wb_datadownload_api.Rmd

---
title: "Accessing World Bank API on Development Indicators"
author: "S. Sikder"
date: "October 02, 2020"
output: html_document
---

# URL of world Bank API: https://data.worldbank.org/indicator

# try with new package "wbstats" require an updated verion of r

```{r, echo=TRUE}

# Part one: Data download

# install required packages 
install.packages("wbstats")
library(wbstats)


# explore useful information on country, region, indicators, topics, data sources

View((wb_countries())) # useful to call country data give also lat and long 
View((wb_regions())) # can be use for call regional indicator
View((wb_topics())) # give a list of all topics 
View((wb_indicators())) # detail information on each indicator including source of information, it will be helpful to find a indicator of interest




# it is possible to get all country information at once but can take more downloading time, therefore it is a good practice to access information according to need - depending on - Topic of interest, Time range, Area of interest

# Lets say we are interested to study the historical trend of some selected indicators on population, urban development, air population in order to make a comparison on regional and National statistics in last 25 year period (from the year 1990)

# now define set of indicator(s) of your interest
## Urban population (% of total): SP.URB.TOTL.IN.ZS
## Urban population growth (annual %):SP.URB.GROW	
## CO2 emissions from transport (% of total fuel combustion): EN.CO2.TRAN.ZS
## Population in the largest city (% of urban population): EN.URB.LCTY.UR.ZS
	
study_indicators <- c("SP.URB.TOTL.IN.ZS", "SP.URB.GROW", "EN.CO2.TRAN.ZS",  "EN.URB.LCTY.UR.ZS")


# download indicators values and store as a dataframe for year of 1975 and 2015 (Germany, region, world)
 
mydata_de <- wb_data(study_indicators, country = "de", start_date = 1975, end_date = 2015) # for germany
mydata_region <- wb_data(study_indicators, country = "regions_only", start_date = 1975, end_date = 2015) # for region
mydata_world <- wb_data(study_indicators, country = "world", start_date = 1975, end_date = 2015) # for world


# Combine all dataframe as one
mydata_de_region_world <- rbind( mydata_de, mydata_region, mydata_world)



# Part 2: Compartive time-serious visualization on urbanization and co2 emission from transportation sector. 


# install require package - ggplot2

install.packages("ggplot2")
library(ggplot2)

# plot points and points size with selected indicators (where x-axis year)
myplot<- ggplot()+geom_point(data=mydata_de_region_world, aes(x=date, y=EN.CO2.TRAN.ZS, size = SP.URB.TOTL.IN.ZS, colour = "red", alpha = 0.3)) #  plot points - with total share of urban populaton (size) and co2 emission from transport sector 

# add another indicators as lines plot
myplot<- myplot+geom_line(data=mydata_de_region_world, aes(x=date, y= EN.URB.LCTY.UR.ZS), colour = "red", cex = 1.5)#  plot line - with population in large cities 

# add another indicators as bar plot
myplot<- myplot+ geom_bar( data=mydata_de_region_world, stat = "identity", aes(x=date, y= SP.URB.GROW)) # fill annual growth of urban population

# add axis label, set axis limit, title

myplot <-myplot+ xlab("YEAR")+xlim(1975,2015)+ylab("IN PERCENTAGE (%)")+ylim(-5,40)+ theme_bw()
myplot <-myplot + ggtitle("Bar: Urban population growth, Line: population in large cities, point: CO2 emissions from transport, point size: urban population")+theme(legend.position = "none", plot.title =element_text(size=12))

# draw multiple plot as one 

myplot+facet_wrap(~country)


# Ref: https://cran.r-project.org/web/packages/wbstats/wbstats.pdf

# some futher idea to explore

# get spatial data on world country, region and combine the indicator data of World Bank  API for interactive visualization.

```