Visualizing counts

library(dopingdata)

if ("pak" %nin% loadedNamespaces()) {
  install.packages("pak", quiet = TRUE)
}
pak::pak("Ryo-N7/tvthemes")
pak::pak("liamgilbey/ggwaffle")
pak::pak("hrbrmstr/hrbrthemes")
pkgs <- c("ggplot2", "extrafont", "dplyr", "stringr", "tidyr",
          "forcats", "ggthemes", "purrr")
pak::pak(pkg = pkgs)

library(ggplot2)
library(extrafont)
loadfonts(quiet = TRUE)
library(tvthemes)
library(ggthemes)
library(hrbrthemes)
library(ggwaffle)
library(dplyr)
library(stringr)
library(tidyr)
library(forcats)

pth <- system.file("extdata", "demo", package = "dopingdata")
get_recent_file(pth, regex = 'substances', ext = '.csv')

File last changed: 2023-12-21 20:33:32.064979
File name: 2023-12-21-tidy_substances.csv
✔ import code pasted to clipboard!

tidy_substances <- read.delim(file = '/Users/mjfrigaard/projects/pkgs/dopingdata/inst/extdata/demo/2023-12-21-tidy_substances.csv', sep = ',')

Adverse Analytical Findings

tidy_substances |>
  dplyr::filter(!is.na(substance_group)) |>
  dplyr::mutate(substance_group = factor(substance_group)) |>
  dplyr::count(substance_group, name = "count") |>
  ggplot2::ggplot(ggplot2::aes(
    x = count,
    y = forcats::fct_reorder(substance_group, count)
  )) +
  ggplot2::geom_col() + 
  ggplot2::labs(
    title = "Total Adverse Analytical Findings",
    subtitle = "What is the most common banned substance?",
    y = "WADA Classification", x = "Counts"
  )

Sports

tidy_substances |>
  dplyr::filter(!is.na(sport)) |>
  dplyr::count(sport, name = "count", sort = TRUE) |>
  head(10) |> 
  ggplot2::ggplot(ggplot2::aes(
    x = count,
    y = forcats::fct_reorder(as.factor(sport), count)
  )) +
  ggplot2::geom_col() + 
  ggplot2::labs(
    title = "Top Ten Sports with Sanctions",
    subtitle = "What sports have the most adverse analytical findings?",
    y = "Sport", x = "Number of Sanctions"
  ) + 
  ggthemes::theme_fivethirtyeight()

Adverse Analytical Findings & Sports

top4_sports <- tidy_substances |>
  dplyr::filter(!is.na(sport)) |>
  dplyr::count(sport, name = "count", sort = TRUE) |>
  head(4) |> 
  dplyr::select(sport) |> 
  purrr::as_vector() |> 
  base::unname()
heatmap_substances <- tidy_substances |> 
  dplyr::filter(!is.na(substance_group) & sport %in% top4_sports) |> 
  dplyr::mutate(sport = dplyr::case_when(
    sport == "mixed martial arts" ~ "MMA",
    sport == "track & field" ~ "Track/Field",
    sport == "cycling" ~ "Cycling",
    sport == "weightlifting" ~ "WL",
    TRUE ~ sport
  )) |> 
  dplyr::mutate(substance_group = factor(substance_group)) |> 
  dplyr::group_by(sport, substance_group) |>
  dplyr::summarise(occurrence = n()) |>
  dplyr::ungroup()

ggplot2::ggplot(data = heatmap_substances, 
       ggplot2::aes(y = substance_group,
           x = sport,
           fill = occurrence)) + 
       ggplot2::geom_raster(hjust = 0.5, vjust = 0.5) + 
  ggplot2::theme(legend.position = "left") + 
  ggplot2::labs(
    title = "Doping Data",
    subtitle = "Top four sports and their most \ncommonly banned substances",
    caption = "MMA = mixed martial arts; WL = weightlifting",
    x = "Sport", y = "", 
    fill = "Sanctions") + 
  hrbrthemes::theme_ipsum()

top3_sports <- tidy_substances |>
  dplyr::filter(!is.na(sport)) |>
  dplyr::count(sport, name = "count", sort = TRUE) |>
  head(3) |> 
  dplyr::select(sport) |> 
  purrr::as_vector() |> 
  base::unname()
tidy_substances |> 
  dplyr::filter(sport %in% top3_sports & 
      substance_group %in% 
      c("S1 ANABOLIC AGENTS", 
        "S6 STIMULANTS", 
        "S4 HORMONE AND METABOLIC MODULATORS", 
        "S5 DIURETICS/MASKING AGENTS", 
        "S2 PEP HORMONES/G FACTORS/MIMETICS", 
        "S8 CANNABINOIDS", 
        "S3 BETA-2 AGONISTS"
)) |> 
  dplyr::mutate(substance_group = factor(substance_group)) |>
  dplyr::count(substance_group, sport, name = "count") |>
  ggplot2::ggplot(ggplot2::aes(
    x = count,
    y = forcats::fct_reorder(substance_group, count), 
    group = sport
  )) +
  ggplot2::geom_col(aes(fill = substance_group), width = 0.45) + 
  ggplot2::facet_wrap(~ sport, scales = 'free_x', nrow = 1) +
  ggplot2::labs(
    title = "Adverse Analytical Findings",
    subtitle = "Most Common Substances in Top Three Sports",
    y = "WADA Classification", 
    x = "Total Sanctions"
  ) + 
  tvthemes::scale_fill_bigHero6(reverse = TRUE) +
  ggthemes::theme_clean() +
  ggplot2::theme(legend.position = 'none')

Substances per Sport

waffle_weightlifting <- tidy_substances |> 
  dplyr::filter(sport == "weightlifting") |>     
  ggwaffle::waffle_iron(aes_d(group = substance_group))

ggplot2::ggplot(data = waffle_weightlifting, 
       ggplot2::aes(x = x, 
           y = y, 
           fill = group)) + 
  ggwaffle::geom_waffle() +
  tvthemes::scale_fill_stevenUniverse(reverse = FALSE) +
  ggplot2::theme(legend.position = "right") +
  ggwaffle::theme_waffle() + 
  labs(title = "Weightlifting",
    subtitle = "Common WADA banned substances",
    x = "", y = "")

waffle_mma <- tidy_substances |> 
  dplyr::filter(sport == "mixed martial arts") |>     
  ggwaffle::waffle_iron(aes_d(group = substance_group))
ggplot2::ggplot(data = waffle_mma, 
       ggplot2::aes(x = x, 
           y = y, 
           fill = group)) + 
  ggwaffle::geom_waffle() +
  ggplot2::theme(legend.position = "right") +
  ggwaffle::theme_waffle() + 
  ggplot2::scale_fill_discrete() +
  labs(title = "Mixed martial arts",
    subtitle = "Most common WADA banned substances",
    x = "", y = "")

waffle_cycling <- tidy_substances |> 
  dplyr::filter(sport == "cycling") |>     
  ggwaffle::waffle_iron(aes_d(group = substance_group))

ggplot2::ggplot(data = waffle_cycling, 
       ggplot2::aes(x = x, 
           y = y, 
           fill = group)) + 
  ggwaffle::geom_waffle() +
  ggplot2::theme(legend.position = "right") +
  ggwaffle::theme_waffle() + 
  labs(title = "Cycling",
    subtitle = "Most common WADA banned substances",
    x = "", y = "")