This vignette is heavily inspired by Tristan Mahr’s post Lists are my secret weapon for reporting stats with knitr. Please read his original for an excellent introduction on how to better organize your data for inline reporting scenarios with lists. I’m going to borrow several examples directly from that post.

## Plug-in reporting

Both Tristan and Yihui Xie call inline reporting the act of interleaving R expressions in the prose of markdown text. When you click the Knit button or call rmarkdown::render() to build your report, knitr evaluates these R expressions, turns them into text and plugs them into your output.

The most common use case is for reporting descriptive statistics. To illustrate, I’ll use the Orange dataset which contains circumference measurements of 5 orange trees at 7 points in time.

Here is some R code we might use to summarize the Orange data:

n_trees <- length(levels(Orange$Tree)) n_timepoints <- length(unique(Orange$age))

And here are some lines we might include in a report about the growth of these trees:

{r setup, include = FALSE}
library(epoxy)


{epoxy}
The dataset contains {nrow(Orange)} tree size measurements
from {n_trees} trees at {n_timepoints} time points in the study.


The dataset contains 35 tree size measurements from 5 trees at 7 timepoints in the study.

With normal R Markdown inline reporting we would have written this in our .Rmd file instead:

The dataset contains r nrow(Orange) tree size measurements
from r n_trees trees at r n_timepoints time points in the study.

The two forms are very similar, but the epoxy chunk approach provides a few advantages, as we’ll discover in this vignette.

## Collect your variables in lists

In the above example, we used normal variables that were available in the global environment of our document. But a small structural change can bring great benefits. It’s worth reading Tristan’s blog post, but to steal his thunder: store your data in lists.

We could, on the one hand, create variables named knitted_when, knitted_where and knitted_with that all store facts about the knitting process. The knitted_ prefix is helpful as an aid to remember that these variables are related.

But you could store those three variables in a single object instead. Bundling everything into a list() allows you to report the results by accessing the list elements by name with $. knitted <- list( when = format(Sys.Date()), where = knitr::current_input(), with = format(utils::packageVersion("knitr")), doc_url = "https://rdrr.io/pkg/knitr/man/knit.html" ) {epoxy} Report prepared on {knitted$when} from {knitted$where} with knitr version {knitted$with} {emo_ji('happy')}.
Read more about [knitr::knit()]({knitted$doc_url}).  Report prepared on 2022-08-28 from inline-reporting.Rmd with knitr version 1.40 😆. Read more about knitr::knit(). This is still essentially equivalent to R Markdown’s inline R chunks. But epoxy chunks include a data chunk argument, which allows us to reference items in the knitted list directly without having to use $.

{epoxy knitted-2, data = knitted}
Report prepared on {when} from {where}
with knitr version {with} {emo_ji('happy')}.
Read more about [knitr::knit()]({doc_url}).


Report prepared on 2022-08-28 from inline-reporting.Rmd with knitr version 1.40 😆. Read more about knitr::knit().

Note that we can still have arbitrary R code in epoxy inline expressions: the emo_ji() function — a vignette-safe version of emo::ji() — exists in my global environment.

## Reporting Model Results

Suppose we have some model results that we’ve prepared into a table (for details, see Tristan’s blog post). These results summarize a linear mixed model estimating population averages for trees grown in several ozone conditions. I’ve copied the resulting data frame into this vignette to avoid taking extra dependencies for this vignette.

text_ready <-
data.frame(
term = c("intercept", "hund_days", "ozone", "hund_days_ozone"),
estimate = c("4.25", "0.34", "&minus;0.14", "&minus;0.04"),
se = c(0.131, 0.013, 0.158, 0.015),
ci = c("[4.00, 4.51]", "[0.31, 0.36]", "[&minus;0.45, 0.17]","[&minus;0.07, &minus;0.01]"),
stringsAsFactors = FALSE
)

We can use split() to make a list of data frames that we can index by the values in the term column.

