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

knitr::opts_chunk$set(
  collapse = TRUE,
  echo=TRUE,
  comment = "#>"
)

Load Packages

library(tidyverse)

Prepare Data

The map is presented with the Bertin 1953 projection

## Getting world map for mapping
world <- rnaturalearth::ne_countries(scale = "small", returnclass = "sf") %>% 
  filter(continent != "Antarctica") %>%  
  # this is the crs from d, which has no EPSG code:
  sf::st_transform(., '+init=epsg:4326')

world2 <- rnaturalearth::ne_countries(scale = "small", returnclass = "sf") %>%  
  filter(continent != "Antarctica") %>%  
  # this is the crs from d, which has no EPSG code:
  #sf::st_transform(., '+init=epsg:4326')
  #sf::st_transform(., '+proj=bertin1953 +R=1 0.72 0.73')
  #sf::st_transform(., '+proj=bertin1953 +x_0=1000')
  sf::st_transform(., '+proj=bertin1953')

#centroids <- sf::st_centroid(world$geometry)
centroids <- sf::st_transform(world$geometry, '+init=epsg:3857') %>%  
  ## Reprojected in order to get centroid
  sf::st_centroid()  %>%
  sf::st_geometry()%>% 
  # this is the crs from d, which has no EPSG code:
  #sf::st_transform(., '+init=epsg:4326')%>%
  #sf::st_transform(., '+proj=bertin1953 +R=1 0.72 0.73') %>%
  #sf::st_transform(., '+proj=bertin1953 +x_0=1000') %>% 
  sf::st_transform(., '+proj=bertin1953') %>% 
  # since we want the centroids in long lat:
  as.data.frame()

world_points <- world  %>%  
  sf::st_drop_geometry() %>%
  cbind(., centroids)

## Loading the stat tables
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)

data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3")) %>%
  filter(Population.type  == "IDP" &
           Year == lastyear & !(is.na(UNHCRBureau))) %>%
  group_by(Year, CountryAsylumName, CountryAsylumCode, UNHCRBureau ) %>%
  summarise(Value2 = sum(Value) )

df3 <- merge(x = data , y = world_points, 
             by.x = "CountryAsylumCode" , by.y = "iso_a3") %>%
      sf::st_as_sf()
df3$quint2 <- Hmisc::cut2(df3$Value, g = 4)

Now let's get World Bank data to join them

wb_data <- wbstats::wb( indicator = c("SP.POP.TOTL",  ## Population total https://data.worldbank.org/indicator/SP.POP.TOTL
                                      "NY.GDP.MKTP.CD", ## GDP current https://data.worldbank.org/indicator/NY.GDP.MKTP.CD
                                      "NY.GDP.PCAP.CD", ## GDP per capita https://data.worldbank.org/indicator/NY.GDP.PCAP.CD 
                                      "NY.GNP.PCAP.CD" ## GNI per capita, Atlas method (current US$) https://data.worldbank.org/indicator/NY.GNP.PCAP.CD
                                      ),
                startdate = 1951, enddate = lastyear, return_wide = TRUE)
# # Renaming variables for further matching
names(wb_data)[1] <- "CountryAsylumCode"
names(wb_data)[2] <- "Year"

df4 <- df3 %>%
   select("CountryAsylumCode", "Year",  "CountryAsylumName", "UNHCRBureau","economy",   "Value2") %>%
    ## Now merge with WB Data 
  left_join(wb_data %>% select("SP.POP.TOTL","NY.GDP.PCAP.CD", "NY.GDP.MKTP.CD","CountryAsylumCode", "Year") %>%
              filter(Year == lastyear-1), 
            by = c( "CountryAsylumCode"    )) %>%  
  mutate(idp.gdp2 = round( Value2 /NY.GDP.MKTP.CD , 4)  )  %>% 
  mutate(idp.gdp = round(  Value2 / NY.GDP.PCAP.CD , 4)  )  %>%
  arrange(desc(Value2)) 
# Discretize the variable
df4$quint2 <- Hmisc::cut2(df4$idp.gdp, g = 4) 

Generate Plot

Maps is created here with MapSF package

# Select a font already installed on your system !!
par(family="Lato")
# set a theme
mapsf::mf_theme(bg = "#E2E7EB",  ## background color --> Used country 
                # bg = "#cdd2d4", "#faebd7ff",  "#cdd2d4",
                mar = c(0, 0, 2, 0), ## margins
                tab = FALSE,  # if TRUE the title is displayed as a 'tab'
                fg = "#0072BC",  ## foreground color --> for the top title - use UNHCR Blue..
                pos = "left", # position, one of 'left', 'center', 'right'
                inner = FALSE, # if TRUE the title is displayed inside the plot area.
                line = 2, #number of lines used for the title
                cex = 1.5, #cex of the title
                #font = "Lato",
                font = 1 ) #font of the title

