R Markdown

This is an R Markdown document about the “Pro kabaddi season 7raidpoints”.It is useful to buy a player for the next season. It can useful to know about the capacity of the player. :

data=read.csv("C:/Users/java/Downloads/Raids_Season7.csv")

##Summary It gives the brief about our data set.It gives about every column in our dataset

summary(data)
##     Player              Club               Played          Raids       
##  Length:45          Length:45          Min.   :11.00   Min.   : 23.00  
##  Class :character   Class :character   1st Qu.:16.00   1st Qu.: 46.00  
##  Mode  :character   Mode  :character   Median :19.00   Median : 62.00  
##                                        Mean   :18.71   Mean   : 83.31  
##                                        3rd Qu.:22.00   3rd Qu.:102.00  
##                                        Max.   :24.00   Max.   :262.00  
##     Raid.Pts        Raid.Avg       Super.Raids    
##  Min.   : 32.0   Min.   : 1.950   Min.   : 0.000  
##  1st Qu.: 60.0   1st Qu.: 3.750   1st Qu.: 1.000  
##  Median : 79.0   Median : 4.460   Median : 2.000  
##  Mean   :105.6   Mean   : 5.422   Mean   : 2.533  
##  3rd Qu.:125.0   3rd Qu.: 6.000   3rd Qu.: 3.000  
##  Max.   :346.0   Max.   :14.420   Max.   :15.000
## It gives us the structure of the columns in our dataset.It gives data type of the every column and also gives no of variables and no of observations.

str(data)
## 'data.frame':    45 obs. of  7 variables:
##  $ Player     : chr  "Pawan Kumar Sehrawat" "Pardeep Narwal" "Naveen Kumar" "Siddharth Sirish Desai" ...
##  $ Club       : chr  "Bengal Warriors" "Patna Pirates" "Dabang Delhi" "Telugu Titans" ...
##  $ Played     : int  24 22 23 22 20 20 21 22 20 22 ...
##  $ Raids      : int  262 234 251 177 171 146 140 112 113 107 ...
##  $ Raid.Pts   : int  346 302 301 217 205 190 162 148 146 132 ...
##  $ Raid.Avg   : num  14.42 13.73 13.09 9.86 10.25 ...
##  $ Super.Raids: int  13 15 2 7 6 6 3 2 2 1 ...
head(data)
##                   Player             Club Played Raids Raid.Pts Raid.Avg
## 1   Pawan Kumar Sehrawat  Bengal Warriors     24   262      346    14.42
## 2         Pardeep Narwal    Patna Pirates     22   234      302    13.73
## 3           Naveen Kumar     Dabang Delhi     23   251      301    13.09
## 4 Siddharth Sirish Desai    Telugu Titans     22   177      217     9.86
## 5         Maninder Singh  Bengal Warriors     20   171      205    10.25
## 6         Vikash Kandola Haryana Steelers     20   146      190     9.50
##   Super.Raids
## 1          13
## 2          15
## 3           2
## 4           7
## 5           6
## 6           6
tail(data)
##                       Player                 Club Played Raids Raid.Pts
## 40 Farhad Rahimi Milaghardan        Telugu Titans     22    38       46
## 41                   Rajnish        Telugu Titans     11    32       45
## 42               Suraj Desai        Telugu Titans     12    35       44
## 43              Meraj Sheykh         Dabang Delhi     17    31       41
## 44 Mohammad Esmaeil Maghsoud        Patna Pirates     20    31       39
## 45               Nitin Rawal Jaipur Pink Panthers     16    23       32
##    Raid.Avg Super.Raids
## 40     2.09           0
## 41     4.09           1
## 42     3.67           1
## 43     2.41           1
## 44     1.95           0
## 45     2.00           1

Including Plots

You can also embed plots, for example:

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.2.1
#Data Layer
ggplot(data = data) + labs(title ="Raiders of pkl")

ggplot(data = data, aes(x = Raids, y = Raid.Pts, col = Raid.Avg))+labs(title = "Raiders of pkl")

Here we plot a graph between raids and raid points. We observe that aas the no of raids are incresing the raid points are also increasing.

ggplot(data = data, aes(x = Raids, y = Raid.Pts, col = Raid.Avg , )) +
  geom_point() +
  labs(title = "Raids vs Raid points", x = "Raids", y = "Raid points")

ggplot(data = data, aes(x = Raids, y = Raid.Pts, size = Raid.Avg)) +
  geom_point() +
  labs(title = "Raids vs Raid points", x = "Raids", y = "Raid points")

ggplot(data = data, aes(x = Raids, y = Raid.Pts, col = factor(Raid.Avg), shape = factor(Super.Raids))) +geom_point() +
labs(title = "Raids vs Raid points", x = "Raids", y = "Raid points")
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 9. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 3 rows containing missing values (geom_point).

ggplot(data = data, aes(x = Raid.Pts)) +
geom_histogram(binwidth = 10,color="black", fill="lightblue") +
labs(title = "Histogram of Raid points", x = "Raid points", y = "No of raiders")

ggplot(data = data, aes(x=Raids, fill=Raid.Pts)) + 
       geom_bar(stat="count")

carb = table(data$Super.Raids)
data.labels = names(carb)
share = round(carb/sum(carb)*1000)
data.labels = paste(data.labels, share)
data.labels = paste(data.labels) 
pie(carb,labels = data.labels,clockwise=TRUE, col=heat.colors(length(data.labels)), main="Frequency of Super Raids")

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.