R/data.R

In MichielNoback/bindata: A Collection of Datasets for Bioinformatics education

#' Allometry of snakes
#'
#' This dataframe holds data for Snout-vent length and mass for a large collection
#' of snake species. Besides genus and species, family association is also included.
#' This dataset was obtained at \url{https://academic.oup.com/biolinnean/article/108/1/161/2415670#supplementary-data}
#' and cleaned manually, so errors are quite possible.
#'
#' @references Feldman and Meiri (2013). Length–mass allometry in snakes. Biological
#' Journal of the Linnean Society, Volume 108, Issue 1, 1 January 2013, Pages 161–172
#'
#' @format A data frame with 1476 observations and 5 variables:
#' \describe{
#' \item{Family}{The taxonomic family the snake species belongs to.}
#' \item{Genus}{The taxonomic genus the snake species belongs to.}
#' \item{Species}{The taxonomic species name the snake species belongs to
#' - this should combine with the genus to get the complete scientific name.}
#' \item{SVL}{The Snout–vent length, in mm.}
#' \item{Mass}{The body mass, in grams.}
#' }
#'
#' @examples
#' head(snakes)
"snakes"



#' Bird species on the British isles
#'
#' Numbers of bird species observed on 42 British Isles, including area,
#' latitude information, distance from Britain and soil variation.
#'
#' @references
#'   M.P. Johnson and D.S. Simberloff (1974). "Environmental Determinants
#'   of Island Species Numbers in the British Isles," Journal of Biogeography,
#'   Vol. 1, pp. 149-154
#'
#'   E.D. McCoy and E.F. Connor (1976). "Enviromnmental Determinants of
#'   Island Species Number in the British Isles: A Reconsideration,"
#'   Journal of Biogeography, Vol. 3, pp. 381-382.
#'
#' @format A data frame with 42 observations and 7 variables:
#' \describe{
#' \item{island}{The name of the island}
#' \item{area}{The surface area of the island, in square kilometer}
#' \item{elevation}{Maximum elevation in meters}
#' \item{soil.types}{The number of different soil types found on the island}
#' \item{latitude}{Midpoint of latitude range in degrees north}
#' \item{dis.from.britain}{Distance in kilometers}
#' \item{num.species}{Number of observed species}
#' }
#' @examples
#' head(britain.species)
"britain.species"



#' Bird observation
#'
#' This dataset contains bird observations of the bird monitoring group called
#' Golden Gate Audubon Society. They are active in the the San Francisco Bay
#' area.
#'
#' @references
#' http://goldengateaudubon.org/birding-resources/observations/
#'
#' @format A data frame with 2019 observations and 13 variables:
#' \describe{
#' \item{Species.No}{An identifier for the species}
#' \item{Genus}{The genus the bird belongs to}
#' \item{Species}{The scientific name of the species (without the genus part)}
#' \item{Common.Name}{The English name of the species}
#' \item{CBRC.Review}{Whether the bird has been reviewed by the California Bird Records Committee}
#' \item{Date.start}{The start date of the observation (as character!)}
#' \item{Date.end}{The last observation date (if any)}
#' \item{Number}{The number of individuals}
#' \item{Location}{The location of the observation - i.e. the name of the place}
#' \item{County}{County}
#' \item{Observer1}{The first (primary) observer}
#' \item{Other.Obs}{Other Observer(s), comma-separated}
#' \item{Notes}{Notes}
#' }
#'
#' @examples
#' head(bird.observations)
"bird.observations"



#' Community Health Status Indicators (CSHI 2015)
#'
#' Some US demographic data.
#'
#' @references
#' data downloaded from:
#' https://catalog.data.gov/dataset/community-health-status-indicators-chsi-to-combat-obesity-heart-disease-and-cancer
#'
#' See \code{\link{health.stats.codebook}} for detailed description
#' @format A data frame with  observations and  variables:
#' \describe{
#'
#' \item{State_FIPS_Code}{}
#' \item{County_FIPS_Code}{}
#' \item{CHSI_County_Name}{Name of county}
#' \item{CHSI_State_Name}{Name of State or District of Columbia}
#' \item{CHSI_State_Abbr}{Two-character postal abbreviation for state name}
#' \item{Strata_ID_Number}{CHSI Peer County Stratum Number}
#' \item{Strata_Determining_Factors}{Listing of strata factors}
#' \item{Number_Counties}{Number of peer counties}
#' \item{Population_Size}{County data, population size}
#' \item{Min_Population_Size}{}
#' \item{Max_Population_Size}{}
#' \item{Population_Density}{County data, population density (people per square mile)}
#' \item{Min_Population_Density}{}
#' \item{Max_Population_Density}{}
#' \item{Poverty}{County data, percentage individuals living below poverty level}
#' \item{Min_Poverty}{}
#' \item{Max_Poverty}{}
#' \item{Age_19_Under}{County data, percentage population under age 19}
#' \item{Min_Age_19_Under}{}
#' \item{Max_Age_19_Under}{}
#' \item{Age_19_64}{County data, percentage population age 19-64}
#' \item{Min_Age_19_64}{}
#' \item{Max_Age_19_65}{}
#' \item{Age_65_84}{County data, percentage population age 65-84}
#' \item{Min_Age_65_84}{}
#' \item{Max_Age_65_85}{}
#' \item{Age_85_and_Over}{County data, percentage population age 85+}
#' \item{Min_Age_85_and_Over}{}
#' \item{Max_Age_85_and_Over}{}
#' \item{White}{County data, percentage White}
#' \item{Min_White}{}
#' \item{Max_White}{}
#' \item{Black}{County data, percentage Black}
#' \item{Min_Black}{}
#' \item{Max_Black}{}
#' \item{Native_American}{County data, percentage American Indian}
#' \item{Min_Native_American}{}
#' \item{Max_Native_American}{}
#' \item{Asian}{County data, percentage Asian/Pacific Islander}
#' \item{Min_Asian}{}
#' \item{Max_Asian}{}
#' \item{Hispanic}{County data, percentage Hispanic origin}
#' \item{Min_Hispanic}{}
#' \item{Max_Hispanic}{}
#' }
#'
#' @examples
#' head(us.demography)
#'
"us.demography"


#' Cancer incidence, prevalence, survival and mortality in the Netherlands
#'
#' Data was downloaded from "Integraal Kankercentrum Nederland" on 2020-01-27
#'
#' @source [https://iknl.nl/nkr-cijfers](https://iknl.nl/nkr-cijfers)
#'
#' A broad selection was made, split over age groups, cancer types and gender (when available) and
#' downloaded as Excel. The long-format data was converted to an R object.
#'
#' @format A data frame with 7525 observations and 5 variables:
#' \describe{
#' \item{Kankersoort}{The type of cancer (Dutch name)}
#' \item{Periode}{The year}
#' \item{Geslacht}{Gender}
#' \item{Leeftijdsgroep}{Age group}
#' \item{Aantal}{Count}
#' }
"cancer_netherlands"