Inian Eco Lecture 02

Getting Started with R Alok Srivastava Lecture - 02

Getting started with R

Alok SrivastavaCRRAO-AIMSCS, Hyderabad, INDIA

Jan 08, 2015

Topics

Basics of R Programming Alok Srivastava 101212

Topics

1 How to use R

2 Data types in R

3 Data creation

4 Data curation

Basics of R programming Lecture - 02Getting Started with R Lecture 02

Topic 1 : How to use R

Basics of R programming Alok Srivastava 101212Basics of R programming Lecture - 02Getting Started with R Lecture 02

Check your Working Path

R installation directory R.home() # R installation directory {which R}

Check your working path getwd() # To get the location of current working

directory

Linux /home/alok/WorkShop/2014/Workshop_UoH_14_Jan/Lecture2

WindowsC:/Users/Alok/Documents

Basics of R Programming Alok Srivastava 101212Basics of R programming Lecture - 02

Itll help to load the path

Getting Started with R Lecture 02

H1

Change your Working Path

Change your working path setwd() # To change the location of working

directory

Recheck your working directory getwd()

Basics of R Programming Alok Srivastava 101212Basics of R programming Lecture - 02Getting Started with R Lecture 02

H2

Strings

Working with Text editor


- Use hash # to comment

Use R as Calculator


Airthmetic Operators

Addition + Subtraction - Multiplication * Division / Exponent ^ OR ** Modulus (x mod y) x%%y Integer Division x%/%y

H3 H4

Variable

H5

Mulitple Variable

Workspace in R

Save Workspace Save workspace

save.image() # Default file .Rdata unlink(.RData) # To remove save.image(mywork.Rdata) # In specific file load(mywork.Rdata) # Load previous work savehistory(file=abc) # Save in txt file, default .Rhistory loadhistory(file=abc) # Load history from file

Quit Session q() # It will ask to save the workspace

image? [y/n/c]


Character variable in R


- to store name or categorical variable

- Use double quotes to store variable

- Use with c operator to store multiple values

H6

Getting help in R Within R:

The ? Command can be used to get help on a specific command within R ? keyword or help(keyword) # Command search library(help=pamr) ??keyword or help.search(keyword) # If dont know function apropos("mean") # list all functions containing

string meanSearch library functions

library(help=base) # List of base function available with R console library(help=samr) # To display the list of function available in

package samr. But to display the help page, first we have to load the library.

Documentation Help files can be accessed in the text file or html format. Manuals, reference cards, tutorials and news about recent developments

are available at http://www.r-project.org/other-docs.html Online help

R-help : https://www.stat.math.ethz.ch/pipermail/r-help/ Bioconductor-help : https://stat.ethz.ch/mailman/listinfo/bioconductor


H6-b


PracticeSession:1


Topic 2 : Data Types in R


Variable types in R Numeric

Integer x = 6is.real(x) # TRUEis.integer(x) # FALSE

Logicalx = c(1,2,3,4,5); y = (x

Vectors in R Vectors may have mode logical,numeric,character.

Examples of Vectorsx = c(45, 90, 135 )

y = c("Kinjal","Madhav","Roopa","Suraj")

z = c(" gene1 " , " gene2 " , " gene3 " , " gene4" )


Arrays in R Vectors may also have mode logical,numeric,character. Two dimension array is same as matrix

Examples of Two dimension arrayx = array(data, dim)

x = array(1:3, c(2,4))

Examples of Three dimension array x = array(1:3, c(2,4,2)) # 2, represent the dimension

x = array(1:3, c(2,4,3)) # 3, represent the dimension


Matrices in R

Col1 Col2 Col3 Col4

Row1

Row2

Row3

Is a matrix

Dimension : 3 X 4

Row names : Row1, Row2, Row3

Column names : Col1, Col2, Col3

Row size: 3

Column size: 4


Data frames in R

Col1 Col2 Col3 Col4

Row1

Row2

Row3

Data frame is a generalization of a matrix

Different column may have different data types

All elements of any column must ,have the same datatype, i.e. all numeric, or all factor, or all character, or all logical

Use for R modeling and graphical functions

If the data is read in using the command read.csv, read.txt etc, it will automatically be saved as a data frame.


