more than that.
#' \describe{
#' \item{name.yob}{a unique identifier for each runner composed of the runner's full name and
#' year of birth.}
#' \item{age}{integer giving the runner's age in the race whose result is being reported.}
#' \item{gun}{the number of minutes elapsed from the starter's gun to the person crossing the finish line}
#' \item{net}{the number of minutes elapsed from the runner's crossing the start line to crossing the finish line.}
#' \item{sex}{the runner's sex}
#' \item{year}{the year of that race}
#' \item{previous}{integer specifying how many times previous to this race the runner had participated in the years 1999 to 2008.}
#' \item{nruns}{integer giving the total number of times that runner participated in the years from 1999 to 2008. The smallest is 2, the largest is 10.}
#' \item{nruns}{integer giving the total number of times that runner participated in the years from 1999 to 2008. The smallest is 2, the largest is 10.}
#' }
#' @details The Cherry Blossom 10 Mile Run is a road race held in Washington,
#' D.C. in April each year. (The name comes from the famous cherry trees
#' that are in bloom in April in Washington.) The results of this race
#' are published at \url{https://www.cherryblossom.org/post-race/race-results/}.
#' @examples
#' if (require(dplyr)) {
#' Cherry %>%
#' group_by(name.yob) %>%
#' count() %>%
#' group_by(n) %>%
#' count(name = "appearances")
#' }
"Cherry"
#' NYC Restaurant Health Violations
#' @docType data
#' @format{
#' A data frame with 480,621 observations on the following 16 variables.
#' \describe{
#' \item{\code{camis}}{unique identifier}
#' \item{\code{dba}}{full name doing business as}
#' \item{\code{boro}}{borough of New York}
#' \item{\code{building}}{building name}
#' \item{\code{street}}{street address}
#' \item{\code{zipcode}}{zipcode}
#' \item{\code{phone}}{phone number}
#' \item{\code{inspection_date}}{inspection date}
#' \item{\code{action}}{action taken}
#' \item{\code{violation_code}}{violation code, see \code{\link{ViolationCodes}}}
#' \item{\code{score}}{inspection score}
#' \item{\code{grade}}{inspection grade}
#' \item{\code{grade_date}}{grade date}
#' \item{\code{record_date}}{recording date}
#' \item{\code{inspection_type}}{inspect type}
#' \item{\code{cuisine_code}}{cuisine code, see \code{\link{Cuisines}}}
#' }
#' }
#' @seealso \code{\link{ViolationCodes}}, \code{\link{Cuisines}}
#' @source NYC Open Data, \url{https://data.cityofnewyork.us/Health/DOHMH-New-York-City-Restaurant-Inspection-Results/43nn-pn8j/}
#' @examples
#' data(Violations)
#' if (require(dplyr)) {
#' Violations %>%
#' inner_join(Cuisines, by = "cuisine_code") %>%
#' filter(cuisine_description == "American") %>%
#' arrange(grade_date) %>%
#' head()
#' }
"Violations"
#' @rdname Violations
#' @docType data
#' @format A data frame with 174 observations on the following 3 variables.
#' \describe{
#' \item{\code{violation_code}}{a factor with many levels}
#' \item{\code{critical_flag}}{is violation critical: a factor with levels \code{N} \code{Y}}
#' \item{\code{violation_description}}{violation description}
#' }
"ViolationCodes"
#' @rdname Violations
#' @docType data
#' @format{
#' A data frame with 84 observations on the following 2 variables.
#' \describe{
#' \item{\code{cuisine_code}}{a character vector}
#' \item{\code{cuisine_description}}{a character vector}
#' }
#' }
"Cuisines"
#' Data Science Papers from arXiv.org
#'
#' @description Papers matching the search string "Data Science" on arXiv.org in August,
#' 2020
#'
#' @name DataSciencePapers
#' @docType data
#' @format A data frame with 1089 observations on the following 15 variables.
#' \describe{ \item{id}{unique arXiv.org identifier for the paper}
#' \item{submitted}{date submitted}
#' \item{updated}{date last updated}
#' \item{title}{title of the paper}
#' \item{abstract}{contents of the abstract}
#' \item{authors}{authors of the paper}
#' \item{affiliations}{affiliations of the authors}
#' \item{link_abstract}{direct link to the abstract}
#' \item{link_pdf}{direct link to the pdf}
#' \item{link_doi}{direct link to the digital object identifier (doi)}
#' \item{comment}{commentary} \item{journal_ref}{reference to
#' the journal (if published)} \item{doi}{digital object identifier}
#' \item{primary_category}{arXiv.org primary category}
#' \item{categories}{arXiv.org categories} }
#' @source \url{https://arxiv.org/}
#' @keywords datasets
#' @examples
#'
#' data(DataSciencePapers)
#' str(DataSciencePapers)
#'
"DataSciencePapers"
#' Medicare Providers
#'
#' Name and location data for the medicare providers in the
#' \code{MedicareCharges} data table.
#'
#' This data table is related to \code{MedicareCharges} data.
#'
#' @name MedicareProviders
#' @docType data
#' @format A data frame with 3337 observations on the following 7 variables.
