shorten long lines identified in R CMD check

also use `styler::style_active_file()` to automate formatting etc.

R/downscale_core.R

@@ -3,10 +3,10 @@
 #' @description
 #' `downscale_core()` is the engine for [`downscale()`].
 #' It takes user-supplied high- and low-resolution rasters as input and downscales to user-specified point locations.
-#' While less user-friendly than [`downscale()`], `downscale_core()` is more flexible in that users can supply their 
-#' own raster inputs. For example, a user could supply their own high-resolution climate map, instead of what is 
-#' available in climr, as the input to `refmap`. Another example is in downscaling a uniform warming level, as shown 
-#' in the example for this function. 
+#' While less user-friendly than [`downscale()`], `downscale_core()` is more flexible in that users can supply their
+#' own raster inputs. For example, a user could supply their own high-resolution climate map, instead of what is
+#' available in climr, as the input to `refmap`. Another example is in downscaling a uniform warming level, as shown
+#' in the example for this function.
 #'
 #' @details
 #' We recommend [`downscale()`] for most purposes.
@@ -31,7 +31,7 @@
 #' @param out_spatial logical. Should a SpatVector be returned instead of a
 #' `data.frame`.
 #' @param plot character. If `out_spatial` is TRUE, the name of a variable to plot.
-#' If the variable exists in `reference`, then its reference values will also be plotted. 
+#' If the variable exists in `reference`, then its reference values will also be plotted.
 #' Otherwise, reference January total precipitation (PPT01) values will be plotted.
 #' Defaults to no plotting (NULL).
 #'
@@ -49,39 +49,55 @@
 #'
 #' @export
 #' @examples
-#' ## 
+#' ##
 #' library(terra)
-#' xyz <- data.frame(lon = runif(10, -130, -106), lat = runif(10, 37, 50), elev = runif(10), id = 1:10)
+#' xyz <- data.frame(
+#' lon = runif(10, -130, -106), lat = runif(10, 37, 50),
+#' elev = runif(10), id = 1:10
+#' )
 #'
 #' ## get bounding box based on input points
 #' thebb <- get_bb(xyz)
-#' 
+#'
 #' ## get database connection
 #' dbCon <- data_connect()
-#' 
-#' # obtain the climatena 1961-1990 normals for the study area. 
+#'
+#' # obtain the climatena 1961-1990 normals for the study area.
 #' refmap <- input_refmap(dbCon, thebb, reference = "refmap_climatena")
-#' 
-#' # obtain the low-resolution climate data for a single gcm, 20-year period, and ssp scenario. 
-#' gcm_raw <- input_gcms(dbCon, thebb, list_gcms()[3], list_ssps()[1], period = list_gcm_periods()[2])
-#' 
+#'
+#' # obtain the low-resolution climate data for a single gcm, 20-year period, and ssp scenario.
+#' gcm_raw <- input_gcms(dbCon, thebb, list_gcms()[3], list_ssps()[1],
+#' period = list_gcm_periods()[2]
+#' )
+#'
 #' # downscale the GCM data
-#' gcm_downscaled <- downscale_core(xyz = xyz, refmap = refmap, gcms = gcm_raw, vars = c("MAT", "PAS"))
-#' 
-#' # create an input of uniform warming of 2 degrees Celsius and no precipitation change, for use as a null comparison to the GCM warming
+#' gcm_downscaled <- downscale_core(
+#' xyz = xyz, refmap = refmap, gcms = gcm_raw,
+#' vars = c("MAT", "PAS")
+#' )
+#'
+#' # create an input of uniform warming of 2 degrees Celsius and no precipitation change,
+#' # for use as a null comparison to the GCM warming
 #' null <- gcm_raw #' use the gcm input object as a template
 #' names(null) <- "null_2C"
-#' names(null[[1]]) <- sapply(strsplit(names(null[[1]]), "_"), function(x) paste("null2C", x[2], x[3], "NA", "NA", "NA", "NA", sep="_"))
-#' for(var in names(null[[1]])){ values(null[[1]][[var]]) <- if(length(grep("PPT", var)==1)) 1 else 2 } #' repopulate with the null values
-#' 
+#' names(null[[1]]) <- sapply(strsplit(names(null[[1]]), "_"), function(x) {
+#' paste("null2C", x[2], x[3], "NA", "NA", "NA", "NA", sep = "_")
+#' })
+#' for (var in names(null[[1]])) {
+#' values(null[[1]][[var]]) <- if (length(grep("PPT", var) == 1)) 1 else 2
+#' } # repopulate with the null values
+#'
 #' # downscale the null values for variables of interest
-#' null_downscaled <- downscale_core(xyz = xyz, refmap = refmap, gcms = null, vars = c("MAT", "PAS"))
+#' null_downscaled <- downscale_core(
+#' xyz = xyz, refmap = refmap, gcms = null,
+#' vars = c("MAT", "PAS")
+#' )
 #' pool::poolClose(dbCon)
-#' 
+#'
 downscale_core <- function(xyz, refmap, gcms = NULL, obs = NULL, gcm_ssp_ts = NULL,
- gcm_hist_ts = NULL, obs_ts = NULL, return_refperiod = TRUE,
- vars = sort(sprintf(c("PPT_%02d", "Tmax_%02d", "Tmin_%02d"), sort(rep(1:12, 3)))),
- ppt_lr = FALSE, nthread = 1L, out_spatial = FALSE, plot = NULL) {
+  gcm_hist_ts = NULL, obs_ts = NULL, return_refperiod = TRUE,
+  vars = sort(sprintf(c("PPT_%02d", "Tmax_%02d", "Tmin_%02d"), sort(rep(1:12, 3)))),
+  ppt_lr = FALSE, nthread = 1L, out_spatial = FALSE, plot = NULL) {
  ## checks
  .checkDwnsclArgs(
  xyz, refmap, gcms, obs, gcm_ssp_ts, gcm_hist_ts,
@@ -363,7 +379,7 @@ downscale_ <- function(xyz, refmap, gcms, gcm_ssp_ts, gcm_hist_ts,
  labels <- nm
  normal_ <- res
  # Reshape (melt / dcast) to obtain final form
- #ref_dt <- tstrsplit(nm, "_")
+ # ref_dt <- tstrsplit(nm, "_")
  ref_dt <- data.table(VAR = nm)
  # setDT(ref_dt)
  # setnames(ref_dt, c("VAR"))
@@ -469,38 +485,40 @@ process_one_climaterast <- function(climaterast, res, xyz, timeseries = FALSE,
  # Cropping will reduce the size of data to load in memory
 
