First of all, I’d like to thank my readers for the lots of feedback on my last post on beautiful outputs in R. I tried to consider all suggestions, updated the existing table-output-functions and added some new ones, which will be described in this post. The updated package is already available on CRAN.
This posting is divided in two major parts:
- the new functions are described, and
- the new features of all table-output-functions are introduced (including knitr-integration and office-import)
First I want to give an overview of the new functions. As you may have noticed, all table-output-functions have new parameters, which enable you to modify the appearance and retrieve objects for knitr-integration and so on. This is described below.
Viewing imported SPSS data sets
As I have mentioned some times before, one purpose of this package is to make it easier for (former) SPSS users to switch to and use R. Beside the data import functions (see all functions beginning with
sji) I now added two functions, where one is specifically useful for SPSS data sets, while the other one is generally useful for data frames.
With the function
sji.viewSPSS you can easily create a kind of “code plan” for your data sets. Note that this function only works for SPSS data sets that have been imported using the
sji.SPSS function (because else variable and value label attributes are missing)! The function call is quite simple. Load the library with
require(sjPlot) and run the following example:
This will give you an overview of: Variable number, variable name, variable label, variable values and value labels:
You can suppress the output of values and value labels if you just want to quickly inspect the variable names. The table can also be sorted either by variable number or by variable name.
Description and content of data frames
If you want to inspect the data frame’s variables, you can use the
sjt.df function. By default, this function calls the describe-function from the psych-package and prints the output as HTML-table:
sjt.df(efc[1:20,1:5], alternateRowColors=TRUE, orderColumn="e42dep", describe=FALSE)
Principal Component Analysis and Correlations
Two more new functions are
sjt.pca for printing results of principal component analyses and
sjt.corr for printing correlations. Printing PCA results will give you an overview of all extracted factors, where the highest factor loading is printed in black, while the other factor loadings are a bit faded (thus, it’s easier to see which item belongs to which factor). Furthermore, you can print the MSA for each item, the Cronbach’s Alpha value for each “scale” and other statistics:
data(efc) # retrieve variable and value labels varlabs <- sji.getVariableLabels(efc) # recveive first item of COPE-index scale start <- which(colnames(efc)=="c82cop1") # recveive last item of COPE-index scale end <- which(colnames(efc)=="c90cop9") # create data frame with COPE-index scale df <- as.data.frame(efc[,c(start:end)]) colnames(df) <- varlabs sjt.pca(df, showMSA=TRUE, showVariance=TRUE)
The next example is a correlation table. Note: This table may look more beautiful if opened in a web browser (because of more space). And second note: See the usage of the CSS-parameter! (more on this later)
sjt.corr(df, pvaluesAsNumbers=TRUE, CSS=list(css.thead="border-top:double black; font-weight:normal; font-size:0.9em;", css.firsttablecol="font-weight:normal; font-size:0.9em;"))
Stacked frequencies and Likert scales
The last new table-output-function is
sjt.stackfrq, which prints stacked frequencies of (Likert) scales.
data(efc) # recveive first item of COPE-index scale start <- which(colnames(efc)=="c82cop1") # recveive first item of COPE-index scale end <- which(colnames(efc)=="c90cop9") # retrieve variable and value labels varlabs <- sji.getVariableLabels(efc) vallabs <- sji.getValueLabels(efc) sjt.stackfrq(efc[,c(start:end)], valuelabels=vallabs['c82cop1'], varlabels=varlabs, alternateRowColors=TRUE)
Similar to the
sjp.stackfrq function (see this posting), you can order the items according to their lowest / highest first value etc.
Tweaking the table-output-functions and integrating output into knitr
In this section, important new parameters of the table-output-functions are described.
sjt function as well as
sji.viewSPSS now have following parameters:
And all of them (invisibly) return at least following values:
- the web page style sheet (
- the web page content (
- the complete html-output (
- the html-table with inline-css for use with knitr (
The table-output is in HTML format, using cascading style sheets to modify the appearance of tables. You can inspect the
page.content parameters to see which CSS classes are used in the HTML-table, for instance:
> value <- sjt.df(efc) > value$page.style  ""
To use the CSS parameter, you must define a
list with values, where the value-name equals the css-class-name with
css. prefix. If you want to change the appearance of the first table column (with variable names), use:
Refer to the function-help to see more examples…
useViewer and no.output
useViewer set to FALSE, you can simply force opening the html-table-output in a web browser, even if a viewer is available. With
no.output set to TRUE, you can suppress the table output completely. This is useful if you want to integrate the tables in your knitr-documents…
As said above, each sjt-function returns an object where you can access the created html-output. The
$knitr object contains the pure html-table (without HTML-pageheader or body-tags) with inline CSS (thus, no class-attributes are used). This allows the simple integration into knitr-documents. Use following code snippet in your knitr-documents and knit it to HTML:
`r sjt.df(efc, no.output=TRUE)$knitr`
Office import improvements
When setting the
file parameter, the table-ouput is saved to a file. This can be opened via MS Word, LibreOffice Writer etc. The import has been improved, so the imported table should render properly now.
Well, enough said. All feature available in the latest sjPlot-package.