How to obtain conditioned results from an R dataframe - r

Question

This is my first message here. I'm trying to solve an R exercise from an edX R course, and I'm stuck in it. It would be great if somebody could help me solve it. Here are the dataframe and question given:
> students
height shoesize gender population
1 181 44 male kuopio
2 160 38 female kuopio
3 174 42 female kuopio
4 170 43 male kuopio
5 172 43 male kuopio
6 165 39 female kuopio
7 161 38 female kuopio
8 167 38 female tampere
9 164 39 female tampere
10 166 38 female tampere
11 162 37 female tampere
12 158 36 female tampere
13 175 42 male tampere
14 181 44 male tampere
15 180 43 male tampere
16 177 43 male tampere
17 173 41 male tampere
Given the dataframe above, create two subsets with students whose height is equal to or below the median height (call it students.short) and students whose height is strictly above the median height (call it students.tall). What is the mean shoesize for each of the above 2 subsets by population?
I've been able to create the two subsets students.tall and students.short (both display the answers by TRUE/FALSE), but I don't know how to obtain the mean by population. The data should be displayed like this:
kuopio tampere
students.short xxxx xxxx
students.tall xxxx xxxx
Many thanks if you can give me a hand!

Answer 1

We can split by a logical vector based on the median height
# // median height
medHeight <- median(students$height, na.rm = TRUE)
# // split the data into a list of data.frames using the 'medHeight'
lst1 <- with(students, split(students, height > medHeight))
Then loop over the list use aggregate from base R
lapply(lst1, function(dat) aggregate(shoesize ~ population,
data = dat, FUN = mean, na.rm = TRUE))
However, we don't need to create two separate datasets or a list. It can be done by grouping with both 'population' and the 'grp' created with logical vector
library(dplyr)
students %>%
group_by(grp = height > medHeight, population) %>%
summarise(shoesize = mean(shoesize))

Answer 2

You can try this:
#Code
students.short <- students[students$height<=median(students$height),]
students.tall <- students[students$height>median(students$height),]
#Mean
mean(students.short$shoesize)
mean(students.tall$shoesize)
Output:
[1] 38.44444
[1] 42.75

Answer 3

You can use pivot_wider() in tidyr and set the argument values_fn as mean.
library(dplyr)
library(tidyr)
df %>%
mutate(grp = if_else(height > median(height), "students.tall", "students.short")) %>%
pivot_wider(id_cols = grp, names_from = population, values_from = height, values_fn = mean)
# # A tibble: 2 x 3
# grp kuopio tampere
# <chr> <dbl> <dbl>
# 1 students.tall 176. 177.
# 2 students.short 164 163.
With a base way, you can try xtabs(), which returns a table object.
xtabs(height ~ grp + population,
aggregate(height ~ grp + population, FUN = mean,
transform(df, grp = ifelse(height > median(height), "students.tall", "students.short"))))
# population
# grp kuopio tampere
# students.short 164.0000 163.4000
# students.tall 175.6667 177.2000
Note: To convert a table object into data.frame, you can use as.data.frame.matrix().