Tag Archives: r tutorial

R Tutorials

R Tutorial: Using the NORC GSS2014 Data File, Creating and Using Subsets

Leave a comment 

R Tutorial:  Using the NORC GSS2014 data file, creating and using subsets

By Douglas M. Wiig

As discussed in the first part of this tutorial, for anyone interested in researching social science questions there is a wealth of survey data available through the National Opinion Research Center (NORC) and its associated research universities. The Center has been conducting a national survey each year since 1972 and has compiled a massive database of data from these surveys. Most if not all of these data files can be accessed and downloaded without charge. I have been working with the 2014 edition of the data and for all part of this tutorial will use the GSS2014 data file that is available for download on the Center’s web site. (See the NORC main website at http://www.norc.org/Research/Projects/Pages/general-social-survey.aspx and at http://www3.norc.org/GSS+Website ).

Accessing and loading the NORC GSS2014 data set was discussed in part one of this tutorial. Refer to it if you need specific information on downloading the data set in STATA or SPSS format.  In this segment we will  use the subset function to select a desired set of cases from all of the cases in the data file that meet certain criteria.  As indicated in my previous tutorial the GSS2014 data set contains a total of 2588 cases and 866 variables.

One of the areas surveyed by NORC each year deals with attitudes toward abortion. One of the questions simply asks respondents if they '...approve of abortion under any circumstances.'  The response is either YES or NO to this question.  Let's assume a researcher is interested in investigating whether or not education has an effect on how the respondent answers the question.

To look at this hypothesis we can use the abortion attitude variable mentioned above, 'abany', and an education variable 'educ' which measures education as the actual number of years of education.  Twelve years of education would be a high school graduate for example, and 16 years would be a college graduate.  We can select a subset of all respondents who indicated 'YES' on the survey question and then generate a mean years of education for this subset.  We can then select a subset of all respondents who indicated 'NO' on the question and calculate a mean years of education for the second subset.

Before starting this code segment be sure that the foreign package is installed and loaded into your R session.  Import the GSS 2014 data file and load it into the data frame ‘Dataset’ using:

########################################################
#import GSS2014 file in SPSS .sav format
#uses foreign package
########################################################
require(foreign)
Dataset <- read.spss("/path to your location/GSS2014.sav", 
                     use.value.labels=TRUE, max.value.labels=Inf, to.data.frame=TRUE)
########################################################

Once the GSS2014 file is loaded use the subset function to select your first subset of respondents who answered the 'abany' question with and 'YES response.  Use the following code to select the subset and store it in a data frame 'SS1':

####################################################
#select subset from Dataset and write to data frame SS1
####################################################
SS1 <- subset(Dataset, abany == "YES", select=educ)
View(SS1)
####################################################

Now select a second subset of respondents who answered the 'abany' question with a 'NO' response. Use the following code to select the subset and store in a data frame 'SS2':

######################################################
#select subset from Dataset and write to data frame SS2
######################################################
SS2 <- subset(Dataset, abany == "NO", select=educ)
View(SS2)
######################################################

In using the subset function as seen above the name of the data set is specified, the criteria for selecting rows is given, and the variables to select from each row specified.  If no 'select' option is given all variables will be shown for the selected row.

Using the View command to examine each subset shows the years of education for each of the 746 respondents who answered ‘YES’ and each of the 907 respondents who answered ‘NO.’ Since the variable ‘educ’ is measured as ratio level numeric data we can calculate a mean and standard deviation for each subset and perform both graphical and statistical analysis of any observed difference between the two means. This will be the subject of the next installment of the tutorial.

R Tutorials

R Tutorial: Using R with NORC GSS Data Part Two, Generating Simple Tables and Using Subsets

Leave a comment

A tutorial by Douglas M. Wiig

Part one of the tutorial  centered on importing NORC GSS data in STATA or SPSS formats in an R data frame. For illustration I used the GSS2014 survey data set that consists of 2538 cases and 866 variables. If a researcher wishes to generate some simple cross tabulations the R CrossTable function is very useful.

