R/data.R

In sptotal: Predicting Totals and Weighted Sums from Spatial Data

#' Data Set with Uncorrelated Poisson Counts.
#'
#' A toy data set that can be used with the \code{sptotal} package. In
#' this example, the true counts are actually uncorrelated, the covariates are
#' generated as uniform random variables, and the sites fall on a
#' regular grid.
#'
#' @format A data frame with 40  rows and 7 variables:
#' \describe{
#'   \item{counts}{counts, with NA values for unsampled sites}
#'   \item{pred1}{a possible predictor}
#'   \item{pred2}{a second possible predictor}
#'   \item{xcoords}{coordinates on the x-axis}
#'   \item{ycoords}{coordinates on the y-axis}
#'   \item{dummyvar}{an extra variable}
#'   \item{areavar}{Variable for the area of each plot}
#'   ...
#' }
"exampledataset"


#' Data Set with Alaska Moose Counts.
#'
#' A data set that can be used with the \code{sptotal} package. In
#' this example, the counts are of moose on 860 sites of equal area.
#'
#' @format A dataframe object. The data frame \code{AKmoose_df} contains 860 rows and 4 columns:
#' \describe{
#'   \item{elev_mean}{The mean elevation for each sitefor each site}
#'   \item{strat}{A stratification variable (either L or M)}
#'   \item{surveyed}{Assigned a 1 if the site was surveyed and a 0 otherwise}
#'   \item{total}{The total moose count on each site (\code{NA} if the site was not surveyed)}
#'   \item{x}{the x-coordinate centroid of the site (TM)}
#'   \item{y}{the y-coordinate centroid of the site (TM)}
#'   \item{lon}{the longitudinal centroid of the site}
#'   \item{lat}{the latitudinal centroid of the site}
#'
#' }
#' @source \href{http://www.adfg.alaska.gov/index.cfm?adfg=hunting.main}{Alaska Department of Fish and Game, Division of Wildlife Conservation} has released this data set under the CC0 (creative commons) license. To the extent possible under law, Alaska Department of Fish and Game, Division of Wildlife Conservation waives all copyright and related or neighboring rights to An Alaskan GSPE (Geospatial Population Estimator) Survey of Moose, AKmoose.rda. This work is published from: United States.
#' @examples
#' data(AKmoose_df)
#' names(AKmoose_df)
#' summary(AKmoose_df)
"AKmoose_df"

#' Simulated Spatially Autocorrelated Data.
#'
#' A simulated data set that can be used with the \code{sptotal} package.
#'
#' @format A data frame object including:
#' \describe{
#'   \item{x}{The x-coordinate for each site}
#'   \item{y}{The y-coordinate for each site}
#'   \item{X1}{Simulated independent variable to be used as a predictor}
#'   \item{X2}{Simulated independent variable to be used as a predictor}
#'   \item{X3}{Simulated independent variable to be used as a predictor}
#'   \item{X4}{Simulated independent variable to be used as a predictor}
#'   \item{X5}{Simulated independent variable to be used as a predictor}
#'   \item{X6}{Simulated spatially correlated random variable to be used as a predictor}
#'   \item{X7}{Simulated spatially correlated random variable to be used as a predictor}
#'   \item{F1}{Simulated factor variable to be used as a predictor}
#'   \item{F2}{Simulated factor variable to be used as a predictor}
#'   \item{Z}{The simulated response variable.}
#'   \item{wts1}{Prediction weights if estimating an overall mean}
#'   \item{wts2}{Prediction weights for estimating a total over a subset of 25 contiguous plots}
#' }
#' @examples
#' data(simdata)
#' names(simdata)
#' summary(simdata)
"simdata"

#' Dissolved Organic Carbon in U.S. Lakes
#'
#' These data contain dissolved organic carbon (DOC) in National Lakes Data from the U.S. Environmental Protection Agency
#'
#' @format A data frame with 1206 rows and 9 variables:
#' \describe{
#'   \item{XCOORD}{x-coordinate from US Contiguous Albers Equal Area Conic projection}
#'   \item{YCOORD}{y-coordinate from US Contiguous Albers Equal Area Conic projection}
#'   \item{DOC_RESULT}{Analyte value, in mg/L, for Dissolved Organic Carbon}
#'   \item{ELEVATION}{Elevation at lake coordinates (LAT_DD_N83, LON_DD_N83) from NHD Digital Elevation Map layer}
#'   \item{FCIBIG_LIT}{Fish cover: index of fish cover due to large structures in the littoral zone}
#'   \item{RVFCGNDBARE_RIP}{riparian zone and vegetation: fraction of ground lacking cover in the riparian zone}
#'   \item{RVFCGNDWOODY_RIP}{riparian zone and vegetation: fraction of ground cover by woody vegetation in the riparian zone}
#'   \item{RVFPUNDWOODY_RIP}{riparian zone and vegetation: fraction of understory with nonwoody cover present in the riparian zone}
#'   \item{UID}{A unique lake identifier in the EPA lake survey databases}
#' }
#' @source \href{https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys}{National Aquatic Resource Surveys} webpage.  We combined \href{https://www.epa.gov/sites/production/files/2016-12/nla2012_wide_siteinfo_08232016.csv}{site data}, \href{https://www.epa.gov/sites/production/files/2016-12/nla2012_waterchem_wide.csv}{DOC data}, and \href{https://www.epa.gov/sites/production/files/2016-12/nla2012_wide_phabmet_10202016.csv}{habitat metrics} to create a data set of 1206 lakes in the conterminous United States.
#' @examples
#' data(USlakes)
#' names(USlakes)
#' summary(USlakes)
"USlakes"