Tour De France
Jim Gruman
April 7, 2020
Last updated: 2021-09-08
Checks: 7 0
Knit directory: myTidyTuesday/
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Visualising Tour De France Data
Inspired by the work of Alastair Rushworth at Visualising Tour De France Data In R
and by the R4DS learning community #TidyTuesday
Importing Libraries and Datasets
suppressPackageStartupMessages({
library(tidyverse)
library(paletteer)
library(ggtext)
library(rvest)
library(lubridate)
library(patchwork)
})
source(here::here("code","_common.R"),
verbose = FALSE,
local = knitr::knit_global())Registered S3 method overwritten by 'tune':
method from
required_pkgs.model_spec parsnipggplot2::theme_set(theme_jim(base_size = 12))
tuesdata <- tidytuesdayR::tt_load('2020-04-07')--- Compiling #TidyTuesday Information for 2020-04-07 ------- There are 3 files available ------ Starting Download ---
Downloading file 1 of 3: `stage_data.csv`
Downloading file 2 of 3: `tdf_stages.csv`
Downloading file 3 of 3: `tdf_winners.csv`--- Download complete ---tdf_winners <- tuesdata$tdf_winners %>%
mutate(year = ymd(year(start_date),truncated = 2L),
speed = distance / time_overall)
stage_data <- tuesdata$stage_data
tdf_stages <- tuesdata$tdf_stages %>%
janitor::clean_names() %>%
mutate(year = year(date))
showtext::showtext_auto()cf_io <- read_html("https://www.countryflags.io/")
country_ids <- cf_io %>%
html_nodes(".item_country") %>%
lapply(function(el) {
key <- el %>%
html_text() %>%
str_split("\\n") %>%
`[[`(1) %>%
trimws() %>%
{
.[nchar(.) > 0]
}
data.frame(
code = key[1],
nationality = key[2]
)
}) %>%
bind_rows()
get_country_flag <- function(x) {
urls <- sapply(x, function(x) {
code <- country_ids$code[which(country_ids$nationality == x)]
file.path("https://www.countryflags.io", code, "flat/64.png")
})
paste0("<img src='", urls, "' width='30' />")
}Nationalities of the Winners
tdf_nations <- tdf_winners %>%
mutate(
nationality = stringr::str_squish(nationality),
nationality = case_when(
nationality == "Great Britain" ~ "United Kingdom",
TRUE ~ nationality
)
) %>%
count(nationality, sort = TRUE) %>%
mutate(nationality = fct_reorder(nationality, n)) %>%
top_n(8, n)
flag_labels <- get_country_flag(tdf_nations$nationality)
pal <- RColorBrewer::brewer.pal("Set1", n = 8)
nations <- tdf_nations %>%
ggplot(aes(n, nationality)) +
geom_bar(fill = 2, stat = "identity") +
scale_y_discrete(name = NULL, labels = flag_labels) +
scale_fill_discrete(guide = FALSE) +
theme_jim(base_size = 10) +
theme(
axis.text.y = ggtext::element_markdown(color = "black", size = 11),
axis.title.y = element_blank(),
plot.title.position = "plot",
panel.grid.major.y = element_blank()
) +
expand_limits(x = c(0, 45)) +
labs(
title = "National Wins",
caption = paste0("@Jim_Gruman | #TidyTuesday | ", Sys.Date())
)Physical characteristics and race characteristics by decade
by_decade <- tdf_winners %>%
group_by(decade = 10 * (year(year) %/% 10)) %>%
summarize(
winner_age = mean(age, na.rm = TRUE),
winner_height = mean(height, na.rm = TRUE),
winner_weight = mean(weight, na.rm = TRUE),
winner_margin = mean(time_margin, na.rm = TRUE),
winner_time = mean(time_overall, na.rm = TRUE),
winner_speed = mean(speed, na.rm = TRUE)
)p1 <- by_decade %>%
ggplot(aes(decade, winner_age)) +
geom_line(color = 4, size = 3, show.legend = FALSE) +
expand_limits(y = 0) +
labs(
y = "",
title = "Average Age of Tour de France Winners By Decade",
subtitle = "source: Alastair Rushworths R Data Package tdf and Kaggle",
caption = paste0("@Jim_Gruman | #TidyTuesday | ", Sys.Date())
)
p1
p2 <- by_decade %>%
ggplot(aes(decade, winner_margin)) +
geom_line(color = 5, size = 3) +
expand_limits(y = 0) +
labs(
y = "Hours",
title = "Margin of Victory of Tour de France Winners By Decade",
subtitle = "source: Alastair Rushworths R Data Package tdf and Kaggle",
caption = paste0("@Jim_Gruman | #TidyTuesday | ", Sys.Date())
)
p2
p3 <- by_decade %>%
ggplot(aes(decade, winner_speed)) +
geom_line(color = 6, size = 3) +
expand_limits(y = 0) +
labs(
y = "Hours",
title = "Average Speed of Tour de France Winners By Decade",
subtitle = "source: Alastair Rushworths R Data Package tdf and Kaggle",
caption = paste0("@Jim_Gruman | #TidyTuesday | ", Sys.Date())
)
p3
Life Expectancy of TDF winners with survival analysis
extrapolated for the riders 38 still alive (not yet dead)
library(survival)
surv_model <- tdf_winners %>%
distinct(winner_name, .keep_all = TRUE) %>%
transmute(
birth_year = year(born),
death_year = year(died),
dead = as.integer(!is.na(death_year))
) %>%
mutate(age_at_death = coalesce(death_year, 2020) - birth_year) %>%
survfit(Surv(age_at_death, dead) ~ 1, data = .)
surv_model %>%
plot(main = "K-M Survival of Tour De France Winners")
library(broom)
glance(surv_model) %>% knitr::kable()| records | n.max | n.start | events | rmean | rmean.std.error | median | conf.low | conf.high | nobs |
|---|---|---|---|---|---|---|---|---|---|
| 63 | 63 | 63 | 38 | 69.62491 | 2.685674 | 77 | 71 | 82 | 63 |
Of the 63 Tour De France winners, 38 are still alive. After accounting for survival expectations for the living, the median life expectancy of a Tour de France winner is estimated as 77 years old.
Stage data
p <- stage_data %>%
group_by(decade = 10 * (year %/% 10)) %>%
distinct(rider, edition, age) %>%
ggplot(aes(decade, age)) +
geom_jitter(color = 7, size = 0.5, alpha = 0.1)
p4 <- p +
geom_line(
data = by_decade, aes(decade, winner_age),
color = 8, size = 3, show.legend = FALSE
) +
expand_limits(y = 0) +
labs(
y = "", x = "",
title = "Average Age of Tour de France Winners By Decade",
subtitle = "source: Alastair Rushworths R Data Package tdf and Kaggle",
caption = paste0("@Jim_Gruman | #TidyTuesday | ", Sys.Date())
)
p4
stages_joined <- stage_data %>%
tidyr::extract(stage_results_id, "stage", "stage-(.*)") %>%
mutate(
stage = if_else(year %in% 1967:1968 & stage == 0, "1a", stage),
stage = if_else(year %in% 1967:1968 & stage == 1, "1b", stage),
stage = if_else(year %in% 1969:2012 & stage == 0, "P", stage)
) %>%
filter(year < 2018) %>%
left_join(tdf_stages, by = c("year", "stage")) %>%
select(-winner, bib_number, winner_country) %>%
mutate(rank = as.integer(rank)) %>%
group_by(year, stage) %>%
mutate(finishers = sum(!is.na(rank))) %>%
ungroup() %>%
mutate(percentile = 1 - rank / finishers)p5 <- stages_joined %>%
count(year, stage) %>%
ggplot(aes(n)) +
geom_histogram(fill = 11) +
labs(title = "Number of Stages Joined Each Year")
p5
It appears that some racers are eliminated or drop out as stages are completed
total_points <- stages_joined %>%
group_by(year, rider) %>%
summarize(
total_points = sum(points, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(points_rank = percent_rank(total_points))Does the winner of the first stage predict their final point ranking?
p6 <- stages_joined %>%
filter(stage == "1") %>%
inner_join(total_points, by = c("year", "rider")) %>%
select(year, rider,
percentile_first_stage = percentile,
points_rank
) %>%
filter(!is.na(percentile_first_stage)) %>%
mutate(first_stage_bin = cut(percentile_first_stage, seq(0, 1, 0.1))) %>%
filter(!is.na(first_stage_bin)) %>%
ggplot(aes(first_stage_bin, points_rank)) +
geom_boxplot(fill = 12) +
scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
scale_y_continuous(labels = scales::percent) +
labs(
x = "Decile Perforance in the First Stage",
y = "Overall Points Percentile",
title = "Relationship of TDF First Stage Finish w/ Overall Finish",
subtitle = "source: Alastair Rushworths R Data Package tdf and Kaggle",
caption = paste0("@Jim_Gruman | #TidyTuesday | ", Sys.Date())
)
p6
Lets explore the names and life durations of the Tour de France winners
winners <- tdf_winners %>%
select(edition, winner_name, born, died, nickname, nationality, start_date, year) %>%
# factor and reorder the winners by birth date
mutate(winner_name = fct_reorder(winner_name, desc(born))) %>%
# compute a life duration in numeric years
mutate(life_duration = as.numeric(as.duration(ymd(born) %--% ymd(died)), "years")) %>%
filter(!is.na(life_duration))
pal <- RColorBrewer::brewer.pal("Set1", n = 3)
life_wins <- winners %>%
ggplot() +
geom_linerange(aes(
xmin = born,
xmax = died,