stats <- split(text_ready, text_ready$term) We now have a list of one-row dataframes: str(stats) #> List of 4 #>$ hund_days      :'data.frame': 1 obs. of  4 variables:
#>   ..$term : chr "hund_days" #> ..$ estimate: chr "0.34"
#>   ..$se : num 0.013 #> ..$ ci      : chr "[0.31, 0.36]"
#>  $hund_days_ozone:'data.frame': 1 obs. of 4 variables: #> ..$ term    : chr "hund_days_ozone"
#>   ..$estimate: chr "&minus;0.04" #> ..$ se      : num 0.015
#>   ..$ci : chr "[&minus;0.07, &minus;0.01]" #>$ intercept      :'data.frame': 1 obs. of  4 variables:
#>   ..$term : chr "intercept" #> ..$ estimate: chr "4.25"
#>   ..$se : num 0.131 #> ..$ ci      : chr "[4.00, 4.51]"
#>  $ozone :'data.frame': 1 obs. of 4 variables: #> ..$ term    : chr "ozone"
#>   ..$estimate: chr "&minus;0.14" #> ..$ se      : num 0.158
#>   ..$ci : chr "[&minus;0.45, 0.17]" Now we can write up our results with inline reporting: {epoxy} The average log-size in the control condition was {stats$intercept$estimate} units, 95% Wald CI {stats$intercept$ci}. There was not a statistically clear difference between the ozone conditions for their intercepts (day-0 values), *B* = {stats$ozone$estimate}, {stats$ozone$ci}. For the control group, the average growth rate was {stats$hund_days$estimate} log-size units per 100 days, {stats$hund_days$ci}. The growth rate for the ozone treatment group was significantly slower, *diff* = {stats$hund_days_ozone$estimate}, {stats$hund_days_ozone$ci}.  The average log-size in the control condition was 4.25 units, 95% Wald CI [4.00, 4.51]. There was not a statistically clear difference between the ozone conditions for their intercepts (day-0 values), B = −0.14, [−0.45, 0.17]. For the control group, the average growth rate was 0.34 log-size units per 100 days, [0.31, 0.36]. The growth rate for the ozone treatment group was significantly slower, diff = −0.04, [−0.07, −0.01]. ### Inline reporting with autocomplete What’s extra neat about epoxy — and not readily apparent if you’re reading this vignette — is that RStudio’s autocomplete feature kicks in when you type stats$ inside a braced expression { }.

Actually, because the IDE doesn’t know about the epoxy knitr engine, the autocomplete tries to help out on every word. It’s typically easy to ignore the suggestions for words that are part of the prose, and it’s usually outweighed by the usefulness of being able to autocomplete the names in your data structures.

### Intermittent inline-reporting

Note that you don’t need to write your entire document or even paragraph inside an epoxy chunk; you can wrap only the data-heavy parts as needed.

There was not a statistically clear difference between the
ozone conditions for their intercepts (day-0 values),
{epoxy}
*B* = {stats$ozone$estimate}, {stats$ozone$ci}.

The growth rate for the ozone treatment group was significantly slower,
{epoxy}
*diff* = {stats$hund_days_ozone$estimate}, {stats$hund_days_ozone$ci}.


There was not a statistically clear difference between the ozone conditions for their intercepts (day-0 values), B = −0.14, [−0.45, 0.17]. The growth rate for the ozone treatment group was significantly slower, diff = −0.04, [−0.07, −0.01].

## Repeated inline reporting

Occasionally you may need to re-use the same phrase or document structure but for different slices of your data.

### Vectorized inline reporting chunks

Suppose we summarize the orange tree growth (normally I would use a combination of dplyr::group_by() and dplyr::summarize() here.)

summarize_tree_growth <- function(tree) {
tree <- Orange[Orange$Tree == tree, ] tree <- data.frame( tree = tree$Tree[1],
age_range = diff(range(tree$age)), circumference_first = tree$circumference[1],
circumference_last = tree$circumference[nrow(tree)] ) tree$growth_rate <- with(tree, (circumference_last - circumference_first) / age_range)
tree
}

orange_summary <- lapply(1:5, summarize_tree_growth)
orange_summary <- do.call(rbind, orange_summary)
orange_summary
#>   tree age_range circumference_first circumference_last growth_rate
#> 1    1      1464                  30                145  0.07855191
#> 2    2      1464                  33                203  0.11612022
#> 3    3      1464                  30                140  0.07513661
#> 4    4      1464                  32                214  0.12431694
#> 5    5      1464                  30                177  0.10040984

epoxy chunks, like glue::glue(), are vectorized, so if we find ourselves needing to repeat the same thing over and over again, we can use this feature to our advantage.

A quick recap of the growth observed in the orange trees:

{epoxy data = orange_summary}
- Tree number {tree} started out at {circumference_first}mm and,
over {age_range} days, grew to be {circumference_last}mm.


A quick recap of the growth observed in the orange trees:

• Tree number 1 started out at 30mm and, over 1464 days, grew to be 145mm.
• Tree number 2 started out at 33mm and, over 1464 days, grew to be 203mm.
• Tree number 3 started out at 30mm and, over 1464 days, grew to be 140mm.
• Tree number 4 started out at 32mm and, over 1464 days, grew to be 214mm.
• Tree number 5 started out at 30mm and, over 1464 days, grew to be 177mm.

### Template inline reporting chunks

By using knitr’s reference labels feature, and the epoxy data chunk option we saw above, you can create an epoxy template that you can re-use like a parameterized chunk.

You start by creating a labelled epoxy chunk with eval = FALSE

{epoxy average-growth, eval=FALSE}
an average of {signif(growth_rate * 7, 2)}mm per week.


that you can later use in your prose by referencing the chunk with ref.label and providing a different slice of data via the data chunk option.

The fourth tree was the largest tree at the end of the study, growing
{epoxy ref.label="average-growth", data = summarize_tree_growth(4)}

Meanwhile, the smallest tree was the third, which grew at
{epoxy ref.label="average-growth", data = summarize_tree_growth(3)}


The fourth tree was the largest tree at the end of the study, growing an average of 0.87mm per week. Meanwhile, the smallest tree was the third, which grew at an average of 0.53mm per week.