- R is a free software environment for statistical computing and graphics. It has an active user community and supports graphical capabilities. - R can import and export data, perform data manipulation and summaries. It provides various plotting functions and control structures to control program flow. - Debugging tools in R include traceback, debug, browser and trace which help identify and fix issues in functions.

1. R Language
dwivedishashwat@gmail.com

2. Background
• 1991: Created by Ross Ihaka and Robert Gentleman
• 2000: R version 1.0.0 is released
• Latest version is 2.15.2 released in Oct „12
• R version 2.15.3 is scheduled to release in Mar „12
and 3.0.0 is scheduled to be released in Apr ‟13
• http://www.r-project.org (basic information about R)
• http://www.cran.r-project.org (base system and
additional packages)
• help() or ?help, help.search() or ??help

3. Background
• R is a free software environment for
statistical computing and graphics
• Very active and vibrant user community
• Graphical capabilities
• Physical memory
• Base R and around 4000 packages
1/21/2014

4. Introduction
 memory.limit(): To find out maximum amount of available
physical memory
 memory.size(): To find out how much memory is in use
 getwd(): Shows the path of your current working directory
 setwd(path): Allows you to set a new path for your current
working directory
 dir(): List down all the files in your working directory
 Program Editor (open, load, run, save)
• ls(): List all objects in your workspace
• rm(): Removes object from your workspace

5. Introduction
 Commands to R are expressions (4/3) or assignments (x <- 4/3)
 R is case sensitive
 Everything in R is a object
 Normally R objects are accessed by their names which is made up from letters,
and digits (0 to 9) or a period (“.”) in non-initial positions.
 Every object has a class
 R has 5 basic classes of objects
 character
 numeric (real numbers)
 integers
 complex
 logical (True / False)
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The most basic object is a vector
 A vector can only contains objects of the same class

6. Background
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Ex.
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 x <- 1 # assignment
 Print(x) # explicit printing
 X # auto printing
Ex.
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Q.
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x <- c(0.5, 0.6) # numeric
X <- c(TRUE, FALSE) # logical
X <- c(T, F) # logical
X <- c(“a”, “b”, “c”, “d”) # character
X <- 1:20 # integer
X <- c(1+0i, 2+4i) #complex
seq(from=1, to=10, by=1)
rep(c(1,2,3,4,5), times=2, each=2)
X <- c(1.7, “a”)
X <- c(TRUE, 2)
X <- c(“a”, TRUE)
When different objects are mixed in a vector, coercion occurs so that every element in the
vector is of same class

7. Session 1 Remaining
• rep(x, times, length.out, each)
• rm()
• rm(list=ls(pattern=“^test”))
• rm(list=ls(pattern=“test”))
• rm(list=setdiff(ls(), “test”))
• rm(list=ls())

8. Introduction
• Ex.
 X <- 0:6
 X <- c(“a”, “b”, “c”)
 X <- c(1, 2, 3)
 Numbers in R generally treated as numeric (i.e. double precision
real numbers)
 If you explicitly wants an integer then you need to specify L suffix
 Special number Inf (1/0), it actually a real number, 1/Inf will give
you 0
 Undefined value NaN (0/0) Not a Number, it can be though of as
missing value
• # indicates comments

9. Data Types
 R objects can have attributes (attributes())
 Class (class())
 Length (length())
 names (colnames for a matrix), dimnames (rownames, colnames for a matrix)
 dimensions (dim())
 other user defined attributes
 Various data types in R
 Vectors
 Vector(mode, length)
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Lists: Special type of vectors which can contain objects of different
classes.
 x <- list(1,2,3,“a”,”b”,”c”)
 x <- list(a=c(1,2,3), b=1:4, c=c(“a”,”b”,”c”))

10. Data Types
 Matrix: vectors with dimension attribute. Dimension itself is an
integer vector of length 2 (nrow, ncol). Matrices are constructed
column wise.
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m <- matrix(nrow=2, ncol=3)
m <- matrix(1:6, nrow=2, ncol=3)
x <- 1:3
y <- 10:12
cbind(x, y)
rbind(x,y)
 Data frames (data.frame())
 https://stat.ethz.ch/pipermail/rhelp/attachments/20101027/05a229bb/attachment.pl
 Factors: Used for categorical data i.e. Male & Female or analyst,
senior analyst, manager etc.
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x <- factor(c(“a”, “b”, “b”, “c”, “c”, “c”, “d”))
levels()
unclass(x)
levels([4:6])
Levels([4:6, drop=TRUE])

