Computing for Data Analysis R statistics programming environment Ming Ni mingni@buffalo.edu...

Introduction to the R language

Computing for Data AnalysisR statistics programming environment

Ming Ni

mingni@buffalo.edu11/14/2014

http://tinyurl.com/ise-r-talk

Outline

1.Overview and History of R

2.Data types in R

3.Reading and Writing Data

4.Plotting Data

Overview and History of R

What is S?

• R is a dialect of S language

• S is a language that was developed by John Chambers and

others at Bell Labs.

• S was initiated in 1976 as an internal statistical analysis

environment – originally implemented as Fortran libraries.

• Version 4 of the S language was release in 1998 and is the

version we use today

• 1991: Created in New Zealand by Ross Ihaka and Robert Gentleman

• 1993: First announcement of R to public

• 1995:Use the GNU General Public License to make R free software

• 1997: The R Core Group is formed. The core group controls the source code for R.

• 2000: R version 1.0.0 is released

• 2014: R version 3.1.2 is most recently released.

What is R?

Overview and History of R

Features of R

1. It is free!

2. The syntax and semantics are very similar to S

3. R is case sensitive

4. Commands are separated either by ; or by a newline

5. Run on almost any standard computing platform/OS (Windows, Mac, Linux even

on the PlayStation 4

Overview and History of R

6. Frequent releases (annual + bugfix releases); active development

7. Core software is quite lean; Functionality is divided into modular packages

8. Graphics capabilities are very sophisticated

9. Useful for interactive work, but contains a powerful programming language for

developing new tools

10. Very active and vibrant user community. (mailing lists and Stack Overflow

Features of R, cont’d

Overview and History of R

1. Essentially based on 40 year old technology

2. Little built in support for dynamic or 3-D graphics

3. No help line you can call for support or explaining features

4. Objects must generally be stored in physical memory of computer! (Big data age

5. Not ideal for all possible situation. R cannot do everything!

Overview and History of R

Drawbacks of R

Other Data Analysis Software

The number of analytics jobs for the more popular software (250 jobs or more, 2/2014).

Overview and History of R

Number of scholarly articles found for each software (2/2014).

Overview and History of R

Other Data Analysis Software

honorable mention:• Python with package numpy, pandas, Scipy• SPSS modeler

Easy drag and drop nodes to access to advanced data analytics

Overview and History of R

Other Data Analysis Software

http://cran.us.r-project.org/

Overview and History of R

Downloading and Installing R

The R system is divided into 2 conceptual parts:• The “base” R system that you download from

CRAN• Everything else

R functionality is divided into a number of packages• There are 4000+ packages on CRAN• Users contributed and not controlled by R Core• There are also large amount R packages outside

of CRAN

Overview and History of R

Design of the R System

R Console R Script

Overview and History of R

Get start of R

You can work directly in R, but most users prefer a graphical interface.

Integrated Development Environment (IDE):• RStudio• Tinn-R • Deducer• Revolution R (leverage R in Hadoop

environments

Text editor with plugins:• Vim• Eclipse +statET

RStudio server on web browser

Overview and History of R

Get start of R

Interactive environment, where people did not consciously think of themselves as programming• Read tables • Data analysis• User

After sophistication increased and have clear need, people are able to slide gradually into programming• Data processing• Develop the own tools • Programmer

Overview and History of R

Get start of R

Outline

1.Overview and History of R

2.Data types in R

3.Reading and Writing Data

4.Plotting Data

• Basic classes: numeric, integer, character,

logical (TRUE/FALSE), complex

• vector, matrix, list

• factor

• missing value

• data frame

Data types in R

Entering InputAt the R prompt we type expressions. The <- symbol is the assignment operator

Expression: x<- 1Object: x Value: 1Class of x: numeric

Hash symbol

Data types in R

Assignment Operator

When a complete expression is entered at prompt, it is evaluated and result of the evaluated expression is returned. The result may be auto-printed.

The [1] indicates that x is a vector and the first element of the object x is value 1

Data types in R

Printing

The : operator is used to create integer sequences

Data types in R

Printing

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

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

class(object) # class or type of an object

Data types in R

Create Vectors

Objects can be explicitly coerced from one class to another using as.* functions, if available

Data types in R

Explicit Coercion

1. vector: A vector can only contain objects of the same class

2. matrix: Matrix are vectors with a dimension attribute. The dimension

attribute is an integer vector of length 2 (nrow, ncol)

3. list: List are a special type of vector that can contain elements of different

classes. It can be multiple dimensions.

Data types in R

vector, matrix, list

Matrices can be created by column-binding or row-binding with cbind() and rbind().They are also able to be used for data frame.

