Module Directory

This module will introduce the underlying principles and basic concepts of programming with the R language. It will cover a wide range of analytics, provide practical experience of powerful R tools, and present real-world examples of how data and analytics are used to gain insights and to improve a business or industry. These examples include text analytics, Twitter, and Gutenberg digital public domain texts.

Throughout these examples, and many more, we will teach programming techniques that will enable students to apply advanced data science approaches to real-world applications. This module assumes no prior programming skills.

The aims of this module are:

• To introduce the fundamental concepts of programming.


• To introduce the key aspects of programming using the R language.


• To introduce powerful R tools for text analytics.

By the end of this module, students will be expected to have:

1. A comprehensive understanding of techniques applicable to recognise different objects and data types in R including character, numeric, factor and logical data.


2. A comprehensive understanding of techniques applicable to use functions in R and create own functions.


3. A comprehensive understanding of techniques applicable to implement R control structures, conditional expressions, and looping techniques.


4. A comprehensive understanding of techniques applicable to analyse sentiment using free form text, extract insights, and perform string processing methods.


5. Conceptual understanding that enables the student to summarise sentiment analyses and natural language processing.


6. Conceptual understanding that enables the student how to solve and to document a complex coding project.

Syllabus

• Introduction to R.


• What is R? A brief overview of the concepts and features of the R statistical programming environment.


• Help systems in R: A description of how to use different sources of R help.


• Data types: A brief introduction to different data types in R including numeric, complex, character, factor, and logical data.


• Data structure: A summary of data structure in R including vectors, matrices, arrays, data frames and lists.


• Importing data: Describing how to import, edit, save, and export data of different formats from R including Excel, SPSS, STATA, and SAS data files.


• Data manipulation: A description of how to use logical operators to manipulate data.


• Missing values: Describing how R handles missing values.


• Visualisation: Creating, editing, and saving graphics in various formats using R.


• Programming using R.


• Functions: What is an R function? how are they structured and used? how can one understand function`s parameters and how can we create our own functions?


• Control Structures: Describing how we include control structures into R code.


• Conditional expressions: Using `if` and `ifelse` structures in R.


• Loops: Introducing looping techniques in R, with particular focus on `for`, `repeat` and `while` statements.


• `apply` family: using `apply`, `lapply`, `tapply`, `mapply` and `sapply` in R.


• Text analytics using R.


• Text as data: understand opinions and intelligence.


• Case study: Analysis of tweets on Twitter to understand sentiment and public perception.


• Sentiment analysis.

Teaching in the School will be delivered using a range of face to face lectures, classes and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.