styler clean up

R/check_coords_names.R

@@ -9,20 +9,22 @@
 #' @return A vector of length 2 with the valid names, in the correct order
 #' @keywords internal
 
-check_coords_names <-function(data, coords){
-  if (is.null(coords)){
-    valid_names <- list(c("x","y"), c("longitude", "latitude"), c("lon","lat"),
-                        c("X","Y"))
+check_coords_names <- function(data, coords) {
+  if (is.null(coords)) {
+    valid_names <- list(
+      c("x", "y"), c("longitude", "latitude"), c("lon", "lat"),
+      c("X", "Y")
+    )
   } else {
     valid_names <- list(coords)
   }
   # internal function to check that both x and y names are present
-  check_pair <- function(valid_names, var_names){
+  check_pair <- function(valid_names, var_names) {
     all(!is.na(match(valid_names, var_names)))
   }
   # find if we have any pair
   valid_pair <- which(unlist(lapply(valid_names, check_pair, names(data))))
-  if (length(valid_pair)!=1){
+  if (length(valid_pair) != 1) {
     stop("There are no recognised coordinate columns, set their names with 'coords'")
   }
 

R/check_times.R

@@ -1,35 +1,34 @@
 #' Check multiple time variables
 #'
-#' Check that we only have one set of times 
+#' Check that we only have one set of times
 #'
 #' @param time_bp times in bp
 #' @param time_ce times in ce
 #' @param allow_null boolean whether both can be NULL
 #' @returns times in bp
 #' @keywords internal
 
-check_time_vars <- function(time_bp, time_ce, allow_null=TRUE) {
-  if (all(!is.null(time_bp), !is.null(time_ce))){
+check_time_vars <- function(time_bp, time_ce, allow_null = TRUE) {
+  if (all(!is.null(time_bp), !is.null(time_ce))) {
     stop("both time_bp and time_ce were provide, the function can only accept one of the two")
   }
-  if (all(is.null(time_bp), is.null(time_ce))){
-    if (allow_null){
+  if (all(is.null(time_bp), is.null(time_ce))) {
+    if (allow_null) {
       return(NULL)
     } else {
       stop("either time_bp or time_ce should be provided")
     }
-
   }
-  if (is.null(time_ce)){
+  if (is.null(time_ce)) {
     return(time_bp)
   } else {
     # convert it to bp
-    if (inherits(time_ce, "list")){
-      return(lapply(time_ce,function(x){x-1950}))
+    if (inherits(time_ce, "list")) {
+      return(lapply(time_ce, function(x) {
+        x - 1950
+      }))
     } else {
-      return(time_ce-1950)
+      return(time_ce - 1950)
     }
-
-
   }
 }

R/clean_data_path.R

@@ -1,4 +1,4 @@
-#' Clean the data path 
+#' Clean the data path
 #'
 #' This function deletes old reconstructions that have been superseded in the
 #' data_path. It assumes that the only files in data_path are part of `pastclim`
@@ -8,8 +8,8 @@
 #' @returns TRUE if files are deleted successfully
 #' @export
 
-clean_data_path <- function(ask=TRUE) {
-  if (is.null(get_data_path(silent=TRUE))){
+clean_data_path <- function(ask = TRUE) {
+  if (is.null(get_data_path(silent = TRUE))) {
     message("The data path has not been set yet; use set_data_path() first!")
     return(FALSE)
   }
@@ -18,26 +18,30 @@ clean_data_path <- function(ask=TRUE) {
   # current or whether it has become obsolete)
   files_now <- files_now[!files_now %in% c("pastclim_dataset_list.csv")]
   # keep worldclim datasets
-  files_now <- files_now[!substr(files_now,1,2)=="wc"]
+  files_now <- files_now[!substr(files_now, 1, 2) == "wc"]
   # keep etopo datasets
-  files_now <- files_now[!substr(files_now,1,5)=="etopo"]
+  files_now <- files_now[!substr(files_now, 1, 5) == "etopo"]
   # keep chelsa datasets
-  files_now <- files_now[!substr(files_now,1,6)=="chelsa"]
-  
-  
+  files_now <- files_now[!substr(files_now, 1, 6) == "chelsa"]
+
+
   possible_files <- unique(getOption("pastclim.dataset_list")$file_name)
   files_to_remove <- files_now[!files_now %in% possible_files]
-  if (length(files_to_remove)>0){
-    if (ask){
-      this_answer <- utils::menu(choices = c("yes","no"),
-                title = paste("The following files are obsolete:\n",
-                                   paste(files_to_remove,collapse = ", "),
-                                   "\n Do you want to delete them?"))
+  if (length(files_to_remove) > 0) {
+    if (ask) {
+      this_answer <- utils::menu(
+        choices = c("yes", "no"),
+        title = paste(
+          "The following files are obsolete:\n",
+          paste(files_to_remove, collapse = ", "),
+          "\n Do you want to delete them?"
+        )
+      )
     } else { # default to delete if we are not asking
       this_answer <- 1
     }
-    if (this_answer==1){
-      file.remove(file.path(get_data_path(),files_to_remove))
+    if (this_answer == 1) {
+      file.remove(file.path(get_data_path(), files_to_remove))
     }
   } else {
     message("Everything is up-to-date; no files need removing.")

