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app.R
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app.R
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# library(cricketr)
library(plyr)
library(dplyr)
library(ggplot2)
library(plotly)
library(stringr)
library(DT)
library(purrr)
library(RcppArmadillo)
library(shiny)
library(shinythemes)
library(shinydashboard)
# library(shinydashboardPlus)
library(fontawesome)
library(htmltools)
library(scales)
library(reactablefmtr)
# t20 <- read.csv("t20_data_12_May_2022.csv", header = T) ### Reading file from external sources
t20 <- readRDS("./data/t20.rds")
####################### DATA PREPROSESSING ###########################################
# source("Creating_BBB_data.R")
t20_bkp <- t20 ## Backup of the original raw data
# colnames(t20) ## Showing the column names
t20[t20 == ""] <- NA ## Filling the blank entries as NA
t20[is.na(t20)] <- 0 ## Filling the NAs as 0
# glimpse(t20) ## To see the variable structure
t20$over = ceiling(t20$ball) ## Creating a column for nth over number (n = 1 to 20)
t20$over_type = ifelse(t20$over >= 1 & t20$over <= 6, "Powerplay",
ifelse(t20$over >= 7 & t20$over <= 16, "Middle Over", "Death Over"))
t20$isDot = ifelse(t20$runs_off_bat == 0, 1, 0) ## No of dots
t20$isOne = ifelse(t20$runs_off_bat == 1, 1, 0) ## No of 1's
t20$isTwo = ifelse(t20$runs_off_bat == 2, 1, 0) ## No of 2's
t20$isThree = ifelse(t20$runs_off_bat == 3, 1, 0) ## No of 3's
t20$isFour = ifelse(t20$runs_off_bat == 4, 1, 0) ## No of 4's
t20$isSix = ifelse(t20$runs_off_bat == 6, 1, 0) ## No of 6's
t20$isOut = ifelse(t20$wicket_type != "0", 1, 0) ## No of out
t20$Runs = t20$runs_off_bat + t20$extras
tot_mat = t20 |> select(match_id) |> unique() |> nrow()
## Formatting last date of data update
last_date <- t20$start_date |> unique() |> as.Date(format = "%Y-%m-%d") |> max()
m <- format(last_date, "%m") |> as.numeric()
d <- format(last_date, "%d")
y <- format(last_date, "%Y")
last_date_updated <- paste0(month.name[m], " ", d,", ",y)
## For getting all players name as a vector
players_name_list = unique(c(t20$striker, t20$non_striker, t20$bowler)) |> as.character()
### For Batting Analysis
t20 <- t20[t20$innings == 1 | t20$innings == 2, ] # Removing Super-Over Balls
########################### ui ###############################################
header = dashboardHeader(title = "Performance Analysis in T20I",
titleWidth = 450)
sidebar = dashboardSidebar(selectInput("player_name", "Select your Cricketer", choices = as.character(players_name_list)),
sidebarMenu(
menuItem("Batting Analysis", tabName = "bat", icon = icon("untappd", lib = "font-awesome")),
menuItem("Bowling Analysis", tabName = "bowl", icon = icon("basketball-ball", lib = "font-awesome")),
menuItem("About", tabName = "about", icon = icon("address-card", lib = "font-awesome"))
))
body = dashboardBody(
tabItems(
tabItem(tabName = "bat",
span(textOutput("name", inline = T), style = "font-size: 45px; font-style: bold", uiOutput("flag", inline = T)),
tags$br(),
column(12,
valueBoxOutput("value1", width = 2),
valueBoxOutput("value2", width = 2),
valueBoxOutput("value3", width = 2),
valueBoxOutput("value4", width = 2),
valueBoxOutput("value5", width = 2),
valueBoxOutput("value6", width = 2),
valueBoxOutput("value7", width = 2),
valueBoxOutput("value8", width = 2),
valueBoxOutput("value9", width = 2),
valueBoxOutput("value10", width = 2),
valueBoxOutput("value11", width = 2),
valueBoxOutput("value12", width = 2)),
h3("Runs Distribution"),
fluidRow(
box(title = "Histogram of Runs", solidHeader = T, collapsible = T, status = "primary",
