Computation with R

Recommended Prerequisites

Not applicable.

Teaching Methods

Theoretical-practical lessons with computers’ use with the allocation of a data bases to each student at the beginning of the semester. Students must use them throughout all the period of learning. In the end the student must submit a report with the presentation of the data bases using the methodologies studied in the discipline.

Learning Outcomes

Overall objectives: Introduction to the programming language R and how to use data bases write programs and to demonstrate how statistical models are implemented and applied

Specific objectives: Topics include creating data, importing data, accessing subsets of data, exporting data, plotting and graphing, loops and functions. It also will provide a basic knowledge of R that would help master the elementary and more advanced statistical tools available in R.

Generic competencies: How to use quantitative and qualitative data information and how to organize it with R.

Specific competencies: Students will be able to import, manage and structure elementary and complex data files; to write simple program scripts for data analysis; to produce and use illustrative data plots; to carry out some important statistical tests characterizing the data; to use appropriate linear and non-linear models and interpret them correctly.

Work Placement(s)

No

Syllabus

1. An overview of the background and features of the R statistical programming system

2. How to start: installation on Windows, Linux and MacOs, customizing the startup environment, graphic user interfaces and updating

3. Introduction to R commands. Creation and use of scripts, saving data and results. Extending R through packages

4. Creating a dataset, import and export external data

5. Introduction to basic graphs. Creation, editing and storing graphics

6. Data management and data manipulation with logical operators

7. Statistics analysis and hypothesis testing

8. Simple and multiple linear regression

9. Basic programming: conditional statements, looping operations, vector operations and functions

10. Intermediate graphs

11. Sampling, resampling and bootstrapping

12. Principal components and factor analysis

13. The manipulation of big data.

Head Lecturer(s)

António Alberto Ferreira Santos

Assessment Methods

Assessment
Continuous evaluation or final examination.: 100.0%

Bibliography

Manual principal | main textbook:

Robert I. Kabacoff, R in Action, Data Analysis and Graphics with R, Manning Pub Co, 2011

 

Outros manuais complementares | complementary books:

R Development Core Team, An Introduction to R: A Programming Environment for Data Analysis and Graphics, GNU Free Documentation License, http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf, 2012

G. Jay Kerns, Introduction to Probability and Statistics Using R, GNU Free Documentation License, 2011

Winston Chang, Cookbook for R, http://wiki.stdout.org/rcookbook/, 2012

Manual and Documents in R, CRAN, Home Page, http://cran.r-project.org/, GNU Free Documentation License