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Book Reviews

Book Review: R High Performance Programming

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A book review by Douglas M. Wiig

Aloysius Lim and William Tjhi. R High Performance Programming. Birmingham, UK: Packt Publishing Ltd., 2015. bit.ly/14Rhpp

R High Performance Programming is a well written, informative book most suited for the experienced R programmer. This book offers a handy guide for R users who need speed and efficiency for the tasks that they perform.

The authors begin with an informative chapter discussing some of the inherent constraints on R’s computing performance such as CPU and RAM usage, and how R code is interpreted on the fly rather than compiled. A guide to several methods of profiling R’s code execution time, memory allocation and CPU usage is discussed in the next chapter. Sample code included in the chapter allows the reader to experiment with various benchmarking techniques to measure processing time and memory usage. This chapter provides the reader with some good tools for benchmarking R projects and identifying areas where improvements in processing can be made.

As is always the case with technical books from Packt Publishing, ample code examples are used in the chapter and the complete code used in each chapter is available for download with the book. This is a very handy feature and allows readers to do some live programming with R as the book is read.

The authors discuss a number of simple tweaks that can be easily performed to increase processing speed such as using built in functions and using hash tables. The hash table technique is useful for applications that use frequent lookups and can dramatically reduce processing time when compared to the use of lists. Running example code using this technique shows a large decrease in processing time when using the hash table approach as compared to straight list processing lookups.

In chapter 4 the authors discuss the use of compiled R code and integrating compiled languages into R code. They show several examples of using the R package inline that allows users to embed C, C++, Objective-C, Objective-C++ and Fortran code within R. Once again there are ample code examples to illustrate the use of this technique. For more advanced uses of compiled code the authors discuss how to create entire modules coded in C++ using the Rcpp package. Several completed code examples are included to illustrate the technique.

Another interesting approach to speeding up R is discussed in a chapter that explores several R packages designed to exploit the capability of GPU’s (Graphic Processing Cards) that are a used in many computers. These techniques can facilitate creating very fast and efficient statistical modeling code using R and the GPU.

As indicated above, readers can download the code package included with the book and find a well-organized set of ten folders (one for each chapter) containing 51 files. These files contain the sample code from the book as well as other code segments and benchmark code discussed in the book. The authors indicate that the code has been tested on R 3.1.1, Ubuntu 14.04 Trusty Tahr, Mac OS X 10.9 Mavericks, and Windows 8.1. This allows integration of these code segments into the reader’s own projects with minimal changes.

Other chapters in R High Performance Programming discuss simple tweaks to use less memory, techniques to speed processing of large datasets and using parallel processing and clustering techniques. The last chapter contains a discussion of using R and Hadoop to process Big Data (massive datasets with sizes measured in petabytes -one petabyes is 1,048,576 gigabytes). Processing data of this magnitude presents many challenges and is an area that is currently the subject of much program development.

I found R High Performance Programming to be a useful and informative book for the advanced user of R. A working knowledge of statistics, R and other programming languages such as C++ or Java is necessary to realize the full benefit of the techniques presented in the book. The book also serves as a good learning tool for less knowledgeable R users who are seeking to advance their programming skills.

Readers who are interested in the use of Hadoop and cluster computer processing might find the book Raspberry Pi Super Cluster by Andrew K. Dennis of interest. (Packt Publishing, 2013

PAC-14-1987838-1387169). A review of this book can be found on my web site at http://dmwiig.net.

Reviewer Information:

Douglas M. Wiig, Professor of Political Science

Grand View University

Teaching areas include social science statistics and research methods, comparative politics, international politics.

Long time user and developer of computer and statistical applications

Host of Open Source Technology in Higher Education web site at http://dmwiig.net

Creator and moderator of LinkedIn discussion forum “Open Source Technology in Higher Education”

Regular contributor to several LinkedIn discussion forums

Author of numerous tutorials on using the R statistical programming language and Raspberry Pi computer

R Tutorials

Using R for Basic Cross Tabulation Analysis: Part Three, Using the xtabs Function

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Using R to Work with GSS Survey Data Part Three: Using xtabs to Create and Analyze Tables

A tutorial by D. M. Wiig
In Part Two of this series of tutorials I discussed how to find and import a data set from the NORC GSS survey. The focus of that tutorial was on the GSS2010 data set that was imported into the R workspace in SPSS format and then loaded into an R data frame for analysis.

