Getting Started with R - Universiti Teknologi Malaysia€¦ · Workshop:)Ge,ng)Started)with)R.)UiTM)12)July)2017).©)Dr.)Norhaiza)Ahmad) 12 July 2017 "Dr. Norhaiza Ahmad" Department

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Workshop: Ge,ng Started with R. UiTM 12 July 2017 .© Dr. Norhaiza Ahmad

12 July 2017 "Dr. Norhaiza Ahmad"

Department of Mathematical Sciences"Faculty of Science"

Universiti Teknologi Malaysia"

Getting Started with R!for newbies!

!PART C: !

DATA OBJECT STRUCTURE IN R!12 July 2017 "

Dr. Norhaiza Ahmad"Department of Mathematical Sciences"

Faculty of Science"Universiti Teknologi Malaysia"

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



Outline

PART C: DATA OBJECT STRUCTURE IN R 1.  Types of Data object structure

Scalars Vectors Matrices Other structures: Factors, Lists, Data Frames

2.  Checking and Changing Data Object Structure



Example Iris Dataset: Flowers with Measurements Sepal.Length Sepal.Width Petal.Length Petal.Width 5.1 3.5 1.4 0.2 4.9 3.0 1.4 0.2 4.7 3.2 1.3 0.2 4.6 3.1 1.5 0.2 5.0 3.6 1.4 0.2 5.4 3.9 1.7 0.4 4.6 3.4 1.4 0.3 5.0 3.4 1.5 0.2 4.4 2.9 1.4 0.2 4.9 3.1 1.5 0.1

Sepal Length

Sepal Width

Petal Length

Petal Width

1 5.1 3.5 1.4 0.2 2 4.9 3 1.4 0.2 3 4.7 3.2 1.3 0.2 4 4.6 3.1 1.5 0.2 5 5 3.6 1.4 0.2 6 5.4 3.9 1.7 0.4 7 4.6 3.4 1.4 0.3 8 5 3.4 1.5 0.2 9 4.4 2.9 1.4 0.2 10 4.9 3.1 1.5 0.1

Scalar

Vector

Matrix

Computa-onal in R : manipulaton of data structure – Linear Algebra. In this sec-on we learn to understand and manipulate data in R



Example Iris Dataset: Flowers with Measurements Sepal.Length Sepal.Width Petal.Length Petal.Width 5.1 3.5 1.4 0.2 4.9 3.0 1.4 0.2 4.7 3.2 1.3 0.2 4.6 3.1 1.5 0.2 5.0 3.6 1.4 0.2 5.4 3.9 1.7 0.4 4.6 3.4 1.4 0.3 5.0 3.4 1.5 0.2 4.4 2.9 1.4 0.2 4.9 3.1 1.5 0.1

Sepal Length

Sepal Width

Petal Length

Petal Width

1 5.1 3.5 1.4 0.2 2 4.9 3 1.4 0.2 3 4.7 3.2 1.3 0.2 4 4.6 3.1 1.5 0.2 5 5 3.6 1.4 0.2 6 5.4 3.9 1.7 0.4 7 4.6 3.4 1.4 0.3 8 5 3.4 1.5 0.2 9 4.4 2.9 1.4 0.2 10 4.9 3.1 1.5 0.1 Iris data in matrix form

x1,1 x1,2 x1,3 x1,4x2,1 x2,2 x2,3 x2,4 x j1 x j2 x jk x jp x10,1 x10,2 x10,3 x10,4

!

"

########

$

%

&&&&&&&&

Mul-variate data consists of mul-ple vectors and scalars. Computa-onally we are free to work with our data as separate variables



Intro: Data Structure in R

Recall •  In Part A-‐ you have experimented with object

assignment using R > x = 2; x > len = 2; len x=2; len=2;x+2

These are examples of data objects in R

i.e assigned a variable/name to a value



amat =

x11 x12 x1k x1px21 x22 x2k x2 p x j1 x j2 x jk x jp xn1 xn2 xnk xnp

!

"

########

$

%

&&&&&&&&

x =

x1x2xn

!

"

######

$

%

&&&&&&

IntuiQvely We can assign data objects to the entries in a set of data



Outline

PART C: DATA STRUCTURE IN R 1.   Types of Data structure

Scalars Vectors Matrices & Arrays Other structures: Factors, Lists, Data Frames




Intro: Data Object Structure in R

•  There are 6 Types of data object structure in R:

•  Scalars •  Vectors •  Factors •  Matrices & Arrays •  Lists •  Data Frames

NUMERIC LOGICAL MODE (True/False) STRINGS

6 DATA STRUCTURE MODES CONSIST

OF VALUES

IMPORTANT TO KNOW STRUCTURE OF DATA as Different R func-ons might use a par-cular data structure



Types of Data Object Structure: Scalar

•  What are other func-ons in R?

•  Where to find other func-ons?

•  simplest type of data object structure is a scalar. •  Scalar is a data object with one value > x = 5 #create scalar data object!!