The CrossTable function is part of the gmodels package, so before running scripts in this tutorial make sure you have installed and loaded gmodels from your favorite CRAN mirror site. As discussed in part one of the tutorial load the GSS2014 dataset into the global environment using:

>require(foreign)

>Dataset <- read.spss(“E:/research/Documents/GSS2014.sav”,

use.value.labels=TRUE, max.value.labels=Inf, to.data.frame=TRUE)

The CrossTable function allows a basic cross tabulation to be performed and includes a large number of options that can be incorporated into the table. The basic structure is as follows:

Usage

CrossTable(x, y, digits=3, max.width = 5, expected=FALSE, prop.r=TRUE, prop.c=TRUE,

prop.t=TRUE, prop.chisq=TRUE, chisq = FALSE, fisher=FALSE, mcnemar=FALSE,

resid=FALSE, sresid=FALSE, asresid=FALSE,

missing.include=FALSE,

format=c(“SAS”,”SPSS”), dnn = NULL, …)

Arguments

x A vector or a matrix. If y is specified, x must be a vector

y A vector in a matrix or a dataframe

digits Number of digits after the decimal point for cell proportions

max.width In the case of a 1 x n table, the default will be to print the output horizontally.

If the number of columns exceeds max.width, the table will be wrapped for

each successive increment of max.width columns. If you want a single column

vertical table, set max.width to 1

expected If TRUE, chisq will be set to TRUE and expected cell counts from the _2 will be

included

prop.r If TRUE, row proportions will be included

prop.c If TRUE, column proportions will be included

prop.t If TRUE, table proportions will be included

prop.chisq If TRUE, chi-square contribution of each cell will be included

chisq If TRUE, the results of a chi-square test will be included

fisher If TRUE, the results of a Fisher Exact test will be included

mcnemar If TRUE, the results of a McNemar test will be included

resid If TRUE, residual (Pearson) will be included

sresid If TRUE, standardized residual will be included

asresid If TRUE, adjusted standardized residual will be included

missing.include

If TRUE, then remove any unused factor levels

format Either SAS (default) or SPSS, depending on the type of output desired.

dnn the names to be given to the dimensions in the result (the dimnames names).

… optional arguments

(Gregory Warnes, maintainer, Package ‘Gmodels’ February, 2015. http://cran.r-project.org/src/contrib/PACKAGES.html)

In this tutorial I will create a  table to examine the relationship between income and education using the variables ‘degree’ and ‘income6’ from the GSS dataset. Both are categorical factors. To simplify the resulting table only actual frequencies will be reported and the ‘chisq’ option will be used to generate a chi-squared test. The format used will be set to SPSS. Use the following statement:

>Generate a cross table of frequencies with chisq reported

>CrossTable(Dataset$”incom16″,Dataset$”degree”, chisq=TRUE, format=c(“SPSS”),prop.r=FALSE, prop.c=FALSE, prop.t=FALSE, prop.chisq=FALSE)

>

In the above code, the row variable is income the appropriate column of the dataset is selected with the ‘Dataset$”incom16” statement. The column variable for the table is education and the appropriate column of the dataset is selected with the ‘Dataset$”degree” statement. The various cell proportions must be set to ‘FALSE’ as they are defaulted to ‘True.’

When you run the above script the table will be generated in SPSS format on the screen.  I will not reproduce the table here because of formatting problems of fitting the table into the blog format.

In part three of this turorial I will discuss generating subsets of the GSS data file and using subsets for statistical analyses such as t tests and ANOVA.




		
	
	

	
	




	
	

				

			R Tutorials, Uncategorized
		

			
Tutorial:  Using R to Analyze GSS2014 Social Science Data, Part One:  Importing the Database in SPSS or STATA Format

		

			 			Leave a comment
						

	


		

		

For anyone interested in researching social science questions there is a wealth of survey data available through the National Opinion Research Center (NORC) and its associated research universities. The Center has been conducting a national survey each year since 1972 and has compiled a massive database of data from these surveys. Most if not all of these data files can be accessed and downloaded without charge. I have been working with the 2014 edition of the data and for this tutorial will use the GSS2014 data file that is available for download on the Center’s web site. ( See the NORC main website at http://www.norc.org/Research/Projects/Pages/general-social-survey.aspx and at http://www3.norc.org/GSS+Website ).


