Classifying substances
classify.Rmd
if ("pak" %nin% loadedNamespaces()) {
install.packages("pak", quiet = TRUE)
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pkgs <- c("dplyr", "stringr", "tidyr", "forcats")
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#> ✔ 4 pkgs + 17 deps: kept 21 [10.5s]This vignette covers classifying ‘adverse analytic findings’ for a
single banned substance. I’ve written a get_recent_file()
function to quickly import .csv files from a specified
directory:
pth <- system.file("extdata", "demo", package = "dopingdata")
get_recent_file(pth, regex = 'sports', ext = '.csv')File last changed: 2023-12-21 20:33:32.064979
File name: 2023-12-21-tidy_sports.csv
✔ import code pasted to clipboard!This makes it easy to paste the necessary import code into the console (or R markdown file):
tidy_sports <- read.delim(file = '/Library/path/to/dopingdata/extdata/demo/2023-12-21-tidy_sports.csv', sep = ',')AAFs vs ADRVs
The sanctions are divided into two categories:
analytic: Adverse Analytical Finding, AAF; An AAF is a report from a WADA-accredited laboratory that identifies the presence of a prohibited substance and/or its metabolites or markers in a sample.non-analytic: Non-Analytical Anti-doping Rule Violation ADRV; a non-analytical anti-doping rule violation does not stem from a positive urine or blood sample, but instead originates from, and is substantiated by, other evidence of doping or violations by an athlete or athlete support personnel..
Substance/reason
The substance_reason column contains the details of the
sanction, which can include the following information:
The name of the banned substance
A description of the infraction (if non-analytic)
We will use regular expressions to identify the type of substance behind the sanction. See the examples below:
stringr::str_view(tidy_sports[['substance_reason']],
"use \\(epo & hgh\\)", match = TRUE)
#> [638] │ erythropoietin (epo) and non-analytical: <use (epo & hgh)>
stringr::str_view(tidy_sports[['substance_reason']],
"tampering, complicity", match = TRUE)
#> [85] │ non-analytical: <tampering, complicity>Most of the non-analytic sanctions include the terms
non-analytic/non-analytical/etc., as a prefix
in the substance_reason column.
Sanction types
I can pass these terms as regular expressions to create the
sanction_type variable, which will contain two values:
non-analytic and analytic. I’ll save this
variable in a new intermediate substances dataset:
substances <- dplyr::mutate(.data = tidy_sports,
sanction_type = dplyr::case_when(
stringr::str_detect(string = substance_reason,
"non-analytical") ~ "non-analytic",
!stringr::str_detect(substance_reason,
"non-analytical") ~ "analytic",
TRUE ~ NA_character_
)
)
substances |>
dplyr::count(sanction_type, sort = TRUE)
#> sanction_type n
#> 1 analytic 498
#> 2 non-analytic 163Now I can filter substances to the
analytical sanctions in sanction_type.
How can I identify the single vs. multiple substances?
Let’s take a look at four different sanctions in
example_sanction_type:
#> sport substance_reason
#> 1 swimming non-analytical: 3 whereabouts failures
#> 2 track & field cannabinoids
#> 3 triathlon androgenic anabolic steroid; cannabinoids
#> 4 track & field non-analytical: tampering, administration, and traffickingSubstance category
We can see two analytic and two non-analytic sanctions, and each one
has a single and multiple substance/reason. Fortunately, the sanctions
with multiple items are separated by either semicolons (;),
commas (,), or a conjunction (and), and can be
separated by a regular expression:
dplyr::mutate(example_sanction_type,
substance_cat = dplyr::case_when(
# identify the multiple_sr substances using a regular expression
stringr::str_detect(substance_reason, "; |, | and | & | / ") ~ 'multiple',
# negate the regular expression for the single substances
!stringr::str_detect(substance_reason, "; |, | and | & | / ") ~ 'single',
TRUE ~ NA_character_)) |>
dplyr::count(substance_cat, substance_reason) |>
tidyr::pivot_wider(names_from = substance_cat, values_from = n)
#> # A tibble: 4 × 3
#> substance_reason multiple single
#> <chr> <int> <int>
#> 1 androgenic anabolic steroid; cannabinoids 1 NA
#> 2 non-analytical: tampering, administration, and trafficking 1 NA
#> 3 cannabinoids NA 1
#> 4 non-analytical: 3 whereabouts failures NA 1The substance_cat identifier can be used to separate
sanctions with multiple substance/reasons from sanctions with a single
substance or reason.
