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| * DPpackage * ggplot2 * gsubfn * hexbin * languageR * lme4 * MCMCglmm * multcomp * plyr * reshape |
=== Hadleyverse === Hadley Wickham has done more than just about anyone to make R more powerful, expressive, and easy to use for common data analysis tasks. * `ggplot2` — Data visualization using grammar of graphics. * `dplyr` — Data manipulation pipelines made easy. Noticeably distinct from its spiritual predecessor `plyr`. `dplyr` and `plyr` conflict so don't load both at the same time. * `tidyr` — Data cleaning and [[http://blog.rstudio.org/2014/07/22/introducing-tidyr/|tidying]], including reshaping from wide to long (`spread`) and long to wide (`gather`) (replaces `reshape`/`reshape2`). Also has very useful functions like `separate`, for splitting up columns with values like 'beach_b_10' into separate columns with 'beach', 'b', and '10'. * `devtools` — Automate common package development workflows. Most useful for [[http://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/|writing custom packages]] but also provides idiot-proof installation packages from source on github/bitbucket or arbitrary URLs via `devtools::install_github` etc. (see below). * `stringr` and `lubridate` — Processing strings and dates/times with less pain. === Everything else === * `knitr` — Literate programming for R. Mix code with text (markdown or LaTeX), and `knitr::knit` will run the code, format the output all purdy, and generate an HTML/PDF report. * `lme4` — Mixed effects modeling. * `multcomp` — Confidence intervals and stuff I think. * `ez` — Attempt at a unified interface for analyzing output of a wide range of models (`lm`, `glm`, `lmer`, `glmer`, etc.) * `gsubfn` — More powerful string replacement. * `hexbin` — Tired of your boring old square bins? Try some exciting hexbins! Now with two extra sides! * `languageR` — Lots of language-specific datasets and code to go along with Baayan's book, "Analyzing Linguistic Data: A practical introduction to statistics". * `MCMCglmm` — Does what it says on the tin: Bayesian inference via MCMC for generalized linear mixed models. Much more flexible and powerful than `lme4`, but with a steep learning curve. * `DPpackage` — Functions for Bayesian inference via simulation in nonparametric/semiparametric models (e.g. the eponymous Dirichlet Process or "DP"). A quicker way to do it is to copy and paste the following line at your R prompt: {{{#!highlight r numbers=disable install.packages(c("devtools","DPpackage","ggplot2","gsubfn","hexbin","languageR","lme4","MCMCglmm","multcomp","dplyr","tidyr","stringr","lubridate","knitr","ez")) }}} == From github (source) == Sometimes a package isn't available (usually temporarily) as a binary for your platform. If you need to build a package from source, make sure you have the developer tools for your OS installed. For MacOS, they are available on the App Store if you have an up-to-date version of MacOS, or from the [[https://developer.apple.com/downloads/index.action?q=xcode|developer site]] (where you'll need to register for a free account first). You may also need Fortran, available via `homebrew` (`brew install gfortran`; recommended) or from the [[http://cran.r-project.org/bin/macosx/tools/|MacOS tools]] page on CRAN. [[http://scicomp.stackexchange.com/a/2470|This StackExchange answer]] is a good discussion of the pros and cons of various ways to install Fortran on MacOS. The last thing you'll need (for github etc.) is to install `devtools`. Let's say I want to install `dplyr` from the github source. I google it and find that it's hosted at [[http://github.com/hadley/dplyr]]. Then, in R: {{{#!highlight r numbers=disable library(devtools) devtools::install_github('hadley/dplyr') }}} Piece of cake. `devtools` includes a whole family of functions for installing source from pretty much anywhere you might find it. If, for instance, you want to install from the source archive on CRAN (e.g., [[http://cran.r-project.org/src/contrib/dplyr_0.4.1.tar.gz]]), you can use the `install_url` command: {{{#!highlight r numbers=disable library(devtools) devtools::install_url('http://cran.r-project.org/src/contrib/dplyr_0.4.1.tar.gz') }}} |
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| When you install those packages you will get this warning: ```Warning: dependencies ‘marray’, ‘affy’, ‘Biobase’, ‘Rgraphviz’, ‘’ are not available```. To fix it, install the standard packages from [http://www.bioconductor.org/docs/install/ Bioconductor] by doing the following at the R prompt: | |
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| {{{ | '''I have no idea whether this is still necessary but I'm leaving it here for posterity's sake — Dave''' When you install the packages above, you may get this warning: ```Warning: dependencies ‘marray’, ‘affy’, ‘Biobase’, ‘Rgraphviz’, ‘’ are not available```. To fix it, install the standard packages from [[http://www.bioconductor.org/docs/install/|Bioconductor]] by doing the following at the R prompt: {{{#!highlight r numbers=disable |
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| adjusting ```lib``` as appropriate for your OS. The example above is for Mac OS X. | adjusting ```lib``` as appropriate for your OS. The example above is for Mac OS X. For Windows use: {{{#!highlight r numbers=disable biocLite(lib='C:\\Program Files\\R\\R-2.11.1\\library') }}} adjusting the version number as appropriate. (n.b. You must have Administrator privileges to install anything under `C:/Program Files/`) |
R Packages
1. From CRAN
Install these packages from CRAN. Always check the "install dependencies" box.
