What R packages do you find most useful in your daily work? Duplicate thread: I just installed the latest version of R. What packages should I obtain?
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


Thanks all for your answers!
 A: 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. 
A: multicore is quite nice for tool for making faster scripts faster.
cacheSweave saves a lot of time when using Sweave.
A: 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
A: data.table is my favorite now! Very look forward to the new version with the more wishlist implemented. 
A: 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.  

A: Packages I often use are raster, sp, spatstat, vegan and splancs. I sometimes use ggplot2, tcltk and lattice.
A: 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.
A: I imagine graphics and data manipulation are two things that are useful no matter what you are doing. Thus, I'd recommend:


*

*ggplot2 (great graphics)

*lattice (great graphics)

*plyr (useful for data manipulation)

*Hmisc (good for descriptive statistics and much more)

A: 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.
A: zoo and xts are a must in my work!
A: I find lattice along with the companion book "Lattice: Multivariate Data Visualization with R" by Deepayan Sarkar invaluable.
A: You can get user reviews of packages on crantastic
A: I would suggest using some of the packages provided by revolution R. In particular, I quite like the:


*

*multicore package for parallel computing using shared memory processors

*there optimized packages for matrices
A: 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.
A: I use 
car, doBy, Epi, ggplot2, gregmisc (gdata, gmodels, gplots, gtools), Hmisc, plyr, RCurl, RDCOMClient, reshape, RODBC, TeachingDemos, XML.
a lot.
A: This is definitely a question that doesn't have "an answer".  It is completely dependent on what you want to do.  That aside, I'll share the packages that I install as a standard with an R update...
install.packages(c("car","gregmisc","xtable","Design","Hmisc","psych",
                        "CCA", "fda", "zoo", "fields",
                      "catspec","sem","multilevel","Deducer","RQDA"))

and leave it to you to investigate those packages and see if they are valuable to you.
A: If you are working with Latex, I recommend TikZ Device for outputting nice, Latex-formatted (like PSTricks) graphics. The output you get is text-based Latex code, which can be embedded with include(filename) into any figure environment. 
Pros: 


*

*Same font in graphics as in your text

*Professional look


Cons:


*

*Takes longer to compile than PNG or PDF

*for very complex R graphics, there are could be some display errors


https://github.com/Sharpie/RTikZDevice - Project, Packages available from CRAN and R-Forge
A: You can also take a look at Task views on CRAN and see if something suit your needs. I agree with @Jeromy for these must-have packages (for data manipulation and plotting).
A: I use lattice, ggplot2, lubridate, reshape, boot, e1071, car, forecast, and zoo a lot.
A: 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

A: I can recommend the new shiny based packages to everyone, it makes data visualisation and inspection interactive and thus easier than writing code in R espacially in the beginning.
A good example would be ggplotgui
A: I use plyr and ggplot2 the most on a daily basis.
I also rely heavily on time series packages; most especially, the zoo package.
A: We mostly use:


*

*ggplot - for charts

*stats

*e1071 - for SVMs
A: I work with both R and MATLAB and I use R.matlab a lot to transfer data between the two.
A: 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
A: Jeromy mentioned my first pick: Lattice.
I also have found the doBy package and its summaryBy function to be insanely useful. They extend aggregate with a formula syntax that lets you aggregate multiple functions simultaneously in non-trivial ways.  Great if you want, say, mean, std. dev., and length.
A: lattice, car, MASS, foreign, party.
A: For me 
I am using kernlab for Kernel-based Machine Learning Lab and e1071 for SVM and ggplot2 for graphics
A: I use 
ggplot2, vegan and reshape quite often.
A: I like roxygen for its Curry() function.
A: RColorBrewer has not been mentioned here, I use it often for plotting if I need color schemes
A: 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?
A: 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!
A: In a narrow sense, R Core has a recommendation: the "recommended" packages.
Everything else depends on your data analysis tasks at hand, and I'd recommend the Task Views at CRAN.
A: Some packages are very useful in R.
I will just recommand kernlab for Kernel-based Machine Learning Lab and e1071 for SVM and ggplot2 for graphics
A: I use ggplot2, reshape, lattice, knitr more often. 
A: Please see link:
TOP 100 R PACKAGES FOR 2013 (JAN-MAY)
http://www.r-statistics.com/2013/06/top-100-r-packages-for-2013-jan-may/
