Sort a subset of columns for each row

Question

I want to sort my.data[4:10] in descending order by row. Some clues here, but I could not parse it sufficiently: Sort second to fifth column for each row in R.

I al

Answer 1

This answer's approach, which you quote above, is close:

cbind(df[,1], t(apply(df[,-1], 1, sort)))

but it needed two changes:

• You want to sort all but the first three columns, not all but the first. So change [,1] and [,-1] to [, 1:3] and [, -(1:3)], respectively.
• By default, sort sorts in increasing order while you want decreasing order, and drops the NAs out entirely, while you want them last. You can fix this by adding the decreasing=TRUE, na.last=TRUE arguments to sort.

This makes the solution:

cbind(my.data[, 1:3], t(apply(my.data[, -(1:3)], 1, function(v) sort(v, decreasing=TRUE, na.last=TRUE))))

Note that it might be a bit clearer if you split it onto multiple lines:

mysort = function(v) sort(v, decreasing=TRUE, na.last=TRUE)
sorted.cols = t(apply(my.data[, -(1:3)], 1, mysort))
cbind(my.data[, 1:3], sorted.cols)

Answer 2

This seems to work fine:

my.data[,4:10] <- t(apply(my.data[,4:10], 1,  function(x) sort(x, na.last = T, decreasing=T)))


#   habitat NumSites NumSamples Sp1 Sp2 Sp3 Sp4 Sp5 Sp6 Sp7
#1    Marsh        3          6   3   1  NA  NA  NA  NA  NA
#2  Prairie        3          5   2   2   2  NA  NA  NA  NA
#3  Savanna        4          8  67   3   3   1  NA  NA  NA
#4    Swamp        1          2   2  NA  NA  NA  NA  NA  NA
#5 Woodland        4          8   2   1   1   1   1  NA  NA

Answer 3

You don't need an anonymous function on this.

> my.data[4:10] <-t(apply(my.data[4:10],1,sort,decreasing = TRUE,na.last = TRUE))
> my.data
#    habitat NumSites NumSamples Sp1 Sp2 Sp3 Sp4 Sp5 Sp6 Sp7
# 1    Marsh        3          6   3   1  NA  NA  NA  NA  NA
# 2  Prairie        3          5   2   2   2  NA  NA  NA  NA
# 3  Savanna        4          8  67   3   3   1  NA  NA  NA
# 4    Swamp        1          2   2  NA  NA  NA  NA  NA  NA
# 5 Woodland        4          8   2   1   1   1   1  NA  NA