Checking date: 22/05/2022

Course: 2022/2023

Programming in R
Study: Master in Statistics for Data Science (345)


Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


The student will acquire the following knowledge: 1. Proficiency in the R programming language and the R-studio working environment. 2. Mastering the different types of data structures. 3. Exploratory data analysis techniques and presentation of results through data visualization techniques. 4. Familiarity with the main data analysis packages of R. 5. Be able to perform a simulation properly. 6. Accelerate the programs implemented by means of parallel programming. 7. Find errors and bottlenecks in their code and generate reports.
Skills and learning outcomes
Description of contents: programme
1. Basics of Programming I. The R-studio environment. Types of data (Arrays, Lists, Factors, Data Frames,...) and their operations. Control structures. Functions. 2. Basics of Programming II. Advanced data structures. Reading and storage of data. 3. Data visualization. The ggplot2 package. 4. Introduction to some useful packages in R. MASS, Caret, dplyr and data.table packages. 5. Simulations. 6. Parallel programming. 7. Debugging, Profiling and presentation of results with Rmarkdown.
Learning activities and methodology
The course will be taught in 7 theoretical and practical classes. The students will have tutoring support where they will be able to resolve their doubts regarding both the material explained in the classes and the practical assignments that will be evaluated.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • Felicidad Marques Asension. R en profundidad. Programación, gráficos y estadística. RC. 2017
  • Fox, J.. Using the R Commander: A Point-and-click Interface for R. CRC Press.. 2016
  • Irizarry, R.A.. Introduction to data science: data analysis and prediction algorithms with R. Boca Raton, Florida. CRC Press. 2020
  • Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc.. 2016
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.