R/lakeWA-data.R

In nwfsc-timeseries/atsalibrary: Packages, data and scripts for ATSA course and lab book

#' Lake Washington Plankton Data
#'
#' @description Monthly Lake Washington (WA, USA) plankton, temperature, total phosphorous, and pH data 1962 to 1994.
#' 
#' @details  The \code{lakeWA} is a 32-year time series (1962-1994) of monthly plankton counts from Lake Washington, Washington, USA. \code{lakeWA} is a transformed version of the raw data (available in the MARSS package \code{data(lakeWAplanktonRaw, package="MARSS")}).  Zeros have been replaced with NAs (missing).  The plankton counts are logged (natural log) and standardized to a mean of zero and variance of 1 (so logged and then z-scored). Temperature, TP & pH were also z-scored but not logged (so z-score of the untransformed values for these covariates). The single missing temperature value was replaced with -1 and the single missing TP value was replaced with -0.3. The two missing pH values were interpolated. Monthly anomalies for temperature, TP and pH were computed by removing the monthly means (computed over the 1962-1994 period). The anomalies were then z-scored to remove mean and standardize variance to 1.
#'
#' @docType data
#' 
#' @name lakeWA
#'
#' @usage data(lakeWA)
#' 
#' @format Object of class \code{"data.frame"}.
#' #'
#' @keywords datasets
#'
#' @references 
#' Adapted from the Lake Washington database of Dr. W. T. Edmondson, as funded by the Andrew Mellon Foundation; data courtesy of Dr. Daniel Schindler, University of Washington, Seattle, WA.
#' 
#' Hampton, S. E. Scheuerell, M. D. Schindler, D. E. (2006)  Coalescence in the Lake Washington story: Interaction strengths in a planktonic food web. Limnology and Oceanography, 51, 2042-2051.
#'
#' @examples
#' # The lakeWA data frame was created with the following code:
#' \dontrun{
# Load data
#' data(lakeWAplankton, package = "MARSS")
#' lakeWA <- data.frame(lakeWAplanktonTrans)
#' # add on month and date columns
#' lakeWA$Month.abb <- month.abb[lakeWA$Month]
#' lakeWA$Date <- as.Date(paste0(lakeWA$Year, "-", lakeWA$Month,"-01"))
#' # interpolate 2 missing values in pH
#' lakeWA$pH[is.na(lakeWA$pH)] <- MARSS::MARSS(lakeWA$pH)$states[1, is.na(lakeWA$pH)]
#' # create monthly anomalies
#' lakeWA$Temp.anom <- residuals(lm(Temp ~ Month.abb, data=lakeWA))
#' lakeWA$TP.anom <- residuals(lm(TP ~ Month.abb, data=lakeWA))
#' lakeWA$pH.anom <- residuals(lm(pH ~ Month.abb, data=lakeWA))
#' # resort the columns
#' lakeWA <- lakeWA[,c(22,1:2,21,3:5,23:25,6:20)]
#' # zscore everything
#' for (i in 5:25) lakeWA[[i]] <- MARSS::zscore(lakeWA[[i]])
#' save( lakeWA, file="data/lakeWA.RData" )
#' }
#' 
#' library(ggplot2)
#' library(tidyr)
#' df <- lakeWA %>% 
#'      pivot_longer(
#'          cols = Temp:Non.colonial.rotifers,
#'          names_to = "variable",
#'          values_to = "value"
#'          )
#' ggplot(df, aes(x=Date, y=value)) + 
#'      geom_line() + 
#'      facet_wrap(~variable)   
#' 
#' data(lakeWA)
"lakeWA"