Very quick introduction to the language R. It talks about basic data structures, data manipulation steps, plots, control structures etc. Enough material to get you started in R.

1. 1
Introduction to R
What is R?
Getting Started
Data structures
Scalar (number, string,
Boolean, Date-time) ,
Vector, Matrix, Data frame, List
Input / Output
Plots
Control Logic
Working with Strings
Writing Functions
Angshuman Saha

2. 2
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

3. 3
Getting Started in R

4. 4
Getting Started
Double - click on the R icon on your desktop to start R
This 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

5. 5
Data Structure :
Vector

6. 6
Vector > Creation
x = c(10, 12.3 , 45) # create a vector of 3 numbers
x = c(FALSE, TRUE , TRUE, FALSE) # create a vector of 4 logical (boolean)
variables
x = c(“red”, “green” , “blue”) # create a vector of 3 strings
x = c(1:15) # create a vector of integers 1 to 15
x = 1:15 # equivalent to previous code
x = rep( 5.6 , 10) # repeat 5.6, 10 times. Vector of length 10 , all entries equal to
5.6
x = 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

7. 7
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 > Naming
x = 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

8. 8
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 addition
z = x - y # z=(9,40,49) . Element-wise subtraction
z = x * y # z=(10,225,330) . Element-wise multiplication
z = x / y # z=(10,9,1.66667) . Element-wise division
z = 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

9. 9
Data Structure :
Matrix

10. 10
Matrix > Creation
x = 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 12
Row and column names can be assigned
colnames(mat) =c("col1","col2","col3")
rownames( mat ) = paste( “rowID ” , 1:4, sep=“_”)
col1 col2 col3
rowID_1 1 2 3
rowID_2 4 5 6
rowID_3 7 8 9
rowID_4 10 11 12

11. 11
Matrix > Subsetting
Consider 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
col3
rowID_1 1 2 3
rowID_2 4 5 6
rowID_3 7 8 9
rowID_4 10 11 12
Row / column names can be changed
rownames(mat) [3] = “third” ;
colnames(mat)[2]=“second col”
col1 Nm2 col3
rowID_1 1 2 3
rowID_2 4 5 6
third 7 8 9
rowID_4 10 11 12
Set all values > 9 to 99
mat [mat>9] = 99
col1 Nm2 col3
rowID_1 1 2 3
rowID_2 4 5 6
third 7 8 9
rowID_4 99 99 99

12. 12
Matrix > Operations
Element-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

13. 13
Data Structure :
Data Frame

14. 14
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

15. 15
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 ownHouse
1 Bob 67 FALSE
2 John 45 FALSE
3 Jane 52 TRUE
Data frames can also be created by reading data from a csv
cust_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”

16. 16
Data Frame > Creation
Consider two data frames - cust1 & cust2
cust = rbind(cust1 , cust2)
Name Age ownHouse
1 Bob 67 FALSE
2 John 45 FALSE
3 Jane 52 TRUE
Name Age ownHouse
1 Bill 55 TRUE
2 Jack 75 TRUE
3 Deb 49 TRUE
Name Age ownHouse
1 Bob 67 FALSE
2 John 45 FALSE
3 Jane 52 TRUE
4 Bill 55 TRUE
5 Jack 75 TRUE
6 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 ownHouse
1 Bob 67 FALSE
5 Jack 75 TRUE

17. 17
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)

18. 18
Data Frame > Creation
Two 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 ownHouse
1 Bob 67 FALSE
2 John 45 FALSE
3 Jane 52 TRUE
Name PetCount hasCar
1 Bob 1 TRUE
2 John 0 FALSE
3 Jill 5 TRUE
cust= merge(cust1,cust2 ,
by = "Name")
Name Age ownHouse PetCount hasCar
1 Bob 67 FALSE 1 TRUE
2 John 45 FALSE 0 FALSE
cust= merge(cust1,cust2 ,
by = "Name" ,
all = TRUE)
Name Age ownHouse PetCount hasCar
1 Bob 67 FALSE 1 TRUE
2 Jane 52 TRUE NA NA
3 Jill NA NA 5 TRUE
4 John 45 FALSE 0 FALSE

19. 19
Data Structure :
List

20. 20
List > Background
• List can be thought of as a vector, whose elements may be
of different types
LIST
vector matrix
Another
List

21. 21
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) )

22. 22
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

23. 23
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 mylist
mylist[[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

24. 24
Data Structure :
Date & Time

25. 25
Data Structure: Dates
Sys.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

26. 26
Data Structure: Dates
Two 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
difftime
x1 = 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"

27. 27
Data Structure :
Others

28. 28
Data Structures: Others
NULL
NULL 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.
NA
NA 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.
NaN
NaN 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.

29. 29
Input / output

30. 30
Input
Read data (row-column format) from a csv file
x = 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 file
x = 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.

31. 31
Output
Write 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 disk
write.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.table
write.table( x, file = “D:/ out.txt” ,
row.names = FALSE, col.names=TRUE, na = “” , sep = “t” )
# Assume: x is an object in R workspace
save( x, file = “D:/ out.RData”)

32. 32
Plots

33. 33
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 plot
main
ylab
xlab
A large number of options available to control – axes, tick
marks, axes labels, legends, font type and size …. etc

34. 34
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 later
x1 = rnorm(30, mean = 0 , sd = 1)
y1 = rnorm(30, mean = 12 , sd = 0.5)
points(x1,y1,pch = 15 , col="red" , cex=1)

35. 35
Plots – multi panel plot
x = 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)

36. 36
Plots – saving to a file
x = 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

37. 37
Control Logic

38. 38
Control
# Generate k random numbers from N(0,1)
# k is not fixed apriori.
# Stop when sum of the value exceed 5
x = NULL ; stopIter = FALSE
while( !stopIter) {
x= c(x,rnorm(1,mean=0,sd=1) )
sumx=sum(x) ;
if (sumx >5){stopIter = TRUE} }
While ()
for ()
# Example of for loop
x = rnorm(100) ; y = rep(0, length(x))
for(i in 1:length(x) ){ y[i] = x[i] ^3 }

39. 39
Working with
Strings

40. 40
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 list
y = 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 matches
z = grep(pattern =“blue”, x = x, ignore.case = TRUE, value = TRUE)
# z = (“Blue”,”skyblue”) – returns the actual strings that match the pattern

41. 41
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 string
Regular expressions provide a vast number of options in manipulating
strings. Study R help on regular expressions to know more.

42. 42
Writing functions

43. 43
Function
GetSummary = function ( x = NULL){
output = list( SumOfSqr = NA , Mean_x = NA, Failed = TRUE )
#Input Validation
if(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

44. 44
Further
Resources

45. 45
Further Help on R
- http://cran.r-project.org/
- http://www.r-project.org/search.html
This page provides links to search engines specific to R
- Search for “R tutorial” , “R forum” …
Have fun exploring
the world of R