Calculating most frequent level by category with plyr

Question

I would like calculate the most frequent factor level by category with plyr using the code below. The data frame b shows the requested result. Why does

Answer 1

You have pretty much exclusively used existing function names in your example: levels, cat, and mode. Generally, that doesn't create much of a problem--for example, calling a data.frame "df" doesn't break R's df() function. But it almost always leads to more ambiguous or confusing code, and in this case, it made things "break". Arun's answer does a great job of showing why.

You can easily fix your problem by renaming your "mode" function. In the example below, I've simplified it a little bit in addition to renaming it, and it works as you expected.

Mode <- function(x) names(which.max(table(x)))
ddply(a, .(cat), summarise,
      mlevels=Mode(levels))
#   cat mlevels
# 1   1       6
# 2   2       5
# 3   3       9

Of course, there's a really cumbersome workaround: Use get and specify where to search for the function.

> mode <- function(x) names(table(x))[which.max(table(x))]
> ddply(a, .(cat), summarise, mlevels = get("mode", ".GlobalEnv")(levels))
  cat mlevels
1   1       6
2   2       5
3   3       9