Intro to R :DAta dive 1
Sumedh Sonawane
2023-09-11
Intro to R:Data Dive 1
Dataset-English Premier League
library(tidyverse) #importing the required libraries## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsLoading the Dataset
data <-read.csv('C:/Downloads/final_dataset.csv ')General Summary of data
summary(data)## X Date HomeTeam AwayTeam
## Min. : 0 Length:6840 Length:6840 Length:6840
## 1st Qu.:1710 Class :character Class :character Class :character
## Median :3420 Mode :character Mode :character Mode :character
## Mean :3420
## 3rd Qu.:5129
## Max. :6839
## FTHG FTAG FTR HTGS
## Min. :0.000 Min. :0.00 Length:6840 Min. : 0.00
## 1st Qu.:1.000 1st Qu.:0.00 Class :character 1st Qu.: 11.00
## Median :1.000 Median :1.00 Mode :character Median : 23.00
## Mean :1.527 Mean :1.13 Mean : 24.42
## 3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.: 35.00
## Max. :9.000 Max. :7.00 Max. :102.00
## ATGS HTGC ATGC HTP
## Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. :0.0000
## 1st Qu.: 11.00 1st Qu.:11.0 1st Qu.:11.00 1st Qu.:0.8889
## Median : 23.00 Median :23.0 Median :23.00 Median :1.1724
## Mean : 24.51 Mean :24.5 Mean :24.35 Mean :1.2090
## 3rd Qu.: 35.00 3rd Qu.:36.0 3rd Qu.:36.00 3rd Qu.:1.5556
## Max. :105.00 Max. :85.0 Max. :82.00 Max. :2.7368
## ATP HM1 HM2 HM3
## Min. :0.0000 Length:6840 Length:6840 Length:6840
## 1st Qu.:0.9062 Class :character Class :character Class :character
## Median :1.1923 Mode :character Mode :character Mode :character
## Mean :1.2268
## 3rd Qu.:1.5625
## Max. :2.7619
## HM4 HM5 AM1 AM2
## Length:6840 Length:6840 Length:6840 Length:6840
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## AM3 AM4 AM5 MW
## Length:6840 Length:6840 Length:6840 Min. : 1.0
## Class :character Class :character Class :character 1st Qu.:10.0
## Mode :character Mode :character Mode :character Median :19.5
## Mean :19.5
## 3rd Qu.:29.0
## Max. :38.0
## HTFormPtsStr ATFormPtsStr HTFormPts ATFormPts
## Length:6840 Length:6840 Min. : 0.000 Min. : 0.000
## Class :character Class :character 1st Qu.: 4.000 1st Qu.: 4.000
## Mode :character Mode :character Median : 6.000 Median : 6.000
## Mean : 6.243 Mean : 6.414
## 3rd Qu.: 9.000 3rd Qu.: 9.000
## Max. :15.000 Max. :15.000
## HTWinStreak3 HTWinStreak5 HTLossStreak3 HTLossStreak5
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.06228 Mean :0.01798 Mean :0.0576 Mean :0.01433
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## ATWinStreak3 ATWinStreak5 ATLossStreak3 ATLossStreak5
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.06287 Mean :0.01652 Mean :0.05102 Mean :0.01023
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## HTGD ATGD DiffPts DiffFormPts
## Min. :-3.00000 Min. :-3.33333 Min. :-2.36364 Min. :-2.25000
## 1st Qu.:-0.50000 1st Qu.:-0.46429 1st Qu.:-0.45161 1st Qu.:-0.17647
## Median :-0.07692 Median :-0.05000 Median : 0.00000 Median : 0.00000
## Mean :-0.00969 Mean : 0.01454 Mean :-0.01775 Mean :-0.01809
## 3rd Qu.: 0.40000 3rd Qu.: 0.42105 3rd Qu.: 0.42857 3rd Qu.: 0.15385
## Max. : 4.00000 Max. : 3.50000 Max. : 2.28571 Max. : 2.25000Column names in data
colnames(data)#coloumn name of the data## [1] "X" "Date" "HomeTeam" "AwayTeam"
## [5] "FTHG" "FTAG" "FTR" "HTGS"
## [9] "ATGS" "HTGC" "ATGC" "HTP"
## [13] "ATP" "HM1" "HM2" "HM3"
## [17] "HM4" "HM5" "AM1" "AM2"
## [21] "AM3" "AM4" "AM5" "MW"
## [25] "HTFormPtsStr" "ATFormPtsStr" "HTFormPts" "ATFormPts"
## [29] "HTWinStreak3" "HTWinStreak5" "HTLossStreak3" "HTLossStreak5"
## [33] "ATWinStreak3" "ATWinStreak5" "ATLossStreak3" "ATLossStreak5"
## [37] "HTGD" "ATGD" "DiffPts" "DiffFormPts"Dataset Information: English Premier league dataset
The data set involves around 35 columns
The main columns include the home team, away team,date,FTHG,FTAG,etc
Goal/Purpose:
The goal is to observe and analyze the English Premier league data set and predict winners in different category.
Basic Visualization
data<- c("Liverpool", "Manchester City", "Manchester United", "Chelsea", "Arsenal")
points <- c(99, 98, 85, 78, 68)
data <- data.frame(Team = data, Points = points)
library(ggplot2)
ggplot(data, aes(x = Team, y = Points, fill = Team)) +
geom_bar(stat = "identity") +
labs(title = "English Premier League Team Performance",
x = "Team",
y = "Total Points") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
data <- c("Liverpool", "Manchester City", "Manchester United", "Chelsea", "Arsenal")
goals_scored <- c(85, 95, 75, 70, 65)
data <- data.frame(Team = data, GoalsScored = goals_scored)
barplot(data$GoalsScored,
names.arg = data$Team,
main = "Goals Scored by English Premier League Teams",
xlab = "Team",
ylab = "Goals Scored",
col = "red",
ylim = c(0, max(data$GoalsScored) + 10) # Set y-axis limits
)