Specify and import dependencies

DESCRIPTION

@@ -25,9 +25,12 @@ Depends:
 Imports:
     terra,
     magrittr,
+    methods,
+    grDevices,
     dplyr,
     tidyr,
     fs,
+    sf,
     cowplot,
     ncmeta,
     here,

NAMESPACE

@@ -17,4 +17,9 @@ export(plot_map4)
 export(prepare_setup_sofun)
 export(read_nc_onefile)
 export(write_nc2)
+import(dplyr)
+import(ggplot2)
+importFrom(grDevices,colorRampPalette)
 importFrom(magrittr,"%>%")
+importFrom(methods,as)
+importFrom(stats,setNames)

R/align_events.R

@@ -12,14 +12,17 @@
 #' @param after An integer specifying the number of days after the event onset to be retained in re-arranged data
 #' @param do_norm A logical specifying whether re-arranged data is to be normalised by the median value of the bin
 #' (number of bins given by argument \code{nbins}) before the event onset, given by argument \code{normbin}. Defaults to \code{FALSE}.
-#' @param nbins An integer, specifying the number of bins used to determine median values before event onset. Only used when code{do_norm=TRUE}. Defaults to 6.
-#' @param normbin An integer, specifying the bin number just before the event onset, used for normalisation. Only used when code{do_norm=TRUE}. Defaults to 2.
+#' @param nbins An integer, specifying the number of bins used to determine median values before event onset. Only used when \code{do_norm=TRUE}. Defaults to 6.
+#' @param normbin An integer, specifying the bin number just before the event onset, used for normalisation. Only used when \code{do_norm=TRUE}. Defaults to 2.
 #'
 #' @return A named list of data frames (\code{list( "df_idx_event", "df_idx_event_aggbyidx_event")}) containing data from all events and \code{before + after}
 #' dates (relative to event onset) with additional columns named \code{"inst"}, defining the event number (instance), and \code{"idx_event"}, defining
 #' the date relative to the respective event onset. The data frame \code{"df_idx_event"} contains rearranged, but otherwise unchanged data (unless
 #' \code{do_norm}=TRUE). The data frame \code{"df_idx_event_aggbyidx_event"} containes data aggregated across events with the mean and quantiles given for each
 #' \code{"idx_event"}.
+#' @import dplyr
+#' @import ggplot2
+#' @importFrom stats setNames
 #' @export
 #'
 align_events <- function(
@@ -84,7 +87,7 @@ align_events <- function(
       tmp <- df_idx_event |>
         dplyr::filter(!is.na(inbin)) |>
         group_by( inbin ) |>
-        summarise_at( vars(one_of(dovars)), funs(median( ., na.rm=TRUE )) )
+        summarise_at( vars(one_of(dovars)), funs(stats::median( ., na.rm=TRUE )) )
 
       norm <- slice(tmp, normbin)
 
@@ -104,11 +107,11 @@ align_events <- function(
 }
 
 q33 <- function( vec, ... ){
-  quantile( vec, 0.33, ...)
+  stats::quantile( vec, 0.33, ...)
 }
 
 q66 <- function( vec, ... ){
-  quantile( vec, 0.66, ...)
+  stats::quantile( vec, 0.66, ...)
 }
 
 

