4  Report Formatting

flowchart LR
Fig[Figures] --> Lay[Layout<br>Size]
Tab[HTML Tables]
Place[Placement] --> places[Margin<br>Tabs<br>Expand/Hide<br>Mixing Tables and Graphics]
rept[qreport] --> rhf[Report Writing<br>Helper Functions]
OF[Overall Format] --> ofs[HTML]
OF --> ltx[LaTeX] --> pdf[pdf]
OF --> mfd[Multi-Format<br>Reports]
MD[Metadata<br>Report<br>Annotations] --> mds[Variable Labels<br>and Units]

A state-of-the-art way to make reproducible reports is to use a statistical computing language such as R and its knitr package in conjunction with either RMarkdown or Quarto, with the latter likely to replace the former. Both of the report-making systems allow one to produce reports in a variety of formats including html, pdf, and Word. Html is recommended because pages can be automatically resized to allow optimum viewing on devices of most sizes, and because html allows for interactive graphics and other interactive components. Pdf is produced by converting RMarkdown or Quarto-produced markdown elements to \(\LaTeX\).Report formatting is very much enhanced by using variable attributes such as labels and units of measurement that are not considered in base R. Methods for better annotating output using labels and units are given below.

This document can serve as a template for using R with Quarto; one can see the raw script by clicking on Code at the top right of the report. A recommended template for typical statistical reports is here with html and pdf output.

4.1 General Formatting

When one has only one output format target, things are fairly straightforward except some situations where mixed formats are rendered in the same code chunk. Click below for details.

With Hmisc package version 4.8 and later and rms package 6.5-0 and later, rendering in html no longer requires results='asis' in chunk headers, and a chunk can mix plain text and html without any problems. The following applies to earlier versions.

To make use of specialized functions that produce html or \(\LaTeX\) markup, one often has to put results='asis' in the code chunk header to keep the system from disturbing the generated html or \(\LaTeX\) markup so that it will be typeset correctly in the final document. This process works smoothly but creates one complication: if you print an object that produces plain text in the same code chunk, the system will try to typeset it in html or \(\LaTeX\). To prevent this from happening you either need to split the chunks into multiple chunks (some with results='asis' and some not) or you need to make it clear that parts of the output are to be typeset verbatim. To do that a simple function pr can sense if results='asis' is in effect for the current chunk. If so, the object is surrounded by the markdown verbatim indicator—three consecutive back ticks. If not the object is left alone. pr is defined in the marksupSpecs$markdown$pr object, so you can bring it to your session by copying into a local function pr as shown below, which has a chunk option results='asis' to show that verbatim output appears anyway. If the argument obj to pr is a data frame or data table, variables will be rounded to the value given in the argument dec (default dec=3) before printing. If you specify inline=x the object x is printed with cat() instead of print(). inline is more for printing character strings.

An example of something that may not render correctly due to results='asis' being in the chunk header (needed for html(...)):

f <- ols(y ~ rcs(x1, 5))
f    # prints model summary in html format
m <- matrix((1:10)/3, ncol=2)
# use pr(obj=m) to fix

Here are examples of pr usage.

pr <- markupSpecs$markdown$pr
x <- (1:5)/7
pr('x:', x)


[1] 0.1428571 0.2857143 0.4285714 0.5714286 0.7142857
[1] 0.1428571 0.2857143 0.4285714 0.5714286 0.7142857
pr(inline=paste(round(x,3), collapse=', '))

0.143, 0.286, 0.429, 0.571, 0.714

Instead of working to keep certain outputs verbatim you can use knitr::kable() to convert verbatim output to markdown. Also see the yaml df-print html option, for which you may want to set df-print: kable.

knitr/Quarto will by default print data frames and other simple tables using html. Even though this is seldom needed, you can make knitr use plain text printing by putting this code at the top of the report to redefine the default knitr printing function.

knit_print <- knitr::normal_print

4.1.1 Annotating Simple Output

We frequently have a mixture of computations and printing within a single R chunk. But sometimes

  • Quarto splits up chunk code and output in a hard-to-read way
  • the object or calculation being printed is not easy to identify when the user has folded the code to make it currently invisible

The Hmisc package prn function will print the name of an object and its contents. Starting with Hmisc version 5.1-3, the printL function can help one create easy-to-read basic output whether the code is folded or not. printL allows you to specify multiple objects with labels to print for them. It also makes it easy to round scalars, vectors, or columns of data frame/tables before printing. Here are some examples.

w <- pi + 1 : 2
w: 4.14159265358979, 5.14159265358979
printL(w, dec=3)
4.142, 5.142
printL(w=w, 'Some calculation'=exp(1), dec=5)
w: 4.14159, 5.14159

Some calculation: 2.71828
d <- data.frame(x=pi+1:2, y=3:4, z=.q(a, b))
printL('this is it'=c(pi, pi, 1, 2),
       'Consecutive integers\nup to 10'=1:10,
this is it: 3.1416, 3.1416, 1, 2

yyy: 3.1416

       x y z
1 4.1416 3 a
2 5.1416 4 b

Consecutive integers
up to 10:
 [1]  1  2  3  4  5  6  7  8  9 10

4.2 Quarto Syntax for Figures

One can specify sizes, layouts, captions, and more using Quarto markup. Captions are ignored unless a figure is given a label. Figure labels must begin with fig-. The figure can be cross-referenced elsewhere in the document using for example See \@fig-scatterplot. Figure will be placed in front of the figure number automatically. Here is example syntax.This explains how to cross-reference subfigures.

