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recruitR

Saiem Gilani
@saiemgilani @saiemgilani

Source: vignettes/recruitR.Rmd
recruitR.Rmd

Offensive Tackle Example 

if (!requireNamespace('pacman', quietly = TRUE)){
  install.packages('pacman')
}
pacman::p_load_current_gh("sportsdataverse/recruitR")

pacman::p_load(dplyr, ggplot2)

Let’s say that we are interested in seeing how many offensive tackles in the 2020 recruiting cycle were:

  • located in Florida
  • located in the states bordering Florida
  • ranked inside the top 1000
FL_OTs <- cfbd_recruiting_player(2020, recruit_type = 'HighSchool', state='FL', position ='OT')
GA_OTs <- cfbd_recruiting_player(2020, recruit_type = 'HighSchool', state='GA', position ='OT')
AL_OTs <- cfbd_recruiting_player(2020, recruit_type = 'HighSchool', state='AL', position ='OT')
SE_OTs <- dplyr::bind_rows(FL_OTs, GA_OTs, AL_OTs)

SE_OTs_1k <- SE_OTs %>% 
  dplyr::filter(ranking < 1000) %>% 
  dplyr::arrange(ranking)

SE_OTs_1k %>% 
  dplyr::select(ranking, name, school, committed_to, position, 
         height, weight, stars, rating, city, state_province)
#> ── Player recruiting info from CollegeFootballData.com ─────── recruitR 0.0.3 ──
#> ℹ Data updated: 2022-12-13 04:04:58 UTC
#> # A tibble: 20 × 11
#>    ranking name  school commi…¹ posit…² height weight stars rating city  state…³
#>      <int> <chr> <chr>  <chr>   <chr>    <dbl>  <int> <int>  <dbl> <chr> <chr>  
#>  1      11 Brod… Litho… Georgia OT        77      298     5  0.995 Lith… GA     
#>  2      38 Tate… Darli… Georgia OT        78      322     4  0.982 Rome  GA     
#>  3      74 Myle… Great… Stanfo… OT        78      308     4  0.966 Norc… GA     
#>  4     110 Marc… St. T… LSU     OT        77      305     4  0.952 Fort… FL     
#>  5     128 Jale… Oakle… Miami   OT        78      331     4  0.942 Oran… FL     
#>  6     157 Issi… Norla… Florida OT        76      309     4  0.931 Miami FL     
#>  7     271 Josh… Suwan… Florida OT        78      335     4  0.905 Live… FL     
#>  8     318 Conn… Jesuit Stanfo… OT        79      260     4  0.897 Tampa FL     
#>  9     333 Javi… Centr… Alabama OT        77      295     4  0.895 Phen… AL     
#> 10     491 Cayd… Fort … North … OT        78      260     3  0.879 Fort… FL     
#> 11     530 Aust… South… Georgia OT        77      278     3  0.876 Guyt… GA     
#> 12     538 Mich… Lenna… Georgi… OT        77      295     3  0.876 Rusk… FL     
#> 13     562 Jord… Gaine… Georgi… OT        78      310     3  0.874 Gain… GA     
#> 14     577 Brad… St. P… Ole Mi… OT        79      310     3  0.873 Mobi… AL     
#> 15     614 Trey… Roswe… North … OT        78      294     3  0.871 Rosw… GA     
#> 16     658 Gera… Cardi… Florida OT        77      320     3  0.868 Fort… FL     
#> 17     752 Jake… Marie… Colora… OT        77      300     3  0.864 Mari… GA     
#> 18     934 Josh… Centr… Kentuc… OT        76.5    304     3  0.856 Phen… AL     
#> 19     953 Wing… Lee C… Georgi… OT        79      285     3  0.855 Lees… GA     
#> 20     971 Kobe… Herit… Cincin… OT        78      275     3  0.855 Ring… GA     
#> # … with abbreviated variable names ¹​committed_to, ²​position, ³​state_province

Plotting the Offensive Tackles by State

You can also create a plot:

SE_OTs_1k$stars <- factor(SE_OTs_1k$stars,levels = c(5,4,3,2))

SE_OTs_1k_grp <- SE_OTs_1k %>%
  dplyr::group_by(state_province, stars) %>%
  dplyr::summarize(players = dplyr::n()) %>% 
  dplyr::ungroup()

ggplot(SE_OTs_1k_grp ,aes(x = state_province, y = players, fill = factor(stars))) +
  geom_bar(stat = "identity",colour='black') +
  xlab("State") + ylab("Number of Players") +
  labs(title="Top-1000 Offensive Tackles in FL, GA, and AL - Class of 2020",
       subtitle="Figure: @SaiemGilani | Data: @CFB_data with #recruitR")+
  geom_text(aes(label = players),size = 4, position = position_stack(vjust = 0.5))+
  scale_fill_manual(values=c("dodgerblue2","lightskyblue","red3","ghostwhite"))+
  theme(legend.title = element_blank(),
        legend.text = element_text(size = 12, margin=margin(t=0.2,r=0,b=0.2,l=-1.2,unit=c("mm")), 
                                   family = "serif"),
        legend.background = element_rect(fill = "grey99"),
        legend.key.width = unit(.2,"cm"),
        legend.key.size = unit(.3,"cm"),
        legend.position = c(0.25, 0.84),
        legend.margin=margin(t = 0.4,b = 0.4,l=-1.2,r=0.4,unit=c('mm')),
        legend.direction = "horizontal",
        legend.box.background = element_rect(colour = "#500f1b"),
        axis.title.x = element_text(size = 12, margin = margin(0,0,1,0,unit=c("mm")), 
                                    family = "serif",face="bold"),
        axis.text.x = element_text(size = 10, margin=margin(0,0,1,0,unit=c("mm")),
                                   family = "serif"),
        axis.title.y = element_text(size = 12, margin = margin(0,0,0,0,unit=c("mm")), 
                                    family = "serif",face="bold"),
        axis.text.y = element_text(size = 12, margin = margin(1,1,1,1,unit=c("mm")), 
                                    family = "serif"),
        plot.title = element_text(size = 14, margin = margin(t=0,r=0,b=1.5,l=0,unit=c("mm")),
        lineheight=-0.5, family = "serif",face="bold"),
        plot.subtitle = element_text(size = 12, margin = margin(t=0,r=0,b=2,l=0,unit=c("mm")), 
                                     lineheight=-0.5, family = "serif"),
        plot.caption = element_text(size = 12, margin=margin(t=0,r=0,b=0,l=0,unit=c("mm")),
                                    lineheight=-0.5, family = "serif"),
        strip.text = element_text(size = 10, family = "serif",face="bold"),
        panel.background = element_rect(fill = "grey95"),
        plot.background = element_rect(fill = "grey85"),
        plot.margin=unit(c(top=0.4,right=0.4,bottom=0.4,left=0.4),"cm"))