Select values in a table conditional to an external table - r

Question

I'd like to select the first N values of each variables (columns) in a data set, where N varies by column and row and are given in an other table. An example below with the iris data:
data(iris)
head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
## Create a fake external table
ext.tab <- data.table(species=c("setosa","versicolor", "virginica" ),N1=c(1:3),N2=c(3:5),N3=c(5:7),N4=c(7:9))
head(ext.tab)
species N1 N2 N3 N4
1: setosa 1 3 5 7
2: versicolor 2 4 6 8
3: virginica 3 5 7 9
Now for Iris setosa, I'd like to get the first maximum value (N1 in ext.tab) of column 1 ('sepal.length' in iris data), then the three max values (N2 in ext.tab) for column 2 (sepal.width), then the five max values (N3) for column 3 (petal.length) and so forth. Then moving to the Iris versicolor and do the same.
The result can be either a table or a list for each species with the values themselves or row indices for each variable (column). Any idea of a fast way to implement that?

Answer 1

Here is a tidyverse approach using a custom function. The function takes the variable and group names as character scalar and number of maximum values as numeric. Inside the function is a dplyr pipeline using .data pronoun. Then, I reshaped ext.tab to long form and applied get_maximum() row-wise.
library(tidyverse)
get_maximum <- \(.x, .group, .n_max, .dat) {
.dat %>%
filter(Species == .group) %>%
arrange(desc(.data[[.x]])) %>%
slice(seq_len(.n_max)) %>%
pull(.data[[.x]])
}
dat <- as_tibble(ext.tab) %>%
pivot_longer(-species) %>%
mutate(name = recode(
name,
N1 = "Sepal.Length",
N2 = "Sepal.Width",
N3 = "Petal.Length",
N4 = "Petal.Width"
)) %>%
rowwise() %>%
mutate(max_num = list(
get_maximum(name, species, value, iris)
)) %>%
ungroup()
If you need the unique maximum values, you can add distinct() inside the custom function.
get_maximum_unique <- \(.x, .group, .n_max, .dat) {
.dat %>%
filter(Species == .group) %>%
distinct(.data[[.x]]) %>%
arrange(desc(.data[[.x]])) %>%
slice(seq_len(.n_max)) %>%
pull(.data[[.x]])
}

Answer 2

Here is an option using data.table. I have taken the liberty of renaming the column names.
cols <- setdiff(names(ext.tab), "Species")
iris[ext.tab, on=.(Species), by=.EACHI,
.(.(mapply(function(x, n) -head(sort(-x, partial=n), n),
x=mget(cols), n=mget(paste0("i.", cols)), SIMPLIFY=FALSE)))]$V1
data:
library(data.table)
iris <- as.data.table(iris)
ext.tab <- data.table(Species=c("setosa", "versicolor", "virginica"),
Sepal.Length=c(1:3),
Sepal.Width=c(3:5),
Petal.Length=c(5:7),
Petal.Width=c(7:9))
output:
[[1]]
[[1]]$Sepal.Length
[1] 5.8
[[1]]$Sepal.Width
[1] 4.4 4.2 4.1
[[1]]$Petal.Length
[1] 1.9 1.9 1.7 1.7 1.7
[[1]]$Petal.Width
[1] 0.4 0.4 0.6 0.4 0.5 0.4 0.4
[[2]]
[[2]]$Sepal.Length
[1] 7.0 6.9
[[2]]$Sepal.Width
[1] 3.4 3.3 3.2 3.2
[[2]]$Petal.Length
[1] 5.1 4.8 4.9 5.0 4.9 4.8
[[2]]$Petal.Width
[1] 1.7 1.6 1.6 1.8 1.5 1.5 1.6 1.5
[[3]]
[[3]]$Sepal.Length
[1] 7.7 7.9 7.7
[[3]]$Sepal.Width
[1] 3.8 3.8 3.6 3.4 3.4
[[3]]$Petal.Length
[1] 6.4 6.3 6.7 6.9 6.7 6.6 6.1
[[3]]$Petal.Width
[1] 2.5 2.5 2.4 2.5 2.4 2.4 2.3 2.3 2.3
Short explanation:
Perform a left join iris[ext.tab, on=.(Species),
by=.EACHI means for each row of ext.tab
x=mget(cols) gets the columns in iris
mget(paste0("i.", cols)) gets the number of values required for each column
-head(sort(-x, partial=n), n) performs a partial sort and extract the first n values
SIMPLIFY=FALSE and .(.( )) are simply required to return the results as a list