{r setup, include=FALSE} library(flexdashboard)

YELLOW CARD ANALYSIS

Column

player yellow cards

TEAM YELLOW CARDS

Column

games played vs yellowcards

player ranking VS per game yellow card average

barplot for top 10 players with most yellow

pieplot for YELLOWS CARDS 20/21(clubs)

RED CARD ANALYSIS

Column

player yellow cards

{r} library(DT) library(tidyverse) library(dplyr) library(rvest)

#DATA FOR PLAYER RED

url3<-“https://sports.ndtv.com/english-premier-league/stats/most-red-cards-player-statsdetail/2020-21” r=read_html(url3) %>% html_nodes(“table”) %>% html_table() red=data.frame(r)

redcards=head(red,10) redcards[“average”]=(redcards$red.card/redcards$Games.Played) datatable(redcards)

TEAM RED CARDS

{r} library(DT) library(tidyverse) library(dplyr) library(rvest)

url4<-“https://sports.ndtv.com/english-premier-league/stats/most-red-cards-team-statsdetail/2020-21” tr=read_html(url4) %>% html_nodes(“table”) %>% html_table() teamred=data.frame(tr) teamredcards=head(teamred,10) datatable(teamredcards)

Column

games played vs RED cards

{r}

plot(redcards$Games.Played,redcards$red.card,type=“p”,xlab =" games played“, ylab =”red cards“,main=”GAMES PLAYED VS RED CARDS CONCEDED“,col=rainbow(10),pch=19) legend(”topleft",legend = redcards$Player.Name,col = rainbow(10),cex=0.8,fill = rainbow((10)))

player ranking per game red card average

{R} plot(redcards$average,redcards$Rank,type=“p”,xlab =" red card average per game“, ylab =”ranking“,main=”PLAYER RANKING VS PER GAME REDCARD AVERAGE“,col=rainbow(10),pch=19) legend(”center",legend = redcards$Player.Name,col = rainbow(10),cex=0.5,fill = rainbow((10)))

correlation between games played vs per game red card average

{r}

#CORRELATION # Data generation set.seed(1) x <- redcards$average y <- redcards$Games.Played

Creating the plot

plot(x, y,xlab = “Passes”,ylab = “Wins”,main = “RELATIONSHIP BETWEEN PER GAME RED CARD AVERAGE AND GAMES PLAYED”, pch = 19, col = rainbow(10))

Regression line

abline(lm(y ~ x), col = “red”, lwd = 3)

Pearson correlation

text(paste(“Correlation:”, round(cor(x, y), 2)), x = 17000, y = 25) cd=cor(x,y)

pie plot

{r} slices=teamredcards$red.card names=teamredcards$Club.Name pct=round(slices/sum(slices)*100) new=paste(names,“-”,pct,“%”,sep="“) pie(slices,labels = new,main =”RED CARDS 20/21(club)",col=rainbow(11:20))