Workshop: Data Visualization
A.Y. 2024/2025
Learning objectives
This WORKSHOP provides a broad overview of visualization methods frequently used in statistical analyses to summarize collected data, describe the model output, and visualize relationships among variables. All the visualization techniques will be treated jointly with case studies to motivate students and provide ready-to-use R scripting skills.
Expected learning outcomes
At the end of the course, the student must be able to select visualization techniques suited to the analysis's goals and develop R scripts to produce the final output. Using RStudio and Rmarkdown, the student must also be able to create effective reports with graphical, textual, and numerical output.
Lesson period: First semester
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Overview:
The agenda includes the most important visualization techniques available in basic R and using extension packages, like "ggplot2" and "tidyverse".
Specialized graphical techniques are also considered, for example, to summarize the main features of a fitted Bayesian model.
All data visualization techniques are applied to selected case studies and dedicated R code is developed as a relevant part of this Workshop.
Main topics:
* Basic graphics in R: scattergrams, histograms, barcharts, boxplots, coplots, piecharts.
* Visualization in the tidyverse using tibbles: creation, reshaping, filtering, selection, pipes.
* The structure of ggplot2 plots: layers, geoms and summaries; annotations, legends, scales and themes; faceting.
* Introduction to interactive graphs.
* Case studies on descriptive statistics and model output; Bayesian models: diagnostics, summaries and prediction.
The agenda includes the most important visualization techniques available in basic R and using extension packages, like "ggplot2" and "tidyverse".
Specialized graphical techniques are also considered, for example, to summarize the main features of a fitted Bayesian model.
All data visualization techniques are applied to selected case studies and dedicated R code is developed as a relevant part of this Workshop.
Main topics:
* Basic graphics in R: scattergrams, histograms, barcharts, boxplots, coplots, piecharts.
* Visualization in the tidyverse using tibbles: creation, reshaping, filtering, selection, pipes.
* The structure of ggplot2 plots: layers, geoms and summaries; annotations, legends, scales and themes; faceting.
* Introduction to interactive graphs.
* Case studies on descriptive statistics and model output; Bayesian models: diagnostics, summaries and prediction.
Prerequisites for admission
The students should be familiar with basic math, probability and statistics. They should also be familiar with basic operations on a PC (at least one among macOS, Windows, and Linux), such as how to install and use programs.
Teaching methods
Frontal lectures introduce each visualization technique and the R code to obtain the final output. Online sessions are also devoted to the analysis of case studies using RStudio. During the Workshop, students create formatted reports from Rmarkdown script files using a lab PC or their laptops (macOS, Windows, Linux).
Teaching Resources
* Online resources available at the course website.
* Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen, 2024, "ggplot2: Elegant Graphics for Data Analysis (3e)", Springer. Online https://ggplot2-book.org/
* Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, "R for Data Science", (2ed) Online https://r4ds.hadley.nz/
* Paul Morrell, 2019, "R graphics", CRC Press.
* Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen, 2024, "ggplot2: Elegant Graphics for Data Analysis (3e)", Springer. Online https://ggplot2-book.org/
* Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, "R for Data Science", (2ed) Online https://r4ds.hadley.nz/
* Paul Morrell, 2019, "R graphics", CRC Press.
Assessment methods and Criteria
The primary purpose of the exam is to evaluate the achievement of the training objectives, for example, the ability to select and apply the appropriate visualization technique to the types of data collected and according to the nature of the information to be extracted. While developing R code, students must also consider the characteristics of the statistical analyses exploited to answer research questions.
The exam consists of the individual production of reports. Each report is structured as described during the second part of the course. The student must upload the R source and final output files for each assigned report.
The student will receive eligibility for the Data Visualization WORKSHOP only after delivering all the assigned reports containing all the required points adequately implemented.
The exam consists of the individual production of reports. Each report is structured as described during the second part of the course. The student must upload the R source and final output files for each assigned report.
The student will receive eligibility for the Data Visualization WORKSHOP only after delivering all the assigned reports containing all the required points adequately implemented.
Professor(s)
Reception:
by appointment on Tuesday and Wednesday (email)
Via Celoria 10