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passviz.R
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passviz.R
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#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
# install.packages("bslib")
# install.packages("thematic")
library(shiny)
#library(plotly)
library(shinythemes)
library(magrittr)
library(ggplot2)
library(ggsoccer)
library(extrafont)
library(dplyr)
library(tidyverse)
# library(bslib)
# library(thematic)
#thematic::thematic_shiny(font = "auto")
#mapping the instat pitch#
pitch_custom <- list(
length = 105,
width = 68,
penalty_box_length = 16.5,
penalty_box_width = 40.3,
six_yard_box_length = 5.5,
six_yard_box_width = 18.3,
penalty_spot_distance = 11,
goal_width = 7.32,
origin_x = 0,
origin_y = 0
)
#importing the dataset#
islpassultimate<-read.csv("ISL passes.csv")
islpassultimate<-islpassultimate%>%filter(xend!=0,yend!=0)
'%ni%'<-Negate('%in%')
#Defining the functions to use#
kmeansfunc<-function(playername,szn){
soc1<-islpassultimate%>%filter(str_detect(player,playername),season==szn,
x!=105,y%ni%c(0,68),xend!=105,yend!=68)
soc1km<-soc1%>%select(id,team,season,event,x,y,xend,yend)
kmeansoc1<-kmeans(soc1km[,5:8],centers = 6,nstart = 25,iter.max = 30)
soc1km$clust<-kmeansoc1$cluster
playerkmeans<-soc1km
playerkmeans$clustername<-factor(playerkmeans$clust,
levels = c(1,2,3,4,5,6),
labels = c("Cluster 1","Cluster 2","Cluster 3",
"Cluster 4","Cluster 5","Cluster 6"))
return(playerkmeans)
}
clustermeanfunc1<-function(playerdf){
socc1<-playerdf[1:6,]
for (i in 1:nrow(socc1)) {
socc1$id[i]=0
socc1$team[i]=paste0("cluster means",i)
socc1$season[i]="placeholder season"
socc1$event[i]="placeholder event"
socc1$x[i]=mean((playerdf%>%filter(clust==i))$x)
socc1$y[i]=mean((playerdf%>%filter(clust==i))$y)
socc1$xend[i]=mean((playerdf%>%filter(clust==i))$xend)
socc1$yend[i]=mean((playerdf%>%filter(clust==i))$yend)
socc1$clust[i]=i
socc1$clustername[i]=paste0("Cluster ",i)
}
meankmeans<-socc1
return(meankmeans)
}
ui <- fluidPage(
#h1("Title without tags$"),
column(12,titlePanel(strong(tags$p("visualising PASSES")))),
#h3("H3 is fine without tags and so is code here"),
tags$head(tags$style(HTML('* {font-family: "Courier"};'))),
theme = shinytheme("cyborg"),
# App title ----
#titlePanel("Passing Styles"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
width = 3,
selectizeInput("b1","SELECT PLAYER (or SEARCH):",
levels(factor(levels(factor(((islpassultimate%>%group_by(player)%>%summarise(nPass=n()))%>%filter(nPass>100))$player)))),
options = list(maxItems = 1,placeholder="Search player")),
#selectizeInput("b2","Select season:",levels(factor(islpassultimate$season))),
uiOutput("secondSelection"),
actionButton("do","SHOW PASS MAPS"),
br(),
br(),
tags$b("This application maps all successful passes made in ISL history, searchable by player and season."),
br(),
br(),
tags$b("",tags$em("PASS CLUSTERS")," tab shows similar passes of a selected player clustered by a ML algorithm, while ",tags$em("PROGRESSIVE PASSES")," tab shows a map of progressive passes made by that player."),
br(),
br(),
tags$b("ISL data sourced from InStat."),
br(),
br(),
img(src="instat logo.png",height="70%",width="100%"),
# Input: Select the random distribution type ----
# radioButtons("dist", "Distribution type:",
# c("Normal" = "norm",
# "Uniform" = "unif",
# "Log-normal" = "lnorm",
# "Exponential" = "exp")),
#
# br() element to introduce extra vertical spacing ----
br(),
# Input: Slider for the number of observations to generate ----
# sliderInput("n",
# "Number of observations:",
# value = 500,
# min = 1,
# max = 1000)
),
# Main panel for displaying outputs ----
mainPanel(
width = 9,
# Output: Tabset w/ plot, summary, and table ----
tabsetPanel(type = "tabs",
tabPanel("PASS CLUSTERS", plotOutput("plot1",height = "498px")),
tabPanel("PROGRESSIVE PASSES", plotOutput("plot2",height = "498px"))
)
#plotOutput("plot1",height = "498px")
)
)
)
server <- function(input, output, session) {
x<-eventReactive(input$do,{
#islpassultimate%>%filter(player==input$b1,season==input$b2)
kmeansfunc(playername = input$b1,szn = input$b2)
}
)
y<-eventReactive(input$do,{
#kmeans(x()%>%select(x,y,xend,yend),centers=4,nstart=25,iter.max = 30)
clustermeanfunc1(x())
}
)
z<-eventReactive(input$do,{
islpassultimate%>%mutate(xprog=xend-x)%>%
filter(player==input$b1,season==input$b2,xprog>=25,x>=16.5,x!=105,y%ni%c(0,68),xend!=105,yend!=68)
}
)
output$secondSelection<-renderUI({
selectInput("b2","SEASON:",levels(factor((islpassultimate%>%filter(player==input$b1))$season)))
})
output$plot1<-renderPlot(({
ggplot(x())+
