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.
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.
- 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.