part d: data structure in r€¦ · data structure in r. workshop: getting started with r. utm 14...

Workshop:Getting Startedwith R.UTM14Oct2018.©Dr.NorhaizaAhmad

14 October 2018 Dr. Norhaiza Ahmad

Department of Mathematical SciencesFaculty of Science

Universiti Teknologi Malaysia

http://science.utm.my/norhaiza/

Getting Started with

for newbies

PART D: DATA STRUCTURE IN R


Outline

PARTD:DATASTRUCTUREINR

1. TypesofDatastructureScalars&Vectors

MatricesOtherstructures:Factors,Lists,DataFrames

2. CheckingandChangingDataObjectStructure

2


DataStructureinRRecall• InPartA- youhaveexperimentedwithobject

assignmentusingR

> x = 2; x > len = 2; len

x=2; len=2;x+2

TheseareexamplesofdataobjectsinR

i.e assignedavariable/nametoavalue

3


HandyTip

Tocheckthetypei.e modeofdatastructure

> x=5;x> mode(x)> mode(3<4)> mode("Apa khabar?") [1] 5

[1] "numeric”[1] "logical”[1] "character”

4


Checkthestructureofthedataset

> str(iris)

str(iris)'data.frame': 150 obs. of 5 variables:

$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...

$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...>

> iris

CallupanRin-builtdataset

Checkthestructureofthedataset

Typeofdata=dataframe

5


DataObjectStructureinR• ThereisawidevarietyofdataobjectstructureinR:

• Scalars• Vectors• Factors• Matrices&Arrays• Lists• DataFrames

NUMERICLOGICALMODE(True/False)STRINGS

DATASTRUCTUREMODESCONSIST

OFVALUES

IMPORTANTTOKNOWSTRUCTUREOFDATAasDifferentRfunctionsmightuseaparticulardatastructure6


ExampleIrisDataset:FlowerswithMeasurementsSepalLength

SepalWidth

PetalLength

PetalWidth

1 5.1 3.5 1.4 0.22 4.9 3 1.4 0.23 4.7 3.2 1.3 0.24 4.6 3.1 1.5 0.25 5 3.6 1.4 0.26 5.4 3.9 1.7 0.47 4.6 3.4 1.4 0.38 5 3.4 1.5 0.29 4.4 2.9 1.4 0.210 4.9 3.1 1.5 0.1

Scalar

Vector

Matrix

ComputationalinR:manipulaton ofdatastructure– LinearAlgebra.InthissectionwelearntounderstandandmanipulatedatainR 7


TypesofDataObjectStructure:Scalar • WhatareotherfunctionsinR?

• Wheretofindotherfunctions?

• simplesttypeofdataobjectstructureisascalar.• Scalarisadataobjectwithonevalue> x = 5 #create scalar data object

> y = 2

> x*y+2

> ”Apa khabar?”

> 3 < 4

[1] 12

[1]"Apa khabar?”

[1] TRUE8


Typesof DataObjectStructure:Vectors• Avector'svaluescanbenumbers,strings,logicalvalues,oranyothertype,aslongasthey'reallthesametype.

(2)Avector'svaluescanbenumbers > c(2,4,5) [1] 2 4 5

(3)Avector'svaluescanbestrings > c(‘a’,’b’) [1] "a" "b"

> x=c(2,4,5);x [1] 2 4 5

(1)Thisisascalar > 5 [1] 5

AssignthescalartoanRobject > x=5;x [1] 5

• Thec() function(c isshortforCombine/Concatenate)createsanewvectorbycombiningalistofvalues.

AssignthescalartoanRobject

9


Extracting&Assigningelements fromvectors

> x=c(1:10)*2 > x

No Task R Code Output

1 Extractthe6thelementinx > x[6] [1] 12

2 Extractthe2nd to6thelement > x[2:6] [1] 4 6 8 10 12

3 Extractthe1st,3rdand5thelementinx

> x[c(1,3,5)] [1] 2 6 10

4 Extract reverse order > x[3:1] [1] 6 4 2

WecanEXTRACT anelementfromthevectororasubsetofthevectorbyindicatingtheINDEXofTHEELEMENTSusingsquarebrackets[ ].

