Rmarkdown_dashboard.Rmd

---
title: "UNHCR Populations Statistics (2000-2016)"
output: 
  flexdashboard::flex_dashboard:
    orientation: row
    vertical_layout: fill
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(error = FALSE, warning = FALSE, cache = TRUE, message=FALSE)

# read data
library(foreign)
library(readr)

# clean & manipulate data
#library(tidyverse)
library(plyr)
library(dplyr)
#library(reshape2)

# plots
library(ggplot2)

# time series
library(tseries)
library(dynlm)
library(urca)

# forecast
library(forecast)
#library(scales)

# knit
library(knitr)
library(rvest)

# interactive
library(plotly)
library(flexdashboard)

setwd("/Users/tessaschneider/Desktop/Final Data Analysis")

df <- read.csv("unhcr_popstats_refugee-status.csv", skip = 2, stringsAsFactors = F, 
               na.string=c("", "*"))
df2 <- read.csv("unhcr_popstats_export_persons_of_concern_all_data.csv", skip = 3, 
                stringsAsFactors = F, na.string=c("", "*"))

df2$Total.Population[df2$Total.Population=="*"] <- "2.5"
df2$Total.Population <- as.numeric(df2$Total.Population)

dfmerged <- merge(df, df2, by = c("Year", "Country...territory.of.asylum.residence", "Origin"))

Refugees_df <- ddply(dfmerged, .(Year), summarize, year_sum_Refugees = sum(`Total.Population`, na.rm = TRUE))

#Refugees_df <- ddply(POC_df, .(Year), summarize, year_sum_Refugees = sum(Refugees..incl..refugee.like.situations.), na.rm = TRUE)

Returned_Refugees_df <- ddply(dfmerged, .(Year), summarize, year_sum_Returned.Refugees = sum(`Returned.refugees`, na.rm = TRUE))
```

by People
=====================================     
Row
-----------------------------------------------------------------------

### People of Concern Per Year

```{r}
POC <- format(round(mean(POC_df$year_sum_Refugees), digits = 0), 1e6, big.mark=",", scientific=FALSE)
valueBox(POC, icon = "fa-user")
```

### Refugees per Year 

```{r}
Refugees <- mean(Refugees_df$year_sum_Refugees)
gauge(Refugees, min = 0, max = 26900000)
```

### Returned Refugees per Year

```{r}
Returned_Refugees <- mean(Returned_Refugees_df$year_mean_Returned.Refugees)
gauge(Returned_Refugees, min = 0, max = 2491)
```

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Populations of Concern (POC)

```{r}
library(reshape2)
PoC_count <- dfmerged[c(1,15:21)] 

PoC_count <- melt(PoC_count, id=c("Year"))
levels(PoC_count$variable) <- c("Refugees (including refugee-like situations", "Asylum Seekers (pending cases)", "Returned Refugees", "Internally displaced persons", "Returned IDPs", "Stateless Persons", "Other persons")



plot1 <- ggplot(PoC_count,aes(Year,value, na.rm = TRUE)) +
  geom_bar(aes(fill=variable),stat="identity") +
  labs(x="Year", 
       y="Number of People (Millions)")

ggplotly(plot1)
```

### Percent Change in POC Populations

```{r}
Year_Pop <- aggregate(dfmerged$`Total.Population`, by=list(Year = dfmerged$Year), FUN=sum, na.rm = TRUE)

Year_Pop$rate <- NA

Year_Pop$rate[which(Year_Pop$Year>2000)] = 100*(diff(Year_Pop$x)/Year_Pop[-nrow(Year_Pop),]$x)

plot2 <- ggplot(Year_Pop, aes(x= Year, y= rate)) + geom_line() + 
  labs(x="Year", 
       y="Percent Change")

ggplotly(plot2)
```

by Country
=====================================  

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Top Countries of Destination

```{r}
destination_country_total <- dfmerged %>%
  dplyr::group_by(Country...territory.of.asylum.residence, Year) %>%
  dplyr::summarize(Total = sum(Total.Population))

top_destcountries <- destination_country_total %>%
  dplyr::group_by(`Country...territory.of.asylum.residence`) %>%
  dplyr::summarize(Total = sum(Total, na.rm = TRUE)) %>%
  top_n(20)

top_destcountries2 <- as.character(top_destcountries$`Country...territory.of.asylum.residence`)

plot3 <- destination_country_total %>%
  filter(Country...territory.of.asylum.residence %in% top_destcountries2) %>%
  ggplot(mapping = aes(x = Year, y = Total)) +
  geom_line() + coord_cartesian(ylim = c(0, 3e6)) +
  facet_wrap( ~ Country...territory.of.asylum.residence, ncol=4)

plotly::ggplotly(plot3)
```

### Top Countries of Origin
```{r}

library(plotly)
library(dplyr)
library(ggplot2)

origin_country_total <- dfmerged %>%
  group_by(Origin, Year) %>%
  summarize(Total = sum(`Total.Population`))

top_origcountries <- origin_country_total %>%
  group_by(Origin) %>%
  summarize(Total = sum(Total, na.rm = TRUE)) %>%
  top_n(20)

top_origcountries2 <- as.character(top_origcountries$Origin)

plot4 <- origin_country_total %>%
  filter(Origin %in% top_origcountries2) %>%
  ggplot(mapping = aes(x = Year, y = Total)) +
  geom_line() + coord_cartesian(ylim = c(0, 1e7)) +
  facet_wrap( ~ Origin, ncol=4)

plotly::ggplotly(plot4)
```

Time Series
=====================================  

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------
```{r}
Germany_Poc <- dfmerged %>% group_by(`Country...territory.of.asylum.residence`, Year) %>% 
  filter('Germany'  %in% `Country...territory.of.asylum.residence`) %>% 
  summarise(German_Total = sum(`Total.Population`, na.rm = TRUE))

df_ts <- merge(Germany_Poc, Year_Pop, by = "Year")
  
df_ts$Year <- ts(df_ts$Year)
df_ts$German_Total<- ts(df_ts$German_Total)
df_ts$x <- ts(df_ts$x)
```

### Weak Dependence (Yes)
```{r}
acf(df_ts$German_Total, na.action = na.pass, lag.max = 5)
```

### Stationarity (No)
```{r}
par(mfrow = c(1, 2))
plot(df_ts$German_Total) #Total POCs in Germany
plot(df_ts$x) #Total POCs in the world
```

Forecasting
=====================================  

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------
```{r}
df3 <- read.csv("unhcr_popstats_export_asylum_seekers_monthly_2017_12_04_203715.csv", 
                skip = 2, stringsAsFactors = F)

df3$Value[df3$Value=="*"] <- "0"
df3$Value <- as.numeric(df3$Value)

Germany_Total.Monthly <- df3 %>%
  group_by(`Country...territory.of.asylum.residence`, Year, Month) %>%
  summarize(Total = sum(Value))

Germany_monthly <- ts(Germany_Total.Monthly$Total, 
                      start = c(1999, 1), frequency = 12)
```

### Decompose
```{r}
plot(decompose(Germany_monthly))
```

### ARIMA Forecasting
```{r}
plot(forecast(auto.arima(Germany_monthly), 30), 
     main = "ARIMA Forecast: Germany Asylum Seeker Arrivals", 
     ylab = "Number of Asylum Seekers", 
     xlab = "Year", ylim=c(0, 90000))
```

### TBATS Forecasting
```{r}
plot(forecast(tbats(Germany_monthly), 30), 
     main = "TBATS Forecast: Germany Asylum Seeker Arrivals", 
     ylab = "Number of Asylum Seekers", 
     xlab = "Year", ylim=c(0, 90000))
```