Removing all columns summing to zero with dplyr - r

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

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))

Answer 2

Without using any package, we can use rowSums of the 'Spp' columns (subset the columns using grep) and double negate so that rows with sum>0 will be TRUE and others FALSE. Use this index to subset the rows.
data[!!rowSums(data[grep('Spp', names(data))]),]
Or using dplyr/magrittr, we select the 'Spp' columns, get the sum of each row with Reduce, double negate and use extract from magrittr to subset the original dataset with the index derived.
library(dplyr)
library(magrittr)
data %>%
select(matches('^Spp')) %>%
Reduce(`+`, .) %>%
`!` %>%
`!` %>%
extract(data,.,)
data
data <- 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 3

You should convert to tidy data with tidyr::gather() and the data frame will be much easier to manipulate.
library(tidyr)
library(dplyr)
A.B.Flood %>% gather(Species, Sp.Count, -Site, -LOC, -TYPE) %>%
group_by(Species) %>%
filter(Sp.Count > 0)
Voila, your tidy data minus the zero counts.
# Site LOC TYPE Species Sp.Count
# <fctr> <fctr> <fctr> <chr> <int>
#1 S01 A FLOOD Spp1 2
#2 S02 A REG Spp1 4
#3 S11 B REG Spp1 1
#4 S01 A FLOOD Spp2 4
#5 S10 B FLOOD Spp2 1
Personally I'd keep it like this. If you want your original format back with the zero counts for the non-discarded species, just add %>% spread(Species, Sp.Count, fill = 0) to the pipeline.
# Site LOC TYPE Spp1 Spp2
#* <fctr> <fctr> <fctr> <dbl> <dbl>
#1 S01 A FLOOD 2 4
#2 S02 A REG 4 0
#3 S10 B FLOOD 0 1
#4 S11 B REG 1 0

Answer 4

There is an even easier and quicker way to do this (and also more in line with your question: using dplyr).
A.B.flood.subset <- A.B.flood[, colSums(A.B.flood != 0) > 0]
or with a MWE:
df <- data.frame (x = rnorm(100), y = rnorm(100), z = rep(0, 100))
df[, colSums(df != 0) > 0]

Answer 5

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))