11. Date & Time
 Converting a character variable to a date variable
 as.Date(variable_name, input_format)
 strptime(variable_name, input_format)
 Output will be %Y-%m-%d %H:%M:%S
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%Y: Year with century
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%m: Month as decimal number (01-12)
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%d: Day of the month as decimal number(01-31)
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%H: Hrs as decimal numbers (00-23)
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%M: Minutes as decimal numbers (00-59)
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%S: Seconda as decimal numbers (00-59)
 Converting a date variable to a character variable / formatting a date
variable
 strftime(date_variable_name, output_format)
 format(data_variable_name, output_format)
 as.character(date_variable_name, output_format)

12. Sub-setting
 [ always returns an object of the same class as the original; can be
used to select more than one element
 [[ is used to extract elements of list or data frames; it can only be
used to extract single element and the class of the returned object
will not necessarily be a list or data frame
 $ is used to extract elements of a list or data frames by names;
semantics are similar to [[

13. Operators
 <: Less than
 <=: Less than equals to
 >: Greater than
 >=: Greater than equals to
 ==: Exactly equals to
 !=: Not equal to
 | or II: OR
 & or &&: AND
 !: NOT

14. Some Examples
 x <- c(“a”, “b”, “c”, “c”, “d”, “a”)
 x[1], x[1:4], x[x > “a”], u <- x >”a”
 x <- matrix(1:6,2,3)
 x[1,2], x[1,], x[,1], x[1,2, drop=FALSE]
 x <- list(var_1=c(1:10), var_2=c(“a”, “b”, “c”), var_3=0.6)
 x[1], x[[1]], x$var_1
 name <- “var_1”, x[name], x[[name]], x$name
 x[c(1,3)], x[[c(1,3)]], x[[1]][[3]]
 Produce a character vector containing var_1, var_2, var_3… var_999
 Remove missing values from x <- c(1, 2, 3, NA, 4, 5, NA, 6)
 y <- c(“a”, “b”, NA, NA, “c”, “d”, “e”, “f”), prepare a matrix containing
two columns x & y and does not have any missing value
 What is the sum & mean of Wind for the observations which has
temperature greater then 60 & month equals to 5
 How to create a new directory with a given name

15. Reading / Writing Data Set
 Principle functions for reading data into R
 read.table(), read.csv(): Used for reading tabular data
 readLines(): For reading lines of a text file
 source(): For reading in R code file
 dget(): For reading in R code file
 load(): For reading in saved workspaces
 unserialize(): For reading single R objects in binary form
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Principle functions for writing data to files
 write.table()
 writeLines()
 dump()
 dput()
 save()
 serialize()

16. Importing / Exporting Data
 Read.table() is one of the most commonly used function for reading data.
Few important arguments;
 file, name of the file to be read,
 header, logical indicating if the file has a header line
 sep, a string indicating how the columns are separated
 colClasses, a character vector indicating class of each column in the dataset
 nrows, the maximum number of rows to be read in the dataset
 na.strings, a character vector of strings which are to be interpreted as NA values
 comment.char, a character string indicating the comment character
 skip, number of lines to skip from beginning
 stringAsFactors, logical indicating should character variables be codes as factors
 Write.table()
 X, the object to be written, preferable a matrix or a data frame
 File, path and name of the file to be created
 Sep, a string indicating how the columns are separated
 Row.names, col.names, logical indicating whether the row names or col names to be
written along with x

17. Data Summary / Manipulation
 attach(x): For attaching a file
 detach(x): For detaching a file
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summary(x): For displaying summary statistics of a data set
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str(x): For displaying summary statistics of a data set in a different
manner then summary()
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sort(): For sorting a vector or factor
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order(): For ordering along more than one variable
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merge(): Merge two data frames by common columns or row names, or
do other versions of database join operations
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cut(x, breaks, labels): Divides the range of x into intervals and codes
the values in x according to which interval they fall. The leftmost
interval corresponds to level one, the next leftmost to level two and
so on.
 cut(x, 10, 1:10)

