Exploratory Data Analysis

Reading Data into R Environment

# reading rda file
load("IPLData.rda")
attach(IPLData)

Dimensions of the data frame

# dimentions of the data
dim(IPLData)

## [1] 612  14

Column names of the data frame

# column names of the data frame
colnames(IPLData)

##  [1] "Year"        "City"        "Match.No"    "Team"        "Won"        
##  [6] "TossWon"     "BatFrist"    "HomeMatch"   "PPRuns"      "PPWickets"  
## [11] "FourCount"   "SixCount"    "WicketsLost" "TotelRuns"

Structure of the data frame

# structure names of the data frame]
str(IPLData)

## 'data.frame':    612 obs. of  14 variables:
##  $ Year       : Factor w/ 6 levels "A2019","B2018",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ City       : Factor w/ 21 levels "Abu Dhabi","Ahemdabad",..: 9 9 16 16 18 18 10 10 3 3 ...
##  $ Match.No   : int  1 1 2 2 3 3 4 4 5 5 ...
##  $ Team       : Factor w/ 12 levels "CSK","DC","GL",..: 10 7 6 11 3 4 8 5 7 2 ...
##  $ Won        : Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 2 1 ...
##  $ TossWon    : Factor w/ 2 levels "0","1": 1 2 1 2 1 2 1 2 2 1 ...
##  $ BatFrist   : Factor w/ 2 levels "0","1": 2 1 2 1 2 1 2 1 2 1 ...
##  $ HomeMatch  : Factor w/ 2 levels "0","1": 2 1 1 2 1 1 1 1 2 1 ...
##  $ PPRuns     : int  59 54 61 59 52 73 50 50 41 43 ...
##  $ PPWickets  : int  1 1 1 1 1 0 1 1 2 2 ...
##  $ FourCount  : int  17 15 12 18 18 18 11 11 14 10 ...
##  $ SixCount   : int  9 6 11 6 8 8 7 7 7 5 ...
##  $ WicketsLost: int  4 10 8 3 4 0 6 4 8 9 ...
##  $ TotelRuns  : int  207 172 184 187 183 184 163 164 157 142 ...

Descriptive Statistics of the data frame

library(psych)
describe(IPLData)[,c(2:5,8:9)]

##               n   mean    sd median min max
## Year*       612   3.37  1.73    3.0   1   6
## City*       612  10.49  5.09   11.0   1  21
## Match.No    612  29.46 15.98   30.0   1  56
## Team*       612   5.76  3.23    5.0   1  12
## Won*        612   1.50  0.50    1.0   1   2
## TossWon*    612   1.50  0.50    1.5   1   2
## BatFrist*   612   1.50  0.50    1.5   1   2
## HomeMatch*  612   1.41  0.49    1.0   1   2
## PPRuns      612  47.67 12.01   48.0  15 105
## PPWickets   612   1.35  1.08    1.0   0   5
## FourCount   612  13.61  4.23   14.0   2  31
## SixCount    612   6.00  3.47    6.0   0  20
## WicketsLost 612   5.73  2.41    6.0   0  10
## TotelRuns   612 160.12 31.98  162.0  41 248

