Nothing
R/data.R
In 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 [tibble::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 [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 [Lahman::Teams]
#'
#' @source The [Lahman::Teams] table from [Lahman::Lahman-package]
#' and <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,
#' <https://www.cia.gov/the-world-factbook/>
#' @seealso [mosaic::CIAdata]
#' @references <https://github.com/factbook/factbook/blob/master/CATEGORIES.md>
#' @keywords datasets
#' @examples
#' str(CIACountries)
#'
"CIACountries"
#' Election Statistics from the 2013 Minneapolis Mayoral Election
#' @docType data
#' @format An object of class [tibble::tbl_df] with 117 rows and 13 columns.
#' \describe{
#' \item{Ward}{Number of the ward}
#' \item{Precinct}{Number of the precinct}
#' \item{Registered Voters at 7am}{Number of registered votes as of 7 am}
#' \item{Voters Registering at Polls}{Number of voters registering at the polls}
#' \item{Voters Registering by Absentee}{Number of voters registering by absentee}
#' \item{Total Registrations}{Total number of registered voters}
#' \item{Voters at Polls}{Number of voters at the polls}
#' \item{Absentee Voters}{Number of absentee voters}
#' \item{Total Ballots Cast}{Number of total ballots cast}
#' \item{Total Turnout}{Total number of voters turning out}
#' \item{Percentage Absentee}{Percentage of absentee voters}
#' \item{% Registered to Total (Election Day)}{Percentage of voters relative to total number of people}
#' \item{Spoiled Ballots}{Number of spolied ballots}
#' }
#' @source <https://vote.minneapolismn.gov/results-data/election-results/2013/mayor/>
"Elections"
#' Cherry Blossom runs
#' @docType data
#' @format An object of class [tibble::tbl_df] 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 <https://www.cherryblossom.org/post-race/race-results/>.
#' @source <https://www.cherryblossom.org/post-race/race-results/>.
#' @seealso *Data Science in R*, Nolan and Temple Lang (ISBN 978-1482234817), Ch. 2
#' @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{camis}{unique identifier}
#' \item{dba}{full name doing business as}
#' \item{boro}{borough of New York}
#' \item{building}{building name}
#' \item{street}{street address}
#' \item{zipcode}{zipcode}
#' \item{phone}{phone number}
#' \item{inspection_date}{inspection date}
#' \item{action}{action taken}
#' \item{violation_code}{violation code, see [ViolationCodes]}
#' \item{score}{inspection score}
#' \item{grade}{inspection grade}
#' \item{grade_date}{grade date}
#' \item{record_date}{recording date}
#' \item{inspection_type}{inspect type}
#' \item{cuisine_code}{cuisine code, see [Cuisines]}
#' }
#' @seealso [ViolationCodes], [Cuisines]
#' @source [NYC Open Data](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
#' @format
#' A data frame with 174 observations on the following 3 variables.
#' \describe{
#' \item{violation_code}{a factor with many levels}
#' \item{critical_flag}{is violation critical: a factor with levels `N`, `Y`}
#' \item{violation_description}{violation description}
#' }
"ViolationCodes"
#' @rdname Violations
#' @format
#' A data frame with 84 observations on the following 2 variables.
#' \describe{
#' \item{cuisine_code}{a character vector}
#' \item{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 <https://arxiv.org/>
#' @keywords datasets
#' @examples
#'
#' data(DataSciencePapers)
#' str(DataSciencePapers)
#'
"DataSciencePapers"
#' Medicare Providers
#'
#' Name and location data for the medicare providers in the
#' [MedicareCharges] data table.
#'
#' This data table is related to [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
#' <https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/>
#' @keywords datasets
#' @seealso [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 `DiagnosisRelatedGroup`, which gives a
#' description of the medical procedure associated with each DRG, and
#' [MedicareProviders], which translates `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 `drg` across each state}
#' }
#' @source Data from the Centers for Medicare and Medicaid Services.
