Adding map_data R function

NAMESPACE

@@ -1,4 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
+export(map_data)
 importFrom(magrittr,"%>%")

R/Functions.R

@@ -272,6 +272,7 @@ individuals_by <- function(individual_data = individuals, ind_id = nom, group =
                  clickAction = click_action)
   }
 }
+
 replace_other <- function(data = individuals, group = NULL, group_other = paste0("autre_", group), group_other_name = NULL, RAS = FALSE){
   group_var <- group
   # if (group_other == "autre_pays"){

R/initiative_by.R

@@ -27,7 +27,6 @@
 #' @param click_action 
 #'
 #' @return Returns a network graph object
-#' @export
 #'
 #' @examples # todo
 initiative_by <- function(initiative_data, by = "pays", filter_var = NULL, filter_vals = NULL,

R/map_data.R

@@ -0,0 +1,62 @@
+#' Map Data for Network Visualisation
+#'
+#' This function takes a data frame and generates a network visualization using the \code{forceNetwork} function 
+#' from the \code{networkD3} package. It separates and expands the data based on the specified values and node values,
+#' and creates links between them.
+#'
+#' @param data The original data frame.
+#' @param values The column name representing the primary values to be mapped.
+#' @param node_values A character vector specifying the column names representing the node values.
+#'
+#' @return A network visualisation generated using the \code{forceNetwork} function.
+#' @export
+#'
+#' @examples
+#' # An example with simple data 
+#' df_original <- data.frame(initiative = c("A", "B", "C", "D"),
+#'                           country = c("BF", "BF Niger", "Niger", "Niger"))
+#' map_data(df_original, "initiative", "country")
+#' 
+#' 
+#' df_original2 <- data.frame(individuals = c("A", "B", "C", "D"),
+#'                            institutions = c("a b", "a", "a", "b c"),
+#'                            initiatives = c("z", "z", "y z", "y"))
+#' map_data(df_original2, "individuals", c("institutions", "initiatives"))
+#' 
+map_data <- function(data, values, node_values) {
+  # repeat for each in node_values because of different sizes in cols
+  # separate_longer_delim doesn't repeat for each col, but groups
+  for (i in node_values){
+    data <- data %>%
+      separate_longer_delim(cols = {{ i }}, " ")
+  }
+
+  # Step 1: Expand data for the node data
+  df_expanded <- data %>%
+    tidyr::pivot_longer(cols = c({{ values }}, {{ node_values }}),
+                        names_to = "group",
+                        values_to = "id") %>%
+    dplyr::arrange(group) %>%
+    dplyr::distinct() %>%
+    dplyr::mutate(id_index = row_number() - 1)
+
+  # Step 2: Create the linked data
+  df_mapped <- data %>%
+    dplyr::left_join(df_expanded, by = setNames("id", values)) %>%
+    dplyr::rename(source = id_index) 
+
+  df_mapped <- df_mapped %>%
+    tidyr::pivot_longer(cols = all_of({{ node_values }}), values_to = "id") %>%
+    dplyr::distinct() %>%
+    dplyr::left_join(df_expanded, by = "id") %>%
+    dplyr::rename(target = id_index) 
+
+  df_mapped <- df_mapped %>%
+    dplyr::select(c(target, source))
+
+  networkD3::forceNetwork(Links = df_mapped, Nodes = df_expanded,
+                          Source = "source", Target = "target",
+                          NodeID = "id",
+                          Group = "group",
+                          legend = TRUE)
+}