plotlyOutput("hist")),
box(title = "Dismissal Type", solidHeader = T, collapsible = T, status = "primary",
plotlyOutput("donut"))),
fluidRow(
box(title = "Phase of Play", solidHeader = T, collapsible = T, status = "primary",
reactableOutput("table1", height = "200px", width = "600px")))
),
tabItem(tabName = "bowl", h1("Bowling Analysis"),tags$hr(),
column(12,
valueBoxOutput("value13", width = 2),
valueBoxOutput("value14", width = 2),
valueBoxOutput("value15", width = 2),
valueBoxOutput("value16", width = 2),
valueBoxOutput("value17", width = 2),
valueBoxOutput("value18", width = 2),
valueBoxOutput("value19", width = 2),
valueBoxOutput("value20", width = 2),
valueBoxOutput("value21", width = 2),
valueBoxOutput("value22", width = 2),
valueBoxOutput("value23", width = 2)),
# valueBoxOutput("value24", width = 2))
fluidRow(
box(title = "Bowling Performance against Opponents", solidHeader = T, collapsible = T, status = "primary",
reactableOutput("table2", height = "auto", width = "600px")))
),
tabItem(tabName = "about", h2("About the R Shiny App"),
h4(p(style = "text-align: justify; font-size = 14px",
"The R Shiny App", tags$b("T20performR"), "is intended to attract cricket enthusiasts who wish to accumulate
every bit of information about their favourite players. This is an open-source encyclopaedia of cricketers
those who are cricbees and have an analytical mindset. The work is designed to demonstrate the performance
analysis of T20I cricketers statistically, whereas exploratory data analysis and visualisation tools provide
the key insights of them. Not only it will capture the individual performances of the cricket stars at the
granular level just by selecting them from the dropdown menu but also it equips the comparative study of
multiple cricketers by some appropriate metrics. The summary statistics of batting and bowling of the
individuals are displayed separately on two pages. This dashboard is based on the ball-by-ball data from the
website", tags$a(href = "https://cricsheet.org/", "cricsheet"), "filtered by T20 International matches for
men only. The dataset is updated in the backend on every final day of each month. The algorithm uses the
necessary R libraries (packages) as well as the scratch codes to process the assimilated data and the required analysis.")),
h5(tags$em(tags$b(paste("*Last updated on", last_date_updated, "23:59:59 IST (", tot_mat,
" matches covered)"))), align = "right"), ## Date Update
tags$br(),
tags$br(),
tags$br(),
tags$br(),
h4("Developed by", tags$b(tags$i("Samrit Pramanik")), align = "center"),
h6(tags$hr()),
h1(tags$a(href = "https://www.linkedin.com/in/samritpramanik24/", icon("linkedin")),
tags$a(href = "mailto:[email protected]", icon("envelope")),
tags$a(href = "https://www.facebook.com/samrit.pramanik24ps/" , icon("facebook-square")),
tags$a(href = "https://github.com/samrit2442", icon("github")),
tags$a(href = "https://www.instagram.com/dark_cosmos24/", icon("instagram")),
tags$a(href = "https://api.whatsapp.com/send?phone=919038337857", icon("whatsapp")), # tags$style(HTML("color: green"))
tags$a(href = "https://t.me/darkcosmos24", icon("telegram")),
tags$a(href = "https://twitter.com/Samrit2442", icon("twitter")), align = "center"),
tags$br(),
h1(icon("r-project"), style = "font-size: 100px", align = "center"),
tags$br(),
tags$br(),
tags$br(),
# tags$blockquote("Shiny-Box is still under continuous development. Please look forward to future updates!"),
h5("Copyright", icon("copyright"), " 2023 ", tags$a(href = "https://shiny.rstudio.com/", "Shiny - RStudio"), ". All Rights Reserved."))
