Introduction to R for Data Analysis

Not only will you acquire a great deal of technical knowledge on R language but you will also gain insights into some sophisticated statistical and analytical concepts and the way analysis supports planning and strategic management processes.

You will learn how to

• create and manipulate R data structures
• import data from external sources
• clean and transform your data
• create sophisticated functions
• use statistical functions for useful organisational purposes and present findings using R graphics, Tableau and Power BI
• Apply R techniques to the work of a data analyst and use them to support the planning and strategic management processes.

This wide-ranging data analysis with R course, covers all aspects of R from basics, through to sophisticated graphics, advanced programming techniques and data mining algorithms. It has strong business focus, illustrating how analytical findings can be used for organisational planning purposes.

Who is it for?

This course is ideal for data analysts wanting to use R to extract data from large datasets.

Timetable

Introduction to R for Data Analysis is taught one evening a week for 10 consecutive weeks.

City Short Courses follow the academic year, delivering courses over three terms. These include:

• Autumn - October
• Spring - January
• Summer - April

Benefits

• Delivered by industry professionals
• Taught online to small classes
• Awarded a City, University of  London

Data structures:
Vectors, factors, matrices, lists and especially data frames. Manipulation of these using aggregative functions, indexing and other more sophisticated functions including the apply() family. How to use these techniques to best advantage with large organisational datasets.

Graphics:
We learn R’s basic plotting techniques (plot(), hist() etc.), but soon move on to more sophisticated techniques (ggplot2(), Tableau, Power BI). How to use these to further analyse organisational data and to present your analytic findings to co-workers.

Statistics:
With the emphasis very much on practical applications, not mathematical theory, we learn about descriptives, distribution, regression and correlation (including multiple regression), t-tests, ANOVA and categorical data analysis (including chi-squared). There is a strong emphasis of the applicability of statistical techniques to organisational problems, refining our models and rigourously testing them for reliability.

Programming:
We learn the basics of procedural programming – variables, control structures and writing simple functions – before moving on to building more sophisticated functions geared to manipulating large datasets.

Data loading, cleaning and transformation:
Loading data from Excel, SQL, XML and the web, using SQL notation to query R data, cleaning and transforming your data (missing values, recoding and converting variables, creating new variables), merging and sampling data.

You will be awarded an official City, University of London certificate if you attend over 70 per cent of the classes. The course is not formally accredited.

Assessment

Teaching is in the form of lectures interspersed with exercises to test and expand your knowledge. There is no formal assessment as part of this course.

There is also a continuous data analysis project, where you will use R techniques to gain insights into a particular client group and how they might be approached to best advantage by an organisation.

While no prior knowledge is required, you will find it useful to have a little knowledge of statistics (descriptives, regression and distribution), some basic SQL (up to using GROUP BY and ORDER BY), and the fundamentals of procedural programming (manipulating variables, ifs and whiles, writing simple functions) that can have been gained using any other programming language.

You must be IT literate.

English requirements

Applicants must be proficient in written and spoken English.

R in Action. Manning (2015). Robert I Kabacoff

Other useful texts will be suggested during the course.