#' \describe{
#' \item{idProvider}{a unique number assigned to each
#' provider}
#' \item{nameProvider}{Name of the provider. (text string)}
#' \item{addressProvider}{Street address of the provider. (text
#' string)}
#' \item{cityProvider}{The name of the city in which the
#' provider is located. (factor)}
#' \item{stateProvider}{The two-letter
#' postal code of the state in which the provider is located. (factor)}
#' \item{zipProvider}{The provider's ZIP code. (factor)}
#' \item{referralRegion}{An identifier for the region serviced by the
#' provider.}
#' }
#' @source Extracted from the highly repetitive table provided by the Centers
#' for Medicare and Medicaid Services.
#' See
#' \url{https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/}
#' @keywords datasets
#' @seealso \code{\link{MedicareCharges}}
#' @examples
#'
#' data(MedicareProviders)
#'
"MedicareProviders"
#' Charges to and Payments from Medicare
#'
#' These data for 2011, released in May 2013, describe how much hospitals
#' charged Medicare for various inpatient procedures, how many were performed,
#' and how much Medicare actually paid.
#'
#' These data are part of a set with \code{DiagnosisRelatedGroup}, which gives a
#' description of the medical procedure associated with each DRG, and
#' \code{MedicareProviders}, which translates \code{idProvider} into a name,
#' address, state, Zip, etc..
#'
#' These data have been pre-aggregated by state.
#'
#' @name MedicareCharges
#' @docType data
#' @format A data frame with 5,025 observations on the following 4 variables.
#' \describe{
#' \item{drg}{Code for the Diagnosis Related Group: a
#' character string that looks like a number.}
#' \item{stateProvider}{the state providing the care.}
#' \item{num_charges}{the total number of charges.}
#' \item{mean_charge}{the average charge for each \code{drg} across each state}
#' }
#' @source Data from the Centers for Medicare and Medicaid Services.
#' See
#' \url{https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/}
#' @keywords datasets
#' @seealso \code{\link{MedicareProviders}}
#' @examples
#'
#' data(MedicareCharges)
#'
"MedicareCharges"
#' Text of Macbeth
#'
#' The entire text of Macbeth, stored in a character vector of length 1.
#'
#' @docType data
#' @format A character vector of length 1
#' @source Project Gutenberg, \url{https://www.gutenberg.org/ebooks/1129/}
"Macbeth_raw"
#' State SAT scores from 2010
#'
#' SAT results by state for 2010
#'
#' @docType data
#' @format A data.frame with 50 rows and 9 variables.
#' \describe{
#' \item{\code{state}}{a factor with levels for each state}
#' \item{\code{expenditure}}{average expenditure per student (in each state)}
#' \item{\code{pupil_teacher_ratio}}{pupil to teacher ratio in that state}
#' \item{\code{salary}}{teacher salary (in 2010 US $)}
#' \item{\code{read}}{state average Reading SAT score}
#' \item{\code{math}}{state average Math SAT score}
#' \item{\code{write}}{state average Writing SAT score}
#' \item{\code{total}}{state average Total SAT score}
#' \item{\code{sat_pct}}{percent of students taking SAT in that state}
#' }
#' @details See also the earlier \code{\link[mosaicData]{SAT}} dataset.
#' @seealso \code{\link[mosaicData]{SAT}}
"SAT_2010"
#' Cities and their populations
#'
#' A list of cities
#'
#'
#' @name world_cities
#' @docType data
#' @format A data frame with 4,428 observations on the following 10 variables.
#' \describe{
#' \item{geoname_id}{integer id of record in geonames database}
#' \item{name}{name of geographical point in plain ascii characters}
#' \item{latitude}{latitude in decimal degrees (wgs84)}
#' \item{longitude}{longitude in decimal degrees (wgs84)}
#' \item{country}{ISO-3166 2-letter country code}
#' \item{country_region}{fipscode}
#' \item{population}{Population}
#' \item{timezone}{the iana timezone id}
#' \item{modification_date}{date of last modification} }
#' @source GeoNames: \url{http://download.geonames.org/export/dump/}
#' @keywords datasets
#' @examples
#'
#' world_cities
#'
"world_cities"
#' Votes from Scottish Parliament
#' @docType data
#' @description Votes recorded on each ballot by each member of the Scottish Parliament in
#' 2008 along with information about party affiliation.
#' @format \code{Votes} is a data.frame with 103582 rows and 3 variables.
#' \describe{
#' \item{\code{bill}}{an identifier for the bill}
#' \item{\code{name}}{the name of the member of parliament}
#' \item{\code{vote}}{1 means a vote for, -1 a vote against. 0 is an abstention.}
#' }
#' \code{Parties} is a data.frame with 134 rows, one for each member of parliament, and 2 variables.
#' \describe{
#' \item{\code{party}}{the name of the political party the member belongs to}
#' \item{\code{name}}{the name of the member of parliament}
#' }
#'
#' @details Almost all of the members of parliament belongs to a political party. This table
#' identifies that party. These data were provided by Caroline Ettinger and form part of her
#' senior honor's project at Macalester College. Prof. Andrew Beveridge supervised the
#' thesis. Ms. Ettinger used the vote data to explore how to extract the party association
#' of members purely from voting records. The \code{Parties} data was used to evaluate
#' the success of methods.
"Votes"
#' @rdname Votes
"Parties"
#' Ballots in the 2013 Mayoral election in Minneapolis
#'
#' @description The choices marked on each (valid) ballot for the election, which was run
#' using a rank-choice, instant runoff system.
