This is a repo for materials used for a module on quantitative research resources, taught at Glasgow International College, a University of Glasgow College.
Course Instructor: Graciela Carrillo
Module Coordinator: Dr. Molly Huq
Find it here: https://gracecarrillo.github.io/course-in-a-box-r/
R is a very popular programming language for the world of data analysis, data science and statistical computing. The journey of the R language from a rudimentary text editor to the interactive R Studio, Jupyter Notebooks and more recently, Google Collaboratory notebooks, has engaged many scientists across the world.
This was possible only because of generous contributions by R users globally. The inclusion of powerful packages in R has made it more and more powerful with time. Packages such as dplyr, tidyr, readr, data.table, SparkR, ggplot2 have made data manipulation, visualization and computation much faster.
In this crash course, we dive a bit into the world of data science and machine learning using R. The goal of this course is to give you an introduction to the most important tools needed in a typical data science project, whatever your field of study.
This course was created using the Jekyll Course template from P2PU and following the tutorial at howto.p2pu.org