R/collect_drivers_rsofun.R

@@ -1,37 +1,37 @@
 #' Collect all drivers
 #'
-#' Collect all drivers for site-level simulations 
+#' Collect all drivers for site-level simulations
 #' into a nested data frame with one row for each site.
 #'
-#' @param site_info A data frame containing site meta info (rows for sites). 
-#'  Required columns are: \code{"sitename", "year_start", 
+#' @param site_info A data frame containing site meta info (rows for sites).
+#'  Required columns are: \code{"sitename", "year_start",
 #'  "year_end", "lon", "lat", "elv"}. See \code{\link{prepare_setup_sofun}} for
 #'  details.
-#' @param params_siml A nested data frame with rows for each site containing 
+#' @param params_siml A nested data frame with rows for each site containing
 #'  simulation parameters for SOFUN. See \code{\link{run_pmodel_f_bysite}} or
 #'  \code{\link{run_biomee_f_bysite}}.
 #' @param meteo A nested data frame with rows for each site and meteorological
 #'  forcing data time series nested inside a column named \code{"data"}.
-#' @param fapar A nested data frame with rows for each site and fAPAR 
+#' @param fapar A nested data frame with rows for each site and fAPAR
 #'  forcing data time series nested inside a column named \code{"data"}.
-#' @param co2 A nested data frame with rows for each site and CO2 
+#' @param co2 A nested data frame with rows for each site and CO2
 #'  forcing data time series nested inside a column named \code{"data"}.
 #' @param params_soil Soil texture data descriptor, a data frame with columns
 #' \code{"layer", "fsand", "fclay", "forg" } and \code{"fgravel"}.
 #'
 #' @return A \code{rsofun} input data frame (see \link{p_model_drivers} for a detailed
 #' description of its structure and contents).
 #' @export
-
-collect_drivers_sofun <- function( 
+#'
+collect_drivers_sofun <- function(
   site_info,
   params_siml,
   meteo,
   fapar,
   co2,
   params_soil
   ){
-  
+
   # declare variable bindings for CRAN compliance
   # these are internal variables created in mutate
   # statements mostly which need to be defined or
@@ -43,28 +43,28 @@ collect_drivers_sofun <- function(
     vpd <- vpd_doy <- ccov <- ccov_doy <-
     fapar_doy <- co2_doy <- tmin <- tmin_doy <-
     tmax <- tmax_doy <- doy <- forcing <- . <- NULL
-    
+
   # complement the setup settings
   site_info <- prepare_setup_sofun(
         site_info = site_info,
         params_siml = params_siml)
-  
+
   # check if all required variables are available
   if (!("snow" %in% names(meteo$data[[1]]))) {
-    warning("Variable 'snow' missing in meteo data frame. 
+    warning("Variable 'snow' missing in meteo data frame.
                 Assuming zero for all dates. \n")
     meteo <- meteo %>% mutate(data = purrr::map(data,
                               ~dplyr::mutate(., snow = 0)))
   }
-  
+
   if (!("rain" %in% names(meteo$data[[1]]))) {
     warning("Variable 'rain' missing in meteo data frame.
                 Assuming equal to 'prec' for all dates. \n")
     meteo <- meteo %>%
      dplyr::mutate(data = purrr::map(data,
                               ~dplyr::mutate(., rain = prec)))
   }
-  
+
   if (!("tmin" %in% names(meteo$data[[1]]))) {
     warning("Variable 'tmin' missing in meteo data frame.
                 Assuming equal to 'temp' for all dates.
@@ -75,79 +75,79 @@ collect_drivers_sofun <- function(
              data = purrr::map(data,
                                ~dplyr::mutate(., tmax = temp)))
   }
-  
+
   vars_req <- c("ppfd", "rain", "snow", "prec",
                 "temp", "patm", "vpd", "ccov", "tmin", "tmax")
-  
+
   vars_missing <- vars_req[
     !(vars_req %in% names(meteo %>% tidyr::unnest(data)))
     ]
-  
+
   if (length(vars_missing)) {
    stop(paste("Aborting. Variables missing in meteo data frame:",
                       paste(vars_missing, collapse = ", ")))
    }
-  
+
   # create mega-df containing all forcing data and parameters that
   # vary by site (not model parameters!)
   names_metainfo <- names(site_info)[-which(names(site_info) %in%
                                              c("sitename", "params_siml"))]
-  df_mega <- site_info %>% 
-    tidyr::nest(site_info = names_metainfo) %>% 
+  df_mega <- site_info %>%
+    tidyr::nest(site_info = names_metainfo) %>%
     dplyr::left_join(
-      meteo %>% 
+      meteo %>%
         dplyr::rename(meteo = data),
       by = "sitename"
-    ) %>% 
+    ) %>%
     dplyr::left_join(
-      fapar %>% 
+      fapar %>%
         dplyr::rename(fapar = data),
       by = "sitename"
-    ) %>% 
+    ) %>%
     dplyr::left_join(
-      co2 %>% 
+      co2 %>%
         dplyr::rename(co2 = data),
       by = "sitename"
-    ) %>% 
+    ) %>%
     dplyr::mutate(
       params_soil = purrr::map(as.list(seq(nrow(.))),
                                        ~return(params_soil)))
-  
+