R/datasets_docs.R

@@ -10,9 +10,9 @@ NULL
 
 #' Documentation for the Beyer2020 dataset
 #'
-#' This dataset covers the last 120k years, at intervals of 1/2 k years, and a 
+#' This dataset covers the last 120k years, at intervals of 1/2 k years, and a
 #' resolution of 0.5 degrees in latitude and longitude.
-#' 
+#'
 #' If you use this dataset, make sure to cite the original publication:
 #'
 #' Beyer, R.M.,
@@ -35,12 +35,12 @@ NULL
 #' \url{https://pubs.usgs.gov/ds/691/ds691.pdf}
 #'
 #' Changelog
-#' 
-#' v1.1.0 Added monthly variables. Files can be downloaded from: 
+#'
+#' v1.1.0 Added monthly variables. Files can be downloaded from:
 #' \url{https://zenodo.org/deposit/7062281}
-#' 
+#'
 #' v1.0.0 Remove ice sheets and internal seas, and use correct formula for bio15.
-#' Files can be downloaded from: 
+#' Files can be downloaded from:
 #' \doi{doi.org/10.6084/m9.figshare.19723405.v1}
 #'
 #'
@@ -50,9 +50,9 @@ NULL
 
 #' Documentation for the Krapp2021 dataset
 #'
-#' This dataset covers the last 800k years, at intervals of 1k years, and a 
+#' This dataset covers the last 800k years, at intervals of 1k years, and a
 #' resolution of 0.5 degrees in latitude and longitude.
-#' 
+#'
 #' The units of several variables have been changed to match what is used
 #' in WorldClim.
 #'
@@ -69,14 +69,14 @@ NULL
 #' \url{https://pubs.usgs.gov/ds/691/ds691.pdf}
 #'
 #' Changelog
-#' 
+#'
 #' v1.4.0 Change units to match WorldClim. Fix variable duplication found
 #' on earlier versions of the dataset.
-#' 
+#'
 #' v1.1.0 Added monthly variables. Files can be downloaded from:
 #' \url{https://zenodo.org/record/7065055}
-#' 
-#' v1.0.0 Remove ice sheets and use correct formula for bio15. 
+#'
+#' v1.0.0 Remove ice sheets and use correct formula for bio15.
 #' Files can be downloaded from:
 #' \doi{doi.org/10.6084/m9.figshare.19733680.v1}
 #'
@@ -86,47 +86,47 @@ NULL
 
