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

```{css, echo=FALSE} .col-ruler { column-rule: 2px solid #0072bc; column-gap: 30px; }

.center { text-align: center; }

```r
# `r paste0(params$country, " - ",params$year) `
knitr::opts_chunk$set(echo = FALSE,
                      comment = "#>",
                      message=FALSE, 
                      warning=FALSE,
                      fig.width = 6.3,
                      fig.height = 4.4,
                      fig.retina = 2,
                      fig.align = "center",
                      fig.showtext = TRUE,
                      dev = "ragg_png",
                      dpi = 300)
library(testthat)
library(ggplot2)
library(unhcrthemes)

##  make sure to get last version of the data
if ( packageVersion("ForcedDisplacementStat") != "0.0.1"){pak::pkg_install("edouard-legoupil/ForcedDisplacementStat")}
library(ForcedDisplacementStat)

##  make sure to get last version of the chart library
if ( packageVersion("unhcrdatapackage") != "0.1.8"){pak::pkg_install("edouard-legoupil/unhcrdatapackage")}
library(unhcrdatapackage)

theme_set( unhcrthemes::theme_unhcr())

## Key figures can be highlighterd with icons https://fontawesome.com/icons/categories/humanitarian 
total_poc <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()

total_ref <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country,
                Population.type == "REF"
                ) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()

total_idp <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country,
                Population.type == "IDP"
                ) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()


total_asy <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country,
                Population.type == "ASY"
                ) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()


total_ooc <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country,
                Population.type == "OOC"
                ) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()


total_oip <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country,
                Population.type == "OIP"
                ) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()


total_sta <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country,
                Population.type == "STA"
                ) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()


total_poc_last_year <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == as.numeric(params$year)-1,
                CountryAsylumCode == params$country) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()

perc_change_poc <- ((total_poc - total_poc_last_year)/total_poc_last_year)*100

ref_asy__last_year <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == as.numeric(params$year)-1,
                CountryAsylumCode == params$country,
                Population.type == "REF" | Population.type == "ASY"
                ) |> 
  dplyr::summarise(sum(Value, na.rm = TRUE)) |> 
  dplyr::pull()


perc_change_asy_ref <- (( (total_ref + total_asy) - ref_asy__last_year)/ref_asy__last_year)*100




top_ref <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country,
                Population.type == "REF") |> 
  dplyr::group_by(CountryOriginName) |> 
  dplyr::summarise(Value = sum(Value, na.rm = TRUE)) |> 
  dplyr::arrange(desc(Value)) |> 
  dplyr::mutate(perc = scales::percent(
    Value / sum(Value),
    accuracy = 1.5,
    trim = FALSE
  )) |> 
  dplyr::slice(1:3)


top_asy <- ForcedDisplacementStat::end_year_population_totals_long |> 
  dplyr::filter(Year == params$year,
                CountryAsylumCode == params$country,
                Population.type == "ASY") |> 
  dplyr::group_by(CountryOriginName) |> 
  dplyr::summarise(Value = sum(Value, na.rm = TRUE)) |> 
  dplyr::arrange(desc(Value)) |> 
  dplyr::mutate(perc = scales::percent(
    Value / sum(Value),
    accuracy = 1.5,
    trim = FALSE
  )) |> 
  dplyr::slice(1:3)

Key figures for r params$countryname

:::: {.col2 .col-ruler style="display: flex;"}

::: {.column width="50%"}

::: {.left data-latex=""}

[r format(round(total_ref, 0), big.mark=",")]{.blue .lead} Refugees

[r format(round(total_asy, 0), big.mark=",")]{.blue .lead} Asylum-seekers

[r format(round(total_ooc, 0), big.mark=",")]{.blue .lead} *Others of Concerns

:::

:::

::: {.column width="50%"}

::: {.left data-latex=""}

[r format(round(total_oip, 0), big.mark=",")]{.blue .lead} Other people in need of international protection

[r format(round(total_idp, 0), big.mark=",")]{.blue .lead} Internally displaced people

[r format(round(total_sta, 0), big.mark=",")]{.blue .lead} Stateless People

:::

:::

::::

# 
plot_ctr_treemap(year = params$year,
                 country_asylum_iso3c = params$country,
                 pop_type = c("REF", "ASY", "DIP","OOC", "STA", "OIP")
         )

\newpage

How the different Categories of Population of concern to UNHCR are evolving over time?

