R/teamWicketsAcrossOversOppnAllMatches.R

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

##########################################################################################
# Designed and developed by Tinniam V Ganesh
# Date : 4 Nov 2021
# Function: teamWicketsAcrossOversOppnAllMatches.R
# This function computes wickets across overs in all matches against opposition in powerplay, middle and death overs
#
###########################################################################################
#' @title
#' Compute the wickets by team against team in powerplay, middle and death overs in all matches
#'
#' @description
#' This function  plots the wickets by team against team in in powerplay, middle and death overs
#'
#' @usage
#' teamWicketsAcrossOversOppnAllMatches(matches,t1,t2,plot=1)
#'
#' @param matches
#' The dataframe of the matches
#'
#' @param t1
#' The 1st team of the match
#'
#' @param t2
#' the 2nd team in the match
#'
#' @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
#' teamWicketsAcrossOversOppnAllMatches.R(matches,'England',"Pakistan")
#' }
#' @seealso
#' \code{\link{getBatsmanDetails}}\cr
#' \code{\link{getBowlerWicketDetails}}\cr
#' \code{\link{batsmanDismissals}}\cr
#' \code{\link{getTeamBattingDetails}}\cr
#'
#' @export
#'
teamWicketsAcrossOversOppnAllMatches <- function(matches,t1,t2,plot=1) {
  team=ball=totalRuns=total=wicketPlayerOut=meanWickets=type=count=opposition=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+)?$")), 0.1, 5.9))
  a2 <- select(a1,team,date,wicketPlayerOut)
  a3 <- a2 %>% group_by(team,date) %>% filter(wicketPlayerOut != "nobody") %>% mutate(count =n())
  a4 = select(a3,team,count)  %>% group_by(team) %>% summarise(meanWickets=mean(count))
  a4$opposition=t2
  a4$type="1-Power Play"

  # Middle overs
  b1 <- a %>% filter(between(as.numeric(str_extract(ball, "\\d+(\\.\\d+)?$")), 6.1, 15.9))
  b2 <- select(b1,team,date,wicketPlayerOut)
  b3 <- b2 %>% group_by(team,date) %>% filter(wicketPlayerOut != "nobody") %>% mutate(count =n())
  b4 = select(b3,team,count)  %>% group_by(team) %>% summarise(meanWickets=mean(count))
  b4$opposition=t2
  b4$type="2-Middle Overs"

  #Death overs
  c1 <- a %>% filter(between(as.numeric(str_extract(ball, "\\d+(\\.\\d+)?$")), 16.1, 20.0))
  c2 <- select(c1,team,date,wicketPlayerOut)
  c3 <- c2 %>% group_by(team,date) %>% filter(wicketPlayerOut != "nobody") %>% mutate(count =n())
  c4 = select(c3,team,count)  %>% group_by(team) %>% summarise(meanWickets=mean(count))
  c4$opposition=t2
  c4$type="3-Death Overs"

  ####################
  # Filter the performance of team2
  a <-filter(matches,team==t2)
  # Power play
  a11 <- a %>% filter(between(as.numeric(str_extract(ball, "\\d+(\\.\\d+)?$")), 0.1, 5.9))
  a21 <- select(a11,team,date,wicketPlayerOut)
  a31 <- a21 %>% group_by(team,date) %>% filter(wicketPlayerOut != "nobody") %>% mutate(count =n())
  a41 = select(a31,team,count)  %>% group_by(team) %>% summarise(meanWickets=mean(count))
  a41$opposition=t1
  a41$type="1-Power Play"

  # Middle overs I
  b11 <- a %>% filter(between(as.numeric(str_extract(ball, "\\d+(\\.\\d+)?$")), 6.1, 15.9))
  b21 <- select(b11,team,date,wicketPlayerOut)
  b31 <- b21 %>% group_by(team,date) %>% filter(wicketPlayerOut != "nobody") %>% mutate(count =n())
  b41 = select(b31,team,count)  %>% group_by(team) %>% summarise(meanWickets=mean(count))
  b41$opposition=t1
  b41$type="2-Middle Overs"

  #Midle overs 2
  c11 <- a %>% filter(between(as.numeric(str_extract(ball, "\\d+(\\.\\d+)?$")), 12.1, 16.9))
  c21 <- select(c11,team,date,wicketPlayerOut)
  c31 <- c21 %>% group_by(team,date) %>% filter(wicketPlayerOut != "nobody") %>% mutate(count =n())
  c41 = select(c31,team,count)  %>% group_by(team) %>% summarise(meanWickets=mean(count))
  c41$opposition=t1
  c41$type="3-Death Overs"



  m=rbind(a4,b4,c4,a41,b41,c41)

  plot.title= paste("Wickets across 20 overs by ",t1, "and", t2, "in all matches", sep=" ")

  # Plot both lines
  if(plot ==1){ #ggplot2
    ggplot(m,aes(x=type, y=meanWickets, fill=opposition)) +
      geom_bar(stat="identity", position = "dodge") +
      ggtitle(bquote(atop(.(plot.title),
                          atop(italic("Data source:http://cricsheet.org/"),""))))



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

    ggplotly(g)

  }


}