Leveraging Rmarkdown and Nextflow for automatic report generation
The R language ecosystem offers an unparalleled experience for interactive data analysis, with its stream-of-consciousness fluidity that lets you focus on discovery rather than syntax. Thanks to the thoughtfully designed Posit IDE (formerly RStudio), creating and knitting markdown reports feels effortless and natural. However, there’s a catch - stepping outside this comfortable IDE environment often requires some clever problem-solving to replicate these seamless workflows in other contexts.
Working extensively with Nextflow, I found myself wanting to combine its pipeline automation with R’s reporting capabilities. After some experimentation, I developed a reliable approach that I’ll share in this guide.
Here’s what we’re aiming to achieve:
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Execute a bioinformatics pipeline (orchestrating various command-line tools)
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Dynamically feed pipeline results into a parameterized markdown document
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Automatically generate polished reports in various formats (.html, .pdf, .md)
Implementing this workflow turns out to be surprisingly straightforward, offering a lightweight approach to enhance your Nextflow pipeline with visually engaging reports. However, there are many potential ways to integrate the two languages, some of which work better than others. Let me share the patterns I like to use to help you avoid the pitfalls.
A straw-man problem
Let’s work through a practical example: creating a report that analyzes the number of reads in a FASTQ file. Thanks to Knitr’s powerful features, we can easily generate a professional-looking report complete with dynamic tables, informative figures, and an automatically generated table of contents - all with minimal effort on our part.
My preferred approach starts with rapid prototyping in the RStudio IDE, where we can quickly iterate and visualize our report’s structure. For the sake of illustration, here’s what a basic report might look like:
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---
title: "FASTQ Read Count Report"
output:
md_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r main}
fq_lines <- readLines(gzfile('/path/to/in.fastq.gz'))
n_reads <- nrow( fq_lines ) / 4
```
There are `r n_reads` in the FASTQ file.
| While this example has some obvious limitations (like loading an entire FASTQ file into memory), don’t get too caught up in the specific implementation details. The goal here is to illustrate the mechanics of how data flows between Nextflow and R markdown - we’re using a deliberately simple example to keep the focus on these core concepts. |
Building the Workflow Structure
Now that we have our report template, let’s design the workflow that will generate it. I’ll start with a simple yet effective structure that demonstrates the core concepts:
The workflow diagram above elegantly translates into this Nextflow code:
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process analysis {
// ...
}
process reporting {
// ...
}
workflow {
input = channel.fromPath(params.input)
analysis(input)
reporting( analysis.out )
}
Breaking Free from the IDE: Automating Report Generation
Now comes an interesting challenge: how do we replicate RStudio’s seamless report generation in an automated pipeline? While the IDE makes report compilation feel magical with its "Knit" button, we need a programmatic approach that works without user interaction.
The solution lies in R’s command-line interface. After consulting Yihui Xie’s excellent R Markdown: The Definitive Guide, I discovered we can trigger report generation with a simple command:
$ Rscript -e "rmarkdown::render('report.Rmd')"
This command does everything the IDE’s "Knit" button does: it loads the R markdown document, executes all code chunks, and generates the final report. Let’s integrate this into our Nextflow process, designing it to:
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Accept our analysis results (the count data)
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Include metadata (like sample IDs) for report customization
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Output a polished HTML report ready for sharing
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process reporting {
input:
tuple( val(meta), path('counts.csv') )
output:
tuple( val(meta), path('Report.html') )
}
Adding in our Knitting command above we have:
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process reporting {
input:
tuple( val(meta), path('counts.csv') )
output:
tuple( val(meta), path('Report.html') )
script:
"""
Rscript -e "rmarkdown::knit('report.Rmd')"
"""
}
Running this code reveals our first challenge - Rmarkdown fails with an error:
Error in abs_path(input) : The file 'report.Rmd' does not exist. Calls: <Anonymous> -> setwd -> dirname -> abs_path In addition: Warning message: In normalizePath(x, winslash = winslash, mustWork = must_work) : path[1]="report.Rmd": No such file or directory Execution halted
The issue is straightforward: our process needs access to the R markdown template file. There are two main approaches to solving this:
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Container-based: Bundle the .Rmd template inside your Docker container, treating it as an integral part of the process. This ensures the template is always available when the process runs, but makes template updates more cumbersome as they require rebuilding the container.