  climaterast <- crop(climaterast, ex, snap = "out")
- gc(reset = TRUE)  ## free unused memory
- 
+ gc(reset = TRUE) ## free unused memory
+
  climaterast <- try(extract(x = climaterast, y = xyz[, .(lon, lat)], method = "bilinear"))
- 
+
  ## we may have run out of memory if there are MANY rasters
- ## attempt to get only unique raster cell values 
+ ## attempt to get only unique raster cell values
  ## (i.e. xyz may be at higher res than the climaterast leading to extracting the same values many times)
  if (is(climaterast, "try-error")) {
  if (grepl("bad_alloc", climaterast)) {
  message("System is out of memory to extract climate values for the supplied coordinates")
- stop("Insufficient memory to downscale climate data for these many points/climate layers.\n",
- " Try reducing number of points/layers.")
- }
- } 
-
+ stop(
+ "Insufficient memory to downscale climate data for these many points/climate layers.\n",
+ " Try reducing number of points/layers."
+ )
+ }
+ }
+
  # else { Ceres not sure what this is for but it's always causing fails
  # stop("Climate value extraction failed.",
  # "\n Please contact developers with a reproducible example and the error:\n",
- # climaterast) 
+ # climaterast)
  # }
- 
+
  # Create match set to match with res names
- 
-
-  labels <- vapply(
-  strsplit(nm, "_"),
-  \(x) {
-  paste0(x[2:3], collapse = "_")
-  },
-  character(1)
-  )
- 
+
+
+ labels <- vapply(
+ strsplit(nm, "_"),
+ \(x) {
+ paste0(x[2:3], collapse = "_")
+ },
+ character(1)
+ )
+
  if (type %in% c("obs")) {
  ## Create match set to match with res names
  labels <- nm
@@ -525,12 +543,12 @@ process_one_climaterast <- function(climaterast, res, xyz, timeseries = FALSE,
  }
 
  setDT(ref_dt)
- if (type %in% c("obs","obs_ts")) {
+ if (type %in% c("obs", "obs_ts")) {
  if (timeseries) {
  setnames(ref_dt, c("DATASET", "VAR", "MONTH", "PERIOD"))
  set(ref_dt, j = "variable", value = nm)
  } else {
- setnames(ref_dt, c("VAR","MONTH"))
+ setnames(ref_dt, c("VAR", "MONTH"))
  set(ref_dt, j = "variable", value = nm)
  set(ref_dt, j = "PERIOD", value = "2001_2020")
  }
@@ -686,7 +704,7 @@ unpackRasters <- function(ras) {
  if (!inherits(xyz$id, colTypes)) {
  stop("'xyz$id' must be an column of type ", paste(colTypes, collapse = ", "))
  }
- 
+
  return(xyz)
 }
 
@@ -701,18 +719,18 @@ unpackRasters <- function(ras) {
  obs_ts = NULL, return_refperiod = FALSE,
  out_spatial = FALSE, plot = NULL, vars = list_vars()) {
  vars <- match.arg(vars, list_vars(), several.ok = TRUE)
- 
+
  if (!return_refperiod %in% c(TRUE, FALSE)) {
  stop("'return_refperiod' must be TRUE or FALSE")
  }
  if (!out_spatial %in% c(TRUE, FALSE)) {
  stop("'out_spatial' must be TRUE or FALSE")
  }
- 
+
  plot <- if (!is.null(plot)) {
- match.arg(plot,list_vars())
+ match.arg(plot, list_vars())
  }
- 
+
  if (!isTRUE(attr(refmap, "builder") == "climr")) {
  stop(
  "Please use `input_refmap` function to create `refmap`.",

