How to remove some variables in R with names that are dates - r

Question

I have a dataset in R where some of the variable names are dates, see a simplified example of the input data below (in Excel):
What I want to do with this data is to remove some of the columns with names that are dates that are older than or equal to a certain date, e.g. 2019-01-31. See a simplified example of the desired output data below (in Excel):
Now, I am able to achieve this by transposing the data, filtering out rows with a date lower than or equal to 31 January 2019 and finally transposing the data back. However I am wondering whether there is a different way to do this using just the column names without pivoting back and forth?
# Example data to copy and paste into R for easy reproduction of problem:
df <- data.frame (id = c("apples", "pears", "grapes", "tomatoes", "carrots", "cucumber", "rabbit", "cat", "dog"),
type = c("fruit", "fruit", "fruit", "veggies", "veggies", "veggies", "pets", "pets", "pets"),
color = c("red", "green", "purple", "red", "orange", "green", "grey", "black", "brown"),
'2019-04-30' = c(353, 91, 270, 2029, 107, 62, 30, 61, 137),
'2019-03-31' = c(349, 90, 267, 2028, 104, 60, 29, 59, 133),
'2019-02-28' = c(345, 89, 264, 2027, 101, 58, 28, 57, 129),
'2019-01-31' = c(341, 88, 261, 2026, 98, 56, 27, 55, 125),
'2018-12-31' = c(337, 87, 258, 2025, 95, 54, 26, 53, 121),
'2018-11-30' = c(333, 86, 255, 2024, 92, 52, 25, 51, 117),
check.names = FALSE)

Answer 1

We can do this in base R. Your dates are conveniently in YYYY-MM-DD format, which means they will be ordered correctly by the >= and <= operators. We can also use a simple regex to preserve any columns that are not in date format:
df[!grepl('\\d{4}-\\d{2}-\\d{2}', colnames(df)) | colnames(df) >= '2019-02-28']
id type color 2019-04-30 2019-03-31 2019-02-28
1 apples fruit red 353 349 345
2 pears fruit green 91 90 89
3 grapes fruit purple 270 267 264
4 tomatoes veggies red 2029 2028 2027
5 carrots veggies orange 107 104 101
6 cucumber veggies green 62 60 58
7 rabbit pets grey 30 29 28
8 cat pets black 61 59 57
9 dog pets brown 137 133 129

Answer 2

The approach is as follows:
extract the column names
transform to Date if possible and NA if not date like
create boolean vector to filter too old dates and non dates (i.e. NAs in the step before) columns
Sample Data
## sample data frame
m <- matrix(1, 3, 10)
colnames(m) <- c("a", "b", as.character(seq.Date(as.Date("2021-1-1"), length.out = 8, by = "days")))
(d <- as.data.frame(m))
# a b 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08
# 1 1 1 1 1 1 1 1 1 1 1
# 2 1 1 1 1 1 1 1 1 1 1
# 3 1 1 1 1 1 1 1 1 1 1
Filter
r <- vapply(names(d), as.Date, numeric(1), optional = TRUE)
d[, is.na(r) | r <= as.Date("2021-1-3")]
# a b 2021-01-01 2021-01-02 2021-01-03
# 1 1 1 1 1 1
# 2 1 1 1 1 1
# 3 1 1 1 1 1
r <- vapply(names(df), as.Date, numeric(1), optional = TRUE)
df[, is.na(r) | r >= as.Date("2019-1-31")]
# id type color 2019-04-30 2019-03-31 2019-02-28 2019-01-31
# 1 apples fruit red 353 349 345 341
# 2 pears fruit green 91 90 89 88
# 3 grapes fruit purple 270 267 264 261
# 4 tomatoes veggies red 2029 2028 2027 2026
# 5 carrots veggies orange 107 104 101 98
# 6 cucumber veggies green 62 60 58 56
# 7 rabbit pets grey 30 29 28 27
# 8 cat pets black 61 59 57 55
# 9 dog pets brown 137 133 129 125

Answer 3

Description
One can re-shape the data to the long format and filter based on the date column.
Data
Same data as provided in the example
df <- data.frame (id = c("apples", "pears", "grapes", "tomatoes", "carrots", "cucumber", "rabbit", "cat", "dog"),
type = c("fruit", "fruit", "fruit", "veggies", "veggies", "veggies", "pets", "pets", "pets"),
color = c("red", "green", "purple", "red", "orange", "green", "grey", "black", "brown"),
'2019-04-30' = c(353, 91, 270, 2029, 107, 62, 30, 61, 137),
'2019-03-31' = c(349, 90, 267, 2028, 104, 60, 29, 59, 133),
'2019-02-28' = c(345, 89, 264, 2027, 101, 58, 28, 57, 129),
'2019-01-31' = c(341, 88, 261, 2026, 98, 56, 27, 55, 125),
'2018-12-31' = c(337, 87, 258, 2025, 95, 54, 26, 53, 121),
'2018-11-30' = c(333, 86, 255, 2024, 92, 52, 25, 51, 117),
check.names = FALSE)
Solution
library(dplyr)
library(tidyr)
df %>%
tidyr::pivot_longer(cols = !c(id, type, color), names_to = 'date', values_to = 'value') %>%
dplyr::mutate(date = as.Date(date, format = '%Y-%m-%d')) %>%
dplyr::filter( date >= as.Date('2019-01-31')) %>%
tidyr::pivot_wider(names_from = 'date', values_from = 'value')
Desired output
id type color `2019-04-30` `2019-03-31` `2019-02-28` `2019-01-31`
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 apples fruit red 353 349 345 341
2 pears fruit green 91 90 89 88
3 grapes fruit purple 270 267 264 261
4 tomatoes veggies red 2029 2028 2027 2026
5 carrots veggies orange 107 104 101 98
6 cucumber veggies green 62 60 58 56
7 rabbit pets grey 30 29 28 27
8 cat pets black 61 59 57 55
9 dog pets brown 137 133 129 125