substances <- substances |>
dplyr::mutate(substance_cat = dplyr::case_when(
stringr::str_detect(substance_reason, "; |, | and | & | / ") ~ 'multiple',
!stringr::str_detect(substance_reason, "; |, | and | & | / ") ~ 'single',
TRUE ~ NA_character_))
substances |>
dplyr::count(substance_cat, sort = TRUE)
#> substance_cat n
#> 1 single 478
#> 2 multiple 183Single analytic substances
First create a dataset that contains the sanctions with a
single substance listed. Store these in
single_analytic_substances.
single_analytic_substances <- substances |>
dplyr::filter(substance_cat == 'single' & sanction_type == "analytic")View the top ten single analytic substances:
single_analytic_substances |>
dplyr::count(substance_reason, sort = TRUE) |>
head(10)
#> substance_reason n
#> 1 androgenic anabolic steroid 49
#> 2 cannabinoids 38
#> 3 ostarine 37
#> 4 clomiphene 18
#> 5 methylhexaneamine 12
#> 6 stanozolol 11
#> 7 testosterone 10
#> 8 furosemide 9
#> 9 spironolactone 8
#> 10 dehydroepiandrosterone (dhea) 7Multiple analytic substances
Next create a dataset with the sanctions listing multiple
substances in substance_reason. Store these in
multiple_analytic_substances.
multiple_analytic_substances <- substances |>
dplyr::filter(substance_cat == 'multiple' & sanction_type == "analytic")View the top ten multiple analytic substances:
multiple_analytic_substances |>
dplyr::count(substance_reason, sort = TRUE) |>
head(10)
#> substance_reason n
#> 1 hydrochlorothiazide and chlorothiazide 4
#> 2 19-norandrosterone (19-na) and 19-noretiocholanolone 3
#> 3 clomiphene and its metabolite 3
#> 4 ostarine; lgd-4033 3
#> 5 androgenic anabolic steroid & 19-norandrosterone (19-na) 2
#> 6 androgenic anabolic steroid and modafinil 2
#> 7 benzoylecgonine and methylecgonine 2
#> 8 hydrochlorothiazide & chlorothiazide 2
#> 9 methylphenidate and its metabolite 2
#> 10 ostarine; lgd-4033; gw1516 2Tidying substances
Tidying the sanctions with multiple WADA banned substances (i.e., one substance per athlete per row) will result in certain athletes appearing in the dataset more than once. The regular expressions below cover a range of semicolons, tabs, and spaces to identify and separate each substance.
add_match_col()
I’ve written the
add_match_col()function, which creates a new'matched'column with the matched regular expression pattern (it’s likestringr::str_view(), but in adata.frame/tibble). I usedadd_match_col()while determining the correct pattern to match on (i.e., any substances listingmetabolites):
dplyr::mutate(example_sanction_type,
# add matched column
punct_match = add_match_col(
string = substance_reason,
pattern = "[[:punct:]]")) |>
dplyr::select(substance_reason, dplyr::last_col())
#> substance_reason punct_match
#> 1 non-analytical: 3 whereabouts failures -, :
#> 2 cannabinoids <NA>
#> 3 androgenic anabolic steroid; cannabinoids ;
#> 4 non-analytical: tampering, administration, and trafficking -, :, ,, ,The rows above are all matching correctly on the regular expression pattern.