#'
#' @details Ballot information for the 2013 Minneapolis Mayoral election, which was run
#' as a rank-choice election. In rank-choice, a voter can indicate first,
#' second, and third choices. If a voter's first choice is eliminated (by
#' being last in the count across voters), the second choice is promoted to
#' that voter's first choice, and similarly third -> second. Eliminations are
#' done successively until one candidate has a majority of the first-choice
#' votes.
#'
#' @name Minneapolis2013
#' @docType data
#' @format A data frame with 80,101 observations on the following 5 variables.
#' All are stored as character strings.
#' \describe{
#' \item{Precinct}{Precincts are sub-divisions within Wards}
#' \item{First}{The voter's first choice}
#' \item{Second}{The voter's second choice}
#' \item{Third}{The voter's third choice}
#' \item{Ward}{The city is divided spatially into districts or 'wards'.
#' These are further subdivided into precincts. }
#' }
#' @references Description of ranked-choice voting:
#' \url{https://vote.minneapolismn.gov/ranked-choice-voting/}
#'
#' A Minnesota Public Radio story about the election ballot tallying process:
#' \url{https://www.mprnews.org/2013/11/22/politics/ranked-choice-vote-count-programmers/}
#'
#' The Wikipedia article about the election:
#' \url{https://en.wikipedia.org/wiki/2013_Minneapolis_mayoral_election}
#' @source Ballot data from the Minneapolis city government:
#' \url{https://vote.minneapolismn.gov/results-data/election-results/2013/mayor/}
#' @keywords datasets
#' @examples
#'
#' data(Minneapolis2013)
"Minneapolis2013"
#' Birds captured and released at Ordway, complete and uncleaned
#'
#' The historical record of birds captured and released at the Katharine Ordway
#' Natural History Study Area, a 278-acre preserve in Inver Grove Heights,
#' Minnesota, owned and managed by Macalester College.
#'
#' There are many extraneous levels of variables such as species. Part of the
#' purpose of this data set is to teach about data cleaning.
#'
#' @name ordway_birds
#' @docType data
#' @format A data frame with 15,829 observations on the bird's species, size,
#' date found, and band number.
#' \describe{
#' \item{\code{bogus}}{a character vector}
#' \item{\code{Timestamp}}{Timestamp indicates when the data were entered into an electronic record,
#' not anything about the bird being described}
#' \item{\code{Year}}{a character vector}
#' \item{\code{Day}}{a character vector}
#' \item{\code{Month}}{a character vector}
#' \item{\code{CaptureTime}}{a character vector}
#' \item{\code{SpeciesName}}{a character vector}
#' \item{\code{Sex}}{a character vector}
#' \item{\code{Age}}{a character vector}
#' \item{\code{BandNumber}}{a character vector}
#' \item{\code{TrapID}}{a character vector}
#' \item{\code{Weather}}{a character vector}
#' \item{\code{BandingReport}}{a character vector}
#' \item{\code{RecaptureYN}}{a character vector}
#' \item{\code{RecaptureMonth}}{a character vector}
#' \item{\code{RecaptureDay}}{a character vector}
#' \item{\code{Condition}}{a character vector}
#' \item{\code{Release}}{a character vector}
#' \item{\code{Comments}}{a character vector}
#' \item{\code{DataEntryPerson}}{a character vector}
#' \item{\code{Weight}}{a character vector}
#' \item{\code{WingChord}}{a character vector}
#' \item{\code{Temperature}}{a character vector}
#' \item{\code{RecaptureOriginal}}{a character vector}
#' \item{\code{RecapturePrevious}}{a character vector}
#' \item{\code{TailLength}}{a character vector}
#' }
#'
#' Timestamp indicates when the data were entered into an electronic record,
#' not anything about the bird being described.
#' @source Jerald Dosch, Dept. of Biology, Macalester College: the manager of
#' the Study Area.
#' @references \url{https://www.macalester.edu/ordway/}
#' @keywords datasets
#' @examples
#'
#' ordway_birds
#'
"ordway_birds"
#' Deaths and Pumps from 1854 London cholera outbreak
#' @docType data
#' @format An object of class \code{\link[sf]{sf}}
#' whose data attribute has 250 rows and 2 columns.
#' @details Both spatial objects are projected in EPSG:27700, aka the British
#' National Grid.
#' @source \url{https://blog.rtwilson.com/john-snows-cholera-data-in-more-formats/}
#' @examples
#' if (require(sf)) {
#' plot(st_geometry(CholeraDeaths))
#' }
"CholeraDeaths"
#' @rdname CholeraDeaths
#' @format An object of class \code{\link[sf]{sf}}.
#' @docType data
"CholeraPumps"
#' Gene expression in cancer
#'
#' The data come from a National Cancer Institute study of gene expression in
#' cell lines drawn from various sorts of cancer.
#'
#'
#'
#' \code{\link{Cancer}} gives information about each cell line.
#'
#' @docType data
#' @format The expression data, \code{NCI60_tiny} is a dataframe of 41,078 gene probes (rows)
#' and 60 cell lines (columns). The first column, \code{Probe} gives the name
#' of the Agilent microarray probe. Each of the remaining columns is named for
#' a cell line. The value is the log-2 expression associated with that probe
#' for the cell line.
#'
#' \describe{
#' \item{Probe}{the name of the Agilent microarray probe}
#' }
#'
#' For \code{Cancer}:
#' \describe{
#' \item{otherCellLine}{a character vector giving the name of one cell line}
#' \item{cellLine}{a character vector giving the name of another cell line}
#' \item{correlation}{the correlation between the two cell lines.
