Reading & visualizing “Ease of doing business” data.

Data Capturing

Step 1: Download data from below URLs

  1. Download from http://www.doingbusiness.org/data/distance-to-frontier and rename file to DistanceToFrontier.csv
  2. Download from http://www.doingbusiness.org/rankings and rename file to EconomyRankings.csv

Step 2 : Get three digit country code.

Get three digit country code for each country/economy (through Wikipedia or other sources) and put them as first column - name it as “code” - in each of above files.

Step 3: Reading data

suppressWarnings(library(plotly))
economyRanking<-read.csv("EconomyRankings.csv")
economyRanking<-subset(economyRanking, select=c("code","Economy", "Ease.of.Doing.Business.Rank"))

#renaming column
colnames(economyRanking)[3] <- "Rank"

Step 4 : Visualizing

l <- list(color = toRGB("grey"), width = 0.5)
g <- list(
  showframe = FALSE,
  showcoastlines = FALSE,
  projection = list(type = 'Mercator')
)

p <- plot_geo(economyRanking) %>%
    add_trace(
        z = ~Rank, color = ~Rank, 
        text = ~Economy, locations = ~code, marker = list(line = l)
    ) %>%
    
    layout(
        title = 'Ease of doing business ranking on world map',
        geo = g
    )
p

As we can see, mostly African nations and few middle east countries are at the bottom whereas countries on top and Australia at down has a good ranking.

Step 5: Reading Distance to Frontier data to observe data across years.

Significant positive changes of countries over time

Here we are getting countries whose ranking increased significantly from 2010 to 2017.

yearWiseRanking<-economyRanking<-read.csv("DistanceToFrontier.csv",na.strings = "?",stringsAsFactors=FALSE)


setnames(yearWiseRanking, old = c('DB.2010','DB.2011','DB.2012','DB.2013','DB.2014','DB.2015','DB.2016','DB.2017'), new = c('2010','2011','2012','2013','2014','2015','2016','2017'))

yearWiseRanking$temp<-yearWiseRanking$'2017'-yearWiseRanking$`2010`
year<-yearWiseRanking[order(-yearWiseRanking$temp),]
head<-head(year,5)

gatheredData<-gather(head, "Year", "scoreOutOf100", 2:8)



p <- plot_ly(gatheredData, x = ~Year, y = ~Economy,  type = 'scatter', mode = 'markers', color = ~scoreOutOf100, 
             marker = list(size = ~scoreOutOf100, opacity = 0.5),hoverinfo = 'text',text = ~paste('Year:',Year,', Economy:',Economy,', Score:',scoreOutOf100)) %>%
    layout(autosize = F, width = 990, height = 500,title = 'Significant positive changes of countries over time',
           xaxis = list(showgrid = FALSE),
           yaxis = list(showgrid = FALSE))

p



Significant negative changes of countries over time

Here we are getting countries whose ranking decreased significantly from 2010 to 2017.

head<-tail(subset(year, temp != 'NA'),5)
gatheredData<-gather(head, "Year", "scoreOutOf100", 2:8)


p <- plot_ly(gatheredData, x = ~Year, y = ~Economy,  type = 'scatter', mode = 'markers', color = ~scoreOutOf100, 
             marker = list(size = ~scoreOutOf100, opacity = 0.5),hoverinfo = 'text',text = ~paste('Year:',Year,', Economy:',Economy,', Score:',scoreOutOf100)) %>%
    layout(autosize = F, width = 990, height = 500,title = 'Significant negative changes of countries over time',
           xaxis = list(showgrid = FALSE),
           yaxis = list(showgrid = FALSE),showlegend = FALSE)

p