As noted above the datasets that are available for download are available in both SPSS format and STATA format. To work with either of these formats using R it is necessary to read the file into a data frame using one of a couple of different packages. The first option I will discuss uses the Hmisc package. The second option I will discuss uses the foreign package. Install both of these packages from your favorite CRAN mirror site before starting the code in this tutorial.


For this tutorial I am using the one year release file GSS2014. This file contains 2538 cases and 866 variables. Download the file   from the web site listed above in both SPSS and STATA formats. Use the following code to load the Hmisc package into your R global environment:


                >require(Hmisc)


Now load the GSS2014.sav SPSS version from your storage device using the following line of code. I am using the filename GSS2014 for my data file and loading the file into the data frame ‘gss14’:


>#load the GSS data file in SPSS format


                >put data into data frame ‘gss14’


                gss14 <- spss.get(F:/research/Documents/GSS2014.sav”,                     use.value.labels=TRUE)


                >


To view the data that was loaded use the command:


>View(gss14)


This will produce a spreadsheet-like matrix of rows and columns containing the data. To load the data file in STATA format download the STATA version of the file from the NORC web site a discussed above. My STATA file is also named GSS2014, but with the STATA .dta extension. Load the file into a data frame using:


>load STATA format file into data frame ‘Dataset2’


                >Datatset2 <- read.dta(“F:/resarch/Documents/GSS2014.dta”)


               >


Once again, you can view the data frame loaded using the command:


>View(dataset2)


Both the STATA and SPSS formats of the data set can also be loaded into R using the foreign package. The procedure is the same for both SPSS and STATA


>load SPSS version


                >require(foreign)


                >Dataset <- read.spss(“F:/research/Documents/GSS2014.sav”,   use.value.labels=TRUE)


 >load STATA version into data frame ‘Dataset3’


>Dataset3 <- read.dta(“E:/research/Documents/GSS2014.dta”) 


Use the ‘View()’ command to view the data frame.


In part two I will discuss some techniques using R to create and analyze subsets of the GSS2014 data file.


 

	

	
	




	
	

				

			R Tutorials
		

			
Using R for Nonparametric Analysis, The Kruskal-Wallis Test Part Four:  R Script and Some Notes on IDE’s

		

			 			Leave a comment
						

	


		

		

 


 Using R for Nonparametric Analysis, The Kruskal-Wallis Test: R Script and Some Notes on IDE’s


 


A tutorial by Douglas M. Wiig


 


In the previous three parts of this tutorial I discussed using R to enter a data set and perform a nonparametric Kruska-Wallis test for ranked means. In this final part the commented script that was used in the first three parts is listed.  


 


If you are going to use R for the majority of your statistical analysis it is highly advisable that you investigate some of the IDE’s (Integrated Development Environments) that are available to assist in coding and debugging R script and creating R packages for personal use or distribution. I think one of the easiest to use is R Studio. R Studio is available in both free open source and commercial versions and can be downloaded at http://www.rstudio.com.     There are versions available for Windows, various Linux distributions, and Mac OS.




The R studio console provides a number of useful tools that facilitate coding. The screen is divided into four sections with one section providing a code editor that features syntax highlighting, code completion and many other features such as line or block code execution. Another window contains R and all displays output, error messages and warnings when code is executed from the editor. A third window displays all of the current environmental variables that are active and can also show all currently loaded R packages. A fourth window can show graphic output from executed code, can be used to manage, download and install R packages, and can be used to access the CRAN database of online help. There are other useful tools that are too numerous to discuss here.