Use the following code to load the data set into an R workspace:

>install.packages(“Hmisc”) #need for file import
>install.packages(“foreign”) #need for file import
>#get spss gss file and put into data frame
>library(Hmisc)
>gssdataframe <- spss.get(“/path-to-your-file/GSS2010.sav”, use.value.labels=TRUE)

The xtabs function provides a quick way to generate and view a cross tabulation of two variables and allows the user to specify one or more control variables in the cross tabulation. Using the variables “ partyid” and “polviews” the cross tablulation is generated with:

>#use xtabs to produce a table
>gsstab <- xtabs(~ partyid + polviews, data=gssdataframe)

To view the resulting table use:

>gsstab #show table

To view summary statistics generated use:

summary(gsstab)

This summary shows the number of cases in the table, the number of factors and the Chi-square value for the table.

Variables used in social science research are often interrelated so it is desirable to control for one or more variables in order to further examine the variables of interest. The table created in the gsstab data frame shows the relationship between political ideology and political party affiliation. To look at the relationship by gender use the following:

>#use xtabs to produce a table with a control variable
>gsstab2 <- xtabs(~ partyid + polviews+ sex, data=gssdataframe)

To view the new table use:

>gsstab2

To view summary statistics for the table enter:

>summary(gsstab2)

As noted above xtabs is a quick and powerful function to create N x N tables with or without control variables. In the next tutorial I explore the use of the ca function to produce a basic Correspondence analysis of underlying dimensions in an N x N table.

R Tutorials, Uncategorized

Using R to Work with GSS Survey Data: Cross Tabulation Tables

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Using R to Work with GSS Survey Data: Viewing Datasets and Performing Cross Tabulations

A tutorial by D. M. Wiig

In a previous tutorial I discussed how to import datasets from the NORC General Social Science Survey using R to write the SPSS formatted data to an R data frame. Once the data has been imported into the R working environment it can be viewed and analyzed. There is a wealth of survey research data available at the NORC web site located at www.norc.org. In this tutorial the dataset gss2010.sav will be used. The dataset is available from www3.norc.org/GSS+Website.

From that page click on the “Quick Downloads” link on the right hand side of the page to access the list of available datasets. From the next page choose SPSS to access ‘.sav’ format files and finally “2010” under the heading “GSS 1972-2012 Release 6.” Please note that this is a rather large data file with 2044 observations of 794 variables. Download the file to a directory that you can access from your R console.

As discussed in a previous tutorial the SPSS format file can be loaded into an R data frame. Make sure that the R packages Hmisc and foreign have been installed and loaded before attempting to import the SPSS file. The following code will load the ‘.sav’ file:

>install.packages(“Hmisc”) #need for file import

>install.packages(“foreign”) #need for file import

>#get spss gss file and put into data frame

>library(Hmisc)

>gssdataframe <- spss.get(“/path-to-your-file/GSS2010.sav”, use.value.labels=TRUE)

Once the file is read into an R data frame it can be viewed in a spreadsheet like interface by using the command:

>View(gssdataframe)

Using the arrow keys, the home key, end key, and the page up and page down keys allows navigating and browsing the file.

Survey data such as that found in the GSS file is usually a mixture of data types ranging from ratio level numbers to categorical data. Cross tabulations are often used to explore relationships among variables that are ordinal or categorical in nature. R has a number of functions available for cross tabulations. The Table function is a quick way to generate a cross tabulation table with a number of options available. The following results in a frequency table of the variables “partyid” and “polviews” both of which are measured in categories:

>#use the gssdataframe

>#the variables partyid and polviews are used

>attach(gssdataframe)

>#create a table named ‘gsstable’

>gsstable <- table(partyid, polviews)