#| label: fig-myplot
#| fig-cap: “An example caption (use one long line for caption)”
#| fig-height: 3
#| fig-width: 4
plot(1:7, abs(-3 : 3))

If the code produces multiple plots you can combine them into one with a single overall caption and include subcaptions for the individual panels:

#| label: fig-myplot
#| fig-cap: “Overall caption …”
#| fig-height: 3
#| fig-width: 4
#| layout-ncol: 2
#| fig-subcap:
#| - “Subcaption for panel (a)”
#| - “Subcaption for panel (b)”
plot(1:7, abs(-3 : 3))

To include an existing image while making use of Quarto for sizing and captioning etc. use this example.

```{r out.width=“600px”}
#| label: fig-mylabel
#| fig-cap: “…”

If you don’t need to caption or cross-reference the figure use e.g.

Other examples are in the next section.

The qreport package has helper functions for building a table of figures. To use those, put addCap() or addCap(scap="short caption for figure") as the first line of code in the chunk. The full caption is taken as the fig-cap: markup. If you don’t specify scap too addCap the short caption will be taken as the fig-scap: markup, or if that is missing, the full caption. At the end of the report you can print the table of figures using the following syntax (but surround the last line with back ticks).

# Figures

r printCap()

For chunks having #| label: fig- you can automatically have knitr call addCap at the start of a chunk, extracting the needed information, if you run the qreport function hookaddcap() in a chunk before the first chunk that produced a graph. This procedure is used through this book. addCap makes use of fig-scap: for short captions.

4.3 Quarto Built-in Syntax for Enhancing R Output

Helper functions described below allow one to enhance graphical and tabular R output by taking advantage of Quarto formatting features. These functions allow one to produce different formats within one code chunk, e.g., a plot in the margin and a table in a collapsible note appearing after the code chunk. But if you need only one output format within a chunk you can make use of built-in syntax as described here. The yaml-like syntax also allows you to specify heights and widths for figures, plus multi-figure layouts.

Here is some example code with all the markup shown.

#| column: margin
#| fig-height: 1
#| fig-width: 3
par(mar=c(2, 2, 0, 0), mgp=c(2, .5, 0))
x <- rnorm(1000)
hist(x, nclass=40, main=’’)
x[1:3] # ordinary output stays put
knitr::kable(x[1:3]) # html output put in margin
hist(x, main=’’)

This results follow.

par(mar=c(2, 2, 0, 0), mgp=c(2, .5, 0))
x <- rnorm(1000)
hist(x, nclass=40, main='')

x[1:3]               # ordinary output stays put
[1] -0.6264538  0.1836433 -0.8356286
knitr::kable(x[1:3]) # html output put in margin
hist(x, main='')

Here are a few markups for figure layout inside R chunks.

Wide page (takes over the margins) and put multiple plots in 1 row:

#| column: screen-inset
#| layout-nrow: 1

What I use the most: a wide column that just expands a little into the right margin, especially appropriate when the table of contents is on the right:

#| column: screen-right

When plotting 3 figures put the first 2 in one row and the third in the second row and make it wide.

#| layout: [[1,1], [1]]

Make the top left panel be wider than the top right one.

#| layout: [[70,30], [100]]

Top left and top right panels have equal widths but devote 0.1 of the total width to an empty region between the two top panels.

#| layout: [[45, -10, 45], [100]]

See here for details about figure specifications inside code chunks.

You can put some .aside information to the right of R output.

Tab sets and collapsible text are frequently helpful in report writing. Tricks can be used to flip all tabs with a single button. For example, if a series of analyses were done in parallel using both parametric and nonparametric methods, one can use CSS so that when clicking a Nonparametric tab all the nonparametric analysis results will show throughout the document.

4.4 Quarto Report Writing Helper Functions

Helper functions are defined when you activate the qreport package. You can get help on these functions by the usual way of typing ?functionname at the console. Several of the functions construct Quarto callouts which are fenced-off sections of markup that trigger special formatting, especially when producing html. The special formatting includes collapsible sections and marginal notes. Here is a summary of some of the qreport (plus a few from Hmisc) helper functions. For most of these functions you have to put results='asis' in the chunk header.

Function Purpose
dataChk run a series of logical expressions for checking data consistency, put results in separate tabs using maketabs, and optionally create two summary tabs
dataOverview runs a data overview report
missChk creates a series of analyses of the extent and patterns of missing values in a data table or data frame, and puts graphical summaries in tabs
hookaddcap makes knitr automatically extract figure labels, captions, short captions for use in list of figures
htmlList print a named list using the names as headers
kabl front-end to knitr::kable and kables. If you run kabl on more than one object it will automatically call kables.
makecallout generic Quarto callout maker used by makecnote, makecolmarg
makecnote print objects or run code and place output in an initially collapsed callout note
makecolmarg print objects or run code and place output in a marginal note
maketabs print objects or run code placing output in separate tabs
makemermaid makes a mermaid diagram with R variable values included in the diagram
makegraphviz similar to makemermaid but using graphviz
varType classify variables in a data table/frame or a vector as continuous, discrete, or non-numeric non-discrete
conVars use varType to extract list of continuous variables
disVars use varType to extract list of discrete variables
