Check Columns for Non-Dates

Identifies any elements in the column(s) that would be changed to NA if as.Date is used on the column(s). This is useful for quickly identifying only the "problem" entries of ostensibly date column(s) that is/are read in as a character.

Usage

date_check(data = NULL, col = NULL)

Arguments

data

(dataframe) object containing at least one column of supposed dates

col

(character or numeric) name(s) or column number(s) of the column(s) containing putative dates in the data object

Value

(list) malformed dates from each supplied column in separate list elements

Examples

# Make a dataframe to test the function
loc <- c("LTR", "GIL", "PYN", "RIN")
time <- c("2021-01-01", "2021-01-0w", "1990", "2020-10-xx")
time2 <- c("1880-08-08", "2021-01-02", "1992", "2049-11-01")
time3 <- c("2022-10-31", "tomorrow", "1993", NA)

# Assemble our vectors into a dataframe
sites <- data.frame("site" = loc, "first_visit" = time, "second" = time2, "third" = time3)

# Use `date_check()` to return only the entries that would be lost
date_check(data = sites, col = c("first_visit", "second", "third"))
#> For 'first_visit', 3 non-dates identified: '2021-01-0w' | '1990' | '2020-10-xx'
#> For 'second', 1 non-dates identified: '1992'
#> For 'third', 2 non-dates identified: 'tomorrow' | '1993'
#> $first_visit
#> [1] "2021-01-0w" "1990"       "2020-10-xx"
#> 
#> $second
#> [1] "1992"
#> 
#> $third
#> [1] "tomorrow" "1993"    
#>