I would like to summarize my experimental data every time a condition changes. For example: > df=data.frame(tos=1:9, temp=rep(c(25,50,25), each=3), response=c(3.2,3.3,3.3, 6.5, 6.5, 6.5, 3.5,3.6,
R Version 2.11.1 32-bit on Windows 7 I got two data sets: data_A and data_B: data_A USER_A USER_B ACTION
I like to write a function using ddply that outputs the summary statistics based on the name of two columns of data.frame mat.
I\'m trying to learn how to write function in R/plyr. I am aware that there are easier ways to do what I show below, but that\'s not thepoint.
Input row.nocolumn开发者_高级运维2column3column4 1bbeeup 2bbeedown 3bbeeup 4bbyydown 5bbzzup I have a rule to remove row 1 and 2 and 3, as while column2 and column3 for row 1, 2 and 3 are the same,
For some reason I\'m getting more results than I expected since the upgrade to R-2.13.0 - and the upgrade to plyr_1.5.1.tar.gz... I tried this on an old version of plyr (version unsure unfortunately a
Every time I get a new data set the first thing I do is check out the summary statistics. The summary function does a pretty good job, but I\'m frequently interested in standard deviations, quantiles
This code works: library(plyr) x <- data.frame(V= c(\"X\", \"Y\", \"X\", \"Y\", \"Z\" ), Z = 1:5) ddply(x, .(V), function(df) sum(df$Z),.parallel=FALSE)
I have the following data.frame d from an experiment: - Variable y (response, continuous) - Factor f (500 levels)
I have a data.frame with 20 columns. The first two are factors, and the rest are numeric. I\'d like to use the first two columns as split variables and then apply the mean() to the remaining columns.
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