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Nextflow-managed: Let Nextflow handle the template file as a process input, taking advantage of its built-in file staging capabilities. This approach offers more flexibility and easier template maintenance.
After experimenting with both methods, I strongly prefer the Nextflow-managed approach. Here’s how to implement it:
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process reporting {
input:
tuple( val(meta), path('counts.csv') )
path('report.Rmd') (1)
output:
tuple( val(meta), path('Report.html') )
script:
"""
Rscript -e "rmarkdown::knit('report.Rmd')"
"""
}
workflow {
input = // ...
report_rmd = file("assets/report.Rmd") (2)
analysis(input)
reporting( analysis.out, report_rmd ) (3)
}
These are the spots that need to be modified:
| 1 | Add the Rmd template as an input to the nextflow process |
| 2 |
Reference the location of
the markdown template. I
like to wrap this path in
file() so that
it can seamlessly resolve
files from the local
filesystem or remote
URLs/s3. I typically store
my report templates in a
folder called "assets" for
files that are accessory to
the pipeline
|
| 3 | Pass the markdown template as an input to the reporting process |
| If you are still getting an error after passing the report as an input it is possible you are using an old version of knitr. Until recently there was an issue with how knitr treated soft-linked files, which is the default staging mechanism when running nextflow locally. If you are running into this problem try updating your knitr package. |
Passing Context Between Nextflow and Knitr
Creating informative and detailed
reports requires seamless data flow
between your Nextflow workflow and
the R markdown template. While this
is something that you typically
would not even think about if the
script: block of your
task is a simple bash command, I
find there is often more context I
want to pass when generating a
report, so I find it helpful to
consider the exchange that happens
at this
interface explicitly:
While this interaction involves several moving parts, understanding the core mechanics will help you build more dynamic and informative reports. Let’s break down these essential connections.
Method 1: Shared File Conventions
The simplest way to pass data between Nextflow and your report is through shared file naming conventions. In our example, the count statistics needed for tables and figures are passed through a CSV file. This connection relies on both sides agreeing on the filename:
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process reporting {
input:
tuple val(meta), path('counts.csv') (1)
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counts <- readr::read_csv('counts.csv') (1)
| 1 | The fact that both the Nextflow script and the .Rmd template both use the same convention that the counts file is called 'counts.csv' allows information to be passed between the two contexts. |
This approach to information sharing is elegantly simple and makes it easy to trace data flow through your workflow. However, it comes with a caveat: it doesn’t follow the DRY principle and can be somewhat fragile. For instance, renaming the file requires changes in both main.nf and Report.Rmd - miss one, and your pipeline breaks. I view these shared filename conventions as forming an implicit "contract" between different parts of the pipeline. While effective, they require careful attention during maintenance and updates.
Method 2: Dynamic Parameters
While shared file conventions work well for data transfer, sometimes we need more flexibility in how we pass context to our reports. Consider plot titles - using generic labels like "FASTQ read count" makes reports reusable, but potentially less informative and harder to interpret at a glance.
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ggplot() + ... + ggtitle("FASTQ read count")
Personally, I find overly vague titles confusing, it adds mental burden to the viewer What am I looking at? One way to lessen this mental burden is provide the viewer with more relevant context. For example, we could use the actual sample ID in the plot title so the user can more readily recognize what is being plotted.
Working backwards, we ultimately want out plotting code to look like this:
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ggplot() + ... + ggtitle(paste(sampleId,"read count"))
Rather than hard-code a generic title, we use a variable to enable the plot title to be parameterized dynamically when the report is knitted. How can we pass context about how this variable should be set between Nextflow and Knitr?
Luckily, the Rmarkdown authors
have provided an elegant
solution: the knit command can
be parameterized through the
YAML front-matter’s
params section.
