Add columns with date calculations based on reference date
Source:R/add-date-columns.R
add_date_columns.RdUsing a reference date (defaults to current date), columns are appended to the data set describing the number of days, weeks, months, quarters, calendar years and fiscal years since the reference date. If the new columns share names with an existing column, the function will show a warning.
Usage
add_date_columns(
df,
date_col,
ref_date = Sys.Date(),
fiscal_year_offset = 6,
week_start = 1,
drop = FALSE
)Arguments
- df
data frame
- date_col
column with class of 'date'
- ref_date
reference date for calculations, defaults to current date
- fiscal_year_offset
the number of months to offset date, if fiscal year ends in June, use 6
- week_start
integer for start of week where Monday = 1 and Sunday = 7
- drop
some of the generated fields may match the input data frame. When TRUE, the original columns will be removed and replaced with the new field of the same name. Otherwise, columns with the same name will be appended with a '1'
Examples
demo_data() |>
add_date_columns(date_col = date)
#> # A tibble: 10 × 11
#> group sales count on_sale date day week month quarter calendar_year
#> <chr> <dbl> <dbl> <dbl> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a 101 35 1 2022-12-19 0 0 0 0 0
#> 2 a 102 34 0 2022-10-19 -61 -9 -2 0 0
#> 3 b 103 33 1 2022-08-19 -122 -18 -4 -1 0
#> 4 b 104 32 0 2022-06-19 -183 -27 -6 -2 0
#> 5 c 105 31 1 2022-04-19 -244 -35 -8 -2 0
#> 6 c 106 30 0 2022-02-19 -303 -44 -10 -3 0
#> 7 d 107 29 1 2021-12-19 -365 -53 -12 -4 -1
#> 8 d 108 28 0 2021-10-19 -426 -61 -14 -4 -1
#> 9 e 109 27 1 2021-08-19 -487 -70 -16 -5 -1
#> 10 e 110 26 0 2021-06-19 -548 -79 -18 -6 -1
#> # … with 1 more variable: fiscal_year <dbl>
# if columns overlap, you will see a warning
demo_data() |>
dplyr::mutate(week = 1) |>
add_date_columns(date_col = date)
#> Warning: This step produced duplicate names and some fields have new names (see above)
#> Use 'drop = TRUE' to remove the overlapping columns prior to adding the new date fields
#> New names:
#> • `week` -> `week...6`
#> • `week` -> `week...8`
#> # A tibble: 10 × 12
#> group sales count on_sale date week...6 day week...8 month quarter
#> <chr> <dbl> <dbl> <dbl> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a 101 35 1 2022-12-19 1 0 0 0 0
#> 2 a 102 34 0 2022-10-19 1 -61 -9 -2 0
#> 3 b 103 33 1 2022-08-19 1 -122 -18 -4 -1
#> 4 b 104 32 0 2022-06-19 1 -183 -27 -6 -2
#> 5 c 105 31 1 2022-04-19 1 -244 -35 -8 -2
#> 6 c 106 30 0 2022-02-19 1 -303 -44 -10 -3
#> 7 d 107 29 1 2021-12-19 1 -365 -53 -12 -4
#> 8 d 108 28 0 2021-10-19 1 -426 -61 -14 -4
#> 9 e 109 27 1 2021-08-19 1 -487 -70 -16 -5
#> 10 e 110 26 0 2021-06-19 1 -548 -79 -18 -6
#> # … with 2 more variables: calendar_year <dbl>, fiscal_year <dbl>
# to drop the old column and keep the new column use `drop = TRUE`
demo_data() |>
dplyr::mutate(week = 1) |>
add_date_columns(date_col = date, drop = TRUE)
#> # A tibble: 10 × 11
#> group sales count on_sale date day week month quarter calendar_year
#> <chr> <dbl> <dbl> <dbl> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a 101 35 1 2022-12-19 0 0 0 0 0
#> 2 a 102 34 0 2022-10-19 -61 -9 -2 0 0
#> 3 b 103 33 1 2022-08-19 -122 -18 -4 -1 0
#> 4 b 104 32 0 2022-06-19 -183 -27 -6 -2 0
#> 5 c 105 31 1 2022-04-19 -244 -35 -8 -2 0
#> 6 c 106 30 0 2022-02-19 -303 -44 -10 -3 0
#> 7 d 107 29 1 2021-12-19 -365 -53 -12 -4 -1
#> 8 d 108 28 0 2021-10-19 -426 -61 -14 -4 -1
#> 9 e 109 27 1 2021-08-19 -487 -70 -16 -5 -1
#> 10 e 110 26 0 2021-06-19 -548 -79 -18 -6 -1
#> # … with 1 more variable: fiscal_year <dbl>