R is a free programming language and software environment for statistical analysis and graphics. It contains functions for data manipulation, calculation, and graphical displays. Some key features of R include being free, running on multiple platforms, and having extensive statistical and graphical capabilities. Common object types in R include vectors, matrices, data frames, and lists. R also has packages that add additional functions.

1. R Programming
S.Sangeetha

2. R
R Recent Version in Oct 2020 - R 4.0.3
Download : https://www.r-project.org/

3. Why R?
• It's free!
• It runs on a variety of platforms including Windows, Unix and MacOS.
• It contains advanced statistical routines not yet available in other packages.
• It has state-of-the-art graphics capabilities.

4. What Is R?
• Programming “environment”
• Object-oriented
• Freeware
• Provides calculations on matrices
• Excellent graphics capabilities
• Supported by a large user network

6. R Overview
• You can enter commands one at a time at the command prompt
(>) or run a set of commands from a source file.
• There is a wide variety of data types, including vectors
(numerical, character, logical), matrices, data frames, and lists.
To quit R, use
>q()

7. Your First R Session

8. Objects
• Names
• Types of objects: vector, array, matrix, data Frame, list
• Attributes
• Mode: numeric, character, complex, logical
• Length: number of elements in object
• Creation
• Assign a value

9. Assignment
• “<-” used to indicate assignment
• x<-c(1,2,3,4,5,6,7)
• x<-c(1:7)
• x<-1:4

10. Vectors
Vectors are R lists of a single type of element
c ~ concatenate

11. Factors
• A factor - statistical data type used to store categorical variables.
• Categorical variables belong to a limited number of categories.
• Nominal categorical variable- without an implied order.
• Ordinal categorical variable - Natural ordering -"Low", "Medium",
"High" .

12. Factors
> temp <- c("low", "high", "medium", "high", "low", "medium", "high")
> factor(temp)
[1] low high medium high low medium high
>levels(factor(temp))
[1] "high" "low" "medium"
> factor(temp,order=TRUE,levels=c("low","medium","high"))
Levels: low < medium < high
>tempfact<-factor(temp,order=TRUE,levels=c("low","medium","high"))
> summary(tempfact)
low medium high
2 2 3

13. Plot
Lets create two small vectors with data and a scatterplot.
z2 <- c(1,2,3,4,5,6)
z3 <- c(6,8,3,5,7,1)
plot(z2,z3)
title("My first scatterplot")

16. R Attach Packages
To attach another package to the system you can use the menu or the library
function.
Via the menu:
`Packages' → `Load package...',
Via the library function:
> library(MASS)
> shoes
$A
[1] 13.2 8.2 10.9 14.3 10.7 6.6 9.5 10.8 8.8 13.3
$B
[1] 14.0 8.8 11.2 14.2 11.8 6.4 9.8 11.3 9.3 13.6
Functions and datasets to support Venables and Ripley,
"Modern Applied Statistics with S" (4th edition, 2002)

18. R Datasets
R comes with a number of sample datasets that you can experiment with. Type
> data( )
To see the available datasets. The results will depend on which packages you
have loaded. Type
>help(datasetname)
for details on a sample dataset.

22. Irises…
setosavirginica
data() yields many built-in data files. This is iris

23. Subsetting iris data
As with vectors, you can "subset" data frames.
df[rows,cols]

25. Two most common object types for statistics
Matrix
Data frame

26. Matrix
• A matrix is a vector with an additional attribute (dim) that defines the
number of columns and rows
• Only one mode (numeric, character, complex, or logical) allowed
• Can be created using matrix()
x<-matrix(data=0,nr=2,nc=2)
or
x<-matrix(0,2,2)
• All columns in a matrix must have the same data type (numeric,
character, etc.) and the same length.

