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Cleaning dates

clean-dates.Rmd

Below we’ll fetch the live USADA sanctions data and process the text:

usada_raw <- get_sanctions_data()
usada <- process_text(raw_data = usada_raw)

Dates

sanction_announced contains the date the sanction was announced, and some of these contain two values (original and updated). Wrangling these values pose some challenges because they aren’t consistently messy:

bad_dates <- subset(usada,
  grepl("^original", usada[['sanction_announced']]),
  c(athlete, sanction_announced))
bad_dates
#>                    athlete                        sanction_announced
#> 4              boyle, evan original: 04/24/2026; updated: 09/09/2026
#> 30        mitchell, manteo original: 09/26/2025; updated: 02/25/2026
#> 54    qualls, robert jerry original: 06/26/2024; updated: 08/13/2025
#> 126             jha, kanak  original: 3/20/2023; updated: 12/01/2023
#> 210        prempeh, ernest original: 05/07/2019; updated: 02/04/2022
#> 213         ngetich, eliud     original: 09/03/21; updated: 01/25/22
#> 243             gehm, zach original:  11/04/2019;updated: 05/17/2021
#> 274           hudson, ryan   original 12/20/2018; updated 11/04/2020
#> 278      paparella, flavia   original: 10/19/2020updated: 01/05/2021
#> 289         murdock, vince original: 09/05/2019; updated: 08/26/2020
#> 293        rante, danielle original: 07/22/2020, updated: 11/03/2022
#> 322       werdum, fabricio   original 09/11/2018; updated 01/16/2020
#> 334         jones, stirley original: 06/17/2019; updated: 12/16/2019
#> 335               hay, amy original: 10/31/2017; updated: 12/16/2019
#> 362           orbon, joane original: 08/12/2019; updated: 09/10/2019
#> 377          ribas, amanda  original: 01/10/2018; updated 05/03/2019
#> 410     saccente, nicholas original: 02/14/2017; updated: 12/11/2018
#> 411           miyao, paulo  original: 05/10/2017;updated: 11/27/2018
#> 415 garcia del moral, luis  original: 07/10/2012;updated: 10/26/2018
#> 416        bruyneel, johan  original: 04/22/2014;updated: 10/24/2018
#> 417   celaya lazama, pedro  original: 04/22/2014;updated: 10/24/2018
#> 418            marti, jose  original: 04/22/2014;updated: 10/24/2018
#> 419         moffett, shaun   original: 04/24/2018updated: 10/19/2018
#> 427           hunter, adam original: 10/28/2016; updated: 09/26/2018
#> 506           bailey, ryan original: 08/03/2017; updated: 12/01/2017
#> 572          thomas, tammy original: 08/30/2002; updated: 02/13/2017
#> 600           tovar, oscar original: 10/28/2015; updated: 10/04/2016
#> 640       fischbach, dylan original: 12/18/2015; updated: 04/11/2016
#> 661        trafeh, mohamed original: 12/18/2014; updated: 08/25/2015
#> 864          young, jerome original: 11/10/2004; updated: 06/17/2008

clean_dates()

I’ve written a clean_dates() function that takes date_col, split and pattern arguments:

  • df = processed USADA dataset with messy dates

  • date_col = sanction date column (usually sanction_announced)

  • split = regex to pass to split argument of strsplit() (defaults to "updated")

  • pattern = regex for other non-date pattern (defaults to "original")

Below, clean_dates() is demonstrated on a couple of the messy rows found above:

clean_dates(
  df = head(bad_dates, 2),
  date_col = "sanction_announced",
  split = "updated",
  pattern = "original")
#>             athlete                        sanction_announced pattern_date
#> 4       boyle, evan original: 04/24/2026; updated: 09/09/2026   2026-04-24
#> 30 mitchell, manteo original: 09/26/2025; updated: 02/25/2026   2025-09-26
#>    split_date
#> 4  2026-09-09
#> 30 2026-02-25

For usada, split the data into three data.frames (bad_dates, good_dates, and no_dates).

bad_dates <- subset(usada, 
  grepl("^original", usada[['sanction_announced']]))
good_dates <- subset(usada, 
  !grepl("^original", usada[['sanction_announced']]) & sanction_announced != "")
no_dates <- subset(usada,
  athlete == "*name removed" & sanction_announced == "")

Clean dates in bad_dates by splitting the bad dates on "updated" and provided "original" as the pattern (the opposite will also work). The sanction_date column will contain the correctly formatted updated sanction_date.

After formatting good_dates and removing original_date column we can combine the two with rbind().

cleaned_dates <- clean_dates(
  df = bad_dates, 
  date_col = "sanction_announced", 
  split = "updated", 
  pattern = "original")
# address names 
names(cleaned_dates)[names(cleaned_dates) == 'split_date'] <- 'sanction_date'
names(cleaned_dates)[names(cleaned_dates) == 'pattern_date'] <- 'original_date'
# format good_dates
good_dates$sanction_date <- as.Date(x = good_dates[['sanction_announced']], 
                                    format = "%m/%d/%Y")
# get intersecting names 
nms <- intersect(names(cleaned_dates), names(good_dates))
# bind the two datasets 
usada_dates <- rbind(good_dates, cleaned_dates[nms])
str(usada_dates)
#> 'data.frame':    704 obs. of  6 variables:
#>  $ athlete           : chr  "lingafeldt, seth" "samelo do amaral, rider" "oprea, erin" "meador, laura" ...
#>  $ sport             : chr  "weightlifting" "brazilian jiu-jitsu" "triathlon" "weightlifting" ...
#>  $ substance_reason  : chr  "testosterone" "drostanolone; nandrolone; 19-norsteroids" "ostarine; lgd-4033; testosterone" "testosterone" ...
#>  $ sanction_terms    : chr  "1-year suspension; loss of results" "3-year suspension; loss of results" "5-year suspension; loss of results" "4-year suspension; loss of results" ...
#>  $ sanction_announced: chr  "09/24/2026" "09/21/2026" "09/11/2026" "09/03/2026" ...
#>  $ sanction_date     : Date, format: "2026-09-24" "2026-09-21" ...