Lectures

Slides

Communicating and Programming:
Lecture 1: Introduction to R. [html] [pdf]
Lecture 2: Communicating with R Markdown. Elements of programming in R. [html] [pdf]

Exploring data:
Lecture 3: Importing and transforming data. [html] [pdf]
Lecture 4: Visualizing data. [html] [pdf]
Lecture 5: Exploratory Data Analysis (EDA). [html] [pdf]

Modeling data:
Lecture 6: Data modeling and linear regression. [html] [pdf]
Lecture 7: Hypothesis testing and classification. [html] [pdf]
Lecture 8: Unsupervised techniques in R. [html] [pdf]

Disclaimer: these lecture slides are subject to change and can be updated any time. Please check regularly.

Exercises

Exercise 1: [html] [rmd] [solutions]
Exercise 2: [html] [rmd] [solutions]
Exercise 3: [html] [rmd] [solutions]
Exercise 4: [html] [rmd][solutions]
Exercise 5: [html] [rmd] [solutions]
Exercise 6: [html] [rmd][solutions]
Exercise 7: [html] [rmd] [solutions]
Exercise 8: [html] [rmd] [solutions]

Introductory Tutorials

• Lecture 1 related:
  • vectors and matrices,
  • lists and data-frames
• Lecture 2 related:
  • programming
  • an extra tutorial on importing data to R needs to be run locally. You can get it by installing my tutorials with devtools::install_github("nlhuong/rexercises"), Then, run a tutorial with the following command:
    learnr::run_tutorial("data_to_R", package = "rexercises")
• Lecture 4:
  • base plotting

Tutorials might include bugs, or some unclear hints. Please, let me know if you encounter any mistakes in the tutorials so I can fix them.

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