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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")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 New Zealand NZL 92.5 1 209 92.5 1
#> 2 Ireland IRE 91.2 2 182 91.2 2
#> 3 Wales WAL 87.2 3 196 86.7 3
#> 4 England ENG 86.2 4 189 85.5 4
#> 5 South Africa RSA 84.6 5 200 85.1 5
#> 6 Australia AUS 82.4 6 217 83.1 6
#> 7 Scotland SCO 81.8 7 177 81.6 7
#> 8 Fiji FJI 77.9 8 111 76.4 10
#> 9 France FRA 77.3 9 187 78.9 8
#> 10 Argentina ARG 77.0 10 182 77.3 9
#> # ... with 95 more rowsHmmm, 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 Ireland 91.2 2 1.44 92.6 1
#> 2 New Zealand 92.5 1 -1.44 91.1 2Yay, we’d jump into first place (sorry, biased!).
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")
#> # A tibble: 1 x 15
#> team start_year end_year matches won lost draw percent_won `for`
#> <chr> <dbl> <dbl> <int> <int> <int> <int> <dbl> <int>
#> 1 Engl… 1871 2019 137 75 43 18 61.8 1636
#> # ... with 6 more variables: against <int>, 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))
#> # A tibble: 50 x 16
#> player start_year end_year matches start sub points tries conversions
#> <chr> <dbl> <dbl> <int> <int> <int> <int> <int> <int>
#> 1 DW Ca… 2003 2015 112 106 6 1598 29 293
#> 2 RJR O… 2000 2013 130 87 43 1083 16 176
#> 3 SM Jo… 1998 2011 110 91 19 970 7 160
#> 4 PC Mo… 1997 2008 102 90 12 893 25 153
#> 5 FA Vl… 2006 2018 113 89 24 888 11 159
#> 6 M Kvi… 2003 2018 115 94 21 840 17 148
#> 7 CD Pa… 1999 2011 109 96 13 809 22 90
#> 8 Y Kus… 2005 2018 101 89 12 733 10 136
#> 9 MJ Gi… 2002 2016 103 89 14 698 30 106
#> 10 BG Ha… 2004 2016 124 122 2 335 67 0
#> # ... with 40 more rows, and 7 more variables: penalties <int>,
#> # dropgoals <int>, gfm <int>, won <int>, lost <int>, draw <int>,
#> # percent_won <dbl>The sublime Dan Carter!
All of the data acessed by this package is obviously for informational/educational use.
It belongs to World Rugby and ESPN. The algorithm that calculates the rankings also belongs to World Rugby, you can read about it here.
- Black flags?! They’re Github’s fault. The flags represent the
rankings as of the tp 8 at 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.
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