methods@manchester summer school 2019 - Getting Started in R

Description:

Overview

R is an open source programming language and software environment for performing statistical calculations and creating data visualisations. It is rapidly becoming the tool of choice for data analysts with a growing number of employers seeking candidates with R programming skills.

This course will provide you with all the tools you need to get started analysing data in R. We will introduce the tidyverse, a collection of R packages created by Hadley Wickham and others which provides an intuitive framework for using R for data analysis. Students will learn the basics of R programming and how to use R for effective data analysis. Practical examples of data analysis on social science topics will be provided.

1. R and the 'tidyverse'

This session will introduce R & RStudio and cover the basics of R programming and good coding practice. We will also discuss R packages and how to use them, with a particular focus on those that make up the 'tidyverse'. We also introduce R Markdown which will be used to report our analyses throughout the course.

2. Import and Tidy

Data scientists spend about 60% of their time cleaning and organizing data (CrowdFlower Data Science Report 2016: 6). This session will show you how to 'tidy' your data ready for analysis in R. In particular, we'll show you how to take data stored in a flat file, database, or web API, and load it into a dataframe in R. We will also talk about consistent data structures, and how to achieve them.

3. Transform

Together with importing and tidying, transforming data is one of the key element of data analysis. We will cover subsetting your data (to narrow your focus), creating new variables from existing ones, and calculating summary statistics.

4. Visualise

Data visualisation is what brings your data to life. This session will provide you with the skills and tools to create the perfect (static and interactive) visualisation for your data.

5. Bringing it all together

In this last session we review all we have learned on this course, and think about how we can bring it all together in dynamic outputs, such as interactive documents, plots, and Shiny applications.

Course objectives

After this course users should be:

· implement the basic operations of R;

· read data in multiple forms;

· clean, manipulate, explore and visualise data in R

Prerequisites
None

Cost:

Students - £600
University of Manchester Staff - £600
Other attendees - £900

Website and registration:

https://www.methods.manchester.ac.uk/connect/events/summer-school-2019/

Region:

North West

Keywords:

Generalized liner model (GLM), Quantitative Software, R

Related publications and presentations:

Generalized liner model (GLM)
Quantitative Software
R

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