R for Economics and Business

Recommended Prerequisites

NA

Teaching Methods

Theoretical-practical lessons with computers. The main concepts, instructions and routines will be shown to the students using practical examples. The students will then have an opportunity to apply these to several practical applications assoaciated with business and economics.

Learning Outcomes

Overall objectives: Introduction to the programming language R and how to write elementary 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 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. Basic statistics 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

14. Reproducible Research

Assessment Methods

Assessment
Periodic or by final exam as given in the course information: 100.0%

Bibliography

João Sousa Andrade, How to Use R  - an introductory text, pdf, FEUC, 2019

Robert I. Kabacoff, R in Action, Data Analysis and Graphics with R, 2nd Ed., Manning Pub Co, 2015

Hadley Wickham & Garret Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, O'Reilly, 2017

Oscar Baruffa, Big Book of R, https://www.bigbookofr.com/