r-factor

There seems to be a difference between levels and labels of a factor in R. Up to now, I always thought that levels were the 'real' name of factor levels, and labels were the names used for output (such as tables and plots). Obviously, this is not the case, as the following example shows: df <- data.frame(v=c(1,2,3),f=c('a','b','c')) str(df) 'data.frame': 3 obs. of 2 variables: $ v: num 1 2 3 $ f: Factor w/ 3 levels "a","b","c": 1 2 3 df$f <- factor(df$f, levels=c('a','b','c'), labels=c('Treatment A: XYZ','Treatment B: YZX','Treatment C: ZYX')) levels(df$f) [1] "Treatment A: XYZ" "Treatment B:

Given a dataframe column which is a series of integers (age), I want to convert ranges of integers into ordinal variables. My current code doesn't work, how do I do this? df <- read.table("http://dl.dropbox.com/u/822467/df.csv", header = TRUE, sep = ",") df[(df >= 0) & (df <= 14)] <- "Age1" df[(df >= 15) & (df <= 44)] <- "Age2" df[(df >= 45) & (df <= 64)] <- "Age3" df[(df > 64)] <- "Age4" table(df) Use cut to do this in one step: dfc <- cut(df$x, breaks=c(0, 15, 45, 56, Inf)) str(dfc) Factor w/ 4 levels "(0,15]","(15,45]",..: 3 4 3 2 2 4 2 2 4 4 ... Once you are satisfied that the breaks are

I run across this often enough that I figure there has to be a good idiom for it. Suppose I have a data.frame with a bunch of attributes, including "product." I also have a key which translates products to brand + size. Product codes 1-3 are Tylenol, 4-6 are Advil, 7-9 are Bayer, 10-12 are Generic. What's the fastest (in terms of human time) way to code this up? I tend to use nested ifelse 's if there are 3 or fewer categories, and type out the data table and merge it in if there are more than 3. Any better ideas? Stata has a recode command that is pretty nifty for this sort of thing, although

I have a sample data frame like below: data <- data.frame(matrix(sample(1:40), 4, 10, dimnames = list(1:4, LETTERS[1:10]))) I want to know how can I select multiple columns and convert them together to factors. I usually do it in the way like data$A = as.factor(data$A) . But when the data frame is very large and contains lots of columns, this way will be very time consuming. Does anyone know of a better way to do it? Choose some columns to coerce to factors: cols <- c("A", "C", "D", "H") Use lapply() to coerce and replace the chosen columns: data[cols] <- lapply(data[cols], factor) ## as