How to load R Built-in R Data set Mtcars and explore the data
R comes with several built-in data sets, which are generally used as demo data for playing with R functions.
In this vingette,we will describe how to load and use R built-in data sets focusing on the Mtcar dataset.
We will be exploring the basic functions of the dataset using a few basic exploration R functions.
List of pre-loaded data
Once you start your R program, there are example data sets available within R along with loaded packages.
If you just want to play with some test data to see how they load and what basic functions you can run, the default installation of R comes with several data sets.
The benefits of starting off using the pre-loaded data is that it gives you a chance to try analysis and plotting commands and there are a lot of online tutorials that use these sample sets.
To see the list of pre-loaded data, type the function data() into the R console and you will get a listing of pre-loaded data sets:
data()Loading a built-in R data
Load and print mtcars data as follow:
Loading
data(mtcars)Print the first 6 rows
This functions will allow you to view the first 6 rows of the data set.
head(mtcars, 6)## 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 1If you want learn more about mtcars data sets, type this:
?mtcars## starting httpd help server ... doneMost used R built-in data sets
mtcars: Motor Trend Car Road Tests
The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models)
Format
A data frame with 32 observations on 11(numeric) variables.
View the content of mtcars data set:
1. Loading
data("mtcars")2. Print
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 1It contains 32 observations and 11 variables: # Number of rows (observations)
nrow(mtcars)## [1] 32[1] 32 # Number of columns (variables)
ncol(mtcars)## [1] 11If you want to learn more about mtcars, type this:
?mtcarsselect head of mtcars data set
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 1select end of mtcars data set
tail(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 2summaries the data set
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. :8.000quantiles of dataset
quantile(mtcars$wt)## 0% 25% 50% 75% 100%
## 1.51300 2.58125 3.32500 3.61000 5.42400select quantiles by percent
To calculate the quantiles by percent:
quantile(mtcars$wt, c(.2, .4, .8))## 20% 40% 80%
## 2.349 3.158 3.770variance of weight
To calculate the variance of weight:
var(mtcars$wt)## [1] 0.957379Historgram
Histograms are a classic way to assess the shape of the distribution of a single variable.
To get the histogram of hp, the code below will produce a histogram:
hist(mtcars$hp)
Summary
We explored some of the common functions in R that I like to use to explore a data frame before I conduct any statistical analysis. We used the built-in data set mtcars to illustrate these functions.