---
title: "ANLY 512 Dashboard Laboratory: Global Refugee Crisis - United States Region"
author: "by Yuan, Xin, Yun - Chia Lo, Jingwen Nie"
date: "`r Sys.Date()`"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
social: menu
source_code: embed
---
```{r data, include=FALSE}
setwd("C:\\Users\\Hannah\\Desktop\\Assignment\\ANLY512\\Project")
data = read.csv('./USA_RefugeesSeekers.csv', colClasses = c("Year"="numeric",
"Country"="character",
"Origin"="character",
"Refugees"="numeric",
"seekers"="numeric"))
country = read.csv('./all.csv',colClasses = c("name" = "character",
"alpha2" = "character",
"region" = "character",
"subregion" = "character"))
coord = read.csv('./coodinate.csv',colClasses = c("country" = "character",
"latitude" = "numeric",
"longtitude" = "numeric"))
region = c()
subregion = c()
long = c()
lat = c()
for (i in data$Origin){
if (i %in% country$name) {
region = append(region,country[country$name == i,]$region)
subregion = append(subregion,country[country$name == i,]$subregion)
long = append(long, coord[coord$country == country[country$name == i,]$alpha2,]$longtitude)
lat = append(lat, coord[coord$country == country[country$name == i,]$alpha2,]$latitude)
}
else{
region = append(region, 'others')
subregion = append(subregion, 'others')
long = append(long, 0)
lat = append(lat, 0)
}
}
data = cbind(cbind(data,region),subregion)
data = cbind(cbind(data,long),lat)
data$region = as.character(data$region)
data$subregion = as.character(data$subregion)
years = c()
years6 = c()
types = c()
value = c()
RR = c() # region refugee
top5 = c()
top5_nation = c()
idx = 1
for(y in unique(data$Year)){
years = append(years,rep(y , 2))
years6 = append(years6,rep(y , 6))
subdata = data[data$Year == y,]
types = append(types,c('Refugees','seekers'))
value = append(value, c(sum(subdata$Refugees), sum(subdata$seekers)))
top = subdata[order(subdata$Refugees, decreasing = TRUE),]
top5 = cbind(top5, append(top$Refugees[1:5],sum(top$Refugees[-(1:5)])))
top5_nation = cbind(top5_nation, append(top$Origin[1:5],'others'))
colnames(top5)[idx] = y
colnames(top5_nation)[idx] = y
idx = idx +1
top =
for(j in unique(data$region)){
RR = append(RR, sum(data[data$region == j,]$Refugees))
}
}
rregions = rep(unique(data$region),length(unique(data$Year)))
```
Refugee Types
=======================================================================
Column {.tabset data-width=450}
-----------------------------------------------------------------------
### Number of Refugees and Asylum Seekers
```{r}
# Dashbord packages
library(plotly)
library(plyr)
library(flexdashboard)
# plot figures
library(ggthemes)
library(ggplot2)
library(scales)
types[types == "seekers"] <- "Seekers"
# refugees and seekers (stacked barplot)
plot1<-ggplot(data.frame(years,types,value), aes(fill=types, y=value, x=years)) +
geom_bar( stat="identity")+
labs(title = "Number of Refugees and Asylum Seekers", x="Years", y="Numbers") +
theme_economist() + scale_fill_discrete(name = "Type")+
theme(plot.title = element_text(hjust = 0.5), legend.title=element_text(size=14))+
scale_y_continuous(labels = comma)
ggplotly(plot1)
```
### Percentage of Refugees and Asylum Seekers
```{r figure 2}
# refugees and seekers (stacked Percent barplot)
plot2<-ggplot(data.frame(years,types,value), aes(fill=types, y=value, x=years)) +
geom_bar( stat="identity", position="fill")+
labs(title = "Percentage of Refugees and Asylum Seekers", x="Years", y="Percentage of Incidents") +
scale_y_continuous(labels = function(x) paste0(abs(x)*100, "%"))+
theme_economist() +
scale_fill_discrete(name = "Type")+
theme(plot.title = element_text(hjust = 0.5), legend.title=element_text(size=14))
ggplotly(plot2)
```
Refugee Origins
=======================================================================
Column {.tabset data-width=550}
-----------------------------------------------------------------------
