I discovered Plotly some days ago, and I was fascinated by it.
What is Plotly?
Plotly is a service for creating and sharing data visualizations that also offers statistical analysis tools plus a robust API, the ability to graph custom functions and a built-in Python shell. Among its APIs, there is the R one: Plotly interactive visualization can be created directly from R.
Thanks to R-bloggers, I discovered that googleVis 0.4.7 with RStudio integration is available on CRAN.
This is great news, but wasn't this that catched my eye. At the end of the post a beautiful map shows a terrible event of the last days: the devastating typhoon track of Haiyan that hit Southeast Asia in November.
R can be connected with Hadoop through the
rmr2 package. The core of this package is
mapreduce() function that allows to write some custom MapReduce algorithms. The aim of this article is to show how it works and to provide an example.
From release 098.208 the last RStudio IDE comes with a visual debugger. Now debugging with R and RStudio becomes a simple and efficient task.
This short post does not want to be a crash course: “debugging with R” nor can be a full explanation of the RStudio visual debugging capabilities: we guess that all of these will be fully documented as soon as this tool will be officially released.
This post is a simple sharing of something we have learned about R and RStudio.
Many people still save their data into Microsoft Excel files. This is an unhappy choice for many reasons but many was already written about this topic. Furthermore, unfortunately Excel become a de facto standard in many business environment and this routine seems to be difficult to strike out.
Many solutions have been implemented to read Excel files from R: each one has advantages and disadvantages, so an universal solution is not available. Get an overview of all the solutions, allows the choice of the best solution case-by-case.