r programming groundup-basic-section-i

R-Programming –Basics R Programming

Ground Up!

Syed Awase Khirni

Syed Awase earned his PhD from University of Zurich in GIS, supported by EU V Framework Scholarship from SPIRIT

Project (www.geo-spirit.org). He currently provides consulting services through his startup www.territorialprescience.com

and www.sycliq.com

1 Copyright 2008-2016 Syed Awase Khirni TPRI

http://www.geo-spirit.org/







http://www.territorialprescience.com/





http://www.sycliq.com/





R-Programming –Basics

R Project

• R – Free Software environment for statistical computing and graphics.

• https://www.r-project.org

• https://cran.r-project.org/mirrors.html

Copyright 2008-2016 Syed Awase Khirni TPRI 2

https://www.r-project.org/



https://cran.r-project.org/mirrors.html




S

• S Language – Developed by John Chambers et. al at Bell Labs

• 1976 -> internal statistical analysis environment – originally implemented as Fortran Libraries

• 1988-> Rewritten in C – statistical models in S by Chambers and Hastie

• 1998-> S v.4.0

• 1991-> R created in New Zealand by Ross Ihaka and Robert Gentleman.

• 1993 -> public release of R • 1995-> Martin Machler

convinced Ross and Robert to use the GNU GPU License

• 1996 , 1997 -> R Core Group Formed with (S Plus Core Group)

• 2000- R Version 1.0 Released • 2015 R Version 3.1.3 -> March

9, 2015.



Design of the R System

• R –Statistical Programming language based on S language developed by Bell Labs.

• Divided into 2 conceptual parts – Base – Add-on Packages

• Base – R System contains – The base package which is required

to run R and contains the most fundamental functions.

– Other packages contained in the base system include utils, stats, datasets, graphics, grDevices, grid, methods, tools, parallel, compiler, splines, tcltk, stats4

• Add-on Packages are packages that are published by either R Core group or any third party vendors

• Syntax similar to S, making it easy for S-PLUS users to switch over

• Semantics are superficially similar to S, but in reality are quite different

• Runs on almost any standard computing platform/OS



R?

• R is an integrated suite of software facilities for data manipulation, calculation and graphical display

• R has – Effective data handling and

storage facility – A suite of operators for

calculations on arrays and matrices

– A large, coherent, integrated collection of tools for data analysis

– Graphical facilities for data analysis and display

– A well developed, simple and effective programming language



R- Drawbacks

• Little built-in support for dynamic or 3-D graphics

• Functionality is based on consumer demand and user contributions

• Web support provided through third party software.



DATA TYPES AND BASIC OPERATIONS IN R



Data Types

• Objects • Numbers • Attributes • Entering Input and Printing • Vectors, Lists • Factors • Missing Values • Data Frames • Names



Objects in R

• R has five basic or atomic classes of objects – Character

– Numeric (real number)

– Integer

– Complex

– Logical (true/false)

• The most basic object is a vector – A vector can only contain objects of the same class

– The one exception is a list, which is represented as a vector but can contain objects of different classes

– Empty vectors can be created with the vector() function



R Studio



Install.packages()

• To install additional third party packages into your R software. We use

• Install.packages(“XLConnect”) – To install XLConnect

package

– To activate an already installed package we use • Library(“packagename”)


Check if the package is already installed or not. any(grepl("<name of your package>", installed.packages()))


Numbers in R

• Treated as numeric objects (i.e. double precision real numbers)

• Suffix L => integer

• Example : 1 => numeric object – 1L => explicitly gives an

integer

• 1/0 => inf (infinity)

• NaN => not a number or missing value



Attributes

• R objects can have attributes – Names, dimnames

– Dimensions (e.g. matrices, arrays)

– Class

– Length

– Other user-defined attributes/metadata

• Attributes of an object can be accessed using the attributes() function.



Assignment Operator (<-)

• Expressions in R are done using <- assignment operator.