Data lists in R

Row1

Row2

Row3

Data list is arrangement of different lists

Different rows may have different number of variables

All elements of any rows must ,have the same datatype, i.e. all numeric, or all factor, or all character, or all logical


Topic 3 : Data Creation


Variable types in R Numeric

Integer x = 6is.real(x) # TRUEis.integer(x) # FALSE

Logicalx = c(1,2,3,4,5); y = (x

Vectors in R Vectors may have mode logical,numeric,character.

Examples of Vectorsx = c(45, 90, 135 )

y = c("Kinjal","Madhav","Roopa","Suraj")

z = c(" gene1 " , " gene2 " , " gene3 " , " gene4" )


H8

Data Creation : Vectors and Arrays Data creation :

c(1,2,3,4) # combine argument to create a vector

from:to # create sequence from to to

seq(from,to,by=diff) # create airthmetic series

rep(c(1,2,3,4),4) # Replicate Elements of Vectors

array(1:3, c(2,4)) # create array of size 2X4


H9

Arrays in R Vectors may also have mode logical,numeric,character. Two dimension array is same as matrix

Examples of Two dimension arrayx = array(data, dim)

x = array(1:3, c(2,4))

Examples of Three dimension array x = array(1:3, c(2,4,2)) # create two array of size 2X4

# 2, represent the dimension

x = array(1:3, c(2,4,3)) # 3, represent the dimension


H10

Data creation : Matrices in RA = 1:3B = 4:6c = 7:9

# cbind combined object by ColumnX = cbind(a,b,c)

# rbind combined object by RowY = rbind(a,b,c)

# Matrix by defining number of rows and columnsZ = matrix(c(1,4,6,2,3,7.8), nrow=2, ncol=3, byrow=T)

Z = matrix(c(1,4,6,2,3,7.8), nrow=2, ncol=3, byrow=F)

expression_data = matrix(c(1,2,3, 11,12,13), nrow = 2, ncol=3, byrow=TRUE,dimnames = list(c("gene1", "gene2"),c("Sample.1", "Sample.2", "Sample.3")))

# To generate random matrix of 10 rows and 5 columnsreplicate(5, rnorm(10))


H11

Data Creation : Data framesData Frame :

go.term = c (GO0009117,GO0009253,GO0009354) gene.count = c(15,18,25) avg.expression.value = c(0.5432,0.2371,0.7867) go.term.rank.rank= c(2,1,3)

mydata = data.frame (go.term,gene.count,avg.gene.expression,go.term.rank)

mydata2 = data.frame(rank=1:4,gene_name=c("ddr1","apr2","bac","p53"),n=c(.90,.75,.52,.31));


H12

Data Creation : Data ListsData List :

genelist1 = c (abc1,abc2) genelist2 = c(brca1,brca2,tp53,mdm2) genelist3 = c(apr,erpn,myc)

mylist = list (genelist1,genelist2,genelist3)

mylist2 = list(rank=1:4,gene_name=c("ddr1","apr2","bac","p53"),n=c(.90,.75,.52,.31));


H13

List : collection of several objects of any type x1 = c("gene1","gene2","gene3","gene4",gene5) x2 = c(2,4,7,9,11) x = list(x1,x2)


PracticeSession:2

Topic 4 : Data Curation


Variable Information is.na (x) # To identify missing values is.array(x) # To store one, two or more dimension data is.vector(x) # One dimension array is.matrix(x) # Two dimension array is.data.frame(x) is.numeric(x) is.complex(x) is.character(x)


H14

Variable conversion

as.vector(x) as.matrix(x) as.data.frame(x) as.character(x)


H14

Variable attributes

Attributes length(x) # Length of vector dim(x) # Dimension of matrix dimnames(x) # Dimension names


H15

Missing ValuesVariables of each data type (numeric, character, logical) can also take the

value NA: not available.

- NA is not the same as 0

- NA is not the same as

- NA is not the same as FALSE

For any operations (calculations, comparisons) that involve NA, we have to logically indicate whether missing values should be considered or removed.