>gsstable #print table frequencies

The following output results:

                   polviews
partyid              EXTREMELY LIBERAL LIBERAL SLIGHTLY LIBERAL MODERATE
  STRONG DEMOCRAT                   41     105               42       94
  NOT STR DEMOCRAT                  14      62               57      154
  IND,NEAR DEM                      11      47               57      103
  INDEPENDENT                        5      20               33      189
  IND,NEAR REP                       1       4               16       74
  NOT STR REPUBLICAN                 2      10               16       88
  STRONG REPUBLICAN                  0       5                5       22
  OTHER PARTY                        1       5                6       16
                    polviews
partyid              SLGHTLY CONSERVATIVE CONSERVATIVE EXTRMLY CONSERVATIVE
  STRONG DEMOCRAT                      22           25                    6
  NOT STR DEMOCRAT                     28           16                    7
  IND,NEAR DEM                         25           11                    5
  INDEPENDENT                          43           32                    9
  IND,NEAR REP                         49           43                    8
  NOT STR REPUBLICAN                   72           72                   13
  STRONG REPUBLICAN                    23          101                   27
  OTHER PARTY                           3           12                    4

>

There are options available with the Table function that include calculating row and column marginal totals as well a cell percentages. Another quick method to generate tables is with the CrossTable function. The function is contained in the gmodels package and can be used on the table generated with the Table function above. Use the following lines of code to generate a cross table between ‘polviews’ and ‘partyid’ using the gsstable created above:

>library(gmodels)

>#produce basic crosstabs

>CrossTable(gsstable,prop.t=FALSE,prop.r=FALSE,prop.c=FALSE,chisq=TRUE,format=c(“SPSS”))

>

Cell Contents
|-------------------------|
|                   Count |
| Chi-square contribution |
|-------------------------|

Total Observations in Table:  1961 

                   | polviews 
           partyid |    EXTREMELY LIBERAL  |              LIBERAL  |     SLIGHTLY LIBERAL  |             MODERATE  | SLGHTLY CONSERVATIVE  |         CONSERVATIVE  | EXTRMLY CONSERVATIVE  |            Row Total | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
   STRONG DEMOCRAT |                  41  |                 105  |                  42  |                  94  |                  22  |                  25  |                   6  |                 335  | 
                   |              62.014  |              84.219  |               0.141  |               8.312  |              11.962  |              15.026  |               4.163  |                      | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
  NOT STR DEMOCRAT |                  14  |                  62  |                  57  |                 154  |                  28  |                  16  |                   7  |                 338  | 
                   |               0.089  |               6.911  |               7.238  |               5.486  |               6.840  |              26.537  |               3.215  |                      | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
      IND,NEAR DEM |                  11  |                  47  |                  57  |                 103  |                  25  |                  11  |                   5  |                 259  | 
                   |               0.121  |               4.902  |              22.674  |               0.284  |               2.857  |              22.144  |               2.830  |                      | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
       INDEPENDENT |                   5  |                  20  |                  33  |                 189  |                  43  |                  32  |                   9  |                 331  | 
                   |               4.634  |              12.733  |               0.969  |              32.889  |               0.067  |               8.107  |               1.409  |                      | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
      IND,NEAR REP |                   1  |                   4  |                  16  |                  74  |                  49  |                  43  |                   8  |                 195  | 
                   |               5.592  |              18.279  |               2.167  |               0.002  |              19.466  |               4.622  |               0.003  |                      | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
NOT STR REPUBLICAN |                   2  |                  10  |                  16  |                  88  |                  72  |                  72  |                  13  |                 273  | 
                   |               6.824  |              18.702  |               8.224  |               2.190  |              33.411  |              18.786  |               0.364  |                      | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
 STRONG REPUBLICAN |                   0  |                   5  |                   5  |                  22  |                  23  |                 101  |                  27  |                 183  | 
                   |               6.999  |              15.115  |              12.805  |              32.065  |               0.121  |             177.476  |              52.256  |                      | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
       OTHER PARTY |                   1  |                   5  |                   6  |                  16  |                   3  |                  12  |                   4  |                  47  | 
                   |               0.354  |               0.227  |               0.035  |               0.170  |               1.768  |               2.735  |               2.344  |                      | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|
      Column Total |                  75  |                 258  |                 232  |                 740  |                 265  |                 312  |                  79  |                1961  | 
-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  801.8746     d.f. =  42     p =  3.738705e-141 


 
       Minimum expected frequency: 1.797552 
Cells with Expected Frequency < 5: 2 of 56 (3.571429%)

Warning message:
In chisq.test(t, correct = FALSE, ...) :
  Chi-squared approximation may be incorrect

>

This code produces a table of frequencies along with a basic Ch-squared test. Other options include generating cell percentages and using either SPSS or SAS table format. This is accomplished by changing the appropriate flag from FALSE to TRUE and specifying either SPSS or SAS for the format flag. The table formatting is compressed in this example due to the narrow margin requirements of the web page.  Use the scroll bar at the bottom of the page to view the entire table.