Below are a series of regular expression to 1) match the substances
that list metabolites 2) differentiate the multiple
substances, and 3) trims the white space from the tidy
substance_reason column.
The code below tests this pattern on a sample from
multiple_analytic_substances before its passed to
tidyr::separate_rows():
dplyr::sample_n(multiple_analytic_substances, size = 10, replace = FALSE) |>
dplyr::mutate(
# replace plurals
substance_reason = stringr::str_replace_all(substance_reason,
"and its metabolite|and its metabolites|its metabolite",
"(metabolite)")) |>
tidyr::separate_rows(substance_reason,
sep = "; |;\t|\\t|, |;| and |and a |and | & | / ") |>
dplyr::mutate(substance_reason = trimws(substance_reason, "both")) |>
dplyr::select(athlete, substance_reason)
#> # A tibble: 26 × 2
#> athlete substance_reason
#> <chr> <chr>
#> 1 atkinson, annie hydrochlorothiazide
#> 2 atkinson, annie chlorothiazide
#> 3 atkinson, annie triamterene
#> 4 atkinson, annie 4-hydroxytriamterene
#> 5 cardoso, josé henrique 19-norandrosterone (19-na)
#> 6 cardoso, josé henrique 2a-methyl-5a-androstan-3a-ol-17-one
#> 7 cardoso, josé henrique epitrenbolone
#> 8 cardoso, josé henrique methasterone
#> 9 cardoso, josé henrique testosterone
#> 10 prince, david ostarine
#> # ℹ 16 more rowsAfter confirming the pattern is working, the output will be stored in
tidy_multiple_substances.
tidy_multiple_substances <- dplyr::mutate(multiple_analytic_substances,
# replace plurals
substance_reason = stringr::str_replace_all(substance_reason,
"and its metabolite|and its metabolites|its metabolite",
"(metabolite)")) |>
tidyr::separate_rows(substance_reason,
sep = "; |;\t|\\t|, |;| and |and a |and | & | / ") |>
dplyr::mutate(substance_reason = trimws(substance_reason, "both"))With both single and multiple substances in tidy format, they can be
combined together into a single tidy_substances
dataset.
tidy_substances <- rbind(single_analytic_substances, tidy_multiple_substances) The top 10 tidy substances are below:
tidy_substances |>
dplyr::count(substance_reason, sort = TRUE) |>
head(10)
#> substance_reason n
#> 1 androgenic anabolic steroid 65
#> 2 ostarine 60
#> 3 cannabinoids 41
#> 4 clomiphene 31
#> 5 gw1516 17
#> 6 hydrochlorothiazide 17
#> 7 stanozolol 16
#> 8 testosterone 16
#> 9 19-norandrosterone (19-na) 14
#> 10 methylhexaneamine 14Classifying substances
To identify the WADA
banned substances, I’ve written
classify_wada_substances(), a function that scans the
substance_reason column and identifies any substances found
on the WADA
list. See the classify_wada_substances() documentation
for more information.
