# Extract fouls along with contextual information.
# Save for later processing.
library(tidyverse)

## define some useful functions for later
# find the closest previous event number with a score margin entry (can be the
# same event number) and return the score margin
find_score_margin <- function(seasondata, gameid, eventnum, colname = "SCOREMARGIN"){
    score_margin <- filter(seasondata,
                           GAME_ID_INT == gameid,
                           EVENTNUM <= eventnum,
                           !is.na(SCOREMARGIN)) %>%
        arrange(EVENTNUM) %>%
        pull(colname)
    # if no score margin found, there is no score in the game
    # return 0 (though this actually returns NA)
    tail_scoremargin <- ifelse(!is.null(score_margin),
                               yes=tail(score_margin, 1),
                               no=0)
    return(tail_scoremargin)
}

# add a total score column to the fouls data frame
sum_score <- function(scorestr) {
    parts <- str_split(scorestr, '-', simplify=TRUE)
    totalscore <- as.integer(parts[1]) + as.integer(parts[2])
}

# function which reduces the season data into only the foul information
# needed for analysis
reduce_season <- function(filename) {

    seasonID = strsplit(filename, '_pbp.csv')[[1]][1]
    # load a single-season of data
    season <- read_csv(paste0("data/", filename))
    # construct a new dataframe which is a subset of the original
    # also ensure that SCOREMARGIN is a number and create an integer gameID
    # SCOREMARGIN is Home-Away score
    # the foul was committed by PLAYER1 against PLAYER2
    season_subset <- season %>%
        dplyr::select(GAME_ID, EVENTNUM, HOMEDESCRIPTION, VISITORDESCRIPTION,
                      SCORE, SCOREMARGIN, PCTIMESTRING, PERIOD) %>%
        mutate(SCOREMARGIN = as.numeric(SCOREMARGIN)) %>%
        mutate(GAME_ID_INT = as.integer(GAME_ID))

    # clear original season data from memory
    rm(season)

    # find the home team foul entries
    homefouls <- grep("FOUL",
                      x = season_subset$HOMEDESCRIPTION,
                      value=FALSE)
    # and the visitor fouls
    visitorfouls <- grep("FOUL",
                         x = season_subset$VISITORDESCRIPTION,
                         value=FALSE)

    # combine foul indices and get unique entries and create for only fouls
    allfouls <- season_subset[unique(c(homefouls, visitorfouls)),]

    # now go back and search for the game score.

    # associate score margins with the fouls
    scoremargins <- purrr::map2_dbl(.x = allfouls$GAME_ID_INT,
                                    .y = allfouls$EVENTNUM,
                                    ~find_score_margin(season_subset,
                                                       gameid= .x,
                                                       eventnum = .y,
                                                       colname="SCOREMARGIN"))
    # associate scores with the fouls
    scores <- purrr::map2_chr(.x = allfouls$GAME_ID_INT,
                              .y = allfouls$EVENTNUM,
                              ~find_score_margin(season_subset,
                                                 gameid = .x,
                                                 eventnum = .y,
                                                 colname ="SCORE"))

    allfouls$SCOREMARGIN <- replace_na(scoremargins, 0)
    allfouls$SCORE <- replace_na(scores, "0-0")

    # corrected score margin is for consistently plotting the number of fouls
    # when either the home or visiting team is ahead
    allfouls <- mutate(allfouls,
                       SCOREMARGIN_CORR = case_when(is.na(HOMEDESCRIPTION) ~ SCOREMARGIN,
                                                    is.na(VISITORDESCRIPTION) ~ -1*SCOREMARGIN))
    allfouls <- mutate(allfouls,
                       TOTALSCORE = purrr::map_int(.x = allfouls$SCORE,
                                                   ~sum_score(scorestr = .x)))

    # add a column specifying whether the foul was on home or visitor
    allfouls <- mutate(allfouls,
                       FOULTEAM = case_when(!is.na(HOMEDESCRIPTION) ~ "HOME",
                                            !is.na(VISITORDESCRIPTION) ~ "VISITOR"))

    # save the derived "all fouls" dataframe
    write_csv(allfouls, paste0('data/', paste0(seasonID, '-allfouls.csv')))
}

# load in all data files and create a single variable
# with the filename as an ID column
datafiles <- list.files("data", pattern="*_pbp.csv", full.names = FALSE)

purrr::map_df(datafiles,
              ~reduce_season(.x))