Customizing HTML tables

All tab_*-functions create a HTML page with the table output. This table, by default, is opened in the viewer pane of your IDE (in case you’re using an IDE that also supports the viewer pane). If a viewer pane is not available, the created HTML output is saved as temporary file and opened in your default web browser. The temporary files are deleted after your R session ends.

Copying table output to office or word processors

Export table as HTML file to open in word processors

You can save the HTML page as file for further usage by specifying the file-argument The saved HTML file can be opened by word processors like LibreOffice or Microsoft Office.

Drag and drop from browser or RStudio viewer pane

You can directly drag and drop a table from the RStudio viewer pane or browser into your word processor. Simply select the complete table with your mouse and drag it into office.

Customizing table output with the CSS parameter

The table output is in in HTML format. The table style (visual appearance) is formatted using Cascading Style Sheets (CSS). If you are a bit familiar with these topics, you can easily customize the appearance of the table output.

Many table elements (header, row, column, cell, summary row, first row or column…) have CSS-class attributes, which can be used to change the table style. Since each sjt.* function as well as tab_model() has different table elements and thus different class attributes, you first need to know which styles can be customized.

Retrieving customizable styles

The table functions invisibly return several values. The return value page.style contains the style information for the HTML table. You can print this style sheet to console using the cat()-function:

library(sjPlot)
data(efc)
m <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc)
tab <- tab_model(m)
cat(tab$page.style)
#> <style>
#> html, body { background-color: white; }
#> table { border-collapse:collapse; border:none; }
#> caption { font-weight: bold; text-align:left; }
#> td {  }
#> .thead { border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; }
#> .tdata { padding:0.2cm; text-align:left; vertical-align:top; }
#> .arc { background-color:#f2f2f2; }
#> .summary { padding-top:0.1cm; padding-bottom:0.1cm; }
#> .summarydata { text-align:left; }
#> .fixedparts { font-weight:bold; text-align:left; }
#> .randomparts { font-weight:bold; text-align:left; padding-top:.8em; }
#> .zeroparts { font-weight:bold; text-align:left; padding-top:.8em; }
#> .simplexparts { font-weight:bold; text-align:left; padding-top:.8em; }
#> .lasttablerow { border-bottom: double; }
#> .firsttablerow {  }
#> .firstsumrow { border-top:1px solid; }
#> .labelcellborder { border-bottom:1px solid; }
#> .depvarhead { text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; }
#> .depvarheadnodv { border-top: double; text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; }
#> .leftalign { text-align:left; }
#> .centeralign { text-align:center; }
#> .firsttablecol { text-align:left; }
#> .footnote { font-style:italic; border-top:double black; text-align:right; }
#> .subtitle { font-weight: normal; }
#> .modelcolumn1 {  }
#> .modelcolumn2 {  }
#> .modelcolumn3 {  }
#> .modelcolumn4 {  }
#> .modelcolumn5 {  }
#> .modelcolumn6 {  }
#> .modelcolumn7 {  }
#> .col1 {  }
#> .col2 {  }
#> .col3 {  }
#> .col4 {  }
#> .col5 {  }
#> .col6 {  }
#> </style>

The HTML code is in the page.content return value. The following code prints the HTML code of the table to the R console:

cat(tab$page.content)
#> <table>
#> <tr>
#> <th class="thead firsttablerow firsttablecol col1">&nbsp;</th>
#> <th colspan="3" class="thead firsttablerow">Total score BARTHEL INDEX</th>
#> </tr>
#> <tr>
#> <td class="depvarhead firsttablerow firsttablecol col1">Predictors</td>
#> <td class="depvarhead firsttablerow col2">Estimates</td>
#> <td class="depvarhead firsttablerow col3">CI</td>
#> <td class="depvarhead firsttablerow col4">p</td>
#> </tr>
#> <tr>
#> <td class="tdata firsttablecol col1">(Intercept)</td>
#> <td class="tdata centeralign modelcolumn1 col2">90.06</td>
#> <td class="tdata centeralign modelcolumn1 col3">77.95&nbsp;&ndash;&nbsp;102.18</td>
#> <td class="tdata centeralign modelcolumn1 col4"><strong>&lt;0.001</strong></td>
#> </tr>
#> <tr>
#> <td class="tdata firsttablecol col1">carer' age</td>
#> <td class="tdata centeralign modelcolumn1 col2">&#45;0.22</td>
#> <td class="tdata centeralign modelcolumn1 col3">&#45;0.36&nbsp;&ndash;&nbsp;-0.08</td>
#> <td class="tdata centeralign modelcolumn1 col4"><strong>0.002</strong></td>
#> </tr>
#> <tr>
#> <td class="tdata firsttablecol col1">average number of hours<br>of care per week</td>
#> <td class="tdata centeralign modelcolumn1 col2">&#45;0.28</td>
#> <td class="tdata centeralign modelcolumn1 col3">&#45;0.31&nbsp;&ndash;&nbsp;-0.24</td>
#> <td class="tdata centeralign modelcolumn1 col4"><strong>&lt;0.001</strong></td>
#> </tr>
#> <tr>
#> <td class="tdata firsttablecol col1">carer's gender</td>
#> <td class="tdata centeralign modelcolumn1 col2">&#45;0.26</td>
#> <td class="tdata centeralign modelcolumn1 col3">&#45;4.36&nbsp;&ndash;&nbsp;3.83</td>
#> <td class="tdata centeralign modelcolumn1 col4">0.900</td>
#> </tr>
#> <tr>
#> <td class="tdata firsttablecol col1">carer's level of<br>education</td>
#> <td class="tdata centeralign modelcolumn1 col2">&#45;0.76</td>
#> <td class="tdata centeralign modelcolumn1 col3">&#45;3.55&nbsp;&ndash;&nbsp;2.02</td>
#> <td class="tdata centeralign modelcolumn1 col4">0.592</td>
#> </tr>
#> <tr>
#> <td class="tdata leftalign summary firstsumrow">Observations</td>
#> <td class="tdata summary summarydata firstsumrow" colspan="3">821</td>
#> </tr>
#> <tr>
#> <td class="tdata leftalign summary">R<sup>2</sup> / R<sup>2</sup> adjusted</td>
#> <td class="tdata summary summarydata" colspan="3">0.270 / 0.266</td>
#> </tr>
#> 
#> </table>

Now you can see which table elements are associated with which CSS class attributes.

Customizing table output with the CSS parameter

You can customize the table output with the CSS parameter. This parameter requires a list of attributes, which follow a certain pattern:

  1. each attributes needs a css. prefix
  2. followed by the class name (e.g. caption, thead, centeralign, etc.)
  3. equal-sign
  4. the CSS format (in (single) quotation marks)
  5. the CSS format must end with a colon (;)

Example:

tab_model(
  m,
  CSS = list(
    css.depvarhead = 'color: red;',
    css.centeralign = 'text-align: left;', 
    css.firsttablecol = 'font-weight: bold;', 
    css.summary = 'color: blue;'
  )
)
  Total score BARTHEL INDEX
Predictors Estimates CI p
(Intercept) 90.06 77.95 – 102.18 <0.001
carer’ age -0.22 -0.36 – -0.08 0.002
average number of hours
of care per week 		-0.28 -0.31 – -0.24 <0.001
carer’s gender -0.26 -4.36 – 3.83 0.900
carer’s level of
education 		-0.76 -3.55 – 2.02 0.592
Observations 821
R2 / R2 adjusted 0.270 / 0.266

In the above example, the header row lost the original style and just became red. If you want to keep the original style and just add additional style information, use the plus-sign (+) as initial character for the parameter attributes. In the following example, the header row keeps its original style and is additionally printed in red:

tab_model(m, CSS = list(css.depvarhead = '+color: red;'))
  Total score BARTHEL INDEX
Predictors Estimates CI p
(Intercept) 90.06 77.95 – 102.18 <0.001
carer’ age -0.22 -0.36 – -0.08 0.002
average number of hours
of care per week 		-0.28 -0.31 – -0.24 <0.001
carer’s gender -0.26 -4.36 – 3.83 0.900
carer’s level of
education 		-0.76 -3.55 – 2.02 0.592
Observations 821
R2 / R2 adjusted 0.270 / 0.266

Pre-defined Table-Layouts

There are a few pre-defined CSS-themes, which can be accessed with the css_theme()-function. There are more pre-defined themes planned for the future.

tab_model(m, CSS = css_theme("cells"))
  Total score BARTHEL INDEX
Predictors Estimates CI p
(Intercept) 90.06 77.95 – 102.18 <0.001
carer’ age -0.22 -0.36 – -0.08 0.002
average number of hours
of care per week 		-0.28 -0.31 – -0.24 <0.001
carer’s gender -0.26 -4.36 – 3.83 0.900
carer’s level of
education 		-0.76 -3.55 – 2.02 0.592
Observations 821
R2 / R2 adjusted 0.270 / 0.266