2016 Olympics in Rio de Janeiro

library(tidyverse)

#data wrangle
  if (! "tidyverse" %in% installed.packages()) install.packages("tidyverse")
  if (! "skimr" %in% installed.packages()) install.packages("skimr")
  if (! "janitor" %in% installed.packages()) install.packages("janitor")
  if (! "scales" %in% installed.packages()) install.packages("scales")
#communication
  if (! "DT" %in% installed.packages()) install.packages("DT")
  if (! "kableExtra" %in% installed.packages()) install.packages("kableExtra")

Intro

Dados extraídos do kaggle https://www.kaggle.com/rio2016/olympic-games?select=athletes.csv

This dataset consists of the official statistics on the 11,538 athletes and 306 events at the 2016 Olympic Games in Rio de Janeiro. The athletes file includes the name, nationality (as a three letter IOC country code), gender, age (as date of birth), height in meters, weight in kilograms, sport, and quantity of gold, silver, and/or bronze medals won for every Olympic athlete at Rio. The events file lists the name, sport, discipline (if available), gender of competitors, and venue(s) for every Olympic event at Rio 2016.

CREDITS: Source Data: Rio 2016 website ; Data Files: GitHub user flother

• alternativy

https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results

This is a historical dataset on the modern Olympic Games, including all the Games from Athens 1896 to Rio 2016. I scraped this data from www.sports-reference.com in May 2018. The R code I used to scrape and wrangle the data is on GitHub. I recommend checking my kernel before starting your own analysis.

Note that the Winter and Summer Games were held in the same year up until 1992. After that, they staggered them such that Winter Games occur on a four year cycle starting with 1994, then Summer in 1996, then Winter in 1998, and so on. A common mistake people make when analyzing this data is to assume that the Summer and Winter Games have always been staggered.

Content

The file contains 271116 rows and 15 columns. Each row corresponds to an individual athlete competing in an individual Olympic event (athlete-events). The columns are:

ID - Unique number for each athlete Name - Athlete’s name Sex - M or F Age - Integer Height - In centimeters Weight - In kilograms Team - Team name NOC - National Olympic Committee 3-letter code Games - Year and season Year - Integer Season - Summer or Winter City - Host city Sport - Sport Event - Event Medal - Gold, Silver, Bronze, or NA

Read - separate databases

Athletes

athletes_raw <- readr::read_csv("data/athletes.csv") %>% 
  dplyr::glimpse()

## Rows: 11,538
## Columns: 11
## $ id          <dbl> 736041664, 532037425, 435962603, 521041435, 33922579, 1730…
## $ name        <chr> "A Jesus Garcia", "A Lam Shin", "Aaron Brown", "Aaron Cook…
## $ nationality <chr> "ESP", "KOR", "CAN", "MDA", "NZL", "AUS", "USA", "AUS", "E…
## $ sex         <chr> "male", "female", "male", "male", "male", "male", "male", …
## $ dob         <chr> "10/17/69", "9/23/86", "5/27/92", "1/2/91", "11/26/90", "1…
## $ height      <dbl> 1.72, 1.68, 1.98, 1.83, 1.81, 1.80, 2.05, 1.93, 1.80, 1.65…
## $ weight      <dbl> 64, 56, 79, 80, 71, 67, 98, 100, 62, 54, 63, 66, NA, 49, 6…
## $ sport       <chr> "athletics", "fencing", "athletics", "taekwondo", "cycling…
## $ gold        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ silver      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ bronze      <dbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Countries

countriess_raw <- readr::read_csv("data/countries.csv") %>% 
  dplyr::glimpse()

## Rows: 201
## Columns: 4
## $ country        <chr> "Afghanistan", "Albania", "Algeria", "American Samoa*",…
## $ code           <chr> "AFG", "ALB", "ALG", "ASA", "AND", "ANG", "ANT", "ARG",…
## $ population     <dbl> 32526562, 2889167, 39666519, 55538, 70473, 25021974, 91…
## $ gdp_per_capita <dbl> 594.3231, 3945.2176, 4206.0312, NA, NA, 4101.4722, 1371…

Events

events_raw <- readr::read_csv("data/events.csv") %>% 
  dplyr::glimpse()

## Rows: 306
## Columns: 6
## $ id         <dbl> 701492, 305278, 708010, 729643, 567019, 607924, 519818, 778…
## $ sport      <chr> "aquatics", "aquatics", "aquatics", "aquatics", "aquatics",…
## $ discipline <chr> "backstroke", "backstroke", "backstroke", "backstroke", "br…
## $ name       <chr> "Women's 100m Backstroke", "Women's 200m Backstroke", "Men'…
## $ sex        <chr> "female", "female", "male", "male", "female", "female", "ma…
## $ venues     <chr> "Olympic Aquatics Stadium", "Olympic Aquatics Stadium", "Ol…