#' See
#' <https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/>
#' @keywords datasets
#' @seealso [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, <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{state}{a factor with levels for each state}
#' \item{expenditure}{average expenditure per student (in each state)}
#' \item{pupil_teacher_ratio}{pupil to teacher ratio in that state}
#' \item{salary}{teacher salary (in 2010 US $)}
#' \item{read}{state average Reading SAT score}
#' \item{math}{state average Math SAT score}
#' \item{write}{state average Writing SAT score}
#' \item{total}{state average Total SAT score}
#' \item{sat_pct}{percent of students taking SAT in that state}
#' }
#' @details See also the earlier [mosaicData::SAT] dataset.
#' @seealso [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: <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 [Votes] is a data.frame with 103582 rows and 3 variables.
#' \describe{
#' \item{bill}{an identifier for the bill}
#' \item{name}{the name of the member of parliament}
#' \item{vote}{1 means a vote for, -1 a vote against. 0 is an abstention.}
#' }
#' [Parties] is a data.frame with 134 rows, one for each member of parliament, and 2 variables.
#' \describe{
#' \item{party}{the name of the political party the member belongs to}
#' \item{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 [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:
#' <https://vote.minneapolismn.gov/ranked-choice-voting/>
#'
#' A Minnesota Public Radio story about the election ballot tallying process:
#' <https://www.mprnews.org/2013/11/22/politics/ranked-choice-vote-count-programmers/>
#'
#' The Wikipedia article about the election:
#' <https://en.wikipedia.org/wiki/2013_Minneapolis_mayoral_election>
#' @source Ballot data from the Minneapolis city government:
#' <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{bogus}{a character vector}
#' \item{Timestamp}{Timestamp indicates when the data were entered into an electronic record,
#' not anything about the bird being described}
#' \item{Year}{a character vector}
#' \item{Day}{a character vector}
#' \item{Month}{a character vector}
#' \item{CaptureTime}{a character vector}
#' \item{SpeciesName}{a character vector}
#' \item{Sex}{a character vector}
#' \item{Age}{a character vector}
#' \item{BandNumber}{a character vector}
#' \item{TrapID}{a character vector}
#' \item{Weather}{a character vector}
#' \item{BandingReport}{a character vector}
#' \item{RecaptureYN}{a character vector}
#' \item{RecaptureMonth}{a character vector}
#' \item{RecaptureDay}{a character vector}
#' \item{Condition}{a character vector}
#' \item{Release}{a character vector}
#' \item{Comments}{a character vector}
#' \item{DataEntryPerson}{a character vector}
#' \item{Weight}{a character vector}
#' \item{WingChord}{a character vector}
#' \item{Temperature}{a character vector}
#' \item{RecaptureOriginal}{a character vector}
#' \item{RecapturePrevious}{a character vector}
#' \item{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 <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 [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 <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 [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.
#'
#'
#'
#' [Cancer] gives information about each cell line.
#'
#' @docType data
#' @format The expression data, [NCI60_tiny] is a dataframe of 41,078 gene probes (rows)
#' and 60 cell lines (columns). The first column, `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 [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 [stats::cor()]}
#' }
#'
#' @seealso [Cancer]
#' @references
#' - Staunton et al. (2001), *PNAS* (\doi{10.1073/pnas.191368598})
#' - D.T. Ross et al. (2000) *Nature Genetics*, 24(3):227-234 (\doi{10.1038/73432})
#' - [CellMiner](https://discover.nci.nih.gov/cellminer/)
#'
#' @examples
#' data(NCI60_tiny)
#'
"NCI60_tiny"
#' @rdname NCI60_tiny
"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 `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 <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 `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 <https://www.stat.berkeley.edu/~nolan/data/spam/SpamAssassinMessages.zip>
#' @seealso *Data Science in R*, Nolan and Temple Lang (ISBN 978-1482234817), Ch. 3
"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 [tibble::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 [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 [Lahman::Teams]
#'
#' @source The [Lahman::Teams] table from [Lahman::Lahman-package]
#' and <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,
#' <https://www.cia.gov/the-world-factbook/>
#' @seealso [mosaic::CIAdata]
#' @references <https://github.com/factbook/factbook/blob/master/CATEGORIES.md>
#' @keywords datasets
#' @examples
#' str(CIACountries)
#'
"CIACountries"
#' Election Statistics from the 2013 Minneapolis Mayoral Election
#' @docType data
#' @format An object of class [tibble::tbl_df] with 117 rows and 13 columns.
