• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
Finance Train

Finance Train

High Quality tutorials for finance, risk, data science

  • Home
  • Data Science
  • CFA® Exam
  • PRM Exam
  • Tutorials
  • Careers
  • Products
  • Login

Accessing Built-in Datasets in R

Data Science

This lesson is part 11 of 29 in the course Data Visualization with R

R comes with many built-in datasets which are quite useful while learning R. To begin learning the basics of data visualization in R, we will make use of some of these datasets.

Datasets in the ‘datasets’ package

Many datasets are included in a package called datasets which is distributed with R so these datasets are instantly available to you for use. For example, two datasets namely cars and pressure are included in this default datasets package. So, you can access their data by using functions such as head(cars), summary(cars), etc. The following examples show results of calls to these functions:

> head(cars)
  speed dist
1     4    2
2     4   10
3     7    4
4     7   22
5     8   16
6     9   10
>
> summary(cars)
     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00  
>
> head(pressure)
  temperature pressure
1           0   0.0002
2          20   0.0012
3          40   0.0060
4          60   0.0300
5          80   0.0900
6         100   0.2700
>
> summary(pressure)
  temperature     pressure       
 Min.   :  0   Min.   :  0.0002  
 1st Qu.: 90   1st Qu.:  0.1800  
 Median :180   Median :  8.8000  
 Mean   :180   Mean   :124.3367  
 3rd Qu.:270   3rd Qu.:126.5000  
 Max.   :360   Max.   :806.0000  
>

To learn more about a dataset, you can use the help function as help(cars).

If you want to get a list of all the datasets, you can do so using the data() function.

Datasets in Other Packages

Any R package can choose to include datasets. You can access the data from a package using thedata() function by using the package argument as follows:

data(datasetname, package="packagename")

For example, there’s a popular package called MASS which contains datasets (such as Cars93). We can access the Cars93 dataset by calling the data() function.

> data(Cars93, package="MASS")

After this call to data(), the Cars93 dataset is available for use in R.

Previous Lesson

‹ Graphic Systems in R

Next Lesson

How to Create a Scatter Plot in R ›

Join Our Facebook Group - Finance, Risk and Data Science

Posts You May Like

How to Improve your Financial Health

CFA® Exam Overview and Guidelines (Updated for 2021)

Changing Themes (Look and Feel) in ggplot2 in R

Coordinates in ggplot2 in R

Facets for ggplot2 Charts in R (Faceting Layer)

Reader Interactions

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Primary Sidebar

In this Course

  • Overview of Data Visualization
  • When to Use Bar Chart, Column Chart, and Area Chart
  • What is Line Chart and When to Use It
  • What are Pie Chart and Donut Chart and When to Use Them
  • How to Read Scatter Chart and Bubble Chart
  • What is a Box Plot and How to Read It
  • Understanding Japanese Candlestick Charts and OHLC Charts
  • Understanding Treemap, Heatmap and Other Map Charts
  • Visualization in Data Science
  • Graphic Systems in R
  • Accessing Built-in Datasets in R
  • How to Create a Scatter Plot in R
  • Create a Scatter Plot in R with Multiple Groups
  • Creating a Bar Chart in R
  • Creating a Line Chart in R
  • Plotting Multiple Datasets on One Chart in R
  • Adding Details and Features to R Plots
  • Introduction to ggplot2
  • Grammar of Graphics in ggplot
  • Data Import and Basic Manipulation in R – German Credit Dataset
  • Create ggplot Graph with German Credit Data in R
  • Splitting Plots with Facets in ggplots
  • ggplot2 – Chart Aesthetics and Position Adjustments in R
  • Creating a Line Chart in ggplot 2 in R
  • Add a Statistical Layer on Line Chart in ggplot2
  • stat_summary for Statistical Summary in ggplot2 R
  • Facets for ggplot2 Charts in R (Faceting Layer)
  • Coordinates in ggplot2 in R
  • Changing Themes (Look and Feel) in ggplot2 in R

Latest Tutorials

    • Data Visualization with R
    • Derivatives with R
    • Machine Learning in Finance Using Python
    • Credit Risk Modelling in R
    • Quantitative Trading Strategies in R
    • Financial Time Series Analysis in R
    • VaR Mapping
    • Option Valuation
    • Financial Reporting Standards
    • Fraud
Facebook Group

Membership

Unlock full access to Finance Train and see the entire library of member-only content and resources.

Subscribe

Footer

Recent Posts

  • How to Improve your Financial Health
  • CFA® Exam Overview and Guidelines (Updated for 2021)
  • Changing Themes (Look and Feel) in ggplot2 in R
  • Coordinates in ggplot2 in R
  • Facets for ggplot2 Charts in R (Faceting Layer)

Products

  • Level I Authority for CFA® Exam
  • CFA Level I Practice Questions
  • CFA Level I Mock Exam
  • Level II Question Bank for CFA® Exam
  • PRM Exam 1 Practice Question Bank
  • All Products

Quick Links

  • Privacy Policy
  • Contact Us

CFA Institute does not endorse, promote or warrant the accuracy or quality of Finance Train. CFA® and Chartered Financial Analyst® are registered trademarks owned by CFA Institute.

Copyright © 2021 Finance Train. All rights reserved.

  • About Us
  • Privacy Policy
  • Contact Us