• The grammar of the language determines whether an expression is complete or not

• The # character indicates a comment. Anything to the right of the # (including the # itself) is ignored

• [1] indicates that x is a vector and 123781213412 is the first element


//auto printing

Ctrl+L to clear console


Vectors in R

• The c() function can be used to create vectors of objects.



Vectors in R

• Using the vector() function



Mixing Objects

• When different objects are mixed in a vector, coercion occurs so that every element in the vector is of the same class.



Explicit Coercion

• Objects can be explicitly coerced from one class to another using the as.* functions.



Matrices • Vectors with a dimension

attribute are called Matrices. The dimension attribute is itself an integer vector of length 2(nrow, ncol)

• Matrices are constructed column-wise, so entries can be thought of starting from the upper left corner and running down the columns.

• Matrices can also be created directly from vectors by adding a dimension attribute.



Cbind-ing

• Matrices can be created by Column-binding with cbind() function



Rbind-ing

• Matrices can be created by row-binding using rbind() function.



Lists in R

• Lists are a special type of vector that can contain elements of different classes.

• Lists are a very important data type in R



Factors

• Used to represent categorical data. Factors can be unordered or ordered.

• Factors are treated specially by modelling functions like lm() and glm()

• Using factors with labels is better than using integers because factors are self-describing, having a variable that has values.



Missing Values

• Many existing, industrial and research datasets contain Missing values.

• These can occur due to various reasons such as manual data entry procedures, equipment errors and incorrect measurements.

• Missing values can appear in the form of outliers or even wrong data (i.e out of boundaries)


• Missing values are denoted by NA or NaN for undefined mathematical operations

– Is.na() is used to test objects if they are NA

– Is.nan() is used to test for NaN

– NA values have a class also, so there are integerNA, characterNA etc.

– A NaN value is also NA but the converse is not true.


Missing Values

• Three type of problems are usually associated with missing values

– Loss of efficiency

– Complications in handling and analyzing the data

– Bias resulting from differences between missing and complete data.


Identifying NA values using is.na() and is.nan()


Data Frames

• Used to store tabular data (table of values) – They are represented as a

special type of list, where every element of the list has to have the same length.

– Each element of the list can be thought of as a column and the length of each element of the list is the number of the rows

• Data frames can store different classes of objects in each column, while matrices must have every element of the same class

• Data frames also have a special attribute called row.names.

• Data frames are usually created by calling read.table() or read.csv()

• Can be converted to a matrix by calling data.matrix() method



Data Frames



Data Frame in R



Names in R

• R Objects can also have names, which is very useful for writing readable code and self-describing objects



Subsetting

• Extracting subsets from an existing dataset is called subsetting – []Always returns an

object of the same class as the original

– [[]]Used to extract elements of a list or a data frame.

– $ is used to extract element of a list or data frame by name; semantics are similar to that of [[]].



Subsetting Matrix



Subsetting List



Subsetting Nested Elements



Partial Matching

• Partial matching of names is allowed with [[]] and $



Remove NA values

• A common task is to remove missing value (NAs) prior to performing any analysis.



Vectorized Operations

• Many operations in R are vectorized making code more efficient, concise and easier to read.



Vectorized Matrix Operations



Reading Data

• R provides some useful functions to read data

– Read.table, read.csv for reading tabular data

– readLines, for reading lines of a text file

– Source: for reading in R code files (inverse of dump)

– dget: for reading in R code files (inverse of dput)

– Load: for reading in saved workspaces

– Unserialize, for reading single R objects in binary form.



Writing Data

• R provides a set of functions to write data into files

– Write.table: to write data in table format

– writeLines: to write lines

– Dump

– Dput

– Save

– serialize



Reading data files with read.table

• For small to moderately sized datasets, we can just call read.table without specifying any other arguments.

