Update to functions

DESCRIPTION

@@ -15,8 +15,9 @@ RoxygenNote: 7.3.2
 Imports: 
     colorRamps,
     data.table,
-    distillery,
+    databook,
     dplyr,
+    distillery,
     extRemes,
     fields,
     forcats,
@@ -40,4 +41,5 @@ Imports:
     tidyselect,
     zoo
 Remotes:
+    IDEMSInternational/databook,
     IDEMSInternational/instatExtras

NAMESPACE

@@ -13,10 +13,8 @@ export(convert_yy_to_yyyy)
 export(cumulative_inventory)
 export(dd_to_dms)
 export(fourier_series)
-export(get_default_significant_figures)
 export(ggwalter_lieth)
 export(import_from_iri)
-export(make_factor)
 export(other_rose_plots)
 export(output_CPT)
 export(plot_declustered)

R/climdex.R

@@ -1,7 +1,6 @@
-#' Calculate climate indices
+#' Calculate Climate Indices
 #'
-#' @description
-#'  This function calculates climate indices based on temperature and precipitation data.  
+#' @description This function calculates climate indices based on temperature and precipitation data.  
 #'  
 #' @param data A data frame containing the temperature and precipitation data.
 #' @param station The name of the column in the data frame that represents the station.

R/climdex_single_station.R

@@ -1,7 +1,5 @@
-
-#' climdex_single_station
-#' @description
-#' This function calculates climate indices for a single weather station based on the provided parameters. The function utilizes the climdex.pcic package to compute various climate indices. The indices can be calculated either on an annual or monthly basis, depending on the specified frequency.
+#' Climdex Single Station
+#' @description This function calculates climate indices for a single weather station based on the provided parameters. The function utilizes the climdex.pcic package to compute various climate indices. The indices can be calculated either on an annual or monthly basis, depending on the specified frequency.
 #'
 #' @param ci
 #' The climate data for the weather station.
@@ -36,8 +34,6 @@
 #'#data <- read.csv("climate_data.csv")
 #'#climdex_single_station(data, freq = "annual", indices = c("fd", "su"),
 #'# year = "Year")
-
-
 climdex_single_station <- function(ci, freq = "annual", indices, year, month,
                                    spells.can.span.years = FALSE, gsl.mode = gsl.mode,
                                    threshold = 1) {
@@ -91,5 +87,4 @@ climdex_single_station <- function(ci, freq = "annual", indices, year, month,
     if (indices[[i]] == "rnnmm") names(df_ind)[ncol(df_ind)] <- paste(indices[[i]], threshold, sep = "_")
   }
   return(df_ind)
-}
-
+}

R/get_default_significant_figures.R

@@ -5,11 +5,6 @@
 #' @param data \code{numeric(1)} A numerical vector
 #'
 #' @return If the data is numeric, "3", otherwise NA.
-#' @export
-#'
-#' @examples 
-#' x <- 1:8
-#' get_default_significant_figures(x)
 get_default_significant_figures <- function(data) {
   if(is.numeric(data)) return(3)
   else return(NA)  

R/make_factor.R

@@ -9,8 +9,6 @@
 #' and the ordered parameter is consistent with the existing ordering, the input data is returned as is.
 #' Otherwise, the input data is converted into a factor variable with the specified ordering.
 #'
-#' @export
-#'
 #' @examples
 #' # Create a factor from a numeric vector
 #' #make_factor(c(1, 2,3,3,2,2,1,1,1, 3, 2), ordered = TRUE)

R/spei_input.R

@@ -1,7 +1,7 @@
 #' Calculate SPEI Input Data
 #' 
 #' @description
-#' This function calculates the Standardized Precipitation-Evapotranspiration Index (SPEI) input data from a given data frame. The function sorts the data by year and month, checks for data completeness, and prepares the data for further SPEI calculations.
+#' This function calculates the Standardised Precipitation-Evapotranspiration Index (SPEI) input data from a given data frame. The function sorts the data by year and month, checks for data completeness, and prepares the data for further SPEI calculations.
 #' 
 #' #' @details
 #' The function expects the input data to be in a data frame format. It calculates the SPEI input data by performing the following steps:

R/spei_output.R

@@ -2,7 +2,7 @@
 #'
 #' @description
 #' 
-#' This function extracts the Standardized Precipitation-Evapotranspiration Index (SPEI) or Standardized Precipitation Index (SPI) column from a 'spei' object. It is designed to work with the original data and handle multiple stations if present. The function removes NA values introduced when unstacking the data to return a vector of the correct length.
+#' This function extracts the Standardised Precipitation-Evapotranspiration Index (SPEI) or Standardized Precipitation Index (SPI) column from a 'spei' object. It is designed to work with the original data and handle multiple stations if present. The function removes NA values introduced when unstacking the data to return a vector of the correct length.
 #' 
 # It requires the original data in order to return a vector of the correct length by removing NA values introduced when unstacking.
 # An alternative to this is to have a single wrapper SPEI/SPI function to handle this.

