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Offensive Tackle Example

This is a basic example which shows you how to solve a common problem:

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, committed_to, position, 
         height, weight, stars, rating, city, state_province)
## ── Player recruiting info from CollegeFootballData.com ─────── recruitR 0.0.3 ──
##  Data updated: 2022-06-17 20:44:10 UTC
## # A tibble: 20 × 10
##    ranking name           committed_to position height weight stars rating city 
##      <int> <chr>          <chr>        <chr>     <dbl>  <int> <int>  <dbl> <chr>
##  1      11 Broderick Jon… Georgia      OT         77      298     5  0.995 Lith…
##  2      38 Tate Ratledge  Georgia      OT         78      322     4  0.982 Rome 
##  3      74 Myles Hinton   Stanford     OT         78      308     4  0.966 Norc…
##  4     110 Marcus Dumerv… LSU          OT         77      305     4  0.952 Fort…
##  5     128 Jalen Rivers   Miami        OT         78      331     4  0.942 Oran…
##  6     157 Issiah Walker… Florida      OT         76      309     4  0.931 Miami
##  7     271 Joshua Braun   Florida      OT         78      335     4  0.905 Live…
##  8     318 Connor McLaug… Stanford     OT         79      260     4  0.897 Tampa
##  9     333 Javion Cohen   Alabama      OT         77      295     4  0.895 Phen…
## 10     491 Cayden Baker   North Carol… OT         78      260     3  0.879 Fort…
## 11     530 Austin Blaske  Georgia      OT         77      278     3  0.876 Guyt…
## 12     538 Michael Ranki… Georgia Tech OT         77      295     3  0.876 Rusk…
## 13     562 Jordan Willia… Georgia Tech OT         78      310     3  0.874 Gain…
## 14     577 Brady Ward     Ole Miss     OT         79      310     3  0.873 Mobi…
## 15     614 Trey Zimmerman North Carol… OT         78      294     3  0.871 Rosw…
## 16     658 Gerald Mincey  Florida      OT         77      320     3  0.868 Fort…
## 17     752 Jake Wray      Colorado     OT         77      300     3  0.864 Mari…
## 18     934 Joshua Jones   Kentucky     OT         76.5    304     3  0.856 Phen…
## 19     953 Wing Green     Georgia Tech OT         79      285     3  0.855 Lees…
## 20     971 Kobe McAllist… Cincinnati   OT         78      275     3  0.855 Ring…
## # … with 1 more variable: state_province <chr>

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()
## `summarise()` has grouped output by 'state_province'. You can override using
## the `.groups` argument.
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(1.5,"mm"),
        legend.key.size = unit(2.0,"mm"),
        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"))