Programming and Text Analytics with R
Undergraduate: Level 4
Monday 18 January 2021
Friday 26 March 2021
10 September 2020
Requisites for this module
The 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 IBM Watson.
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 purpose of this module is to introduce:
Fundamental concepts of programming.
The key aspects of programming using the R language.
Powerful R tools for text analytics.
At the end of this module a student will be able to:
A. A systematic, extensive and comparative knowledge and understanding of different objects and data types in R including character, numeric, factor and logical data.
B. A systematic, extensive and comparative knowledge and understanding of functions in R and create own functions.
C. A comprehensive knowledge and familiarity of R control structures, conditional expressions, and looping techniques.
D. A comprehensive knowledge and familiarity of sentiment using free form text, extract insights, and perform string processing methods.
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.
This module has 35 contact hours that will be structured as follows:
Lectures: 15 hours
Computer labs: 20 hours
This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.
Assessment items, weightings and deadlines
|Coursework / exam
Module supervisor and teaching staff
Dr Osama Mahmoud, email: email@example.com.
Dr Osama Mahmoud & Dr Joe Bailey
Dr Osama Mahmoud (firstname.lastname@example.org), Dr Joe Bailey (email@example.com)
No external examiner information available for this module.
Available via Moodle
Of 2146 hours, 0 (0%) hours available to students:
2146 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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