vClus run Hmisc::varclus on a dataset after reducing it

The input to maketabs, as will be demonstrated later, may be a named list, or more commonly, a series of formulas whose right-hand sides are executed and the result of each formula is placed in a separate tab. The left side of the formula becomes the tab label. For makecolmarg there should be no left side of the formula as marginal notes are not labeled. For the named list option the list names become the tab names. Examples of both approaches appear later in this report. In formulas, a left side label must be enclosed in back ticks and not quotes if it is a multi-word string. A wide argument is used to expand the width of the output outside the usual margins. An initblank argument creates a first tab that is empty. This allows one to show nothing until one of the other tabs is clicked. Alternately you can specify as the first formula ` ` ~ ` `. See this for another way to generate tabs. See this for more information about dynamic generation of markdown text and knitr components with R.

The two approaches to using maketabs also apply to makecnote and makecolmarg. Examples of the “print an object and place it inside a callout” are given later in the report for makecnote and makecolmarg. Here is an example of the more general formula method that can render any object, including html widgets as produced by plotly graphics. An interactive plotly graphic appears at the bottom of the plots in the right margin. You can single click on elements in the legend to turn them off and on, and double click within the legend to restore to default values.

options(plotlyauto=TRUE)  # makes Hmisc use plotly's auto size option
                          # rather than computing height, width
x <- round(rnorm(100, 100, 15))
makecolmarg(~ table(x) + raw + hist(x) + plot(ecdf(x)) + histboxp(x=x))
 67  70  73  77  78  79  81  82  83  84  86  87  88  89  90  91  92  93  94  95 
  1   1   1   1   1   1   2   1   1   1   1   1   1   3   1   6   1   3   3   2 
 96  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 116 117 
  1   6   5   3   2   1   2   2   4   5   2   2   6   2   3   3   1   2   1   3 
118 120 121 122 123 124 130 133 136 
  2   1   1   1   1   2   1   1   1 

# or try makecnote(`makecnote example` ~ kabl(table(x)) + hist(x) + ...
# Avoid raw by using kabl(table(x)) instead of table(x)

Adding + raw to a formula in makecnote, makecolmarg, or maketabs forces printed results to be treated as raw verbatim R output.

makecallout is a general Quarto callout maker that implements different combinations of the following: list or formula, print or run code, defer executing and only produce the code to execute vs. running the code now, and close the callout or leave it open for more calls.

qreport also has helper functions for interactively accessing information to help in report and analysis building:

Function Purpose
htmlView view html-converted objects in RStudio View pane
htmlViewx view html-converted objects in external browser

However the automatic viewing of html objects in the RStudio Viewer will satisfy most needs.

4.5 Multi-Output Format Reports

To allow one report to be used to render multiple output formats, especially html and pdf, it is helpful to be able to sense which output format is currently in play, and to use different functions or options to render output explicitly for the current format. Here is how to create variables that can be referenced simply in code throughout the report, and to invoke the plotly graphics package if output is in html to allow interactivity. A small function ggp is defined so that if you run any ggplot2 output through it, the result will be automatically converted to plotly using the ggplotly function, otherwise it is left at standard static ggplot2 output if html is not the output target.See this for examples of articles rendered in both html and PDF from the same script.

outfmt <- if(knitr::is_html_output ()) 'html'  else 'pdf'
markup <- if(knitr::is_latex_output()) 'latex' else 'html'
ishtml <- outfmt == 'html'
if(ishtml) require(plotly)
ggp <- if(ishtml) ggplotlyr else function(ggobject, ...) ggobject
# See below for more about ggplotlyr (a front end for ggplotly that can
# correct a formatting issue with hover text)

Quarto has a excellent facility for conditionally including document sections depending on the currently chosen output format.

The Hmisc, rms, and rmsb packages have a good deal of support for creating \(\LaTeX\) output in addition to html. They require some special \(\LaTeX\) packages to be accessed. In addition, if using any of Quarto’s nice features for making marginal notes, there is another \(\LaTeX\) package to attach. Below you’ll find what needs to be added to the yaml prologue at the top of your script if using Quarto. You have to modify pdf-engine to suit your needs. I use luatex because it handles special unicode characters. In the future (approximately July 2022) a bug in Pandoc will be fixed and you can put links-as-notes: true in the yaml header instead of redefining href and linking in hyperref.

    self-contained: true
    . . .
    pdf-engine: lualatex
    toc: false
    number-sections: true
    number-depth: 2
    top-level-division: section
    reference-location: document
    listings: false
      \usepackage{marginnote, here, relsize, needspace, setspace, hyperref}

The href redefinition above turns URLs into footnotes if running \(\LaTeX\).

There is one output element provided by Quarto that will not render correctly to \(\LaTeX\): a marginal note using the markup .column-margin. To automatically use an alternate in-body format, define a function that can be used for both typesetting formats.

mNote <- if(ishtml) '.column-margin'
                    '.callout-note appearance="minimal"'

Then use r mNote enclosed in back ticks in place of the .column-margin callout for generality.

