# What R packages do you find most useful in your daily work?

What are the R packages you couldn't imagine your daily work with data? Please list both general and specific tools.

UPDATE: As for 24.10.10 ggplot2 seems to be the winer with 7 votes.

Other packages mentioned more than one are:

• plyr - 4
• RODBC, RMySQL - 4
• sqldf - 3
• lattice - 2
• zoo - 2
• Hmisc/rms - 2
• Rcurl - 2
• XML - 2

## locked by whuber♦Aug 20 '15 at 14:24

This question exists because it has historical significance, but it is not considered a good, on-topic question for this site, so please do not use it as evidence that you can ask similar questions here. This question and its answers are frozen and cannot be changed. More info: help center.

• Very subjective question: this question cannot be answered, and is not suitable for a QA site. – Egon Willighagen Jul 19 '10 at 19:58
• Should probably be community wiki; useful question here but doesn't have definitive answer. – Shane Jul 19 '10 at 20:05
• @Shane: good point. moved. @ Egon: subjective indeed. but if the answers come from knowledgeable people i don't mind dose of subjectivity. i've started learning R quite recently and have couple of dozens installed to explore, however i notice that there are tools that I use much more often irrespectively of the task at hand. – radek Jul 19 '10 at 20:06
• It would be interesting if StackExchange could support some method of linking community wiki posts across sites. Because I will bet this question has been asked on Stackoverflow and I also think that Statistical Analysis may attract some people that wouldn't usually visit SO. – Sharpie Jul 19 '10 at 20:19
• @Sharpie: there have been several interesting SO posts like stackoverflow.com/questions/1295955/… or stackoverflow.com/questions/1535021/… however they are not focused on packages. and i agree, linkage of community wiki could be really useful. – radek Jul 19 '10 at 20:37

I use plyr and ggplot2 the most on a daily basis.

I also rely heavily on time series packages; most especially, the zoo package.

I use the xtable package. The xtable package turns tables produced by R (in particular, the tables displaying the anova results) into LaTeX tables, to be included in an article.

multicore is quite nice for tool for making faster scripts faster.
cacheSweave saves a lot of time when using Sweave.

ggplot2 - hands down best visualization for R.

RMySQL/RSQLite/RODBC - for connecting to a databases

sqldf - manipulate data.frames with SQL queries

Hmisc/rms - packages from Frank Harrell containing convenient miscellaneous functions and nice functions for regression analyses.

GenABEL - nice package for genome-wide association studies

Rcmdr - a decent GUI for R if you need one.

Also check out CRANtastic - this link has a list of the most popular R packages. Many on the top of the list have already been ment

data.table is my favorite now! Very look forward to the new version with the more wishlist implemented.

Packages I often use are raster, sp, spatstat, vegan and splancs. I sometimes use ggplot2, tcltk and lattice.

For me personally, I use the following three packages the most, all available from the awesome Omega Project for Statistical Computing (I do not claim to be an expert, but for my purposes they are very easy to use):

• RCurl: It has lots of options which allows access to websites that the default functions in base R would have difficulty with I think it's fair to say. It is an R-interface to the libcurl library, which has the added benefit of a whole community outside of R developing it. Also available on CRAN.

• XML: It is very forgiving of parsing malformed XML/HTML. It is an R-interface to the libxml2 library and again has the added benefit of a whole community outside of R developing it Also available on CRAN.

• RJSONIO: It allows one to parse the text returned from a json call and organise it into a list structure for further analysis.The competitor to this package is rjson but this one has the advantage of being vectorised, readily extensible through S3/S4, fast and scalable to large data.

Sweave lets you embed R code in a LaTeX document. The results of executing the code, and optionally the source code, become part of the final document.

So instead of, for example, pasting an image produced by R into a LaTeX file, you can paste the R code into the file and keep everything in one place.

• Just a hint for all those who want to get started on reproducible research with R. I would advice you to have a look at the newish package knitr instead of Sweave. It's basically Sweave on steroids. It is as easy, if not easier, to learn and far more flexible. – Christoph_J Feb 18 '13 at 21:05

zoo and xts are a must in my work!

I find lattice along with the companion book "Lattice: Multivariate Data Visualization with R" by Deepayan Sarkar invaluable.

If you are doing any kind of predictive modeling, caret is a godsend. Especially combined with the multicore package, some pretty amazing things are possible.

Day-to-day the most useful package must be "foreign" which has functions for reading and writing data for other statistical packages e.g. Stata, SPSS, Minitab, SAS, etc. Working in a field where R is not that commonplace means that this is a very important package.

I use

car, doBy, Epi, ggplot2, gregmisc (gdata, gmodels, gplots, gtools), Hmisc, plyr, RCurl, RDCOMClient, reshape, RODBC, TeachingDemos, XML.

a lot.

I could not live without:

• lattice for graphics
• xlsx or XLConnect for reading Excel files
• rtf to create reports in rtf format (I would prefer Sword or R2wd but I cannot install statconn at work; I will surely try odfWeave soon.)
• nlme and lme4 for mixed models
• ff for working with large arrays

RODBC for accessing data from databases, sqldf for performing simple SQL queries on dataframes (although I am forcing myself to use native R commands), and ggplot2 and plyr

I work with both R and MATLAB and I use R.matlab a lot to transfer data between the two.

We mostly use:

• ggplot - for charts
• stats
• e1071 - for SVMs
• You may also want to check out kernlab and caret for SVMs. They're interesting (thought not necessarily better) alternatives. – Zach May 8 '11 at 2:27

lattice, car, MASS, foreign, party.

For me I am using kernlab for Kernel-based Machine Learning Lab and e1071 for SVM and ggplot2 for graphics

I use ggplot2, vegan and reshape quite often.

I like roxygen for its Curry() function.

RColorBrewer has not been mentioned here, I use it often for plotting if I need color schemes

I am a big fan of RCPP when I need a rapid for loop or to perform non very R compliant treatments. It is very well implemented in the R eco system, can receive Matrix / sparse Matrix without conversion as arguments in a function.

C++ syntax is easy when you are doing simple stuff (which is often my case).

Really, you don't need to be a package maker to need this awesome lib.

Did I say C++ is very rapid?

The doParallel and foreach packages have made my life so much easier by allowing me to parallelize my code and run it on a compute-optimized instance on Amazon EC2! I use them very often. But that would not have been possible without the RStudio AMIs released by Louis Aslett. Finally, I have to mention the stringr package which really makes working with strings a walk in the park. Use it in every text mining application. And I also use knitr very frequently to produce high quality reports of my work. Many thanks for this amazing package Yihui Xie!

I use ggplot2, reshape, lattice, knitr more often.