 #' Documentation for the WorldClim datasets
 #'
-#' WorldClim version 2.1 is a database of high spatial resolution global weather and 
+#' WorldClim version 2.1 is a database of high spatial resolution global weather and
 #' climate data, covering both the present and future projections. If you use this
 #' dataset, make sure to cite the original publication:
 #'
-#' Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution 
-#' climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315. 
+#' Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution
+#' climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.
 #' \doi{doi.org/10.1002/joc.5086}
-#' 
-#' **Present-day reconstructions** are based on the mean for the period 1970-2000, 
+#'
+#' **Present-day reconstructions** are based on the mean for the period 1970-2000,
 #' and are available at multiple resolutions of
 #' 10 arc-minutes, 5 arc-minutes, 2.5 arc-minute and 0.5 arc-minutes. The resolution
-#' of interest can be obtained by changing the ending of the dataset name 
+#' of interest can be obtained by changing the ending of the dataset name
 #' *WorldClim_2.1_RESm*, e.g. *WorldClim_2.1_10m* or *WorldClim_2.1_5m*
 #' (currently, only 10m and 5m are currently available in `pastclim`). In `pastclim`, the datasets are given
 #' a date of 1985 CE (the mid-point of the period of interest), corresponding to
-#' a time_bp of 35. There are 19 “bioclimatic” variables, as well as monthly 
+#' a time_bp of 35. There are 19 “bioclimatic” variables, as well as monthly
 #' estimates for minimum, mean, and maximum temperature, and precipitation.
-#' 
+#'
 #' **Future projections** are based on the models in CMIP6, downscaled and de-biased
 #' using WorldClim 2.1 for the present as a baseline. Monthly values of minimum
 #' temperature, maximum temperature, and precipitation, as well as 19 bioclimatic
 #' variables were processed for
-#' 23 global climate models (GCMs), and for four 
+#' 23 global climate models (GCMs), and for four
 #' Shared Socio-economic Pathways (SSPs): 126, 245, 370 and 585. Model and
 #' SSP can be chosen by changing the ending of the dataset name
-#' *WorldClim_2.1_GCM_SSP_RESm*. 
-#' 
-#' Available values for GCM are: "ACCESS-CM2", 
+#' *WorldClim_2.1_GCM_SSP_RESm*.
+#'
+#' Available values for GCM are: "ACCESS-CM2",
 #' "BCC-CSM2-MR", "CMCC-ESM2", "EC-Earth3-Veg", "FIO-ESM-2-0",
 #' "GFDL-ESM4", "GISS-E2-1-G", "HadGEM3-GC31-LL", "INM-CM5-0", "IPSL-CM6A-LR",
 #' "MIROC6", "MPI-ESM1-2-HR", "MRI-ESM2-0", and "UKESM1-0-LL". For SSP, use: "ssp126",
 #' "ssp245",	"ssp370",	and "ssp585". RES takes the same values as for present reconstructions
-#' (i.e. "10m", "5m", "2.5m", and "0.5m"). Example dataset names are 
+#' (i.e. "10m", "5m", "2.5m", and "0.5m"). Example dataset names are
 #' *WorldClim_2.1_ACCESS-CM2_ssp245_10m* and *WorldClim_2.1_MRI-ESM2-0_ssp370_5m*
-#'  
-#' The dataset are averages over 20 year 
+#'
+#' The dataset are averages over 20 year
 #' periods (2021-2040, 2041-2060, 2061-2080, 2081-2100).
 #' In `pastclim`, the midpoints of the periods (2030, 2050, 2070, 2090) are used as the time stamps. All 4 periods
 #' are automatically downloaded for each combination of GCM model and SSP, and are selected
 #' as usual by defining the time in functions such as [region_slice()].
-#' 
+#'
 #'
 #' @name WorldClim_2.1
 NULL
@@ -152,33 +152,33 @@ NULL
 #' derived bioclimatic variables covering the last 5 Ma (Pliocene–Pleistocene),
 #' at intervals of 1,000 years, and a spatial resolution of 1
 #' degrees (see Barreto et al., 2023 for details).
-#' 
-#' PALEO-PGEM-Series is downscaled to 1° × 1° spatial resolution from the 
-#' outputs of the PALEO-PGEM emulator (Holden et al., 2019), which emulates 
-#' reasonable and extensively validated global estimates of monthly temperature 
-#' and precipitation for the Plio-Pleistocene every 1 kyr at a spatial 
+#'
+#' PALEO-PGEM-Series is downscaled to 1° × 1° spatial resolution from the
+#' outputs of the PALEO-PGEM emulator (Holden et al., 2019), which emulates
+#' reasonable and extensively validated global estimates of monthly temperature
+#' and precipitation for the Plio-Pleistocene every 1 kyr at a spatial
 #' resolution of ~5° × 5° (Holden et al., 2016, 2019).
-#' 
+#'
 #' PALEO-PGEM-Series includes the mean and the standard deviation (i.e.,
 #' standard error) of the emulated climate over 10 stochastic GCM emulations
-#' to accommodate aspects of model uncertainty. This allows users to estimate 
-#' the robustness of their results in the face of the stochastic aspects of 
+#' to accommodate aspects of model uncertainty. This allows users to estimate
+#' the robustness of their results in the face of the stochastic aspects of
 #' the emulations. For more details, see Section 2.4 in Barreto et al. (2023).
-#' 
+#'
 #' Note that this is a very large dataset, with 5001 time slices. It takes
 #' approximately 1 minute to set up each variable when creating a region_slice or
 #' region_series. However, once the object has been created, other operations tend
 #' to be much faster (especially if you subset the dataset to a small number
 #' of time steps of interest).
-#' 
+#'
 #' If you use this dataset, make sure to cite the original publications:
-#' 
-#'  Barreto, E., Holden, P. B., Edwards, N. R., & Rangel, T. F. (2023). 
+#'
+#'  Barreto, E., Holden, P. B., Edwards, N. R., & Rangel, T. F. (2023).
 #'  PALEO-PGEM-Series: A spatial time series of the global climate over the
 #'   last 5 million years (Plio-Pleistocene). Global Ecology and
 #'    Biogeography, 00, 1– 12.
 #'    \doi{doi.org/10.1111/geb.13683}
-#'    
+#'
 #' Holden, P. B., Edwards, N. R., Rangel, T. F., Pereira, E. B., Tran, G. T.,
 #'  and Wilkinson, R. D. (2019): PALEO-PGEM v1.0: a statistical emulator of
 #'   Pliocene–Pleistocene climate, Geosci. Model Dev., 12, 5137–5155,
@@ -187,5 +187,3 @@ NULL
 #' @name Barreto2023
 NULL
 #> NULL
-
-