Quarto and its workhorse Pandoc now are quite good at creating Word .docx files, even allowing \(\LaTeX\) math expressions to render well and be editable in Word. Missing is the ability to handle marginal notes including .asides.

When collaborating with a Word user by sending her a .docx file whenever a report is updated, it is hard but necessary to discourage her from editing the docx file instead of communicating changes back to you to make in the primary .qmd file. But often the collaborator is using parts of your report to build another Word document. In that case it is important for the collaborator to be able to see what changed since the last report. The minimal-effort way to do this is to save the last version of the .docx file and send both the last and current versions to the collaborator. She can then compare the two versions in Word to see exactly what has changed.

Even when producing only html, one may wish to save individual graphics for manuscript writing. For non-interactive graphics you can right click on the image and download the .png file. For interactive plots, plotly shows a “take a snapshot” icon when you hover over the image. Clicking this icon will produce a static .png snapshot of the graph. Some graphs are not appropriate for static documents, and the variables created in the code above can be checked so that, for example, an alternative graph can be produced when making a .pdf file. But in other cases one just produces an additional static plot that is not shown in the html report. See the margin note near Figure 15.18 for an example.

As done with various Hmisc and rms package functions, one can capitalize on Hmisc’s special formatting of variable labels and units when constructing tables in \(\LaTeX\) or html. The basic constructs are shown in the code below.

# Retrieve a set of markup functions depending on typesetting format
# See below for definition of ishtml
specs    <- markupSpecs[[if(ishtml) 'html' else 'latex']]
# Hmisc markupSpecs functions create plain text, html, latex,
# markdown, or plotmath code
varlabel <- specs$varlabel  # retrieve an individual function
# Format text describing variable named x
# hfill=TRUE typesets units to be right-justified in label
# Use the following character string as a row label
# Default specifies the string to use if there is no label
# (usually taken as the variable name)
varlabel(label(x, default='x'), units(x), hfill=TRUE)

For plotting and sometimes for html, the Hmisc hlab function is used. It makes label and units lookups easy. For plain text formatting of labels/units, the Hmisc vlab function is easy to use.

4.6 HTML Tables

Nicely formatted tables can be created in multiple ways:

  • using customized code that directly writes html markup
  • using customized code that directly writes \(\LaTeX\) markup
  • using customized code that writes markdown markup (e.g., “pipe” tables)
  • hand coding markdown (usually pipe tables)

The latter two provide less flexibility but have the advantage of being automatically converted to html or \(\LaTeX\) depending on your destination format.

Here is an example of a hand coded markdown pipe table. Note (1) the second line of the markup indicates that the first column is to be left-justified and the second column right-justified, and (2) you can include computed values from R expressions. On the caption line we specify that the first column occupies 2/3 of the width. We could have specified tbl-colwidths="[67,33]" to get the same result.

| This Column | That Column |
| cat | dog |
| `r pi` | `r 2+3` |
: Table caption {tbl-colwidths=“[2,1]”}

The result is

Table caption
This Column That Column
cat dog
3.1415927 5

There is an automatic feature of html that makes it especially attractive as a destination format: If a cell contains a long string of characters, those strings will be line-wrapped appropriately, with the line length depending on the width of the display device.

The knitr package kable function provides an easy way to produce html tables from data tables/frames and matrices, and knitr::kables allows one to put several tables together. The qreport package kabl function combines the features of kable and kables. The kableExtra package allows you to greatly extend what kable can do.

There are many R packages and functions for making advanced html tables. See for example the Table 1 tab in Chapter 9. This table was produced by the Hmisc package summaryM function, which used the htmlTable function in the htmlTable package. Other packages to consider are gt (see Section 4.10) and its uncredited predecessor tangram, and packages discussed here.

The central guide for basic table making in Quarto is here.

4.6.1 gt Package

The gt package is to tables as ggplot2 is to graphs. gt provides a wide variety of formatting opportunities allowing one to flexibly create fairly complex tables that can contain interactive elements. Like ggplot2, table elements (column headings, rows, columns, or row-column combinations) are specified by adding layers to an accumulating gt object using a pipe operator (“pass along to”) such as |>. Unlike ggplot2, gt can translate markdown elements to html on-the-fly. This allows you to do things like including bullet lists and small tables inside gt table cells. See for example the Categorical tab in Section 2.9 for an example where small markdown tables appear inside a larger gt table.

Here is an example that includes many of the gt features that are commonly needed. See Section 4.10 for how to put graphics in gt table cells, and see Section 11.3 for another gt example.

# Define a data frame that forms the table rows and columns
d <- data.frame(
  Item              = c(runif(3), NA),
  chi               = rchisq(4, 3),
  '$$X_2$$'         = rnorm(4),
  Markdown          = c('* part 1\n* part 2\n* part 3', '', '', '**xxx**'),
  Y                 = c('$$\\alpha_{3}^{4}$$', rep('', 3)),
