I have this dataset:
my_data = structure(list(col = c("A", "B", "C"), `2000-01-01` = c(86L,
43L, 73L), `2000-01-02` = c(99L, 77L, 12L)), class = "data.frame", row.names = c(NA,
-3L))
col 2000-01-01 2000-01-02
1 A 86 99
2 B 43 77
3 C 73 12
My goal is to transform this dataset into the following format:
date col count
1 2000-01-01 A 86
2 2000-01-01 B 43
3 2000-01-01 C 73
4 2000-01-02 A 99
5 2000-01-02 B 77
6 2000-01-02 C 12
Have I done this correctly?
Here is my code:
library(tidyr)
# how come this seems to works for all columns even though I only specified "2001-01-01"?
my_data %>%
pivot_longer(!col, names_to = "2001-01-01", values_to = "count")
# A tibble: 6 x 3
col `2001-01-01` count
<chr> <chr> <int>
1 A 2000-01-01 86
2 A 2000-01-02 99
3 B 2000-01-01 43
4 B 2000-01-02 77
5 C 2000-01-01 73
6 C 2000-01-02 12
Thanks!
I would have done it like this:
library(tidyr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
structure(list(col = c("A", "B", "C"), `2000-01-01` = c(86L,
43L, 73L), `2000-01-02` = c(99L, 77L, 12L)), class = "data.frame", row.names = c(NA,
-3L)
) %>%
pivot_longer(-col, names_to = 'date', values_to = 'count') %>%
arrange(date, col)
#> # A tibble: 6 × 3
#> col date count
#> <chr> <chr> <int>
#> 1 A 2000-01-01 86
#> 2 B 2000-01-01 43
#> 3 C 2000-01-01 73
#> 4 A 2000-01-02 99
#> 5 B 2000-01-02 77
#> 6 C 2000-01-02 12
Created on 2022-12-09 with reprex v2.0.2