On occasion of the 10,000th R package: The eoda Top 10

January 28, 2017

(This article was first published on eoda english R news – Der Datenanalyse Blog von eoda, and kindly contributed to R-bloggers)

R just passed another milestone: 10,000 packages on CRAN. From A3 to zyp, from ABC analysis to zero-inflated models – 10,000 R packages mean great variety and methods for almost every use case. On occasion of this event we collected the top 10 R packages in collaboration with the ones who should know best: our data scientists.

The eoda Top 10
The eoda Top 10 R-Packages

Our Top 10 R packages

  • Hmisc: This was one of the first R packages to be used at eoda on a regular basis. Today we barely use Hmisc anymore but nonetheless it had to be part of our top 10 simply for nostalgic reasons.
  • data.table: R as an in-memory database with data.table? Who said R was slow?
  • TraceR: Excellent profiling package. Will find every bottleneck.
  • dplyR: Not only fast when it comes to evaluation but also easy to master. Intuitive data management with R has a name – dplyR.
  • ggplot2: A guarantee for easily creating descriptive graphics.
  • magrittr: %>%. The pipe operater %>% turns complicated nested function calls into readable chains.
  • shiny: Interactive web application where programming in HTML and JavaScript can be done with little effort.
  • tidyr: Very good functionality for restructuring data. Particularly remarkable: the functions gather and spread for converting data from long to wide format or from wide to long format.
  • caret: This package unifies the data mining algorithms in one interface.
  • rcpp: A package that we don’t use extensively ourselves but is nevertheless indispensable in our top 10 list because it is an important component of many other great R packages.

We are already curious to see which R packages will become indispensable tools in the future and make it to the top 10 list on occasion of the 20,000th R package.

But for now, only that much: Congratulations to the world-wide R community and the R core team.

To leave a comment for the author, please follow the link and comment on their blog: eoda english R news – Der Datenanalyse Blog von eoda.

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