  check.names = FALSE)   # allows illegal R column names

gt(d)                                                |>
  tab_header(title=md('**Main Title $\\beta_3$ Using `gt`**'),
             subtitle='Some Subtitle')               |>
  tab_options(table.width=pct(65))                   |>
  tab_spanner('Numeric Variables', columns=1:3)      |>
  tab_spanner('Non-Numeric Variables',
              columns=c(Markdown, Y))                |>
  tab_row_group(md('**After** Intervention'),  rows=3:4) |>
  tab_row_group('Before Intervention', rows=1:2)     |>
  sub_missing(missing_text='')                       |>
  fmt_number(columns=c(Item, '$$X_2$$'), decimals=2) |>
  cols_label(Y   ~ html('Velocity<br>of Thing'),
             chi ~ md('$$\\chi^2_{3}$$'))            |>
  cols_width(Markdown ~ px(160))                     |>
  cols_align(align='center', columns=Y)              |>
  fmt_markdown(columns=Markdown, rows=1)             |>
            locations=cells_body(columns=Markdown))  |>
  tab_style(style=cell_text(color='blue', align='right'),
            locations=cells_column_labels(columns='$$X_2$$')) |>
  tab_source_note(md('_Note_: There is a bug in `tab_row_group` in `gt` version 0.9.0 causing the row group labels to appear in the reverse order in which they were named.  This is why the `tab_row_group` were reversed in the code.  The problem is reported [here](https://github.com/rstudio/gt/issues/717).'))    |>
  tab_footnote(md('Carefully calculated based on _bad_ assumptions'),
                                    rows=Item==min(Item, na.rm=TRUE)))
md() allows you to specify markdown syntax
Make the table have 65% of the report body width
Instead of printing NA for missing values of Item, print blank
Two digits to the right of the decimal point for two columns
Rename the Y column and stack two lines for the label, marking this as html; rename chi using markdown math notation
Make the Markdown column 160 pixels wide
Transform markdown text in column named Markdown (quotes not needed in gt) but only for the first row. The fourth row rendered ** literally instead of using bold face.
Make column Markdown have a small font
Make the X_2 column label be blue and right-aligned. Right alignment did not work for math mode.
Footnote automatically placed on the row where Item has its lowest value
Main Title \(\beta_3\) Using gt
Some Subtitle
Numeric Variables Non-Numeric Variables
Item $$\chi^2_{3}$$ $$X_2$$ Markdown Velocity
of Thing
Before Intervention
1 0.27 5.543782 0.74
  • part 1
  • part 2
  • part 3
0.37 5.354397 0.58
After Intervention
0.57 2.915247 −0.31

5.053191 1.51 **xxx**
Note: There is a bug in tab_row_group in gt version 0.9.0 causing the row group labels to appear in the reverse order in which they were named. This is why the tab_row_group were reversed in the code. The problem is reported here.
1 Carefully calculated based on bad assumptions

To remove certain table elements use these examples.

gt(d) |> tab_options(column_labels.hidden=TRUE)                          |>
         tab_options(table_body.hlines.width=0, table.border.top.width=0)|>
# or
g <- ...
g |> cols_hide(columns=Pvalue)
remove column headings
remove top line
remove columns named X1 and X2
some operation that creates a gt object, e.g., print(describe(mydata, 'continuous'))
remove a column and finally render the table

4.7 CSS

When producing reports in html, you can create custom html styles that quarto will use. These styles are defined using HTML5’s CSS (cascading style sheets). An example .css file is at hbiostat.org/rflow/h.css, and your report may gain access to such a .css file by including a line like css: h.css in the top-level quarto yaml header under the html: section.

Two of the styles defined by defined by h.css are smaller and smaller2. smaller will shrink the font size of a block of text (even one containing code and R output, but it does not apply to tables) to 80% of its original size. smaller2 will make it 64% of the original size. To invoke these styles we use quartodivs” as follows:

::: {.smaller2}
This is text that will appear smaller ...


Here is an example using smaller2.

This is text that will appear smaller. More of the same. More of the same. More of the same. More of the same. More of the same. More of the same. More of the same. More of the same. More of the same. More of the same. More of the same. More of the same. More of the same.

2.3 4.5
2.2 3.3
x <- pi
[1] 3.141593

Another style in h.css is quoteit which is useful for including quotations. The text is italicized, dark blue, 80% of regular size, and has 10% left and right margins. Here is an example.

::: {.quoteit}
Some eloquent quote appears here.  The author of the quote is assumed to know what they are talking about, and seem to be able to express themselves.

Some eloquent quote appears here. The author of the quote is assumed to know what they are talking about, and seem to be able to express themselves.

As discussed here you can use Quarto's markdown syntax to style text with CSS, e.g., the color is [red]{style="color: red;"}. This can be handy when you change a report and you want someone else to see what’s changed. Suppose that changed text is to appear in blue. Define a “mark changed text” character variable Ch as follows.Unfortunately, these HTML colors will not render in Word.

Ch <- '{style="color:blue;"}'

Then you can type “[This text]`r Ch` has changed” to render the following: This text has changed. You can also define a helper function to be generic if you want to use more than one color:

# substitute keeps you from having to quote a word
col <- function(co) {
  co <- as.character(substitute(co))
  paste0('{style="color:', co, ';"}')

Try it: [This]`r col(red)` is red and [This other thing]`r col(blue)` is blue.

which renders:

This is red and this other thing is blue if rendering to HTML.

4.8 Advanced Tables That Render to Both HTML and Word