> y = 2 !!

> x*y+2!!> ”Apa khabar?”!!> 3 < 4!!!!!!

!!!![1] 12!![1]"Apa khabar?”!![1] TRUE!!!!!!



Handy Tip

To check the mode of data object:

> x=5;x!> mode(x)!> mode(3<4)!> mode("Apa khabar?")!!

[1] 5![1] "numeric”![1] "logical”![1] "character”!



Outline

PART C: DATA STRUCTURE IN R

1.   Types of Data structure Scalars Vectors Matrices Other structures: Factors, Lists, Data Frames




Types of Data Object Structure: Vectors

•  What are other func-ons in R?

•  Where to find other func-ons?

•  A vector's values can be numbers, strings, logical values, or any other type, as long as they're all the same type.

(3) A vector's values can be numbers > c(2,4,5) [1] 2 4 5!

(5) A vector's values can be strings > c(‘a’,’b’) [1] "a" "b"!

(4) Assign a vector to an R object > x=c(2,4,5);x [1] 2 4 5!

(1) This is a scalar > 5 [1] 5!

(2) Assign the scalar to an R object > x=5;x [1] 5!

•  The c() func-on (c is short for Combine/Concatenate) creates a new vector by combining a list of values.



Vector Arithme-c

Vectors can be used on the usual arithme-c opera-ons in R.

If the vectors are of different length, the vector with the shorter length is repeated to the length of the longer vector:

TIP: Use length() to measure the length of a vector

> x1=c(1,2,3) > x2=c(4,5,6) > y=x1+x2;y

[1] 5 7 9

> x=1:10;x > y=c(1,3);y > x*y

[1] 1 2 3 4 5 6 7 8 9 10 [1] 1 3 [1] 1 6 3 12 5 18 7 24 9 30



Sequence Vectors in R

To generate a sequence of real numbers use the func-on seq()

(2) Other op-ons in seq!(increment of 0.5)!

> seq(1,3,0.5) [1] 1.0 1.5 2.0 2.5 3.0!

(3) vector with integers from 3 down to 1:

> 3:1 [1] 2 4 5!

(1) A vector from 1 to 3 > 1:3 [1] 1 2 3!

alterna-vely.. > seq(1,3) [1] 1 2 3!

[1] 3 2 1!

Seek/Link help document for seq!



Repeat Sequence Vectors in R

Seek/Link help document for rep()!

To generate a sequence of repea-ng sequences use rep()

> x1=rep(1:2,3);x1 [1] 1 2 1 2 1 2!



TASK: Generate vector sequence

1. Generate the following sequences: [1]8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4 9.6 9.8 10.0

2. Generate the following sequences: [1]10.0 9.8 9.6 9.4 9.2 9.0 8.8 8.6 8.4 8.2 8.0 3.Write the command to produce 3 5 1 3 5 1 using R command ,rep()!



Generate vector (Random Sequence)

R can generate a random sequence from a number of probability density func-ons. The general format for genera-ng such sequences is: rdensity(num,p1,p2,...) where density is the probability density func-on, num is the number of values to generate and p1, p2, ... are the values needed to generate from the density func-on.



No Task R Code Output

1 Generate 5 values from a Normal distribuQon with mean 5 and standard deviaQon 1.5

> rnorm(10,5,1.5)!!!>r1=rnorm(5,5,1.5)!

[1] 2.597671 4.021968 5.820759![4] 4.130905 2.859552!!> r1![1] 4.600297 4.410904 3.577039![4] 4.812063 6.642143!!

2 Random genera-on for the Poisson distribu-on with parameter lambda. package <stats>

> rpois(30,2)! [1] 1 2 2 1 2 4 1 0 3 1 2 3 3 2 2![16] 2 2 4 1 0 0 2 1 0 1 1 0 5 1 4!

!

Seek help document for the related R func-ons!

[NOTE: What is the difference between the two commands in Task 1?

Generate vector (Random Sequence)



Task: Generate vector (Random Sequence)

1.   Generate 100 values from a binomial distribuQon of size 23 and

probability 0.25. 2. Generate 80 values from a standard normal distribuQon, stored in an R object called r8. Then find the mean, standard deviaQon of this dataset. [NOTE: use summary() to your set of data in quesQon 2. What is the output for this?]



ExtracQng & Assigning elements from vectors

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


1 Extract the 6th element in x > x[6]! [1] 12!

2 Extract the 2nd to 6th element > x[2:6] ! [1] 4 6 8 10 12!!

3 Extract the 1st, 3rd and 5th element in x

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

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

We can EXTRACT an element from the vector or a subset of the vector by indica-ng the INDEX of THE ELEMENTS using square brackets [ ].

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



We can EXTRACT and assign subsets to a vector that we have extracted


5 Extract Dis-nct 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 Extract Repeated index > x[rep(c(9,10),2)]! [1] 18 20 18 20!

