R programming Basic & Advanced

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©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )

Why ???

• Manage your Workspace • Data types • Fiddle with Data Types • Lists Vs Vectors • R as calculator!!! • Decision making statements, looping, functions • Interact with R!!! • Visualization!!! • Time for U!!! • Clustering • Regression (with curve fitting)

Data types

• R has five basic or “atomic” classes of objects: 1. character 2. numeric (real numbers) 3. integer 4. complex 5. logical (True/False)

• The most basic object is a vector • Empty vectors can be created with the vector()

function.

Fiddle with Data types

Lists Vs Vecto s

Lists are set of heterogeneous elements. Vectors is a set of homogeneous elements.

num = c(22, 33, 55) # vector HOMOGENEOUS srs= c("aaa", "bbb", "cc", "dd", "ee") # vector HOMOGENEOUS bool = c(TRUE, FALSE, FALSE, FALSE, FALSE) # vector

HOMOGENEOUS x = list(num, srs, bool, 3) # x contains copies of num, srs, bool;

Lists HETEROGENEOUS

as calculato !!!

5+6 5+6*5 y <- c(1,2,4,5) z <- 1:4

“kaisi ho??”

Decision statements, Looping & Functions

a<-function(x,y){ if(x>y){ return (x) } else { return (y) } } a(20,10)

for(m in 1:3){ print(m) }

x=0 while(x<5){ print("hi") x=x+1 }

lapply(1:3, function(x) x^2)

x=0 repeat{ print("hi") x=x+1 if(x==5){ break } }

R doesn’t process loops quickly. Try lapply.

mydata<-read.csv("solaRdata.csv") #loading & reading data mydata summary(mydata) #statistical overview read.csv(file = "solaRdata.csv", sep= ",")[ ,1:2] #display only first and second column, EXTRACTION of relevant data mydata<-na.omit(mydata) #to omit the columns having ‘NA’ #cleaning of data max(mydata) #shows maximum value present in the entire data, analysis of data max(mydata[ ,c('Attitude')]) #select ur girl friend ->max atitude max(mydata[ ,c('Look')]) #select your girl friend on basis of look

write.csv(mydata, file = "MyData.csv") read.csv("Mydata.csv")

P.S. “solaRdata.csv” must be present in your current working directory.

Lets see how it looks!!!

x<-read.csv(file = "solaRdata.csv", sep= ",")[ ,1] y<-read.csv(file = "solaRdata.csv", sep= ",")[ ,2] plot(x,y) # analysis of data hist(y, col="pink", xlab="Solar Radaition",ylab="Frequency") slices <- c(50,105,300) lbls <- c("2012", "2013", "2014(counting)") pie(slices, labels = lbls,col=rainbow(length(lbls)), main="No. of fans of HIT-K ACM Student chapter")

Its time fo U!!!!

1. Display first and second rows of solaRdata.csv

2. Display the minimum value present in the entire dataset

3. Find maximum element in First row

1. read.csv(file = "solaRdata.csv", sep= ",")[1:2,] 2. min(mydata) 3. max(read.csv(file = "solaRdata.csv", sep= ",")[ 1,])

Welcome to Advanced 101

2 Inte esting Applications!!

CLUSTERING

CURVE FITTING

Curve Fitting

• Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints.

If you just had a feel its like the ones you learn in your degree courses…….here is some you can try hands on..

Did this cause a problem??

This is where R kicks in!!

• THE REGRESSION MODEL

Types of egression

• Linear Regression

• Non linear regression

Some more theory

THE DATA: mydata<-read.csv(“Rdata.csv”) newdata <- mydata[,1:2] print(newdata) y<-newdata[,2] x<-newdata[,1] plot(x,y)

• f<-lm(y~x)

• xx <- seq(00,350, length=350)

• lines(xx, predict(f, data.frame(x=xx)), col="red")

• f<-lm(y~poly(x,2,raw=TRUE))

• lines(xx, predict(f, data.frame(x=xx)), col="green")

• f<-lm(y~poly(x,10,raw=TRUE))

• lines(xx, predict(f, data.frame(x=xx)), col="blue")

• summary(f)

Many to pick from!!

–Connectivity based clustering

–Centroid based clustering

–Distribution based clustering

–Density based clustering

–A Lot more…

Means(Centroid based Approach)

1.Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.

2.Assign each object to the group that has the closest centroid.

3.When all objects have been assigned, recalculate the positions of the K

centroids.

4.Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized

can be calculated.

So here is how it goes….

• Mydata<-read.csv("Rdata.csv")

• newdata<-mydata[,2:3]

• print (newdata)

• (kc <- kmeans(newdata, 3))

• summary(kc)

• plot(newdata[c("Complextion", "Smartness")], col=kc$cluster)

• points(kc$centers[,c("Complextion", "Smartness")], col=1:3, pch=8, cex=2)

Bibliography

• http://www.slidesshare.net

• http://images.google.com/

• http://en.wikipedia.org

• http://cran.r-project.org/

• http://www.statmethods.net/

R programming Basic & Advanced

Data & Analytics