Introduction to the R language Computing for Data Analysis R statistics programming environment Ming Ni [email protected] 11/14/2014 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 Overview and History of R What is R? • 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. 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 Features of R, cont’d 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 Overview and History of R Drawbacks 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 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 Other Data Analysis Software 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 Downloading and Installing R http://cran.us.r-project.org/ Overview and History of R Design of the R System 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 Get start of R 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 Outline 1.Overview and History of R 2.Data types in R 3.Reading and Writing Data 4.Plotting Data Data types in R • Basic classes: numeric, integer, character, logical (TRUE/FALSE), complex • vector, matrix, list • factor • missing value • data frame Data types in R Assignment Operator Entering Input At the R prompt we type expressions. The <- symbol is the assignment operator Expression: x<- 1 Object: x Value: 1 Class of x: numeric Hash symbol Data types in R Printing 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  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 Create Vectors 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 Explicit Coercion Objects can be explicitly coerced from one class to another using as.* functions, if available Data types in R vector, matrix, list 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 cbind-ing and rbind-ing 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 • Basic classes: numeric, integer, character, logical (TRUE/FALSE), complex • vector, matrix, list • factor • missing value • data frame Data types in R Factor 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 Missing Values 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 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 Reading and Writing 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 read.table(file, header, sep, colClasses, nrows, skip, stringAsFactors) The read.table function is one of most commonly used function for reading data. It has few important arguments: • 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? The help file for the read.table function from R Documentation: 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) 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. Outline 1.Overview and History of R 2.Data types in R 3.Reading and Writing Data 4.Plotting Data 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 Plot Title Y label Margin 1,2,3,4 X label Plotting Data Some Important Base Graphics Parameters The par() function is used to specify global graphics parameters that affect all plots in an R session. • 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) Plotting Data R base plotting Demo Ming Ni Student of Industrial and Systems Engineering, State University of New York at Buffalo Email: [email protected] Advisor: Qing He, Ph.D.