R/convertAllYaml2RDataframesT20.R

In tvganesh/yorkr: Analyze Cricket Performances Based on Data from Cricsheet

##########################################################################################
# Designed and developed by Tinniam V Ganesh
# Date : 20 Mar 2016
# Function: convertAllYaml2RDataframesT20
# This function converts all yaml files to dataframes and stores as .RData from a given source
# directory to target directory.
# The target file is the name of the opposing teams and the date of the match
#
###########################################################################################
#' @title
#' Convert and save all T20 Yaml files  to dataframes
#'
#' @description
#' This function coverts all T20 Yaml files from source directory to data frames. The data frames
#' are then stored as .RData. The saved files are of the format team1-team2-date.RData
#' For e.g. England-India-2008-04-06.RData etc
#' @usage
#' convertAllYaml2RDataframesT20(sourceDir=".",targetDirMen=".",targetDirWomen=".")
#'
#' @param sourceDir
#' The source directory of the yaml files
#'
#' @param targetDirMen
#' The target directory in which the data frames of men are stored as RData files
#'
#' @param targetDirWomen
#' The target directory in which the data frames of women are stored as RData files
#'
#' @return None
#' @references
#' \url{https://cricsheet.org/}\cr
#' \url{https://gigadom.in/}\cr
#' \url{https://github.com/tvganesh/yorkrData/}
#' @author
#' Tinniam V Ganesh
#' @note
#' Maintainer: Tinniam V Ganesh \email{tvganesh.85@gmail.com}
#'
#' @examples
#' \dontrun{
#' # In the example below ../yamldir is the source dir for the yaml files
#' convertAllYaml2RDataframesT20("../yamldir","../data")
#' }
#' @seealso
#' \code{\link{bowlerMovingAverage}}\cr
#' \code{\link{bowlerWicketsVenue}}\cr
#' \code{\link{convertYaml2RDataframeT20}}\cr
#'
#' @export
#'

convertAllYaml2RDataframesT20 <- function(sourceDir=".",targetDirMen=".",targetDirWomen="."){
    yaml.load_file=info.dates=info.match_type=info.overs=info.venue=NULL
    info.teams=matchType=winner=result=venue=info.gender=ball=team=batsman=NULL
    bowler=nonStriker=byes=legbyes=noballs=wides=nonBoundary=penalty=runs=extras=totalRuns=NULL
    wicketFielder=wicketKind=wicketPlayerOut=replacementIn=replacementOut=replacementReason=replacementTeam=NULL
    files <- list.files(sourceDir)
    print(length(files))
    for(iii in 1:length(files)){
        overs <- NULL
        pth = paste(sourceDir,"/",files[iii],sep="")

        cat("i=",iii,"  file=",pth,"\n")


        # Load yaml file
        a <- yaml.load_file(pth)

        # This is a temporary change.
        # Removing elements of Players,Registry and balls per over from yaml file
        a[[2]][['players']] <- NULL #Players
        a[[2]][['registry']] <- NULL #Registry
        a[[2]][['balls_per_over']] <- NULL  #balls per over

        # Cast as data frame for easy processing
        tryCatch(b <- as.data.frame(a),
                 error = function(e) {
                     print("Error!")
                     eFile <- files[iii]
                     errorFile <- paste(targetDirMen,"/","errors.txt",sep="")
                     write(eFile,errorFile,append=TRUE)

                 }
        )
        sz <- dim(b)

        # Gather the meta information
        meta <- select(b,info.dates,info.match_type,info.overs, info.venue,
                       info.teams,info.gender)
        names(meta) <- c("date","matchType","overs","venue","team1","gender")

        # Check if there was a winner or if there was no result (tie,draw)
        if(!is.null(b$info.outcome.winner)){
            meta$winner <- b$info.outcome.winner
            meta$result <- "NA"
        } else if(!is.null(b$info.result)){
            meta$winner <- "NA"
            meta$result <- b$info.result
        } else if(!is.null(b$info.outcome.result)){
            meta$winner <- "NA"
            meta$result <- b$info.outcome.result
        }
        meta$team2 = meta[2,5]
        meta <- meta[1,]

        #Reorder columns
        meta <- select(meta,date,matchType,overs,venue,team1,team2,winner,result,gender)


        # Remove the innings and deliveries from the column names
        names(b) <-gsub("innings.","",names(b))
        names(b) <- gsub("deliveries.","",names(b))


        # Fix for changed order of columns-28 Apr 2020
        idx = which(names(b) == "1st.team") # Search for column name 1st.team
        # Select the column after that which includes ball-by-ball detail
        idx=idx+1
        m <- b[1,idx:sz[2]]
        #Transpose to the details of the match
        match <- t(m)
        rnames <- rownames(match)

        match <- as.data.frame(cbind(rnames,match))

