From c08623a3e42892db30a62e093c15d79f4172050c Mon Sep 17 00:00:00 2001 From: Andrew Weatherman Date: Thu, 8 Feb 2024 11:36:45 -0500 Subject: [PATCH] update readme to remove gt theme and gt helper -- both in cbbplotR --- README.Rmd | 36 ------ README.md | 147 +++++++++-------------- man/figures/README-unnamed-chunk-5-1.png | Bin 35122 -> 41475 bytes man/figures/README-unnamed-chunk-6-1.png | Bin 17338 -> 17306 bytes 4 files changed, 56 insertions(+), 127 deletions(-) diff --git a/README.Rmd b/README.Rmd index 6f806cd..bf42f31 100644 --- a/README.Rmd +++ b/README.Rmd @@ -177,39 +177,3 @@ cbbdata::cbd_kenpom_authorization(password = 'xxx') - Year-end ratings (`cbd_kenpom_ratings`) - Day-by-day ratings (`cbd_kenpom_ratings_archive`) -### Other - -`cbbdata` ships with the `gt_theme_athletic` theme for `gt` tables. The theme is perfect for stat-focused tables, giving them a classic, monospaced feel. - -```{r, message=FALSE, warning=FALSE, eval=FALSE} -cbbdata::cbd_torvik_ratings(year=2024) %>% - dplyr::slice(1:5) %>% - dplyr::select(team, conf, barthag, adj_o, adj_d) %>% - gt::gt() %>% - cbbdata::gt_theme_athletic() -``` - -```{r, echo=FALSE, warning=FALSE, message=FALSE} -knitr::include_graphics('man/figures/gt_theme.png') -``` - -Also included in `cbbdata` is a helper function for plotting team logos with `gt`. Simply pass in a dataframe with team names, specify where your 'team' logo resides, and the name of the new column (using the same name as your 'team' column will replace that column -- perfect for `gt`). - -```{r, message=FALSE, warning=FALSE, eval=FALSE} -cbbdata::cbd_torvik_ratings(year=2024) %>% - dplyr::slice(1:5) %>% - dplyr::select(team, conf, barthag, adj_o, adj_d) %>% - cbbdata::cbd_gt_logos(team, team) %>% - gt::gt() %>% - cbbdata::gt_theme_athletic() %>% - gt::fmt_markdown(team) %>% - gt::cols_align(columns = team, 'left') -``` - -```{r, echo=FALSE, warning=FALSE, message=FALSE} -knitr::include_graphics('man/figures/with_logos.png') -``` - -## Support - -The CBBData API is free to use but hosting it is not! If you find this resource helpful, please kindly consider [supporting me and the project on Ko-fi](https://ko-fi.com/andrewweatherman). diff --git a/README.md b/README.md index 6f4bbc7..9c24bf4 100644 --- a/README.md +++ b/README.md @@ -108,9 +108,9 @@ with it a rich collection of Barttorvik data. #### Metric Ratings: -- Year-end ratings (`cbd_torvik_ratings`) -- Day-by-day ratings (`cbd_torvik_ratings_archive`) -- Team four factor splits (`cbd_torvik_team_factors`) +- Year-end ratings (`cbd_torvik_ratings`) +- Day-by-day ratings (`cbd_torvik_ratings_archive`) +- Team four factor splits (`cbd_torvik_team_factors`) E.g., if you want to see what the no-bias T-Rank top 10 looks like: @@ -118,26 +118,27 @@ E.g., if you want to see what the no-bias T-Rank top 10 looks like: cbbdata::cbd_torvik_team_factors(year = 2024, no_bias = TRUE) %>% dplyr::slice(1:10) %>% dplyr::select(team, barthag, adj_o, adj_d) +#> API Key set! #> # A tibble: 10 × 4 #> team barthag adj_o adj_d #> -#> 1 Houston 0.983 116. 81.2 -#> 2 Purdue 0.972 124. 91.0 -#> 3 Arizona 0.971 119. 87.7 -#> 4 Connecticut 0.963 125. 93.9 -#> 5 BYU 0.956 118. 90.1 -#> 6 Oklahoma 0.948 114. 88.8 -#> 7 Auburn 0.947 119. 93.0 -#> 8 Iowa St. 0.946 116. 90.1 -#> 9 Alabama 0.940 129. 102. -#> 10 Marquette 0.933 118. 