#' See \code{\link{cor}}}
#' }
#'
#' @seealso \code{\link{Cancer}}
#' @references Staunton et al. (2001), \emph{PNAS} (\doi{https://doi.org/10.1073/pnas.191368598})
#'
#' D.T. Ross et al. (2000) \emph{Nature Genetics}, 24(3):227-234
#' (\doi{https://doi.org/10.1038/73432})
#'
#' \href{https://discover.nci.nih.gov/cellminer/}{CellMiner}
#' @examples
#'
#' data(NCI60_tiny)
#'
"NCI60_tiny"
#' @rdname NCI60_tiny
#' @docType data
"Cancer"
#' Headlines_train
#' @description This data comes from Chakraborty et. al., which combines headlines from
#' a variety of news and clickbait sources. Some headlines contain
#' subject matter inappropriate for classroom use. Given the volume of headlines
#' containing such language (especially for \code{clickbait == TRUE}), this filtering
#' might not catch all problematic headlines. User discretion is advised.
#' The training dataset is a random sample of approximately 80\% of the observations
#' from the original dataset.
#'
#' @docType data
#' @format A data frame with 18,360 rows and 3 variables:
#' \describe{
#' \item{title}{a character vector}
#' \item{clickbait}{a logical vector}
#' \item{ids}{an integer vector}
#' }
#' @source \url{https://github.com/bhargaviparanjape/clickbait/}
#' @references \doi{10.1109/ASONAM.2016.7752207}
"Headlines_train"
#' @rdname Headlines_train
#' @description The testing dataset is a random sample of the remaining 20\% of the observations
#' not found in the training set.
#' @docType data
#' @format A data frame with 4,589 rows and 3 variables:
#' @examples
#' nrow(Headlines_train)
#' nrow(Headlines_test)
"Headlines_test"
#' Email Train
#'
#' The training dataset includes a set of email subject lines used for classification
#' of whether the message is spam (unsolicited commercial content) or not.
#' Many subject lines include subject matter inappropriate for classroom use.
#' Given the volume of headlines containing such language
#' (especially for \code{spam == TRUE}), user discretion is advised.
#' This dataset is a random sample of 80\% of the emails data.
#' @docType data
#' @format A data frame with 5,526 rows and 3 variables:
#' \describe{
#' \item{ids}{an integer vector}
#' \item{subjectline}{a character vector}
#' \item{type}{a character vector}
#' }
#' @source Originally retrieved from http://www.rdatasciencecases.org/Data.html
"Emails_train"
#' @rdname Emails_train
#' @description The testing dataset is a random sample of 20\% of the emails data.
#' @docType data
#' @format A data frame with 1,382 rows and 3 variables:
#' @examples
#' nrow(Emails_train)
#' nrow(Emails_test)
"Emails_test"
#' Saratoga Houses
#' @docType data
#' @format A tibble with 1728 rows and 16 variables:
#' \describe{
#' \item{price}{},
#' \item{lot_size}{},
#' \item{waterfront}{},
#' \item{age}{},
#' \item{land_value}{},
#' \item{construction}{},
#' \item{air_cond}{},
#' \item{fuel}{},
#' \item{heat}{},
#' \item{sewer}{},
#' \item{living_area}{},
#' \item{pct_college}{},
#' \item{bedrooms}{},
#' \item{fireplaces}{},
#' \item{bathrooms}{},
#' \item{rooms}{}
#' }
#' @examples
#' saratoga_houses
"saratoga_houses"
#' @rdname saratoga_houses
#' @docType data
"saratoga_codes"
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#' Data about recent major league baseball teams
#'
#' A dataset containing information about Major League Baseball teams
#' from 2008-2014.
#'
#' @docType data
#' @format A \code{\link{tbl_df}} object.
#' \describe{
#' \item{yearID}{season in which the team played}
#' \item{teamID}{the team's three character identifier}
#' \item{lgID}{the league in which the team played}
#' \item{W}{number of wins}
#' \item{L}{number of losses}
#' \item{WPct}{winning percentage}
#' \item{attendance}{number of fans in attendance}
#' \item{normAttend}{number of fans in attendance, relative to the team with
#' the highest attendance in this sample (the 2008 New York Yankees)}
#' \item{payroll}{the sum of the salaries of the players on each team. Note
#' that this number is only an estimate of the actual team payroll -- and may
#' not even be a very good one. Salaries are accumulated from \code{\link[Lahman]{Salaries}}}
#' \item{metroPop}{the size of the team's home city's metropolitan population, according
#' to Wikipedia and the 2010 US Census}
#' \item{name}{the full name of the team}
#' }
#'
#' @seealso \code{\link[Lahman]{Teams}}
#'
#' @source The \code{\link[Lahman]{Teams}} table from \code{\link[Lahman]{Lahman-package}}
#' and \url{https://en.wikipedia.org/wiki/List_of_Metropolitan_Statistical_Areas}
#'
"MLB_teams"
#' Several variables on countries from the CIA Factbook, 2014.
#'
#' The CIA Factbook has geographic, demographic, and economic data on a
#' country-by-country basis.
#' In the description of the variables, the 4-digit
#' number indicates the code used to specify that variable on the data and
#' documentation web site.
#'
#' @name CIACountries
#' @docType data
#' @format A data frame with the following variables for each of the Countries
#' in the World. (236 countries are given.)