Data : Athletes

DT::datatable(athletes_raw)

Adjusts

f_transform_yy_in_yyyy <- function(x){
  ifelse(as.numeric(str_sub(x, start = -2, end = -1)) > 20,
         paste0(str_sub(x, start = 1, end = -3), "19", str_sub(x, start = -2, end = -1)),
         paste0(str_sub(x, start = 1, end = -3), "20", str_sub(x, start = -2, end = -1))
         )
  } 

athletes <- athletes_raw %>% 
  dplyr::mutate(id = as.character(id)) %>% 
  #IMC
    dplyr::mutate(IMC = weight/(height^2)) %>% 
    dplyr::relocate(IMC, .after = weight) %>%
  #Age
    dplyr::mutate(dob = f_transform_yy_in_yyyy(dob)) %>%
    dplyr::mutate(dob = lubridate::mdy(dob)) %>%
    dplyr::mutate(age = 2016 - lubridate::year(dob)) %>% 
    dplyr::relocate(age, .after = dob) %>% 
  #Medals Total
    dplyr::mutate(medal_total = gold + silver + bronze) %>% 
    dplyr::mutate(medal_total_nonZero = ifelse(medal_total == 0, NA, medal_total)) %>% 
  #Medals Rank
    dplyr::mutate(medal_rank = 
                    dplyr::case_when(
                      gold > 0 ~ "gold",
                      silver > 0 ~ "silver",
                      bronze > 0 ~ "bronze",
                      TRUE ~ "none"
                    )) %>% 
    dplyr::mutate(medal_rank = as.ordered(medal_rank)) %>%
    dplyr::mutate(medal_rank = forcats::fct_relevel(medal_rank, c("none", "bronze", "silver", "gold"))) %>%
  dplyr::glimpse()

## Rows: 11,538
## Columns: 16
## $ id                  <chr> "736041664", "532037425", "435962603", "521041435"…
## $ name                <chr> "A Jesus Garcia", "A Lam Shin", "Aaron Brown", "Aa…
## $ nationality         <chr> "ESP", "KOR", "CAN", "MDA", "NZL", "AUS", "USA", "…
## $ sex                 <chr> "male", "female", "male", "male", "male", "male", …
## $ dob                 <date> 1969-10-17, 1986-09-23, 1992-05-27, 1991-01-02, 1…
## $ age                 <dbl> 47, 30, 24, 25, 26, 26, 23, 25, 28, 25, 19, 20, 24…
## $ height              <dbl> 1.72, 1.68, 1.98, 1.83, 1.81, 1.80, 2.05, 1.93, 1.…
## $ weight              <dbl> 64, 56, 79, 80, 71, 67, 98, 100, 62, 54, 63, 66, N…
## $ IMC                 <dbl> 21.63332, 19.84127, 20.15100, 23.88844, 21.67211, …
## $ sport               <chr> "athletics", "fencing", "athletics", "taekwondo", …
## $ gold                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ silver              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ bronze              <dbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ medal_total         <dbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0,…
## $ medal_total_nonZero <dbl> NA, NA, 1, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, …
## $ medal_rank          <ord> none, none, bronze, none, none, none, bronze, none…

EDA - All athletes

skimr::skim(athletes)

Data summary

Name	athletes
Number of rows	11538
Number of columns	16
_______________________
Column type frequency:
character	5
Date	1
factor	1
numeric	9
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id	0	1	5	9	0	11538	0
name	0	1	4	40	0	11517	0
nationality	0	1	3	3	0	207	0
sex	0	1	4	6	0	2	0
sport	0	1	4	17	0	28	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dob	1	1	1954-05-20	2002-11-26	1990-05-21	5595

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
medal_rank	0	1	TRUE	4	non: 9681, bro: 642, gol: 619, sil: 596

