R/plot_ctr_diff_in_pop_groups.R

In Edouard-Legoupil/unhcrdatapackage: UNHCR Population Statistics Database with Chart Library and Factsheet Template

# WARNING - Generated by {fusen} from /dev/dev_plot_Country.Rmd: do not edit by hand

#' Increases and Decreases in Population Groups
#'
#' @param year Numeric value of the year (for instance 2020)
#' @param country_asylum_iso3c Character value with the ISO-3 character code of the Country of Asylum
#' @param pop_type Vector of character values. Possible population type (e.g.: REF, IDP, ASY, OIP, OOC, STA)
#' 
#' @importFrom ggplot2  ggplot  aes  coord_flip   element_blank element_line
#'             element_text expansion geom_bar geom_col geom_hline unit stat_summary
#'             geom_label geom_text labs  position_stack  scale_color_manual scale_colour_manual 
#'             geom_text
#'             scale_fill_manual scale_x_continuous scale_x_discrete  scale_y_continuous   sym theme  
#' @importFrom utils  head
#' @importFrom tidyselect where
#' @importFrom stringr  str_replace 
#' @importFrom scales cut_short_scale label_percent label_number breaks_pretty
#' @importFrom stats  reorder aggregate 
#' @importFrom dplyr  desc select  case_when lag mutate group_by filter summarise ungroup
#'               pull distinct n arrange across slice left_join
#' @importFrom tidyr pivot_longer  gather separate spread
#' @importFrom unhcrthemes theme_unhcr
#' 
#' @return a ggplot2 object
#' 
#' @export
#'

#' @examples
#' # 
#' plot_ctr_diff_in_pop_groups(year = 2022,
#'                             country_asylum_iso3c = "ROU",
#'                             pop_type = c("REF", "ASY")
#'          )
#' 
plot_ctr_diff_in_pop_groups <- function(year = 2021,
                                        country_asylum_iso3c = country_asylum_iso3c,
                                        pop_type = pop_type
) {
  
  
  
  # diff_perc <- function(x){
  #   x = as.numeric((x - lag(x))/lag(x)) + 0
  #   
  # }
#  \(x) as.numeric((x - lag(x))/lag(x)) + 0
  
  df <- ForcedDisplacementStat::end_year_population_totals  |> 
    filter(CountryAsylumCode != "UKN",
           !is.na(CountryAsylumCode),
           (Year == year-1 | Year == year),  #### Parameter
           CountryAsylumCode == country_asylum_iso3c #### Parameter
    ) |> 
    group_by(Year, CountryAsylumName) |> 
    summarise(across(where(is.numeric), sum)) |> 
    ungroup() |> 
    arrange(Year) |> 
    select(-c(Year)) |> 
    group_by(CountryAsylumName) |> 
    summarise(across(where(is.numeric), 
                     list(diffabs = diff,
                         # diffperc = diff_perc),
                          diffper = \(x) as.numeric((x - lag(x))/lag(x)) + 0 ),
                     
                     .names = "{.col}_{.fn}")) |> 
    ungroup() |> 
    slice(2) 
  
  df <- replace(df, is.na(df), 0)
  
  df <- df |> 
    tidyr::gather(v, value, REF_diffabs:HCO_diffper) |> 
    tidyr::separate(v, c("population_type", "value_type"), sep = "\\_") |> 
    tidyr::spread(key = value_type, value = value) 
  
  
  df <- df |>         
 mutate(diffper = label_percent(accuracy = 1,  trim = FALSE) (diffper))
   #    mutate(diffper = label_percent(accuracy = 1,  trim = FALSE) (\(x) as.numeric((x - lag(x))/lag(x)) + 0 ))
  
  
  df <- df |> 
    filter(population_type %in% pop_type)
  
