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# R programming Basic & Advanced

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Presentation on R programming. Topics covered are: 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)

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### R programming Basic & Advanced

1. 1. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
2. 2. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
3. 3. Why ??? ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
4. 4. • 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) ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
5. 5. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
6. 6.         ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
7. 7. 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. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
8. 8. Fiddle with Data types              ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
9. 9. 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 ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
10. 10. as calculato !!! 5+6 5+6*5 y <- c(1,2,4,5) z <- 1:4 “kaisi ho??” ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
11. 11. 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. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
12. 12.  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. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
13. 13. 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") ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
14. 14. 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 ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
15. 15. 1. read.csv(file = "solaRdata.csv", sep= ",")[1:2,] 2. min(mydata) 3. max(read.csv(file = "solaRdata.csv", sep= ",")[ 1,]) ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
16. 16. Welcome to Advanced 101 ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
17. 17. 2 Inte esting Applications!! CLUSTERING CURVE FITTING ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
18. 18. 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.. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
19. 19. Did this cause a problem?? ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
20. 20. This is where R kicks in!! • THE REGRESSION MODEL ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
21. 21. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
22. 22. Types of egression • Linear Regression • Non linear regression ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
23. 23. Some more theory ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
24. 24. THE DATA: mydata<-read.csv(“Rdata.csv”) newdata <- mydata[,1:2] print(newdata) y<-newdata[,2] x<-newdata[,1] plot(x,y) ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
25. 25. • 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) ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
26. 26. luste  Many to pick from!! –Connectivity based clustering –Centroid based clustering –Distribution based clustering –Density based clustering –A Lot more… ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
27. 27. 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. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
28. 28. 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) ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
29. 29. Bibliography • http://www.slidesshare.net • http://images.google.com/ • http://en.wikipedia.org • http://cran.r-project.org/ • http://www.statmethods.net/ ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )
30. 30. ©Angan Mitra (anganconnorlinconmitra@gmail.com), Sohom Ghosh (sohom1ghosh@gmail.com )