How To Load Mpg Data In R

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

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

Related: A Consummate 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 past using the post-obit command:

                          information(mtcars)

Nosotros tin can take a await at the offset half dozen 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   six  160 110 3.xc 2.620 sixteen.46  0  i    4    4 Mazda RX4 Wag     21.0   half dozen  160 110 3.90 2.875 17.02  0  1    four    iv Datsun 710        22.8   iv  108  93 iii.85 ii.320 18.61  1  1    4    i Hornet 4 Drive    21.four   vi  258 110 3.08 three.215 nineteen.44  ane  0    3    1 Hornet Sportabout 18.7   eight  360 175 3.15 3.440 17.02  0  0    iii    2 Valiant           18.ane   6  225 105 2.76 three.460 20.22  i  0    3    one

Summarize the mtcars Dataset

Nosotros can use the summary() office to quickly summarize each variable in the dataset:

                                          #summarize mtcars dataset                summary(mtcars)        mpg             cyl             disp             hp         Min.   :ten.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0    1st Qu.:fifteen.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.seven   Mean   :146.7    3rd Qu.:22.80   3rd Qu.:eight.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.fifty   Min.   :0.0000    1st Qu.:3.080   1st Qu.:2.581   1st Qu.:xvi.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   Hateful   :0.4375    3rd Qu.:three.920   3rd Qu.:3.610   3rd Qu.:xviii.90   tertiary Qu.:one.0000    Max.   :4.930   Max.   :v.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 :iv.000   Median :2.000    Hateful   :0.4062   Mean   :3.688   Mean   :2.812    3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:iv.000    Max.   :1.0000   Max.   :5.000   Max.   :8.000

For each of the 11 variables nosotros tin 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 tin 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 xi

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

We tin can also use the names() office to display the column names of the data frame:

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

Visualize the mtcars Dataset

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

For instance, we can use the hist() office to create a histogram of the values for a sure variable:

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

Nosotros could likewise 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                ',         edge='                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',      chief='Scatterplot',      xlab='mpg',      ylab='wt',      pch=19)

By using these built-in functions in R, we tin can learn a not bad deal about the mtcars dataset.

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