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
age	1	1.00	26.70	5.38	14.00	23.00	26.00	30.00	62.00	▃▇▁▁▁
height	330	0.97	1.77	0.11	1.21	1.69	1.76	1.84	2.21	▁▁▇▅▁
weight	659	0.94	72.07	16.18	31.00	60.00	70.00	81.00	170.00	▂▇▂▁▁
IMC	680	0.94	22.87	3.41	11.40	20.72	22.42	24.43	53.40	▂▇▁▁▁
gold	0	1.00	0.06	0.26	0.00	0.00	0.00	0.00	5.00	▇▁▁▁▁
silver	0	1.00	0.06	0.24	0.00	0.00	0.00	0.00	2.00	▇▁▁▁▁
bronze	0	1.00	0.06	0.24	0.00	0.00	0.00	0.00	2.00	▇▁▁▁▁
medal_total	0	1.00	0.18	0.43	0.00	0.00	0.00	0.00	6.00	▇▁▁▁▁
medal_total_nonZero	9681	0.16	1.09	0.38	1.00	1.00	1.00	1.00	6.00	▇▁▁▁▁

EDA - By sex

athletes %>% 
  dplyr::group_by(sex) %>% 
  skimr::skim()  

Data summary

Name	Piped data
Number of rows	11538
Number of columns	16
_______________________
Column type frequency:
character	4
Date	1
factor	1
numeric	9
________________________
Group variables	sex

Variable type: character

skim_variable	sex	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id	female	0	1	5	9	0	5205	0
id	male	0	1	5	9	0	6333	0
name	female	0	1	4	40	0	5197	0
name	male	0	1	4	37	0	6320	0
nationality	female	0	1	3	3	0	202	0
nationality	male	0	1	3	3	0	206	0
sport	female	0	1	4	17	0	28	0
sport	male	0	1	4	17	0	28	0

Variable type: Date

skim_variable	sex	n_missing	complete_rate	min	max	median	n_unique
dob	female	0	1	1954-05-20	2002-11-26	1990-09-30	3593
dob	male	1	1	1955-08-05	2002-04-14	1990-02-02	4045

Variable type: factor

skim_variable	sex	n_missing	complete_rate	ordered	n_unique	top_counts
medal_rank	female	0	1	TRUE	4	non: 4332, bro: 301, gol: 294, sil: 278
medal_rank	male	0	1	TRUE	4	non: 5349, bro: 341, gol: 325, sil: 318

Variable type: numeric

skim_variable	sex	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
age	female	0	1.00	26.20	5.26	14.00	23.00	26.00	29.00	62.00	▅▇▁▁▁
age	male	1	1.00	27.11	5.44	14.00	23.00	26.00	30.00	61.00	▃▇▂▁▁
height	female	139	0.97	1.70	0.09	1.21	1.64	1.70	1.75	2.03	▁▁▇▆▁
height	male	191	0.97	1.82	0.10	1.45	1.75	1.82	1.89	2.21	▁▃▇▂▁
weight	female	199	0.96	62.64	11.25	31.00	55.00	61.00	68.00	150.00	▂▇▁▁▁
weight	male	460	0.93	80.10	15.38	39.00	70.00	78.00	88.00	170.00	▂▇▂▁▁
IMC	female	209	0.96	21.58	2.93	13.26	19.84	21.23	22.77	48.98	▅▇▁▁▁
IMC	male	471	0.93	23.96	3.41	11.40	21.91	23.53	25.34	53.40	▁▇▁▁▁
gold	female	0	1.00	0.06	0.26	0.00	0.00	0.00	0.00	4.00	▇▁▁▁▁
gold	male	0	1.00	0.05	0.25	0.00	0.00	0.00	0.00	5.00	▇▁▁▁▁
silver	female	0	1.00	0.06	0.25	0.00	0.00	0.00	0.00	2.00	▇▁▁▁▁
silver	male	0	1.00	0.05	0.23	0.00	0.00	0.00	0.00	2.00	▇▁▁▁▁
bronze	female	0	1.00	0.06	0.25	0.00	0.00	0.00	0.00	2.00	▇▁▁▁▁
bronze	male	0	1.00	0.06	0.24	0.00	0.00	0.00	0.00	2.00	▇▁▁▁▁
medal_total	female	0	1.00	0.19	0.45	0.00	0.00	0.00	0.00	5.00	▇▁▁▁▁
medal_total	male	0	1.00	0.17	0.41	0.00	0.00	0.00	0.00	6.00	▇▁▁▁▁
medal_total_nonZero	female	4332	0.17	1.11	0.42	1.00	1.00	1.00	1.00	5.00	▇▁▁▁▁
medal_total_nonZero	male	5349	0.16	1.08	0.34	1.00	1.00	1.00	1.00	6.00	▇▁▁▁▁