1.1. Hadleyverse
Hadley Wickham has done more than just about anyone to make R more powerful, expressive, and easy to use for common data analysis tasks.
ggplot2 — Data visualization using grammar of graphics.
dplyr — Data manipulation pipelines made easy. Noticeably distinct from its spiritual predecessor plyr. dplyr and plyr conflict so don't load both at the same time.
tidyr — Data cleaning and tidying, including reshaping from wide to long (spread) and long to wide (gather) (replaces reshape/reshape2). Also has very useful functions like separate, for splitting up columns with values like 'beach_b_10' into separate columns with 'beach', 'b', and '10'.
devtools — Automate common package development workflows. Most useful for writing custom packages but also provides idiot-proof installation packages from source on github/bitbucket or arbitrary URLs via devtools::install_github etc. (see below).
stringr and lubridate — Processing strings and dates/times with less pain.
1.2. Everything else
knitr — Literate programming for R. Mix code with text (markdown or LaTeX), and knitr::knit will run the code, format the output all purdy, and generate an HTML/PDF report.
lme4 — Mixed effects modeling.
multcomp — Confidence intervals and stuff I think.
ez — Attempt at a unified interface for analyzing output of a wide range of models (lm, glm, lmer, glmer, etc.)
gsubfn — More powerful string replacement.
hexbin — Tired of your boring old square bins? Try some exciting hexbins! Now with two extra sides!
languageR — Lots of language-specific datasets and code to go along with Baayan's book, "Analyzing Linguistic Data: A practical introduction to statistics".
MCMCglmm — Does what it says on the tin: Bayesian inference via MCMC for generalized linear mixed models. Much more flexible and powerful than lme4, but with a steep learning curve.
DPpackage — Functions for Bayesian inference via simulation in nonparametric/semiparametric models (e.g. the eponymous Dirichlet Process or "DP").
A quicker way to do it is to copy and paste the following line at your R prompt:
install.packages(c("devtools","DPpackage","ggplot2","gsubfn","hexbin","languageR","lme4","MCMCglmm","multcomp","dplyr","tidyr","stringr","lubridate","knitr","ez"))
2. From github (source)
Sometimes a package isn't available (usually temporarily) as a binary for your platform. If you need to build a package from source, make sure you have the developer tools for your OS installed. For MacOS, they are available on the App Store if you have an up-to-date version of MacOS, or from the developer site (where you'll need to register for a free account first). You may also need Fortran, available via homebrew (brew install gfortran; recommended) or from the MacOS tools page on CRAN. This StackExchange answer is a good discussion of the pros and cons of various ways to install Fortran on MacOS. The last thing you'll need (for github etc.) is to install devtools.
Let's say I want to install dplyr from the github source. I google it and find that it's hosted at http://github.com/hadley/dplyr. Then, in R:
library(devtools)
devtools::install_github('hadley/dplyr')
Piece of cake.
devtools includes a whole family of functions for installing source from pretty much anywhere you might find it. If, for instance, you want to install from the source archive on CRAN (e.g., http://cran.r-project.org/src/contrib/dplyr_0.4.1.tar.gz), you can use the install_url command:
library(devtools)
devtools::install_url('http://cran.r-project.org/src/contrib/dplyr_0.4.1.tar.gz')
3. From Bioconductor
I have no idea whether this is still necessary but I'm leaving it here for posterity's sake — Dave
When you install the packages above, you may get this warning: Warning: dependencies ‘marray’, ‘affy’, ‘Biobase’, ‘Rgraphviz’, ‘’ are not available. To fix it, install the standard packages from Bioconductor by doing the following at the R prompt:
source("http://bioconductor.org/biocLite.R")
biocLite(lib='/Library/Frameworks/R.framework/Resources/library/')
adjusting lib as appropriate for your OS. The example above is for Mac OS X. For Windows use:
biocLite(lib='C:\\Program Files\\R\\R-2.11.1\\library')
adjusting the version number as appropriate. (n.b. You must have Administrator privileges to install anything under C:/Program Files/)