• Data <- read.table(“sampledata.txt”)



R-DataSets

• https://vincentarelbundock.github.io/Rdatasets/datasets.html

• http://openflights.org/data.html • http://www.public.iastate.edu/~hofmann/data_i

n_r_sortable.html • https://r-dir.com/reference/datasets.html • http://fimi.ua.ac.be/data/ • https://datamarket.com/data/list/?q=provider:ts

dl • https://www.data.gov/


https://vincentarelbundock.github.io/Rdatasets/datasets.html

https://vincentarelbundock.github.io/Rdatasets/datasets.html

http://openflights.org/data.html

http://www.public.iastate.edu/~hofmann/data_in_r_sortable.html

http://www.public.iastate.edu/~hofmann/data_in_r_sortable.html

https://r-dir.com/reference/datasets.html



http://fimi.ua.ac.be/data/

https://datamarket.com/data/list/?q=provider:tsdl

https://datamarket.com/data/list/?q=provider:tsdl

https://www.data.gov/


Directory/get working directory

• Setting and getting the current working directory


> setwd("<path to your folder>")


Reading CSV files



Airmile data



Mocking sample data with mockaroo


https://www.mockaroo.com/

https://www.mockaroo.com/


Reading large datasets with read.table



Write.csv()

• One of the easiest ways to save an R data frame is to write it to a csv file or tsv file or text file.



dput()

• Writes an ASCII text representation of an R object to a file or connection, or uses one to recreate the object



Head and Tail of DataSet

• Returns the first or the last part of an object , i.e. vector, matrix, table, data frame or function.



Loading “foreign” data

• Sometimes, we would like to import data from other statistical packages like SAS,SPSS and Stata

• Reading stata (.dta) files with foreign library

• Writing data files from R into Stata is also very straightforward.



Library”foreign”data

• SPSS Data

– For data files in SPSS format, it can be opened with the function read.spss from “foreign” package.

– “to.data.frame” option set to TRUE to return a data frame.



Loading “foreign”data

• Excel data

– Sometimes, we have data in xls format that needs to be imported into R prior to its use.

– Library(gdata)



Loading”foreign”data

• Using XLConnect package

• Install.packages(“XLConnect”);



Loading”foreign data”

• Minitab

– For importing minitab portable worksheets into R. We can use foreign library.



Computing Memory Requirements

• An integer takes 8bytes for numeric data type.

• Imagine you have a data frame with 100,000 rows and 100 columns.

• 100,000 X100X8bytes/numeric

– 220 bytes/MB

– Which accounts for 785 MB of memory is required.



Text Formats

• dump and dput are useful because the resulting textual format is editable and in the case of corruption, potentially recoverable

• In the case of writing out to a table or CSV file, dump and dput preserve the metadata (sacrificing some readability), so that another user doesn’t have to specify it all over again.

• Textual formats can work much better with version control programs like GIT and SVN, used to track changes meaningfully

• Text formats have longer life and adhere to “unix philosophy”

• However, the format is not very space-efficient.



Dump() function

• Creates a file in a format that can be read with the source() function or pasted in with the copy/paste edit functions of the windowing system.



Dput() function

• Dput function saves data as an R expression, which means that the resulting file can actually be copied and pasted into the R console.

• Creates and uses an ASCII file representing the object

• Writes an ASCII version of the object onto the file.



Functions in R

• Functions are a fundamental building block of R – Functions can be

assigned to variables

– Functions can be stored in lists,

– Functions can be passed as arguments to other functions

– Functions can have nested functions.

• Anonymous functions are functions that have no name.

• We use functions to incorporate sets of instructions that we want to use repeatedly or that because of their complexity, are better self-contained in a sub-program and called when needed.



User Defined Functions in R

• UDF are defined to accomplish a particular task and are not aware that a dedicated function or library exists already.



Infix Operators in R

• They are unique functions and methods that facilitate basic data expressions or transformations.

• They refer to the placement of the arithmetic operator between variables.

