World Bank Development Indicator

---
title: "ANLY512_narrative_storyboard.rmd"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source: embed
    storyboard: true
---
World Bank Development Indicator


### Where Are Mammal Species Threatened
```{r}
indicators <- read.csv("/Users/yh101251/Desktop/HU/512/week10/indicators.csv")
``` 

```{r}
library(dplyr)

threatened=filter(indicators, IndicatorCode %in% c("EN.MAM.THRD.NO","EN.BIR.THRD.NO","EN.FSH.THRD.NO","EN.HPT.THRD.NO","NY.GDP.PCAP.CD","ER.MRN.PTMR.ZS"))
#mammal species, bird species, fish species, plant species, GDP per capita, marine protected water

library(countrycode)

threatened$continent <- factor(countrycode(sourcevar = threatened[, "CountryName"],
                                         origin = "country.name",
                                          destination = "continent"))

library(zoo)
library(tidyr)
threatened_wide <- threatened %>%
  spread(IndicatorCode, Value) %>%
  group_by(CountryName) %>%
  mutate_all(funs(na.locf(., na.rm = FALSE, fromLast = FALSE)))%>%filter(row_number()==n())

## after spread()
## CountryName   Var1    Var2    Var3    Var4   Var5
## A             12      11      33      NA      NA
## A             112     115     NA      NA      NA
## reference: Combine rows by group with differing NAs in each row: https://stackoverflow.com/questions/45201654/combine-rows-by-group-with-differing-nas-in-each-row

threatened_wide_matched <- na.omit(threatened_wide)

``` 


```{r echo=F, results='hide'}
library(rworldmap)
##Joining data to a country map
matched <- joinCountryData2Map(as.data.frame(threatened_wide_matched),joinCode = "ISO3",nameJoinColumn = "CountryCode")
```

```{r}
##displaying the data
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapCountryData(matched, nameColumnToPlot="EN.MAM.THRD.NO", mapTitle = "Number of Mammal Species Threatened in 2015",catMethod = "pretty",colourPalette = "topo") 
```

```{r echo=F, results='hide'}
attach(threatened_wide_matched)
newdata <- threatened_wide_matched[order(EN.MAM.THRD.NO),] 
tail(newdata,10)
```

*** 
Top 10 countries:

Indonesia - 185

Madagascar - 119

Mexico - 101

India - 93

Brasil - 82

China - 74

Malaysia - 70

Colombia - 56 

Australia - 56

Peru - 55

### Relationship between GDP and threatened mammal species
```{r} 
library(ggplot2)
library(ggthemes)

ggplot(data =threatened_wide_matched, aes(x=NY.GDP.PCAP.CD, y= EN.MAM.THRD.NO,colour=continent, alpha = 0.5)) +
  geom_point() + 
  geom_smooth(method='lm',formula=y~x) +
  facet_wrap(~continent) +
  theme_bw() +
  labs(x="GDP per capita", y="Number of species threatened", title = "GDP per capita and Mammal Species Threatened")
```

***
The distribution of countries shows Africa, America and Asia have countries with more mammal species threatened.
Countries with lower GDP generally have more mammal species threatened, except for Oceania.

### Relationship between GDP, fish species and protected water
```{r}
library(ggplot2)
library(ggthemes)

ggplot() +
  geom_point(data = threatened_wide_matched, aes(x=NY.GDP.PCAP.CD, y=ER.MRN.PTMR.ZS, colour=continent, size = EN.FSH.THRD.NO), alpha = 0.5) + 
  geom_smooth(method='lm',formula=y~x) +
  labs(title = "Relationship: Fish Species Threatened and Protected Water", subtitle = "Each point represents one country", x="GDP per Capita in U.S. $", y = "Percentage of Water Protected") +
  theme_bw() 
  
```



***
European countries - which have higher GDP and higher percetage of water protected - have the fewer fish species threatened. 

### Protected water and threatened fish species
```{r}
library(ggplot2)
library(ggthemes)

ggplot(data = threatened_wide_matched, aes(x=ER.MRN.PTMR.ZS, y=EN.FSH.THRD.NO, colour = continent, alpha = 0.5)) +
  geom_point() + 
  geom_smooth(method='lm',formula=y~x) +
  labs(title = "Relationship: Protected Water and Fish Species Threatened", subtitle = "Each point represents one country", x="Percentage of Marine Water Protected", y = "Number of Species Threatened") +
  theme_bw() +
  facet_wrap(~continent)
  
```

```{r echo=F, results='hide'}
attach(threatened_wide_matched)
newdata <- threatened_wide_matched[order(EN.FSH.THRD.NO),] 
tail(newdata,10)
```

***
European countries - which have higher GDP and higher percetage of water protected - have the fewer fish species threatened. 
Top 10 countries in with fish species threatened:

South Africa - 247

Australia	-	216

Cameroon - 179

China	-	175

Turkey - 150

Indonesia	- 131	

Tanzania - 131	

Mexico - 119	

India -	111

United States - 103