There are many functions available in R to analyze data in tabular format. In my next tutorial I will examine using the xtabs function to produce basic cross tabulation with control variables.

R Tutorials

R Tutorial: Using R to Work With Datasets From the NORC General Social Science Survey

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R Tutorial: Using R to Work With Datasets From the NORC General Social Science Survey

A tutorial by D. M. Wiig

Part One:

When I teach classes in social science statistics and social science research methods I like to use “live” data as much as possible both in classroom lectures and in homework assignments. For the social sciences one excellent and readily available source of live data is the ongoing General Social Science Survey project, The National Data Program for the Sciences. This is a project of NORC, a National Science Research Center at the University of Chicago (see www.norc.org for the projects main web site.)

There a a number of datasets available in different formats. The quick download datasets that I like to use are primarily SPSS data files. Many institutions have SPSS available for students and faculty but the use of SPSS is my no means universal. I have found that it is easy to use R to read the .sav format files into an R data frame and then write the file out to a comma separated value, .csv format that can be read my almost any statistics software package. As I will discuss in this an future tutorials it is also quite effective to use R to analyze the GSS files.

To create R datasets using the GSS files we can use some of the file import/export features available in R. To begin, make sure that the R packages “Hmisc” and “foreign” are installed and loaded in your R session environment. This can be accomplished using:

> install.packages(“Hmisc”) #need for file import

> install.packages(“foreign”) #need for file import

As an example, the following code will load the GSS data file “gss2010x.sav” into an R data frame using the spss.get function:

>library(Hmisc)

>gssdataframe <- spss.get(“/path-to-your-file/gss2010x.sav”, use.value.labels=TRUE)

The file “gss2010x.sav” contains 500 observations of 47 variables. Codebooks and other information about the data in these datasets is readily avaiable for download from the NORC web site. After the data is loaded into the data frame it can be viewed using:

>gssdataframe

To convert and save the file to a comma separated value (.csv) format use the following use the write.table function:

>#write dataframe to .csv file

>write.table(gssdataframe, “/path-to-your-file/gss2010x.csv”,sep=”,”)

The file, now in a .csv format can be accessed with virtually any statistics package or other software. In my next tutorial I will discuss working with GSS data using the various table and cross table functions available in R.

 

R Tutorials

How To: Download and install the latest version or R on your Linux Ubuntu OS

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How To: Download and install the latest version or R on your Linux Ubuntu OS

(A Tutorial by D.M. Wiig)

I have several computers that use Linux operating systems and I have installed R on all of them. I use Debian on some of the machines and Ubuntu on others. When downloading R using the distribution’s package manager or from the command line I have notice that I will get versions of R ranging from 2.13.xx to 2.15.xxx depending on the Linux distribution. That has not been a problem until the release of the current version of R, version 3.0.3. Since this version is not backwards compatible with earlier releases it is necessary to upgrade to the new version to take advantage of new packages that are rapidly being developed as well as modification to existing packages to accommodate R 3.0.3. This tutorial will cover the installation of R 3.0.3 on the Ubuntu distribution of Linux.

When installing R 3.0.3 it is necessary to make sure that the current binaries are installed to your version of the Linux OS. If you are running a Ubuntu distribution you can edit the sources.list file on your computer to access the most up to date CRANs. Open a terminal program and enter the following from the command line:

$ cd /etc/apt/

$ dir

Make sure the file sources.list is in the directory and then edit the file opening the nano editor:

$ sudo nano sources.list

You should see a file in the editor that is similar to the file shown below:

—————————————————————————————————-

deb cdrom:[Kubuntu 11.10 _Oneiric Ocelot_ – Release i386 (20111012)]/ oneiric main restricted

deb http://streaming.stat.iastate.edu/CRAN/bin/linux/ubuntu precise/

# See http://help.ubuntu.com/community/UpgradeNotes for how to upgrade to

# newer versions of the distribution.

deb http://us.archive.ubuntu.com/ubuntu/ precise main restricted

deb-src http://us.archive.ubuntu.com/ubuntu/ precise main restricted

## Major bug fix updates produced after the final release of the

## distribution.

deb http://us.archive.ubuntu.com/ubuntu/ precise-updates main restricted

deb-src http://us.archive.ubuntu.com/ubuntu/ precise-updates main restricted

## N.B. software from this repository is ENTIRELY UNSUPPORTED by the Ubuntu

## team. Also, please note that software in universe WILL NOT receive any

## review or updates from the Ubuntu security team.

deb http://us.archive.ubuntu.com/ubuntu/ precise universe

deb-src http://us.archive.ubuntu.com/ubuntu/ precise universe

deb http://us.archive.ubuntu.com/ubuntu/ precise-updates universe

deb-src http://us.archive.ubuntu.com/ubuntu/ precise-updates universe

## N.B. software from this repository is ENTIRELY UNSUPPORTED by the Ubuntu

^G Get Help ^O WriteOut ^R Read File ^Y Prev Page ^K Cut Text ^C Cur Pos

^X Exit ^J Justify ^W Where Is ^V Next Page ^U UnCut Text ^T To Spell

—————————————————————————

I have highlighted the line that I added to this file. This line will force Linux to access the CRAN for the latest version of R in a library that is not normally searched for updates if you have an earlier version of R installed. For an Ubuntu distribution change the line to one of the following depending on the distribution that you have installed:

http://<myfavorite-cran-mirror&gt; /bin/linux/ubuntu saucy/

http://<myfavorite-cran-mirror&gt; /bin/linux/ubuntu quantal/

http://<myfavorite-cran-mirror&gt; /bin/linux/ubuntu precise/

http://<myfavorite-cran-mirror&gt; /bin/linux/ubuntu lucid/

Replace <myfavorite-cran-mirror> with the CRAN repository of your choice found at the web site http://cran.r.project.org/mirrors.html. In my case as shown above I used a CRAN repository here in Iowa at Iowa State University. Once the line has been entered in your sources.list file press ctrl-o to save the file, and press ctrl-x to exit the editor. Be sure when you invoke nano that you have root privileges (by using sudo nano) or you will not be able to write out the modified file.

Once you have successfully modified the sources.list file proceed with the R 3.0.3 installation by issuing the command:

$ sudo apt-get update (to make sure all supporting files are current)

and then:

$ sudo apt-get install r-base

When the update runs you should see that R 3.0.x is downloaded and is being installed. After the installation is complete test it by issuing the command:

$ R

You will see the output as shown below:

———————————————

R version 3.0.3 (2014-03-06) — “Warm Puppy”

Copyright (C) 2014 The R Foundation for Statistical Computing

Platform: i686-pc-linux-gnu (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.

You are welcome to redistribute it under certain conditions.

Type ‘license()’ or ‘license()’ for distribution details.

Natural language support but running in an English locale

R is a collaborative project with many contributors.

Type ‘contributors()’ for more information and

‘citation()’ on how to cite R or R packages in publications.

Type ‘demo()’ for some demos, ‘help()’ for on-line help, or

‘help.start()’ for an HTML browser interface to help.

Type ‘q()’ to quit R.

>

You are now up and running with the latest version of R. The process for installation of R 3.0.x is similar for Debian and Fedora distributions. Each of these will be covered in a future tutorial.

 

R Tutorials

Using R in Nonparametric Statistics: Basic Table Analysis, Part Two

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Using R in Nonparametric Statistics: Basic Table Analysis, Part Two

A Tutorial by D.M. Wiig

As discussed in a previous tutorial one of the most common methods display ng and analyzing data is through the use of tables. In this tutorial I will discuss setting up a basic table using R and exploring the use of the CrossTable function that is available in the R ‘gmodel’ package. I will use the same hypothetical data table that I created in Part One of this tutorial, data that examines the relationship between income and political party identification among a group of registered voters. The variable “income” will be considered ordinal in nature and consists of categories of income in thousands as follows:

“< 25”; “25-50”; “51-100” and “>100”

Political party identification is nominal in nature with the following categories:

“Dem”, “Rep”, “Indep”