EDA - relation

sex

athletes %>% 
  janitor::tabyl(sex) %>% 
  dplyr::arrange(sex) %>% 
  janitor::adorn_totals("row") %>% 
  dplyr::mutate(percent = scales::percent(percent)) %>% 
  kableExtra::kbl() %>% 
  kableExtra::kable_styling(full_width = FALSE)

sex	n	percent
female	5205	45.1%
male	6333	54.9%
Total	11538	100.0%

medal_rank

athletes %>% 
  dplyr::mutate(medal_rank = forcats::fct_rev(medal_rank)) %>% 
  janitor::tabyl(medal_rank) %>% 
  dplyr::arrange(medal_rank) %>% 
  janitor::adorn_totals("row") %>% 
  dplyr::mutate(percent = scales::percent(percent)) %>% 
  kableExtra::kbl() %>% 
  kableExtra::kable_styling(full_width = FALSE)

medal_rank	n	percent
gold	619	5.36%
silver	596	5.17%
bronze	642	5.56%
none	9681	83.91%
Total	11538	100.00%

medal_rank vs. sex (absoluts)

athletes %>% 
  janitor::tabyl(medal_rank, sex) %>% 
  dplyr::arrange(desc(medal_rank)) %>% 
  kableExtra::kbl() %>% 
  kableExtra::kable_styling(full_width = FALSE)

medal_rank	female	male
gold	294	325
silver	278	318
bronze	301	341
none	4332	5349

athletes %>% 
  janitor::tabyl(medal_rank, sex) %>% 
  dplyr::arrange(desc(medal_rank)) %>% 
  janitor::adorn_totals("col") %>%
  kableExtra::kbl() %>% 
  kableExtra::kable_styling(full_width = FALSE)

medal_rank	female	male	Total
gold	294	325	619
silver	278	318	596
bronze	301	341	642
none	4332	5349	9681

athletes %>% 
  janitor::tabyl(medal_rank, sex) %>% 
  dplyr::arrange(desc(medal_rank)) %>% 
  janitor::adorn_totals("row") %>%
  kableExtra::kbl() %>% 
  kableExtra::kable_styling(full_width = FALSE)

medal_rank	female	male
gold	294	325
silver	278	318
bronze	301	341
none	4332	5349
Total	5205	6333

medal_rank vs. sex (relatives)

athletes %>% 
  janitor::tabyl(medal_rank, sex) %>% 
  dplyr::arrange(desc(medal_rank)) %>% 
  janitor::adorn_totals(c("row", "col")) %>%
  janitor::adorn_percentages("all") %>% 
  dplyr::mutate(female = scales::percent(female)) %>% 
  dplyr::mutate(male = scales::percent(male)) %>% 
  dplyr::mutate(Total = scales::percent(Total)) %>% 
  kableExtra::kbl() %>% 
  kableExtra::kable_styling(full_width = FALSE)

medal_rank	female	male	Total
gold	2.548%	2.817%	5.36%
silver	2.409%	2.756%	5.17%
bronze	2.609%	2.955%	5.56%
none	37.546%	46.360%	83.91%
Total	45.112%	54.888%	100.00%

medal_rank vs. sex (relative by Cols)

athletes %>% 
  janitor::tabyl(medal_rank, sex) %>% 
  dplyr::arrange(desc(medal_rank)) %>% 
  janitor::adorn_totals(c("row", "col")) %>%
  janitor::adorn_percentages("col") %>% 
  dplyr::mutate(female = scales::percent(female)) %>% 
  dplyr::mutate(male = scales::percent(male)) %>% 
  dplyr::mutate(Total = scales::percent(Total)) %>% 
  kableExtra::kbl() %>% 
  kableExtra::kable_styling(full_width = FALSE)

medal_rank	female	male	Total
gold	5.65%	5.13%	5.36%
silver	5.34%	5.02%	5.17%
bronze	5.78%	5.38%	5.56%
none	83.23%	84.46%	83.91%
Total	100.00%	100.00%	100.00%

medal_rank vs. sex (relative by Rows)

athletes %>% 
  janitor::tabyl(medal_rank, sex) %>% 
  dplyr::arrange(desc(medal_rank)) %>% 
  janitor::adorn_totals(c("row", "col")) %>%
  janitor::adorn_percentages("row") %>% 
  dplyr::mutate(female = scales::percent(female)) %>% 
  dplyr::mutate(male = scales::percent(male)) %>% 
  dplyr::mutate(Total = scales::percent(Total)) %>% 
  kableExtra::kbl() %>% 
  kableExtra::kable_styling(full_width = FALSE)

medal_rank	female	male	Total
gold	47.50%	52.50%	100%
silver	46.64%	53.36%	100%
bronze	46.88%	53.12%	100%
none	44.75%	55.25%	100%
Total	45.11%	54.89%	100%