93.8 +#> 1 Houston 0.979 117. 83.9 +#> 2 Purdue 0.974 126. 92.3 +#> 3 Auburn 0.955 120. 91.7 +#> 4 Connecticut 0.955 123. 94.4 +#> 5 Arizona 0.949 120. 92.9 +#> 6 Tennessee 0.944 118. 91.9 +#> 7 Alabama 0.941 126. 98.9 +#> 8 Iowa St. 0.940 115. 90.2 +#> 9 BYU 0.934 120. 95.3 +#> 10 Marquette 0.930 117. 93.1 ``` #### Player Data: -- Individual game logs (`cbd_torvik_player_game`) -- Season averages (`cbd_torvik_player_season`) -- Season splits (`cbd_torvik_player_split`) +- Individual game logs (`cbd_torvik_player_game`) +- Season averages (`cbd_torvik_player_season`) +- Season splits (`cbd_torvik_player_split`) E.g., if you want to see which ACC player averages the most points at home: @@ -147,15 +148,16 @@ cbbdata::cbd_torvik_player_split(year = 2024, conf = 'ACC', split = 'location') dplyr::filter(games >= 3) %>% dplyr::slice_max(pts, n = 1) %>% dplyr::select(team, player, pts, games) -#> team player pts games -#> 1: Syracuse Judah Mintz 26 5 +#> team player pts games +#> +#> 1: North Carolina RJ Davis 23.66667 6 ``` #### Team + Conference Data: -- Team stats splits (`cbd_torvik_team_split`) -- Team histories (`cbd_torvik_team_history`) -- Conference four factor splits (`cbd_torvik_conf_factors`) +- Team stats splits (`cbd_torvik_team_split`) +- Team histories (`cbd_torvik_team_history`) +- Conference four factor splits (`cbd_torvik_conf_factors`) E.g., if you want to see which conferences shoot the best at home against top 100 teams: @@ -168,19 +170,19 @@ cbbdata::cbd_torvik_conf_factors(2024, venue = 'home', top = 100) %>% #> # A tibble: 5 × 3 #> conf games efg #> -#> 1 P12 6 55.5 -#> 2 SEC 13 51.1 -#> 3 BE 14 50.7 -#> 4 ACC 12 50.4 -#> 5 Amer 5 49.5 +#> 1 P12 39 55.3 +#> 2 MWC 36 52.4 +#> 3 B10 64 52 +#> 4 SEC 62 51.1 +#> 5 B12 69 51.1 ``` #### Game Data: -- Individual game box (`cbd_torvik_game_box`) -- Individual game four factors (`cbd_torvik_game_factors`) -- Individual game stats (box + factors) (`cbd_torvik_game_stats`) -- Season schedule (`cbd_torvik_season_schedule`) +- Individual game box (`cbd_torvik_game_box`) +- Individual game four factors (`cbd_torvik_game_factors`) +- Individual game stats (box + factors) (`cbd_torvik_game_stats`) +- Season schedule (`cbd_torvik_season_schedule`) E.g., if you want to track how Duke’s offense has performed this season: @@ -197,9 +199,9 @@ cbbdata::cbd_torvik_game_factors(year = 2024, team = 'Duke') %>% #### Predictions -- Individual game predictions (`cbd_torvik_game_prediction`) -- Team season predictions (`cbd_torvik_season_prediction`) -- Team season simulations (`cbd_torvik_season_simulation`) +- Individual game predictions (`cbd_torvik_game_prediction`) +- Team season predictions (`cbd_torvik_season_prediction`) +- Team season simulations (`cbd_torvik_season_simulation`) E.g., if you want to run 10,000 simulations of Duke’s season with their performance as of today: @@ -216,12 +218,11 @@ cbbdata::cbd_torvik_season_simulation('Duke', 2024) %>% #### Tournament Results + Resumes -- Daily NET rankings and quadrant records - (`cbd_torvik_current_resume`) -- Tournament performance (`cbd_torvik_ncaa_results`) -- Tournament “committee sheets” (`cbd_torvik_ncaa_sheets`) -- Resume database (`cbd_torvik_resume_database`) -- Similar team tournament resumes (`cbd_torvik_similar_resumes`) +- Daily NET rankings and