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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  1. R programming track
  2. -a-Introduction to R

--1-Intro to basics

#1-How it works

-In the editor there is already some sample code. Can you see which lines are actual R code and which are comments? comments: #

-Add a line of code that calculates the sum of 6 and 12

3+4

6+5

#2-Arithmetic with R

-Type 2^5 in the editor to calculate 2 to the power 5

-Type 28 %% 6 to calculate 28 modulo 6

# An addition
5 + 5 
# A subtraction
5 - 5 
# A multiplication
3 * 5
# A division
(5 + 5)/2 
# Exponentiation
2^5
# Modulo
28%%6

#3-Variable assignment

# Assign the value 42 to x
x <- 42
# Print out the value of the variable x
x

#4-Variable assignment (2)

# Assign the value 5 to the variable my_apples
my_apples<-5
# Print out the value of the variable my_apples
my_apples

#5-Variable assignment (3)

# Assign a value to the variables my_apples and my_oranges
my_apples <- 5
my_oranges<-6
​
# Add these two variables together
my_apples+my_oranges
​
# Create the variable my_fruit
my_fruit<-my_apples+my_oranges

#6-Apples and oranges

# Assign a value to the variable my_apples
my_apples <- 5 
​
# Fix the assignment of my_oranges
my_oranges <- 6 
​
# Create the variable my_fruit and print it out
my_fruit <- my_apples + my_oranges 
my_fruit

#7-Basic data types in R

#Change the value of the:
#my_numeric variable to 42.
#my_character variable to "universe". 
#Note that the quotation marks indicate that "universe" is a character.
#my_logical variable to FALSE.
#Note that R is case sensitive!

# Change my_numeric to be 42
my_numeric <- 42

# Change my_character to be "universe"
my_character <- "universe"

# Change my_logical to be FALSE
my_logical <- FALSE

#8-What's that data type?

# Declare variables of different types
my_numeric <- 42
my_character <- "universe"
my_logical <- FALSE 
​
# Check class of my_numeric
class(my_numeric)
​
# Check class of my_character
class(my_character)
​
# Check class of my_logical
class(my_logical)

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Last updated 6 years ago