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rugger is a rrrrugby package 🏉. Get stats on teams, players, rankings and calculate changes in the rankings for a certain match.
You can install rugger with:
remotes::install_github("RobertMyles/rugger")
I won’t be burdening CRAN with it.
You can see the current world rankings with get_rankings()
:
library(rugger)
get_rankings()
#> # A tibble: 105 x 7
#> team team_abbr points rank played previous_points previous_rank
#> <chr> <chr> <dbl> <int> <int> <dbl> <int>
#> 1 South Africa RSA 94.2 1 212 94.2 1
#> 2 New Zealand NZL 92.1 2 220 92.1 2
#> 3 England ENG 88.8 3 204 88.8 3
#> 4 Wales WAL 85.0 4 212 85.0 4
#> 5 Ireland IRE 84.4 5 196 84.4 5
#> 6 Australia AUS 81.9 6 227 81.9 6
#> 7 France FRA 80.9 7 199 80.9 7
#> 8 Japan JPN 79.3 8 174 79.3 8
#> 9 Scotland SCO 79.2 9 190 79.2 9
#> 10 Argentina ARG 78.3 10 190 78.3 10
#> # … with 95 more rows
Hmmm, what would happen if New Zealand played Ireland tomorrow, and Ireland won by 5 points?
calculate_rank("New Zealand", "Ireland", score = c(15, 20))
#> # A tibble: 2 x 6
#> team points rank points_exchanged new_points new_rank
#> <chr> <dbl> <int> <dbl> <dbl> <int>
#> 1 New Zealand 92.1 2 -2 90.1 1
#> 2 Ireland 84.4 5 2 86.4 2
Let’s have a look at the history between England and Scotland, the first two teams to play the game:
get_team_records("England", "Scotland")
#> Data courtesy of ESPN, http://stats.espnscrum.com/statsguru/rugby/
#> # A tibble: 1 x 15
#> team start_year end_year matches won lost draw percent_won `for` against
#> <chr> <dbl> <dbl> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 Engl… 1871 2019 137 75 43 19 61.7 1674 1225
#> # … with 5 more variables: difference <int>, tries <int>, conversions <int>,
#> # penalties <int>, dropgoals <int>
England winning almost 62% of the matches there.
I wonder which player has scored most tries in rugby?
library(dplyr)
get_team_records(type = "player") %>%
arrange(desc(points)) %>%
select(player, points)
#> # A tibble: 50 x 2
#> player points
#> <chr> <int>
#> 1 DW Carter (NZ) 1598
#> 2 RJR O'Gara (Ire/Lions) 1083
#> 3 SM Jones (Lions/Wales) 970
#> 4 FA Vlaicu (Rom) 951
#> 5 M Kvirikashvili (Georg) 840
#> 6 CD Paterson (Scot) 809
#> 7 Y Kushnarev (Russ) 777
#> 8 MJ Giteau (Aust) 698
#> 9 BG Habana (SA) 335
#> 10 BG O'Driscoll (Ire/Lions) 250
#> # … with 40 more rows
The sublime Dan Carter!
All of the data acessed by this package is obviously for informational/educational use, and a big thanks to all for making it available.
It belongs to World Rugby and ESPN. The algorithm that calculates the rankings also belongs to World Rugby, you can read about it here. Some of the data is also pulled from Pick and Go by Lassen Creative Technologies.
- Black flags?! They’re Github’s fault. The flags represent the
rankings as of the end of 2018 – New Zealand, Ireland, Wales,
England, South Africa, Australia, Scotland and Fiji. You can see
them in R with
emo::ji("wales")
, for example, using the emo package, available from https://github.com/hadley/emo.