Data Analysis and Visualisation Courses

DAV101 - Introduction to Data Analysis and Visualisation with R

Duration: 5 Days

Course Background

This course provides an intensive introduction to the use of R for data analyis and visualisation. Starting with an overview of R and its uses it then goes on to explore data management and retrieval from within R, import and export of data between R and spreadsheet packages such as Excel, the R programming language and the accessing relational databases from R. The course also explores a variety of data visualisation techniques available in R as well as the implementation of various programs in R to automate much of the work of data analysis and visualisation. It is a hands-on course filled with real data and examples, case studies, and in-class mini projects. It should be considered as a foundational course that lays the foundation for more advanced data analysis and visualisation projects involving the analysis and mining of large and complex data sets.

Course Prerequisites and Target Audience

Strudents are expected to have a basic knowledge of statistics and programming, such as might have been gained in as part of an undergraduate satistics course in e.g. Economics, Business Studies or Psychology. The level of programming assumed is such as might have been gained by writing e.g. shell programs or simple python scripts, or basic VBA programming.

Course Outline

Overview and Introduction to R

R as a computing environment
R interface, session, and language overview
The RStudio IDE

Data Manipulation

Basic data types and operations
Data Frames
Importing, saving, exporting, and re-using data
Standard R functions for numbers, factors, text, and dates
Vector-oriented computation
Sorting, ranking, and printing

R as a Programming Language

Data types and variables
Loops and conditions
Functions - implementation and use
Object-oriented aspects of R programming – lists and classes
Code optimisation and efficient data processing

Exploratory Data Analysis

Basic data summary functions
Tables and cross-tabulations

Graphics programming in R

Basic plots and charts
Advanced plotting functions
Modifying plots, labels, titles, and advanced customization
Trellis graphics
Saving graphics and graphic formats

Statistics and Modeling in R

Overview of modeling and statistical data analysis
Basic statistical tests, power, and sample size functions
Regression and analysis of variance
Basic data mining functions

Modular programming in R

Object oriented programming and the object oriented features of R
Controlling object evaluation and R's scoping rules
Understanding base functions such as with, subset and transform
Capturing user input without evaluating it
Controllng when and where R evaluates expressions and calls

R’s rules for dynamic and lexical scoping
Writing code that modifies code

R functions as first class objects

Creating anonymous functions
Writing closures – functions that return functions
Building higher-order functions – functions that take other functions as input
Working with lists of functions
S3 and S4 generic functions and classes
R5 - Reference classes

Adding libraries and CRAN
Operating system functions and system calls
Working with R from the command line
R and SQL

Overview of relational databases and SQL
Using sqldf to write SQL commands to extract data from a data frame
Using the DBI package to interact with relational databases from R

Data visualisation in R

Overview of ggplot and plyr
Creating informative scatterplots
Adding extra variables with aesthetics (e.g. color, shape and size) or facetting.
Create graphics for large data

Histograms and bar charts for displaying distributional summaries
Boxplots
Scatterplots variations that overcome the over-plotting problems associated with large data.

Designing and critiquing a graphic

Advanced layered techniques
Overlaying graphic elements using ggplot layers
Combining raw data with statistical summaries and contextual information.
Polishing and tweaking plots for maximum presentation impact;
Design heuristics
Colour theory
Effective use of labels, legends and axes
Creating and perfecting plot themes.