Summary Statistics of the data frame

# summary statistics of the data frame
summary(IPLData)

##     Year            City        Match.No          Team     Won     TossWon
##  A2019:112   Mumbai   : 74   Min.   : 1.00   KXIP   : 79   0:307   0:306  
##  B2018:112   Kolkata  : 72   1st Qu.:16.00   RCB    : 78   1:305   1:306  
##  C2017:110   Bengaluru: 70   Median :30.00   DC     : 77                  
##  D2016:104   Delhi    : 70   Mean   :29.46   MI     : 77                  
##  E2015: 62   Hyderabad: 70   3rd Qu.:43.00   KKR    : 76                  
##  F2014:112   Mohali   : 42   Max.   :56.00   SRH    : 74                  
##              (Other)  :214                   (Other):151                  
##  BatFrist HomeMatch     PPRuns         PPWickets       FourCount    
##  0:306    0:363     Min.   : 15.00   Min.   :0.000   Min.   : 2.00  
##  1:306    1:249     1st Qu.: 40.00   1st Qu.:1.000   1st Qu.:11.00  
##                     Median : 48.00   Median :1.000   Median :14.00  
##                     Mean   : 47.67   Mean   :1.355   Mean   :13.61  
##                     3rd Qu.: 55.00   3rd Qu.:2.000   3rd Qu.:16.00  
##                     Max.   :105.00   Max.   :5.000   Max.   :31.00  
##                                                                     
##     SixCount       WicketsLost       TotelRuns    
##  Min.   : 0.000   Min.   : 0.000   Min.   : 41.0  
##  1st Qu.: 3.000   1st Qu.: 4.000   1st Qu.:142.0  
##  Median : 6.000   Median : 6.000   Median :162.0  
##  Mean   : 5.997   Mean   : 5.729   Mean   :160.1  
##  3rd Qu.: 8.000   3rd Qu.: 7.000   3rd Qu.:181.0  
##  Max.   :20.000   Max.   :10.000   Max.   :248.0  
##

Two way Frequency Tables

Percentage winnings of IPL matches with TossWon

tab1 <- table(Won,TossWon)
tab2 <- prop.table(tab1)
round(tab2*100,2)

##    TossWon
## Won     0     1
##   0 27.29 22.88
##   1 22.71 27.12

Percentage winnings of IPL matches with BatFrist

tab1 <- table(Won,BatFrist)
tab2 <- prop.table(tab1)
round(tab2*100,2)

##    BatFrist
## Won     0     1
##   0 22.55 27.61
##   1 27.45 22.39

Percentage winnings of IPL matches with HomeMatch

tab1 <- table(Won,HomeMatch)
tab2 <- prop.table(tab1)
round(tab2*100,2)

##    HomeMatch
## Won     0     1
##   0 33.50 16.67
##   1 25.82 24.02

Summary Statistics in Groups

Average total runs broken down by BatFrist

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    mean = round(mean(TotelRuns),2),
                      sd = round(sd(TotelRuns),2)),
                      by = (BatFrist)][order(BatFrist)]
tab1

##    BatFrist   N   mean    sd
## 1:        0 306 154.81 32.08
## 2:        1 306 165.44 31.03

Average TotelRuns broken down by TossWon

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    mean = round(mean(TotelRuns),2),
                      sd = round(sd(TotelRuns),2)),
                      by = (TossWon)][order(TossWon)]
tab1

##    TossWon   N   mean    sd
## 1:       0 306 165.35 30.91
## 2:       1 306 154.90 32.22

Average TotelRuns broken down by HomeMatch

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    mean = round(mean(TotelRuns),2),
                      sd = round(sd(TotelRuns),2)),
                      by = (HomeMatch)][order(HomeMatch)]
tab1

##    HomeMatch   N   mean    sd
## 1:         0 363 157.20 32.21
## 2:         1 249 164.39 31.21

Average TotelRuns broken down by Won

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    mean = round(mean(TotelRuns),2),
                      sd = round(sd(TotelRuns),2)),
                      by = (Won)][order(Won)]
tab1

##    Won   N   mean    sd
## 1:   0 307 152.94 32.06
## 2:   1 305 167.36 30.26

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by TossWon

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    TotelRuns = round(mean(TotelRuns),2),
                    PPRuns    = round(mean(PPRuns),2),
                    PPWickets = round(mean(PPWickets),2),
                    FourCount = round(mean(FourCount),2),
                    SixCount  = round(mean(SixCount),2),
                    WicketsLost = round(mean(WicketsLost),2)),
                      by = (TossWon)][order(TossWon)]
tab1

##    TossWon   N TotelRuns PPRuns PPWickets FourCount SixCount WicketsLost
## 1:       0 306    165.35  46.82      1.31     13.85     6.27        5.89
## 2:       1 306    154.90  48.52      1.40     13.37     5.73        5.57

