Rstats

rMaps Mexico map

It’s exciting when great people help each other get things done This is a simple networking story, which might not be surprising to some but I was happily surprised by it. This is how the story goes: Two weeks ago rMaps (Vaidyanathan, 2014) was released. After making a blog post about it I thought about using it to make a map of the homicide rate in Mexico over the recent years.

Automatically coloring your R output in the terminal using colorout

Thanks to Alyssa Frazee I just learned about the colorout package (Aquino, 2013). It modifies R so that the output is in different colors, making it much more pleasant to use R in the terminal. Do note that colorout is not available from CRAN, but you can easily install by following the instructions on the colorout site (Official site) reproduced below: download.file("http://www.lepem.ufc.br/jaa/colorout_1.0-2.tar.gz", destfile = "colorout_1.0-2.tar.gz") install.packages("colorout_1.0-2.tar.gz", type = "source", repos = NULL) The next step is to then load colorout automatically when I start R.

rMaps released

Ramnath Vaidyanathan just released his new R interactive package, rMaps (Vaidyanathan, 2014). The packages relies on the development version of his widely known rCharts package (Vaidyanathan, 2013) as well as javascript libraries that specialize in maps. If you don’t know Ramnath, he is one of the most active R developers out there!! You can see that from his GitHub profile. The package is very new and still under development, but I bet that Ramnath released it to get us users excited and maybe find some helpful hands to document it and further develop it.

How to upload files to Dropbox and Google Docs from R

Have you ever wondered whether you can upload files from R to Dropbox and/or Google Docs? I recently asked myself this question while making my most recent Shiny app (more later). The answer is yes, you can upload files from R to these cloud services! Dropbox As far as I know, the best R package for uploading files to Dropbox is rDrop (Ram & Temple Lang, 2012). The whole setup is very well explained in it’s GitHub repository (Karthik).

Creating awesome reports for multiple audiences using knitrBootstrap

As a biostatistics student, I use R very frequently when analyzing data. At the same time, I interact with other researchers, some who know how to use R (R crowd) and some who don’t (yet!): no-R crowd. This means that I have to be able to communicate my results to two crowds. It is important that I can quickly provide the code in case that the R savvy want to look at it: maybe they find a bug and report it ^^.

Trying to reduce the memory overhead when using mclapply

I am currently trying to understand how to reduce the memory used by mclapply. This function is rather complicated and others have explained the differences versus parLapply (A_Skelton73, 2013; lockedoff, 2012 ) and also made it clear that in mclapply each job does not know if the others are running out of memory and thus cannot trigger gc (Urbanek, 2012). While I still struggle to understand all the details of mclapply, I can successfully use it to reduce computation time at the expense of a very high memory load.

Creating your Jekyll-Bootstrap powered blog for R blogging

As you might have noticed, I recently decided to move Fellgernon Bit from Tumblr to GitHub. There are a couple of reasons why I made this change. I wanted a more professional-looking blog. There are not many R blogs on Tumblr, and well, long text posts are not really meant for Tumblr. Better code highlighting. I had enabled R code highlighting using the highlighting instructions from Jeffrey Horner (Horner, Part I).

ggplot Tutorial

ggplot TutorialI liked the following ggplot2 tutorial which is featured in Gabriela de Queiroz’s blog called unbiasedestimator. The tutorial looks very neatly presented and I’m sure that it will be very helpful to anyone just getting started with ggplot2 before they jump into ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham or R Graphics Cookbook by Winston Chang. The tutorial is very nicely formatted with code in bold highlighting parts that change something in the plot.

userR2013 data analysis contest: data exploration

Description The useR2013 conference is organizing a data analysis contest, check the rules here. They have a package called useR2013DAC with two data sets: one from La Liga and the other one from the Formula 1. Once you download and install the package (available here), you can quickly explore the data using the following R commands: Data exploration ## Load the package library(useR2013DAC) ## Explore laliga data data(laliga) head(laliga) ## Season Week HomeTeam AwayTeam ## 1 200809 1 Athletic Club Bilbao Union Deportiva Almeria ## 2 200809 1 Atlético Madrid Málaga CF ## 3 200809 1 Betis Sevilla Real Club Recreativo Huelva ## 4 200809 1 CA Osasuna Villarreal CF ## 5 200809 1 CD Numancia FC Barcelona ## 6 200809 1 Deportivo de La Coruña Real Madrid CF ## HomeGoals AwayGoals ## 1 1 3 ## 2 4 0 ## 3 0 1 ## 4 1 1 ## 5 1 0 ## 6 2 1 summary(laliga) ## Season Week HomeTeam AwayTeam ## Length:1900 Min.

Reading an R file from GitHub

Lets say that I want to read in this R file from GitHub into R. The first thing you have to do is locate the raw file. You can do so by clicking on the Raw button in GitHub. In this case it’s https://raw.github.com/lcolladotor/ballgownR-devel/master/ballgownR/R/infoGene.R One would think that using source() would work, but it doesn’t as shown below: source("https://raw.github.com/lcolladotor/ballgownR-devel/master/ballgownR/R/infoGene.R") ## Warning: unsupported URL scheme ## Error: cannot open the connection However, thanks again to Hadley Wickham you can do so by using the devtools (Wickham & Chang, 2013 ) package.