INFSCI0310 HW1. Amy Zhang

Load the mtcars dataset from a CSV file

#importing the dataset
df <- read.csv("mtcars-3.csv")

Data Understanding:

Displaying the First Few Rows using head() function

head(df)

Print the dimension of the dataset

dim(df)

[1] 32 12

Print the data structure of variable df (class function)

class(df)

[1] "data.frame"

Print data types of column model, mpg, hp and am

#model
class(df$model)

[1] "character"

#mpg
class(df$mpg)

[1] "numeric"

#hp
class(df$hp)

[1] "integer"

#am
class(df$am)

[1] "integer"

Use summary() function to get some details of this dataset.

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  

Change the datatype/class of variable ‘am’ from integer to boolean/logical

boolean = as.logical(df$am)
class(boolean)

[1] "logical"

Data Visualization

Scatterplot:

Here, explore how “mpg” (miles per gallon) and “hp” (horsepower) relate to each other. The x-axis could represent “hp,” and the y-axis could represent “mpg.” Scatter plots are useful for identifying patterns or correlations between variables. Also mention your interpretation of the plot using a markdown block.

plot(df$hp, df$mpg, main = "Scatterplot Relating MPG and HP",
     xlab= "Horsepower (HP)", ylab= "Miles per Gallon (MPG)"
     )

The scatterplot shows a negative correlation between hp and mpg, it shows that as hp increases, mpg decreases. The higher the horsepower, the less miles per gallon the car uses.

Bar Chart:

please create a bar chart to display the count or distribution of cars with different numbers of cylinders (e.g., 4 cylinders, 6 cylinders, 8 cylinders). Each bar in the chart represents a category, and the height of the bar corresponds to the count of cars in that category.

df$cyl <- as.factor(df$cyl)
barplot(table(df$cyl),
        main ="distribution of cars with different numbers of cylinders",
        xlab= "number of cylinders",
        ylab="count"
        )

Histogram:

Create a histogram to visualize the distribution of a numeric variable “mpg” (miles per gallon)

hist (df$mpg, xlab= "MPG", main= "MPG Histogram")