# Tour de France

The Tour de France is an annual men’s multiple stage bicycle race primarily held in France, while also occasionally passing through nearby countries. Like the other Grand Tours (the Giro d’Italia and the Vuelta a España), it consists of 21 day-long stages over the course of 23 days. It has been described as “the world’s most prestigious and most difficult bicycle race”.

The data this week comes from Alastair Rushworth’s Data Package tdf and Kaggle.

Alastair has a very nice walkthrough of his data package at his blog!

I’ve added the Kaggle data which goes through 2017 for some additional stage-specific data not captured in his dataset. Please note that for the most part these datasets COULD be joined by year/edition.

Some other stats and records can be found on Wikipedia.

Get the data here

# Get the Data

tdf_winners <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-04-07/tdf_winners.csv')

# Or read in with tidytuesdayR package (https://github.com/dslc-io/tidytuesdayR)
# PLEASE NOTE TO USE 2020 DATA YOU NEED TO USE tidytuesdayR version ? from GitHub

# Either ISO-8601 date or year/week works!

# Install via pak::pak("dslc-io/tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2020-04-07')
tuesdata <- tidytuesdayR::tt_load(2020, week = 15)


tdf_winners <- tuesdata$tdf_winners

Data Dictionary

`tdf_winners.csv`

variable	class	description
edition	integer	Edition of the Tour de France
start_date	double	Start date of the Tour
winner_name	character	Winner’s name
winner_team	character	Winner’s team (NA if not on a team)
distance	double	Distance traveled in KM across the entire race
time_overall	double	Time in hours taken by the winner to complete the race
time_margin	double	Difference in finishing time between the race winner and the runner up
stage_wins	double	Number of stage wins (note that it is possible to win the GC without winning any stages at all)
stages_led	double	Stages led is the number of stages spent as the race leader (wearing the yellow jersey) by the eventual winner
height	double	Height in meters
weight	double	Weight in kg
age	integer	Age as winner
born	double	year born
died	double	Year died
full_name	character	Full name
nickname	character	Nickname
birth_town	character	Birth town
birth_country	character	Birth country
nationality	character	Nationality

`stage_data.csv`

variable	class	description
edition	integer	Race edition
year	double	Year of race
stage_results_id	character	Stage ID
rank	character	Rank of racer for stage
time	double	Time of racer
rider	character	Rider name
age	integer	Age of racer
team	character	Team (NA if not on team)
points	integer	Points for the stage
elapsed	double	Time elapsed stored as `lubridate::period`
bib_number	integer	Bib number

`tdf_stages.csv`

variable	class	description
Stage	character	Stage Number
Date	double	Date of stage
Distance	double	Distance in KM
Origin	character	Origin city
Destination	character	Destination city
Type	character	Stage Type
Winner	character	Winner of the stage
Winner_Country	character	Winner’s nationality

Cleaning Script

library(tidyverse)
library(tdf) # install at: https://github.com/alastairrushworth/tdf

winners <- tdf::editions %>% 
  select(-stage_results)

all_years <- tdf::editions %>% 
  unnest_longer(stage_results) %>% 
  mutate(stage_results = map(stage_results, ~ mutate(.x, rank = as.character(rank)))) %>% 
  unnest_longer(stage_results) 

stage_all <- all_years %>% 
  select(stage_results) %>% 
  flatten_df()

combo_df <- bind_cols(all_years, stage_all) %>% 
  select(-stage_results)

stage_clean <- combo_df %>% 
  select(edition, start_date,stage_results_id:last_col()) %>% 
  mutate(year = lubridate::year(start_date)) %>% 
  rename(age = age...25) %>% 
  select(edition, year, everything(), -start_date)

winners %>% 
  write_csv(here::here("2020", "2020-04-07", "tdf_winners.csv"))

stage_clean %>% 
  write_csv(here::here("2020", "2020-04-07", "stage_data.csv"))