GenEpi-BioTrain - Virtual training 14 - R data analysis and visualisation for beginners

Topic outline

• Introduction

  Introduction

  ℹ️ Please Note

  You are welcome to explore the available resources and video recordings. Please note that the resources in this course come from the Virtual Training 14 held in February 2025, and certificates are only issued to participants who attended the live webinar sessions.

  Welcome to the virtual training on "R data analysis and visualisation for beginners"!
  
  

  This course aims at providing you with a general introduction to working in R and RStudio.  It will cover the basics of R and is aimed into providing an understanding and enabling independent usage of the programming language.

  This training covers the basic syntax, operations, data types, structures, manipulation and visualisation with tuberculosis data providing the thematic framework. If you already have experience of working with R and RStudio and understand the syntax of the language this course may not be for you.

  On the other hand, if you want to use R or specific R packages for ANY type of data analysis this course will provide you with the necessary understanding to do so.

   

  What is R?

  R is a powerful and open-source programming language and software environment specifically designed for statistical computing, data analysis, and graphical visualisation. Widely used by statisticians, data scientists, and researchers, R excels at handling and analyzing large datasets, performing complex statistical computations, and creating high-quality visualisations.

  
  

  IMPORTANT! Prerequisites

  The main assumption of this course is that in order to work with R, you need to work in RStudio. Therefore, to be ready to start this course you need to have both R and RStudio preinstalled and running on your PC. The installation of both of these programs is explained below.

  Note that the course material and demonstration will be performed on a windows laptop but both R and RStudio are macOS and linux compatible. R version 4.4.2 and RStudio version 2024.12.0+467 will be used but earlier versions should also perform without errors.
  

  How to install R(win only) and RStudio from this website
  

  1. Go to the Resources folder of Session 1, Introduction to R, and   download the executable(.exe) files for your operating system. Follow the instructions for installation. 
     

  
  

  How to install R from the web
  

  1. Download the installation file of the newest R version from CRAN. Choose your operation system (e.g. Download R for Windows), and then choose either Install R for the first time, if you haven't installed R before on your computer, or base if you already did and you are installing a new version; download the installation file and proceed further.
  2. Install the R program - you may keep the default location of installation files in Program Files/R. On Windows, the R folder in your Program Files folder may contain several subfolders with different R versions installed in parallel, e.g. R-2.10.1, R-2.11.1 etc.

  
  

  
  

  How to install RStudio from the web
  

  Download the latest version from RStudio website; you will need Desktop version, Open Source Edition for your system; you may click here to get directly to the selection of the current RStudio version. Follow the instructions for installation.

  
  

  Sessions

  The four sessions will take place daily on 17-20 February 2025 at 09:00-12:30 (CET).

  As with all GenEpi-BioTrain Virtual Trainings, the webinars will be recorded and you will be able to access the recordings at any time, provided you are enrolled in the course.

   

  • ForumAnnouncements
  • PageLegal terms and conditions
  • PagePresenters Biography
  • FileInformation on the GenEpi-BioTrain programme, Code of conduct
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  Session 1 (17 February) - Introduction to R

  Session 1 (17 February) - Introduction to R

  The aim of the first session is to introduce the participants to the R programming language and RStudio environment. Emphasis will be placed on understanding the RStudio interface for ease of navigation and on the basic data types and structures of the R programming language to enable first coding steps.
  

  After attending this session, participants will be able to:

  • Understand and use RStudio interface
    
  • Execute basic R commands
    
  • Create and modify data structures

  
  

  Module 1: Introduction

  1. What is R?
  • Overview of R and its applications
  • Introduction to RStudio IDE
  2. Getting Started with R
  • RStudio interface: Console, Script, Environment, Plots, and Help panels
  • Running R commands interactively
  3. Basic Syntax and Operations
  • Variables and assignment operators
  • Basic arithmetic operations
  • working with comments
    

  Module 2: Data Types and Structures part 1
  

  1. Basic Data Types (Classes)
     
  • Numeric, Character, Logical, Factor
  2. Data Structures (Objects)
     
  • Vectors
  • Matrices
  3. Indexing and Subsetting
  • Accessing elements in vectors, and matrices
    
  • Using logical conditions to subset data

  
  

  • FolderResources
  • FileLesson_1_Introduction_to_R
  • PageRecording of session 1
• Session 2 (18 February) - Data import & Functions

  Session 2 (18 February) - Data import & Functions
  The aim of the second session is to expand the knowledge of basic R commands to import and export data and to write their own functions.
  