Although there are many advanced table making tools in R for producing HTML, most of these will not properly render to Word much of the time. Functions that directly write HTML markup such as those in the Hmisc and htmlTable packages produce HTML that Quarto and pandoc know how to faithfully render to Word. An example multi-format output script is here, with HTML output and .docx output.

4.9 Diagrams

Quarto builds in two diagramming languages: mermaid and graphviz. Section 8.1 has detailed examples using mermaid, which uses a simpler format than graphviz. graphviz allows for more complex diagrams exemplified here and also provides more control. graphviz nodes can include HTML tables, and you can even have arrows drawn between table cells or between a table cells and other non-table nodes. Here is an example, taken from this excellent post. Connections between diagram elements are made possible by assigning port identifiers to elements. The chunk header refers to dot which is a primary module of graphviz, for directed graphs.

digraph {
  graph [pad="0.5", nodesep="0.5", ranksep="2"]
  //  splines=ortho for square connections
  node  [shape=plain]

Foo [label=<
<table border="0" cellborder="0" cellspacing="0">
  <tr><td><b><i>InputFoo</i></b></td><td><font color="blue">two</font> </td>   </tr><HR/>
  <tr>  <td port="1">one</td><td> two </td></tr>
  <tr>  <td port="2">two</td><td> two </td></tr>
  <tr>  <td port="3">three</td><td> two </td></tr>
  <tr>  <td port="4">four</td><td> two </td></tr>
  <tr>  <td port="5">five</td><td port="a"> two </td></tr>
  <tr>  <td port="6">six</td><td port="b"> two </td></tr>
Bar [label=<This and that<br/><font face="courier" color="darkblue">and that and <b>that</b></font>>];

Foo:3:w -> Foo:2:w; 
// node name:port:direction (n,ne,e,se,s,sw,w,nw,c,_)
// c=center within node, _=use appropriate node side
// See graphviz.org/docs/attr-types/portPos
Foo:3:w -> Foo:6:w;
Foo:6:w -> Foo:1:w;
Foo:1:w -> Foo:a:e;
Foo:b:e -> Bar;

Foo InputFoo two one two two two three two four two five two six two Foo:w->Foo:w Foo:w->Foo:w Foo:w->Foo:w Foo:w->Foo:e Bar This and that and that and that Foo:e->Bar

The qreport makegraphviz function allows variable insertions into graphviz diagrams, and if a variable to be inserted is a data frame it will be converted to a simple HTML table that graphviz can handle. Here is an example. {u} is the syntax for inserting the value of variable u.

x <- data.frame(x1=round(runif(3), 3), x2=.q(a,b,c))
     x1 x2
1 0.875  a
2 0.339  b
3 0.839  c
z <- 'digraph {node [shape=plain];
  Foo [shape=oval label=<Information about <font color="blue">{{g}}</font>>];
  Bar [label=<{{u}}>];  // add shape=box to box the table
  Foo -> Bar}'
makegraphviz(z, g='states', u=x, file='gvtest.dot')

The diagram is then rendered with a dot chunk containing a special file: gvtest.dot markup.

Foo Information about states Bar x1 x2 0.875 a 0.339 b 0.839 c Foo->Bar

See Section 8.1 for a more advanced graphviz example that is along these lines. See this for some excellent graphviz flowchart examples.

Note: As of 2022-12-11 Quarto has withdrawn support for tooltips. I hope that is added back someday.

As exemplified in Chapter 8, Mermaid provides an easy way to make many types of diagrams. Diagrams are more valuable when they are dynamic. Mermaid provides an easy way to include pop-up tooltips in diagram nodes, to provide deeper information about the node. When the tooltips contain tables whose columns need to line up, you need to put the following in your document so that tooltips will used a fixed-width font and preserve white space. The best way to include this is to put it in a .css file that is reference in the report’s yaml, or to surround the four lines with <style></style>.

mermaidTooltip {
      font-family: courier;
      white-space: pre;

Quarto has excellent support for Graphviz charts, facilitated by the qreport package makegraphviz function to help insert variables and data tables inside diagrams. The Graphviz approach also allows fine control of fonts and colors. It is best to spend a little more time learning the Graphviz dot language with Quarto, with and without using makegraphviz.

4.10 Mixing Graphics and Tables

You may need to compose a matrix of outputs where some elements are graphical and some are tabular. R and Quarto provide a variety of methods for accomplishing this, summarized below, with links.

  • Base graphics: produce one large image using functions such as lines, points, text; simple to understand but takes a good deal of composition work to compute \(x,y\) coordinates for placing text and for keeping the correct column justifications
  • flextable
  • patchwork + gridExtra: tables are converted to graphics then layed out using elegant patchwork syntax as exemplified here
  • ggtext
  • kableExtra
  • gt possibly with gtExtras or sparkline
  • Native Quarto + gridExtra
  • Native Quarto table with some cells created by converting graphics output to svg (scalable vector graphic using the svglite package) and marked as html

The last two options are appealing because of their minimal dependencies. Here is an example using the next-to-last option based on the layout syntax described in Section 4.3. The page is divided into two rows, with a graph and a table appearing left to right in the first row, and a large graph taking up the whole second row. Between the two elements in the first row, 10% of the width is left blank to separate the two. The gridExtra package is used to convert a table to a plot, and math notation using R plotmath is included.

# parse=TRUE: make grid.table respect plotmath notation
# also increase font size for the table
tt <- gridExtra::ttheme_minimal(parse=TRUE, base_size=30)
d <- cbind('A[3]'=1:2, B=c('alpha^33', 'frac(i+j,sqrt(n) + sqrt(m, 3))'))
gridExtra::grid.table(d, theme=tt)


See this tutorial for ways to format specific rows/columns in a grid table.