#' \describe{
#' \item{country}{Name of the country}
#' \item{pop}{number of people, 2119}
#' \item{area}{area (sq km), 2147}
#' \item{oil_prod}{Crude oil - production (bbl/day), 2241}
#' \item{gdp}{Gross Domestic Product per capita ($/person), 2001}
#' \item{educ}{education spending (\% of GDP), 2206}
#' \item{roadways}{Roadways per unit area (km/sq km), 2085}
#' \item{net_users}{Fraction of Internet users (\% of population), 2153}
#' }
#' @source From the CIA World Factbook,
#' \url{https://www.cia.gov/the-world-factbook/}
#' @seealso \code{\link[mosaic]{CIAdata}}
#' @references \url{https://github.com/factbook/factbook/blob/master/CATEGORIES.md}
#' @keywords datasets
#' @examples
#' str(CIACountries)
#'
"CIACountries"
#' Election Statistics
#' @docType data
#' @format An object of class \\code{tbl_df} (inherits from \\code{tbl}, \\code{data.frame})
#' with 117 rows and 13 columns.
#' \describe{
#' \item{Ward}{Name of the country}
#' \item{Precinct}{number of people, 2119}
#' \item{Registered.Voters.at.7am}{area (sq km), 2147}
#' \item{Voters.Registering.at.Polls}{Crude oil - production (bbl/day), 2241}
#' \item{gdp}{Gross Domestic Product per capita ($/person), 2001}
#' \item{educ}{education spending (\% of GDP), 2206}
#' \item{roadways}{Roadways per unit area (km/sq km), 2085}
#' \item{net_users}{Fraction of Internet users (\% of population), 2153}
#' }
"Elections"
#' Cherry Blossom runs
#' @docType data
#' @format An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame})
#' with 41,248 rows and 8 columns. Each row refers to an individual runner in one race of the
#' Cherry Blossom Ten Miler. The data cover the years 1999 to 2008. All of the runners listed ran
#' at least two of the races in that period, some ran many more than that.
#' \describe{
#' \item{name.yob}{a unique identifier for each runner composed of the runner's full name and
#' year of birth.}
#' \item{age}{integer giving the runner's age in the race whose result is being reported.}
#' \item{gun}{the number of minutes elapsed from the starter's gun to the person crossing the finish line}
#' \item{net}{the number of minutes elapsed from the runner's crossing the start line to crossing the finish line.}
#' \item{sex}{the runner's sex}
#' \item{year}{the year of that race}
#' \item{previous}{integer specifying how many times previous to this race the runner had participated in the years 1999 to 2008.}
#' \item{nruns}{integer giving the total number of times that runner participated in the years from 1999 to 2008. The smallest is 2, the largest is 10.}
#' \item{nruns}{integer giving the total number of times that runner participated in the years from 1999 to 2008. The smallest is 2, the largest is 10.}
#' }
#' @details The Cherry Blossom 10 Mile Run is a road race held in Washington,
#' D.C. in April each year. (The name comes from the famous cherry trees
#' that are in bloom in April in Washington.) The results of this race
#' are published at \url{https://www.cherryblossom.org/post-race/race-results/}.
#' @examples
#' if (require(dplyr)) {
#' Cherry %>%
#' group_by(name.yob) %>%
#' count() %>%
#' group_by(n) %>%
#' count(name = "appearances")
#' }
"Cherry"
#' NYC Restaurant Health Violations
#' @docType data
#' @format{
#' A data frame with 480,621 observations on the following 16 variables.
#' \describe{
#' \item{\code{camis}}{unique identifier}
#' \item{\code{dba}}{full name doing business as}
#' \item{\code{boro}}{borough of New York}
#' \item{\code{building}}{building name}
#' \item{\code{street}}{street address}
#' \item{\code{zipcode}}{zipcode}
#' \item{\code{phone}}{phone number}
#' \item{\code{inspection_date}}{inspection date}
#' \item{\code{action}}{action taken}
#' \item{\code{violation_code}}{violation code, see \code{\link{ViolationCodes}}}
#' \item{\code{score}}{inspection score}
#' \item{\code{grade}}{inspection grade}
#' \item{\code{grade_date}}{grade date}
#' \item{\code{record_date}}{recording date}
#' \item{\code{inspection_type}}{inspect type}
#' \item{\code{cuisine_code}}{cuisine code, see \code{\link{Cuisines}}}
#' }
#' }
#' @seealso \code{\link{ViolationCodes}}, \code{\link{Cuisines}}
#' @source NYC Open Data, \url{https://data.cityofnewyork.us/Health/DOHMH-New-York-City-Restaurant-Inspection-Results/43nn-pn8j/}
#' @examples
#' data(Violations)
#' if (require(dplyr)) {
#' Violations %>%
#' inner_join(Cuisines, by = "cuisine_code") %>%
#' filter(cuisine_description == "American") %>%
#' arrange(grade_date) %>%
#' head()
#' }
"Violations"
#' @rdname Violations
#' @docType data
#' @format A data frame with 174 observations on the following 3 variables.
#' \describe{
#' \item{\code{violation_code}}{a factor with many levels}
#' \item{\code{critical_flag}}{is violation critical: a factor with levels \code{N} \code{Y}}
#' \item{\code{violation_description}}{violation description}
#' }
"ViolationCodes"
#' @rdname Violations
#' @docType data
#' @format{
#' A data frame with 84 observations on the following 2 variables.
