ifelse with sorted values by row - r

Question

I have a dataframe (example data):
id <- c(1, 2, 3)
ex1 <- c(0.8, 0.2, 0.3)
ex2 <- c(0.1, 0.4, 0.04)
ex3 <- c(0.04, 0.3, 0.5)
ex <- c(1, 1, 1)
ran <- c(0.5, 0.7, 0.6)
dat <- data.frame(id, ex1, ex2, ex3, ex, ran)
dat
id ex1 ex2 ex3 ex ran
1 1 0.8 0.10 0.04 1 0.5
2 2 0.2 0.40 0.30 1 0.7
3 3 0.3 0.04 0.50 1 0.6
I want to change the values of "ex" with an if-else-condition. "ex" should change to 5 (arbitrary) when "ran" is smaller or equal then the highest value for the ex$-variables. It should be greater then the other ex$-variables aswell, but they should be sorted - the second largest value added to the smallest value. Here are examples for all id's, beginning with id 1:
dat$ex <- ifelse(dat$ran <= dat$ex1 & dat$ran > dat$ex1 + dat$ex2, 5, dat$ex)
Here, ex1 is the largest value, followed by ex1 and ex2.
For id 2, it should be:
dat$ex <- ifelse(dat$ran <= dat$ex2 & dat$ran > dat$ex3 + dat$ex1, 5, dat$ex)
Here, ex2 is the largest value, followed by ex3 and then ex1.
For id 3:
dat$ex <- ifelse(dat$ran <= dat$ex3 & dat$ran > dat$ex1 + dat$ex2, 5, dat$ex)
Here, ex3 is the largest value, followed by ex1 and then ex2.
Now to the problem: How to generalize the ifelse-statement? Note: It is important that the summation of the two smaller values is performed as implemented in the examples. I need to identify the sorted values for ex1, ex2 and ex3 within ifelse by id.

Answer 1

Here is a way how we could achieve the task using dplyr and tidyr:
library(dplyr)
library(tidyr)
dat %>%
pivot_longer(
cols = ex1:ex3
) %>%
arrange(id, desc(value)) %>%
group_by(id) %>%
mutate(ex = ifelse(ran <= value[1] & ran > sum(value[2], value[3]), 5, ex)) %>%
pivot_wider(
names_from=name
)
output:
id ex ran ex1 ex2 ex3
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 5 0.5 0.8 0.1 0.04
2 2 1 0.7 0.2 0.4 0.3
3 3 1 0.6 0.3 0.04 0.5

Answer 2

We may use pmax
library(dplyr)
library(purrr)
dat %>%
mutate(ex= case_when(ran <=invoke(pmax, across(matches('^ex\\d+'))) ~ 5,
TRUE ~ ex))
id ex1 ex2 ex3 ex ran
1 1 0.8 0.10 0.04 5 0.5
2 2 0.2 0.40 0.30 1 0.7
3 3 0.3 0.04 0.50 1 0.6

Answer 3

exes <- t(apply(subset(dat, select = grep("^ex.+", names(dat))), 1, function(z) c(max(z), sum(z[-which.max(z)]))))
exes
# [,1] [,2]
# 1 0.8 0.14
# 2 0.4 0.50
# 3 0.5 0.34
ifelse(dat$ran <= exes[,1] & dat$ran > exes[,2], 5, dat$ran)
# 1 2 3
# 5.0 0.7 0.6
Walk-through:
subset(dat, ...) is a way to dynamically extract columns from a frame regardless of its type (e.g., data.frame, tbl_df, or data.table), and without risk of dropping the frame to a column (i.e., see that mtcars[,2] is no longer a frame); there are other ways to do this, some in base R, some in other packages like dplyr or data.table
apply(dat, 1, ..) operates on the rows of the respective columns; because when MARGIN=1 (second arg), it transposes the results, so we need to t(.) it back into the right shape;
exes is now a matrix whose first column contains the max of the ex# variables, and the second column contains the sum of the non-max ex# variables
From here, I think the use of exes is the "general" solution you were looking for.