Frequency counts of individuals that fall into each category are numeric. In the first example we will create a table by entering the data as a data frame and displaying the results. When using this method it is a good idea to set up the table on paper before entering the data into R. This will help to make sure that all cases and factors are entered correctly. The table I want to generate will look like this:

party
income                Dem Rep Indep
<25 1                          5     5      10
26-50                      20    15    15
51-100                  10     20    10
>100                        5       30    10

When using the CrossTable() function the data should be entered in matrix format. Enter the data from the table above as follows:

>#enter data as table matrix creating the variable ‘Partyid’
>#enter the frequencies
>Partyid <-matrix(c(15,20,10,5, 5,15,20,30, 10,15,10,10),4,3)
>#enter the column dimension names and column heading categories
>dimnames(Partyid) = list(income=c(“<25”, “25-50″,”51-100”, “>100”), party=c(“Dem”,”Rep”,”Indep”))

To view the structue of the created data matrix use the command:

> str(Partyid)
num [1:4, 1:3] 15 20 10 5 5 15 20 30 10 15 …
– attr(*, “dimnames”)=List of 2
..$ income: chr [1:4] “<25” “25-50” “51-100” “>100”
..$ party : chr [1:3] “Dem” “Rep” “Indep”
>

To view the table use the command:

> Partyid
                                                     party
income                       Dem Rep Indep
<25                                   15     5      10
25-50                             20     15    15
51-100                         10      20   10
>100                               5        30   10
>  

Remember that R is case sensitive so make sure you use upper case if you named your variable ‘Partyid.’

Once the table has been entered as a matrix it can be displayed with a number of available options using the CrossTable() function. In this example I will produce a table in SAS format(default format), display both observed and expected cell frequencies, the proportion of the Chi-square total contributed by each cell, and the results of the chi-square analysis. The script is:
> #make sure gmodels package is loaded
> require(gmodels)
> #CrossTable analysis
> CrossTable(Partyid,prop.t=FALSE,prop.r=FALSE,prop.c=FALSE,expected=TRUE,chisq=TRUE,prop.chisq=TRUE)

Cell Contents
|—————————–|
|                                                    N |
|                             Expected N |
| Chi-square contribution |
|—————————-|
Total Observations in Table: 165
                                             | party
income | Dem | Rep | Indep | Row Total |
<25        |    15     | 5              | 10        | 30                   |
                 | 9.091 | 12.727 |8.182  |                          |
                 | 3.841 | 4.692 | 0.404 |                             |

25-50 |      20             15             | 15 |      |50

                 15.152 | 21.212 | 13.636 | |
               | 1.552   | 1.819    | 0.136 | |

51-100 | 10           | 20            | 10 |         40 |
              | 12.121 | 16.970 | 10.909 | |
|                 0.371 |   0.541 |    0.076 | |
————-|———–|———–|———–|———–|
>100 |        5 |          30             | 10 |        45 |
          | 13.636 | 19.091 |    12.273 | |
           | 5.470 |   6.234 |         0.421 | |
————-|———–|———–|———–|———–|
Column Total | 50 | 70 | 45 | 165 |
————-|———–|———–|———–|———–|
Statistics for All Table Factors
Pearson’s Chi-squared test
————————————————————
Chi^2 = 25.55608 d.f. = 6 p = 0.0002692734

>

As seen above row marginal totals and column marginal totals are displayed by default with the SAS format. There are other options available for the CrossTable() function. See the CRAN documentation for a detailed description of all of the options available. In the next installment of this tutorial I will examine some of the measures of association that are available in R for nominal and ordinal data displayed in a table format.

 

R Tutorials

Using R in Nonparametric Statistics: Basic Table Analysis, Part One

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Using R in Nonparametric Statistics: Basic Table Analysis, Part One

A Tutorial by D.M. Wiig
One of the most common methods displaying and analyzing data is through the use of tables. In this tutorial I will discuss setting up a basic table using R and performing an initial Chi-Square test on the table. R has an extensive set of tools for manipulating data in the form of a matrix, table, or data frame. The package ‘vcd’ is specifically designed to provide tools for table analysis. Before beginning this tutorial open an R session in your terminal window. You can install the vcd package using the following command:

>install.packages()

Depending on your R installation you may be asked to designate a CRAN reflector to download from or you may see a list of available packages in your default CRAN mirror. Select the package ‘vcd’ and download it. I might add at this point that if you are running the newest release of R, R-3.0.x you will have to reload a number of dependencies that will not work under the latest version of R. Any time you are installing a package and see the ‘non-zero exit status’ error message look the dialog over to see which packages have to be reinstalled to work with the newest version of R. If you are using R-2.xx.x the vcd package will install without any other re-installations.

