Introduction to

1
Introduction to
3.11.13

• Dror Hollander
• Gil Ast Lab
• Sackler Medical School

2
Lecture Overview

• What is R and why use it?
• Setting up R RStudio for use
• Calculations, functions and variable classes
• File handling, plotting and graphic features
• Statistics
• Packages and writing functions

3
What is ?

• R is a freely available language and environment
  for statistical computing and graphics
• Much like , but bette !

4
Why use ?

• SPSS and Excel users are limited in their ability
  to change their environment. The way they
  approach a problem is constrained by how Excel
  SPSS were programmed to approach it
• The users have to pay money to use the software

5
s Strengths

• Data management manipulation
• Statistics
• Graphics
• Programming language
• Active user community
• Free

6
s Weaknesses

• Not very user friendly at start
• No commercial support
• Substantially slower than programming languages
  (e.g. Perl, Java, C)

7
Lecture Overview

• What is R and why use it?
• Setting up R RStudio for use
• Calculations, functions and variable classes
• File handling, plotting and graphic features
• Statistics
• Packages and writing functions

8
Installing

• Go to R homepage http//www.r-project.org/

And just follow the installation instructions
9
Installing RStudio

• RStudio is a integrated development environment
  (IDE) for R
• Install the desktop edition from this link
  http//www.rstudio.org/download/

10
Using RStudio
Script editor
View variables in workspace and history file
View help, plots files manage packages
R console
11
Set Up Your Workspace

• Create your working directory
• Open a new R script file

12
Lecture Overview

• What is R and why use it?
• Setting up R RStudio for use
• Calculations, functions and variable classes
• File handling plotting and graphic features
• Statistics
• Packages and writing functions

13
- Basic Calculations

• Operators take values (operands), operate on
  them, and produce a new value
• Basic calculations (numeric operators)
• , - , / , ,
• Lets try an example. Run this
• (170.35)(1/3)Before you do

Script editor
Use to write comments (script lines that are
ignored when run)
Click here / Ctrlenter to run code in RStudio
R console
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- Basic Functions

• All R operations are performed by functions
• Calling a functiongt function_name(x)
• For examplegt sqrt(9) 1 3
• Reading a functions help file gt ?sqrt Also,
  when in doubt Google it!

15
Variables

• A variable is a symbolic name given to stored
  information
• Variables are assigned using either or lt-
  gt xlt-12.6 gt x1 12.6

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Variables - Numeric Vectors

• A vector is the simplest R data structure. A
  numeric vector is a single entity consisting of a
  collection of numbers.
• It may be created
• Using the c() function (concatenate)
  xc(3,7.6,9,11.1)gt x1 3 7.6 9 11.1
• Using the rep(what,how_many_times) function
  (replicate)xrep(10.2,3)
• Using the operator, signifiying a series of
  integersx415

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Variables - Character Vectors

• Character strings are always double quoted
• Vectors made of character stringsgt
  yc("I","want","to","go","home") gt y 1 "I"
  "want" "to" "go" "home"
• Using rep()gt rep("bye",2) 1 "bye" "bye"
• Notice the difference using paste() (1
  element)gt paste("I","want","to","go","home")1
  "I want to go home"

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Variables - Boolean Vectors

• Logical either FALSE or TRUE
• gt 5gt3 1 TRUE
• gt x15gt x1 1 2 3 4 5 gt xlt3 1 TRUE TRUE
  FALSE FALSE FALSEzxlt3

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RStudio Workspace History

• Lets review the workspace and history tabs
  in RStudio

20
Manipulation of Vectors

• Our vector xc(101,102,103,104)
• are used to access elements in x
• Extract 2nd element in xgt x21 102
• Extract 3rd and 4th elements in x gt x34 or
  xc(3,4)1 103 104

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Manipulation of Vectors Cont.

• gt x 1 101 102 103 104
• Add 1 to all elements in xgt x1 1 102 103
  104 105
• Multiply all elements in x by 2gt x2 1 202
  204 206 208

22
More Operators

• Comparison operators
• Equal
• Not equal !
• Less / greater than lt / gt
• Less / greater than or equal lt / gt
• Boolean (either FALSE or TRUE)
• And
• Or
• Not !

