popdata demo

demo_popdata.R

library(popdata)
library(unhcrthemes)
library(tidyverse)
library(janitor)

### Most function use "pd_" prefix
### "pd" stands for PopData

### Log into popdata
# pd_login()

###
demo_asr_2020 <- pd_asr(table = "demographics",
                        year = 2020)
glimpse(demo_asr_2020)

### How many refugees in Burkina Faso as of December 2020 ?
demo_asr_2020 |>
  filter(asylum == "BKF", populationType == "REF") |>
  group_by(asylum) |>
  summarize(ref = sum(total, na.rm = TRUE)) |>
  ungroup()


### How many refugees and IDPs in Burkina Faso as of December 2020 ?
demo_asr_2020 |>
  filter(asylum == "BKF", populationType %in% c("REF", "IDP")) |>
  group_by(asylum, populationType) |>
  summarize(val = sum(total, na.rm = TRUE)) |>
  ungroup()

### Wide columns
demo_asr_2020 |>
  filter(asylum == "BKF", populationType %in% c("REF", "IDP")) |>
  group_by(asylum, populationType) |>
  summarize(val = sum(total, na.rm = TRUE)) |>
  ungroup() |>
  pivot_wider(names_from = populationType,
              values_from = val)


### IDP and REF in Burkina Faso and Nigeria
demo_asr_2020 |>
  filter(asylum %in% c("BKF", "NIG"),
         populationType %in% c("REF", "IDP")) |>
  group_by(asylum, populationType) |>
  summarize(val = sum(total, na.rm = TRUE)) |>
  ungroup() |>
  pivot_wider(names_from = populationType,
              values_from = val)


### Do it for the past 3 years
map_dfr(2018:2020, function(y) {
  pd_asr(table = "demographics", year = y) |>
    mutate(year = y) |>
    filter(asylum %in% c("BKF", "NIG"),
         populationType %in% c("REF", "IDP")) |>
    group_by(year, asylum, populationType) |>
    summarize(val = sum(total, na.rm = TRUE)) |>
    ungroup() |>
    pivot_wider(names_from = populationType,
                values_from = val) |>
    arrange(asylum)
})

### How many refugees by region
demo_asr_2020 <- pd_asr(table = "demographics",
                        year = 2020)

demo_asr_2020 <- demo_asr_2020 |>
  pd_augment(col = "asylum", prefix = "asylum")

demo_asr_2020 |>
  filter(populationType == "REF") |>
  group_by(asylum_bureau) |>
  summarise(ref = sum(total, na.rm = TRUE)) |>
  ungroup()


demo_asr_2020 |>
  filter(is.na(asylum_bureau)) |>
  View()


### Age gender break down
demo_asr_2020 |>
  clean_names() |>
  select(asylum, origin, population_type,
         starts_with(c("total_female", "total_male")),
         -ends_with("total")) |>
  pivot_longer(cols = contains(c("total_female", "total_male")),
               names_to = c("sex", "age"),
               names_pattern = "total_(male|female)_(.*)",
               values_to = "count")