Combining MIN and MAX into a rowise function in R - r

Question

I have a bit of code that I used in an excel spreadsheet that used min and max that I'm trying to transfer over to R.
I have two columns, "mini" and "maxi" which represent a range of possible values. The third column I'm trying to populate is the proportion of that range that falls between 5 and 19. Looking at the first row in the example, if "mini" was 10 and "maxi" was 15, the value of the 5-19 column should be 1, since the range falls completely in that span. In row 9, the "mini" is 1 and the "maxi" is 3, meaning it falls completely outside of the 5-19 range, and should therefore be 0. Row 3 however, straddles this range, and only 25% falls in the range of 5-19, so the output value should be 0.25.
Edit I have updated R and although several solutions worked before, I am now getting the error:
Error in mutate_impl(.data, dots, caller_env()) :
attempt to bind a variable to R_UnboundValue
Here's an example of how the DF looks:
ID mini maxi
1 10 15
2 17 20
3 2 5
4 40 59
5 40 59
6 21 39
7 21 39
8 17 20
9 1 3
10 4 6
The code that I used previously was something like this:
=MAX((MIN(maxi,19)-MAX(mini,5)+1),0)/(maxi-mini+1)
I was initially trying to use something like
percentoutput <- mutate(DF, output = MAX((MIN(maxi,19) - MAX(mini,5) + 1),0)/(maxi-mini + 1))
This resulted in the ouput column being full of NAs.
I wasn't sure if this is a situation where I'd need to run an apply function, but I'm not sure how to go about setting it up. Any guidance is appreciated!
Here is an example DF:
structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), min = c(10,
17, 2, 40, 40, 21, 21, 17, 1, 4), max = c(15, 20, 5, 59, 59,
39, 39, 20, 3, 6)), class = c("spec_tbl_df", "tbl_df", "tbl",
"data.frame"), row.names = c(NA, -10L), spec = structure(list(
cols = list(ID = structure(list(), class = c("collector_double",
"collector")), mini = structure(list(), class = c("collector_double",
"collector")), maxi = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))

Answer 1

We can calculate ratio of min to max values that are in range of 5:19 using rowwise.
library(dplyr)
df %>% rowwise() %>% mutate(ratio = mean(min:max %in% 5:19))
# ID min max ratio
# <dbl> <dbl> <dbl> <dbl>
# 1 1 10 15 1
# 2 2 17 20 0.75
# 3 3 2 5 0.25
# 4 4 40 59 0
# 5 5 40 59 0
# 6 6 21 39 0
# 7 7 21 39 0
# 8 8 17 20 0.75
# 9 9 1 3 0
#10 10 4 6 0.667
and similarly in base R using apply :
df$ratio <- apply(df[-1], 1, function(x) mean(x[1]:x[2] %in% 5:19))

Answer 2

Here is a vectorized version using data.table:
DT[, portion := {
mn <- pmax(mini, lb)
mx <- pmin(maxi, ub)
fifelse(mn <= mx, (mx - mn + 1L) / (maxi - mini + 1L), 0)
}]
Or equivalently in base R:
DF$mn <- pmax(DF$mini, lb)
DF$mx <- pmin(DF$maxi, ub)
DF$portion <- ifelse(DF$mn <= DF$mx, (DF$mx - DF$mn + 1L) / (DF$maxi - DF$mini + 1L), 0)
output:
ID mini maxi portion
1: 1 10 15 1.0000000
2: 2 17 20 0.7500000
3: 3 2 5 0.2500000
4: 4 40 59 0.0000000
5: 5 40 59 0.0000000
6: 6 21 39 0.0000000
7: 7 21 39 0.0000000
8: 8 17 20 0.7500000
9: 9 1 3 0.0000000
10: 10 4 6 0.6666667
data:
library(data.table)
DT <- fread("ID mini maxi
1 10 15
2 17 20
3 2 5
4 40 59
5 40 59
6 21 39
7 21 39
8 17 20
9 1 3
10 4 6")
lb <- 5L
ub <- 19L

Answer 3

We can use map2
library(dplyr)
library(purrr)
df %>%
mutate(ratio = map2_dbl(min, max, ~ mean(.x:.y %in% 5:19)))