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Introduction to RWhat is R?

Getting Started

Data structuresScalar (number, string,

Boolean, Date-time) , Vector, Matrix, Data frame, List

Input / Output

Plots

Control Logic

Working with Strings

Writing Functions

Angshuman Saha

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What is R?

• R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

• R can be downloaded and installed from CRAN website (http://www.r-project.org/)

• CRAN stands for Comprehensive R Archive Network

• Installation comes with base, stat and a few other packages. Other than that, there are hundreds of contributed packages enabling users to a variety of specialized computation on data

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Getting Started in R

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Getting StartedDouble - click on the R icon on your desktop to start RThis launches the R GUI window

In the command prompt you can directly type your code and hit Enter. This will run the code. This however runs the code one line at a time.

1. Using command prompt

You can use a standard text editor like Notepad to create your R code and save it in a text file. You can manually copy the whole code from there and paste it in the RGUI window. This will run the whole code.

2. Using external text files

You may save your R code in a text file with extension “.r”. You can then source this file to run the code. Use “File>Source R code” from the menu to do this. Alternatively, you may type following command in R prompt source(“D:/myFirstRcode.r”) to run the code. You need to specify the full path of your R code file within double-quotes, while using source().

3. Using .r files

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Data Structure : Vector

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Vector > Creation

x = c(10, 12.3 , 45) # create a vector of 3 numbersx = c(FALSE, TRUE , TRUE, FALSE) # create a vector of 4 logical (boolean) variablesx = c(“red”, “green” , “blue”) # create a vector of 3 strings

x = c(1:15) # create a vector of integers 1 to 15x = 1:15 # equivalent to previous code

x = rep( 5.6 , 10) # repeat 5.6, 10 times. Vector of length 10 , all entries equal to 5.6x = rep( c(1,2) , c(3,2) ) # x= (1,1,1,2,2)

x = seq( 10 , 14 , 2) # sequence from 10 to 14 in steps of 2. x=(10,12,14)

x = vector(mode="numeric", length=0) # Initialize a zero length numeric vector, values will be put inside

it later

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Vector > Accessing Elements

x = c(10, 12.3 , 45, 55, 65, 75, 85) # create a vector y=x[2] # y has value 12.3 y=x[c(5,6,7)] # y is a vector with 5th,6th and 7th value of x y=x[ -c(5,6,7) ] # y is a vector with all but 5th,6th and 7th value of x y=x[c(1,1,3,4,7,7)] # y = (10,10,45,55,85,85)

Vector > Namingx = c(10, 45, 55 ) # create a vector names(x) = c(“first”, ”second”, ”third”) # name the elements of x

y=x[ “second” ] # y= 45. Elements can be accessed by name.

a = “third” ; y=x[ a ] # y = 55. Name can be passed through another variable

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Vector > operations

x = c(10, 45, 55 ) ; y = c(1, 5, 6 ) # create two vectors x and y

z = x + y # z=(11,50,61) . Element-wise additionz = x - y # z=(9,40,49) . Element-wise subtractionz = x * y # z=(10,225,330) . Element-wise multiplicationz = x / y # z=(10,9,1.66667) . Element-wise divisionz = x ^2 # z=(100,2025,3025) . Element-wise squaring

z = x[x>20] # z=(45,55) . All elements of x that are >20

z= which(x>20) # z= (2,3). Indices of x where x>20

z1 = x[x>20] ; z2 = x[ which( x>20 ) ] ; u= which(x>20) ; z3=x[u]# z1 z2 and z3 are all identical

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Data Structure : Matrix

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Matrix > Creationx = matrix( 10, nrow=3 , ncol = 5) # x is a 3 by 5 matrix with all entries = 10

Matrix can be created from a vector x = 1:12 ; mat = matrix(x , nrow = 4 , ncol=3)

[,1] [,2] [,3][1,] 1 5 9[2,] 2 6 10[3,] 3 7 11[4,] 4 8 12

By default, numbers are stacked column wise.To change that , use byrow = TRUE x = 1:12 ; mat = matrix(x , nrow = 4 , ncol=3 , byrow = TRUE)

