Course for Learning R

Content

This repository contains the material to learn about the R programming language. The topics are divided in four parts:

• Basics starting point if you have few or zero experience with R. The basic concepts are explained here.

  1. Introduction about me, R and this course
  2. Exploring data using the ggplot2 and dplyr packages
  3. Presenting results using the rmarkdown package
  4. More exercises about exploring data
  5. Transforming data to a tidy format using the tidy package
  6. Make calculations with times and dates using the lubridate package
  7. Reading data from a file into R using the readr package, or connect to a database using the DBI package
  8. An introduction into statistical models
  9. Making a simple linear model
  10. Making a simple logistic regression model

• ML here more advanced techniques for modeling a dataset are explained

  1. Explanation of re sampling techniques like cross validation and bootstrapping
  2. Automatic feature selection
  3. Decision trees and random forests
  4. More examples for modeling and the Simpsons paradox

• Big Data explanation of what is big data, hadoop and spark and how to use it in combination with R

  1. Introduction to Big Data
  2. Introduction to Hadoop, MapReduce ans Spark
  3. Introduction to the sparklyr package
  4. Introduction to Cloudera

• Advanced here more advanced R topics are explained

  1. Functional programming using the purrr package
  2. Making interactive web pages using shiny
  3. Using asynchronous programming in a shiny application
  4. Scraping information from an internet page using the rvest package

How to Use

Every chapter has the following parts:

• An {chapter_name}.md file that contains the presentation used for the chapter
• An *.R or *.Rmd file that contains the exercises for the chapter

In the answers map all the answers for the exercises can be found. The extra folder contains examples that are not closely related to one of the chapters, or new chapters that are beeing made. The datasets folder contains the external datasets that are used during this course. Because I gave this course first in Colombia there is also a presentaciones_en_español folder with the presentations in Spanish for most of the material. Finally there is a references.md file which contains all the sources I used for creating this material.