#' \describe{
#' \item{\code{cuisine_code}}{a character vector}
#' \item{\code{cuisine_description}}{a character vector}
#' }
#' }
"Cuisines"
#' Data Science Papers from arXiv.org
#'
#' @description Papers matching the search string "Data Science" on arXiv.org in August,
#' 2020
#'
#' @name DataSciencePapers
#' @docType data
#' @format A data frame with 1089 observations on the following 15 variables.
#' \describe{ \item{id}{unique arXiv.org identifier for the paper}
#' \item{submitted}{date submitted}
#' \item{updated}{date last updated}
#' \item{title}{title of the paper}
#' \item{abstract}{contents of the abstract}
#' \item{authors}{authors of the paper}
#' \item{affiliations}{affiliations of the authors}
#' \item{link_abstract}{direct link to the abstract}
#' \item{link_pdf}{direct link to the pdf}
#' \item{link_doi}{direct link to the digital object identifier (doi)}
#' \item{comment}{commentary} \item{journal_ref}{reference to
#' the journal (if published)} \item{doi}{digital object identifier}
#' \item{primary_category}{arXiv.org primary category}
#' \item{categories}{arXiv.org categories} }
#' @source \url{https://arxiv.org/}
#' @keywords datasets
#' @examples
#'
#' data(DataSciencePapers)
#' str(DataSciencePapers)
#'
"DataSciencePapers"
#' Medicare Providers
#'
#' Name and location data for the medicare providers in the
#' \code{MedicareCharges} data table.
#'
#' This data table is related to \code{MedicareCharges} data.
#'
#' @name MedicareProviders
#' @docType data
#' @format A data frame with 3337 observations on the following 7 variables.
#' \describe{
#' \item{idProvider}{a unique number assigned to each
#' provider}
#' \item{nameProvider}{Name of the provider. (text string)}
#' \item{addressProvider}{Street address of the provider. (text
#' string)}
#' \item{cityProvider}{The name of the city in which the
#' provider is located. (factor)}
#' \item{stateProvider}{The two-letter
#' postal code of the state in which the provider is located. (factor)}
#' \item{zipProvider}{The provider's ZIP code. (factor)}
#' \item{referralRegion}{An identifier for the region serviced by the
#' provider.}
#' }
#' @source Extracted from the highly repetitive table provided by the Centers
#' for Medicare and Medicaid Services.
#' See
#' \url{https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/}
#' @keywords datasets
#' @seealso \code{\link{MedicareCharges}}
#' @examples
#'
#' data(MedicareProviders)
#'
"MedicareProviders"
#' Charges to and Payments from Medicare
#'
#' These data for 2011, released in May 2013, describe how much hospitals
#' charged Medicare for various inpatient procedures, how many were performed,
#' and how much Medicare actually paid.
#'
#' These data are part of a set with \code{DiagnosisRelatedGroup}, which gives a
#' description of the medical procedure associated with each DRG, and
#' \code{MedicareProviders}, which translates \code{idProvider} into a name,
#' address, state, Zip, etc..
#'
#' These data have been pre-aggregated by state.
#'
#' @name MedicareCharges
#' @docType data
#' @format A data frame with 5,025 observations on the following 4 variables.
#' \describe{
#' \item{drg}{Code for the Diagnosis Related Group: a
#' character string that looks like a number.}
#' \item{stateProvider}{the state providing the care.}
#' \item{num_charges}{the total number of charges.}
#' \item{mean_charge}{the average charge for each \code{drg} across each state}
#' }
#' @source Data from the Centers for Medicare and Medicaid Services.
#' See
#' \url{https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/}
#' @keywords datasets
#' @seealso \code{\link{MedicareProviders}}
#' @examples
#'
#' data(MedicareCharges)
#'
"MedicareCharges"
#' Text of Macbeth
#'
#' The entire text of Macbeth, stored in a character vector of length 1.
#'
#' @docType data
#' @format A character vector of length 1
#' @source Project Gutenberg, \url{https://www.gutenberg.org/ebooks/1129/}
"Macbeth_raw"
#' State SAT scores from 2010
#'
#' SAT results by state for 2010
#'
#' @docType data
#' @format A data.frame with 50 rows and 9 variables.
#' \describe{
#' \item{\code{state}}{a factor with levels for each state}
#' \item{\code{expenditure}}{average expenditure per student (in each state)}
#' \item{\code{pupil_teacher_ratio}}{pupil to teacher ratio in that state}
#' \item{\code{salary}}{teacher salary (in 2010 US $)}
#' \item{\code{read}}{state average Reading SAT score}
#' \item{\code{math}}{state average Math SAT score}
#' \item{\code{write}}{state average Writing SAT score}
#' \item{\code{total}}{state average Total SAT score}
#' \item{\code{sat_pct}}{percent of students taking SAT in that state}
#' }
#' @details See also the earlier \code{\link[mosaicData]{SAT}} dataset.
#' @seealso \code{\link[mosaicData]{SAT}}
"SAT_2010"
#' Cities and their populations
#'
#' A list of cities
#'
#'
#' @name world_cities
#' @docType data
#' @format A data frame with 4,428 observations on the following 10 variables.