Now consider the last option. Instead of converting table elements to graphics we convert graphics elements to html by rendering them to svg text using the svglite package and marking the text as html with htmltools::HTML. Here is a function that makes this easy to do. The expr argument is any R expression that produces a graph. It must be enclosed in braces if the expression has more than one command. Arguments ps, cex.lab, cex.axis, ... are ignored when using ggplot2.

msvg <- function(expr, w=5, h=4, ps=10, cex.lab=.9, cex.axis=0.6, 
                 bg='transparent', ...) {
  f <- tempfile(fileext='.svg')
  svglite::svglite(f, width=w, height=h, pointsize=ps, bg=bg)
  qreport::spar(cex.lab=cex.lab, cex.axis=cex.axis, ...)
  .x. <- expr
  if(inherits(.x., 'ggplot')) print(.x.)

In the following example the width of column 1 was specified to be twice the width of column 2, and column 2 is right-justified. An R base graphic is placed in row 1 column 1, and a ggplot2 graphic in row 2 column 2. Column 2 is centered.

`r p1 <- msvg(plot(rnorm(20), ylab=’’), w=4, h=2)`
`r p2 <- msvg(ggplot(mapping=aes(x=1:10, y=rnorm(10))) + geom_point(), w=2.5, h=1.4)`

| A | B |
| `r p1` | Row 1 column 2 |
| $\alpha_{3}^{47}$ | `r p2` |
: Example table with svg graphics {tbl-colwidths=“[2,1]”}
Example table with svg graphics
5 10 15 20 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 Index Row 1 column 2
\(\alpha_{3}^{47}\) -1 0 1 2 2.5 5.0 7.5 10.0 1:10 rnorm(10)

The svg graphics, being scalable, will have full resolution for any level of magnification of the table.

kableExtra and other packages such as gt,flextable, and htmlTable can provide table enhancements. kableExtra would not preserve html for the graphics cell. Here is an example using gt. See this for gt examples where the same graphics form is used for all the rows for a column. In this gt approach a data frame is constructed with placeholders for graphics, with the placeholder value being the name of the svg graphics object. Then specific rows and columns are replaced with svg graphics character strings.

# Must use double $ for LaTeX math inside gt tables
d <- data.frame(A=c('p1',             '$$\\alpha_{3}^{47}$$'),
                B=c('Row 1 column 2', 'p2'                  ) )
# Define a function that will retrieve the correct graph
s  <- function(x) c(p1 = p1, p2 = p2)[x]

gt(d) |> tab_header(title='Main Title', subtitle='Some Subtitle') |>
         tab_options(column_labels.hidden=TRUE) |>
         tab_options(table_body.hlines.width=0, table.border.top.width=0) |>
         cols_width(A ~ pct(67), B ~ pct(33)) |>
         cols_align(align='left',   columns=A) |>
         cols_align(align='center', columns=B) |>
         text_transform(locations=cells_body(rows=1, columns=A), fn=s) |>
         text_transform(locations=cells_body(rows=2, columns=B), fn=s)
Main Title
Some Subtitle
5 10 15 20 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 Index Row 1 column 2
$$\alpha_{3}^{47}$$ -1 0 1 2 2.5 5.0 7.5 10.0 1:10 rnorm(10)

gt has a special function for putting a ggplot in a table cell. Let’s try it. But note that ggplot_image produces a png file that is not scalable. Let’s also replace the first graph with a spike histogram for a normal distribution sample using Hmisc function pngNeedle which produces a png file. Use the gt local_image file to include it.

g <- ggplot(mapping=aes(x=1:10, y=rnorm(10))) + geom_point()
x         <- rnorm(10000)
sp        <- spikecomp(x, method='grid', normalize=FALSE)
spikehist <- pngNeedle(sp$y / max(sp$y), h=14, w=3, lwd=2)
gt(d) |> text_transform(locations=cells_body(rows=1, columns=A), 
                        fn=function(x) local_image(spikehist, height=14)) |>
         text_transform(locations=cells_body(rows=2, columns=B),
                                                   ggplot_image(g, height=200, aspect=1.5)) |>
spikecomp is in the Hmisc package and computes coordinates of spike histograms, rounding continuous values to pretty numbers.
pngNeedle in Hmisc plots a spike histogram without labeling the original data values, and returns the name of a .png file containing the short and wide plot. It needs values to be in \([0,1]\) so the input vector consists of counts divided by the maximum over all counts. Height h is in pixels.
aspect is the width:height aspect ratio.
Row 1 column 2

Instead of using a static spike histogram in the upper left column let’s use the sparkline package’s sparkline function to draw an interactive spike histogram, using similar examples from here and these jQuery javascript options. See also this. A disadvantage of this approach is that bar charts drawn using sparkline use \(y\) coordinates (here, relative frequency) as tooltips (mouse hover text) instead of the more informative \(x\) coordinates.

sparkline(0)   # load javascript dependencies
spike <- htmltools::HTML(spk_chr(values=round(sp$y / sum(sp$y), 4), type='bar',
                                 chartRangeMin=0, zeroColor='lightgray',
                                 barWidth=1, barSpacing=1, width=200))
gt(d) |> text_transform(locations=cells_body(rows=1, columns=A), 
                        fn=function(x) spike) |>
         text_transform(locations=cells_body(rows=2, columns=B),
                        fn=function(x) ggplot_image(g, height=200, aspect=1.5)) |>
         tab_options(column_labels.hidden=TRUE) |>
         cols_width(A ~ pct(40), B ~ pct(60))
Row 1 column 2

Let’s improve the information by including \(x\) coordinates in tooltips, using an example from here (see also here). At the lowest x value, expand the tooltip to include quartiles, min, max, \(n\), and mean. Include the frequency count in addition to relative frequency.

spike <- function(x, w=200) {
  x        <- x[! is.na(x)]
  sp       <- spikecomp(x, method='grid', normalize=FALSE)
  freq     <- sp$y
  xvals    <- paste0('x=', sp$x, '<br>n=', freq)
