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Preprocessing Data with R


Admission requirements

The only prerequisite of the course is to know a programming language and understanding algorithms.


In this course students will learn how to program in R and how to use R for preprocessing data. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions and scripts, and operations for cleaning, filtering and organizing data.


  1. Introduction to R and RStudio
  2. Data Visualization with ggplot2
  3. Workflow: Basics
  4. Data transformation with dplyr
  5. Workflow: scripts & functions
  6. Exploratory data analysis
  7. Workflow: projects
  8. Working with open data

Course objectives

The objectives of the course are learning and development of skills for data processing. Students will learn to autonomously manage data and to prepare it for later analysis. Therefore, all sessions are completely practical. The type of class is totally practical and dynamic.


The most recent timetable can be found on the students' website.

Mode of instruction


Course Load

Hours of study: 28 hrs (= 1 EC)
Lectures : 8 hrs
Self-study: 20 hrs

Assessment method

  • Practical assignments to be done in the class.

Reading list

  • G. Grolemund y H. Wickham, “R for Data Science” O’Reilly January 2017.

  • Wim P. Krijnen, Applied Statistics using R, 2009.


  • You have to sign up for the course in uSis. Check this link for information about how to register for courses.


Lecturer: dr. Victoria López


Please note that this is an extracurricular course that can only be taken by Master Computer Science students.