#' \describe{
#' \item{geoname_id}{integer id of record in geonames database}
#' \item{name}{name of geographical point in plain ascii characters}
#' \item{latitude}{latitude in decimal degrees (wgs84)}
#' \item{longitude}{longitude in decimal degrees (wgs84)}
#' \item{country}{ISO-3166 2-letter country code}
#' \item{country_region}{fipscode}
#' \item{population}{Population}
#' \item{timezone}{the iana timezone id}
#' \item{modification_date}{date of last modification} }
#' @source GeoNames: \url{http://download.geonames.org/export/dump/}
#' @keywords datasets
#' @examples
#'
#' world_cities
#'
"world_cities"
#' Votes from Scottish Parliament
#' @docType data
#' @description Votes recorded on each ballot by each member of the Scottish Parliament in
#' 2008 along with information about party affiliation.
#' @format \code{Votes} is a data.frame with 103582 rows and 3 variables.
#' \describe{
#' \item{\code{bill}}{an identifier for the bill}
#' \item{\code{name}}{the name of the member of parliament}
#' \item{\code{vote}}{1 means a vote for, -1 a vote against. 0 is an abstention.}
#' }
#' \code{Parties} is a data.frame with 134 rows, one for each member of parliament, and 2 variables.
#' \describe{
#' \item{\code{party}}{the name of the political party the member belongs to}
#' \item{\code{name}}{the name of the member of parliament}
#' }
#'
#' @details Almost all of the members of parliament belongs to a political party. This table
#' identifies that party. These data were provided by Caroline Ettinger and form part of her
#' senior honor's project at Macalester College. Prof. Andrew Beveridge supervised the
#' thesis. Ms. Ettinger used the vote data to explore how to extract the party association
#' of members purely from voting records. The \code{Parties} data was used to evaluate
#' the success of methods.
"Votes"
#' @rdname Votes
"Parties"
#' Ballots in the 2013 Mayoral election in Minneapolis
#'
#' @description The choices marked on each (valid) ballot for the election, which was run
#' using a rank-choice, instant runoff system.
#'
#' @details Ballot information for the 2013 Minneapolis Mayoral election, which was run
#' as a rank-choice election. In rank-choice, a voter can indicate first,
#' second, and third choices. If a voter's first choice is eliminated (by
#' being last in the count across voters), the second choice is promoted to
#' that voter's first choice, and similarly third -> second. Eliminations are
#' done successively until one candidate has a majority of the first-choice
#' votes.
#'
#' @name Minneapolis2013
#' @docType data
#' @format A data frame with 80,101 observations on the following 5 variables.
#' All are stored as character strings.
#' \describe{
#' \item{Precinct}{Precincts are sub-divisions within Wards}
#' \item{First}{The voter's first choice}
#' \item{Second}{The voter's second choice}
#' \item{Third}{The voter's third choice}
#' \item{Ward}{The city is divided spatially into districts or 'wards'.
#' These are further subdivided into precincts. }
#' }
#' @references Description of ranked-choice voting:
#' \url{https://vote.minneapolismn.gov/ranked-choice-voting/}
#'
#' A Minnesota Public Radio story about the election ballot tallying process:
#' \url{https://www.mprnews.org/2013/11/22/politics/ranked-choice-vote-count-programmers/}
#'
#' The Wikipedia article about the election:
#' \url{https://en.wikipedia.org/wiki/2013_Minneapolis_mayoral_election}
#' @source Ballot data from the Minneapolis city government:
#' \url{https://vote.minneapolismn.gov/results-data/election-results/2013/mayor/}
#' @keywords datasets
#' @examples
#'
#' data(Minneapolis2013)
"Minneapolis2013"
#' Birds captured and released at Ordway, complete and uncleaned
#'
#' The historical record of birds captured and released at the Katharine Ordway
#' Natural History Study Area, a 278-acre preserve in Inver Grove Heights,
#' Minnesota, owned and managed by Macalester College.
#'
#' There are many extraneous levels of variables such as species. Part of the
#' purpose of this data set is to teach about data cleaning.
#'
#' @name ordway_birds
#' @docType data
#' @format A data frame with 15,829 observations on the bird's species, size,
#' date found, and band number.
#' \describe{
#' \item{\code{bogus}}{a character vector}
#' \item{\code{Timestamp}}{Timestamp indicates when the data were entered into an electronic record,
#' not anything about the bird being described}
#' \item{\code{Year}}{a character vector}
#' \item{\code{Day}}{a character vector}
#' \item{\code{Month}}{a character vector}
#' \item{\code{CaptureTime}}{a character vector}
#' \item{\code{SpeciesName}}{a character vector}
#' \item{\code{Sex}}{a character vector}
#' \item{\code{Age}}{a character vector}
#' \item{\code{BandNumber}}{a character vector}
#' \item{\code{TrapID}}{a character vector}
#' \item{\code{Weather}}{a character vector}
#' \item{\code{BandingReport}}{a character vector}
#' \item{\code{RecaptureYN}}{a character vector}
#' \item{\code{RecaptureMonth}}{a character vector}
#' \item{\code{RecaptureDay}}{a character vector}
#' \item{\code{Condition}}{a character vector}
#' \item{\code{Release}}{a character vector}
#' \item{\code{Comments}}{a character vector}
#' \item{\code{DataEntryPerson}}{a character vector}
#' \item{\code{Weight}}{a character vector}
#' \item{\code{WingChord}}{a character vector}
#' \item{\code{Temperature}}{a character vector}
#' \item{\code{RecaptureOriginal}}{a character vector}
#' \item{\code{RecapturePrevious}}{a character vector}
#' \item{\code{TailLength}}{a character vector}
#' }
#'
#' Timestamp indicates when the data were entered into an electronic record,
#' not anything about the bird being described.