[1] 2 4 6 8 10 12 14 16 18 20

10


WecanEXTRACT andassignsubsetstoavectorthatwehaveextracted


5 ExtractDistinct ranges > x[c(1:3,5:6)]

> x[c(1:3,7,10)]

[1] 2 4 6 10 12

[1] 2 4 6 14 20

6 ExtractRepeatedindex > x[rep(c(9,10),2)] [1] 18 20 18 20

7 Extract andassignsubsettoavector

>ab=x[c(1:3,7,10)] > ab[1] 2 4 6 14 20

8 Extractlogicalvector > x>10

> x[x>10]

[1]FALSEFALSEFALSEFALSEFALSETRUE[7]TRUETRUETRUETRUE

[1]1214161820

Extracting&Assigningelements fromvectors

11


ExcludingelementsfromvectorsWecanEXCLUDE elementsfromavectorbyindicatingtheNEGATIVEindexoftheelement(s)usingsquarebrackets[ ].

No Task R Code Output9 Excludethe

6thelementinx

> x[-6] [1] 2 4 6 8 10 14 16 18 20

10 Excludethe2ndto6thelement

> x[-(2:6)] [1] 2 14 16 18 20

11 Excludethe1st,3rdand5thelementinx

> x[-c(1,3,5)] [1] 4 8 12 14 16 18 20

12


Otherbasicfunctions:appliedtovectors> x=6:15> length(x) #Number of elements in x[1] 10> max(x) #Largest value in x[1] 15> min(x) #Least value in x[1] 6> sum(x) #Sum of all values in x[1] 105> prod(x) #Product of all values in x[1] 10897286400> mean(x) #Average of all values in x[1] 10.5> range(x) #Range of vector x[1] 6 15> var(x) #Variance of x[1] 9.166667> sd(x) #Standard deviation of x[1] 3.02765> sqrt(var(x)) #Square root of variance=sd of x[1] 3.02765

13


OutlinePARTD:DATASTRUCTUREINR

1. TypesofDatastructureScalars&Vectors

MatricesOtherstructures:Factors,Lists,DataFrames


14


TypesofDataObjectStructure:Matrices

• Rstoresdataelementsina2-dimensionalmatrixusingthefunctionmatrix()

• Computationallyefficient- Manipulatedataasmatrices

Arrayisamatrixwithmorethan2-dimension 15


MatricesinR

> x.mat=matrix(c(2,5,1,5,6,8),nrow=3,ncol=2)

> x.mat=matrix(c(2,5,1,5,6,8),ncol=2)

A=255618

[,1] [,2][1,] 2 5[2,] 5 6[3,] 1 8

[,1] [,2][1,] 2 5[2,] 5 6[3,] 1 8

> x.mat=matrix(c(2,5,1,5,6,8),3,2)[,1] [,2]

[1,] 2 5[2,] 5 6[3,] 1 8

16


Extractingelement(s)fromamatrix


1 Extractelementin3rdrowof 1st column

> x.mat[3,1] [1] 1

2 Extractallobservationsinthe2nd column

> x.mat[ ,2] [1] 5 6 8

3 Extracttallobservationsinthe3rd row ? [1] 1 8

4 Extractsubmatrices > x.mat[1:2,] [,1] [,2][1,] 2 5[2,] 5 6

Justlikevectorselementsareindicatedbythelabelsinthematrices> x.mat

[,1] [,2][1,] 2 5[2,] 5 6[3,] 1 8

[ROW,COLUMN]

17


Combiningmatrixorvectors(matrixform) toamatrix

No Task R Code Output1 Combining

avector> y.mat=matrix(1:3,3,1)

[,1][1,] 1[2,] 2[3,] 3

> cbind(x.mat,y.mat) > cbind(x.mat,y.mat)[,1] [,2] [,3]

[1,] 2 5 1[2,] 5 6 2[3,] 1 8 3

(Providedthatthelengthisappropriate)

> x.mat[,1] [,2]

[1,] 2 5[2,] 5 6[3,] 1 8

Combine matrices by columnscbind()

18


Combiningmatrixorvectors(matrixform)toamatrix

No Task R Code Output1 Creatematrix

z.mat in R ? z.mat[,1] [,2]

[1,] 1 4[2,] 2 5

2 Combinematrixx.mat andz.mat byrowsandassignthenewmatrixasA.mat

? A.mat[,1] [,2]

[1,] 2 5[2,] 5 6[3,] 1 8[4,] 1 4[5,] 2 5

Combine matrices by row rbind()

19


Transposingamatrix:t()No Task R Code Output

1 Transpose amatrix

> t(x.mat) [,1] [,2] [,3][1,] 2 5 1[2,] 5 6 8

20


MatrixArithmeticOperations:No Task R Code Output

1 Checkdimension ofmatrix

> dim(x.mat)> dim(y.mat)