#' \describe{
#' \item{Ward}{Number of the ward}
#' \item{Precinct}{Number of the precinct}
#' \item{Registered Voters at 7am}{Number of registered votes as of 7 am}
#' \item{Voters Registering at Polls}{Number of voters registering at the polls}
#' \item{Voters Registering by Absentee}{Number of voters registering by absentee}
#' \item{Total Registrations}{Total number of registered voters}
#' \item{Voters at Polls}{Number of voters at the polls}
#' \item{Absentee Voters}{Number of absentee voters}
#' \item{Total Ballots Cast}{Number of total ballots cast}
#' \item{Total Turnout}{Total number of voters turning out}
#' \item{Percentage Absentee}{Percentage of absentee voters}
#' \item{% Registered to Total (Election Day)}{Percentage of voters relative to total number of people}
#' \item{Spoiled Ballots}{Number of spolied ballots}
#' }
#' @source <https://vote.minneapolismn.gov/results-data/election-results/2013/mayor/>
"Elections"
#' Cherry Blossom runs
#' @docType data
#' @format An object of class [tibble::tbl_df] 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 <https://www.cherryblossom.org/post-race/race-results/>.
#' @source <https://www.cherryblossom.org/post-race/race-results/>.
#' @seealso *Data Science in R*, Nolan and Temple Lang (ISBN 978-1482234817), Ch. 2
#' @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{camis}{unique identifier}
#' \item{dba}{full name doing business as}
#' \item{boro}{borough of New York}
#' \item{building}{building name}
#' \item{street}{street address}
#' \item{zipcode}{zipcode}
#' \item{phone}{phone number}
#' \item{inspection_date}{inspection date}
#' \item{action}{action taken}
#' \item{violation_code}{violation code, see [ViolationCodes]}
#' \item{score}{inspection score}
#' \item{grade}{inspection grade}
#' \item{grade_date}{grade date}
#' \item{record_date}{recording date}
#' \item{inspection_type}{inspect type}
#' \item{cuisine_code}{cuisine code, see [Cuisines]}
#' }
#' @seealso [ViolationCodes], [Cuisines]
#' @source [NYC Open Data](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
#' @format
#' A data frame with 174 observations on the following 3 variables.
#' \describe{
#' \item{violation_code}{a factor with many levels}
#' \item{critical_flag}{is violation critical: a factor with levels `N`, `Y`}
#' \item{violation_description}{violation description}
#' }
"ViolationCodes"
#' @rdname Violations
#' @format
#' A data frame with 84 observations on the following 2 variables.
#' \describe{
#' \item{cuisine_code}{a character vector}
#' \item{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 <https://arxiv.org/>
#' @keywords datasets
#' @examples
#'
#' data(DataSciencePapers)
#' str(DataSciencePapers)
#'
"DataSciencePapers"
#' Medicare Providers
#'
#' Name and location data for the medicare providers in the
#' [MedicareCharges] data table.
#'
#' This data table is related to [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
#' <https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/>
#' @keywords datasets
#' @seealso [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 `DiagnosisRelatedGroup`, which gives a
#' description of the medical procedure associated with each DRG, and
#' [MedicareProviders], which translates `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 `drg` across each state}
#' }
#' @source Data from the Centers for Medicare and Medicaid Services.