#' @source Jerald Dosch, Dept. of Biology, Macalester College: the manager of
#' the Study Area.
#' @references \url{https://www.macalester.edu/ordway/}
#' @keywords datasets
#' @examples
#'
#' ordway_birds
#'
"ordway_birds"
#' Deaths and Pumps from 1854 London cholera outbreak
#' @docType data
#' @format An object of class \code{\link[sf]{sf}}
#' whose data attribute has 250 rows and 2 columns.
#' @details Both spatial objects are projected in EPSG:27700, aka the British
#' National Grid.
#' @source \url{https://blog.rtwilson.com/john-snows-cholera-data-in-more-formats/}
#' @examples
#' if (require(sf)) {
#' plot(st_geometry(CholeraDeaths))
#' }
"CholeraDeaths"
#' @rdname CholeraDeaths
#' @format An object of class \code{\link[sf]{sf}}.
#' @docType data
"CholeraPumps"
#' Gene expression in cancer
#'
#' The data come from a National Cancer Institute study of gene expression in
#' cell lines drawn from various sorts of cancer.
#'
#'
#'
#' \code{\link{Cancer}} gives information about each cell line.
#'
#' @docType data
#' @format The expression data, \code{NCI60_tiny} is a dataframe of 41,078 gene probes (rows)
#' and 60 cell lines (columns). The first column, \code{Probe} gives the name
#' of the Agilent microarray probe. Each of the remaining columns is named for
#' a cell line. The value is the log-2 expression associated with that probe
#' for the cell line.
#'
#' \describe{
#' \item{Probe}{the name of the Agilent microarray probe}
#' }
#'
#' For \code{Cancer}:
#' \describe{
#' \item{otherCellLine}{a character vector giving the name of one cell line}
#' \item{cellLine}{a character vector giving the name of another cell line}
#' \item{correlation}{the correlation between the two cell lines.
#' See \code{\link{cor}}}
#' }
#'
#' @seealso \code{\link{Cancer}}
#' @references Staunton et al. (2001), \emph{PNAS} (\doi{https://doi.org/10.1073/pnas.191368598})
#'
#' D.T. Ross et al. (2000) \emph{Nature Genetics}, 24(3):227-234
#' (\doi{https://doi.org/10.1038/73432})
#'
#' \href{https://discover.nci.nih.gov/cellminer/}{CellMiner}
#' @examples
#'
#' data(NCI60_tiny)
#'
"NCI60_tiny"
#' @rdname NCI60_tiny
#' @docType data
"Cancer"
#' Headlines_train
#' @description This data comes from Chakraborty et. al., which combines headlines from
#' a variety of news and clickbait sources. Some headlines contain
#' subject matter inappropriate for classroom use. Given the volume of headlines
#' containing such language (especially for \code{clickbait == TRUE}), this filtering
#' might not catch all problematic headlines. User discretion is advised.
#' The training dataset is a random sample of approximately 80\% of the observations
#' from the original dataset.
#'
#' @docType data
#' @format A data frame with 18,360 rows and 3 variables:
#' \describe{
#' \item{title}{a character vector}
#' \item{clickbait}{a logical vector}
#' \item{ids}{an integer vector}
#' }
#' @source \url{https://github.com/bhargaviparanjape/clickbait/}
#' @references \doi{10.1109/ASONAM.2016.7752207}
"Headlines_train"
#' @rdname Headlines_train
#' @description The testing dataset is a random sample of the remaining 20\% of the observations
#' not found in the training set.
#' @docType data
#' @format A data frame with 4,589 rows and 3 variables:
#' @examples
#' nrow(Headlines_train)
#' nrow(Headlines_test)
"Headlines_test"
#' Email Train
#'
#' The training dataset includes a set of email subject lines used for classification
#' of whether the message is spam (unsolicited commercial content) or not.
#' Many subject lines include subject matter inappropriate for classroom use.
#' Given the volume of headlines containing such language
#' (especially for \code{spam == TRUE}), user discretion is advised.
#' This dataset is a random sample of 80\% of the emails data.
#' @docType data
#' @format A data frame with 5,526 rows and 3 variables:
#' \describe{
#' \item{ids}{an integer vector}
#' \item{subjectline}{a character vector}
#' \item{type}{a character vector}
#' }
#' @source Originally retrieved from http://www.rdatasciencecases.org/Data.html
"Emails_train"
#' @rdname Emails_train
#' @description The testing dataset is a random sample of 20\% of the emails data.
#' @docType data
#' @format A data frame with 1,382 rows and 3 variables:
#' @examples
#' nrow(Emails_train)
#' nrow(Emails_test)
"Emails_test"
#' Saratoga Houses
#' @docType data
#' @format A tibble with 1728 rows and 16 variables:
#' \describe{
#' \item{price}{},
#' \item{lot_size}{},
#' \item{waterfront}{},
#' \item{age}{},
#' \item{land_value}{},
#' \item{construction}{},
#' \item{air_cond}{},
#' \item{fuel}{},
#' \item{heat}{},
#' \item{sewer}{},
#' \item{living_area}{},
#' \item{pct_college}{},
#' \item{bedrooms}{},
#' \item{fireplaces}{},
#' \item{bathrooms}{},
#' \item{rooms}{}
#' }
#' @examples
#' saratoga_houses
"saratoga_houses"
#' @rdname saratoga_houses
#' @docType data
"saratoga_codes"
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