R for Statistical Analysis Schulung

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Kurs Code



14 hours (üblicherweise 2 Tage inklusive Pausen)


There are no specific requirements needed to attend this course.


Organisation - Laborarbeit und praktische Beispiele zu realen Daten .

Machine Translated


Day One

  1. Introduction to R & Rstudio (2 hours)
    • Making R more friendly, R and available GUIs
    • Rstudio
    • Scripting in Rstudio
    • Navigation, sections and code folding
    • Troubleshooting and code debugging in RStudio
    • Related software and documentation
    • Getting help with functions and features
    • Projects in RStudio
    • Creating analytical reports with RStudio
    • Keyboard shortcuts and useful features
  2. Importing/Exporting data (1 hour)
    • Flat files – txt, csv
    • Spredsheet files – xls, xlsx
    • SPSS, SAS and other formats data
    • Accessing data from SQL data sources
    • SQL database connectivity and operations
  3. Organising data (2 hours)
    • Data types and classes
    • Data storage in R – Rdata format
    • Objects structure
    • Numbers and vectors
    • Matrix and table
    • Factors
    • Lists
    • Data Frames
    • Date and time
  4. Tabular representation (3 hours)
    • Overview of packages for data tables – dplyr, tidyr, data.table
    • Indexes and subscripts
    • Selecting, subsetting observations and variables
    • Filtering, grouping
    • Recoding transformations
    • Reshaping data
    • Merging data
    • Character manipulation, stringr package
    • Regular expressions

Day Two

  1. Related software and documentation (1 hour)
    • Rstudio and GIT - versioning
    • Markdown
    • Reports and presentations with LaTeX
    • Shiny web applications
  2. R and Statistics (2 hours)
    • Probability and Normal Distribution
    • Random numbers
    • Descriptive Statistics
    • Standarization and Normalization
    • Confidence Intervals
    • Hypothesis Testing
    • ANOVA
    • Qualitative data analysis
  3. Linear regression (2 hours)
    • Correlation coefficient and interpretation
    • Simple and multiple linear regression
    • Estimation methods – Least squares
    • Model validation – tests for violation of assumptions
    • Selecting variables – different approaches
    • Regulatizations – ridge and lasso regression
    • Generalized least square – nonlinearity
    • Logistic regression
  4. Graphical procedures (2 hours)
    • Basic plots for 1 variable
    • Visualizations for 2 and more variables
    • Graphical parameters
    • Special plots
    • Exporting plots to png, pdf and jpeg files
    • Extending graphical capabilities of R with ggplot2
  5. Help in R (1 hour)
    • Searching through documentation of R
    • R packages and documentation
    • R Cran Task View – search for problem solution



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