y = winner_name,
color = life_duration
),
lwd = 1.1
) +
labs(
x = "",
y = "Year"
) +
geom_point(aes(
y = winner_name,
x = year
),
shape = 19,
size = 2,
color = "grey"
) +
scale_shape_identity("",
labels = "Won the\nTour de France",
breaks = c(19),
guide = "legend"
) +
scale_colour_gradient2("Lifetime\n(years)",
low = pal[1], mid = pal[2],
high = pal[3], midpoint = 60
) +
labs(
title = "Lifespans of Riders",
subtitle = "source: Alastair Rushworths R Data Package tdf and Kaggle"
) +
guides(
colour = guide_legend(order = 1),
shape = guide_legend(order = 2)
) +
theme(
legend.position = "top",
legend.background = element_rect(color = "white")
) +
theme_jim(base_size = 10)(life_wins | nations) +
plot_annotation("Tour de France Winners") +
theme(aspect.ratio = 3)
The original tweet submission:
tweetrmd::include_tweet("https://twitter.com/jim_gruman/status/1248662506102497281")#TidyTuesday: Tour de France #rstats #cycling
— Jim Gruman📚🚵♂️⚙ (@jim_gruman) April 10, 2020
Code: https://t.co/PjmnaAGBnE pic.twitter.com/qdwEp9siDL
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] broom_0.7.9 survival_3.2-11 patchwork_1.1.1 lubridate_1.7.10
[5] rvest_1.0.1 ggtext_0.1.1 paletteer_1.4.0 forcats_0.5.1
[9] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.1
[13] tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5 tidyverse_1.3.1
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 systemfonts_1.0.2
[4] workflows_0.2.3 plyr_1.8.6 selectr_0.4-2
[7] tidytuesdayR_1.0.1 splines_4.1.1 listenv_0.8.0
[10] usethis_2.0.1 digest_0.6.27 foreach_1.5.1
[13] htmltools_0.5.2 yardstick_0.0.8 viridis_0.6.1
[16] parsnip_0.1.7.900 fansi_0.5.0 magrittr_2.0.1
[19] tune_0.1.6 tzdb_0.1.2 recipes_0.1.16
[22] globals_0.14.0 modelr_0.1.8 gower_0.2.2
[25] extrafont_0.17 R.utils_2.10.1 vroom_1.5.4
[28] sysfonts_0.8.5 extrafontdb_1.0 hardhat_0.1.6
[31] rsample_0.1.0 dials_0.0.9.9000 colorspace_2.0-2
[34] textshaping_0.3.5 haven_2.4.3 xfun_0.25
[37] RCurl_1.98-1.4 crayon_1.4.1 jsonlite_1.7.2
[40] iterators_1.0.13 glue_1.4.2 gtable_0.3.0
[43] ipred_0.9-11 R.cache_0.15.0 tweetrmd_0.0.9
[46] Rttf2pt1_1.3.9 future.apply_1.8.1 scales_1.1.1
[49] infer_1.0.0 DBI_1.1.1 showtextdb_3.0
[52] Rcpp_1.0.7 viridisLite_0.4.0 gridtext_0.1.4
[55] bit_4.0.4 GPfit_1.0-8 lava_1.6.10
[58] prodlim_2019.11.13 httr_1.4.2 RColorBrewer_1.1-2
[61] ellipsis_0.3.2 farver_2.1.0 R.methodsS3_1.8.1
[64] pkgconfig_2.0.3 nnet_7.3-16 sass_0.4.0
[67] dbplyr_2.1.1 janitor_2.1.0 utf8_1.2.2
[70] here_1.0.1 labeling_0.4.2 tidyselect_1.1.1
[73] rlang_0.4.11 DiceDesign_1.9 later_1.3.0
[76] munsell_0.5.0 cellranger_1.1.0 tools_4.1.1
[79] cachem_1.0.6 cli_3.0.1 generics_0.1.0
[82] evaluate_0.14 fastmap_1.1.0 ragg_1.1.3
[85] yaml_2.2.1 rematch2_2.1.2 knitr_1.33
[88] bit64_4.0.5 fs_1.5.0 showtext_0.9-4
[91] future_1.22.1 whisker_0.4 R.oo_1.24.0
[94] xml2_1.3.2 compiler_4.1.1 rstudioapi_0.13
[97] png_0.1-7 curl_4.3.2 reprex_2.0.1
[100] lhs_1.1.1 bslib_0.3.0 stringi_1.7.4
[103] highr_0.9 gdtools_0.2.3 hrbrthemes_0.8.0
[106] lattice_0.20-44 Matrix_1.3-4 markdown_1.1
[109] styler_1.5.1 conflicted_1.0.4 vctrs_0.3.8
[112] tidymodels_0.1.3 pillar_1.6.2 lifecycle_1.0.0
[115] furrr_0.2.3 jquerylib_0.1.4 bitops_1.0-7
[118] httpuv_1.6.2 R6_2.5.1 promises_1.2.0.1
[121] gridExtra_2.3 parallelly_1.27.0 codetools_0.2-18
[124] MASS_7.3-54 assertthat_0.2.1 rprojroot_2.0.2
[127] withr_2.4.2 parallel_4.1.1 hms_1.1.0
[130] grid_4.1.1 rpart_4.1-15 timeDate_3043.102
[133] class_7.3-19 snakecase_0.11.0 rmarkdown_2.10
[136] git2r_0.28.0 pROC_1.18.0