R/wwr_report.R

@@ -84,7 +84,7 @@ wwr_export <- function(data, year, month, mean_station_pressure, mean_sea_level_
   if (!missing(mean_station_pressure)) {
     df_2_means <- data %>%
       dplyr::group_by(!!! rlang::syms(c(link, year))) %>%
-      dplyr::summarise(mean = sprintf("%6s", round(summary_mean(.data[[mean_station_pressure]], na.rm = TRUE), 1)), .groups = "keep")
+      dplyr::summarise(mean = sprintf("%6s", round(databook::summary_mean(.data[[mean_station_pressure]], na.rm = TRUE), 1)), .groups = "keep")
     data[[mean_station_pressure]] <- ifelse(is.na(data[[mean_station_pressure]]), 
                                             "", round(data[[mean_station_pressure]], 1))
     data[[mean_station_pressure]] <- sprintf("%6s", data[[mean_station_pressure]])
@@ -96,7 +96,7 @@ wwr_export <- function(data, year, month, mean_station_pressure, mean_sea_level_
   if (!missing(mean_sea_level_pressure)) {
     df_3_means <- data %>%
       dplyr::group_by(!!! rlang::syms(c(link, year))) %>%
-      dplyr::summarise(mean = sprintf("%6s", round(summary_mean(.data[[mean_sea_level_pressure]], na.rm = TRUE), 1)), .groups = "keep")
+      dplyr::summarise(mean = sprintf("%6s", round(databook::summary_mean(.data[[mean_sea_level_pressure]], na.rm = TRUE), 1)), .groups = "keep")
     data[[mean_sea_level_pressure]] <- ifelse(is.na(data[[mean_sea_level_pressure]]), 
                                               "", round(data[[mean_sea_level_pressure]], 1))
     data[[mean_sea_level_pressure]] <- sprintf("%6s", data[[mean_sea_level_pressure]])
@@ -108,7 +108,7 @@ wwr_export <- function(data, year, month, mean_station_pressure, mean_sea_level_
   if (!missing(mean_temp)) {
     df_4_means <- data %>%
       dplyr::group_by(!!! rlang::syms(c(link, year))) %>%
-      dplyr::summarise(mean = sprintf("%6s", round(summary_mean(.data[[mean_temp]], na.rm = TRUE), 1)), .groups = "keep")
+      dplyr::summarise(mean = sprintf("%6s", round(databook::summary_mean(.data[[mean_temp]], na.rm = TRUE), 1)), .groups = "keep")
     data[[mean_temp]] <- ifelse(is.na(data[[mean_temp]]), 
                                 "", round(data[[mean_temp]], 1))
     data[[mean_temp]] <- sprintf("%6s", data[[mean_temp]])
@@ -120,7 +120,7 @@ wwr_export <- function(data, year, month, mean_station_pressure, mean_sea_level_
   if (!missing(total_precip)) {
     df_5_means <- data %>%
       dplyr::group_by(!!! rlang::syms(c(link, year))) %>%
-      dplyr::summarise(mean = sprintf("%6s", format(summary_sum(.data[[total_precip]], na.rm = TRUE), digits = 1, nsmall = 1)), .groups = "keep")
+      dplyr::summarise(mean = sprintf("%6s", format(databook::summary_sum(.data[[total_precip]], na.rm = TRUE), digits = 1, nsmall = 1)), .groups = "keep")
     data[[total_precip]] <- ifelse(is.na(data[[total_precip]]), 
                                    "", ifelse(data[[total_precip]] <= 0.05, 0, format(data[[total_precip]], digits = 1, nsmall = 1)))
     data[[total_precip]] <- sprintf("%6s", data[[total_precip]])
@@ -132,7 +132,7 @@ wwr_export <- function(data, year, month, mean_station_pressure, mean_sea_level_
   if (!missing(mean_max_temp)) {
     df_6_means <- data %>%
       dplyr::group_by(!!! rlang::syms(c(link, year))) %>%
-      dplyr::summarise(mean = sprintf("%6s", round(summary_mean(.data[[mean_max_temp]], na.rm = TRUE), 1)), .groups = "keep")
+      dplyr::summarise(mean = sprintf("%6s", round(databook::summary_mean(.data[[mean_max_temp]], na.rm = TRUE), 1)), .groups = "keep")
     data[[mean_max_temp]] <- ifelse(is.na(data[[mean_max_temp]]), 
                                     "", round(data[[mean_max_temp]], 1))
     data[[mean_max_temp]] <- sprintf("%6s", data[[mean_max_temp]])
@@ -144,7 +144,7 @@ wwr_export <- function(data, year, month, mean_station_pressure, mean_sea_level_
   if (!missing(mean_min_temp)) {
     df_7_means <- data %>%
       dplyr::group_by(!!! rlang::syms(c(link, year))) %>%
-      dplyr::summarise(mean = sprintf("%6s", round(summary_mean(.data[[mean_min_temp]], na.rm = TRUE), 1)), .groups = "keep")
+      dplyr::summarise(mean = sprintf("%6s", round(databook::summary_mean(.data[[mean_min_temp]], na.rm = TRUE), 1)), .groups = "keep")
     data[[mean_min_temp]] <- ifelse(is.na(data[[mean_min_temp]]), 
                                     "", round(data[[mean_min_temp]], 1))
     data[[mean_min_temp]] <- sprintf("%6s", data[[mean_min_temp]])
@@ -157,7 +157,7 @@ wwr_export <- function(data, year, month, mean_station_pressure, mean_sea_level_
     if (any(data[[mean_rel_hum]] < 0 | data[[mean_rel_hum]] > 100, na.rm = TRUE)) stop("Mean Relative Humidity must be a percentage between 0 and 100.")
     df_8_means <- data %>%
       dplyr::group_by(!!! rlang::syms(c(link, year))) %>%
-      dplyr::summarise(mean = sprintf("%6s", round(summary_mean(.data[[mean_rel_hum]], na.rm = TRUE), 0)), .groups = "keep")
+      dplyr::summarise(mean = sprintf("%6s", round(databook::summary_mean(.data[[mean_rel_hum]], na.rm = TRUE), 0)), .groups = "keep")
     data[[mean_rel_hum]] <- ifelse(is.na(data[[mean_rel_hum]]), 
                                    "", round(data[[mean_rel_hum]], 1))
     data[[mean_rel_hum]] <- sprintf("%6s", data[[mean_rel_hum]])