7 Extract and assign subset to a vector

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

8 Extract logical vector > x>10!!> x[x>10]!

[1] FALSE FALSE FALSE FALSE FALSE TRUE [7] TRUE TRUE TRUE TRUE [1] 12 14 16 18 20

ExtracQng & Assigning elements from vectors



Excluding elements from vectors

We can EXCLUDE elements from a vector by indica-ng the NEGATIVE index of the element(s) using square brackets [ ].

No Task R Code Output 9 Exclude the

6th element in x

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

10 Exclude the 2nd to 6th element

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

11 Exclude the 1st, 3rd and 5th element in x

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



Other useful funcQons

We can EXTRACT an element from the vector or a subset of the vector using square brackets [ ].

> 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!



Outline

PART C: DATA STRUCTURE IN R

1.   Types of Data structure Scalars Vectors

Matrices Other structures: Factors, Lists, Data Frames




Types of Data Object Structure: Matrices

•  R stores data elements in a 2-‐dimensional matrix using the func-on matrix()!

•  Computa-onally efficient -‐ Manipulate data as matrices

Array is a matrix with more than 2-‐dimension



Matrices in R A= 2 5 5 6 1 8

> 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)!

[,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!



ExtracQng element(s)from a matrix


1 Extract element in 3rd row of 1st column

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

2 Extract all observa-ons in the 2nd column

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

3 Extract t all observa-ons in the 3rd row ?! [1] 1 8!

!

4 Extract submatrices > x.mat[1:2,]! !

[,1] [,2]![1,] 2 5![2,] 5 6!

Just like vectors elements are indicated by the labels in the matrices > x.mat! [,1] [,2]![1,] 2 5![2,] 5 6![3,] 1 8!

[ROW,COLUMN]



Combining matrix or vectors(matrix form) to a matrix

No Task R Code Output 1 Combining

a vector > 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!

Provided the length is appropriate > x.mat! [,1] [,2]![1,] 2 5![2,] 5 6![3,] 1 8!

Combine matrices by columns!cbind()



Combining matrix or vectors(matrix form) to a matrix

No Task R Code Output 1 Create matrix

z.mat in R ?!

z.mat! [,1] [,2]![1,] 1 4![2,] 2 5!

2 Combine matrix x.mat and z.mat by rows and assign the new matrix as A.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()



Transposing a matrix: t()

No

Task R Code Output

1 Transpose a matrix

> t(x.mat)!!

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



Matrix ArithmeQc OperaQons: No Task R Code Output

1 Check dimension of matrix

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

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

2 Matrix mul-plica-on

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

[1] 2 3! [,1]![1,] 15![2,] 41!

3 Inverse of a matrix #solve() func-on. The matrix must be square and not singular.

>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!

Matrix mul-plica-ons

%*%!Ensure appropriate dimension



Outline

PART C: DATA STRUCTURE IN R 1.   Types of Data structure





Other Types of Data Object Structure:

Factor •  type of character/string vector •  Typically used to describe the data-‐ not for calcula-ons •  Labels for qualita-ve variables

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

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

factor()




Lists •  data objects containing ‘every’ elements •  Contains element of miscellaneous modes •  Useful for organising informa-on > 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]]!

Elements on a list can be extracted




Data Frames •  similar to a spreadsheet •  Each column is a vector. Elements in each vector has the same mode. Different vectors can have different modes.

•  All vectors in the data frame must be the same length

data.frame()

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

> A.df! x y z!1 1 a 100!2 2 b 200!




Data Frames •  If the elements are defined within a dataframe, •  Use anach(name of data frame) to read the elements

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.df!x1 y1 c1!1 1 a 100!2   2 b 200!

Error: object 'x1' not found!

> x1![1] 1 2!

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



Handy Tip ●  To call a variable in a dataframe. We could use $ sign or anach(<dataframe>) then call the variable. ●  However, if the name of the variable in the dataframe has already been

defined earlier as a data object outside the dataframe (Global environment), Thus-‐Calling the variable in a dataframe arer anach() might fail, as the variable in the dataframe could be ‘masked’ by the data object.

●  Say >x1=c(“here”,”there”) #defined as a data object . Then > A.df=data.frame(x1=1:2,y1=c("a","b"),c1=c(100,200))!Thus, if > attach(A.df);x1!Then x1 is displayed as “here”,”there” instead of 1:2!!●  One way is to remove the data object x1

> rm(x1)!Then attach again!!



Outline

PART C: DATA STRUCTURE IN R 1.  Types of Data structure


2.   Checking and Changing Data Object Structure



Checking Data Object Structure •  Iden-fy types of Data structure: vector, matrix, list etc

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 !



Change Data Object Structure •  Forcing a structure to another

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!



NEXT

● PART D: Reading Data

Documents

Getting Started with R - Universiti Teknologi Malaysia€¦ · Workshop:)Ge,ng)Started)with)R.)UiTM)12)July)2017).©)Dr.)Norhaiza)Ahmad) 12 July 2017 "Dr. Norhaiza Ahmad" Department