 


Another program that is worth looking at is RKWard which combines an IDE with a graphics GUI for R statistical analysis. Information and downloads for RKWard can be found at https://rkward.kde.org. This program is also free and open source and can be run on a Windows platform, Mac OS, or various distributions of Linux. The program has been optimized for Linux. A discussion of these IDE’s is beyond the scope of this posting.


 


Shown below is the commented R script for all three parts of the Kruskal-Wallis tutorial. For ease of reading code portions are shown in bold print.


 


#packages that must be present in the global environment before running these scripts


 


#stats; graphics; grDevices; utils; datasets; methods; base


 


#


 


#code to enter data using the data editor


 


#KW data entry, define file kruskal as a data frame


 


kruskal <-data.frame()


 


#invoke the data editor


 


kruskal <-edit(kruskal)


 


#define group as containing 3 factors; tell R which data column goes with which factor


 


group <- factor(1,2,3)


 


#alternative data entry method


 


#Define factor Group as containing three categories


 


Group <- c(1,1,1,1,1,2,2,2,2,2,3,3,3,3)


 


#create a vector defined as authscore and enter values


 


authscore <- c(96,128,83,61,101,82,121,132,135,109,115,149,166,147)


 


#create data frame kruskal matching each group factor to individual scores


 


kruskal <- data.frame(Group, authscore)


 


#use the following line to look at the structure of the data frame created


 


str(kruskal)


 


#run the basic Kruskal-Wallis test


 


#


 


kruskal.test(authscore ~ Group, data=kruskal)


 


#


 


#the following code is used to conduct a post-hoc comparison of the ranked means


 


#it is useful to first do a simple boxplot for a visual comparison


 


#Use this script to save the boxplot graphic to a .png data file


 


#save output in pdf file authplot


 


#send output to screen and file


 


sink()


 


authplot <- boxplot(authscore ~ Group, data=kruskal, main=”Group Comparison”, ylab=”authscore”)


 


#save .png file


 


png(“authplot.png”)


 


#now return all output to console


 


dev.off()


 


#


 


#make sure that the package PMCMR is loaded before running the following script


 


library(PMCMR)


 


#use the ‘with’ function to pass the data from the kruskal data frame to the post hoc


 


#test script; specify the Tukey HSD method for determining significance of each


 


# pair of comparisions


 


#


 


with(kruskal, {


 




 


posthoc.kruskal.nemenyi.test(authscore, Group, “Tukey”)


 


})


 


#


 


#NOTE: if using a version of R < 3.xx then use the package pgirmess instead of PMCMR


 


#


 


#the following lines show the post hoc analysis using the pgirmess package


 


#note the function kuskalmc is used for the comparisons


 


library(pgirmess)


 


authscore <- c(96,128,83,61,101,82,121,132,135,109,115,149,166,147)


 


Group <- c(1,1,1,1,1,2,2,2,2,2,3,3,3,3)


 


kruskalmc(authscore ~ Group, probs=.05, cont=NULL)


 


#


 

	

	
	




	
	

				

			R Tutorials
		

			
Using R for Nonparametric Statistics:  The Kruskal-Wallis Test, Part Two

		

			 			2 Comments
						

	


		

		

Using R for Nonparametric Statistics:  The Kruskal-Wallis Test, Part Two


A Tutorial by Douglas M. Wiig


Before we can run the Kruskal-Wallis test we need to define which column contains the factors (independent variables) and which contains the authoritarianism scores (dependent variable). Once we define the factor column R will match the correct score to each of the 14 observations.

As set up in the study, ‘Group’ is the factor(independent variable), and ‘authscore’ is the dependent variable. Use the command:


> Group <-factor(1,2,3)


This designates which observation belongs to each group. To make sure the data structure has been set up correctly use the command:


> str(kruskal)

‘data.frame’: 14 obs. of 2 variables:

 $ Group : num 1 1 1 1 1 2 2 2 2 2 …

 $ authscore: num 96 128 83 61 101 82 124 132 135 109 …

>


The output of this command shows a summary of the structure of the data frame created. We can now run the Kruskal Wallis test with the command:


> kruskal.test(authscore ~ Group, data=kruskal)


The output will be:


Kruskal-Wallis rank sum test


data: authscore by Group

Kruskal-Wallis chi-squared = 6.4057, df = 2, p-value = 0.04065


>


As seen in the above output the analysis of authoritarianism score by group indicates that the probability of differences in scores among the three groups being due to chance alone is less that the .05 alpha level that was set for the study. (pobt < .05). Further post hoc analysis would be necessary to determine the exact nature of the differences among the scores of the three groups. This will be the topic of a future tutorial.


More to come:  Part Three will explore the use of multiple comparison techniques to analyze ranked means

	

	
	




	
	

				

			R Tutorials
		

			
Using R for Nonparametric Analysis:  The Kruskal-Wallis Test, Part One

		

			 			Leave a comment
						

	


		

		

 


Using R for Nonparametric Data Analysis: The Kruskal-Wallis Test


A tutorial by Douglas M. Wiig


Analysis of variance(ANOVA) is a commonly used technique for examining the effect of an independent variable on three or more dependent variables. There are several types of ANOVA ranging from simple one-way ANOVA to the more complex multiple analysis of variance, MANOVA. ANOVA makes several assumptions about the sample data being used such as the assumption of normal distribution of the variables in the parent population, underlying continuous distribution of the variables, and interval or ratio level measurement of all variables. If any of these assumptions cannot be met a researcher can turn to a nonparametric counterpart to ANOVA for the analysis. This tutorial will discuss the use of the Kruskal-Wallis test, the nonparametric counterpart to analysis of variance.


In this tutorial I will explore a simple example and discuss entering the sample data into a data file using the R data editor. I will then discuss setting up the data for analysis and using the Kruskal-Wallis test.


I am going to assume that the reader has a working knowledge of ANOVA with parametric data. Since ANOVA uses sample means and variances as the basis of the statistical test interval or ratio level measurement is necessary to insure valid results in addition to the assumptions indicated above. With the nonparametric Kruskal-Wallis test the only assumptions to be met are ordinal or better measurement and the assumption of an underlying continuous measurement. The example to be used here is taken from a book on nonparametric statistics by Sidney Seigel.(Sidney Seigel, Nonparametric Statistics for the Behavioral Sciences, New York: McGraw-Hill, 1956, pp-184-196).


A researcher wishes to test the hypothesis that school administrators are typically more authoritarian than classroom teachers. He also believes that many classroom teachers are adminstration-oriented in their professional aspirations which may, in turn, have an effect on their authoritarianism. 14 subjects are selected and divided into three groups: teaching-oriented teachers (classroom teachers who wish to remain in a teaching position), administration-oriented teachers (classroom teachers who aspire to become administrators), and practicing administrators.(Seigel, p. 186). The level of authoritarianism of each subject is measured through a survey that assigns an authoritarianism score that is considered to be at least ordinal in nature. Higher scores indicate higher levels of authoritarianism. (Siegel, p. 186). The null hypothesis is that there is no difference in mean authoritarianism scores among the three groups. The alternative hypothesis is that the mean authoritarianism scores among the three groups are different. The alpha level for rejecting the null hypothesis is p = .05. (Seigel, p. 186).


Since we make no assumption about a normal distribution of scores, have a small sample size of n = 14, and ordinal measure we will use the nonparametric test which is based on median scores and ranks rather than means and variances as used in parametric ANOVA. The mathematical details of how this is done is beyond the scope of this tutorial. See Seigel, p. 187-189 for details. The authoritarian scores for the three groups are shown below:


Authoritarianism Scores of Three Groups of Educators


Teacher-Oriented        Admin-oriented    Administrators


teachers   n=5                teachers   n=5                n=4


—————————————————————————————-


96                                  82                               115


128                              124                               149


83                               132                               166


61                               123                               147


101                             109


—————————————————————————————-


(Seigel, p. 187)


The first task is to create an R data frame with the scores from the table. We will enter the scores using the R data editor. We will name the data frame ‘kruskal.’   Invoke the editor using the following commands:


