96343 - PROGRAMMING LAB 2

Conoscenze e abilità da conseguire

Students develop transversal skills with a focus on the development of skills complementary to the quantitative methods’ courses. In particular, students acquire skills in data analysis and in the use of dedicated software and programming languages such as R or Python, as well as skills in data visualization.

Contenuti

Quarto

R package tidyverse:

• web scraping with rvest
• data tidying with tidyr
  
• data manipulation (subsetting, merging, summarising) with dplyr
• data visualization with ggplot2
  

R programming:

• user-written functions
• loops

Testi/Bibliografia

Suggested readings:

• Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science. O'Reilly.
  

Further readings:

• Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly.
  
• Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
• Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R Markdown: The definitive guide. CRC Press.

Metodi didattici

Computer laboratory sessions, group work.

In consideration of the type of activity and the teaching methods adopted, the attendance of this training activity requires the prior participation of all students in the training modules 1 and 2 on safety in the study places, in e-learning mode. (https://elearning-sicurezza.unibo.it/)

Modalità di verifica e valutazione dell'apprendimento

Pass/fail exam consisting in a take-home project; group work (max. 4 people) is allowed, accompanied by precise indications about the division of tasks.

The exam aims at evaluating the acquired skills in the use of R for data analysis and visualization.

Although attending classes is not mandatory, it is strongly recommended.

Strumenti a supporto della didattica

• R scripts
• Datasets
• Slides

Orario di ricevimento

Consulta il sito web di Marco Berrettini