I am struggling to relocate the descriptive statistics columns close to the original columns
iris2 <- iris %>% dplyr::select(c(2:4)) %>% dplyr::mutate(across(.cols = where(is.numeric), .fns = list(
n_miss = ~ sum(is.na(.x)),
mean = ~ mean(.x, na.rm = TRUE),
median = ~ median(.x, na.rm = TRUE),
min = ~ min(.x, na.rm = TRUE),
max = ~ max(.x, na.rm = TRUE)
)))
As you can see I have the descriptive columns generated after computing descriptive stats are located at the end of the dataframe, and I would like to be ordered.
So I have 2 questions related; I will paste some of the syntax (none of them works):
#First: how to pass the pattern using str_detect or grep to relocate descriptive columns
iris2 <- iris2 %>% dplyr::relocate(str_detect(colnames, "Sepal.Width"), .after = Sepal.Width)
# Second: would it be possible to do it for all columns, passing the name in a vector
iris2 <- iris2 %>% dplyr::relocate(str_detect(colnames, c("Sepal.Width", "Petal.Width"), .after = list(Sepal.Width, Petal.Width)))
The expected output would be something like this for every column (I paste for Sepal.Width)
expected_output <- structure(list(Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Sepal.Width_mean = c(3.05733333333333,
3.05733333333333, 3.05733333333333, 3.05733333333333, 3.05733333333333,
3.05733333333333), Sepal.Width_median = c(3, 3, 3, 3, 3, 3),
Sepal.Width_min = c(2, 2, 2, 2, 2, 2), Sepal.Width_max = c(4.4,
4.4, 4.4, 4.4, 4.4, 4.4), Petal.Length = c(1.4, 1.4, 1.3,
1.5, 1.4, 1.7)), row.names = c(NA, 6L), class = "data.frame")
I haven't tried the arrange function because I expected to perform these operations in the pipe, but if not possible I will do it in two steps
Thank you!
Use
nms <- sub("_.*", "", names(iris2))
iris2[, order(ordered(nms, unique(nms)))]
If you want to pipe this use:
iris2 %>%
select(order(str_remove(names(.), "_.*") %>%
ordered(unique(.))))
And the structure looks as shown below:
'data.frame': 150 obs. of 18 variables:
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Sepal.Width_n_miss : int 0 0 0 0 0 0 0 0 0 0 ...
$ Sepal.Width_mean : num 3.06 3.06 3.06 3.06 3.06 ...
$ Sepal.Width_median : num 3 3 3 3 3 3 3 3 3 3 ...
$ Sepal.Width_min : num 2 2 2 2 2 2 2 2 2 2 ...
$ Sepal.Width_max : num 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4 ...
$ Petal.Length : num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Length_n_miss: int 0 0 0 0 0 0 0 0 0 0 ...
$ Petal.Length_mean : num 3.76 3.76 3.76 3.76 3.76 ...
$ Petal.Length_median: num 4.35 4.35 4.35 4.35 4.35 4.35 4.35 4.35 4.35 4.35 ...
$ Petal.Length_min : num 1 1 1 1 1 1 1 1 1 1 ...
$ Petal.Length_max : num 6.9 6.9 6.9 6.9 6.9 6.9 6.9 6.9 6.9 6.9 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Petal.Width_n_miss : int 0 0 0 0 0 0 0 0 0 0 ...
$ Petal.Width_mean : num 1.2 1.2 1.2 1.2 1.2 ...
$ Petal.Width_median : num 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 ...
$ Petal.Width_min : num 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 ...
$ Petal.Width_max : num 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 ...