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.
To install rMaps package, you just write the following commands on R console.
require(devtools) install_github("ramnathv/rCharts@dev") install_github("ramnathv/rMaps")
(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.
library(WDI) library(rMaps) library(dplyr) library(countrycode) # Get CO2 emission data from World bank # Data source : http://data.worldbank.org/indicator/EN.ATM.CO2E.KT/ df <- WDI(country=c("all"), indicator="EN.ATM.CO2E.KT", 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 summarize(value=EN.ATM.CO2E.KT[which.max(year)]) # 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.