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The mtcars dataset in R is a built-in dataset that contains data on the performance of different models of cars. It can be analyzed using various R functions to explore the data, such as summary(), which gives a summary of numerical data, or plot(), which produces a graph of the data. Other R functions, such as subset(), can also be used to further analyze the data.
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:
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$mpg,
col='steelblue',
main='Histogram',
xlab='mpg',
ylab='Frequency')
We could also use the boxplot() function to create a to visualize the distribution of values for a certain variable:
#create boxplot of values for mpg
boxplot(mtcars$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 that explains how to fit linear regression models and generalized linear models using the mtcars dataset.
The following tutorials explain how to perform other common tasks in R: