I hope you find the information contained on this blog to be of use as you explore R and R programming. Feel free to help me expand my reach by adding your own tutorials that you have written or perhaps papers that you have delivered or are preparing for conferences or publication. Just remember the goal of this blog is to explore and facilitate R statistics and programming and the use of R in a number of environments.
Doug W.
I have spent the past week or two working with Peppermint operating system v. 6. This is an operating system that is a hybrid with Google’s Chrome operating system integrated with an Ubuntu platform. Peppermint OS provides a fast easy to use GUI and is optimized for using Google based cloud tools.
The idea is to keep the clutter to a minimum on the home computer and utilize the cloud to advantage with the numerous Google tools and applications that are available. Of course, Peppermint OS is free and open source. The degree of customization possible is only limited by the users imagination and programming abilities.
I will write more on this later as I utilize more of the programs features. Check out the Peppermint OS web site at:
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.
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:
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 |
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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.
Due to unkown reasons my original server became corrupted and had to be shut down. I am back with a new site and over the next few days with be re-adding most of the content from our original blog. Check back often!
D.M. Wiig
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R Statistics and Programming 