))
ui <- dashboardPage(header = header, sidebar = sidebar, body = body, skin = "black")
####################################### server ################################
server <- function(session, input, output) {
# updateSelectizeInput(session, , server = T)
stat_react <- reactive({
## Computing Total Number of Dismissal
diss <- t20 |> dplyr::filter(player_dismissed == input$player_name) |>
dplyr::select(match_id, wicket_type, over_type, bowler)
# Removing Wide Balls
plyr_data <- t20 |> dplyr::filter(striker == input$player_name & wides == 0)
## Computing Total Innings played
innings <- t20 |> dplyr::filter(striker == input$player_name | non_striker == input$player_name) |>
dplyr::summarise(Innings = n_distinct(match_id)) |> as.numeric()
## Deducing Innings wise Statistics
stat1 <- plyr_data |> dplyr::filter(striker == input$player_name) |>
dplyr::group_by(start_date, match_id) |>
dplyr::summarise(Runs = sum(runs_off_bat), Balls = length(runs_off_bat),
SR = round(Runs/Balls*100,2),
Fours = sum(isFour), Sixes = sum(isSix),
Dots = sum(isDot))
stat2 <- left_join(stat1, diss) ## Innings wise Player's Data
stat2$wicket_type <- ifelse(is.na(stat2$wicket_type), "not out", stat2$wicket_type)
stat2$isThirty <- ifelse(stat2$Runs >= 30 & stat2$Runs < 50, 1, 0) # No of 30's
stat2$isFifty <- ifelse(stat2$Runs >= 50 & stat2$Runs < 100, 1, 0) # No of 50's
stat2$isHundred <- ifelse(stat2$Runs >= 100, 1, 0) # No of 100's
stat2$isNO <- ifelse(stat2$wicket_type == "not out", 1, 0) ## Not Out Innings flag
stat3 <- plyr_data |> dplyr::filter(striker == input$player_name) |>
dplyr::group_by(match_id) |> dplyr::select(match_id, innings, bowling_team, venue) |> unique()
stat4 <- left_join(stat2, stat3)
stat5 <- t20 |> dplyr::group_by(match_id, innings) |>
dplyr::summarise(team_runs = sum(Runs))
stat6 <- left_join(stat4, stat5, by = c("match_id", "innings"))
stat6$contribution = round(stat6$Runs/stat6$team_runs*100, 2)
stat6$Year = substr(stat6$start_date, 1, 4)
stat6$bowler <- ifelse(stat6$wicket_type == "run out", NA, stat6$bowler)
stat6 # Complete innings wise player data
}) # Creating Innings wise dataset
# output$flag <- renderUI({
# tags$img(src = "india.png", width = 50, height = 50)
# })
output$name <- renderText({paste("Career Overview of", input$player_name)})
output$value1 <- renderValueBox({
valueBox(nrow(stat_react()), "Total Innings Played", color = "light-blue")
}) # Total Innings Played
output$value2 <- renderValueBox({
valueBox(sum(stat_react()$Runs), "Total Runs Scored", color = "blue")
}) # Total Runs Scored
output$value3 <- renderValueBox({
valueBox(sum(stat_react()$Balls), "Total Balls Faced", color = "teal")
}) # Total Balls Faced
output$value4 <- renderValueBox({
valueBox(sum(stat_react()$isNO), "Not Outs", color = "green")
}) # Not Outs
output$value5 <- renderValueBox({
valueBox(round(sum(stat_react()$Runs)/(nrow(stat_react())-sum(stat_react()$isNO)),2), "Batting Average", color = "navy")
}) # Batting Average
output$value6 <- renderValueBox({
valueBox(round(mean(stat_react()$Runs),2), "Runs Per Innings", color = "black")
}) # Runs Per Innings
output$value7 <- renderValueBox({
valueBox(round(sum(stat_react()$Runs)/(sum(stat_react()$Balls))*100,2), "Strike Rate", color = "red")
}) # Strike Rate
output$value8 <- renderValueBox({
valueBox(paste0(sum(stat_react()$isThirty), "/",
sum(stat_react()$isFifty), "/",
sum(stat_react()$isHundred)), "30+ / 50s / 100s", color = "olive")
}) # 30s/50s/100s
output$value9 <- renderValueBox({
HS = ""
if(stat_react()[which.max(stat_react()$Runs), "isNO"] == 1)