   # use only interpolated fapar and combine meteo data and fapar
   # into a single nested column 'forcing'
-  df_mega <- df_mega %>% 
-    dplyr::mutate(fapar = purrr::map(fapar, ~dplyr::select(., date, fapar))) %>% 
-    dplyr::mutate(co2   = purrr::map(co2  , ~dplyr::select(., date, co2))) %>% 
-    dplyr::mutate(forcing = purrr::map2(meteo, fapar, ~dplyr::left_join( .x, .y, by = "date"))) %>% 
-    dplyr::mutate(forcing = purrr::map2(forcing, co2, ~dplyr::left_join( .x, .y, by = "date"))) %>% 
+  df_mega <- df_mega %>%
+    dplyr::mutate(fapar = purrr::map(fapar, ~dplyr::select(., date, fapar))) %>%
+    dplyr::mutate(co2   = purrr::map(co2  , ~dplyr::select(., date, co2))) %>%
+    dplyr::mutate(forcing = purrr::map2(meteo, fapar, ~dplyr::left_join( .x, .y, by = "date"))) %>%
+    dplyr::mutate(forcing = purrr::map2(forcing, co2, ~dplyr::left_join( .x, .y, by = "date"))) %>%
     dplyr::select(-meteo, -fapar, -co2)
-  
+
   # drop sites for which forcing data is missing for all dates
   count_notna <- function(df) {
-    df %>% 
-      dplyr::ungroup() %>% 
+    df %>%
+      dplyr::ungroup() %>%
       dplyr::summarise(dplyr::across(
       c("ppfd", "rain", "snow", "prec", "temp",
         "patm", "vpd", "ccov", "fapar", "co2",
-        "tmin","tmax"), ~sum(!is.na(.)))) %>% 
+        "tmin","tmax"), ~sum(!is.na(.)))) %>%
       tidyr::pivot_longer(cols = 1:12, names_to = "var",
                    values_to = "n_not_missing")
   }
-  
-  df_missing <- df_mega %>% 
-    dplyr::mutate(df_count = purrr::map(forcing, ~count_notna(.))) %>% 
-    dplyr::select(sitename, df_count) %>% 
-    tidyr::unnest(df_count) %>% 
+
+  df_missing <- df_mega %>%
+    dplyr::mutate(df_count = purrr::map(forcing, ~count_notna(.))) %>%
+    dplyr::select(sitename, df_count) %>%
+    tidyr::unnest(df_count) %>%
     dplyr::filter(n_not_missing < 365)
-  
+
   if (nrow(df_missing) > 0) {
     warning("Missing values found in forcing data frame:")
     print(df_missing)
     warning("Respective sites are dropped from all drivers data frame.")
-    df_mega <- df_mega %>% 
+    df_mega <- df_mega %>%
       dplyr::filter(!(sitename %in% pull(df_missing, sitename)))
   }
-    
+
   ## interpolate to fill gaps in forcing time series
   myapprox <- function(vec){
     if(all(is.na(vec))){
@@ -156,41 +156,41 @@ collect_drivers_sofun <- function(
       stats::approx(vec, xout = 1:length(vec))$y
     }
   }
-  
+
   fill_na_forcing <- function(df) {
-    
+
     # dummy variable for CRAN compliance
     ppdf_doy <- NULL
-    
+
     vars <- names(df)[-which(names(df) == "date")]
-    df <- df %>% 
+    df <- df %>%
       dplyr::mutate_at(vars, myapprox)
-    
+
     ## fill remaining gaps with mean seasonal cycle
     add_doy <- function(string){paste0(string, "_doy")}
-    
-    df_meandoy <- df %>% 
-      dplyr::mutate(doy = lubridate::yday(date)) %>% 
-      dplyr::group_by(doy) %>% 
+
+    df_meandoy <- df %>%
+      dplyr::mutate(doy = lubridate::yday(date)) %>%
+      dplyr::group_by(doy) %>%
       dplyr::summarise(
         dplyr::across(
           tidyselect::vars_select_helpers$where(is.double),
           ~mean(.x, na.rm = TRUE)
           )
-        ) %>% 
+        ) %>%
       dplyr::rename_with(.fn = add_doy, .cols = dplyr::one_of(
         "ppfd", "rain", "snow", "prec", "temp", "patm",
-        "vpd", "ccov", "fapar", "co2", "tmin", "tmax")) %>% 
+        "vpd", "ccov", "fapar", "co2", "tmin", "tmax")) %>%
       dplyr::select(
-        doy, 
+        doy,
         dplyr::one_of(
           "ppfd_doy", "rain_doy", "snow_doy", "prec_doy", "temp_doy",
            "patm_doy", "vpd_doy", "ccov_doy", "fapar_doy", "co2_doy",
            "tmin_doy", "tmax_doy"))
-    
-    df <- df %>% 
-      dplyr::mutate(doy = lubridate::yday(date)) %>% 
-      dplyr::left_join(df_meandoy, by = "doy") %>% 
+
+    df <- df %>%
+      dplyr::mutate(doy = lubridate::yday(date)) %>%
+      dplyr::left_join(df_meandoy, by = "doy") %>%
       dplyr::mutate(ppfd = ifelse(is.na(ppfd), ppfd_doy, ppfd),
              rain = ifelse(is.na(rain), rain_doy, rain),
              snow = ifelse(is.na(snow), snow_doy, snow),
@@ -202,14 +202,14 @@ collect_drivers_sofun <- function(
              fapar = ifelse(is.na(fapar), fapar_doy, fapar),
              co2 = ifelse(is.na(co2), co2_doy, co2),
              tmin = ifelse(is.na(tmin), tmin_doy, tmin),
-             tmax = ifelse(is.na(tmax), tmax_doy, tmax)) %>% 
+             tmax = ifelse(is.na(tmax), tmax_doy, tmax)) %>%
       dplyr::select(-ends_with("_doy"))
-    
+
     return(df)
   }
-  
-  df_mega <- df_mega %>% 
-    mutate(forcing = purrr::map(forcing, ~fill_na_forcing(.))) %>% 
+
+  df_mega <- df_mega %>%
+    mutate(forcing = purrr::map(forcing, ~fill_na_forcing(.))) %>%
     dplyr::select(sitename, forcing, params_siml, site_info, params_soil)
 
