gistfile1.rmd

install.packages("devtools") # windows users install Rtools. Also: for mac and linux.
library("devtools")
install_github("ropensci/plotly")  # Plotly is part of the super cool rOpenSci.

library(plotly)  # install libraries we’ll be using
library(ggplot2)
library(reshape)

py <- plotly("ggplot2examples", "3gazttckd7")  # sign up (https://plot.ly/ssi/) and get a key  or use our public key to initiate a graph object

library(WDI) # now we’ll make the plot from the blog post
library(ggplot2)
 
dat = WDI(indicator='NY.GNP.PCAP.CD', country=c('CL','HU','UY'), start=1960, end=2012) # Grab GNI per capita data for Chile, Hungary and Uruguay

wb <- ggplot(dat, aes(year, NY.GNP.PCAP.CD, color=country)) + geom_line() 
+     xlab('Year') + ylab('GDI per capita (Atlas Method USD)') 
+     labs(title <- "GNI Per Capita ($USD Atlas Method)")

py$ggplotly(wb)  # call Plotly