olympicAthletes covers every modern Olympic Games from
Athens 1896 through Milano-Cortina
2026. Three core datasets carry all the information; the rest
of the package is convenience subsets of them (single sports, single
Games, pre-counted summaries) built for teaching examples. This vignette
walks through the three core datasets and shows a few representative
analyses.
The three core datasets at a glance
| Dataset | Rows | One row per… |
|---|---|---|
olympic_athletes |
315,094 | (athlete, Games, event) participation |
medal_table |
1,929 | (Games, NOC) verified medal totals |
editions |
62 | Olympic edition (incl. cancelled Games) |
olympic_athletes is the headline long-format table.
medal_table and editions are companion tables
that make per-team-event medal counting and edition-level lookups easy
without re-aggregating 300k+ rows. The convenience subsets
(e.g. athletics_athletes,
olympic_athletes_2024, usa_summer_medals) are
documented in help(package = "olympicAthletes") and keep
the same columns as their parent dataset.
olympic_athletes: athlete-event participations
data(olympic_athletes)
str(olympic_athletes, give.attr = FALSE, vec.len = 2)
#> 'data.frame': 315094 obs. of 16 variables:
#> $ id : int 12068 35094 35698 38123 41160 ...
#> $ name : chr "Arthur Charles Blake" "Angelos Fetsis" ...
#> $ sex : chr "M" "M" ...
#> $ age : int 24 NA 22 23 21 ...
#> $ height : num NA NA NA 154 NA ...
#> $ weight : num NA NA NA 45 NA ...
#> $ team : chr "United States" "Greece" ...
#> $ noc : chr "USA" "GRE" ...
#> $ games : chr "1896 Summer" "1896 Summer" ...
#> $ year : int 1896 1896 1896 1896 1896 ...
#> $ season : chr "Summer" "Summer" ...
#> $ city_local_latin: chr "Athína" "Athína" ...
#> $ city_english : chr "Athens" "Athens" ...
#> $ sport : chr "Athletics" "Athletics" ...
#> $ event : chr "Athletics Men's 1,500 metres" "Athletics Men's 1,500 metres" ...
#> $ medal : chr "Silver" NA ...A quick sanity check — participations per Games, by season:
tab <- table(olympic_athletes$year, olympic_athletes$season)
tail(tab, 10)
#>
#> Summer Winter
#> 2008 13602 0
#> 2010 0 4402
#> 2012 12920 0
#> 2014 0 4891
#> 2016 13688 0
#> 2018 0 5119
#> 2020 14535 0
#> 2022 0 5249
#> 2024 13660 0
#> 2026 0 5415A note on team medals
Medals in olympic_athletes are
per-player: every member of a gold-winning ice hockey
roster has their own medal = "Gold" row. It is the right
shape for athlete-level analysis, but it inflates team counts when you
sum naively.
# Every roster member of the men's ice hockey gold medal team
# at Beijing 2022 gets a Gold row:
hockey_2022 <- subset(
olympic_athletes,
year == 2022 &
event == "Ice Hockey Men's Ice Hockey" &
medal == "Gold"
)
nrow(hockey_2022)
#> [1] 0For headline medal-table totals, use medal_table
instead.
medal_table: verified per-edition totals
medal_table covers every Olympic edition from Athens
1896 through Milano-Cortina 2026 and uses the IOC per-team-event
convention.
data(medal_table)
head(subset(medal_table, year == 2024), 5)
#> edition_id games year season noc country gold
#> 1808 63 2024 Summer 2024 Summer USA United States 40
#> 1809 63 2024 Summer 2024 Summer CHN People's Republic of China 40
#> 1810 63 2024 Summer 2024 Summer JPN Japan 20
#> 1811 63 2024 Summer 2024 Summer AUS Australia 18
#> 1812 63 2024 Summer 2024 Summer FRA France 16
#> silver bronze total notes
#> 1808 44 42 126
#> 1809 27 24 91
#> 1810 12 13 45
#> 1811 19 16 53
#> 1812 26 22 64Top five NOCs at Paris 2024 by gold count:
paris <- subset(medal_table, year == 2024)
paris <- paris[order(-paris$gold, -paris$total), ]
head(paris[, c("noc", "country", "gold", "silver", "bronze", "total")], 5)
#> noc country gold silver bronze total
#> 1808 USA United States 40 44 42 126
#> 1809 CHN People's Republic of China 40 27 24 91
#> 1810 JPN Japan 20 12 13 45
#> 1811 AUS Australia 18 19 16 53
#> 1812 FRA France 16 26 22 64
editions: metadata for every Games
editions includes one row per Olympic edition (62 in
total), including the handful of Games cancelled by the World Wars.
data(editions)
head(editions[, c("games", "city_local_latin", "city_english", "country",
"opening_ceremony", "closing_ceremony",
"participants", "medal_events")], 10)
#> games city_local_latin city_english country opening_ceremony
#> 1 1896 Summer Athína Athens Greece 1896-04-06
#> 2 1900 Summer Paris Paris France <NA>
#> 3 1904 Summer St. Louis St. Louis United States 1904-05-14
#> 4 1906 Summer Athína Athens Greece 1906-04-22
#> 5 1908 Summer London London Great Britain 1908-07-13
#> 6 1912 Summer Stockholm Stockholm Sweden 1912-07-06
#> 7 1916 Summer Berlin Berlin Germany <NA>
#> 8 1920 Summer Antwerpen Antwerp Belgium 1920-08-14
#> 9 1924 Summer Paris Paris France 1924-07-05
#> 10 1924 Winter Chamonix Chamonix France 1924-01-24
#> closing_ceremony participants medal_events
#> 1 1896-04-15 176 43
#> 2 <NA> 1241 95
#> 3 <NA> 650 95
#> 4 1906-05-02 841 74
#> 5 1908-07-25 2024 110
#> 6 1912-07-15 2409 107
#> 7 <NA> NA NA
#> 8 1920-08-30 2680 162
#> 9 1924-07-27 3255 131
#> 10 1924-02-05 312 17Use it to enrich olympic_athletes with edition-level
facts (host country, opening date, total medal events) without scraping
anything yourself.
Provenance recap
- 1896-2016 rows come from rgriff23/Olympic_history, originally scraped from sports-reference.com.
- 2018-2026 rows were scraped from olympedia.org using the pipeline at ismayc/olympic-moderndive-data.
- The build script in
data-raw/DATASET.Rreproduces every.rdaindata/from the CSVs indata-raw/.
See ?olympic_athletes, ?medal_table, and
?editions for full column-level documentation.