HS = paste0(max(stat_react()$Runs),"*","(", stat_react()[which.max(stat_react()$Runs), "Balls"],")")
else
HS = paste0(max(stat_react()$Runs),"(", stat_react()[which.max(stat_react()$Runs), "Balls"],")")
valueBox(HS, "Highest Score", color = "purple")
}) # Highest Score
output$value10 <- renderValueBox({
valueBox(paste0(sum(stat_react()$Fours), "/", sum(stat_react()$Sixes)), "Fours / Sixes", color = "maroon")
}) # 4s/6s
output$value11 <- renderValueBox({
valueBox(paste0(round((sum(stat_react()$Fours) + sum(stat_react()$Sixes))/sum(stat_react()$Balls)*100,2),"%"), "Boundary Percentage", color = "orange")
}) # Boundary %
output$value12 <- renderValueBox({
valueBox(paste0(round(sum(stat_react()$Dots)/sum(stat_react()$Balls)*100,2),"%"), "Dot Percentage", color = "fuchsia")
}) # Dot %
stat_react2 <- reactive({
fig1 <- plot_ly(x = stat_react()$Runs, type = "histogram")
fig1
})
output$hist <- renderPlotly({stat_react2()}) # Histogram of Runs
stat_react3 <- reactive({
stat9 = stat_react() |> dplyr::filter(wicket_type != "not out")
dd = data.frame(table(stat9$wicket_type))
fig <- dd |> plot_ly(labels = ~Var1, values = ~Freq) |>
add_pie(hole = 0.5) |>
layout(showlegend = T,
xaxis = list(showgrid = F, zeroline = F, showticklabels = F),
yaxis = list(showgrid = F, zeroline = F, showticklabels = F))
fig
})
output$donut <- renderPlotly({stat_react3()}) # Donut Chart of Dismissal Types
stat_react4 <- reactive({
data1 <- t20 |> dplyr::filter(striker == input$player_name & wides == 0)
stat7 <- data1 |> dplyr::group_by(over_type) |>
dplyr::summarise(Runs = sum(runs_off_bat), Six = sum(isSix), Four = sum(isFour),
SR = round(sum(runs_off_bat)/length(runs_off_bat)*100,2))
stat8 <- table(stat_react()$over_type) |> t() |> as.data.frame()
stat8 <- stat8[,-1]
colnames(stat8) = c("over_type", "Dismissed")
table1 <- left_join(stat7, stat8) |> dplyr::arrange(desc(over_type))
colnames(table1) <- c("Over Type","Runs","Sixes","Fours","SR","Dismissed")
reactable(
table1,
pagination = FALSE,
compact = TRUE,
defaultColDef = colDef(
cell = data_bars(table1,
fill_color = c("blue", "black"),
fill_gradient = TRUE,
bar_height = 30,
background = "lightgrey")
)
)
# tab1 <- plot_ly(
# type = 'table',
# columnorder = 1:6,
# columnwidth = rep(120,6),
# header = list(
# values = c("<b>Over Type</b>","<b>Runs</b>","<b>Sixes</b>","<b>Fours</b>","<b>SR</b>","<b>Dismissed</b>"),
# align = rep('center', ncol(table1)),
# line = list(width = 1.5, color = 'black'),
# fill = list(color = 'rgb(235, 100, 230)'),
# font = list(size = 16, color = "white"),
# height = 50
# ),
# cells = list(
# values = t(as.matrix(unname(table1))),
# align = rep('center', ncol(table1)),
# line = list(color = "black", width = 1.2),
# fill = list(color = 'rgba(228, 222, 249, 0.65)'),
# font = list(size = 14, color = "black"),
# height = 55
# ))
#
# tab1
})
output$table1 <- renderReactable({stat_react4()}) # Table-1
stat_react_bowl1 <- reactive({
wk_var = c("stumped", "caught", "hit wicket", "bowled", "caught and bowled", "lbw")
t20$isBowler_wicket = ifelse(t20$wicket_type %in% wk_var,1,0)
## To find out Runs and Wickets at each innings
bw_stat1 <- t20 |> dplyr::filter(bowler == input$player_name & legbyes == 0 & byes == 0 & penalty == 0) |>
dplyr::group_by(start_date, match_id) |>
dplyr::summarise(Runs = sum(Runs), Wickets = sum(isBowler_wicket))
## To find out Dot Balls at each innings
bw_stat2 <- t20 |> dplyr::filter(bowler == input$player_name) |>
dplyr::mutate(isBowlDot = ifelse(runs_off_bat + wides + noballs == 0,1,0)) |>