  > kruskal <- data.frame()


   > kruskal <- edit(kruskal)


You should see the data entry editor open in a separate window. In order to process the data properly it needs to be entered into two columns. The first column will be the factors (which group the scores belong to), and the second column will contain the actual scores. Label column 1 ‘Group’ and column 2 ‘authscore.’ When the data are entered your editor should look like this:


———————-


Group  authscore


1    1               96


2    1            128


3    1             83


4    1            61


5    1           101


6    2            82


7    2           121


8    2          132


9    2          135


10   2       109


11   3       115


12   3       149


13   3       166


14   3       147


———————-


Make sure that each column of numbers is of the data type “Real.” l Close the data editor by clicking ‘Quit’ and the data will be saved in the working directory for access. To see what has been entered in the data editor use the command:


> kruskal


Group authscore


1     1             96


2     1            128


3     1             83


4     1            61


5     1           101


6     2            82


7     2           121


8     2            132


9     2            135


10     2         109


11     3         115


12     3         149


13     3         166


>


You should see the output as above. If you need to make changes simple invoke the editor with:


> kruskal <-edit(kruskal)


The editor will open and you can make any changes you need to. Be sure to click on ‘Quit’ to save the changes to the working directory.


Part Two will continue the analysis

	

	
	




	
	

			
	

				

			R Tutorials
		

			
R Tutorial:  A Simple Script to Create and Analyze a Data File, Part Two

		

			 			1 Comment
						

	


		

		

A simple R script to create and analyze a data file:part two:    A tutorial by D.M. Wiig


In part one I discussed creating a simple data file containing the height and weight of 10 subjects.  In part two I will discuss the script needed to create a simple scatter diagram of the data and perform a basic Pearson correlation.  Before attempting to continue the script in this tutorial make sure that you have created and save the data file as discussed in part one.


To conduct a correlation/regression analysis of the data we want to first view a simple scatter plot. Load a library named ‘car’ into R memory. Use the command:


 > library(car)


Then issue the following command to plot the graph:


> plot(Height~Weight, log=”xy”, data=Sampledatafile)


The output is seen below:


scatter1


We can calculate a Pearson’s Product Moment correlation coefficient by using the command:


> # Pearson rank-order correlations between height and weight


> cor(Sampledatafile[,c(“Height”,”Weight”)], use=”complete.obs”, method=”pearson”)


Which results in:


Height Weight


Height 1.0000000 0.8813799


Weight 0.8813799 1.0000000


To run a simple linear regression for Height and Weight use the following code. Note that the dependent variable (Weight) is listed firt:


> model <-lm(Weight~Height, data=Sampledatafile)


> summary(model)


Call:


lm(formula = Weight ~ Height, data = Sampledatafile)


Residuals:


Min 1Q Median 3Q Max 


-30.6800 -16.9749 -0.8774 19.9982 25.3200 


Coefficients:


Estimate Std. Error t value Pr(>|t|) 


(Intercept) -337.986 98.403 -3.435 0.008893 ** 


Height 7.518 1.425 5.277 0.000749 ***


—


Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Residual standard error: 21.93 on 8 degrees of freedom


Multiple R-squared: 0.7768, Adjusted R-squared: 0.7489 


F-statistic: 27.85 on 1 and 8 DF, p-value: 0.0007489


> 


To plot a regression line on the scatter diagram use the following command line. Note that we enter the y (dependent)variable first and then the x (independent)variable:


 > scatterplot(Weight~Height, log=”xy”, reg.line=lm, smooth=FALSE, spread=FALSE,


 + data=Sampledatafile)


 > 


This will produce a graph as seen below. Note that box plots have also been included in the output:


scatter2


This tutorial has hopefully demonstrated that complex tasks can be accomplished with relatively simple command line script. I will explore more of these simple scripts in future tutorials.


More to Come:


 

	

	
	




	
	

				

			R Tutorials
		

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

		

			 			1 Comment
						

	


		

		

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
		

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

		

			 			Leave a comment
						

	


		

		

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.