**R Studio Environment**

**R Location (OSX)**

$ ls –l /Library/Frameworks/R.framework/Versions

**#Get R Version**

version

**Environment**

getwd()

setwd(“/Users/avkashchauhan/work/global”)

getwd()

dir()

**#Getting Help**

help(getwd)

**#Reading a File**

help(read.csv)

filename <- “test.csv”

filex <- read.csv(filename, header = TRUE, sep=”,”)

filex

summary(filex)

filex$id

filex$name

filex$age

filex$zip

names(filex)

attributes(filex)

**# Listing All Vars**

ls()

# ls() – List of all variables

**# DataTypes & number Assignment**

asc <- c(1,2,3,4,5,6,7,8,9,10)

# What is c? c is “combine”

asc[2]

asc[5]

asc[5:6]

asc[1:9]

View(asc)

a <- 10

a

a[1]

a[3]

help(sqrt)

a <- sqrt(10)

a

a <- sqrt(10*a)

a

asc

mean(asc)

median(asc)

help(var)

typeof(asc)

typeof(a)

# String data type

a <- c(“this”, “is”, “so”, “fun”)

a

a[1]

typeof(a)

**#Understanding c or combine**

a <- 10

> a

[1] 10

> a[1]

[1] 10

> a[2]

[1] NA

> a <- c(10)

> a

[1] 10

> a[2]

[1] NA

# DATAFRAME

**# creating a data frame**

a <- c(1,2,3,4,5,6,7,8,9,10)

b <- c(10,20,30,40,50,60,70,80,90,100)

ab <- data.frame(first=a, second=b)

ab

ab[1]

ab[1][1]

ab[1][2] ß XXX

ab[2]

ab[2][1]

ab[2][2] ß XXX

ab$first

ab$second

ab$second[1]

ab$second[3]

ab$first[10]

View(ab)

**#Logical**

a <- c(TRUE)

a

typeof(a)

a <- c(FALSE)

a

typeof(a)

**#Conditions in R**

a <- c(TRUE)

if(!a) a <- c(FALSE)

a **ß Still TRUE**

if(a) a <- c(FALSE)

a ß FALSE Now

a <- c(TRUE,FALSE)

a

a[1]

a[2]

if (a[1]) a[2] <- TRUE

a

**Factor in R** – A “**factor**” is a vector whose elements can take on one of a specific set of values. For example, “Sex” will usually take on only the values “M” or “F,” whereas “Name” will generally have lots of possibilities. The set of values that the elements of a **factor** can take are called its levels.

a <- factor(c(“Male”, “Female”, “Female”, “Male”, “Male”))

a

a <- factor(c(“A”,”A”,”B”,”A”,”B”,”B”,”C”,”A”,”C”))

a

**Tables: (One way and two way)**

a <- factor(c(“Male”, “Female”, “Female”, “Male”, “Male”))

a

mytable <- table(a)

a

mytable

summary(a)

attributes(a)

**#datatype check R**

**#Example #1**

a <- c(1,2,4)

is.numeric(a)

is.factor(a)

**#Example #2**

b <- factor(c(“M”, “F”))

b

is.factor(b)

is.numeric(b)

**Graph Plotting in R**

Using Library **ggplot2**

**#installing ggplot2**

install.packages(“ggplot2”)

also installing the dependencies ‘colorspace’, ‘Rcpp’, ‘stringr’, ‘RColorBrewer’, ‘dichromat’, ‘munsell’, ‘labeling’, ‘plyr’, ‘digest’, ‘gtable’, ‘reshape2’, ‘scales’, ‘proto’

**Using ggplot2 Library**

** **

library(ggplot2)

detach(package:ggplot2)

head(diamonds)

View(diamonds)

qplot(clarity, data=diamonds, fill=cut, geom=”bar”)

qplot(clarity, data=diamonds, geom=”bar”, fill=cut, position=”stack”)

qplot(clarity, data=diamonds, geom=”freqpoly”, group=cut, colour=cut, position=”identity”)

qplot(carat, data=diamonds, geom=”histogram”, binwidth=0.1)

qplot(carat, data=diamonds, geom=”histogram”, binwidth=0.01)

Graph Source: http://www.ceb-institute.org/bbs/wp-content/uploads/2011/09/handout_ggplot2.pdf

Keywords: R, Analysis, ggplot,