[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6[3,] 7 8 9[4,] 10 11 12Row and column names can be

assigned colnames(mat) =c("col1","col2","col3")

rownames( mat ) = paste( “rowID ” , 1:4, sep=“_”)

col1 col2 col3rowID_1 1 2 3rowID_2 4 5 6rowID_3 7 8 9rowID_4 10 11 12

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Matrix > SubsettingConsider the Matrix – mat in previous example x = mat[ 2, ] # a vector containing second row of mat y = mat[ ,3 ] # a vector containing third column of mat x = mat[ “rowID_3”, ] # third row of mat x = mat[ ,”col2” ] # second column of mat newmat = mat[ 1:2, 2:3 ] # sub-matrix of mat newmat = mat[ c(1,2,4) , c(1,3) ] # sub-matrix of mat

diag_entries = diag(mat) # vector (1,5,9)

col1 col2 col3rowID_1 1 2 3rowID_2 4 5 6rowID_3 7 8 9rowID_4 10 11 12

Row / column names can be changed rownames(mat) [3] = “third” ; colnames(mat)[2]=“second col”

col1 Nm2 col3rowID_1 1 2 3rowID_2 4 5 6third 7 8 9rowID_4 10 11 12

Set all values > 9 to 99mat [mat>9] = 99

col1 Nm2 col3rowID_1 1 2 3rowID_2 4 5 6third 7 8 9rowID_4 99 99 99

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Matrix > OperationsElement-wise operations

mat1 = matrix(1:12, nrow=4, ncol = 3) mat2 = matrix( 10*(1:12), nrow=4, ncol = 3) mat3 = mat1 + mat2 # element-wise addition # Similarly we can have element-wise # subtraction , multiplication , division

mat1 = matrix(1:16, nrow=4, ncol = 4) mat2 = matrix( 10*(1:16), nrow=4, ncol = 4) mat3 = mat1 %*% mat2 # matrix-multiplication

Matrix multiplication

mat1 = matrix( rnorm(16) ,4,4) mat2 = solve( mat1 )

Matrix inversion

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Data Structure : Data Frame

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Data Frame > Background

• Data frame can be thought of as a matrix where the columns may be of different types (e.g. text, date, number, logical)

• Most datasets we work with can be stored as data frame

• Row / column subsetting works just like matrices

• Row and column names can be assigned

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Data Frame > Creation

Data frames can be created by stacking individual vectors column-wise

cust = c(“Bob” , “John” , “Jane”) age= c(67, 45, 52) ownHouse = c( FALSE , FALSE, TRUE) cust_dat = data.frame( Name= cust, Age = age, ownHouse = ownHouse)

Name Age ownHouse1 Bob 67 FALSE2 John 45 FALSE3 Jane 52 TRUE

Data frames can also be created by reading data from a csvcust_dat = read.csv( file = “custData.csv” , header = TRUE, stringsAsFactors = FALSE)

header = TRUE says that the 1st row of the file contains column names stringsAsFactors = FALSE do not convert character vectors to “factors”

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Data Frame > CreationConsider two data frames - cust1 & cust2

cust = rbind(cust1 , cust2)

Name Age ownHouse1 Bob 67 FALSE2 John 45 FALSE3 Jane 52 TRUE

Name Age ownHouse1 Bill 55 TRUE2 Jack 75 TRUE3 Deb 49 TRUE

Name Age ownHouse1 Bob 67 FALSE2 John 45 FALSE3 Jane 52 TRUE4 Bill 55 TRUE5 Jack 75 TRUE6 Deb 49 TRUE

Two data frames can be stacked below each other

A new data frame can be created by subsetting an existing data frame

cust = cust[cust$Age > 60 , ]

Name Age ownHouse1 Bob 67 FALSE5 Jack 75 TRUE

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Data Frame > Creation

cust0 = data.frame( Name=character(0) , Age=numeric(0) , ownHouse = logical(0) )

[1] Name Age ownHouse<0 rows> (or 0-length row.names)

An empty data frame can be created by specifying column names and types. It can be populated later.

An empty data frame can be created from an existing data frame

cust0 = cust[0,]

[1] Name Age ownHouse<0 rows> (or 0-length row.names)

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Data Frame > CreationTwo data frames can be merged by a common column

By default, only common records are returned.Using options - all , all.x , all.y – different record sets are obtained. Records may contain missing values.