dplyr::group_by(match_id) |>
dplyr::summarise(Dots = sum(isBowlDot))
bw_stat <- left_join(bw_stat1, bw_stat2, by = "match_id")
## To find out total balls bowled in each innings
bw_stat3 <- t20 |> dplyr::filter(bowler == input$player_name & wides == 0 & noballs == 0) |>
dplyr::group_by(match_id) |>
dplyr::summarise(Balls = length(runs_off_bat))
bw_stat <- left_join(bw_stat, bw_stat3, by = "match_id")
# https://youtu.be/UmDItbiDV6o (23:08 - 23:45) ### Highligths of Ban vs SL 18 Sep 2007
bw_stat4 <- t20 |> dplyr::filter(bowler == input$player_name) |>
dplyr::mutate(BowlRuns = runs_off_bat + wides + noballs) |>
dplyr::group_by(match_id, over) |>
dplyr::summarise(isMaiden = ifelse(sum(BowlRuns) == 0,1,0)) |>
dplyr::group_by(match_id) |> dplyr::summarise(Maiden = sum(isMaiden))
bw_stat <- left_join(bw_stat, bw_stat4, by = "match_id")
bw_stat5 <- t20 |> dplyr::filter(bowler == input$player_name) |>
dplyr::group_by(match_id) |> dplyr::select(match_id, innings, venue, batting_team) |> unique()
bw_stat <- left_join(bw_stat, bw_stat5, by = "match_id")
bw_stat <- bw_stat |> dplyr::mutate(Econ = round(Runs/Balls*6,2),
is4wkt = ifelse(Wickets == 4, 1, 0),
is5wkt = ifelse(Wickets >= 5, 1, 0))
bw_stat
}) ### For Bowling Analysis
stat_react_bowl2 <- reactive({
bw_stat6 <- stat_react_bowl1() |> dplyr::group_by(batting_team) |>
summarise(Innings = length(match_id), Wickets = sum(Wickets),
Dots = sum(Dots))
reactable(
bw_stat6,
pagination = FALSE,
compact = TRUE,
defaultColDef = colDef(
cell = data_bars(bw_stat6,
fill_color = c("blue", "black"),
fill_gradient = TRUE,
bar_height = 30,
background = "lightgrey")
))
})
output$table2 <- renderReactable({stat_react_bowl2()}) # Table-2
output$value13 <- renderValueBox({
valueBox(nrow(stat_react_bowl1()), "Total Innings Bowled", color = "light-blue")
}) # Innings
output$value14 <- renderValueBox({
valueBox(sum(stat_react_bowl1()$Balls), "Total Balls Bowled", color = "aqua")
}) # Balls
output$value15 <- renderValueBox({
valueBox(sum(stat_react_bowl1()$Runs), "Total Runs Conceded", color = "navy")
}) # Runs
output$value16 <- renderValueBox({
valueBox(sum(stat_react_bowl1()$Wickets), "Total Wickets Taken", color = "red")
}) # Wickets
output$value17 <- renderValueBox({
BBI = ""
W = max(stat_react_bowl1()$Wickets)
R = min(stat_react_bowl1()$Runs[which(stat_react_bowl1()$Wickets == W)])
if (sum(stat_react_bowl1()$Wickets) == 0)
BBI = paste0("-")
else
BBI = paste0(W,"/",R)
valueBox(BBI, "Best Bowling Figure", color = "teal")
}) # BBI
output$value18 <- renderValueBox({
valueBox(sum(stat_react_bowl1()$is4wkt), "4 Wickets Hauls", color = "olive")
}) # 4Wkts
output$value19 <- renderValueBox({
valueBox(sum(stat_react_bowl1()$is5wkt), "5 Wickets Hauls", color = "orange")
}) # 5Wkts
output$value20 <- renderValueBox({
valueBox(round(sum(stat_react_bowl1()$Runs)/sum(stat_react_bowl1()$Wickets),2), "Bowling Average", color = "lime")
}) # Bowling Average
output$value21 <- renderValueBox({
valueBox(round(sum(stat_react_bowl1()$Runs)/sum(stat_react_bowl1()$Balls)*6,2), "Economy Rate", color = "maroon")
}) # Economy Rate
output$value22 <- renderValueBox({
valueBox(round(sum(stat_react_bowl1()$Balls)/sum(stat_react_bowl1()$Wickets),2), "Bowling Strike Rate", color = "purple")
}) # Bowling Strike Rate
output$value23 <- renderValueBox({
valueBox(paste0(round(sum(stat_react_bowl1()$Dots)/sum(stat_react_bowl1()$Balls)*100,2),"%"), "Dot Ball Percentage", color = "purple")
}) # Dot%
}
# .rs.files.restoreBindings()
shinyApp(ui = ui, server = server)