#' See
#' <https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/>
#' @keywords datasets
#' @seealso [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, <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{state}{a factor with levels for each state}
#' \item{expenditure}{average expenditure per student (in each state)}
#' \item{pupil_teacher_ratio}{pupil to teacher ratio in that state}
#' \item{salary}{teacher salary (in 2010 US $)}
#' \item{read}{state average Reading SAT score}
#' \item{math}{state average Math SAT score}
#' \item{write}{state average Writing SAT score}
#' \item{total}{state average Total SAT score}
#' \item{sat_pct}{percent of students taking SAT in that state}
#' }
#' @details See also the earlier [mosaicData::SAT] dataset.
#' @seealso [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: <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 [Votes] is a data.frame with 103582 rows and 3 variables.
#' \describe{
#' \item{bill}{an identifier for the bill}
#' \item{name}{the name of the member of parliament}
#' \item{vote}{1 means a vote for, -1 a vote against. 0 is an abstention.}
#' }
#' [Parties] is a data.frame with 134 rows, one for each member of parliament, and 2 variables.
#' \describe{
#' \item{party}{the name of the political party the member belongs to}
#' \item{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 [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:
#' <https://vote.minneapolismn.gov/ranked-choice-voting/>
#'
#' A Minnesota Public Radio story about the election ballot tallying process:
#' <https://www.mprnews.org/2013/11/22/politics/ranked-choice-vote-count-programmers/>
#'
#' The Wikipedia article about the election:
#' <https://en.wikipedia.org/wiki/2013_Minneapolis_mayoral_election>
#' @source Ballot data from the Minneapolis city government:
#' <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{bogus}{a character vector}
#' \item{Timestamp}{Timestamp indicates when the data were entered into an electronic record,
#' not anything about the bird being described}
#' \item{Year}{a character vector}
#' \item{Day}{a character vector}
#' \item{Month}{a character vector}
#' \item{CaptureTime}{a character vector}
#' \item{SpeciesName}{a character vector}
#' \item{Sex}{a character vector}
#' \item{Age}{a character vector}
#' \item{BandNumber}{a character vector}
#' \item{TrapID}{a character vector}
#' \item{Weather}{a character vector}
#' \item{BandingReport}{a character vector}
#' \item{RecaptureYN}{a character vector}
#' \item{RecaptureMonth}{a character vector}
#' \item{RecaptureDay}{a character vector}
#' \item{Condition}{a character vector}
#' \item{Release}{a character vector}
#' \item{Comments}{a character vector}
#' \item{DataEntryPerson}{a character vector}
#' \item{Weight}{a character vector}
#' \item{WingChord}{a character vector}
#' \item{Temperature}{a character vector}
#' \item{RecaptureOriginal}{a character vector}
#' \item{RecapturePrevious}{a character vector}
#' \item{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 <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 [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 <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 [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.
#'
#'
#'
#' [Cancer] gives information about each cell line.
#'
#' @docType data
#' @format The expression data, [NCI60_tiny] is a dataframe of 41,078 gene probes (rows)
#' and 60 cell lines (columns). The first column, `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 [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 [stats::cor()]}
#' }
#'
#' @seealso [Cancer]
#' @references
#' - Staunton et al. (2001), *PNAS* (\doi{10.1073/pnas.191368598})
#' - D.T. Ross et al. (2000) *Nature Genetics*, 24(3):227-234 (\doi{10.1038/73432})
#' - [CellMiner](https://discover.nci.nih.gov/cellminer/)
#'
#' @examples
#' data(NCI60_tiny)
#'
"NCI60_tiny"
#' @rdname NCI60_tiny
"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 `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 <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 `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 <https://www.stat.berkeley.edu/~nolan/data/spam/SpamAssassinMessages.zip>
#' @seealso *Data Science in R*, Nolan and Temple Lang (ISBN 978-1482234817), Ch. 3
"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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