  p <- ggplot() +
    geom_col(data = df, 
           aes(x = population_type, 
                 y = diffabs,
                 fill = population_type),
             width = 0.8) +
    scale_fill_manual(
      values = c("ASY" = "#18375F",
                 "REF" = "#0072BC",
                 "OIP" = "#EF4A60", 
                 #"VDA" = "#EF4A60", 
                 "OOC" = "#999999",
                 "IDP" = "#00B398",
                 "STA" = "#E1CC0D"),
      drop= TRUE, limits = force, guide="none"    ) +
    scale_x_discrete(labels= c("ASY" = "Asylum-seekers",
                               "REF" = "Refugees",
                               #"VDA" = "Venezuelans Displaced\nAbroad", ,
                               "OIP" = "Other people in need\n of international protection", 
                               "OOC" = "Others of Concern\nto UNHCR",
                               "IDP" = "Internally Displaced\nPersons",
                               "STA" = "Stateless Persons"),
                     drop = TRUE,
                     limits = force) +
    
    ## Adding labels with conditionned positions...
    
    geom_text(  data = subset(df, diffabs >= 0 & diffabs < max(diffabs) / 1.5),
                aes(x = population_type,
                    y = diffabs,
                    label = label_number(accuracy = 1,
                                         scale_cut = cut_short_scale())(diffabs)),
                vjust = -0.5 , colour = "black", size = 5   ) +
    
    geom_text( data = subset(df, diffabs >= 0 & diffabs < max(diffabs) / 1.5),
               aes(  x = population_type,
                     y = diffabs,
                     label = paste(intToUtf8(9650), diffper)),
               vjust = -2.0 , colour = "black", size = 5  ) +
    
    geom_text( data = subset(df, diffabs >= 0 & diffabs >= max(diffabs) / 1.5),
               aes(  x = population_type,
                     y = diffabs,
                     label = label_number(accuracy = 1,
                                          scale_cut = cut_short_scale())(diffabs) ),
               vjust = 2.7 ,   colour = "white",   size = 5  ) + 
    
    geom_text(  data = subset(df, diffabs >= 0 & diffabs >= max(diffabs) / 1.5),
                aes(x = population_type,
                    y = diffabs,
                    label = paste(intToUtf8(9650), diffper)),
                vjust = 1.2 ,   colour = "white",   size = 5    ) + 
    
    geom_text( data = subset(df, diffabs < 0/ 1.5),
               aes(  x = population_type,
                     y = diffabs,
                     label = label_number(accuracy = 1,
                                          scale_cut = cut_short_scale())(diffabs)),
               vjust =  1.2, colour = "black", size = 5  ) +
    
    geom_text( data = subset(df, diffabs < 0  / 1.5),
               aes( x = population_type,
                    y = diffabs,
                    label = paste(intToUtf8(9660), diffper)),
               vjust = 2.7,  colour = "black",  size = 5  ) +
    
    geom_hline(yintercept = 0, color = "black") +
    
    labs(title = paste0(df |> 
                          distinct(CountryAsylumName) |> 
                          pull(), 
                        ": Increases and Decreases in Population Groups | ",
                        year-1,
                        "-",
                        year),
         subtitle = "Number of people and percentage",
         caption = "Source: UNHCR.org/refugee-statistics")  + 
    theme_unhcr(grid = FALSE, axis = "x",
                axis_title = FALSE, axis_text = "x",
                font_size = 14) +
    theme(axis.line.x = element_line(color="white"),
          axis.text.x = element_text(size=10))
  
  # geom_text(
  #   data = subset(df, diffabs < 0 & diffabs <= min(diffabs) / 1.5),
  #   aes(
  #     x = population_type,
  #     y = diffabs,
  #     label = label_number(accuracy = 1,
  #                                  scale_cut = cut_short_scale())(diffabs)
  #   ),
  #   vjust = -2.0 ,
  #   colour = "white",
  #   size = 5
  # )  +
  #   geom_text(
  #     data = subset(df, diffabs < 0 & diffabs <= min(diffabs) / 1.5),
  #     aes(
  #       x = population_type,
  #       y = diffabs,
  #       label = diffper
  #     ),
  #     vjust = -0.5 ,
  #     colour = "white",
  #     size = 5
  #   ) +
  
  
  return(p) # print(p)
}