Name Age ownHouse1 Bob 67 FALSE2 John 45 FALSE3 Jane 52 TRUE

Name PetCount hasCar1 Bob 1 TRUE2 John 0 FALSE3 Jill 5 TRUE

cust= merge(cust1,cust2 , by = "Name")

Name Age ownHouse PetCount hasCar1 Bob 67 FALSE 1 TRUE2 John 45 FALSE 0 FALSE

cust= merge(cust1,cust2 ,

by = "Name" , all = TRUE)

Name Age ownHouse PetCount hasCar1 Bob 67 FALSE 1 TRUE2 Jane 52 TRUE NA NA3 Jill NA NA 5 TRUE4 John 45 FALSE 0 FALSE

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Data Structure : List

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List > Background

• List can be thought of as a vector, whose elements may be of different types

LIST

vector

matrix

Another List

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List > Creation

An empty list mylist = list() # nothing is known about the list mylist = vector(mode=“list”, length=5) # length is known upfront

Non- empty list mylist = list( c(1,5,7) , “abc” , matrix(0,3,3) )

List with names mylist = list( comp1 = c(1,5,7) , comp2 = “abc” , comp3 = matrix(0,3,3) )

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List > Accessing the entries

By Index mylist = list( c(1,5,7) , “abc” , matrix(0,3,3) ) x = mylist[[1]] # x is a vector (1,5,7) x = mylist[[2]] # x is a string “abc” x = mylist[[1]] # x is a 3-by-3 matrix of zeros

By Name mylist = list( comp1 = c(1,5,7) , comp2 = “abc” , comp3 = matrix(0,3,3) ) x = mylist$comp1 # x is a vector (1,5,7) x = mylist$comp2 # x is a string “abc” x = mylist$comp3 # x is a 3-by-3 matrix of zeros

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List > Updating entries

By Index

By Name

mylist = list( comp1 = c(1,5,7) , comp2 = “abc” , comp3 = matrix(0,3,3) )

mylist[[4]] = 1024 # create a new entry at 4th position a number 1024 mylist = mylist[-3] # drop the third entry from mylistmylist[[2]] = “New Entry” # update the second entry

mylist$comp99 = 1024 # create a new entry at 4th position its name “comp99” mylist$comp1 = c(10,10) # update the entry – “comp1”

mylist = list( comp1 = c(1,5,7) , comp2 = “abc” , comp3 = matrix(0,3,3) ) names( mylist) # returns the vector – (“comp1” , “comp2” , “comp3”) names( mylist) = c(“A”,”B”,”C”) # change the names of the components names( mylist)[2] =”second” # change only the name of the second component

Renaming components

Subsets newlist = mylist[ c(1,3,4) ] # new list contains the first, third and fourth entry of mylist

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Data Structure : Date & Time

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Data Structure: DatesSys.time() # Returns the current system date and time.

x = strptime("02-07-2012",format="%m-%d-%Y")

x = strptime("02-feb-2012",format="%d-%b-%Y")

x = strptime("02-feb-2012 15:45:10",format="%d-%b-%Y %H:%M:%S")

String to Date-time

x = Sys.time() # on typing x in console you see : "2012-06-22 11:44:01 IST"

y = strftime(x , format="%d-%b-%Y") # "22-Jun-2012"

y = strftime(x , format="date: %d-%b-%Y >> Time: %H+%M+%S")

# "date: 22-Jun-2012 >> Time: 11+44+01« 

y = strftime(x , format="%d-%b-%Y %a >> Time: %H hour %M min %S sec")

#"22-Jun-2012 Fri >> Time: 11 hour 44 min 01 sec"

Date-time to String

Study R help on date-time variables to learn about a large number of possible format options

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Data Structure: DatesTwo main (internal) formats for date-time are : POSIXct and POSIXlt

POSIXct : A short format of date-time, typically used to store date-time columns in a data frame

POSIXlt : A long format of date-time, various other sub-units of time can be extracted from here

x = Sys.time() # on typing x in console you see : "2012-06-22 11:44:01 IST"

y = as.POSIXlt(x) # Convert from POSIXct to POSIXlt

z = c(y$mon, y$year, y$hour, y$min, y$wday) # z = (5, 112, 11, 51, 5)

Examples

difftimex1 = strptime("02-07-2012 14:20:34",format="%m-%d-%Y %H:%M:%S ")

x2 = strptime("11-07-2012 14:20:34",format="%m-%d-%Y %H:%M:%S ")

y = x2-x1 # y is a difftime object

x1 + as.difftime( 1 , units="days") # "2012-02-08 14:20:34 IST“

x1 + as.difftime( 10 , units=“mins")# "2012-02-07 14:30:34 IST"

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Data Structure : Others

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Data Structures: Others

NULLNULL is typically used for initializing variables. The code “x=NULL” creates a

variable x of length zero. It can later be converted to other values by overwriting x with some other values. The function is.null() returns TRUE of FALSE and tells whether a variable is NULL or not.

