The **mtcars** dataset is a built-in dataset in R that contains measurements on 11 different attributes for 32 different cars.

This tutorial explains how to explore, summarize, and visualize the **mtcars** dataset in R.

**Related:** A Complete Guide to the Iris Dataset in R

**Load the mtcars Dataset**

Since the **mtcars** dataset is a built-in dataset in R, we can load it by using the following command:

**data(mtcars)**

We can take a look at the first six rows of the dataset by using the **head()** function:

**#view first six rows of mtcars dataset
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
**

**Summarize the mtcars Dataset**

We can use the **summary()** function to quickly summarize each variable in the dataset:

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

For each of the 11 variables we can see the following information:

**Min**: The minimum value.**1st Qu**: The value of the first quartile (25th percentile).**Median**: The median value.**Mean**: The mean value.**3rd Qu**: The value of the third quartile (75th percentile).**Max**: The maximum value.

We can use the **dim()** function to get the dimensions of the dataset in terms of number of rows and number of columns:

**#display rows and columns
dim(mtcars)
[1] 32 11
**

We can see that the dataset has **32 **rows and **11** columns.

We can also use the **names()** function to display the column names of the data frame:

**#display column names
names(mtcars)
[1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
[11] "carb"
**

**Visualize the mtcars Dataset**

We can also create some plots to visualize the values in the dataset.

For example, we can use the **hist()** function to create a histogram of the values for a certain variable:

**#create histogram of values for mpg
hist(mtcars$mpg,
col='steelblue',
main='Histogram',
xlab='mpg',
ylab='Frequency')
**

We could also use the **boxplot()** function to create a boxplot to visualize the distribution of values for a certain variable:

**#create boxplot of values for mpg
boxplot(mtcars$mpg,
main='Distribution of mpg values',
ylab='mpg',
col='steelblue',
border='black')**

We can also use the **plot()** function to create a scatterplot of any pairwise combination of variables:

**#create scatterplot of mpg vs. wt
plot(mtcars$mpg, mtcars$wt,
col='steelblue',
main='Scatterplot',
xlab='mpg',
ylab='wt',
pch=19)**

By using these built-in functions in R, we can learn a great deal about the **mtcars** dataset.

If you’d like to perform more advanced statistical analysis with this dataset, check out this tutorial that explains how to fit linear regression models and generalized linear models using the **mtcars** dataset.

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

The Easiest Way to Create Summary Tables in R

How to Calculate Five Number Summary in R

How to Perform Simple Linear Regression in R