So You Need to Learn R
Learn R programming from scratch in this comprehensive course. Gain the skills to analyze, model, and visualize data using base R. Suitable for beginners with no programming experience, this course covers key concepts and real-world challenges. Perfect for researchers, analysts, and those looking to enter the data science field.
What you’ll learn
- How to write R code in scripts and functions
- Obtain summary statistics, aggregate data, and create statistical models
- How to work with data: load, transform, aggregate, and graph
- Basic brogramming concepts like data structures and loops
So you need to learn R, but you’ve never programmed before. Don’t panic! In this course, we’ll take you on a guided tour of how to code and do data analysis in base R. You’ll learn to load data, summarize it, analyze it, model it, and visualize it. You’ll learn about thorny real-world issues, like handling missing values and loading mis-formatted data files.
We’ll cover the fundamentals of programming. We’ll talk about key programming concepts, like data structures and the flow of control. And you’ll practice writing code as we go, tackling problems that progressively challenge you and build your confidence, on carefully crafted example data sets.
I won’t take shortcuts, gloss over details, or just give you easy cut-and-paste examples. I want you to walk away from this class as a junior programmer in R, able to approach whatever real data problems you face at work, in school, or in your research. I’ve worked in R almost every day for over a decade, in research and industry, to solve real data problems — and I want to help you do the same.
This course is appropriate for someone with no programming experience. High-school level statistics, and a familiarity with tabular data (like in Excel) is helpful.
It addresses base R. It does not cover the Tidyverse collection of libraries.
Who this course is for:
- Researchers and academics with limited programming experience
- R users who want a more formal introduction to the language
- Analysts who want to add another tool to their skillset
- People planning a switch into a data science career
- Graduate students in the sciences
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