  qu       <- paste0('Q<sub>', 1:3, '</sub>  : ', round(quantile(x, (1:3)/4), 3))
  ot       <- paste0(c('n   :', 'Min :', 'Max :', 'Mean :'),
                     c(length(x), round(c(range(x, na.rm=TRUE), mean(x)), 3)))
  stats    <- paste(c(ot[1:2], qu, ot[3:4]), collapse='<br>')
  xvals[1] <- paste0(stats, '<br><br>', xvals[1])
  htmltools::HTML(spk_chr(values=round(freq / sum(freq), 4), type='bar',
                          chartRangeMin=0, zeroColor='lightgray',
                          barWidth=1, barSpacing=1, width=w,

# Define javascript function to construct the tooltip
tt <- function(xv)
      "function(sparkline, options, field){
       return %s[field[0].offset] + '<br/>' + field[0].value;
      jsonlite::toJSON(xv) ) )

sp <- spike(x)
gt(d) |> text_transform(locations=cells_body(rows=1, columns=A), 
                        fn=function(x) sp) |>
         text_transform(locations=cells_body(rows=2, columns=B),
                        fn=function(x) ggplot_image(g, height=200, aspect=1.5)) |>
         tab_options(column_labels.hidden=TRUE) |>
         cols_width(A ~ pct(40), B ~ pct(60))
Row 1 column 2

In many situations we need the same type of micrographic constructed for all rows. Creat a gt table with a spike histogram for each of several continuous variables in the support dataset.

X <- subset(support, select=c(age, slos, totcst, meanbp, hrt, temp, crea))
s <- sapply(X, spike, w=300)         # apply spike to each variable in X
# Or: require(data.table)
#     setDT(support)
#     s <- support[, sapply(.SD, spike, w=250), .SDcols=.q(age,slos,totcst,meanbp,hrt,temp,crea)]
d <- data.frame(Variable          = names(X),
                Label             = sapply(X, label),
                Units             = .q(y, d, '$', 'mmHg', bpm, '$$^\\circ C$$', 'mg/dL'),
                'Spike Histogram' = names(X),
                check.names=FALSE)   # allow space inside name
gt(d) |> text_transform(locations=cells_body(columns=4), fn=function(x) s) |>
         tab_style(style=cell_text(weight='bold'), locations=cells_body(columns=Variable)) |>
         tab_style(style=cell_text(size='small'), locations=cells_body(columns=Label)) |>
         tab_style(style=cell_text(size='small', style='italic'), locations=cells_body(columns=Units)) |>
Variable Label Units Spike Histogram
age Age y
slos Days from Study Entry to Discharge d
totcst Total RCC cost $
meanbp Mean Arterial Blood Pressure Day 3 mmHg
hrt Heart Rate Day 3 bpm
temp Temperature (celcius) Day 3 $$^\circ C$$
crea Serum creatinine Day 3 mg/dL

This approach forms the basis of the Hmisc print.describe function when options(prType='html') and you code print(describe(...), 'continuous'). Hover over the smallest value in a spike histogram sparkline to see the 5 smallest distinct data values, or over the largest value to see the 5 largest. The column: screen-inset yaml markup is used to show this very wide table.

des <- describe(support)
print(des, 'continuous')
support Descriptives
24 Continous Variables of 35 Variables, 1000 Observations
Variable Label n Missing Distinct Info Mean Gini |Δ| Quantiles
.05 .10 .25 .50 .75 .90 .95
age Age 1000 0 970 1.000 62.47 18.2 33.76 38.91 51.81 64.90 74.50 81.87 86.00
slos Days from Study Entry to Discharge 1000 0 88 0.998 17.86 17.3 4 4 6 11 20 37 53
d.time Days of Follow-Up 1000 0 582 1.000 475.7 576.4 5.0 8.0 27.0 256.5 725.0 1464.3 1757.1
edu Years of Education 798 202 25 0.969 11.78 3.897 6 8 10 12 14 16 18
scoma SUPPORT Coma Score based on Glasgow D3 1000 0 11 0.650 11.74 19.34 0.0 0.0 0.0 0.0 9.0 44.0 62.4
charges Hospital Charges 975 25 967 1.000 56271 69155 3757 4688 10029 26499 63622 147109 223582
totcst Total RCC cost 895 105 895 1.000 30490 36194 2484 3081 5899 15110 37598 72906 114932
totmcst Total micro-cost 628 372 617 1.000 26168 30192 1653 2548 5297 13828 33691 66229 96753
avtisst Average TISS, Days 3-25 994 6 241 1.000 22.64 14.86 6.00 8.00 12.00 19.00 31.75 43.33 48.00
meanbp Mean Arterial Blood Pressure Day 3 1000 0 122 1.000 84.98 30.88 47.00 55.00 64.75 78.00 107.00 120.00 128.05
wblc White Blood Cell Count Day 3 976 24 282 1.000 12.4 8.577 2.475 4.800 6.899 10.449 15.500 22.248 27.524
hrt Heart Rate Day 3 1000 0 124 1.000 97.87 35.69 54.0 60.0 72.0 100.0 120.0 135.0 146.1
resp Respiration Rate Day 3 1000 0 45 0.993 23.49 10.33 9 10 18 24 29 36 40
temp Temperature (celcius) Day 3 1000 0 64 0.999 37.08 1.374 35.50 35.80 36.20 36.70 38.09 38.80 39.20
pafi PaO2/(.01*FiO2) Day 3 747 253 463 1.000 244.2 126 92.61 115.00 156.33 226.66 310.00 400.00 442.81
alb Serum Albumin Day 3 622 378 38 0.998 2.917 0.8797 1.800 2.000 2.400 2.800 3.400 4.000 4.199
bili Bilirubin Day 3 703 297 115 0.997 2.527 3.385 0.3000 0.3000 0.5000 0.7999 1.7998 5.8594 12.5896
crea Serum creatinine Day 3 997 3 87 0.997 1.808 1.468 0.6000 0.7000 0.8999 1.2000 1.8999 3.6396 5.5996
sod Serum sodium Day 3 1000 0 42 0.997 137.7 6.706 129 131 134 137 141 145 148
ph Serum pH (arterial) Day 3 750 250 53 0.998 7.416 0.08433 7.289 7.319 7.380 7.420 7.470 7.500 7.520
glucose Glucose Day 3 530 470 226 1.000 156.4 85.09 74.0 82.0 100.0 128.0 185.0 269.3 327.5
bun BUN Day 3 545 455 106 1.000 32.61 27.12 7.0 9.0 14.0 23.0 43.0 68.6 88.8
urine Urine Output Day 3 483 517 359 1.000 2194 1562 141.7 600.0 1208.5 1925.0 2900.0 4087.6 4822.5
adlsc Imputed ADL Calibrated to Surrogate 1000 0 251 0.967 1.98 2.185 0.000 0.000 0.000 1.670 3.042 5.000 6.000
print(des, 'categorical')
support Descriptives
11 Categorical Variables of 35 Variables, 1000 Observations
Variable Label n Missing Distinct Info Sum Mean Gini |Δ|
death Death at any time up to NDI date:31DEC94 1000 0 2 0.665 668 0.668 0.444
sex 1000 0 2

hospdead Death in Hospital 1000 0 2 0.567 253 0.253 0.3784
dzgroup 1000 0 8

dzclass 1000 0 4

num.co number of comorbidities 1000 0 8 0.937
1.886 1.449
income 651 349 4

race 995 5 5

adlp ADL Patient Day 3 366 634 8 0.842
1.246 1.766
adls ADL Surrogate Day 3 690 310 8 0.899
1.755 2.295
sfdm2 841 159 5