Quantitative analysis with R & RStudio

Quantitative analysis with R & RStudio

Public courses


- Anyone can join the training
- Course outline as presented on the website
- Small groups, 3-10 people

Private courses

Price set individually

- Training workshop just for your team
- You choose date and location of the training
- Course outline tailored to your needs

About the training

During this training, you will learn to create and manage RStudio projects. The main focus of this training is placed on organizing your work properly, creating documentation, working on a code in collaboration and sharing your work with others.

This training is delivered using Live Script method, meaning that entire training is done in RStudio environment. Forget about boring and unproductive PowerPoints. In each section of the course, the trainer will guide you through the topics, showing how it can be performed in RStudio. Then you will be able to practice the knowledge by completing the exercises prepared for each section. The final part of the course is a case study – a complete project done by participants with the assistance of the trainer.

Who is this training for?

The training is addressed to those, who want to move from ad hoc analyses to structured, repeatable, and well-planned analyses in R. In particular, this training meets needs of professional researchers, analysts, developers and managers who work or would like to start working with R and RStudio.

What will I learn?

After completing the training, you will be able to:

  • Manage analytical projects
  • Manage quantitative analysis in R using RStudio
  • Manage program versions using Git
  • Import data from multiple sources
  • Prepare data for analysis
  • Write clear and concise codes for reproducible analyses
  • Create automated reports and presentations using R Markdown
  • Create R packages and share them within the company

Course outline

  1. Analytical Projects
    • The concept of analytical project
    • Defining and planning your project
    • Execution
    • Monitoring and control
    • Closing the project
    • Why R and RStudio?
  2. Introduction to R
    • R basics
    • Objects
    • Indexing
    • Functions
    • Data import
    • Memory
    • Packages
  3. Introduction to RStudio
    • RStudio overview
    • Writing scripts in RStudio
    • Editing code
    • Navigation, sections, code folding
  4. RStudio advanced features
    • Creating packages
    • Creating documentation
    • Error diagnosis
    • Debugging
  5. Managing RStudio projects
    • Project environment in RStudio
    • Creating a project
    • File and folder structure
    • GIT version control and Subversion
    • Group work on RStudio project
    • Reporting tools
    • Context management
    • Managing packages and dependencies  – packrat
    • Good practices
  6. Files – accessing, storing, versioning
    • Using Dropbox to store data
    • Using GitHub to store data
    • Accessing files
    • Versioning
    • RStudio and Git
  7. Organizing and managing data
    • Data loading and update process
    • Importing local data
    • Importing data from database
    • Importing data from internet sources
    • Data loading process automation
  8. Preparing data for analysis, feature engineering
    • From raw data to data technically complete data
      • Changing data dimension
      • Renaming variables, adding variables
      • Sorting data
      • Filtering observations and variables
      • Working with text
      • Changing class of variable
      • Joining two data sets
    • From technically complete data to data ready for analysis
      • Missing observations
      • Outliers
      • Data discrepancy
      • Correcting data
      • Data imputation
  9. Reproducible analysis – Introduction
    • Creating reproducible analyses
    • knitr and R Markdown basics
    • File extensions
    • Combining blocks of code with text
    • Hooks
    • knitr and markdown integration in RStudio
    • Good practices
  10. Analysis and results
    • Code and results presentation
    • R Markdown, knitr, LaTeX, Beamer, HTML
    • Combining text with results of analysis
    • Presenting results in tabular form
    • LaTeX, HTML tables
    • Graphics and charts integration
    • ggplot2, googleVis charts
  11. Results presentation using knitr and LaTeX
    • Latex basics
    • Sections, chapters
    • Maths
    • Lists, Bullets
    • List of references – BibTeX
  12. Creating reports and presentations using knitr and LaTeX
    • Creating large reports using LaTeX
    • Creating large reports using knitr
    • Joining documents
    • LaTeX Beamer presentations
  13. Web sharing with R Markdown
    • R Markdown basics
    • R Markdown editor
    • Sections, chapters, paragraphs
    • URLs, symbols
    • Lists. Bullets
    • CSS and Markdown
  14. Creating reports and presentations with R Markdown
    • Creating large HTML reports with R Markdown
    • HTML presentations with R Markdown
    • LaTeX Beamer presentations in R Markdown
    • Publishing
  15. Introduction to creating R packages in RStudio
    • Basics
    • Package structure
    • Folder structure
    • Building your own package
    • Testing

Course Curriculum

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