> NA==1

[1] NA

> 1+NA

[1] NA

> max(c(NA, 4, 7))

[1] NA

> max(c(NA, 4, 7), na.rm=T)

[1] 7


H16

Data selection and manipulation Slicing and Extracting Data : Vectors

x[n] # nth element x[-n] # all but nth element x[1:n] # first n element x[-c(1:n)] # elements from n+1 to end x[c(2,5,7)] # specific elements x[x>5] # all elements greater than 5 x[x5 & x < 9] # all elements between 5 and 9 x[x %in% c("ab","sh")] # elements in given vector

Data selection from list and data frame : x[[n]] # nth element of the list x$name # extract x attribute with variable name attributes(x) # attributes of data frame


H17

Basic Matrix operation Matrix curation :

x[r,c] # element at rth row and cth column x[r,] # row r X[,c] # column c x[c(2,5,8)] # To select specific column

Matrix operation: dim(x) # Dimesnion of matrix x+y # Sum of matrix x and y dim(x) # Dimesnion of matrix t(x) # Transpose of matrix diag(x) # Diagonal element of matix nrow(x) # numer of rows rownames(x) # row names rowSums(x) # row sum rowMeans(x) # row means

cor(x) # correlation matirx var(x) # variance matrix


H18

Data selection and manipulation Data selection and manipulation :

X*2 # scalar multiplication length(x) # length of the vector sum (x) # sum of element in vector max, min # max and min values rev # reverse in order sort # sorting unique # unique rle # run length encoding table(a,b) # comparison table sample(x) # for random sampling of the data which.max(x) # return index of the max elements of x. Which.min(x) # return index of the min elements of x. Which (x == a) # returns a vector of indices of x, if

comparsion operator is TRUE Which (x %in% a) # return index which matches with a choose (n,k) # combinations of k events among n repetitions. rank(x) # ranking round(x,3) # round the element of x to 3 decimal places order


H19

Basic Math and Statistics function Basic Maths and Statistics functions:

sqrt(x) # square root of the function sin(x), cos(x), # trignometry functions asin(x), acos(x) # inverse trignometry functions log(x), log10(x), log(x,base) # log exp(2) # exponential function max(x), min(x), # min and max value range(x) # range sum(x) # sum of x

mean(x) # mean of the elements of x median(x) # median of the elements of x var(x) # variance of the element of x sd(x) # standard deviation of x


H20

Advance R Built-in function functions:

abs(x) # absolute values ceiling(x) # Ceiling floor(x) # floor trunc(x) # trunc round(3.4578) # round, decimal place signif(3.4578) # signif, significant digits


H21


PracticeSession:3


Exercise:1


Exercise 1: Round off the number 3.543321 up to three decimal place.

Exercise 2: Generate a sequence, x=seq(1,524,d). where d is a random number between 2 to 9. Find

length(x) sum(x) cube root of x extract 5, 7th element from vector x extract 2nd to 5th element from vector x create vector without 2nd to 5th element from

vector x which elements of vector x are greater than 10 find a vector whose elements are greater than 10 find a vector whose elements are greater than 10

and less than 50 find: max, min, rev, sort, unique, range



Exercise 3: Explore the commands a=rep(2,5) b=rep(3,7) c=rep(4,2) z2= c(a,b,c) z= sample(z2) # analyze z u = rle(z) sort (z) unique (z) what sample command does? attributes of u analyze u # Interpret what rle does mean, median, var,sd, convert z into log scale



Exercise 4: Generate 20 replicate of TRUE sample denoted by T, and 10 replicate of FALSE sample denoted by F.

Exercise 5: Generate a vector z1 from 2 to 5, second vector z3 from 12 to 15, and combine them into a new vector z.

Exercise 6: Write the sequence expression for 5 10 15 20 25 30 35 40 45 50

Exercise 7: Generate a sequence start with 19 to 957, with a difference of 17.

Exercise 8: Generate any 3X4 matrix using command matrix

Exercise 9: Ceate 3 vectors, a,b,c of size 5, generate a matrix using cbind and rbind, calculate the dimension of matrix.



Exercise 10: A class of 20 student, appeared for maths and biology exam, secure marks between 20 to 90. Genearte random marks, satisfying the above criteria in a matrix, that contain First Row as name of the student as S1, S2, ...., S20, and first column as the subject math1 and bio1 respectively.Save the name of the students and marks of the student who,

Secure more than 70 % marks in either of two subjects, and

Fail in either of two. Average marks secure by students in both subjects



DontforgettosaveWorkspace..


THANK YOU .........

Alok SrivastavaAssistant Professor, CRRAO- AIMSCS, Hyderabad, INDIA

Date 8-01-15


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