Learning Path overview
Have you ever wondered how you can turn your massive datasets in to useful information for your team, organisation or business? Then R is the language you need to learn.
This learning path introduces the fundamental concepts and knowledge you need to use the programming language R, a mathematical and statistical modelling language used extensively in data analysis and Big Data.
By attending this course you will learn how to write programmes using R to create effective statistical outputs and to visualize data using R’s library.
Aimed at all who wish to learn the R programming language.
Prerequisites of the Course
No prior knowledge of R is assumed
Delegates should already be familiar with basic programming concepts such as variables, scope and functions
Experience of another scripting language such as Python or Perl would be an advantage
Having an understanding of mathematical concepts will be beneficial
At the end of this course you will be able to understand:
- The basics of R
- How to download and install RStudio
- What makes RStudio unique
- Datasets in RStudio
- Basic operations in R
- Variables, Booleans, operations and integers in R
- Data Structures in R
- Functions in R
- Operators in R
- Conditional Statements in R
- Implicit Control Flow in R
- Plotting in R
This Learning Path contains videos, PDFs, and labs for 10 courses.
We begin with an introduction to your trainer Kunal, and what you can expect from the videos in this Learning Path.
Module 1: Interacting in R
- Introductio to RStudio
- Understanding RStudio
- What makes RStudio unique?
- Adding comments in R
- Useful keyboard shortcuts in RStudio
- Viewing a dataset in RStudio
Module 2: Introduction to Data
- Basic calculator operations in R
- How to assign variables in R
- How to transmit to output devices in R
- How to install a package in R
Module 3: Beginner Data Structures in R
- Vectors in R
- Generating Vectors in R
- Element access in R
- Operations in R
- Vector Recycling in R
- Booleans in R
- Characters in R
- Missing data in R
- Infinity in R
Module 4: Intermediate Data Structures in R
- Objects in R
- Integers in R
- Implicit coersion in R
- Explicit coercion in R
- Matrices in R
- Subsetting matrices in R
- Arrays in R
- Lists in R
Module 5: Advanced Data Structures in R
- Factors in R
- Data frames in R
- Modifying data frames in R
- Subsetting data frames in R
- Factors in a data frame in R
Module 6: Functions in R
Introduction to functions in R
Using built in functions in R
Creating functions in R
What is returned from a function in R
Functions of functions in R
Input arguments of functions in R
Named arguments of functions in R
Module 7: Operators in R
- Operators in R
- Introduction to Conditional Statements in R
- Vectorised comparisons to generate one result in R
- Vectorised comparisons to generate many results in R
Module 8: Conditional Statements in R
- Explicitly repeating operations in R
- For loops can be nested in R
- Implicitly repeating operations in R
- Repetitively apply actions in R
- Repetitively apply user defined functions in R
- Repetitively apply anonymous functions in R
- Repetitively apply actions on vectors in R
Module 9: Implicit Control Flow in R
- Bring in data from a file into R
- Saving and loading all objects in R
- Interacting with clipboard in R
- Connecting to files in R
- Reading from a file in R
- Writing to a file in R
Module 10: Plotting in R
- Basic plots in R
- Types of lots in R
- Graphical parameters of plots in R
- Modifying a plot in R
The last section offers a brief summary, and provides you with some next steps in your journey in R.
We welcome all feedback and suggestions - please contact us at email@example.com to let us know what you think.
Kunal has worked with data for most of his career, ranging from diffusion markov chain processes to migrating reporting platforms.
Kunal has helped clients with early stage engagement and formed multi week training programme curriculum.
Kunal has a passion for statistics and data; he has delivered training relating to Hypothesis Testing, Exploring Data, Machine Learning Algorithms, and the Theory of Visualisation.
Data Scientist at a credit management company; applied statistical analysis to distressed portfolios.
Business Data Analyst at an investment bank; project to overhaul the legacy reporting and analytics platform.
Statistician within the Government Statistical Service; quantitative analysis and publishing statistical findings of emerging levels of council tax data.
Structured Credit Product Control at an investment bank; developing, maintaining, and deploying a PnL platform for the CVA Hedging trading desk.