R Programming
  • The wikipedia of R by me
  • Hello R
    • -What is R & RStudio
    • -Learning sources
    • -R online editor
    • -R environment
  • Data types
    • -Dealing with Number
    • -Dealing with String
    • -Dealing with Dates
    • -Dealing with NA's
    • -Dealing with Logicals
    • -Dealing with Factors
  • R data
    • -R object
    • -Data structures
      • --Basics
      • --Managing Vectors
      • --Managing Matrices
      • --Managing Data Frames
    • -Functions
    • -Importing/exporting data
    • -Shape&Transform data
    • -R management
  • Visualizations
  • Intro to R Bootcamp
    • -01-introduction
    • -02-data preparation
    • -03-data transformation
    • -04-visualization
  • R programming track
    • -a-Introduction to R
      • --1-Intro to basics
      • --2-Vectors
      • --3-Matrices
      • --4-Factors
      • --5-Data frames
      • --6-Lists
    • -b-Intermediate R
      • --1-Conditionals and Control Flow
      • --2-Loops
      • --3-Functions
      • --4-The apply family
      • --5-Utilities
    • -d-Writing Functions in R
      • --1-A quick refresher
      • --2-When and how you should write a function
      • --3-Functional programming
      • --4-Advanced inputs and outputs
      • --5-Robust functions
  • Data Wrangling with R
  • R-tutor
    • #R introduction
    • #Elementary Statistics with R
  • Hands-On Programming with R
  • R for Data Science
  • Advanced R
  • ggplot2
  • R packages
  • Statistik-1
  • Statistik-2
  • Statistik-3
  • Zeitreihen & Prognosen
  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • R Graphics Cookbook
    • ggplot2 intro
    • ggplot2 custome
    • ggplot top-50
  • #Exploratory Data Analysis
    • -Data Summary
    • -Checklist Solution
  • #Data Mining
    • Untitled
    • Untitled
  • #Machine Learning
    • Intro to ML
    • Intro alghorithms
    • 1. Supervised Learning
  • Master R for Data Science
    • Learning R
    • Untitled
    • Untitled
  • Data Science Projects
    • Simple linear regression:
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Hands-On Programming with R

An introduction to R from Hadley Wickham

Find the R code in this link:

  1. ​The Very Basics​

  2. ​Packages and Help Pages

  3. R Objects​

  4. R Notation​

  5. Modifying Values​

  6. Environments

  7. Programs

  8. S3

  9. Loops

  10. Speed

  11. Appendix:

  • A. Installing R and RStudio​

  • B. R Packages​

  • C. Updating R and Its Packages​

  • D. Loading and Saving Data in R​

  • E. Debugging R Code

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Last updated 6 years ago