In this fourth lesson of our introduction to data analysis in R course, you will continue adding to your R programming skills as you begin to bring them into a more realistic data analysis workflow context.

In this lesson, you'll learn about the Tidyverse packages — a collection of open-source tools to make data analysis using R easier and more efficient. Then, you'll work step-by-step through a real data analysis workflow. You'll indentify the type of dataset you're working with, import and store the data using R, and use your new programming skills to explore it.

Then, you'll learn a little about how to visualize the data stored in your data set. (Data visualization is also covered in much more depth in our Data Visualization in R course, which comes later in this Data Analyst in R path, but you'll get a little taste of it here). 

After you finish this lesson, you'll be familiar with the data analysis workflow: importing data, exploring it, and visualizing it.


  • Import and store data using R.
  • Explore your data set with R.
  • Visualize the data using R.

Lesson Outline

  1. Data Analysis in R
  2. R Toolset for Data Analysis: Tidyverse Packages
  3. Describing Our Dataset
  4. Identifying a Dataset Type
  5. Importing and Storing Data in R
  6. Characterizing a Dataset
  7. Exploring a Dataset: View the First Lines
  8. Exploring a Dataset: View the Last Lines
  9. Visualizing Data Science Salaries
  10. Visualizing Daya Science Salaries by Job Type
  11. Next Steps