18. Data Summary / Manipulation
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pretty(x, n): Compute a sequence of about n+1 equally spaced „round‟
values which cover the range of the values in x.
 pretty(x, 100)
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substr(x, start, stop) <- value: Extract or replace substrings in a
character vector.
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strsplit(): Split the elements of a character vector x into substrings
according to the matches to substring split within them.
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rank(): Returns the sample ranks of the values in a vector. Ties (i.e.,
equal values) and missing values can be handled in several ways
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aggregate(): Splits the data into subsets, computes summary statistics
for each, and returns the result in a convenient form.
 ddply(): For each subset of a data frame, apply function then combine
results into a data frame.

19. Control Structures
 Allows you to control the flow of execution of the program
 if, else (testing a condition)
 if (condition) {do something} else if {do something different} else {do something
different}
 for (executing a loop fixed number of times)
 for (i in 1:10) { do something}
 while (executing a loop while a condition is true)
 while (condition) { do something}
 repeat (execute a infinite loop)
 break (break the execution of a loop)
 next (skip a iteration of a loop)
 return (exit a function)
 Create a vector with all integers from 1 to 1000 and replace all even
number by their inverse

20. Loop Functions
 lapply: Returns a list of the same length as X, each element of which
is the result of applying FUN to the corresponding element of X
 lapply(airquality, mean)
 Calculate sum of all the variables of the airquality dataset excluding NAs
 sapply: Sapply is a user-friendly version of lapply by default returning
a vector or matrix if appropriate
 sapply(airquality, mean)
 Repeat the problem present in lapply using sapply and see the difference
 apply: Returns a vector or array or list of values obtained by applying
a function to margins of an array or matrix
 apply(airquality, 1, sum)
 Calculate deciles including min and max of all the variables of the dataset
airquality excluding NAs
 Calculate square of each element of a matrix with dimensions 10 & 2 and
entries 1 to 20

21. Loop Functions
 tapply: Apply a function to each cell of a ragged array, that is to
each (non-empty) group of values given by a unique combination of
the levels of certain factors
 tapply(airquality$Ozone, aiqruality$Month, sum)
 Calculate sum of Ozone variable for observations having month equals
to 5
 mapply: mapply is a multivariate version of sapply. mapply applies
FUN to the first elements of each argument, the second elements,
the third elements, and so on
 mapply(rep, 1:4, 4:1)
 Calculate sum of two lists with dimensions 10 & 2 and having entries 1
to 20, 101 to 120, 201 to 220 & 301 to 320

22. Plotting Functions
 plot(x,y)
 hist(x)
 par()
 pch: plotting symbol
 lty: line type
 lwd: line width
 col: plotting color
 las: axis label orientation
 bg: background color
 mar: margin size
 oma: outer margin size
 mfrow: number of plots per row, column (plots are filled row-wise)
 mfcol: number of plots per row, column (plots are filled column-wise)

23. Plotting Functions
 lines: add lines to the plot
 points: add points to the plot
 text: add text labels to the plot
 title: add annotations to x, y axis labels, title, subtitle, outer
margin
 mtext: add text to the margins of the plot
 axis: adding axis ticks/labels

24. Functions
 function ()
 Exact match –> Partial match –> Positional match
 Return value of a function is the last expression in the function body
to be evaluated
 Functions can be nested, so that a function can be defined inside
another function
 Functions can be passed as arguments to other functions

25. Debugging
• Primary tools for debugging functions in R
 traceback: prints out the function call stack after an error occurs; does
nothing if there is no error
 debug: flags a function for debug mode which allows you to step through
execution of a function one line at a time
 browser: suspends the execution of a function whenever it is called and
puts the function in debug mode
 trace: allows you to insert debugging code into a function at specific
places
 recover: allows you to modify the error behavior so that you can browse
the function call stack

26. Debugging
 Indications that something‟s is not right
 message: a generic notification/diagnostic message produced by the
message function; execution of the function continues
 warning: an indication that something is wrong but not necessarily
fatal produced by warning function‟ execution of the function
continues
 error: an indication that a fatal problem has occurred produced by
stop function; execution stops
 condition: a generic concept for indicating that something
unexpected can occur; programmers can create their own conditions

27. Thanks a lot
For Question Read more 
dwivedishashwat@gmail.com