Advanced R Training
About the training
R is currently one of the most popular tools for data analysis. It is also a tool that helps automating data analysis. You can use R to create your own functions, do complicated calculations, transform, and aggregate data, build statistical models or use data mining techniques and in the end, present your results on practical and esthetic charts and reports. Since R is an open source language and software there is a large community involved in developing and improving it. Thanks to that R is continuously updated and contains packages using the latest and most advanced techniques in data analysis and statistics. R is widely popular among companies, banks, universities and public institutions.
With this Advanced R Training, you will learn techniques that will make your work with R even faster and more effective and allow you to conduct complex analyses effortlessly.
Who is this training for?
Advanced R Training is aimed at people who already have some R programming experience, but would like to feel more comfortable in the environment and speed up their work. If you are not sure if this is the best training for you feel free to contact us. We will help you choose the best training for your needs!
This training is aimed at:
Our clients work in different fields (finance, banking, production, medicine, biology, etc.), but have one thing in common: they want to make the best use of the data and create top-level analyses. During the training, we concentrate on those R elements that would be the most useful for your everyday work.
What will I learn?
This is an advanced training. After completing this training participants should be able to use even advanced R features freely. We will also show you common mistakes and problems that occur while programming with R and how to solve them.
After completing the training you will be able to:
- Solve most of the common R problems – at the beginning of working with R user often come across various problems and frequently make mistakes. It is normal, especially when using advanced functions and packages. We will show you how to deal with them and what are the best practices to avoid them.
- Create advanced functions – R allows you to create user-defined functions. It is very useful when you are doing complex calculations and analyses. We will show you how to build them properly and efficiently.
- Use loops and conditional code execution – Loops and If statements are the basics of every programming language. You will learn how and when to use them in R.
- Use R for statistical analyses – It is the possibility of creating advanced analyses and a large selection of statistical packages that makes R programming language so popular amongst statisticians and data scientists. We will show you how you can make your work easier and better by using R.
- Create simulations to solve complex problems – We will show you how to approach complex problems using simulations.
- Automate daily analytical tasks and reports – You will learn how to automate your daily tasks and reports, so you don’t have to waste your time and energy doing the same thing over and over again.
- Increase code efficiency – You will learn how to write a good code that is efficient, easy to read and edit in the future. We will show you why using loops is often not so good idea and what to do instead.
Forget about PowerPoint presentations. We want you to gain practical knowledge of the topic, so you will work in R during the entire training. Each section of the course ends with practical exercises.
- Introduction and R basics
- Overview of RStudio environment
- Creating reproducible analyses
- Analyzing code with tryCatch
- Debugging your code
- Conditional code execution
- Creating user-defined functions
- Managing function arguments
- Default argument values
- Values returned by functions
- Local memory vs function scope
- Recursive functions
- Mathematical functions and simulations
- Basic mathematical functions
- Matrix and vector calculations
- Probability distributions
- Random number generation
- Creating simulations
- Working with text
- Overview of text functions
- Regular expressions
- Stringr package
- Debugging code
- Basics of debugging
- Syntax and logical errors
- Using debug and browser functions
- Using trace function
- Code efficiency: speed and memory
- Optimizing R code
- Loops vs vectorization
- Memory problems
- Monitoring code using Rproof
- Managing large datasets
- Bigmemory package