R-intro-2018

Overview

In this introductory course to R you will learn how to master the basics of this beautiful and immensely useful open-source language. R currently has a growing number of users and its applications in both academia and in the private sector are also increasing in a yearly basis.

The course will provide an introduction to the fundamental elements of R, starting from basic data structures and functions up to more complex stuff such as if conditionals and for loops. Some basic notions regarding the tidyverse approach to data manipulation (dplyr package) and visualization (ggplot2) will also be explored.

With the knowledge acquired from this course, you will be ready to undertake your own data analysis and (hopefully) be capable of pursuing more advanced topics.

Our exploration will start by focusing on some key aspects of R as programming language, its syntax and slowly introduce some of the functions used for basic statistics such as those used for summarizing data (e.g., mean, standard-deviation), pairwise correlation and linear models.

Pre-requisites

Don't worry we will start slowly! :-) As such, you don't really need any prior knowledge of computer programming (although some previous experience will make things easier) however, some familiarity with basic mathematical and statistical concepts and notions is assumed (e.g., distributions, centrality and dispersion measures, quantiles).

Course content by session

From vectors to sophisticated graphical data summaries with the ggplot2 package these are the contents of this course:

Session 1

Exploring RStudio environment and workflow
Understanding the basics of Rmarkdown format
Starting a work session
Using the help system
R as a (sophisticated) calculator
Vectors and matrices - understanding the differences
Vector and matrix indexation

Session 2

Time for recap session #1
Missing data: NA values
Data frames (as an extension of matrices)
Exploratory Data Analysis: scatter-plots and correlation analyses
Factor variables
Plotting and comparing distributions with boxplots
Hypothesis testing (t-Test)
Analysis of variance (one-way ANOVA)

Session 3

Lists
User-defined/custom functions
If conditionals
For loops
Combining it all together

Session 4

A brief tour to the tidyverse
Data manipulation with the dplyr package
- Main dplyr verbs
- The pipe %>% operator
Making pretty graphics with ggplot2 - design principles
Introduction to main types of plots/graphics:
- Scatter-plots
- Regression, trend and smooth lines
- Histograms
- Boxplots
- Modifying plots
- Making plots by groups

joaofgoncalves / R-intro-2018