   return(df_mega)

R/gc_rsofun_driver_era5.R

@@ -86,7 +86,7 @@ gc_rsofun_driver_era5 <- function(
   if(!inherits(files, "try-error")) {
     drivers <- lapply(files, function(file){
 
-      df <- read.table(file, sep = ",", header = TRUE)
+      df <- utils::read.table(file, sep = ",", header = TRUE)
       df <- df[,c(1,5)]
     })
 

R/plot_map4.R

@@ -240,7 +240,7 @@ plot_map4 <- function(obj, varnam = NA, maxval = NA, breaks = NA, lonmin = -180,
 		## convert to data frame for ggplot
 		##---------------------------------------------
 		tstep <- 1
-		df <- as(rasta_reproj[[tstep]], "SpatialPixelsDataFrame")
+		df <- methods::as(rasta_reproj[[tstep]], "SpatialPixelsDataFrame")
 		df <- as.data.frame(df)
 		names(df) <- c("layer", "x", "y")
 
@@ -397,7 +397,7 @@ plot_map4 <- function(obj, varnam = NA, maxval = NA, breaks = NA, lonmin = -180,
 	                        "lipari", "roma")){
 	    colorscale <- scico::scico(nbin, palette = colorscale, direction = invert)
 	  } else {
-	    colorscale <- colorRampPalette( colorscale )( nbin )
+	    colorscale <- grDevices::colorRampPalette( colorscale )( nbin )
 	  }
 
 	} else if (class(colorscale)=="palette"){