### Refugees Origin
```{r figure 3}
# Refugees origin (group barplot)
rregions[rregions == "others"] <- "Others"
plot3<-ggplot(data.frame(years6,rregions,RR), aes(fill=rregions, y=RR, x=years6)) +
geom_bar(position="dodge", stat="identity")+
labs(title = "Refugees Origin", x="Years", y="Numbers of Refugees") +
theme_economist() + scale_fill_discrete(name = "Origin Region")+
scale_y_continuous(labels = comma)+
theme(plot.title = element_text(hjust = 0.5), legend.title=element_text(size=14))
ggplotly(plot3)
```
```{r data for figure 4, , include=FALSE}
datar<-as.data.frame(cbind(data$Year, data$region, data$Refugees))
colnames(datar)=c("Year","Region","Refugees")
datas<-as.data.frame(cbind(data$Year, data$region, data$seekers))
colnames(datas)=c("Year","Region","Seekers")
datar_sum<-as.data.frame(aggregate(.~Year+Region, datar, sum))
datas_sum<-as.data.frame(aggregate(.~Year+Region, datas, sum))
n<-nrow(datar_sum)
datar_sum$Group = c(rep("Refugees", n))
datas_sum$Group = c(rep("Seekers", n))
colnames(datar_sum)=c("Year", "Region", "Number", "Group")
colnames(datas_sum)=c("Year", "Region", "Number", "Group")
data_sum<-rbind(datar_sum, datas_sum)
```
### Number of Refugees and Asylum Seekers by Regions over Time
```{r figure 4}
datar<-as.data.frame(cbind(data$Year, data$region, data$Refugees))
colnames(datar)=c("Year","Region","Refugees")
datas<-as.data.frame(cbind(data$Year, data$region, data$seekers))
colnames(datas)=c("Year","Region","Seekers")
datar_sum<-as.data.frame(aggregate(.~Year+Region, datar, sum))
datas_sum<-as.data.frame(aggregate(.~Year+Region, datas, sum))
n<-nrow(datar_sum)
datar_sum$Group = c(rep("Refugees", n))
datas_sum$Group = c(rep("Seekers", n))
colnames(datar_sum)=c("Year", "Region", "Number", "Group")
colnames(datas_sum)=c("Year", "Region", "Number", "Group")
data_sum<-rbind(datar_sum, datas_sum)
plot4<-ggplot(data_sum, aes(Year, Number, group=Group, colour=Group))+
geom_line() +
geom_point()+
facet_wrap(~Region, strip.position = "bottom")+
xlab("Years") + ylab("Numbers") +
ggtitle("Number of Refugees and Asylum Seekers by Regions over Time") +
theme_bw()+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.x = element_text(size = 7))
ggplotly(plot4)
```
###Refugees Migration Map
```{r figure 5}
library(maps)
library(geosphere)
xlim <- c(-170, 170)
ylim <- c(-70, 90)
map("world", col="#191919", fill=TRUE, bg="gray", lwd=0.05, xlim=xlim, ylim=ylim)
US = c(-95.712891, 37.09024)
year = 2017
subdata = data[data$Year == year,]
# Color
pal = colorRampPalette(c("#333333", "white", "#1292db"))
colors = pal(100)
max5 = tail(sort(subdata$Refugees),5)[1]
for (i in 1:length(subdata$Origin)) {
cnt = min(subdata$Refugees[i], max5)
inter = gcIntermediate(c(subdata$long[i], subdata$lat[i]), US, n=1000, addStartEnd=TRUE, breakAtDateLine = TRUE)
colindex = round( (cnt / max5) * length(colors) )
if (length(inter) == 2){
lines(inter[[1]], col=colors[colindex], lwd=0.6)
lines(inter[[2]], col=colors[colindex], lwd=0.6)
}
else{
lines(inter, col=colors[colindex], lwd=0.6)
}
}
```
###Asylum Seekers Migration Map
```{r figure 6}
xlim <- c(-170, 170)
ylim <- c(-70, 90)
map("world", col="#191919", fill=TRUE, bg="gray", lwd=0.05, xlim=xlim, ylim=ylim)
US = c(-95.712891, 37.09024)
subdata = data[data$Year == year,]
# Color
pal = colorRampPalette(c("#333333", "white", "#1292db"))
colors = pal(100)
max5 = tail(sort(subdata$seekers),5)[1]
for (i in 1:length(subdata$Origin)) {
cnt = min(subdata$seekers[i], max5)
inter = gcIntermediate(c(subdata$long[i], subdata$lat[i]), US, n=1000, addStartEnd=TRUE, breakAtDateLine = TRUE)
colindex = round( (cnt / max5) * length(colors) )
if (length(inter) == 2){
lines(inter[[1]], col=colors[colindex], lwd=0.6)
lines(inter[[2]], col=colors[colindex], lwd=0.6)
}
else{
lines(inter, col=colors[colindex], lwd=0.6)
}
}
```