R/plot_timeSeries_input.R

@@ -1,62 +1,66 @@
 #' Input data for the time series climate change plot
 #'
 #' @description
-#' Input data for the [`plot_timeSeries()`] function. Since these inputs are time-consuming to generate, 
+#' Input data for the [`plot_timeSeries()`] function. Since these inputs are time-consuming to generate,
 #' the purpose of conducting the generation of the input table in a separate function is to allow users
-#' to make multiple calls to [`plot_timeSeries()`] (e.g., for comparing different climate variables) 
+#' to make multiple calls to [`plot_timeSeries()`] (e.g., for comparing different climate variables)
 #' without needing to generate the inputs each time.
-#' 
+#'
 #' @details
 #' This function generates standardized inputs for one or multiple locations at any spatial scale.
-#' If multiple locations are specified, the output is the average of the climate variables for all locations. 
-#' 
-#' Downloads of GCM time series take a long time. The `plot_timeSeries_input()` function can take >1hr 
-#' to run for the first time it is called for a location. We are looking into ways to speed this up, but until then 
-#' we recommend users dedicate some time to run this function in background. Once the time series are cached, they 
-#' don't need to be downloaded again. 
+#' If multiple locations are specified, the output is the average of the climate variables for all locations.
+#'
+#' Downloads of GCM time series take a long time. The `plot_timeSeries_input()` function can take >1hr
+#' to run for the first time it is called for a location. We are looking into ways to speed this up, but until then
+#' we recommend users dedicate some time to run this function in background. Once the time series are cached, they
+#' don't need to be downloaded again.
 #'
 #' @template xyz
 #' @inheritParams downscale
 #' @template vars
 #'
 #' @return `data.table` of average downscaled climate variables for all locations.
-#' 
+#'
 #' @examples
-#' if(FALSE){
-#' # data frame of arbitrary points
-#' my_points <- data.frame(lon = c(-127.7300,-127.7500), lat = c(55.34114, 55.25), elev = c(711, 500), id = 1:2)
-#' 
-#' # generate the input data
-#' my_data <- plot_timeSeries_input(my_points)
-#' 
-#' # use the input to create a plot
-#' plot_timeSeries(my_data, variable1 = "Tmin_sm")
+#' if (FALSE) {
+#' # data frame of arbitrary points
+#' my_points <- data.frame(
+#' lon = c(-127.7300, -127.7500),
+#' lat = c(55.34114, 55.25),
+#' elev = c(711, 500),
+#' id = 1:2
+#' )
+#'
+#' # generate the input data
+#' my_data <- plot_timeSeries_input(my_points)
+#'
+#' # use the input to create a plot
+#' plot_timeSeries(my_data, variable1 = "Tmin_sm")
 #' }
-#' #' 
+#' #'
 #' @export
-
 plot_timeSeries_input <- function(
- xyz, 
+ xyz,
  gcms = list_gcms(),
  ssps = list_ssps(),
  max_run = 10,
- obs_ts_dataset = c("cru.gpcc", "climatena"), 
+ obs_ts_dataset = c("cru.gpcc", "climatena"),
  obs_years = 1901:2022,
- gcm_hist_years = 1850:2014, 
- gcm_ssp_years = 2015:2100, 
- vars = list_vars()
-) {
- data <- downscale(xyz = xyz, 
-  gcms = gcms,
-  ssps = ssps,
-  max_run = max_run,
-  obs_ts_dataset = obs_ts_dataset, 
-  obs_years = obs_years,
-  gcm_hist_years = gcm_hist_years, 
-  gcm_ssp_years = gcm_ssp_years, 
-  vars = vars
+ gcm_hist_years = 1850:2014,
+ gcm_ssp_years = 2015:2100,
+ vars = list_vars()) {
+ data <- downscale(
+  xyz = xyz,
+ gcms = gcms,
+ ssps = ssps,
+ max_run = max_run,
+ obs_ts_dataset = obs_ts_dataset,
+ obs_years = obs_years,
+ gcm_hist_years = gcm_hist_years,
+ gcm_ssp_years = gcm_ssp_years,
+ vars = vars
  )
- data.agg <- data[, lapply(.SD, mean), by = .(GCM, SSP, RUN, PERIOD, DATASET), .SDcols = -c("id", "GCM", "SSP", "RUN", "PERIOD", "DATASET")]
+ data.agg <- data[, lapply(.SD, mean), by = .(GCM, SSP, RUN, PERIOD, DATASET),
+ .SDcols = -c("id", "GCM", "SSP", "RUN", "PERIOD", "DATASET")]
  return(data.agg)
 }
-