Hi,
if the rows in your data.frame are numeric, this solution will work.
(numeric.index <- unlist(lapply(df, is.numeric)))
df[, numeric.index] <- apply(df[,numeric.index], 2, pct)
This does not work for the example you gave, unless you coerce the columns
with the your numerics to numeric:
c1 <- c("A", "B", "C", "C")
c2 <- factor(c(1, 1, 2, 2), labels = c("Y","N"))
x <- c(0.5234, 0.6919, 0.2307, 0.1160)
y <- c(0.9251, 0.7616, 0.3624, 0.4462)
df <- data.frame(c1, c2, x, y)
df$y <- as.numeric(df$y)
df$x <- as.numeric(df$x)
pct <- function(x) round(100*x, 1)
(numeric.index <- unlist(lapply(df, is.numeric)))
df[, numeric.index] <- apply(df[,numeric.index], 2, pct)
Christoph
2011/10/12 <michael.laviole...@dhhs.state.nh.us>
>
> My data frame consists of character variables, factors, and proportions,
> something like
>
> c1 <- c("A", "B", "C", "C")
> c2 <- factor(c(1, 1, 2, 2), labels = c("Y","N"))
> x <- c(0.5234, 0.6919, 0.2307, 0.1160)
> y <- c(0.9251, 0.7616, 0.3624, 0.4462)
> df <- data.frame(c1, c2, x, y)
> pct <- function(x) round(100*x, 1)
>
> I want to apply the pct function to only the numeric variables so that the
> proportions are computed to percentages, and retain all the columns:
>
> c1 c2 x1 x2
> 1 A Y 52.3 92.5
> 2 B Y 69.2 76.2
> 3 C N 23.1 36.2
> 4 C N 11.6 44.6
>
> I've been approaching it with the ddply and colwise functions from the plyr
> package, but in that case each I need each row to be its own group and
> retain all columns. Am I on the right track? If not, what's the best way to
> do this?
>
> Thanks in advance,
> M. L.
>
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