        # Gather details for first team

        # Set the number of overs to 50 for ODI matches
        numOver <- seq(from=0,to=20,by=1)

        # Create string of delivery in each over upto delivery 16 in case of no balls,
        # wides etc.
        # Note: The over can be more than .6 when you have no balls, wides etc
        d <- c(".1",".2",".3",".4",".5",".6",".7",".8",".9",".1",".1",
               ".1",".1",".1",".1")

        m <- 1
        # Create a vector of deliveries from 0 to 50 by concatenating string
        delivery <- NULL
        for(k in 1:length(numOver)){
            for(l in 1:length(d)){
                delivery[m] <- paste(numOver[k],d[l],sep="")
                m=m+1
            }
        }


        #Create string for 1st team
        print("first loop")
        s <- paste("1st.",delivery,"\\.",sep="")
        team1 <- b$`1st.team`[1]
        # Parse the yaml file over by over and store as a row of data
        overs1 <- parseYamlOver(match,s,team1,delivery,meta)


        # Create string for 2nd team
        print("second loop")
        s1 <- paste("2nd.",delivery,"\\.",sep="")
        team2 <- b$`2nd.team`[1]
        overs2 <- parseYamlOver(match,s1,team2,delivery,meta)

        # Row bind the 1dst
        overs <- rbind(overs1,overs2)

        colList=list("batsman"="batsman","bowler"="bowler","non_striker"="nonStriker","runs.batsman" ="runs",
                     "runs.extras" ="extras","runs.total"="totalRuns","byes"="byes","legbyes"="legbyes","noballs"="noballs",
                     "wides"="wides","runs.non_boundary"="nonBoundary","penalty" ="penalty","wicket.fielders"="wicketFielder",
                     "wicket.kind"="wicketKind","wicket.player_out" ="wicketPlayerOut",
                     "replacements.match.in"="replacementIn","replacements.match.out"="replacementOut",
                     "replacements.match.reason"="replacementReason","replacements.match.team"="replacementTeam",
                     "ball"="ball","team"="team","date"="date","matchType"="matchType","overs" ="overs",
                     "venue"="venue","team1" ="team1","team2" ="team2", "winner"="winner","result"="result",
                     "gender"="gender")

        # Get the column names for overs
        cols <- names(overs)
        newcols <- NULL
        for(i in 1: length(cols)){
            newcols <- c(newcols, colList[[cols[i]]])
        }

        # Rename the columns
        names(overs) <- newcols
        overs <- select(overs, ball,team,batsman,bowler,nonStriker,
                        byes,legbyes,noballs,
                        wides,nonBoundary,penalty, runs,
                        extras,totalRuns,wicketFielder,
                        wicketKind,wicketPlayerOut,
                        replacementIn, replacementOut, replacementReason,
                        replacementTeam,date,matchType,overs,venue,team1,team2,winner,result,gender)

        # Change factors to appropiate type
        overs$byes <- as.numeric(as.character(overs$byes))
        overs$legbyes <- as.numeric(as.character(overs$legbyes))
        overs$wides <- as.numeric(as.character(overs$wides))
        overs$noballs <- as.numeric(as.character(overs$noballs))
        overs$nonBoundary <- as.numeric(as.character(overs$nonBoundary))
        overs$penalty <- as.numeric(as.character(overs$penalty))
        overs$runs <- as.numeric(as.character(overs$runs))
        overs$extras <- as.numeric(as.character(overs$extras))
        overs$totalRuns <- as.numeric(as.character(overs$totalRuns))
        overs$date = as.Date(overs$date)
        overs$overs <- as.numeric(as.character(overs$overs))
        # Add missing elements
        overs[overs$wicketFielder == 0,]$wicketFielder="nobody"
        overs[overs$wicketKind == 0,]$wicketKind="not-out"
        overs[overs$wicketPlayerOut==0,]$wicketPlayerOut ="nobody"
        sapply(overs,class)

        teams <- as.character(unique(overs$team))
        #Create a unique file which is based on the opposing teams and the date of the match

        filename <- paste(meta$team1,"-",meta$team2,"-",meta$date,".",
                          "RData",sep="")

        # Write men and women in separate folders
        gender=overs$gender

        if(gender[1] =="male"){
            to <- paste(targetDirMen,"/",filename,sep="")
        }
        else{
            to <- paste(targetDirWomen,"/",filename,sep="")
        }

        # Save as .RData
        save(overs,file=to)

        # Write the name of the file that was converted and the converted file for reference
        convertedFile <- paste(files[iii],filename,sep=":")
        if(gender[1] == "male")
            outputFile <- paste(targetDirMen,"/","convertedFiles.txt",sep="")
        else
            outputFile <- paste(targetDirWomen,"/","convertedFiles.txt",sep="")
        write(convertedFile,outputFile,append=TRUE)


    }

}