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plot(cars)

df <- read.csv("/Users/lichuqiao/Desktop/INF0310/mtcars-3.csv")
head(df,1)

dim(df)

[1] 32 12

class(df)

[1] "data.frame"

str(df)

'data.frame':   32 obs. of  12 variables:
 $ model: chr  "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
 $ mpg  : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl  : int  6 6 4 6 8 6 8 4 4 6 ...
 $ disp : num  160 160 108 258 360 ...
 $ hp   : int  110 110 93 110 175 105 245 62 95 123 ...
 $ drat : num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt   : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec : num  16.5 17 18.6 19.4 17 ...
 $ vs   : int  0 0 1 1 0 1 0 1 1 1 ...
 $ am   : int  1 1 1 0 0 0 0 0 0 0 ...
 $ gear : int  4 4 4 3 3 3 3 4 4 4 ...
 $ carb : int  4 4 1 1 2 1 4 2 2 4 ...

summary(df)

    model                mpg             cyl             disp      
 Length:32          Min.   :10.40   Min.   :4.000   Min.   : 71.1  
 Class :character   1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8  
 Mode  :character   Median :19.20   Median :6.000   Median :196.3  
                    Mean   :20.09   Mean   :6.188   Mean   :230.7  
                    3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0  
                    Max.   :33.90   Max.   :8.000   Max.   :472.0  
       hp             drat             wt             qsec      
 Min.   : 52.0   Min.   :2.760   Min.   :1.513   Min.   :14.50  
 1st Qu.: 96.5   1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89  
 Median :123.0   Median :3.695   Median :3.325   Median :17.71  
 Mean   :146.7   Mean   :3.597   Mean   :3.217   Mean   :17.85  
 3rd Qu.:180.0   3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90  
 Max.   :335.0   Max.   :4.930   Max.   :5.424   Max.   :22.90  
       vs               am              gear            carb      
 Min.   :0.0000   Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4375   Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :1.0000   Max.   :5.000   Max.   :8.000  

y<-(df[10])
y_num<- as.numeric(unlist(y))
y_num<-as.logical(y_num)
str(y_num)

 logi [1:32] TRUE TRUE TRUE FALSE FALSE FALSE ...

df[10]<-y_num
str(df)

'data.frame':   32 obs. of  12 variables:
 $ model: chr  "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
 $ mpg  : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl  : int  6 6 4 6 8 6 8 4 4 6 ...
 $ disp : num  160 160 108 258 360 ...
 $ hp   : int  110 110 93 110 175 105 245 62 95 123 ...
 $ drat : num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt   : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec : num  16.5 17 18.6 19.4 17 ...
 $ vs   : int  0 0 1 1 0 1 0 1 1 1 ...
 $ am   : logi  TRUE TRUE TRUE FALSE FALSE FALSE ...
 $ gear : int  4 4 4 3 3 3 3 4 4 4 ...
 $ carb : int  4 4 1 1 2 1 4 2 2 4 ...

df

# Scatter Plot
input <- mtcars[, c('hp', 'mpg')]
plot(x = input$hp, y = input$mpg,
    xlab = "horsepower",
    ylab = "miles per gallon",
    main = "horsepower vs miles per gallon"
)

# Bar Chart
barplot(table(df$cyl), xlab = "Cylinders", ylab = "Count", main ="Bar-Chart")

# Histogram
df <- read.csv("/Users/lichuqiao/Desktop/INF0310/mtcars-3.csv")
hist(df$mpg,main ="Histogram",xlab="bin",ylab="count")

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