Use dplyr to substitute apply - r

Question

I have table like this (but number of columns can be different, I have a number of pairs ref_* + alt_*):
+--------+-------+-------+-------+-------+
| GeneID | ref_a | alt_a | ref_b | alt_b |
+--------+-------+-------+-------+-------+
| a1 | 0 | 1 | 1 | 3 |
| a2 | 1 | 1 | 7 | 8 |
| a3 | 0 | 1 | 1 | 3 |
| a4 | 0 | 1 | 1 | 3 |
+--------+-------+-------+---------------+
and need to filter out rows that have ref_a + alt_a < 10 and ref_b + alt_b < 10. It's easy to do it with apply, creating additional columns and filtering, but I'm learning to keep my data tidy, so trying to do it with dplyr.
I would use mutate first to create columns with sums and then filter by these sums. But can't figure out how to use mutate in this case.
Edited:
Number of columns is not fixed!

Answer 1

You do not need to mutate here. Just do the following:
require(tidyverse)
df %>%
filter(ref_a + alt_a < 10 & ref_b + alt_b < 10)
If you want to use mutate first you could go with:
df %>%
mutate(sum1 = ref_a + alt_a, sum2 = ref_b + alt_b) %>%
filter(sum1 < 10 & sum2 < 10)
Edit: The fact that we don't know the number of variables in advance makes it a bit more complicated. However, I think you could use the following code to perform this task (assuming that the variable names are all formated with "_a", "_b" and so on. I hope there is a shorter way to perform this task :)
df$GeneID <- as.character(df$GeneID)
df %>%
gather(variable, value, -GeneID) %>%
rowwise() %>%
mutate(variable = unlist(strsplit(variable, "_"))[2]) %>%
ungroup() %>%
group_by(GeneID, variable) %>%
summarise(sum = sum(value)) %>%
filter(sum < 10) %>%
summarise(keepGeneID = ifelse(n() == (ncol(df) - 1)/2, TRUE, FALSE)) %>%
filter(keepGeneID == TRUE) %>%
select(GeneID) -> ids
df %>%
filter(GeneID %in% ids$GeneID)
Edit 2: After some rework I was able to improve the code a bit:
df$GeneID <- as.character(df$GeneID)
df %>%
gather(variable, value, -GeneID) %>%
rowwise() %>%
mutate(variable = unlist(strsplit(variable, "_"))[2]) %>%
ungroup() %>%
group_by(GeneID, variable) %>%
summarise(sum = sum(value)) %>%
group_by(GeneID) %>%
summarise(max = max(sum)) %>%
filter(max < 10) -> ids
df %>%
filter(GeneID %in% ids$GeneID)