Other than the data structures described so far, there are a few very useful data types.

NANA is used for denoting missing values. The code “x=NA” creates a

variable x with missing values. The function is.na() returns TRUE of FALSE and tells whether a variable is NA or not.

NaNNaN stands for “Not a Number”. The code “x= sqrt(-10) ; y = log(-10)”

sets value of x and y to NaN. Also prints a warning message in console. The function is.nan() lets you check whether the value of a variable is NaN or not.

Inf Inf stands for “Infinity”. The code “x= 10/0 ; y = -3/0” sets value of x to Inf and y to -Inf.The function is.finite() lets you check whether the value of a variable is infinity or not.

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Input / output

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InputRead data (row-column format) from a csv filex = read.csv(file = “D:/mydata.csv” , header = TRUE, stringsAsFactors = FALSE)# x is a data frame containing the data in csv

Read data (row-column format) from a delimited filex = read.table( file = “D:/mydata.csv” , sep = “,” , header = TRUE, stringsAsFactors = FALSE)# x is a data frame containing the data in csv# read.csv is a special case of read.table with sep=“,”. # In read.table you may specify any character(s) of your choice as a separator

Reading arbitrary data using a lower level function : scan()Using scan() user can read character by character from a file.

These functions have many more optional input arguments to let user control the way in which data is read.

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OutputWrite a R object in R workspace to disk

Write a data frame to a file on disk# Assume: x is a data frame

# write.csv() writes it to a csv file on diskwrite.csv( x, file = “D:/ out.csv” , row.names = FALSE, col.names=TRUE, na = “”)

# write.table() writes it to any user-specified file. # write.csv(0 is a special case of write.tablewrite.table( x, file = “D:/ out.txt” ,

row.names = FALSE, col.names=TRUE, na = “” , sep = “\t” )

# Assume: x is an object in R workspacesave( x, file = “D:/ out.RData”)

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Plots

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Plots – xy plot

x = rnorm(100, mean = 2 , sd = 2)y = rnorm(100, mean = 10 , sd = 1)plot(x,y, xlab = "x-variable" , ylab = "y-variable", main = "scatter plot example" , pch = 19 , cex= 0.7, col="blue")

X-y scatter plotmain

ylab

xlab

A large number of options available to control – axes, tick marks, axes labels, legends, font type and size …. etc

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Plots - overlay

x = rnorm(100, mean = 2 , sd = 2)y = rnorm(100, mean = 10 , sd = 1)plot(x,y,xlab = "x-variable" , ylab = "y-variable", main = "scatter plot example" , pch = 19 , cex= 0.7, col="blue")

Generate a plot

Add red points laterx1 = rnorm(30, mean = 0 , sd = 1) y1 = rnorm(30, mean = 12 , sd = 0.5)

points(x1,y1,pch = 15 , col="red" , cex=1)

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Plots – multi panel plotx = rnorm(100, mean = 2 , sd = 2)y = rnorm(100, mean = 10 , sd = 1)

par(mfrow=c(2,2))

plot(x,y,xlab = "x-variable" , ylab = "y-variable", main = "scatter plot example" , pch = 19 , cex= 0.7, col="blue")

hist(x, xlab = "x-variable" , ylab = "frequency", main = "histogram-x" , col = "grey", border="blue" , lwd=2 )

hist(y, xlab = "y-variable" , ylab = "frequency", main = "histogram-y" , col = "grey", border="blue" , lwd=2 )

plot(density(x),col="limegreen",lwd=2, xlab="x",ylab="density",main="density plot")

par( mfrow=c(2,2)) splits the plot region into a 2-by2 matrix.Next 4 plot commands create plots in cells (1,1),(1,2),(2,1),(2,2)

