R/teamSRAcrossOversAllOppnAllMatches.R

In yorkr: Analyze Cricket Performances Based on Data from Cricsheet

##########################################################################################
# Designed and developed by Tinniam V Ganesh
# Date : 5 Nov 2021
# Function: teamSRAcrossOversAllOppnAllMatches
# This function computes strike rate across overs in all matches against allopposition in powerplay, middle and death overs
#
###########################################################################################
#' @title
#' Compute the strike rate by team against all team in powerplay, middle and death overs in all matches
#'
#' @description
#' This function  plots the SR by team against  all team in in powerplay, middle and death overs
#'
#' @usage
#' teamSRAcrossOversAllOppnAllMatches(matches,t1,plot=1)
#'
#' @param matches
#' The dataframe of the matches
#'
#' @param t1
#' The team of the matches
#'
#' @param plot
#' Plot=1 (static), Plot=2(interactive)
#'
#' @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{
#'
#' # Plot tne match worm plot
#' teamSRAcrossOversAllOppnAllMatches(matches,"Pakistan")
#' }
#' @seealso
#' \code{\link{getBatsmanDetails}}\cr
#' \code{\link{getBowlerWicketDetails}}\cr
#' \code{\link{batsmanDismissals}}\cr
#' \code{\link{getTeamBattingDetails}}\cr
#'
#' @export
#'
teamSRAcrossOversAllOppnAllMatches <- function(matches,t1,plot=1) {
    team=ball=totalRuns=total=type=SR=meanSR=str_extract=NULL
    ggplotly=NULL


    # Filter the performance of team1
    a <-filter(matches,team==t1)

    # Power play
    a1 <- a %>% filter(between(as.numeric(str_extract(ball, "\\d+(\\.\\d+)?$")), 0.1, 5.9))
    a2 <- select(a1,team, totalRuns,date)
    a3 <- a2 %>% group_by(team,date) %>% summarise(total=sum(totalRuns),count=n())
    a3$SR = a3$total/a3$count *100
    a4 = a3 %>% select(team,SR) %>% summarise(meanSR=mean(SR))
    a4$type="1-Power Play"

    # Middle overs I
    b1 <- a %>% filter(between(as.numeric(str_extract(ball, "\\d+(\\.\\d+)?$")), 6.1, 15.9))
    b2 <- select(b1,team, totalRuns,date)
    b3 <- b2 %>% group_by(team,date) %>% summarise(total=sum(totalRuns),count=n())
    b3$SR = b3$total/b3$count *100
    b4 = b3 %>% select(team,SR) %>% summarise(meanSR=mean(SR))
    b4$type="2-Middle Overs"

    #Death overs 2
    c1 <- a %>% filter(between(as.numeric(str_extract(ball, "\\d+(\\.\\d+)?$")), 16.1, 20.0))
    c2 <- select(c1,team, totalRuns,date)
    c3 <- c2 %>% group_by(team,date) %>% summarise(total=sum(totalRuns),count=n())
    c3$SR = c3$total/c3$count *100
    c4 = c3 %>% select(team,SR) %>% summarise(meanSR=mean(SR))
    c4$type="3-Death Overs"


    m=rbind(a4,b4,c4)
    plot.title= paste("Strike rate across 20 overs by ",t1, "in all matches against all teams", sep=" ")



    # Plot both lines
    if(plot ==1){ #ggplot2

        ggplot(data = m,mapping=aes(x=type, y=meanSR, fill=team)) +
            geom_bar(stat="identity", position = "dodge") +
            ggtitle(bquote(atop(.(plot.title),
                                atop(italic("Data source:http://cricsheet.org/"),""))))


    }else { #ggplotly
        g <-ggplot(data = m,mapping=aes(x=type, y=meanSR, fill=team)) +
            geom_bar(stat="identity", position = "dodge") +
            ggtitle(plot.title)

        ggplotly(g)

    }

}