  
  

  After attending this session, the participants will be able to:

  • Import/export csv and excel files
  • Explore the imported datasets
    
  • Write their own functions
  • perform base R plotting
    

  
  

  Module 2: Data Types and Structures part 2
  

  1. Data Structures (Objects)
     
  • Data Frames
  2. Indexing and Subsetting
  • Accessing elements in data frames
  • Using logical conditions to subset data

  Module 3: Importing and Exporting Data

  1. Reading Data
  • Importing CSV files using read.csv()
  • Importing Excel files using readxl package
  • Reading data from other formats (TXT)
  2. Saving Data
  • Exporting data to CSV or Excel
  3. Exploring Data
  • Using head(), tail(), summary(), and str() to inspect datasets

  Module 4: Writing Functions and Basic Programming

  1. Creating Functions
  • Writing reusable code with function()
  • Function arguments and return values

  Module 5: Data Visualisation part 1
  

  1. Introduction to Data Visualisation in R
  • Base R plotting functions

  
  

  
  

  • FolderResources
  • FileLesson_2_Data_Import_and_Functions
  • PageRecording of session 2
• Session 3 (19 February) - Data Manipulation and Visualisation

  Session 3 (19 February) - Data Manipulation and Visualisation

  In this third session participants will learn to manipulate tabular data using real world datasets. They will be introduced to packages in general as well as those that make selecting and filtering data easier. Finally they will visualise data using base R plotting functions as well as ggplot package.
  

  
  

  After attending this session, the participants will be able to:

  • Use tidyverse package
    
  • Manipulate tabular data
    
  • Understand ggplot2 grammar

  
  

  Module 6: Data Manipulation

  1. Introduction to the dplyr Package
  • Overview of the tidyverse
  • Selecting and filtering data
  • Adding or transforming columns
  2. Grouping and Summarizing Data
  • Grouped operations with group_by() and summarize()
  • Aggregation techniques

  
  

  Module 7: Data Visualisation part 2
  

  1. Introduction to Data Visualisation in ggplot2
  • Understanding ggplot2 grammar
  2. Creating Basic Charts with ggplot2
     
  • Histograms, Bar plots, Line charts, and Scatter plots
  • Customizing plots: Titles, Labels, Legends, and Themes

  
  

  
  

  • FolderResources
  • FileLesson_3_Data_Manipulation_and_Visualisation
  • PageRecording of session 3
• Session 4 (20 February) - Real dataset analysis

  Session 4 (20 February) - Real dataset analysis

  In this final session we will combine all that we have learned and analyze a real-world tuberculosis dataset.
  

  After attending this session, the participants will be able to:

  • Read in a tabular dataset
    
  • Filter columns and rows
    
  • Plot resistance change over time
    
    

  Module 8: Project and Practice

  1. Mini-Project
  • Analyzing and visualising a real-world dataset
  • Applying learned skills to clean, manipulate, and visualise data
  2. Challenges and Exercises
  • Hands-on practice questions for reinforcement
  3. Q&A and Wrap-Up
  • Addressing common beginner questions
  • Resources for further learning (books, online courses, communities)

  
  

  • FolderResources
  • FileLesson_4_Final_Assignment
  • PageRecording of session 4
• Contacts

  Contacts

  For any questions regarding the this webinar series, please use the discussion forum.

  If you have questions regarding other training initiatives please contact: courses@ecdc.europa.eu.

  If you have technical issues please contact ECDC Helpdesk - ICT Front Office ICT.FrontOffice@ecdc.europa.eu.