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Open source, computers, Africa and other more (or less) interesting stuff.

Tag: R

Configuring R

I took some time today to configure my R experience. I’m mostly using R from Emacs using ESS (Emacs Speaks Statistics), which means I had to configure some settings there as well.

Previously, my settings only consisted of setting a customised directory in which to install my packages and an alias to start R without asking for saving the histroy when quitting. This I did by setting the following environment variable in my .bashrc and/or .zshrc, as well as an alias:

# Set the library path for R
export R_LIBS_USER=${R_LIBS_USER}:~/Programmeren/R/lib
 
if [ -n "$(/usr/bin/which R 2>/dev/null)" ]; then
alias R="$(/usr/bin/which R) --no-save"
fi

However, Emacs didn’t pick up either of these variables, so high time to fix that. This meant creating two files with the following content:

~/.Rprofile:

# Set the default CRAN repository used by install.packages()
options("repos" = c(CRAN = "http://cran-mirror.cs.uu.nl/"))

~/.Renviron:

R_LIBS="~/Programmeren/R/lib"

I added the following to my .emacs file to start R with the --no-save option:

(setq inferior-R-args "--no-save ")

Additionally I have the following in there to turn on a spelling checker and have line-wraps enabled:

(add-hook 'ess-mode-hook
          (lambda ()
            ;; Set pdflatex as the default command for Sweave (default: texi2pdf)
            (setq ess-swv-pdflatex-commands (quote ("pdflatex"
                                                    "texi2pdf"
                                                    "make")))
            (auto-fill-mode t)
            (flyspell-prog-mode)
            ))

Doing a quick fixed-effects meta-analysis using the Rmeta package

This is a quick example of how to do a fixed meta-analysis using the R package Rmeta, just so I dont have to look it up again next time:

## Create data frame containing betas and standard errors
df <- data.frame()
df <- rbind(df, c(2., 0.2))
df <- rbind(df, c(2.5, 0.4))
df <- rbind(df, c(2.2, 0.2))
 
## Add study names
df <- cbind(df, c("study 1", "study 2", "study 3"))
 
colnames(df) <- c("beta", "se_beta", "name") 
 
## Do the meta-analysis 
ms <- meta.summaries(df$beta, df$se_beta, names=df$name)
 
## Add some colors
mc <- meta.colors(summary="darkgreen", zero="red")
 
## Make a forest plot
plot(ms, xlab=expression(beta ~ " (mmol/l)"), 
     ylab="Study", colors=mc, zero=2.6)

The resulting plot looks like this:
Forest plot of fake data

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