Tag Archives: table analysis

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


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.

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


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.

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


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.

 

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


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.

 

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


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.