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

thisbureau <- "Americas"
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)

topasyl <-  unhcrdatapackage::asylum_decisions %>%
  ## Add reference for the filters
  dplyr::left_join( unhcrdatapackage::reference %>% 
                      select(coa_region = `UNHCRBureau`, iso_3),  by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(coa_region == thisbureau & Year == lastyear) %>% 
 # select(region_asy, ctry_asy, year, recognizedConvMandate, recognizedOther, appliedDuringTheYear, meta_applicationAveragePersonsPerCase) %>%
  mutate(DecisionsAveragePersonsPerCase = map_dbl(DecisionsAveragePersonsPerCase, ~replace_na(max(as.numeric(.), 1), 1))) %>%
  group_by(CountryAsylumName) %>% 
  summarize(Recognized = sum(Recognized * DecisionsAveragePersonsPerCase, na.rm = TRUE),
            ComplementaryProtection = sum(ComplementaryProtection * DecisionsAveragePersonsPerCase, na.rm = TRUE),
            TotalDecided = sum(TotalDecided * DecisionsAveragePersonsPerCase, na.rm = TRUE)) %>%
  mutate(RefugeeRecognitionRate = (Recognized ) / TotalDecided,
         GlobalRecognitionRate = (Recognized + ComplementaryProtection) / TotalDecided ) %>%
  filter(TotalDecided  != 0) %>%
  filter(TotalDecided  > 1000)  %>%
  mutate(CountryAsylumName = str_replace(CountryAsylumName, "United States of America", "USA"))



topOrigin <-  unhcrdatapackage::asylum_decisions %>%
  ## Add reference for the filters
  dplyr::left_join( unhcrdatapackage::reference %>% 
                      select(coa_region = `UNHCRBureau`, iso_3),  by = c("CountryOriginCode" = "iso_3")) %>% 
  filter(coa_region == thisbureau & Year == lastyear) %>% 
 # select(region_asy, ctry_asy, year, recognizedConvMandate, recognizedOther, appliedDuringTheYear, meta_applicationAveragePersonsPerCase) %>%
  mutate(DecisionsAveragePersonsPerCase = map_dbl(DecisionsAveragePersonsPerCase, ~replace_na(max(as.numeric(.), 1), 1))) %>%
  group_by(CountryOriginName) %>% 
  summarize(Recognized = sum(Recognized * DecisionsAveragePersonsPerCase, na.rm = TRUE),
            ComplementaryProtection = sum(ComplementaryProtection * DecisionsAveragePersonsPerCase, na.rm = TRUE),
            TotalDecided = sum(TotalDecided * DecisionsAveragePersonsPerCase, na.rm = TRUE)) %>%
  mutate(RefugeeRecognitionRate = (Recognized ) / TotalDecided,
         GlobalRecognitionRate = (Recognized + ComplementaryProtection) / TotalDecided) %>%
  filter(TotalDecided  != 0) %>%
  filter(TotalDecided  > 1000)  %>%
  mutate(CountryOriginName = str_replace(CountryOriginName, " \\(Bolivarian Republic of\\)", ""))


topasyl2 <-  topasyl  %>%
  arrange(desc(RefugeeRecognitionRate)) %>%
  head(10)  

## Filter top 10 
topOrigin2 <-  topOrigin  %>%
  arrange(desc(RefugeeRecognitionRate)) %>%
  head(10) 

Generate plot

recoasyl <- ggplot(topasyl2, aes(y = RefugeeRecognitionRate, 
             x = reorder(CountryAsylumName, RefugeeRecognitionRate))) + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) +
  #facet_grid(.~ ctry_asy) +  
  geom_bar( stat ="identity", fill = unhcRstyle::unhcr_blue) +
  coord_flip() +
  labs(#title = " ",
       subtitle = "Per country of asylum",
       x = " ", 
       y = " " ) +
  unhcRstyle::unhcr_theme(base_size = 9) +
  theme(#axis.text.x = element_blank(),
     # legend.position = "none",
    panel.grid.major.x = element_line(color = "#cbcbcb"), 
    panel.grid.major.y = element_blank()) + ### changing grid line  
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333")  


recoorigin <- ggplot(topOrigin2, aes(y = RefugeeRecognitionRate, 
             x = reorder(CountryOriginName, RefugeeRecognitionRate))) + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) +
  #facet_grid(.~ ctry_asy) +  
  geom_bar( stat ="identity", fill = unhcRstyle::unhcr_blue) +
  coord_flip() +
  labs(#title = " ",
       subtitle = "Per country of Origin ",
       x = " ", 
       y = " " ) +
  unhcRstyle::unhcr_theme(base_size = 9) +
  theme(#axis.text.x = element_blank(),
     # legend.position = "none",
    panel.grid.major.x = element_line(color = "#cbcbcb"), 
    panel.grid.major.y = element_blank() ) + ### changing grid line  
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") 

##########################
#joining charts
##################
library(patchwork)
patchworkRSD <- recoasyl + recoorigin
patchworkRSDa2 <- patchworkRSD +
 # unhcRstyle::unhcr_theme(base_size = 8)  +  ## Insert UNHCR Style
  theme(legend.position = "none") +
  plot_annotation(
    title = 'Refugee Recognition Rate Comparison in the Americas in 2020 ',
   # subtitle = ' ',
    caption = 'Source: UNHCR.org/refugee-statistics - Only Countries of Origin with more than 1,000 application during 2020 -\n Because of different types of procedure, data from the US may \n includes multiple applications per applicants compared to other countries  '
  )


patchworkRSDa2

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