• The types of infix operators used in R include functions for data extraction, arithmetic sequences, comparison, logical testings, variable assignments and custom data functions



Infix Operator in R

• Infix operators, are used between operands, these operators do a function call in the background.



Predefined infix Operators in R

Operator Rank Description

%% 6 Reminder operator

%/% Integer Division

%*% 6 Matrix Multiplication

%o% 6 Outer Product

%x% 6 Kronecker product

%in% 9 Matching operator

:: 1 Extract -> extract function from a package namespace.

::: 1 Extract-> extract a hidden function from a namespace

$ 2 Extract list subset, extract list data by name

@ 2 Extract attributes by memory slot or location.

[[]] 3 Extract data by index



Predefined infix operators in R

Operator Rank Description

^ 4 Arithmetic Exponential Operator

: 5 Generate sequence of number

! 8 Not/Negation Operator

Xor 10 Logical/Exclusive OR

& 10 Logical and element

&& 10 Logical and control

~ 11 Assignment(equal) used in formals and model building

<<- 12 Permanent Assignment

<- 13 Left assignment

-> 13 Right assignment



User Defined infix in R



User defined infix function in R



CONTROL FLOW IN R SYED AWASE KHIRNI



If If..else



Ifelse()

• Vectors form the basic building block of R programming.

• Most functions in R take vector as input and output a resultant vector

• Vectorization of code will be much faster than applying the same function to each element of the vector individually.

• Ifelse() is a vector equivalent of if..else statement

• Test_expression must be a logical vector (or an object that can be coerced to logical)

• Return value is a vector with the same length as test_expression



forloop



While



Break Next



Repeat Loop

• A repeat loop is used to iterate over a block of code multiple number of time

• There is no condition check in repeat loop to exit the loop

• We must put a condition explicitly inside the body of the loop and use the break statement to exit the loop



OBJECTS AND CLASSES IN R SYED AWASE KHIRNI



OOP in R

• An object is a data structure have some attributes and methods which act on the attributes

• A class is a blue print for the object.

• R has three(3) class systems

– S3 Class System

– S4 Class System

– Reference Class System



S3 Class System

• Primitive in nature

• Lacks a formal definition and object of this class can be simply created by adding a class attribute.

• Objects are created by setting the class attribute

• Attributes are accessed using $

• Methods belong to generic function

• Follows copy-on-modify semantics

S4 Class System

• A formally defined structure which helps in making object of the same class look more or less similar.

• Class components are properly defined using the setClass() function and objects are created using the new() function.

• Attributes are accessed using @

• Methods belong to generic function

• Follows copy-on-modify semantics



Reference Class System

• Similar to the object oriented programming we are used to in C# and Java.

• Basically an extension of S4 class system with an environment added to it.

• Reference Class System

– Class defined using SetRefClass()

– Objects are created using generator functions

– Attributes are accessed using $

– Methods belong to the class

– Does not follow copy-on-modify semantics



S3 Class System



S3 Class



S3 Class Method



S3 class with methods



Inheritance – S3 Class System



S4 Class System in R

• S4 class is defined using the setClass() function

• Member variables are called slots

• When defining a class, we need to set the name and the slots (along with class of the slot)



S4 Class System in R

Accessing Slots

• Slots of an object are accessed using @

Modifying Slots


• A slot can be modified through reassignment operations as shown below


Inheritance in S4



R Reference Class System

• Reference class in R are similar to the object oriented programming, we are used to seeing in C++, Java, Python.

• Unlike S3 and S4 classes, methods belong to class rather than generic functions.

• Reference class are internally implemented as S4 classes with an environment added to it.

• setRefClass() returns a generator function which is used to create objects of that class



Reference Class in R

Accessing Fields in R

• Fields of the object can be accessed using the $ operator

Modifying Fields in R


• Fields can be modified by reassignment


Reference Methods .copy()



Reference Methods



Inheritance in Reference Class



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