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analysis on ipl 2023 auction dataset

#ipl 2023 auction dataset
data=read.csv("C:/Users/java/Downloads/ipl_2023_dataset.csv")

#summary and structure of data
summary(data)

##  Player.Name         Base.Price            Type           Cost.in.Rs...CR. 
##  Length:568         Length:568         Length:568         Min.   : 0.0000  
##  Class :character   Class :character   Class :character   1st Qu.: 0.0000  
##  Mode  :character   Mode  :character   Mode  :character   Median : 0.0000  
##                                                           Mean   : 0.6872  
##                                                           3rd Qu.: 0.2000  
##                                                           Max.   :18.5000  
##                                                           NA's   :325      
##  Cost.in....K.     X2022.Squad        X2023.Squad       
##  Min.   :   0.00   Length:568         Length:568        
##  1st Qu.:   0.00   Class :character   Class :character  
##  Median :   0.00   Mode  :character   Mode  :character  
##  Mean   :  82.47                                        
##  3rd Qu.:  24.00                                        
##  Max.   :2220.00                                        
##  NA's   :325

str(data)

## 'data.frame':    568 obs. of  7 variables:
##  $ Player.Name     : chr  "Shivam Mavi" "Joshua Little" "Kane Williamson" "K.S. Bharat" ...
##  $ Base.Price      : chr  "4000000" "5000000" "20000000" "2000000" ...
##  $ Type            : chr  "BOWLER" "BOWLER" "BATSMAN" "WICKETKEEPER" ...
##  $ Cost.in.Rs...CR.: num  6 4.4 2 1.2 0.5 0.5 0.2 0 0 0 ...
##  $ Cost.in....K.   : int  720 528 240 144 60 60 24 0 0 0 ...
##  $ X2022.Squad     : chr  "KKR" "" "SRH" "DC" ...
##  $ X2023.Squad     : chr  "GT" "GT" "GT" "GT" ...

Including Plots

#top few players
head(data)

##       Player.Name Base.Price         Type Cost.in.Rs...CR. Cost.in....K.
## 1     Shivam Mavi    4000000       BOWLER              6.0           720
## 2   Joshua Little    5000000       BOWLER              4.4           528
## 3 Kane Williamson   20000000      BATSMAN              2.0           240
## 4     K.S. Bharat    2000000 WICKETKEEPER              1.2           144
## 5    Mohit Sharma    5000000       BOWLER              0.5            60
## 6     Odean Smith    5000000  ALL-ROUNDER              0.5            60
##   X2022.Squad X2023.Squad
## 1         KKR          GT
## 2                      GT
## 3         SRH          GT
## 4          DC          GT
## 5                      GT
## 6        PBKS          GT

#few players from down
tail(data)

##           Player.Name Base.Price        Type Cost.in.Rs...CR. Cost.in....K.
## 563    Divyansh Joshi    2000000 ALL-ROUNDER               NA            NA
## 564       Dhruv Patel    2000000 ALL-ROUNDER               NA            NA
## 565   Jack Prestwidge    2000000 ALL-ROUNDER               NA            NA
## 566    Aditya Sarvate    2000000 ALL-ROUNDER               NA            NA
## 567     Sagar Solanki    2000000 ALL-ROUNDER               NA            NA
## 568 Prenelan Subrayen    2000000 ALL-ROUNDER               NA            NA
##     X2022.Squad X2023.Squad
## 563                  Unsold
## 564                  Unsold
## 565                  Unsold
## 566                  Unsold
## 567                  Unsold
## 568                  Unsold

for the plots,we are using ggplot2 library.ggplot2 is a popular R data visualization package that provides an intuitive and flexible framework for creating a wide range of high-quality, customized graphs and plots for data analysis and presentation.

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.2.3

#barplot 
ggplot(data, aes(x = Player.Name, y = Base.Price)) + 
  geom_bar(stat = "identity", fill = "skyblue") + 
  labs(title = "player vs base price", x = "players", y = "base price")

from this plot, we observed that most of the players were either retained by their respective teams or have a base price of 20 lakh rupees.

#histogram
ggplot(data, aes(x = Cost.in.Rs...CR.)) + 
  geom_histogram(binwidth = 5, fill = "pink", color = "brown") + 
  labs(title = "players", x = "price", y = "Frequency")

## Warning: Removed 325 rows containing non-finite values (`stat_bin()`).

we can conclude the same over here also

#scatter plot
ggplot(data, aes(x = Cost.in.Rs...CR., y = Base.Price)) + 
  geom_point(color = "green") + 
  labs(title = "Scatter Plot Example", x = "X Variable", y = "Y Variable")

## Warning: Removed 325 rows containing missing values (`geom_point()`).

this plot didn’t include unsold players.

a=table(data$X2023.Squad)
b=names(a)
a#displaying no of players in each squad

## 
##    CSK     DC     GT    KKR    LSG     MI   PBKS    RCB     RR    SRH Unsold 
##     25     25     25     22     25     24     22     25     25     25    325

share = round(a/sum(a)*100)
a = paste(share,"%",sep="")

the table created above shows no. of players in each squad.

# Create a data frame with the data to be plotted
c <- data.frame(category = b, value = a)

# Create the pie chart using ggplot2
library(ggplot2)
ggplot(c, aes(x = "", y = value, fill = category)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar(theta = "y") +
  geom_text(aes(label = value), position = position_stack(vjust = 0.5)) +
  scale_fill_manual(values = rainbow(length(b))) +
  labs(title = "Pie Chart Example")

shows the above analysis in the form of share of pie.

#boxplot
ggplot(data, aes(x = Type, y =Base.Price)) + 
  geom_boxplot(fill = "grey", color = "black") + 
  labs(title = "Box Plot Example", x = "Category", y = "Value")

no outliers found.