WADA Classes
classify_wada_substances() creates a
substance_group variable with each of the WADA
classifications (stored in dopingdata::wada_classes):
dopingdata::wada_classes
#> Classification
#> 1 S1 ANABOLIC AGENTS
#> 2 S2 PEP HORMONES/G FACTORS/MIMETICS
#> 3 S3 BETA-2 AGONISTS
#> 4 S4 HORMONE AND METABOLIC MODULATORS
#> 5 S5 DIURETICS/MASKING AGENTS
#> 6 S6 STIMULANTS
#> 7 S7 NARCOTICS
#> 8 S8 CANNABINOIDS
#> 9 S9 GLUCOCORTICOIDS
#> 10 S0 UNAPPROVED SUBSTANCES
#> 11 M1 MANIPULATION OF BLOOD
#> 12 M2 CHEMICAL AND PHYSICAL MANIPULATION
#> 13 M3 GENE AND CELL DOPING
#> 14 P1 BETA-BLOCKERSdopingdata stores vectors with each substance group in
the WADA list (the S1 ANABOLIC AGENTS substances are
below):
head(dopingdata::s1_substances, 10)
#> [1] "3α-hydroxy-5α-androst-1-en-17-one"
#> [2] "androgenic anabolic steroid"
#> [3] "androgenic anabolic steroids"
#> [4] "anabolic agent"
#> [5] "anabolic agents"
#> [6] "anabolic steroid"
#> [7] "anabolic steroids"
#> [8] "androstenedione"
#> [9] "metabolites of androstenedione"
#> [10] "1-androstenediol (5α-androst-1-ene-3β,17β-diol)"The substance group vectors are passed to make_regex() to create a
regular expressions (s1_regex), which we can use to match
the substance_reason column on (see the example using
dopingdata::example_tidy_substances dataset):
s1_regex <- make_regex(x = dopingdata::s1_substances)
stringr::str_view(string = example_tidy_substances$substance_reason,
pattern = s1_regex, match = TRUE)
#> [10] │ <androgenic anabolic steroid>The output from classify_wada_substances() can be used
to answer questions like: what substance_group’s appear
the most?
tidy_substances <- classify_wada_substances(
usada_data = tidy_substances,
subs_column = "substance_reason") UNCLASSIFIED single substances
The following single substances are marked as
UNCLASSIFIED:
tidy_substances |>
dplyr::filter(
substance_cat == "single" &
substance_group == "UNCLASSIFIED" &
substance_reason != "") |>
dplyr::distinct(athlete, substance_reason)
#> athlete substance_reason
#> 1 rodriguez, yair 3 whereabouts failuresThe final unclassified substance is actually a result from a
miss-classified sanction type (for rodriguez, yair).
tidy_substances |>
dplyr::filter(athlete == "rodriguez, yair") |>
dplyr::select(athlete, substance_reason, substance_group, sanction_type)
#> athlete substance_reason substance_group sanction_type
#> 1 rodriguez, yair 3 whereabouts failures UNCLASSIFIED analyticFor this particular athlete, 1) the sanction_type should
be non-analytic, and 2) the substance_group
should be missing (NA_character_)
tidy_substances <- tidy_substances |>
dplyr::mutate(sanction_type = dplyr::case_when(
athlete == "rodriguez, yair" ~ "non-analytic",
TRUE ~ sanction_type
)) |>
dplyr::mutate(substance_group = dplyr::case_when(
athlete == "rodriguez, yair" ~ NA_character_,
TRUE ~ substance_group
))
tidy_substances |>
dplyr::filter(athlete == "rodriguez, yair") |>
dplyr::select(athlete, substance_reason, substance_group, sanction_type)
#> athlete substance_reason substance_group sanction_type
#> 1 rodriguez, yair 3 whereabouts failures <NA> non-analyticUNCLASSIFIED multiple substances
tidy_substances |>
dplyr::filter(
substance_cat == "multiple" &
substance_group == "UNCLASSIFIED" &
substance_reason != "") |>
dplyr::distinct(substance_reason)
#> substance_reason
#> 1 2a-methyl-5a-androstan-3a-ol-17-one
#> 2 d-methamphetamine
#> 3 arimistane
#> 4 torasemide
#> 5 possession
#> 6 use/attempted use
#> 7 evading sample collection
#> 8 igf-1
#> 9 human chorionic gonadotropin (hcg)
#> 10 aod-9064
#> 11 s-23
#> 12 intact human chorionic gonadtrophin (hcg)
#> 13 thiazide metabolite 4-amino-6-chloro-1,3-benzenedisulfonamide (acb)
#> 14 methylecgonine
#> 15 propylhexadrine
