Football

library(readxl)
mydata <- read_excel("./Football.xlsx")

head(mydata)

## # A tibble: 6 × 10
##    Rank Name   Position   Age Value Club  Games_played Goals Assists Card_yellow
##   <dbl> <chr>     <dbl> <dbl> <dbl> <chr>        <dbl> <dbl>   <dbl>       <dbl>
## 1     1 Kylia…        4    22   144 Pari…           16     7      11           3
## 2     2 Erlin…        4    21   135 Boru…           10    13       4           1
## 3     3 Harry…        4    28   108 Tott…           16     7       2           2
## 4     4 Jack …        1    26    90 Manc…           15     2       3           1
## 5     5 Moham…        2    29    90 Live…           15    15       6           1
## 6     6 Romel…        4    28    90 Chel…           11     4       1           0

2. Display the frequency distribution pf the players positions

mydata$Position <- factor(mydata$Position,
                          levels = c(1, 2, 3, 4, 5),
                          labels = c( "Midfielder", "Winger", "Defender", "Striker", "Goalkeeper"))

library(ggplot2)
ggplot(mydata, aes( x= Position)) +
         geom_bar(colour = "blue", fill = "lightblue") +
  ylab("Frequency")+
  theme_minimal() +
geom_text(stat = "count",
          aes(label = ..count..),
          vjust = -0.3)

## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

3. Scatterplot

library(ggplot2)
ggplot(mydata, aes( x= Games_played, y= Goals))+
  geom_point(color="blue", size= 3)+
  geom_smooth(method= "lm", se=FALSE, color = "red")+
  labs(title= "Scatterplot")

## `geom_smooth()` using formula = 'y ~ x'

d) Average number of cards for defenders

mean(mydata$Card_yellow[mydata$Position=="Defender"])

## [1] 1.913043

The average number of yellow cards for defenders is 1.91.

Hypothesis testing Ho: mu = 1.91 H1: Mu < 1.91

t.test(mydata[mydata$Position == "Striker", ]$Card_yellow,
       mu= 1.91,
       alternative = "less")

## 
##  One Sample t-test
## 
## data:  mydata[mydata$Position == "Striker", ]$Card_yellow
## t = -0.78247, df = 13, p-value = 0.224
## alternative hypothesis: true mean is less than 1.91
## 95 percent confidence interval:
##      -Inf 2.247475
## sample estimates:
## mean of x 
##  1.642857

We can not reject Ho, because p value is not lower than 0.05. We can not say that the average number of yellow card is smaller for strikers compared to defenders.