Data types in R

cbind-ing and rbind-ing

• Basic classes: numeric, integer, character,

logical (TRUE/FALSE), complex

• vector, matrix, list

• factor

• missing value

• data frame

Data types in R

Factor is special type of vector. Factors are used to represent categorical data.• Factors can be unordered or ordered.• Each element of factors has a label.

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

Data types in R

Factor

generate frequency tables using the table( ) function

Data types in R

Factor

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

• NaA means 0/0 – stands for Not a Number

• NA is generally interpreted as a missing value.

• NA values have a class also, so there integer NA, character NA, logical NA, etc.

• A NaN value is also NA but the converse is not true

Data types in R

Missing Values

Data types in R

Missing Values Functions

Data types in R

Summary

• Basic classes: numeric, integer, character,

logical (TRUE/FALSE), complex

• vector, matrix, list

• factor

• missing value

• data frame

Outline

1.Overview and History of R

2.Data types in R

3.Reading and Writing Data

4.Plotting Data

Principal functions reading data into R.

• read.table, read.csv, for reading tabular data (.csv, .txt

• readLines, for reading lines of a text file

• source, for reading in R code file (.r

• load, for reading in saved workspaces (.rdata

Analogous functions writing data to files.

• write.table (txt, .csv

• writeLines

• dump

• save

Reading and Writing Data

The read.table function is one of most commonly used function for reading data. It has few important arguments:

read.table(file, header, sep, colClasses, nrows, skip, stringAsFactors)

• file, the name of a file, or a connection

• header, logical indicating if the file has a header line

• sep, a string indicting how the columns are separated

• colClasses, a character vector indicating the class of each column in the dataset

• nrows, the number of rows in the dataset

• skip, the number of lines to skip from the beginning

• stringAsFactors, should character variables be coded as factors?

Reading and Writing Data

• read.table(file, header, sep)• The other arguments of the function use default parameters. How to check it?

read.table(file, header = FALSE, sep = "", quote = "\"'", dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"), row.names, col.names, as.is = !stringsAsFactors, na.strings = "NA", colClasses = NA, nrows = -1, skip = 0, check.names = TRUE, fill = !blank.lines.skip, strip.white = FALSE, blank.lines.skip = TRUE, comment.char = "#", allowEscapes = FALSE, flush = FALSE, stringsAsFactors = default.stringsAsFactors(), fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)

The help file for the read.table function from R Documentation:

Reading and Writing Data

Check with the R help Documentation

1. ?read.table: precede the name of the function with ?

2. ??keyword: searches R documentation for keyword

3. Google read.table r

If you cannot follow the help documentation, please

see the example first, which is at end of the webpage

Reading and Writing Data

Data frames are used to store tabular data(Key data type used in R)

1. They are represented as a special type of list where every element of the

list has to have the same length

2. Unlike matrix , data frames can store different classes of objects in each

column (just like lists)

3. Data frames also have a special attribute called row.names, used to

annotate the data

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

5. Can be converted to a matrix by calling data.matrx()

Reading and Writing Data

Demo• The Iris Data Set consists of 50 samples from each of three

species of Iris flowers (Iris setosa, Iris virginica and Iris versicolor).

• 4 attributes were measured from each sample.

Reading and Writing Data

Outline

1.Overview and History of R

2.Data types in R

3.Reading and Writing Data

4.Plotting Data

The plotting and graphics engine in R is in a few base and

recommend packages:

• graphics: contains plotting functions for the “base” graphing

systems, including plot, hist, boxplot, etc.

• lattice;

• Grid;

• grDevices;

Plotting Data

Common questions about R plotting

• Where to plot: R graphic devices.

• How to plot: Function with parameter

• Need to resize: Exportation Format selection

The process of making a R base plotting:

• Base graphics are usually constructed piece by piece.

• Each aspect of the plot handled separately through a series of function calls

• Mirror the thought process

Base plotting is used most commonly and are a very powerful system for creating 2-D Graphics.

Plotting Data

Plotting Data

Plot Title

Y label

X label

Margin 1,2,3,4

Some Important Base Graphics Parameters

The par() function is used to specify global graphics parameters that affect all plots in an R session.

Plotting Data

• pch: the plotting symbol (default is open circle

• lty: the line type (solid line, dashed, dotted• lwd: the line width• col: the plotting color• las: the orientation of the axis labels• bg: the background color• mar: the 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)

DemoR base plotting

Plotting Data

Ming Ni

Student of Industrial and Systems Engineering, State University of New York at Buffalo

Email: mingni@buffalo.eduAdvisor: Qing He, Ph.D.