Introduction to R & Statistical Modelling in R
Who is this course for?
The course requires familiarity with basic statistical methods (e.g. t-tests, box plots) but assumes no previous knowledge of statistical computing.
What will I learn?
The purpose of this course is to introduce participants to the R environment for statistical computing. The course focuses on entering, working with and visualising data in R, and linear regression modelling in R.
By the end of the course, participants will be able to use R to:
- Have a clear understanding of R/RStudio IDE and its background.
- Be familiar with navigating the RStudio IDE.
- Understand the core fundamentals of R.
- Understand functions and arguments.
- Be able to create vectors and applying functions.
- Be exposed to the tibbles and {tidyverse} package.
- Be able to comfortably import, export, and store data in R.
- Have a basic introduction to graphics with {ggplot2}.
- Have a basic understanding of manipulating data manipulation with {dplyr}.
- Understand logical and relational data partitioning.
- Have a thorough understanding of popular statistical techniques.
- Have the skills to make appropriate assumptions about the structure of the data and check the validity of these assumptions in R
- Be able to fit regression models in R between a response variable and understand how to apply these techniques to their own data using R’s common interface to statistical functions.
- Be able to cluster data using standard clustering techniques.
Skills tags:
- data
- R
- programming
- statistics