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.

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

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