In social science research we often use data that is nominal or ordinal in nature. Data is displayed in categories with associated frequency counts. In this tutorial I will use a set of hypothetical data that examines the relationship between income and political party identification among a group of registered voters. The variable “income” will be considered ordinal in nature and consists of categories of income in thousands as follows:

“< 25”; “25-50”; “51-100” and “>100”

Political party identification is nominal in nature with the following categories:

“Dem”, “Rep”, “Indep”

Frequency counts of individuals that fall into each category are numeric. In the first example we will create a table by entering the data as a data frame and displaying the results. When using this method it is a good idea to set up the table on paper before entering the data into R. This will help to make sure that all cases and factors are entered correctly. The table I want to generate will look like this:

party
income                 Dem Rep Indep
<25                             15    5      10
26-50                        20   15    15
51-100                     10   20    10
>100                            5    30    10

To enter the above into a data frame use the following on the command line:

> partydata <- data.frame(expand.grid(income=c(“<25″,”25-50″,”51-100″,”>100″), party=c(“Dem”,”Rep”, “Indep”)),count=c(15,20,10,5,5,15,20,30,10,15,10,10))
>

Make sure the syntax is exactly as shown and make sure the entire script is on the same line or has done an automatic return to the next line in your R console. When the command runs without error you can view the data by entering:

> partydata

The following output is produced:

> partydata
income                    party         count
1 <25                         Dem            15
2 25-50                    Dem            20
3 51-100                 Dem           10
4 >100                      Dem             5
5 <25                         Rep               5
6 25-50                    Rep              15
7 51-100                 Rep              20
8 >100                      Rep              30
9 <25                         Indep          10
10 25-50                 Indep          15
11 51-100              Indep          10
12 >100                   Indep          10
>

At this point the data is in frequency rather that table or matrix form. To view a summary of information about the data use the command:

>str(partydata)

You will see:

> str(partydata)
‘data.frame’: 12 obs. of 3 variables:
$ income: Factor w/ 4 levels “<25″,”26-50”,..: 1 2 3 4 1 2 3 4 1 2 …
$ party : Factor w/ 3 levels “Dem”,”Rep”,”Indep”: 1 1 1 1 2 2 2 2 3 3 …
$ count : num 15 20 10 5 5 15 20 30 10 15 …

To convert the data into tabular format use the command xtabs to perform a cross tabulation. I have named the resulting table “tabs”:

>tabs <- xtabs(count ~income + party, data=partydata)

To view the resulting table use:

> tabs
                                                        party
income                              Dem Rep Indep
<25                                        15      5        10
26-50                                   20      15      15
51-100                                10      20      10
>100                                       5       30      10
>

This produces a table in the desired format. To do a quick analysis of the table that produces a Chi-square statistic use the command:

> summary(tabs)

The output is

> summary(tabs)
Call: xtabs(formula = count ~ income + party, data = partydata)
Number of cases in table: 165
Number of factors: 2
Test for independence of all factors:
Chisq = 25.556, df = 6, p-value = 0.0002693
>

In future tutorials I will discuss many of the other resources that are available with the vcd package for manipulating and analyzing data in a tabular format.

 

R Tutorials

Using R in Nonparametric Statistics: Basic Table Analysis, Part Three, Using assocstats and collapse.table

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A tutorial by D.M. Wiig

As discussed in a previous tutorial one of the most common methods displaying and analyzing data is through the use of tables. In this tutorial I will discuss setting up a basic table using R and exploring the use of the assocstats function to generate several commonly used nonparametric measures of association. The assocstats function will generate the association measures of the Phi-coefficient, the Contingency Coefficient and Cramer’s V, in addition to the Likelihood Ratio and Pearson’s Chi-Squared for independence. Cramer’s V and the Contigency Coefficient are commonly applied to r x c tables while the Phi-coefficient is used in the case of dichotomous variables in a 2 x 2 table.