::: {.bg-blue}

According to official information provided by government authorities, as of December r params$year, the population of interest to UNHCR in r params$country reached r format(round(total_poc, 0), big.mark=",") people. Compared to r as.numeric(params$year)-1, the total population r ifelse(perc_change_poc>0, paste0("has increased ", format(round(perc_change_poc, 1), big.mark=","), "% during the year"), ifelse(perc_change_poc<0, paste0("has decreased ", format(round(perc_change_poc, 1), big.mark=","), "% during the year"), "has not changed")). Moreover, there was a r format(round(perc_change_asy_ref, 0), big.mark=",")% r ifelse(perc_change_asy_ref>0, "growth", ifelse(perc_change_asy_ref<0, "drop","change")) of refugees and asylum seekers in the country.

:::

plot_ctr_population_type_per_year(year = params$year,
                                  lag = 5,
                         country_asylum_iso3c = params$country,
                          pop_type = c("REF",
                                       "ASY",
                                       "OIP",
                                       "OOC",
                                       "STA",
                                       "IDP" ))

What are the main countries of Origin of Forced Displacement across Borders?

The three main countries of origin for Refugees were r top_ref[1,1] (r top_ref[1,3] of the total refugee population), r top_ref[2,1] (r top_ref[2,3] of the total refugee population) and r top_ref[3,1] (r top_ref[3,3] of the total refugee population). Regarding the asylum-seeker population, the three main countries of origin for asylum-seekers were r top_asy[1,1] (r top_asy[1,3] of the total asylum-seekers population), r top_asy[2,1] (r top_asy[2,3] of the total asylum-seekers population) and r top_asy[3,1] (r top_asy[3,3] of the total asylum-seekers population).

Refugees

plot_ctr_population_type_abs(year = params$year,
                    country_asylum_iso3c = params$country,
                    top_n_countries = 9,
                    pop_type = "REF"
                    ) +
  labs(title = paste0("Refugees: Main Countries of origin | ", params$year))

Asylum Seekers

plot_ctr_population_type_abs(year = params$year,
                    country_asylum_iso3c = params$country,
                    top_n_countries = 9,
                    pop_type = "ASY"
                    ) +
  labs(title = paste0("Asylum-seekers: Main Countries of origin | ", params$year))

plot_ctr_diff_in_pop_groups(year = params$year,
                            country_asylum_iso3c = params$country,
                            pop_type = c("REF", "ASY")
         )

Evolution

# 
plot_ctr_origin_history(year = params$year,
                         country_asylum_iso3c  = params$country,
                        lag = 5,
                          pop_type = c("REF", 
                                       "ASY", 
                                       "OIP", 
                                       "IDP" ),
                        otherprop = .02)

What are the Demographics profiles of the population?

Refugees

# 
plot_ctr_pyramid(year = params$year,
                 country_asylum_iso3c  = params$country,
                            pop_type = c("REF" )
         )

Asylum seekers

# 
plot_ctr_pyramid(year = params$year,
                 country_asylum_iso3c  = params$country,
                            pop_type = c(  "ASY")
         )

Refugee Recognition rate

plot_ctr_recognition(year = params$year,
                    country_asylum_iso3c  = params$country,
                    top_n_countries = 10, 
                    measure = "RefugeeRecognitionRate",
                                 order_by = "TotalDecided" )

plot_ctr_recognition(year = params$year,
                    country_asylum_iso3c  = params$country,
                    top_n_countries = 10, 
                    measure = "TotalRecognitionRate",
                                 order_by = "Recognized" )

Asylum Applications & Decision over time

plot_ctr_asylum(year = params$year,
                country_asylum_iso3c  = params$country, 
                lag = 10)

Asylum Processing

plot_ctr_process(year = params$year,
                country_asylum_iso3c  = params$country)

Average Asylum Processing Time

plot_ctr_processing_time(year = params$year,
                              country_asylum_iso3c = params$country)

What are the trends in terms of Solutions?

plot_ctr_solution(year = params$year,
                  country_asylum_iso3c  = params$country,
                  pop_type = c("REF", "ASY"))

What is the share of Forcibly Displaced People in comparison with Migrants in r params$countryname?

plot_ctr_disp_migrant(year = params$year,
                     country_asylum_iso3c  = params$country )

What are the main destination of asylum for nationals from r params$countryname?

plot_ctr_destination(year = params$year,
                     country_origin_iso3c = params$country,
                     pop_type = c("REF", "ASY")
         )

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