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Plots – saving to a filex = rnorm(100, mean = 2 , sd = 2)y = rnorm(100, mean = 10 , sd = 1)

png(file = "D:/testplots.png")

par(mfrow=c(2,2))plot(x,y,xlab = "X" , ylab = "Y", main = " " , pch = 19 , cex= 0.7, col="blue")

plot( 0,0, type="n", axes=F, xlab="",ylab="",main="")

text(0,0, "NO DATA")

hist(y, xlab = "Y" , ylab = "frequency", main = "histogram-y" , col = "grey", border="blue" , lwd=2 )

plot(density(x),col="limegreen",lwd=2, xlab="x",ylab="density",main="density plot (X) ")

dev.off()

The code creates the above plot and saves it

in a png file in the location :

D:/testplots.png

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Control Logic

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Control

# Generate k random numbers from N(0,1) # k is not fixed apriori. # Stop when sum of the value exceed 5x = NULL ; stopIter = FALSEwhile( !stopIter) { x= c(x,rnorm(1,mean=0,sd=1) ) sumx=sum(x) ; if (sumx >5){stopIter = TRUE}

}

While ()

for ()# Example of for loopx = rnorm(100) ; y = rep(0, length(x))for(i in 1:length(x) ){ y[i] = x[i] ^3 }

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Working with Strings

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Working with Strings x= nchar("WRA data Filtering") #counts number of characters – x= 18 in this case

MetID = 2 ; x = paste(“Met”, MetID, sep = “:”) # string concatenation - x= “Met:2”

x = substr(“Met 12”, start=1, stop = 5) # substring from position 1 to 5 - x= “Met 1”

x = strsplit("Met1 has no data" , split = " ") # splits the string by “ ”. Returns a listy = unlist(x) # y is a vector with 4 elements – “Met1” , “has”, “no”, “data”

x= sub( pattern = "Met1” , replacement = “Met2” , x = “Met1 is empty") # replaces the first match - x = “Met2 is empty”

x= gsub("Met1” , “Met2” , x = “Met1 is empty. Met1 has no data.") # replaces all matches - x = “Met2 is empty. Met2 has no data.”

x = c( “red” , “Blue” , “green” , “skyblue” )y = grep(pattern =“blue”, x = x, ignore.case = TRUE) # y = (2,4) – positions of matchesz = grep(pattern =“blue”, x = x, ignore.case = TRUE, value = TRUE)

# z = (“Blue”,”skyblue”) – returns the actual strings that match the pattern

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Regular Expressions x=c("ht_10m","ht:20m"," ht_30m")

y = gsub("^ht_","HT:",x) # y = ("HT:10m" , "ht:20m" , " ht_30m")# Replace “ht_” at the beginning of the string with “HT:”

y = gsub(“m$",”mtr",x) # y = ("ht_10mtr“ , "ht:20mtr“ , " ht_30mtr")# Replace “m” at the end of the string with “mtr”

y = gsub(“[0-9]+",”XXX", x) # y = ("ht_XXXm" , "ht:XXXm" , " ht_XXXm")

# Replace one or more occurrence of digits with “XXX”

y = gsub(“_[0-9]+",”XXX", x) # y = ("htXXXm" , "ht:20m" , " htXXXm") # Replace one or more occurrence of digits preceeded by “_” with

“XXX”

u = grep(“^ht_[0-9]+m", x) ; y = x ; y[-u] = “invalid!” # y = ("ht_10m" , "invalid!“ , "invalid!")

# Used for checking the validity of format of a stringRegular expressions provide a vast number of options in manipulating strings. Study R help on regular expressions to know more.

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Writing functions

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Function

GetSummary = function ( x = NULL){

output = list( SumOfSqr = NA , Mean_x = NA, Failed = TRUE )

#Input Validationif(is.null(x) || length(x) ==0 || ){ return(output) }x1 = x[is.numeric(x)] ; if(length(x1) == 0)

{ return(output) }###############

output$SumOfSqr = sum( x^2 , na.rm = T)output$Mean_x = mean(x , na.rm = T)output$Failed = FALSE

return(output)}

Define the function

Use the function

x = rnorm(1000) ; out = GetSummary(x)

Argument

Default Value

Return Value

Comment

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Further Resources

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Further Help on R- http://cran.r-project.org/

- http://www.r-project.org/search.htmlThis page provides links to search engines specific to

R

- Search for “R tutorial” , “R forum” …

Have fun exploring the

world of R