Unable to summarize the minimum and maximum while using for loop - r

Question

Below is random data.
drop drop1 drop2 ch
15 14 40 1
20 15 45 1
35 16 90 1
40 17 70 0
25 18 80 0
30 18 90 0
11 20 100 0
13 36 11 0
16 70 220 0
19 40 440 1
25 45 1 1
35 30 70 1
40 40 230 1
17 11 170 1
30 2 160 1
I am using code below for variable profiling for continuous variable in R.
library(dplyr)
dt %>% mutate(dec=ntile(drop, n=2)) %>%
count(ch, dec) %>%
filter(ch == 1) -> datcbld
datcbld$N <- unclass(dt %>%
mutate(dec=ntile(drop, n=2)) %>%
count(dec) %>%
unname())[[2]]
datcbld$ch_perc <- datcbld$n / datcbld$N
datcbld$GreaterThan <- unclass(dt %>% mutate(dec=ntile(drop, n=2)) %>%
group_by(dec) %>%
summarise(min(drop)))[[2]]
datcbld$LessThan <- unclass(dt %>%
mutate(dec=ntile(drop, n=2)) %>%
group_by(dec) %>%
summarise(max(drop)))[[2]]
datcbld$Varname <- rep("dt", nrow(datcbld))
And below is output of the code.
ch dec n N ch_perc GreaterThan LessThan Varname
1 1 4 8 0.5 11 25 drop
1 2 5 7 0.714285714 25 40 drop
This code works perfectly fine when I am using it for a single variable.
When I am trying to run it for each column using a for loop it is unable to summarise with min and max for each decile.
Below is my code using for running for loop.
finaldata <- data.frame()
for(i in 1:(ncol(dt) - 1)){
dt %>%
mutate(dec=ntile(dt[, colnames(dt[i])], n = 2)) %>%
count(ch,dec) %>%
filter(ch == 1) -> dat
dat$N <- unclass(dt %>%
mutate(dec=ntile(dt[, colnames(dt[i])], n=2)) %>%
count(dec) %>%
unname())[[2]]
dat$ch_perc <- dat$n / dat$N
dat$GreaterThan <- unclass(dt %>%
mutate(dec=ntile(dt[, colnames(dt[i])], n=2)) %>%
group_by(dec) %>%
summarise(min(dt[, colnames(dt[i])])))[[2]]
dat$LessThan <- unclass(dt %>%
mutate(dec=ntile(dt[, colnames(dt[i])], n=2)) %>%
group_by(dec) %>%
summarise(max(dt[, colnames(dt[i])])))[[2]]
dat$Varname <- rep(colnames(dt[i]), nrow(dat))
finaldata <- rbind(finaldata, dat)
}
But I'm unable to get same result.

Answer 1

We could do this with map by looping over the names and this can be done without breaking off the chain (%>%)
library(tidyverse)
names(dt)[1:3] %>%
map_df(~
dt %>%
select(.x, ch) %>%
mutate(dec = ntile(!! rlang::sym(.x), n = 2)) %>%
group_by(dec) %>%
mutate(N = n(),
GreaterThan = max(!!rlang::sym(.x)),
LessThan = min(!!rlang::sym(.x))) %>%
select(-1) %>%
count(!!! rlang::syms(names(.))) %>%
filter(ch == 1)%>%
mutate(ch_perc = n/N,
Varname = .x))
# A tibble: 6 x 8
# Groups: dec [2]
# dec ch N GreaterThan LessThan n ch_perc Varname
# <int> <int> <int> <dbl> <dbl> <int> <dbl> <chr>
#1 1 1 8 25 11 4 0.5 drop
#2 2 1 7 40 25 5 0.714 drop
#3 1 1 8 18 2 5 0.625 drop1
#4 2 1 7 70 20 4 0.571 drop1
#5 1 1 8 90 1 5 0.625 drop2
#6 2 1 7 440 90 4 0.571 drop2
The issue in the OP's for loop is the use of
dt[, colnames(dt[i])]
within summarise. It will apply the min or max on the full column value instead of applying the function on the column respecting the group by structure
We could convert the column names to symbols as showed above (sym) and do an evaluation or use summarise_at
finaldata <- data.frame()
for(i in 1:(ncol(dt) - 1)){
dt %>%
mutate(dec=ntile(dt[, colnames(dt[i])], n = 2)) %>%
count(ch,dec) %>%
filter(ch == 1) -> dat
dat$N <- unclass(dt %>%
mutate(dec=ntile(dt[, colnames(dt[i])], n=2)) %>%
count(dec) %>%
unname())[[2]]
dat$ch_perc <- dat$n / dat$N
dat$GreaterThan <- unclass(dt %>%
mutate(dec=ntile(dt[, colnames(dt[i])], n=2)) %>%
group_by(dec) %>%
summarise(max(!! rlang::sym(names(dt)[i]))))[[2]]
dat$LessThan <- unclass(dt %>%
mutate(dec=ntile(dt[, colnames(dt[i])], n=2)) %>%
group_by(dec) %>%
summarise(min(!! rlang::sym(names(dt)[i]))))[[2]]
dat$Varname <- rep(colnames(dt[i]), nrow(dat))
finaldata <- rbind(finaldata, dat)
}
finaldata
# A tibble: 6 x 8
# ch dec n N ch_perc GreaterThan LessThan Varname
# <int> <int> <int> <int> <dbl> <dbl> <dbl> <chr>
#1 1 1 4 8 0.5 25 11 drop
#2 1 2 5 7 0.714 40 25 drop
#3 1 1 5 8 0.625 18 2 drop1
#4 1 2 4 7 0.571 70 20 drop1
#5 1 1 5 8 0.625 90 1 drop2
#6 1 2 4 7 0.571 440 90 drop2
data
dt <- structure(list(drop = c(15L, 20L, 35L, 40L, 25L, 30L, 11L, 13L,
16L, 19L, 25L, 35L, 40L, 17L, 30L), drop1 = c(14L, 15L, 16L,
17L, 18L, 18L, 20L, 36L, 70L, 40L, 45L, 30L, 40L, 11L, 2L), drop2 = c(40L,
45L, 90L, 70L, 80L, 90L, 100L, 11L, 220L, 440L, 1L, 70L, 230L,
170L, 160L), ch = c(1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L)), .Names = c("drop", "drop1", "drop2", "ch"),
class = "data.frame", row.names = c(NA,
-15L))