Seamless analytical environment by WDI, dplyr, and rMaps

March 22, 2014

(This article was first published on My Life as a Mock Quant in English, and kindly contributed to R-bloggers)

Recently I found that My R Guru @ramnath_vaidya is developping a new visualization package rMaps.

I was so excited when I saw it for the first time and I think that it's really awesome for plotting any data on a map.

Let me explain how we can

  • Get data(WDI package)
  • Manipulate data(dplyr package)
  • Visualize the result(rMaps package)

with greate R packages.

Except for rMaps package, you can install these packages(WDI, dplyr) from CRAN by usual way.

install.packages(c("WDI", "dplyr"))

To install rMaps package, you just write the following commands on R console.


(Don't forget to install “devtools” package to use install_github function.)

Now, as an example, I show you that

  • Get “CO2 emissions (kt)” data from World Bank by WDI package
  • Summarze it to by dplyr package
  • Visualize it by rMaps package

The result is shown below:


By the way, recently an Japanese R professional guy often posts his greate articles. I recommend you to see these articles if you are interested in visualizing and dplyr especially.

Source codes:

# Get CO2 emission data from World bank
# Data source :
df <- WDI(country=c("all"),
start=2004, end=2013)
# Data manipulation By dplyr
data <- df %.%
na.omit() %.%
#Add iso3c format country code
mutate(iso3c=countrycode(iso2c, "iso2c", "iso3c")) %.%
group_by(iso3c) %.%
#Get the most recent CO2 emission data
# Visualize it by rMaps
i1 <- ichoropleth(value~iso3c, data, map="world")
i1$show("iframesrc", cdn = TRUE) # for blog post
#... or you can direct plot by just evaluating "i1" on R console.

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