annotate_pitch(dimensions = pitch_custom,colour = "white",fill = "#38383b")+
theme_pitch()+
geom_segment(aes(x=x,y=y,xend=xend,yend=yend),lineend = "butt",
linejoin = "mitre",alpha=0.5,colour="#74c69d",
arrow = arrow(ends = "last",length = unit(0.05,"cm"),
type = "closed"))+
facet_wrap(~clustername)+
geom_segment(data = y(),aes(x=x,y=y,xend=xend,yend=yend),lineend = "butt",
linejoin = "mitre",
arrow = arrow(ends = "last",length = unit(0.20,"cm"),
type = "closed"),
colour="red",size=0.8,alpha=0.9)+
labs(title = "PASS CLUSTERS",subtitle = paste0(input$b1,", ",input$b2),
caption = "@abhibharali")+
geom_segment(aes(x=0,y=-3,xend=30,yend=-3),size=1,lineend = "butt",
linejoin = "mitre",
arrow = arrow(type = "closed",length = unit(0.07,"inches")),
colour="azure4")+
theme(
plot.background = element_rect(fill = "white"),
panel.background = element_rect(fill = "white"),
#strip.background = element_rect(fill = "white"),
#strip.text = element_text(family = "Segoe UI Semibold",colour = "black",size = 5),
text = element_text(family = "Courier",colour = "black",hjust = 0),
plot.title = element_text(family = "Courier",colour = "black",hjust = 0,
face = "bold",size = 20),
plot.subtitle = element_text(family = "Courier",colour = "yellow",hjust = 0,
size = 15,color = "slateblue1",face = "bold"),
plot.caption = element_text(size = 10,face = "bold"),
strip.background = element_blank(),
strip.text = element_blank(),
panel.border = element_blank()
#plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")
)
}
))
output$plot2<-renderPlot(({
ggplot(z())+
annotate_pitch(dimensions = pitch_custom,colour = "white",fill = "#38383b")+
theme_pitch()+
geom_segment(aes(x=x,y=y,xend=xend,yend=yend),lineend = "butt",
linejoin = "mitre",alpha=0.9,colour="skyblue4",
arrow = arrow(ends = "last",length = unit(0.20,"cm"),
type = "closed"),size=1)+
labs(title = "PROGRESSIVE PASSES",subtitle = paste0(input$b1,", ",input$b2),
caption = "@abhibharali")+
geom_segment(aes(x=0,y=-3,xend=30,yend=-3),size=1,lineend = "butt",
linejoin = "mitre",
arrow = arrow(type = "closed",length = unit(0.07,"inches")),
colour="azure4")+
theme(
plot.background = element_rect(fill = "white"),
panel.background = element_rect(fill = "white"),
#strip.background = element_rect(fill = "white"),
#strip.text = element_text(family = "Segoe UI Semibold",colour = "black",size = 5),
text = element_text(family = "Courier",colour = "black",hjust = 0.5),
plot.title = element_text(family = "Courier",colour = "black",hjust = 0.5,
face = "bold",size = 20),
plot.subtitle = element_text(family = "Courier",hjust = 0.5,
size = 15,color = "skyblue4",face = "bold"),
plot.caption = element_text(size = 10,face = "bold"),
strip.background = element_blank(),
strip.text = element_blank(),
panel.border = element_blank(),
plot.margin = unit(c(0.5, 0, 0.5, 0), "cm")
)
}))
# Reactive expression to generate the requested distribution ----
# This is called whenever the inputs change. The output functions
# defined below then use the value computed from this expression
# d <- reactive({
# dist <- switch(input$b1,
# norm = rnorm,
# unif = runif,
# lnorm = rlnorm,
# exp = rexp,
# rnorm)
#
# dist(input$n)
# })
# Generate a plot of the data ----
# Also uses the inputs to build the plot label. Note that the
# dependencies on the inputs and the data reactive expression are
# both tracked, and all expressions are called in the sequence
# implied by the dependency graph.
# output$plot <- renderPlot({
# dist <- input$dist
# n <- input$n
#
# hist(d(),
# main = paste("r", dist, "(", n, ")", sep = ""),
# col = "#75AADB", border = "white")
# })
#
# Generate a summary of the data ----
# output$summary <- renderPrint({
# summary(d())
# })
#
# # Generate an HTML table view of the data ----
# output$table <- renderTable({
# d()
# })
}
# # Define UI for application that draws a histogram
# ui <- fluidPage(
#
# # Application title
# titlePanel("Old Faithful Geyser Data"),
#
# # Sidebar with a slider input for number of bins
# sidebarLayout(
# sidebarPanel(
# sliderInput("bins",
# "Number of bins:",
# min = 1,
# max = 50,
# value = 30)
# ),
#
# # Show a plot of the generated distribution
# mainPanel(
# plotOutput("distPlot")
# )
# )
# )
#
# # Define server logic required to draw a histogram
# server <- function(input, output) {
#
# output$distPlot <- renderPlot({
# # generate bins based on input$bins from ui.R
# x <- faithful[, 2]
# bins <- seq(min(x), max(x), length.out = input$bins + 1)
#
# # draw the histogram with the specified number of bins
# hist(x, breaks = bins, col = 'darkgray', border = 'white')
# })
# }
#
# # Run the application
shinyApp(ui = ui, server = server)
#