michael watson (IAH-C <michael.watson <at> bbsrc.ac.uk> writes:
: : Hi
: : I'm seeing some "odd" behaviour with cbind(). My code is:
: : > cat <- read.table("cogs_category.txt", sep="\t", header=TRUE,
: quote=NULL, colClasses="character")
: > colnames(cat)
: [1] "Code" "Description"
: > is.factor(cat$Code)
: [1] FALSE
: > is.factor(cat$Description)
: [1] FALSE
: > is.factor(rainbow(nrow(cat)))
: [1] FALSE
: > cat <- cbind(cat,"Color"=rainbow(nrow(cat)))
: > is.factor(cat$Color)
: [1] TRUE
: > ?cbind
: : I read a text file in which has two columns, Code and Description.
: Neither of these are factors. I want to add a column of colours to the
: data frame using rainbow(). The rainbow function also does not return a
: factor. However, if I cbind my data frame (which has no factors in it)
: and the results of rainbow() (which is a vector, not a factor), then for
: some reason the new column is a factor...??
Others have already explained the problem and given what is likely the best solution but here is one other idea, just in case.
You may require a data frame depending on what you want to do but if you don't then you could alternately use a character matrix since that won't result in any conversions to factor.
Lets call the data frame from read.table, Cat.df, and our matrix, Cat.m. cat is not wrong but its confusing since there is a common R function called cat. Now we can write the following and don't have to worry about factors:
Cat.df <- read.table(...) # create a character matrix and cbind Colors to it Cat.m <- cbind(as.matrix(Cat.df), Color = rainbow(nrow(Cat.df)))
If you do find you need a data frame later you can convert it back like this:
Cat.df <- as.data.frame(Cat.m) Cat.df[] <- Cat.m # clobber factors with character data
For speed, the mApply function in the Hmisc package (used by the Hmisc summarize function) does looping for stratified statistical summaries by operating on matrices rather than data frames. factors are converted to numerics, and service routines can save and restore the levels and other attributes. Here is an example from the summarize help file, plus related examples:
# To run mApply on a data frame: m <- mApply(asNumericMatrix(x), race, h) # Here assume h is a function that returns a matrix similar to x at <- subsAttr(x) # get original attributes and storage modes matrix2dataFrame(m, at) # Get stratified weighted means g <- function(y) wtd.mean(y[,1],y[,2]) summarize(cbind(y, wts), llist(sex,race), g, stat.name='y') mApply(cbind(y,wts), llist(sex,race), g)
# Compare speed of mApply vs. by for computing
d <- data.frame(sex=sample(c('female','male'),100000,TRUE),
country=sample(letters,100000,TRUE),
y1=runif(100000), y2=runif(100000))
g <- function(x) {
y <- c(median(x[,'y1']-x[,'y2']),
med.sum =median(x[,'y1']+x[,'y2']))
names(y) <- c('med.diff','med.sum')
y
}
system.time(by(d, llist(sex=d$sex,country=d$country), g))
system.time({
x <- asNumericMatrix(d)
a <- subsAttr(d)
m <- mApply(x, llist(sex=d$sex,country=d$country), g)
})
system.time({
x <- asNumericMatrix(d)
summarize(x, llist(sex=d$sex, country=d$country), g)
})--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