quadrant records (`cbd_torvik_current_resume`) +- Tournament performance (`cbd_torvik_ncaa_results`) +- Tournament “committee sheets” (`cbd_torvik_ncaa_sheets`) +- Resume database (`cbd_torvik_resume_database`) +- Similar team tournament resumes (`cbd_torvik_similar_resumes`) E.g., if you want to pull the five teams with the most Q1 NET wins: @@ -230,16 +231,20 @@ cbbdata::cbd_torvik_current_resume() %>% dplyr::mutate(q1_wins = readr::parse_number(quad1)) %>% dplyr::slice_max(q1_wins, n = 5) %>% dplyr::select(team, conf, q1_wins, net) -#> # A tibble: 7 × 4 -#> team conf q1_wins net -#> -#> 1 Purdue B10 5 3 -#> 2 Houston B12 4 1 -#> 3 Arizona P12 3 2 -#> 4 Kansas B12 3 13 -#> 5 Clemson ACC 3 15 -#> 6 Wisconsin B10 3 17 -#> 7 Texas A&M SEC 3 22 +#> # A tibble: 11 × 4 +#> team conf q1_wins net +#> +#> 1 Purdue B10 8 2 +#> 2 Connecticut BE 8 3 +#> 3 Houston B12 6 1 +#> 4 Wisconsin B10 6 15 +#> 5 Arizona P12 5 4 +#> 6 Kansas B12 5 12 +#> 7 North Carolina ACC 5 10 +#> 8 Marquette BE 5 11 +#> 9 Baylor B12 5 14 +#> 10 Duke ACC 5 20 +#> 11 Boise St. MWC 5 40 ``` ### KenPom @@ -259,45 +264,5 @@ cbbdata::cbd_kenpom_authorization(password = 'xxx') #### Metric Ratings: -- Year-end ratings (`cbd_kenpom_ratings`) -- Day-by-day ratings (`cbd_kenpom_ratings_archive`) - -### Other - -`cbbdata` ships with the `gt_theme_athletic` theme for `gt` tables. The -theme is perfect for stat-focused tables, giving them a classic, -monospaced feel. - -``` r -cbbdata::cbd_torvik_ratings(year=2024) %>% - dplyr::slice(1:5) %>% - dplyr::select(team, conf, barthag, adj_o, adj_d) %>% - gt::gt() %>% - cbbdata::gt_theme_athletic() -``` - - - -Also included in `cbbdata` is a helper function for plotting team logos -with `gt`. Simply pass in a dataframe with team names, specify where -your ‘team’ logo resides, and the name of the new column (using the same -name as your ‘team’ column will replace that column – perfect for `gt`). - -``` r -cbbdata::cbd_torvik_ratings(year=2024) %>% - dplyr::slice(1:5) %>% - dplyr::select(team, conf, barthag, adj_o, adj_d) %>% - cbbdata::cbd_gt_logos(team, team) %>% - gt::gt() %>% - cbbdata::gt_theme_athletic() %>% - gt::fmt_markdown(team) %>% - gt::cols_align(columns = team, 'left') -``` - - - -## Support - -The CBBData API is free to use but hosting it is not! If you find this -resource helpful, please kindly consider [supporting me and the project -on Ko-fi](https://ko-fi.com/andrewweatherman). +- Year-end ratings (`cbd_kenpom_ratings`) +- Day-by-day ratings (`cbd_kenpom_ratings_archive`) diff --git a/man/figures/README-unnamed-chunk-5-1.png b/man/figures/README-unnamed-chunk-5-1.png index 0cca8a0b67b6ad12fb59d190354b465e98cd3226..e0365d008dc5ec58bead0ecc65473e0b7d6c05f2 100644 GIT binary patch literal 41475 zcmdqJ^;=X?+crEi4BaIq-6DvDGz=vmDS{v%-AGG^Ln8 zeB=FYP5=Ud6WGYgYS_st$U44qbb01vVrHRW;b7rnW2UJf1Az!fMQOjZzN<|p`LV8o zi{(eqXiiQX!L{Ir%2%2{gD3x7TU6Lj-;aM)DIL?dHIr_`#=mr#Wy2{+CQa0qHy=NL zqu<8PXj`})$^E%f<1;*n<8=*Qf;`>Y#QVGd%3W4HG)lv9^VdwnTP37}%rD;KF7-ZPrYlvoxfebVrDOf5XFvS3 zcJ+=W%o@Ngr+ZbW;`^bQEt%9S8u9e;+Xj-I z>5&|WqaMb`R6CE zOwJAS?odJdji}W^?L!jo_xhx92#1^^W?T z3I`Hu=91am+OJG1zI=Wm`1~A3KLGKo^K8Jf!qC55KIxN+v+FyFqi1`wG`lRh@WVCJ zCDVBh+52*y$HmCS$eG?XQ^9uBYe&HDZ^@T3oK>5or_-4-dK28@9OfYs?H%pr%Ibf} zLxZtR9(cP86_Q3#yllYeDRne2^eaMA%7- 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