## Get Break https://riatelab.github.io/mapsf/reference/mf_get_breaks.html
bks <- mapsf::mf_get_breaks(x = df4$idp.gdp,
                           nbreaks = 4, ## Number of class
                           breaks = "quantile") ## "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", "fisher", "jenks" and "dpih" are classIntervals methods. You may need to pass additional arguments for some of them.

df4$quint3 <- cut(df4$idp.gdp, 
                  breaks =bks,
                  labels = c("Class 1","Class 3","Class 2","Class 4"))             


mapsf::mf_init(world2) 
# Plot a shadow
mapsf::mf_shadow(world2, 
          add = TRUE)
mapsf::mf_map(world2, 
              add = TRUE, 
              lwd = 0.5, 
              border = "#93A3AB", 
              col = "#FFFFFF")

mapsf::mf_prop_typo( 
  x = df4, # frame to use.. 
  var = c("Value2", "economy"),  ## First value for size, second for color
  inches = .035, # size of the biggest symbol (radius for circles, half width for squares) in inches
  val_max = 90000,  # maximum value used for proportional symbols
  symbol = "circle", ## type of symbol- 'circle' or 'square'
  border = "grey25", ## border color of symbol
  lwd = 1.5, #  border width of symbol
  pal = "Inferno",  ## Color palette  - https://developer.r-project.org/Blog/public/2019/04/01/hcl-based-color-palettes-in-grdevices/
  alpha = .8,  ## if pal is a hcl.colors palette name, the alpha-transparency level
  leg_no_data = "No data", ## When no data
  col_na = "grey",  ## When no data
  leg_pos = c("topright", "bottomright"),  # position of the legend
  leg_title = c("Number of IDP", "Type of Economy"), # title of the legend
  leg_title_cex = c(.7, .7),  # title font size of the legend
  leg_val_cex = c(.5, .5), # content font size of the legend
  leg_val_rnd = .2, # number of decimal places of the values in the legend
  leg_frame = c(FALSE, FALSE), # add frame around the legend
  add = TRUE
)
# labels for a few  countries - https://riatelab.github.io/mapsf/reference/mf_label.html
mapsf::mf_label(x = df4[df4$Value2 > 5000000,], 
         var = "CountryAsylumCode",  # name(s) of the variable(s) to plot
         cex = 0.9, #  labels cex
         col = "black", 
         font = 4,
         halo = TRUE, # add halo
         bg = "white",  # halo color
         r = 0.1,  # width of the halo
         overlap = FALSE,  # if FALSE, labels are moved so they do not overlap.
         lines = FALSE)

## Annotation - https://riatelab.github.io/mapsf/reference/mf_annotation.html
mapsf::mf_annotation(   
      x = df4[df4$CountryAsylumCode == "COL",], #   an sf object with 1 row, a couple of coordinates (c(x, y)).
      txt= paste0("Displacement started \n in the 90's \n", 
                  as.character(round(data[data$CountryAsylumCode == "COL",c("Value2")], digits = -2) )), #   the text to display
      pos= "topright", #   position of the text, one of "topleft", "topright", "bottomright", "bottomleft"
      cex = .6, #   size of the text
      col_txt = "black", #  text color
      halo= TRUE, #   add a halo around the text
      bg = "white", #   halo color
      col_arrow= "black", #   arrow color
      s = 1.2 #     arrow size (min=1)
)

mapsf::mf_annotation(   
      x = df4[df4$CountryAsylumCode == "COD",], #   an sf object with 1 row, a couple of coordinates (c(x, y)).
      txt= paste0("Displacement started \n in the 90's \n", 
                  as.character(round(data[data$CountryAsylumCode == "COD",c("Value2")], digits = -2) )), #   the text to display
      pos= "bottomright", #   position of the text, one of "topleft", "topright", "bottomright", "bottomleft"
      cex = .6, #   size of the text
      col_txt = "black", #  text color
      halo= TRUE, #   add a halo around the text
      bg = "white", #   halo color
      col_arrow= "black", #   arrow color
      s = 1.2 #     arrow size (min=1)
)

mapsf::mf_annotation(   
      x = df4[df4$CountryAsylumCode == "SYR",], #   an sf object with 1 row, a couple of coordinates (c(x, y)).
      txt= paste0("Conflict started in 2011 \n", 
                  as.character(round(data[data$CountryAsylumCode == "SYR",c("Value2")], digits = -2) )), #   the text to display
      pos= "topright", #   position of the text, one of "topleft", "topright", "bottomright", "bottomleft"
      cex = .6, #   size of the text
      col_txt = "black", #  text color
      halo= TRUE, #   add a halo around the text
      bg = "white", #   halo color
      col_arrow= "black", #   arrow color
      s = 1.2 #     arrow size (min=1)
)

# Set a layout
mapsf::mf_title(txt = "Internal Displacement in 2020 in relation with Country Wealth", fg = "#FFFFFF")
mapsf::mf_credits(txt = "Source: UNHCR.org/refugee-statistics", bg = "#ffffff80") 

Edouard-Legoupil/unhcrdatapackage documentation built on Nov. 6, 2023, 6:10 p.m.