Introduction

The goal in the next two weeks are threefold - the first part is to get you started with using RStudio and being familiar with its environment. The second part of the session aims to introduce you to the basic programming etiquette for basic data management. The third part is focused on building your confidence for using RStudio for data analysis such as descriptive analysis. The last part is to get you to understand what distributions are and using them for basic probability predictions.

At the end of this workshop, you should be able to perform some basic data managing and analysis task in RStudio. The skills learned here will enable you complete the Formative Assignment and prepare you for what’s next in GIF2.

Learning outcomes

Across the three parts, the learning outcomes for this workshop are partitioned are as follows:

  1. The first task includes getting you started with RStudio by installing the needed software(s) (i.e., RStudio and R (Base)) on to your personal laptop.
  2. The second task aims to get you to being familiar with its RStudio’s environment and panels. Here, we begin with you interacting with RStudio’s console to do simple arithmatic and creating objects.
  3. The third task we will begin a soft introduction on the basics of managing data in RStudio. This includes learning how to create various objects in RStudio such as vector and data frame objects which forms the basics of data structures.
  4. The crucial part of this session will be to teach how to set-up working directories, scripting and importing Barcelona datasets in RStudio.
  5. Next, we will learn how to handle the imported data for descriptive analysis using the following techniques: (a.) data types and visualisation; (b.) frequency distribution; and (c.) central tendency measures.
  6. Lastly, we will learn the following techniques: (a.) generating theoretical probability distribution with particular focus on the Normal Distribution; and (b.) to simulate observations from Normal Distribution for making probability predictions based on the mean and standard deviation.

These task will be supported with guidance videos to help with the self-guided learning. Alright, let do this [LINK]!