23
Manipulation of Vectors Cont.

• Our vector x100150
• Elements of x higher than 145gt xxgt145 1 146
  147 148 149 150
• Elements of x higher than 135 and lower than
  140gt x xgt135 xlt140 1 136 137 138 139

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Manipulation of Vectors Cont.

• Our vector gt xc("I","want","to","go","home")
• Elements of x that do not equal wantgt xx !
  "want" 1 "I" "to" "go" "home"
• Elements of x that equal want and homegt xx
  in c("want","home") 1 "want" "home"

Note use for 1 element and in for
several elements
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Variables Data Frames

• A data frame is simply a table
• Each column may be of a different class (e.g.
  numeric, character, etc.)
• The number of elements in each
• row must be identical

• Accessing elements in data frame
• xrow,column
• The age columngt xage orgt x,age or
  gt x,1
• All male rowsgt xxgenderM,

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Variables Matrices

• A matrix is a table of a different class
• Each column must be of the same class (e.g.
  numeric, character, etc.)
• The number of elements in each
• row must be identical

• Accessing elements in matrices
• xrow,column
• The Height columngt x,Height or gt
  x,2
• Note you cannot use gt xWeight

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Exe cise

• Construct the character vector pplNames
  containing 5 names Srulik, Esti, Shimshon,
  Shifra, Ezra
• Construct the numeric vector ages that includes
  the following numbers 21, 12 (twice), 35
  (twice)
• Use the data.frame() function to construct the
  pplAges table out of pplNames ages
• Access the pplAges rows with ages values
  greater than 19

28
Lecture Overview

• What is R and why use it?
• Setting up R RStudio for use
• Calculations, functions and variable classes
• File handling, plotting and graphic features
• Statistics
• Packages and writing functions

29
Wo king With a File

• For example analysis of a gene expression file
• Workflow
• Save file in workspace directory
• Read / load file to R
• Analyze the gene expression table
• 305 gene expression reads in 48 tissues (log10
  values compared to a mixed tissue pool)
• Values gt0 ? over-expressed genes
• Values lt0 ? under-expressed genes
• File includes 306 rows X 49 columns

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File Handling - ead File

• Read file to R
• Use the read.table() function
• Note each function receives input (arguments)
  and produces output (return value)
• The function returns a data frame
• Rungt geneExprss read.table(file
  "geneExprss.txt", sep "\t",header T)
• Check tablegt dim(geneExprss) table
  dimentionsgt geneExprss1, 1st linegt
  class(geneExprss) check variable class
• Or double click on variable name in workspace tab

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Plotting - Pie Chart

• What fraction of lung genes are over-expressed?
• What about the under-expressed genes?
• A pie chart can illustrate our findings

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Using the pie() Function

• Lets regard values gt 0.2 as over-expressed
• Lets regard values lt (-0.2) as under-expressed
• Lets use Length() ? retrieves the number of
  elements in a vector

gt up length (geneExprssLung geneExprssLunggt0.
2) gt down length (geneExprssLung
geneExprssLunglt(-0.2)) gt mid length
(geneExprssLung geneExprssLunglt0.2
geneExprssLunggt(-0.2)) gt pie (c(up,down,mid)
,labels c("up","down","mid"))

• More on saving plots to files in a few slides

33
Plotting - Scatter Plot

• How similar is the gene expression profile of the
  Hippocampus (brain) to that of that of the
  Thalamus (brain)?
• A scatter plot is ideal for the visualization of
  the correlation between two variables

34
Using the plot() Function

• Plot the gene expression profile of
  Hippocampus.brain against that of Thalamus.brain
• gt plot ( geneExprssHippocampus.brain,
  geneExprssThalamus.brain, xlab"Hippocampus",
  ylab"Thalamus")

35
File Handling Load File to

• .RData files contain saved R environment data
• Load .RData file to R
• Use the load() function
• Note each function receives input (arguments)
  and produces output (return value)
• Rungt load (file "geneExprss.RData")
• Check tablegt dim(geneExprss) table
  dimentionsgt geneExprss1, 1st linegt
  class(geneExprss) check variable class
• Or double click on variable name in workspace tab