#> 16 androstatrienedione
#> 17 androst-(2,3)-en-17-one
#> 18 methylclostebol
#> 19 promagnon
#> 20 4-hydroxytriamterene
#> 21 non-anatlyical: administration
#> 22 trafficking
#> 23 human chorionic gonadotrophin (hcg)Re-classifying substances
Any substances that are not classified from the existing WADA list
can be added with reclass_substance() (these substances can
also added to their relative vector and regular expression in
data-raw/)
reclass_substance()
reclass_substance() takes a df,
substance, and value:
# 2a-methyl-5a-androstan-3a-ol-17-one ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=arimistane&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "^2a-methyl-5a-androstan-3a-ol-17-one$",
value = "S1 ANABOLIC AGENTS")
# d-methamphetamine ----
# https://www.usada.org/sanction/hillary-tran-accepts-doping-sanction/
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "d-methamphetamine",
value = "S6 STIMULANTS")
# arimistane ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=arimistane&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "arimistane",
value = "S4 HORMONE AND METABOLIC MODULATORS")
# torasemide ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=torasemide&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "torasemide",
value = "S5 DIURETICS/MASKING AGENTS")
# igf-1 ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=igf-1&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "igf-1",
value = "S2 PEP HORMONES/G FACTORS/MIMETICS")
# human chorionic gonadotropin (hcg) ----
# https://www.usada.org/athletes/antidoping101/athlete-guide-anti-doping/
# https://www.usada.org/spirit-of-sport/education/wellness-and-anti-aging-clinics/
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "^human chorionic gonadotropin \\(hcg\\)$",
value = "S2 PEP HORMONES/G FACTORS/MIMETICS")
# intact human chorionic gonadtrophin (hcg) ----
# https://www.usada.org/athletes/antidoping101/athlete-guide-anti-doping/
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "^intact human chorionic gonadtrophin \\(hcg\\)$",
value = "S2 PEP HORMONES/G FACTORS/MIMETICS")
# human chorionic gonadotrophin (hcg) ----
# https://www.usada.org/athletes/antidoping101/athlete-guide-anti-doping/
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "human chorionic gonadotrophin \\(hcg\\)",
value = "S2 PEP HORMONES/G FACTORS/MIMETICS")
# aod-9064 ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=aod-9064&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "aod-9064",
value = "S2 PEP HORMONES/G FACTORS/MIMETICS")
# s-23 ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=s-23&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "s-23",
value = "S1 ANABOLIC AGENTS")
# methenolone ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=methenolone&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "methenolone",
value = "S1 ANABOLIC AGENTS")
# thiazide metabolite 4-amino-6-chloro-1,3-benzenedisulfonamide (acb) ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=thiazide&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "^thiazide metabolite 4-amino-6-chloro-1,3-benzenedisulfonamide \\(acb\\)$",
value = "S5 DIURETICS/MASKING AGENTS")
# methylecgonine ----
# https://www.usada.org/sanction/mike-alexandrov-accepts-doping-sanction/
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "methylecgonine",
value = "S6 STIMULANTS")
# propylhexadrine ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=propylhexadrine&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "propylhexadrine",
value = "S6 STIMULANTS")
# androstatrienedione ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=androstatrienedione&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "androstatrienedione",
value = "S4 HORMONE AND METABOLIC MODULATORS")
