Removing all columns summing to zero with dplyr

Question

I'm currently working on a dataframe that looks something like this:

Site  Spp1  Spp2  Spp3  LOC  TYPE
S01   2     4     0     A    FLOOD
S02   4     0     0     A    REG
....
S10   0     1     0     B    FLOOD
S11   1     0     0     B    REG

What I'm trying to do is subset the dataframe so I can run some indicator species analysis in R.

The following code works in that I create two subsets of the data, merge them into one frame and then drop the unused factor levels

A.flood <- filter(data, TYPE == "FLOOD", LOC == "A")
B.flood <- filter(data, TYPE == "FLOOD", LOC == "B")
A.B.flood <- rbind(A.flood, B.flood) %>% droplevels.data.frame(A.B.flood, except = c("A", "B"))

What I was also hoping/need to do is to drop all Spp columns (in my real dataset there are ~ 60) that sum to zero. Is there a way to achieve this this with dplyr, and if there is, is it possible to pipe that code onto the existing A.B.flood dataframe code?

Thanks!

EDIT

I managed to remove all the columns that summed to zero, by selecting only the columns that summed to > zero:

A.B.flood.subset <- A.B.flood[, apply(A.B.flood[1:(ncol(A.B.flood))], 2, sum)!=0]

Answer 1

For those who want to use dplyr 1.0.0 with the where keyword, you can do:

A.B.flood %>% 
  select(where( ~ is.numeric(.x) && sum(.x) != 0))

returns:

  Spp1 Spp2
1    2    4
2    4    0
3    0    0
4    4    0

using the same data given by @akrun:

A.B.flood <- structure(
  list(
    Site = c("S01", "S02", "S03", "S04"),
    Spp1 = c(2L,
             4L, 0L, 4L),
    Spp2 = c(4L, 0L, 0L, 0L),
    Spp3 = c(0L, 0L, 0L, 0L),
    LOC = c("A", "A", "A", "A"),
    TYPE = c("FLOOD", "REG",
             "FLOOD",
             "REG")
  ),
  .Names = c("Site", "Spp1", "Spp2", "Spp3", "LOC",
             "TYPE"), class = "data.frame", row.names = c(NA, -4L))

Answer 2

I realize this question is now quite old, but I came accross and found another solution using dplyr's "select" and "which", which might seem clearer to dplyr's enthusiasts:

A.B.flood.subset <- A.B.flood %>% select(which(!colSums(A.B.flood, na.rm=TRUE) %in% 0))