r - How to remove rows where all columns are zero using dplyr pipe

Question

I have the following data frame:

dat <- structure(list(`A-XXX` = c(1.51653275922944, 0.077037240321129, 
0), `fBM-XXX` = c(2.22875185527511, 0, 0), `P-XXX` = c(1.73356698481106, 
0, 0), `vBM-XXX` = c(3.00397859609183, 0, 0)), .Names = c("A-XXX", 
"fBM-XXX", "P-XXX", "vBM-XXX"), row.names = c("BATF::JUN_AHR", 
"BATF::JUN_CCR9", "BATF::JUN_IL10"), class = "data.frame")

dat 
#>                     A-XXX  fBM-XXX    P-XXX  vBM-XXX
#> BATF::JUN_AHR  1.51653276 2.228752 1.733567 3.003979
#> BATF::JUN_CCR9 0.07703724 0.000000 0.000000 0.000000
#> BATF::JUN_IL10 0.00000000 0.000000 0.000000 0.000000

I can remove the row with all column zero with this command:

> dat <- dat[ rowSums(dat)!=0, ]
> dat
                    A-XXX  fBM-XXX    P-XXX  vBM-XXX
BATF::JUN_AHR  1.51653276 2.228752 1.733567 3.003979
BATF::JUN_CCR9 0.07703724 0.000000 0.000000 0.000000

But how can I do it with dplyr's pipe style?

Answer 1

Here's a dplyr option:

library(dplyr)
filter_all(dat, any_vars(. != 0))

#       A-XXX  fBM-XXX    P-XXX  vBM-XXX
#1 1.51653276 2.228752 1.733567 3.003979
#2 0.07703724 0.000000 0.000000 0.000000

Here we make use of the logic that if any variable is not equal to zero, we will keep it. It's the same as removing rows where all variables are equal to zero.

Regarding row.names:

library(tidyverse)
dat %>% rownames_to_column() %>% filter_at(vars(-rowname), any_vars(. != 0))
#         rowname      A-XXX  fBM-XXX    P-XXX  vBM-XXX
#1  BATF::JUN_AHR 1.51653276 2.228752 1.733567 3.003979
#2 BATF::JUN_CCR9 0.07703724 0.000000 0.000000 0.000000