# Fundamentos de Programação em R

## Objectives

First contacts with the statistical software R-project as a working environment for exploratory data analysis and as a tool for all kind of statistical modelling. To learn how to deal with all different types of objects that are available and to use them in practice for the most common statistical analysis for health data. To get acquaintance with the several program libraries.

## General characterization

12019

2.0

##### Responsible teacher

Isabel Cristina Maciel Natário

##### Hours

Weekly - Available soon

Total - 29

Português

### Prerequisites

Basic notions of Analysis and intermediate level notions of Probability and Statistics.

### Bibliography

Crawley, MJ (2007). The R Book. Wiley.

Maindonald J, Braun J (2007). Data Analysis and Graphics Using R. An Example-Based Approach, 2nd edition. Cambridge University Press.

Venables WN, Smith DM, R Core Team (2014). An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics, Version 3.6.1. https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

Venables WN, Ripley BD (1999). Modern Applied Statistics with S-Plus, 3rd edition. Springer.

### Teaching method

The curricular unit is divided into 2 learning units that include each 8-12 videos of oral exposition of the contents, of 10-15 minutes each, along with the presentation of examples and complemented by solved proposed exercises. In the end of each learning unit a revising exercise is delivered. In the end of all units an evaluation assignment is delivered which contributes for the final grading. A timetable for explaining doubts to students made is available via Skype.

### Evaluation method

One practical assignment, needing R programming, where students must analyse a dataset and report it.

## Subject matter

1. Introduction to R, its instalation and documentation
2. Basic commands
3. Types of objects and their use: vector, matrix, arrays, data frame, factor, list, functions
4. Data exploratory analysis: summary measures, plots and graphics
5. Fitting the more common statistical models: linear regression, analysis of variance, non-parametric statistics

## Programs

Programs where the course is taught: