Introduction to R

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Introduction to R


R is probably the best environment for data analysis, and is the choice of many data scientists and business analysts. This 3 day workshop will introduce R to these audiences and provide the basic skills needed for conducting data analysis projects independently in R. The workshop adopts a hands-on, interactive approach.

It is comprised of a few “interactive demonstration” modules that introduce key concepts. In these modules we follow a well documented R script, occasionally deviating from it to answer questions and deepen our understanding of the material presented. Interspersed between these demonstrations are in-class exercises in which participants have the opportunity to solve real (sometimes a bit simplified) business cases, using R by themselves. Finally there is always room for participants to bring their own data to the class, and discuss how to perform their task in R.

This modular structure enables to cater for the specific needs of participants and their organization. The typical workshop takes a minimum of 2 full days (2×8 hours), but 3 are advised. More “open audience” consultation time can be added as well as more modules to cover additional topics and statistical methods.


The workshop is designed with two main audiences in mind:

  • Statisticians & Business Analysts
    It is expected that participants have experience with data analysis, but there is no need for prior programming experience.
  • Programmers that need to develop data oriented software
    Here the assumption is that participants have programming background, but not necessarily statistical background.

Note: Experience shows that it is not a good idea to mix these two populations, as each needs a different emphasis and approach to master the required skills.

Course Topics

Module 1: R Basics

  • Introducing the R and Rstudio Environment
  • Basic Data Structures: vectors, lists & data frames
  • Basic Operations on Data: element-wise operations, selection and summary operations
  • Working with Dates and Strings

Module 2: Data Manipulation

  • Reading and Writing Data from/to Files (including the haven and readxl packages)
  • The dplyr Package: merging, recoding and aggregating data
  • The reshape2 Package: long and wide data formats and their usage

Module 3: Basic Statistics

  • Descriptive Statistics
  • Univariate and Bivariate statistical tests (t-test, chi-square, etc.)
  • Linear regression
  • Logistic regression

Module 4: Data Visualization

  • Basic plot commands
  • The ggplot2 package to cover histograms, scatterplots, boxplots, etc.

Module 5: Process Automation

  • Basics of R programming
  • (if time permits) Template based reporting with the knitr package
  • (if time permits) Generating paper-like tables for publication with stargazer package
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