jcbonilla/analytics

Analytics at NYU by JC Bonilla

analytics's Issues

Class 1_R introduction

Business Analytics Course, New York University

Author: JC Bonilla

[email protected]

Content:

1. Calculations

2. Variables

3. Comments

4. Help

5. Functions

6. Data Types

7. Loading Data

8. Basic Analysis

9. Filtering data

Calculations

After RStudio is started code in the Console (Below) or Debugging tool (above)

Lets enter numbers and perform calculations.

1 + 2

5 + 55

5/1.23

6*6

sqrt(16)

2:20

So, R does scalar arithmetic returning the scalar value.

In actual fact, R returns a vector of length 1 - hence the [1] denoting first element of the vector

# Variables

We assign values to variables with the assignment operator "=" or "<-"

x = 1
y <- 2
x
y

s<-6
p<-4
t<-s+p

# Comments

All text after the pound sign "#" within the same line is considered a comment.

11 + 11 # this is a comment

#### Help

R provides extensive documentation. For example, entering ?c or help(c) at the prompt gives documentation of the function c in R. Please give it a try.

help(c)

# Functions

R functions are invoked by its name, then followed by the parenthesis, and zero or more arguments.

The following apply the function c to combine three numeric values into a vector.

c(1, 2, 3)

vector<-c(5,9,1,0)

series<-1:10

seq(1,9,by=2)

seq(8,20,length=6)

rep(0,100)

rep(1:3,6)

Data Types

There are several R data types that are of frequent occurrence in routine R calculations.

Example are: numeric, integer, complex, logical, character

class(x)
z<-"I love R"
class(z)
data <- c(1,2,3)
class(data)

data.1<-list(1,2,3)
class(data.1)
data
data.1

# Load Data

loading data from .csv file:

data<- read.csv("/Google Drive/_NYU GDrive/Teaching/Business Analytics/BA Data/zagat.csv", header=TRUE,
stringsAsFactors=FALSE) #direct method
setwd("/Google Drive/_NYU GDrive/Teaching/Business Analytics/BA Data") #using working directory
data<- read.csv("zagat.csv", header=TRUE,stringsAsFactors=FALSE)
getwd() # display active directory

exploring file

names(data)
dim(data)
class(data)
data
data[1:4] # brackets [ ] allow indexing, columns 1-4
data[1:10,1:3] # displays 10 rows and 3 columns
data$Price # displays valyes for column "Price"
data$Price[1:10]
price<-data$Price # dollar symbol $ is used to invoce a vector in a matrix

other ways to explore data imports

head(data)
str(data)

Basic Analysis

Basic Functions

mean()
var()
sd()
min()
max()
median()
quantile()
cor()

mean(data$Price)
var(data$Price)
sd(data$Price)
min(data$Price)
max(data$Price)
median(data$Price)
quantile(data$Price)
cor(data$Price,data$Food)
summary(data$Price)

Filtering data

data.1<-subset(data, Price == 50)
data.2<-subset(data, Price > 50)
dim(data.1)
dim(data.2)

Computing z-scores

zagat<-data
service.sd <- sd(zagat$Service)
service.mean <- mean(zagat$Service)
z <- (zagat$Service-service.mean)/service.sd

creating subsets of data by Z value (3, 2, and 1 standard dev)

zagat.z3 <- subset(zagat, z<3)
zagat.z2 <- subset(zagat, z<2)
zagat.z1 <- subset(zagat, z<1)

Computing same calculation for data without outliers

dim(zagat)
summary(zagat$Service)
esd(zagat$Service)

dim(zagat.z3)
summary(zagat.z3$Service)
sd(zagat.z3$Service)

dim(zagat.z2)
summary(zagat.z2$Service)
sd(zagat.z2$Service)

dim(zagat.z1)
summary(zagat.z1$Service)
sd(zagat.z1$Service)