36
Plotting Bar Plot

• How does the expression profile of NOVA1 differ
  across several tissues?
• A bar plot can be used to compare two or more
  categories

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Using the barplot() Function

• Compare NOVA1 expression in Spinalcord, Kidney,
  Heart and Skeletal.muscle by plotting a bar plot
• Sort the data before plotting using the sort()
  function
• barplot() works on a variable of a matrix class
  
• gt tissues c ( "Spinalcord", "Kidney",
  "Skeletal.muscle", "Heart")gt barplot ( sort (
  geneExprss "NOVA1",tissues ) )

38
More Graphic Functions to Keep in Mind

• hist()
• boxplot()
• plotmeans()
• scatterplot()

39
Exe cise

• Use barplot() to compare PTBP1 PTBP2 gene
  expression in Hypothalamus.brain
• Use barplot() to compare PTBP1 PTBP2 gene
  expression in Lung
• What are the differences between the two plots
  indicative of?

40
Save Plot to File - RStudio

• Create a .PNG file

• Create a .PDF file

41
Save Plot to File in

• Before running the visualizing function, redirect
  all plots to a file of a certain type
• jpeg(filename)
• png(filename)
• pdf(filename)
• postscript(filename)
• After running the visualization function, close
  graphic device using dev.off() or graphcis.off()

• For example
• gt load(file"geneExprss.RData")
• gt Tissues c ("Spinalcord", "Kidney",
  "Skeletal.muscle", "Heart")
• gt pdf("Nova1BarPlot.PDF")
• gt barplot ( sort (geneExprss "NOVA1", tissues )
  )
• gt graphics.off()

42
Lecture Overview

• What is R and why use it?
• Setting up R RStudio for use
• Calculations, functions and variable classes
• File handling, plotting and graphic features
• Statistics
• Packages and writing functions

43
Statistics cor.test()
gt geneExprss read.table (file
"geneExprss.txt", sep "\t", header T) gt
cor.test ( geneExprssHippocampus.brain,
geneExprssThalamus.brain, method "pearson") gt
cor.test ( geneExprssHippocampus.brain,
geneExprssThalamus.brain, method "spearman")

• A few slides back we compared the expression
  profiles of the Hippocampus.brain and the
  Thalamus.brain
• But is that correlation statistically
  significant?
• R can help with this sort of question as well
• To answer that specific question well use the
  cor.test() function

44
Statistics More Testing, FYI

• t.test() Student t test
• wilcox.test() Mann-Whitney test
• kruskal.test() Kruskal-Wallis rank sum test
• chisq.test() chi squared test
• cor.test() pearson / spearman correlations
• lm(), glm() linear and generalized linear
  models
• p.adjust() adjustment of P-values for multiple
  testing (multiple testing correction) using FDR,
  bonferroni, etc.

45
Statistics Examine the Distribution of
Your Data

• Use the summary() function
• gt geneExprss read.table (file
  "geneExprss.txt", sep "\t", header T)
• gt summary(geneExprssLiver)
• Min. -1.844001st Qu. -0.17290 Median -0.05145
  Mean -0.08091 3rd Qu. 0.05299 Max. 0.63950

46
Statistics More Distribution Functions

• mean()
• median()
• var()
• min()
• max()
• When using most of these functions remember to
  use argument na.rmT

47
Lecture Overview

• What is R and why use it?
• Setting up R RStudio for use
• Calculations, functions and variable classes
• File handling, plotting and graphic features
• Statistics
• Packages and writing functions

48
Functions Packages

• All operations are performed by functions
• All R functions are stored in packages
• Base packages are installed along with R
• Packages including additional functions can by
  downloaded by user
• Functions can also be written by user

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Install Load Packages - RStudio
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Install Load Packages -

• Use the functions
• Install.packages(package_name)
• update.packages(package_name)
• library(package_name) Load a package

51
Final Tips

• Reading the functions help file (gt
  ?function_name)
• Run the help file examples
• Use http//www.rseek.org/
• Google what youre looking for
• Post on the R forum webpage
• And most importantly play with it, get the hang
  of it, and do NOT despair ?