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by BatFrist

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    TotelRuns = round(mean(TotelRuns),2),
                    PPRuns    = round(mean(PPRuns),2),
                    PPWickets = round(mean(PPWickets),2),
                    FourCount = round(mean(FourCount),2),
                    SixCount  = round(mean(SixCount),2),
                    WicketsLost = round(mean(WicketsLost),2)),
                      by = (BatFrist)][order(BatFrist)]
tab1

##    BatFrist   N TotelRuns PPRuns PPWickets FourCount SixCount WicketsLost
## 1:        0 306    154.81  48.80      1.36     13.40     5.60        5.51
## 2:        1 306    165.44  46.54      1.35     13.82     6.39        5.95

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by HomeMatch

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    TotelRuns = round(mean(TotelRuns),2),
                    PPRuns    = round(mean(PPRuns),2),
                    PPWickets = round(mean(PPWickets),2),
                    FourCount = round(mean(FourCount),2),
                    SixCount  = round(mean(SixCount),2),
                    WicketsLost = round(mean(WicketsLost),2)),
                      by = (HomeMatch)][order(HomeMatch)]
tab1

##    HomeMatch   N TotelRuns PPRuns PPWickets FourCount SixCount WicketsLost
## 1:         0 363    157.20  47.09      1.37     13.37     5.68        5.81
## 2:         1 249    164.39  48.52      1.33     13.95     6.46        5.61

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by HomeMatch,BatFrist

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    TotelRuns = round(mean(TotelRuns),2),
                    PPRuns    = round(mean(PPRuns),2),
                    PPWickets = round(mean(PPWickets),2),
                    FourCount = round(mean(FourCount),2),
                    SixCount  = round(mean(SixCount),2),
                    WicketsLost = round(mean(WicketsLost),2)),
                      by = .(BatFrist,HomeMatch)][order(HomeMatch)]
tab1

##    BatFrist HomeMatch   N TotelRuns PPRuns PPWickets FourCount SixCount
## 1:        0         0 205    152.59  48.00      1.39     13.15     5.36
## 2:        1         0 158    163.18  45.92      1.35     13.66     6.09
## 3:        1         1 148    167.85  47.21      1.34     13.99     6.72
## 4:        0         1 101    159.33  50.44      1.32     13.90     6.09
##    WicketsLost
## 1:        5.68
## 2:        5.99
## 3:        5.91
## 4:        5.16

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by HomeMatch,TossWon

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    TotelRuns = round(mean(TotelRuns),2),
                    PPRuns    = round(mean(PPRuns),2),
                    PPWickets = round(mean(PPWickets),2),
                    FourCount = round(mean(FourCount),2),
                    SixCount  = round(mean(SixCount),2),
                    WicketsLost = round(mean(WicketsLost),2)),
                      by = .(TossWon,HomeMatch)][order(HomeMatch)]
tab1

##    TossWon HomeMatch   N TotelRuns PPRuns PPWickets FourCount SixCount
## 1:       1         0 194    152.37  47.41      1.39     12.99     5.46
## 2:       0         0 169    162.74  46.72      1.34     13.81     5.92
## 3:       0         1 137    168.56  46.94      1.26     13.90     6.69
## 4:       1         1 112    159.29  50.45      1.42     14.02     6.18
##    WicketsLost
## 1:        5.65
## 2:        6.00
## 3:        5.74
## 4:        5.44

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by BatFrist,TossWon

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    TotelRuns = round(mean(TotelRuns),2),
                    PPRuns    = round(mean(PPRuns),2),
                    PPWickets = round(mean(PPWickets),2),
                    FourCount = round(mean(FourCount),2),
                    SixCount  = round(mean(SixCount),2),
                    WicketsLost = round(mean(WicketsLost),2)),
                      by = .(TossWon,BatFrist)][order(BatFrist)]
tab1

