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• R
• RStudio

Resources

• An Introduction to Statistical Learning

Basic Commands

• Functions are used to perform operations

• Concatenate to create a Vector

  •   x = c(1,2,3,4,5) # 1 2 3 4 5
      x <- c(1,2,3,4,5) # 1 2 3 4 5
    

• Help

  •   ?c
    

• Adding two vectors

  •   x = c(1,2,3,4,5)
      y = c(1,2,3,4,5)
      z = x+y # 2  4  6  8 10
      z = z+2 # 4  6  8 10 12
            
      k = c(2,3)
      z = x+k # Error
            
      k = c(2)
      z = x+k # 3 4 5 6 7
    

• Length of vector

  •   length(x) # 5
      length(y) # 5
      length(k) # 2
    

• List of All Objects and Remove Object(s)

  •   ls() # x, y, k, z
      rm(k, z)
            
      rm(list=ls())
    

• Creating Function

  •   f = function(x, y) x^2 + y^2
      f(10, 10) # 200
            
      f = function(x, y){ # Shift+Enter
           z = x^2 + y^2
           z
       }
      f(10, 10) # 200
    

• Matrix

  •   ?matrix
      x = matrix(data=c(1, 2, 3, 4), nrow=2, ncol=2)
            
      x = matrix(c(1, 2, 3, 4), 2, 2)
            
      x = matrix(nrow=2, ncol=2, data=c(1, 2, 3, 4))
            
      dim(x)
    

• Outer

  •   x = 1:10
      y = x
      z = x %o% y # default multiplication operator
      z = outer(x,y)
      z = outer(x, y, "+")
            
      f = function(x, y) x^2 + y^2
      z = outer(x, y, f)
    

• Other Functions

  •   sqrt(x)
      x^2
            
      # rnorm() generates vector of normal variables with mean 0 and std 1
      x = rnorm(50)
      y = rnorm(50, mean=50, sd=0.1)
            
      # Correlation
      cor(x, y)
            
      # Seed
      set.seed(5); x = rnorm(5); x
      set.seed(5); x = rnorm(5); x
            
      # Mean, Variance, Standard Deviation
      mean(x); var(x); sd(x)
    

Graphics

•   x = rnorm(100)
    y = rnorm(100)
      
    plot(x,y)
    plot(x, y, xlim=c(0,2), ylim=c(0,2))
      
    plot(x, y, type="p") # "l" for lines and "b" for both points and lines
      
    plot(x,y, main="Scatter Plot", xlab="x-axis", ylab="y-axis") # xlabel and ylabel
      
    # Saving plot in pdf
    pdf(file = "/Users/naneja/Downloads/xy.pdf")
    plot(x,y, main="xy plot", xlab="x-axis", ylab="y-axis")
    dev.off() # complete plotting
      
    # Saving plot in jpg
    jpeg(file = "/Users/naneja/Downloads/xy.jpg")
    plot(x,y, main="xy plot", xlab="x-axis", ylab="y-axis")
    dev.off() # complete plotting
  

• Sequence of Numbers

  •   # Sequence for vector of integers
      x = seq(1, 10); x
      x = 1:10; x
            
      # Sequence for vector of integers equally spaced
      x = seq(1, 10, length=5); x
      x <- seq(-pi, pi, length = 5); x
    

• 3D Plots

  • First dimension: a vector of the x values
  • Second dimension: a vector of the y values

  • Third dimension: a matrix of the z values whose elements correspond to each pair of (x, y) coordinates

  •   x = 1:10
      y = x
            
      f = function(x, y) cos(y) / (1 + x^2)
      z = outer(x, y, f)
            
      # 3D Plot
      persp(x, y, z)
      persp(x, y, z , theta=30)
      persp(x, y, fa , theta=30, phi=70)
      persp(x, y, fa , theta=30, phi=40)
            
      # Heatmap based on values of z
      image(x, y, z)
    

Indexing Data

•   A = matrix(1:16, 4, 4)
      
    A[4,4] # 16
    A[c(1,2,3), c(1,2,3)] # 3x3
    A[1:3, 1:3]
      
    A[4,]
    A[,4]
      
    A[1:3,]
    A[,1:3]
      
    A[-1,]
    A[-c(1,2),]
    A[-c(1,4),]
  

Loading Data

# https://www.kaggle.com/c/titanic/data
getwd()
setwd("/Users/naneja/Downloads/R")

data = read.csv("titanic/train.csv")
data[0,] # column names

install.packages("ISLR2")
library(ISLR2)

Auto = ISLR2::Auto
Auto = read.csv("data/auto.csv")

View(Auto)
head(Auto)

dim(Auto)

Auto = read.csv("data/auto.csv", header = T, na.strings = c("?"), stringsAsFactors = T)

Auto[1:4, ]

Auto <- na.omit(Auto)
dim(Auto)

names(Auto)

• na.strings
  • chars replaced with NA (missing element)
• stringsAsFactors = T
  • any variable containing character strings should be interpreted as a qualitative variable, and that each distinct character string represents a distinct level for that qualitative variable.

Additional Graphical and Numerical Summaries

• Scatterplot of quantitative variables

  •   library(ISLR2)
      Auto = ISLR2::Auto
            
      names(Auto)
            
      plot(Auto$cylinders , Auto$mpg)
            
      attach(Auto)
      plot(cylinders , mpg)
      detach()
    

• Converting Quantative to Qualitative

  • Cylinders variable has small number of possible values so may be converted to qualitative

  •   cylinders <- as.factor(cylinders)
            
      plot(cylinders , mpg) # Box Plot
            
      plot(cylinders , mpg , col="red")
            
      plot(cylinders , mpg , col="red", varwidth=T)
            
      plot(cylinders , mpg , col="red", varwidth=T, horizontal=T)
            
      plot(cylinders , mpg , col="red", varwidth=T, xlab="cylinders", ylab="MPG")
    

• Histogram

  • Numberic Variable

  •   hist(mpg)
            
      hist(mpg , col=2) # red
            
      hist(mpg , col=2, breaks=15) # breaks is suggestion only
    

• Pairs - Scatterplot Matrix

  •   pairs(Auto) # for every pair of variables
            
      pairs(~ mpg + displacement + horsepower + weight + acceleration, data = Auto) # subset of variables 
    

• Identify

  •   plot(horsepower , mpg)
            
      # Label some points with a variable
      names(Auto) # Field names
            
      identify(horsepower , mpg , name) # Select points to be labeled as Esc
    

• Summary

  •   summary(Auto) # Numeric summary of each variable
            
      summary(mpg) # Summary of single variable