To illustrate the use of assocstats I will use hypthetical data exploring the relationship between level of education and average annual income. Education will be measured using the nominal categories “High School”, “College”, and “Graduate”. Average annual income will be measured using ordinal categories and expressed in thousands:

“< 25”; “25-50”; “51-100” and “>100”

Frequency counts of individuals that fall into each category are numeric.

In the first example a 4 x 3 table created with hypothetical frequencies as shown below:

Income                                Education
(thousands)          High School   College   Graduate

<25                                    15                       8                  5

26-50                              12                       12                8

51-100                           10                       22                25

>100                                  5                       10                 32
The first table, table1, is entered into R as a data frame using the following commands:

#create 4 x 3 data frame
#enter table1 in frequency form
table1 <- data.frame(expand.grid(income=c(“<25″,”25-50″,”51-100″,”>100″), education=c(“HS”,”College”, “Graduate”)),count=c(15,12,10,5,8,12,22,10,5,8,25,32))

Check to make sure the data are in the right row and column categories. Notice that the data are entered in the ‘count’ list by columns.

> table1
income  education     count
1 <25             HS                  15
2 25-50        HS                  12
3 51-100     HS                 10
4 >100          HS                   5
5 <25             College         8
6 25-50        College        12
7 51-100     College        22
8 >100          College        10
9 <25             Graduate      5
10 25-50     Graduate      8
11 51-100   Graduate    25
12 >100       Graduate    32
>

If the stable structure looks correct generate the table, tab1, using the xtabs function:

> #create table tab1 from data.frame
> tab1 <- xtabs(count ~income + education, data=table1)
Show the table using the command:

>tab1
                               education
income         HS College Graduate
<25                   15     8             5
25-50             12     12           8
51-100          10     22          25
>100                 5     10          32
>
Use the assocstats function to generate measures of association for the table. Make sure that you have loaded the vcd package and the vcdExtras packages. Run assocstats with the following commands:

> assocstats(tab1)
X^2 df P(> X^2)
Likelihood Ratio 31.949 6 1.6689e-05
Pearson 32.279 6 1.4426e-05

Phi-Coefficient : 0.444
Contingency Coeff.: 0.406
Cramer’s V : 0.314
>

The measures show an association between the two variables. My intent is not to provide an analysis of how to evaluate each of the measures. There are excellent sources of documention on each measure of association in the R CRAN Literature. Since the Phi-coefficient is designed primarily to measure association between dichotomous variables in a 2 x 2 table,collapse the 4 x 3 table using the collapse.table function to get a more accurate Phi-coefficient. Since we want to go from a 4 x 3 to a 2 x 2 table we essentially collapse the table in two stages. The first stage collapses the table to a 2 x 3 table by combining the “<25” with the “25-50” and the “51-100” with the “>100” categories of income.

The resulting 2 x 3 table is seen below:

Education
Income                High School      College        Graduate

<50                                 27                        20                    13

>50                                15                        32                     57

To collapse the table use the R function collapse.table to combine the “<25” and “26-50” categories and the “50-100” and “>100” categories as discussed above:

> #collapse table tab1 to a 2 x 3 table, table2
> table2 <-collapse.table(tab1, income=c(“<50″,”<50″,”>50″,”>50″))

View the resulting table, table2, with:

> table2
                                education
income          HS        College       Graduate
<50                  27             20                   13
>50                  15             32                   57
>

Now collapse the table to a 2 x 2 table by combining the “College” and “Graduate” columns:
> #collapse 2 x 3 table2 to a 2 x2 table, table3
> table3 <-collapse.table(table2, education=c(“HS”,”College”,”College”))

View the resulting table, table3, with:

> table3
                               education
income             HS             College
<25                     27                  33
>100                  15                  89
>

Use the assocstats function to evaluated the 2 x 2 table:

> #use assocstats on the 2 x 2 table, table3
> assocstats(table3)
X^2 df P(> X^2)
Likelihood Ratio 18.220 1 1.9684e-05
Pearson 18.673 1 1.5519e-05

Phi-Coefficient : 0.337
Contingency Coeff.: 0.32
Cramer’s V : 0.337
>

There are many other table manipulation function available in the R vcd and vcdExtras packages and well as other packages to provide analysis of nonparametric data. This series of tutorials hopefully serves to illustrate some of the more basic and common table functions using these packages. The next tutorial looks at the use of the ca function to perform and graph the results of a basic Correspondence Analysis.