Workshop: data visualization
A.A. 2026/2027
Obiettivi formativi
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.
Risultati apprendimento attesi
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.
Periodo: Primo semestre
Modalità di valutazione: Giudizio di approvazione
Giudizio di valutazione: superato/non superato
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Primo semestre
Programma
Overview:
The agenda includes the most important visualization techniques available in base R and through extension packages, such as 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, using R code developed during the workshop.
Main topics:
* Basic graphics in R: scatterplots, histograms, bar charts, boxplots, coplots, pie charts.
* Visualization in the tidyverse using tibbles: creating, reshaping, filtering, piping, and selecting data.
* The structure of ggplot2 plots: layers, geoms and summaries; annotations, legends, scales and themes; faceting.
* Case studies on descriptive statistics and model output; Bayesian models: diagnostics, summaries, and predictions.
* Introduction to interactive visualizations and structured data.
The agenda includes the most important visualization techniques available in base R and through extension packages, such as 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, using R code developed during the workshop.
Main topics:
* Basic graphics in R: scatterplots, histograms, bar charts, boxplots, coplots, pie charts.
* Visualization in the tidyverse using tibbles: creating, reshaping, filtering, piping, and selecting data.
* The structure of ggplot2 plots: layers, geoms and summaries; annotations, legends, scales and themes; faceting.
* Case studies on descriptive statistics and model output; Bayesian models: diagnostics, summaries, and predictions.
* Introduction to interactive visualizations and structured data.
Prerequisiti
Students should be familiar with basic mathematics, probability, and statistics. They should also be familiar with basic computer operations on at least one of the following operating systems: macOS, Windows, or Linux, including how to install and use software.
Metodi didattici
Lectures performed in a computer lab introduce each visualization technique, along with the corresponding R code required to clean the data, extract information, and produce the intended outputs. The analysis of case studies is performed using RStudio. During the Workshop, students produce formatted reports from R Markdown scripts using either laboratory computers or their own laptops (macOS, Windows, or Linux). Attendance is recommended but not mandatory.
Materiale di riferimento
* Online resources available at the course website.
* Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen, 2024, "ggplot2: Elegant Graphics for Data Analysis (3rd ed.)", Springer. Online https://ggplot2-book.org/
* Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, "R for Data Science", (2nd ed.), O'Reilly. Online https://r4ds.hadley.nz/
* Paul Morrell, 2019, "R graphics", CRC Press. ISBN 9780367780692.
* Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen, 2024, "ggplot2: Elegant Graphics for Data Analysis (3rd ed.)", Springer. Online https://ggplot2-book.org/
* Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, "R for Data Science", (2nd ed.), O'Reilly. Online https://r4ds.hadley.nz/
* Paul Morrell, 2019, "R graphics", CRC Press. ISBN 9780367780692.
Modalità di verifica dell’apprendimento e criteri di valutazione
The primary purpose of the assessment is to evaluate the extent to which the learning objectives have been achieved. In particular, students are expected to demonstrate their ability to select and apply appropriate visualization techniques according to the type of data collected and the nature of the information to be extracted. When developing R code, students must also consider the characteristics and assumptions of the statistical analyses used to address the research questions.
The assessment consists of preparing R Markdown reports, which must be submitted through the official website. Each report must follow the structure presented during the workshop: code, results, and brief comments. For each assigned report, students must submit both the R source files and the corresponding output files. Submission deadlines apply to both attending and non-attending students.
Students who satisfactorily complete all required reports will receive the credits associated with the Data Visualization Workshop. The assessment is based on a pass/fail evaluation, with no numerical grade awarded.
The assessment consists of preparing R Markdown reports, which must be submitted through the official website. Each report must follow the structure presented during the workshop: code, results, and brief comments. For each assigned report, students must submit both the R source files and the corresponding output files. Submission deadlines apply to both attending and non-attending students.
Students who satisfactorily complete all required reports will receive the credits associated with the Data Visualization Workshop. The assessment is based on a pass/fail evaluation, with no numerical grade awarded.
Docente/i
Ricevimento:
Su appuntamento Martedì e Mercoledì (email)
via Celoria 10