27. Data Frame
• A data frame is more general than a matrix, in that different columns
can have different modes (numeric, character, factor, etc.).
• Just like a table in a database or excel sheet.
• Can be created using data.Frame()
L <-letters[1:4] #a b c d
X <-1:4 #1 2 3 4
data.frame(x,l) #create data frame

28. Data Elements
• select only one element
• x[2]
• select range of elements
• x[1:3]
• select all but one element
• x[-3]
• slicing: including only part of the object
• x[c(1,2,5)]
• select elements based on logical operator
• x(x>3)
In R, array indexes start at 1

29. Packages in R
Standard Packages
Standard statistical and graphical functions
• CRAN-Comprehensive R Archive Network

30. Namespaces
• Packages have namespaces
• Hide functions and data that are meant only for internal use
• Prevent functions from breaking when a user picks a name that
clashes package
• Provide a way to refer to an object within a particular package.
• Namespaces prevent the user’s definition from taking precedence

31. Operators in Namespaces
• :: selects definitions from a particular namespace.
Ex.base::t
• ::: acts like the :: but also allows access to hidden objects.

32. Data Input

33. Data Import & Entry

34. Data input
• data.entry()
• create object first, then enter data
• read.table()
• reads in data from an external file

36. Comma Delimited Text File
# first row contains variable names, comma is separator
# assign the variable id to row names
mydata <- read.table("c:/mydata.csv", header=TRUE, sep=",", row.names="id")

37. Keyboard Input
• Usually you will obtain a dataframe by importing it from SAS, SPSS, Excel, Stata, a
database, or an ASCII file. To create it interactively, you can do something like the
following.
# create a dataframe from scratch
age <- c(25, 30, 56)
gender <- c("male", "female", "male")
weight <- c(160, 110, 220)
mydata <- data.frame(age,gender,weight)

38. Exporting Data
To A Tab Delimited Text File
write.table(mydata, "c:/mydata.txt", sep="t")
To SAS
library(foreign)
write. foreign(mydata, "c:/mydata.txt","c:/mydata.sas",package="SAS" )

39. Exporting Data
write.foreign(df, datafile, codefile, package = c("SPSS", "Stata", "SAS"), ...)
df
A data frame
datafile
Name of file for data output
codefile
Name of file for code output
package
Name of package

40. Data Manipulation

41. Numeric Functions
Function Description
abs(x) absolute value
sqrt(x) square root
ceiling(x) ceiling(3.475) is 4
floor(x) floor(3.475) is 3
trunc(x) trunc(5.99) is 5
round(x, digits=n) round(3.475, digits=2) is 3.48
signif(x, digits=n) signif(3.475, digits=2) is 3.5
cos(x), sin(x), tan(x) also acos(x), cosh(x), acosh(x), etc.
log(x) natural logarithm
log10(x) common logarithm
exp(x) e^x

42. Character Functions
Function Description
substr(x, start=n1,
stop=n2)
Extract or replace substrings in a character vector.
x <- "abcdef"
substr(x, 2, 4) is "bcd"
grep(pattern, x , ignore.
Case=FALSE,
fixed=FALSE)
Search for pattern in x. If fixed =FALSE then pattern is a
regular expression. If fixed=TRUE then pattern is a text
string. Returns matching indices.
grep("A", c("b","A","c"), fixed=TRUE) returns 2
sub(pattern, replacement,
x, ignore.case =FALSE,
fixed=FALSE)
Find pattern in x and replace with replacement text. If
fixed=FALSE then pattern is a regular expression.
If fixed = T then pattern is a text string.
sub("s",".","Hello There") returns "Hello.There"

43. strsplit(x, split) Split the elements of character vector x at split.
strsplit("abc", "") returns 3 element vector "a", "b", "c"
toupper(x) Uppercase
tolower(x) Lowercase

44. References
• P. Kuhnert & B. Venables, An Introduction to R: Software for Statistical
Modeling & Computing
• J.H. Maindonald, Using R for Data Analysis and Graphics
• W.N. Venebles & D. M. Smith, An Introduction to R
• G. Jay Kerns, “Introduction to Probability and Statistics Using R”, Third
Edition”