##    TossWon BatFrist   N TotelRuns PPRuns PPWickets FourCount SixCount
## 1:       1        0 271    155.26  48.97      1.36     13.45     5.75
## 2:       0        0  35    151.34  47.51      1.37     12.97     4.46
## 3:       0        1 271    167.15  46.73      1.30     13.96     6.50
## 4:       1        1  35    152.14  45.09      1.71     12.71     5.54
##    WicketsLost
## 1:        5.45
## 2:        5.97
## 3:        5.87
## 4:        6.54

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by BatFrist,TossWon,HomeMatch

# summary statistics by groups
library(data.table)
IPLData.dt <- data.table(IPLData)
tab1 <- IPLData.dt[, .(N = .N,
                    TotelRuns = round(mean(TotelRuns),2),
                    PPRuns    = round(mean(PPRuns),2),
                    PPWickets = round(mean(PPWickets),2),
                    FourCount = round(mean(FourCount),2),
                    SixCount  = round(mean(SixCount),2),
                    WicketsLost = round(mean(WicketsLost),2)),
                      by = .(TossWon,BatFrist,HomeMatch)][order(HomeMatch)]
tab1

##    TossWon BatFrist HomeMatch   N TotelRuns PPRuns PPWickets FourCount
## 1:       1        0         0 180    152.57  48.02      1.37     13.14
## 2:       0        1         0 144    164.48  46.53      1.32     13.92
## 3:       0        0         0  25    152.72  47.84      1.48     13.16
## 4:       1        1         0  14    149.79  39.64      1.64     11.00
## 5:       0        1         1 127    170.19  46.96      1.28     14.01
## 6:       1        0         1  91    160.58  50.85      1.34     14.05
## 7:       1        1         1  21    153.71  48.71      1.76     13.86
## 8:       0        0         1  10    147.90  46.70      1.10     12.50
##    SixCount WicketsLost
## 1:     5.44        5.59
## 2:     6.12        5.94
## 3:     4.76        6.32
## 4:     5.71        6.43
## 5:     6.93        5.80
## 6:     6.35        5.16
## 7:     5.43        6.62
## 8:     3.70        5.10

Correlation Matrix

library(dplyr)
# Subset of Continuous Data Columns
IPLData2 <- select(IPLData,c(9:14))
#dim(dt2)

# Create the Correlation matrix
corMat <- round(cor(IPLData2), 2)#corMat
corMat

##             PPRuns PPWickets FourCount SixCount WicketsLost TotelRuns
## PPRuns        1.00     -0.42      0.45     0.26       -0.16      0.45
## PPWickets    -0.42      1.00     -0.27    -0.18        0.46     -0.33
## FourCount     0.45     -0.27      1.00     0.15       -0.24      0.66
## SixCount      0.26     -0.18      0.15     1.00       -0.24      0.67
## WicketsLost  -0.16      0.46     -0.24    -0.24        1.00     -0.27
## TotelRuns     0.45     -0.33      0.66     0.67       -0.27      1.00

Visualizing Correlation Matrix

library(corrplot)
corrplot(corMat, method="circle")

MBASkills.In

Data Analytics Skills (for MBAs) using R programming

Exploratory Data Analysis

Reading Data into R Environment

Dimensions of the data frame

Column names of the data frame

Structure of the data frame

Descriptive Statistics of the data frame

Summary Statistics of the data frame

Two way Frequency Tables

Percentage winnings of IPL matches with TossWon

Percentage winnings of IPL matches with BatFrist

Percentage winnings of IPL matches with HomeMatch

Summary Statistics in Groups

Average total runs broken down by BatFrist

Average TotelRuns broken down by TossWon

Average TotelRuns broken down by HomeMatch

Average TotelRuns broken down by Won

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by TossWon

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by BatFrist

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by HomeMatch

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by HomeMatch,BatFrist

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by HomeMatch,TossWon

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by BatFrist,TossWon

Average TotelRuns,PPRuns,PPWickets,FourCount,SixCount,WicketsLost broken down by BatFrist,TossWon,HomeMatch

Correlation Matrix

Visualizing Correlation Matrix