# androst-(2,3)-en-17-one ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=androst&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "androst-\\(2,3\\)-en-17-one",
value = "S1 ANABOLIC AGENTS")
# methylclostebol ----
# https://www.wada-ama.org/en/prohibited-list?page=0&q=methylclostebol&all=1#search-anchor
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "methylclostebol",
value = "S1 ANABOLIC AGENTS")
# promagnon ----
# https://www.usada.org/sanction/u-s-judo-athlete-ohara-accepts-sanction-rule-violation/
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "promagnon",
value = "S1 ANABOLIC AGENTS")
# 4-hydroxytriamterene ----
# 4-Hydroxy Triamterene is a diuretic agent and metabolite of Triamterene
# https://www.scbt.com/p/4-hydroxy-triamterene-1226-52-4
tidy_substances <- reclass_substance(
df = tidy_substances,
substance = "4-hydroxytriamterene",
value = "S5 DIURETICS/MASKING AGENTS")After reclassifying the substances above, remaining
UNCLASSIFIED/multiple sanctions are all
non-analytic:
tidy_substances |>
dplyr::filter(
substance_cat == "multiple" &
substance_group == "UNCLASSIFIED" &
substance_reason != "") |>
dplyr::distinct(substance_reason)
#> substance_reason
#> 1 possession
#> 2 use/attempted use
#> 3 evading sample collection
#> 4 non-anatlyical: administration
#> 5 traffickingChanging the sanction_type classification to
non-analytic requires missing (NA_character_) values for
substance_group:
# Change sanction_type to non-analytic
tidy_substances <- tidy_substances |>
dplyr::mutate(
sanction_type = dplyr::case_when(
substance_group == "UNCLASSIFIED" & substance_reason == "possession" ~ "non-analytic",
substance_group == "UNCLASSIFIED" & substance_reason == "use/attempted use" ~ "non-analytic",
substance_group == "UNCLASSIFIED" & substance_reason == "evading sample collection" ~ "non-analytic",
substance_group == "UNCLASSIFIED" & substance_reason == "non-anatlyical: administration" ~ "non-analytic",
substance_group == "UNCLASSIFIED" & substance_reason == "trafficking" ~ "non-analytic",
TRUE ~ sanction_type
)
)
# Change substance_group to NA_character_
tidy_substances <- tidy_substances |>
dplyr::mutate(
substance_group = dplyr::case_when(
sanction_type == "non-analytic" ~ NA_character_,
TRUE ~ substance_group)
) Remove the empty substance_reason values:
tidy_substances <- dplyr::filter(tidy_substances, substance_reason != "")Tidy substances
Now all the substances have been properly classified as Adverse Analytical Findings.
tidy_substances |>
dplyr::count(sanction_type, substance_group) |>
tidyr::pivot_wider(names_from = sanction_type, values_from = n)
#> # A tibble: 14 × 3
#> substance_group analytic `non-analytic`
#> <chr> <int> <int>
#> 1 M1 MANIPULATION OF BLOOD 3 NA
#> 2 M2 CHEMICAL AND PHYSICAL MANIPULATION 1 NA
#> 3 P1 BETA-BLOCKERS 2 NA
#> 4 S0 UNAPPROVED SUBSTANCES 1 NA
#> 5 S1 ANABOLIC AGENTS 327 NA
#> 6 S2 PEP HORMONES/G FACTORS/MIMETICS 42 NA
#> 7 S3 BETA-2 AGONISTS 12 NA
#> 8 S4 HORMONE AND METABOLIC MODULATORS 80 NA
#> 9 S5 DIURETICS/MASKING AGENTS 67 NA
#> 10 S6 STIMULANTS 95 NA
#> 11 S7 NARCOTICS 3 NA
#> 12 S8 CANNABINOIDS 41 NA
#> 13 S9 GLUCOCORTICOIDS 8 NA
#> 14 NA NA 6And the non-analytic sanctions are truly Non-Analytical
Anti-doping Rule Violations.
dplyr::filter(tidy_substances, sanction_type == "non-analytic") |>
dplyr::count(substance_reason, substance_cat) |>
tidyr::pivot_wider(names_from = substance_cat, values_from = n)
#> # A tibble: 6 × 3
#> substance_reason single multiple
#> <chr> <int> <int>
#> 1 3 whereabouts failures 1 NA
#> 2 evading sample collection NA 1
#> 3 non-anatlyical: administration NA 1
#> 4 possession NA 1
#> 5 trafficking NA 1
#> 6 use/attempted use NA 1