refugee-flows/flow4.Rmd at master · danidlsa/refugee-flows · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
---
title: "Refugee Flows 2016-2021"
---

```{r}
library(tidyverse)
library(countrycode)
#remotes::install_github("vidonne/unhcrdesign")
#remotes::install_github("vidonne/unhcrthemes")
#remotes::install_github("vidonne/unhcrdown")
#remotes::install_github("edouard-legoupil/unhcrdatapackage")
#devtools::install_github("FlowmapBlue/flowmapblue.R")
library(flowmapblue)
library(countrycode)
library(unhcrdatapackage)
# Importing refugee data from UNHCR

asylum_applications <- asylum_applications
asylum_decisions <- asylum_decisions

asylum_decisions_2021 <- asylum_decisions %>%
  filter(Year>2015 & Year<=2021) %>%
  select(CountryOriginCode:CountryAsylumName, Recognized, DecisionTypeName ) %>%
  group_by(CountryOriginCode, CountryAsylumCode, CountryOriginName, CountryAsylumName) %>%
  summarize(Recognized=sum(Recognized)) %>%
  ungroup()

asylum_decisions_2000 <- asylum_decisions %>%
  filter(Year>=2000 & Year<=2005) %>%
  select(CountryOriginCode:CountryAsylumName, Recognized, DecisionTypeName ) %>%
  group_by(CountryOriginCode, CountryAsylumCode, CountryOriginName, CountryAsylumName) %>%
  summarize(Recognized=sum(Recognized)) %>%
  ungroup()

asylum_decisions_2010 <- asylum_decisions %>%
  filter(Year>2005 & Year<=2010) %>%
  select(CountryOriginCode:CountryAsylumName, Recognized, DecisionTypeName ) %>%
  group_by(CountryOriginCode, CountryAsylumCode, CountryOriginName, CountryAsylumName) %>%
  summarize(Recognized=sum(Recognized)) %>%
  ungroup()

asylum_decisions_2015 <- asylum_decisions %>%
  filter(Year>2010 & Year<=2015) %>%
  select(CountryOriginCode:CountryAsylumName, Recognized, DecisionTypeName ) %>%
  group_by(CountryOriginCode, CountryAsylumCode, CountryOriginName, CountryAsylumName) %>%
  summarize(Recognized=sum(Recognized)) %>%
  ungroup()


# To graph flows between countries, I will work with their centroids.
# The following dataset includes centroids of each country with its latitude and longitude
longlat_centroids <- readr::read_csv("https://gist.githubusercontent.com/tadast/8827699/raw/7255fdfbf292c592b75cf5f7a19c16ea59735f74/countries_codes_and_coordinates.csv") %>%
  janitor::clean_names()


locations <- longlat_centroids %>%
  mutate(id=alpha_3_code,
         name=country,
         lat=latitude_average,
         lon=longitude_average)

mapboxAccessToken = "pk.eyJ1IjoiZGFuaWRsc2EiLCJhIjoiY2swZ3NzZWs3MGI2NzNtbjVmcndjcHdiZCJ9.UHWAyCOMT-7HsSWlv1y4xQ"


# Period 2016-2021

flows <- asylum_decisions_2021 %>%
  mutate(origin=CountryOriginCode,
         dest=CountryAsylumCode,
         count=Recognized) %>%
  select(origin, dest, count)


map <- flowmapblue(locations, flows, mapboxAccessToken, clustering = F, darkMode = FALSE, animation = F)


map
```