apply conditional on two groups of columns within dataframe - r

Question

I have a df:
a<-c(5,1,5,3,5,3,5,1)
b<-c(1,5,1,5,1,5,3,5)
df<-as.data.frame(rbind(a,b))
names(df)<-c('pre1','post1','pre2','post2','pre3','post3','pre4','post4')
And I have two groups of samples within the columns eg 'pre' and post':
pre<-seq(1,8,by=2)
post<-seq(2,8,by=2)
I would like to apply a conditional that 100% of the pre and 50% of the post pass OR 50% of the pre and 100% of the post
eg
if 100% of 'pre' are 3 or over AND 50% post are 3 or over keep row
OR
if 50% of 'pre' are 3 or over AND 100% post are 3 or over keep row
so in the example df only row 'a' would stay
I have:
test<- ((df[apply(df[pre],1,function(x) sum(x>=3)/length(x)),] &
df[apply(df[post],1,function(x) sum(x>3)/length(x))>=0.5,]) |
(df[apply(df[pre],1,function(x) sum(x>3)/length(x))>=0.5,] &
df[apply(df[post],1,function(x) sum(x>3)/length(x)),]))
But I get a vector of 'TRUEs' which isn't what I want.

Answer 1

We can create a logical vector to compare using rowSums
df[(rowSums(df[pre] >= 3)/length(pre) == 1) &
(rowSums(df[post] >= 3)/length(post) >= 0.5) |
(rowSums(df[post] >= 3)/length(post) == 1) &
(rowSums(df[pre] >= 3)/length(pre) >= 0.5), ]
# pre1 post1 pre2 post2 pre3 post3 pre4 post4
#a 5 1 5 3 5 3 5 1
Using apply we can do
df[apply(df[pre] >= 3, 1, all) & apply(df[post] >= 3, 1, sum)/length(post) >= 0.5 |
apply(df[post] >= 3, 1, all) & apply(df[pre] >= 3, 1, sum)/length(pre) >= 0.5, ]

Answer 2

Here's a much less concise tidyverse solution that could probably be shortened substantially.
library(tidyverse)
pass_val = 3
df %>%
rownames_to_column() %>%
gather(col, val, -rowname) %>%
separate("col", c("type", "num"), sep = -1) %>%
count(rowname, type, pass = val >= pass_val) %>%
spread(pass, n, fill = 0) %>%
transmute(rowname, type, pass_pct = `TRUE`/(`TRUE` + `FALSE`)) %>%
spread(type, pass_pct) %>%
filter(post == 1 & pre >= 0.5 | post >= 0.5 & pre == 1)

Answer 3

Here is one option with tidyverse
library(tidyverse)
library(rap)
crossing(val = c(0.5, 1), cols = c("pre", "post")) %>%
rap(x = ~ df %>%
select(matches(cols)) %>%
{rowMeans(. >=3) >= val}) %>%
group_by(val) %>%
transmute(ind = reduce(x, `&`)) %>%
filter(any(ind)) %>%
pull(ind) %>%
filter(df, .)
# pre1 post1 pre2 post2 pre3 post3 pre4 post4
#1 5 1 5 3 5 3 5 1

Answer 4

Here's a base R solution that splits by row name, checks the conditions using sapply, and uses the output as a logical index on df:
df[sapply(split(df, rownames(df)), function(x) {
(sum(x[pre] > 2)/ncol(x[pre]) >= .5) & (sum(x[post] > 2)/ncol(x[post]) == 1) ||
(sum(x[pre] > 2)/ncol(x[pre]) == 1) & (sum(x[post] > 2)/ncol(x[post]) >= .5)
}),]
#### OUTPUT ####
pre1 post1 pre2 post2 pre3 post3 pre4 post4
a 5 1 5 3 5 3 5 1