Calculate rowwise maximum from columns that have changing names - r

Question

I have the following objects:
s1 = "1_1_1_1_1"
s2 = "2_1_1_1_1"
s3 = "3_1_1_1_1"
Please note that the value of s1, s2, s3 can change in another example.
I then have the follwoing data frame:
set.seed(666)
df = data.frame(draw = c(1,2,3,4,1,2,3,4,1,2,3,4),
resp = c(1,1,1,1,2,2,2,2,3,3,3,3),
"1_1_1_1_1" = runif(12),
"2_1_1_1_1" = runif(12),
"3_1_1_1_1" = runif(12)).
Please note that the column names of may data frame will change based on the values of s1,s2,s3.
I now want to achieve the following:
I want to find out which of last three columns in df has the highest value and store it as a value in a new column (values are supposed to be either of 1,2 or 3, depending on if the highest value is the first, second or third of these variables).
Now that I know which value is the highest per row, I want to group/summarize the result by the column resp and count how often my max value is 1, 2 or 3.
So the outcome from 1. should be:
draw resp 1_1_1_1_1 2_1_1_1_1 3_1_1_1_1 max
1 1 0.774 0.095 0.806 3
2 1 0.197 0.142 0.266 3
...
And the outcome from 2. is supposed to be:
resp first_max second_max third_max
1 1 1 2
2 2 1 1
3 1 2 1
My problem is that tidyverse's rowwise function is deprecated and that I don't know how I can dynamically address columns in a tidyverse pipe by column names which a re stored externally (here in s1, s2, s3). One last note: I might be overcomplicating things by trying to go by the column names, when, in fact, the positions of the columns that I'm interested in are always at column position 3:5.

Answer 1

Here is one way to get what you want. For a sligthly different format, you can use count rather than table but this matches your expected output. Hope this helps!!
library(dplyr)
df %>%
mutate(max_val = max.col(select(., starts_with("X")))) %>%
select(resp, max_val) %>%
table()
max_val
resp 1 2 3
1 1 1 2
2 2 1 1
3 1 2 1
Or, you could do this:
df %>%
mutate(max_val = max.col(.[3:5])) %>%
count(resp, max_val) %>%
mutate(max_val = paste0("max_", max_val)) %>%
spread(value = n, key = max_val)
resp max_1 max_2 max_3
<dbl> <int> <int> <int>
1 1 1 1 2
2 2 2 1 1
3 3 1 2 1

Answer 2

calculate max using pmap(row-wise iteration)
max_cols <- pmap_dbl(unname(df),function(x,y,...){
vals <- unlist(list(...))
return(which(vals == max(vals)))
})
result <- df %>% add_column(max = max_cols)
> result
draw resp X1_1_1_1_1 X2_1_1_1_1 X3_1_1_1_1 max
1 1 1 0.4551478 0.70061232 0.618439890 2
2 2 1 0.3667764 0.26670969 0.024742605 1
3 3 1 0.6806912 0.03233215 0.004014758 1
4 4 1 0.9117449 0.42926492 0.885247456 1
5 1 2 0.1886954 0.34189707 0.985054492 3
6 2 2 0.5569398 0.78043504 0.100714130 2
7 3 2 0.9791164 0.92823982 0.676584495 1
8 4 2 0.9174654 0.74627116 0.485582287 1
9 1 3 0.3681890 0.69622331 0.672346875 2
10 2 3 0.5510356 0.99651637 0.482430518 2
11 3 3 0.4283281 0.12832611 0.018095649 1
12 4 3 0.6168436 0.64381995 0.655178701 3
Reshape the data frame.
reshape2::dcast(result,resp~max,fun.aggregate = length,value.var = "max")
resp 1 2 3
1 1 1 1 2
2 2 2 1 1
3 3 1 2 1