Holcombe:ProgrammingInR
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R is an interactive programming language for statistics. The syntax is very idiosyncratic, and not really in a good way. Try R for programmers for a description. However it may have menudriven versions maybe available R commander we haven't tried that and another one is pmg GTK maybe here
In the lab we have the book Using R for Introductory Statistics. R_Statistics introduces you to R
Dani has posted some example code and graphs on his personal website.
R reference cheatsheet, also a file here Media:Matlabpythonxref.pdf that gives equivalent code for doing array operations in MATLAB, Python, and R plot parameters
There is a wiki with some good tips here. Also Data frame tips, list of R websites
Functions in R can only return one parameter.
dataframe tips
Examining your data frame or object, let's say it's called datos
typeof(datos) #returns "list!" str(datos) #tells you it's a dataframe, number of observations, columns, etc head(datos) str(datos) summary(datos) #good for ggplot objects also
df$varWithExtraLevels = factor(df$varWithExtraLevels)
length(df) #number of columns of dataframe
names(df) #names of columns of dataframe
library(Hmisc); describe(df)
#Calling typeof() on a dataframe returns "list"
rm(objectToBeDeleted) rm(list = ls()) #Delete nearly everything in memory
expand.grid() to create dataframe with every combination of some factors
Check your counterbalancing in your results file. Make a contingency table,
table(dataRaw$speed,dataRaw$relPhaseOuterRing)
Replace certain value with another
thr$thresh[ thr$task=='ident' ] = NA
Creating Graphs (usu. ggplot2)
how I Holcombe:fit psychometric functions and bootstrap
See http://openwetware.org/wiki/Holcombe:Plotting
Debugging in R
How to examine and try things with a questionable variable within a function?
ee << resultsMeans #make global, violating all principles of good coding #DEBUG STOP
After an error, calling traceback() gives you the stack
doing ANOVAs etc
ANOVA with repeated measures walkthrough
R will assume factor is regressor if numeric
I think I had too many error terms reducing error terms
Anovas with repeated measures can be complicated in R.
We have some R books in the lab
Dealing with circular data
von Mises vs. wrapped Gaussian,
see Swindale, N. V. (1998). Orientation tuning curves: empirical description and estimation of parameters. Biol Cybern, 78(1), 4556.