Greg Wilson from Software Carpentry posted a plot challenge yesterday evening: http://software-carpentry.org/blog/2015/02/plot-this.html#comment-1845097980

The challenge was to redo this figure to maximize visual information.

Here is what I would do for a static plot:

library(ggplot2)
per.capita <- read.csv(“~/Desktop/per.capita.csv”)

# New variables
per.capita[,5]<-(per.capita$Attendees)/(per.capita$Population/1000000)
per.capita[,6]<-(per.capita$Instructors)/(per.capita$Population/1000000)
colnames(per.capita)[5]<-“xvar”
colnames(per.capita)[6]<-“yvar”

# This is just to adjust tags so they don’t overlap
per.capita[,7]<-c(0,-0.5,0,1.5,0,0,0,0,0.5,1.5,0,0,0,0.5,1.5,-1,0,0,0,0,2,0,0,0,0,0,0,0,0)
colnames(per.capita)[7]<-“vjust”
per.capita[,8]<-c(-0.2,-0.2,-0.2,-0.2,-0.2,-0.2,-0.2,-0.2,-0.2,0.9,-0.2,-0.2,-0.2,-0.2,1.2,-0.1,-0.2,-0.2,-0.2,-0.2,-0.1,-0.2,-0.2,-0.2,-0.2,-0.2,-0.2,-0.2,-0.2)
colnames(per.capita)[8]<-“hjust”

# Plot
(ggplot(per.capita, aes(x=log(xvar+1), y=log(yvar+1), color=Country))
+ theme(legend.position=”none”)
+ geom_point(shape=10, size=5)
+ scale_x_continuous(limits=c(0,4))
+ geom_text(aes(label=per.capita$Country, vjust=per.capita$vjust,hjust=per.capita$hjust))
+ xlab(“Attendees per Million Habitants (log)”)
+ ylab(“Instructors per Million Habitants (log)”)
+ ggtitle(“Software Carpentry Instructors and Attendees Statistics per Country”))

I also noticed there were some great Ipython interactive maps, definitely going to try something like this: http://nbviewer.ipython.org/gist/jiffyclub/3f3cc34745da55f36fcf

Learning a new language is always a tedious and challenging experiment. It is long. It is frustrating. It is tiring. Then, after a looooong while, it is rewarding. So why should you bother to learn a new statistical language? Why learn the R language? Why should you spend hours, days, weeks, drinking countless coffees, trying to get a grip of R?

So you’ve been using EXCEL, SAS, SPSS, Minitab, or others, for years. You’ve gotten confortable and effective. And now SOMEBODY (What an A**!) tells you that you should switch to R because it’s much more effective/productive #trendy. And you are scared/pissed off/just not interested. But wait a minute; do you know what is R? R is a programing language designed to facilitate data exploratory analysis, classical statistical tests and high-level graphics. R is a flexible, cutting-edge, powerful, statistical tool. Now that you know what R is, what does it do more than EXCEL, SAS, SPSS, and the likes? Why is it worth your time and energy? R language offers many advantages compared to other statistical tools. It is strong enough to handle big messy data. It allows the writing of a reproducible script for cross-scientific validation (which is non-arguable for scientists). It offers you more than 2000 libraries (a plethora of packages developed to perform particular functions and tasks) in a huge variety of fields. These libraries are made by those who use them and for those who use them. R reinforces scientist good habits in statistics and data management. It integrates with many other programing languages such as Java, C++ and Python as well as with other statistical tools such as IBM SPSS. Many big industrial players such as Facebook, the NY Times, Google, and Bank of America use R. And R works on all platforms: Windows, MAC, Linux. R is the leading edge of development in statistics, data mining and data analysis. But most of all, R is free, open source, transparent, open to community critics and reviews thus fast to improve. R is definitely trending, yes, because R is moving now, and it’s moving fast. To include R as one of your assets is a crucial advantage for recruitment. Yes, it is possible that you will swear on a forgotten coma, or be slow on debugging R errors. But R has a ton of blogs and peer-supported communities to help you learn its components and integrate them to your day-to-day work. You have no excuse not to give a shot at learning R.

http://www.r-project.org

http://www.inside-r.org

http://www.revolutionanalytics.com/r-community

http://www.r-statistics.com/tag/r-community/