roty_munge.r

library(tidyverse)
library(nbastatR) # devtools::install_github("abresler/nbastatR")
df_player_dict <-
  nbastatR::get_bref_player_dictionary() %>%
  filter(!is.na(countSeasons))

df_roty_winners <-
  nbastatR::get_bref_awards(awards = c("Rookie of the Year"))

all_data <-
  nbastatR::get_bref_players_seasons(
    seasons = 1952:2018,
    tables = c("advanced", "totals"),
    assign_to_environment = F
  )

df_all_rookies <-
  all_data %>%
  left_join(df_player_dict %>% select(slugPlayerBREF, slugSeasonRookie)) %>%
  mutate(isRookie = ifelse(slugSeason == slugSeasonRookie, TRUE, FALSE)) %>%
  filter(isRookie) %>%
  arrange(yearSeason)

df_all_rookies <-
  df_all_rookies %>%
  filter(minutesTotals >= 200) %>%
  mutate_if(is.numeric,
            funs(ifelse(. %>% is.na(), 0 , .)))

df_all_rookies <-
  df_all_rookies %>%
  mutate_at(c("groupPosition", "idPosition"),
            funs(factor)) %>%
  asbmisc::convert_factors_to_classes() ## another propriatary package -- you need to turn idPosition & groupPositin into dummy variables 

df_all_rookies <-
  df_all_rookies %>%
  left_join(df_roty_winners %>%
              mutate(isROTY = T) %>%
              select(slugSeason, slugPlayerBREF, isROTY)) %>%
  mutate(isROTY = ifelse(isROTY %>% is.na(), FALSE, TRUE) %>% factor(levels = c("TRUE", "FALSE"))) %>%
  select(-c(minutes, isRookie))


training <-
  df_all_rookies %>%
  filter(!yearSeason == 2018)

testing <-
  df_all_rookies %>%
  filter(yearSeason == 2018) %>%
  select(-isROTY)

input_vars <-
  df_all_rookies %>%
  select_if(is.numeric) %>%
  select(-matches("^year|^id")) %>%
  names() %>%
  append("isROTY")

data_training <- training %>% select(one_of(input_vars))
data_testing <- testing %>% select(one_of(input_vars))

dict_caret <- 
  modelR2::dictionary_caret_models()

test_models <-
  modelR2::caret_models(
    data_training = data_training,
    data_testing = data_testing,
    prediction_variable = 'isROTY',
    models = c("ranger", "xgbTree", "glmnet_h2o"),
  )