[1] 3 2[1] 3 1#clearly these two matrices cannot be multiplied

2 Matrixmultiplication

> dim(t(x.mat))> t(x.mat)%*%y.mat

[1] 2 3[,1]

[1,] 15[2,] 41

3 Inverseofamatrix#solve()function. Thematrixmustbesquareandnotsingular.

>A=matrix(sample(4),2,2)

# any matrix

[,1] [,2][1,] 2 3[2,] 4 1

> solve(A)

#inverse of A#TEST that the AA-1=I

[,1] [,2][1,] -0.1 0.3[2,] 0.4 -0.2

Matrixmultiplications

%*%Ensureappropriatedimension

21


OutlinePARTD:DATASTRUCTUREINR1. TypesofDatastructure

ScalarsVectorsMatricesOtherstructures:Factors,Lists,DataFrames


22


OtherTypesofDataObjectStructure:Factor• typeofcharacter/stringvector• Typicallyusedtodescribethedata- notforcalculations

• Labelsforqualitativevariables

> quality=c("High","Medium","Low")> quality=factor(quality)

> quality[1] High Medium Low Levels: High Low Medium

factor()

23


OtherTypesofDataObjectStructure:Lists• dataobjectscontaining‘every’elements• Containselementofmiscellaneousmodes• Usefulfororganising information

> mylist= list(5,6,c(1,2,3),c("blue","red"),x.mat)

> mylist[[1]][1] 5

[[2]][1] 6

[[3]][1] 1 2 3

[[4]][1] "blue" "red"

[[5]][,1] [,2]

[1,] 2 5[2,] 5 6[3,] 1 8

list()

> mylist[[5]]

Elementsonalistcanbeextracted

24


OtherTypesofDataObjectStructure:DataFrames• similartoaspreadsheet• Eachcolumnisavector.Elementsineachvectorhasthesamemode.Differentvectorscanhavedifferentmodes.

• Allvectorsinthedataframemustbethesamelength

data.frame()

> x=1:2> y=c(”a”,”b”)> z=c(100,200)> A.df=data.frame(x,y,z);A.df

> A.dfx y z

1 1 a 1002 2 b 200

25


OtherTypesofDataObjectStructure:DataFrames• Iftheelementsaredefinedwithinadataframe,• Useattach(nameofdataframe)toreadtheelements

data.frame()

> A.df=data.frame(x1=1:2,y1=c("a","b"),c1=c(100,200))

> x1

> attach(A.df)> x1

> # or use $ sign>A.df$x1

> A.dfx1 y1 c11 1 a 1002 2 b 200

Error: object 'x1' not found

> x1[1] 1 2

> A.df$x1[1] 1 2 26


OutlinePARTD:DATASTRUCTUREINR1. TypesofDatastructure

ScalarsVectorsMatricesOtherstructures:Factors,Lists,DataFrames


27


CheckingDataObjectStructure• IdentifytypesofDatastructure:vector,matrix,listetc

is.<what>Eg. is.vector();is.matrix();is.numeric; is.character

> x<-c(1,2,3,4) > #check data object type > is.vector(x)

> is.data.frame(x)

> #check data mode > is.character(x)

> is.numeric(x)

[1] TRUE

[1] FALSE

[1] FALSE

[1] TRUE 28


ChangeDataObjectStructure• Forcingastructuretoanother

as.<what>Eg. as.vector();as.matrix();as.numeric; as.character

> x<-c(1,2,3,4)

> x1=as.matrix(x)

> x[1] 1 2 3 4

> x1[,1]

[1,] 1[2,] 2[3,] 3[4,] 4

29


Next

• PartE:Read/ImportData

30


HandyTip• Tocallavariableinadataframe.Wecoulduse$signorattach(<dataframe>)thencallthevariable.• However,ifthenameofthevariableinthedataframe hasalreadybeendefined

earlierasadataobjectoutsidethedataframe (Globalenvironment),Thus-Callingthevariableinadataframe afterattach()mightfail,asthevariableinthedataframe couldbe‘masked’bythedataobject.

• Say>x1=c(“here”,”there”) #definedasadataobject.Then> A.df=data.frame(x1=1:2,y1=c("a","b"),c1=c(100,200))Thus, if > attach(A.df);x1Then x1 is displayed as “here”,”there” instead of 1:2

• Onewayistoremovethedataobjectx1> rm(x1)Then attach again

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part d: data structure in r€¦ · data structure in r. workshop: getting started with r. utm 14...

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