RSS02.D2 Introduction to R - Confirmed

Confirmed

The guiding logic of the course is to give practical knowledge of the whole data analysis workflow:

Monday – Importing data 
Tuesday – Data wrangling/cleaning 
Wednesday – Visualisation | Exploratory analysis 
Thursday – Analysis | Writing our own functions 
Friday – Reporting the results

Reflecting on the realities of typical research projects, the course focuses on data cleaning and getting data into a shape which allows us to analyze and visualize it properly. The exploratory analysis and data visualization parts are heavily intertwined.

We will learn how to make descriptive statistics, how to group data, and how to explore a given dataset. The course puts strong emphasis on visualization components and we will learn to use the ggplot2 package to produce wonderful looking graphs (as an example, most of the Financial Times' charts are made with R in ggplot2).

As part of learning a programming language, it is inevitable that we must learn how to write our own functions. It is not the most intuitive part, and I will focus on making it as accessible as possible without relying on too much computer science/programming jargon. Alongside this, we’ll look at a few statistical applications in R (t-test and OLS regression).

At the end of the course, we will export our results from R or even write an academic paper or report using RMarkdown.