R/df_from_region_series.R

@@ -1,28 +1,28 @@
 #' Extract data frame from a region series
-#' 
+#'
 #' Extract the climatic information from a region series and organise them as
 #' a data frame.
-#' 
+#'
 #' To extract a data frame from a region slice, see [df_from_region_slice()].
 #'
 #' @param x climate time series generated with [region_series()]
-#' @param xy a boolean whether x and y coordinates should be added 
+#' @param xy a boolean whether x and y coordinates should be added
 #' to the dataframe (default to TRUE)
 #' @returns a data.frame where each cell each raster layer (i.e. timestep) is a
 #' row, and the available variables are columns.
 #'
 #' @export
 
-df_from_region_series <- function(x ,xy=TRUE) {
-  if (!is_region_series(x)){
+df_from_region_series <- function(x, xy = TRUE) {
+  if (!is_region_series(x)) {
     stop("x is not a valid object generated by region_series")
   }
   times_all <- time_bp(x[[1]])
-  for (i in seq_len(terra::nlyr(x[1]))){
-    x_slice <- slice_region_series(x,times_all[i])
-    slice_df <- terra::as.data.frame(x_slice,xy=xy)
+  for (i in seq_len(terra::nlyr(x[1]))) {
+    x_slice <- slice_region_series(x, times_all[i])
+    slice_df <- terra::as.data.frame(x_slice, xy = xy)
     slice_df$time_bp <- times_all[i]
-    if (i==1){
+    if (i == 1) {
       region_df <- slice_df
     } else {
       region_df <- rbind(region_df, slice_df)

R/df_from_region_slice.R

@@ -2,22 +2,22 @@
 #'
 #' Extract the climatic information from a region slice and organise it as
 #' a data frame. This is just a wrapper around [terra::as.data.frame()].
-#' 
+#'
 #' To extract a data frame from a region series, see [df_from_region_series()].
 #'
-#' @param x climate time slice (i.e. a [`terra::SpatRaster`]) 
+#' @param x climate time slice (i.e. a [`terra::SpatRaster`])
 #' generated with [region_slice()]
-#' @param xy a boolean whether x and y coordinates should be added 
+#' @param xy a boolean whether x and y coordinates should be added
 #' to the dataframe (default to TRUE)
 #' @returns a data.frame where each cell the raster is a
 #' row, and the available variables are columns.
 #'
 #' @export
 
-df_from_region_slice <- function(x ,xy=TRUE) {
-  if (!inherits(x,"SpatRaster")){
+df_from_region_slice <- function(x, xy = TRUE) {
+  if (!inherits(x, "SpatRaster")) {
     stop("x is not a valid SpatRaster generated by region_slice")
   }
-  slice_df <- terra::as.data.frame(x,xy=xy)
+  slice_df <- terra::as.data.frame(x, xy = xy)
   return(slice_df)
 }