Asylum_processing.Rmd

---
title: "Asylum Processing"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Asylum Processing}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  echo=TRUE,
  comment = "#>"
)
```


## Load Packages

```{r message=FALSE, warning=FALSE}
library(tidyverse)
```

## Prepare Data

```{r}
thisbureau <- "Americas"
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
end_year_population_totals_long.asy <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))

asylumseekerAM2020 <- end_year_population_totals_long.asy %>%
  filter(Population.type  %in% c("ASY") & 
           Year == lastyear &
           UNHCRBureau  == thisbureau &
           !(is.na(UNHCRBureau)) ) %>%
  group_by( CountryAsylumName) %>%
  summarise(Asylum = sum(Value) )  %>%
  filter(Asylum  > 50000)


asylum_decisionAM <- 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
  ) %>%
  group_by(CountryAsylumName, Year ) %>%
  mutate(Year = as.integer(Year) ) %>%
  summarise(Decided = sum(TotalDecided) ) %>%
  filter(CountryAsylumName  %in%  as.character(asylumseekerAM2020$CountryAsylumName))


asylum_applicationsAM <- unhcrdatapackage::asylum_applications  %>%
  ## 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
  ) %>%
  group_by(CountryAsylumName, Year ) %>%
  mutate(Year = as.integer(Year) ) %>%
  summarise(Applied = sum(NumberApplications) ) %>%
  filter(CountryAsylumName  %in%  as.character(asylumseekerAM2020$CountryAsylumName)) %>%
  filter(Year  > 2017) %>%
  dplyr::left_join( asylum_decisionAM,  by = c("CountryAsylumName", "Year")) %>% 
  mutate(applicationrate = Decided / Applied  * 100 ) %>% 
  mutate(applicationrate.cat = cut(applicationrate, breaks=c(0, 25, 50, 75,100, 500)))

```

## Generate plot

```{r echo=TRUE, fig.height=7, fig.width=7, warning=FALSE, ,message=FALSE}
## First test combine Number of Application & Ratio as color
rsdrate <- ggplot(asylum_applicationsAM,
           aes(x = reorder(CountryAsylumName, Applied),
               y = Applied,
               fill= applicationrate.cat  )) +
      geom_bar( stat ="identity") +
      #scale_fill_grey(start = 0.85, end = 0.15) +
      #scale_fill_gradient(low = "#132B43",   high = "#56B1F7") +
      scale_fill_brewer(palette = "RdYlGn") +
      #scale_fill_brewer(direction = -1) +
      coord_flip() +
      facet_wrap( vars(Year ), ncol = 3) +
      scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
      theme(legend.position="none") +
      geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
      unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
      theme(panel.grid.major.x  = element_line(color = "#cbcbcb"),
            panel.grid.major.y  = element_blank(),
            panel.grid.minor = element_blank()) + ### changing grid line that should appear
      ## and the chart labels
      labs(title = "How much Status decision processing capacity are addressing the demand in the Americas?",
           subtitle = "Colors indicate the ratio between Numbers of Asylum Applications and Decisions ",
           x = "Number of applications",
           y = "",
           caption = "Source: UNHCR.org/refugee-statistics - only for countries with more than 50,000 Asylum Seekers") 

rsdrate

```