Here’s a basic example:
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---
params:
arg: NULL
---
# Access the parameter in your R code
value <- params$arg
For our specific use case,
we’ll define a
sampleId parameter
to customize plot titles:
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---
params:
sampleId: NULL
---
# Use the sample ID for plot customization
sampleId <- params$sampleId
And the "glue" that connects it
with Nextflow is Groovy’s
powerful
String Interpolation
that allows you to pull
information out of a
Map of metadata.
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process reporting {
// ...
script:
"""
Rscript -e "rmarkdown::render('report.Rmd', params = list(sampleId = '${meta.id}'))"
"""
}
This pattern still relies on a shared convention - both the Nextflow script and .Rmd template must agree on parameter names like "sampleId". However, it offers greater flexibility than hard-coded filenames. When a report expects specific filenames like "counts.csv", you’re forced to either rename your files or modify the report. By parameterizing these values instead, the same report template can be easily reused across different contexts without modification.
Making Your Workflow Portable with Rocker Verse Images
The next challenge is deploying your workflow to production environments like cloud services or compute clusters. This can be particularly tricky with R workflows because, unlike compiled languages that produce portable binaries, R requires both the interpreter and all dependent packages to be available at runtime. Enter Docker - the industry standard solution for packaging software environments. Docker lets you bundle your entire software stack, including R, required packages, and system dependencies, into a single portable image. While containerization is widely adopted in bioinformatics, with most tools offering ready-to-use Docker images, Nextflow takes it a step further by providing seamless integration with these containerized environments.
But what about R specifically? Do you need to create custom Dockerfiles to install R and its packages? Given that the R environment can be hundreds of megabytes and many packages require complex compilation steps, this could be daunting.
Fortunately, there’s a simpler solution. For workflows that stick to core tidyverse packages and knitr, we can leverage pre-built images from the Rocker Project. This initiative, maintained by the R community, provides a comprehensive suite of Docker images with R and common packages pre-installed. For our Rmarkdown reports, the "verse" image is perfect - it includes everything we need. Adding it to your workflow is as simple as one line:
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process reporting {
container "rocker/verse"
// ...
}
One place in particular where containerization comes in handy is when the workflow is run in the cloud. For example, cloud executors like AWS Batch require all tasks to be run using a Docker container. If you already have your AWS Batch infrastructure setup, referenceing a publicly hosted docker image like 'rocker/verse' is all that is needed to run the process in the cloud.
Creating Reusable Report Modules
As your collection of workflows expands, you’ll notice certain reporting tasks appearing repeatedly across different projects. Instead of duplicating code through copy-paste, this presents an excellent opportunity to create reusable modules. Consider a standard QC report that you might want to run after various preprocessing and alignment workflows - rather than maintaining multiple copies of this logic, we can create a single, well-tested module that can be easily imported wherever needed.
Nextflow provides a clean mechanism for code reuse through its module system. Let’s look at how to structure our reporting code as a module. First, we’ll organize our code following Nextflow’s conventional module structure:
/nf-lib/
└── modules/
└── reporting/
└── main.nf
I’m using
/nf-lib here as
a simple framework to
demonstrate module
inclusion. A popular
alternative to consider is
nf-core, which provides a
conventional framework for
Nextflow module organization
and reuse.
|
With this structure in place, importing and using our reporting module becomes straightforward:
Here’s how we can import and use our reporting module in a new workflow:
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include {reporting} from '/nf-lib/modules/reporting'
workflow {
some_bespoke_analysis(params.input)
reporting( some_bespoke_analysis.out )
}
This approach will
almost work with our
existing code, but there’s one
crucial detail we need to address:
template resolution. Remember how we
specified the RMarkdown template
path in
Example 6
(Callout #2)? That path is relative
to the main.nf script.
When Nextflow executes, it resolves
paths relative to the current
working directory where you run the
nextflow run command -
not relative to the module’s
location. In our new script
reuse.nf, the
file('assets/report.Rmd')
would resolve relative to
`reuse.nf’s location, which
isn’t what we want. Two
changes are needed to make our
module truly portable:
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Use
${moduleDir}to make the template path relative to the module’s location -
Convert the process into a workflow to properly encapsulate the template handling
Here’s how these changes look in practice:
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report_rmd = file("${moduleDir}/assets/report.Rmd")
workflow reporting {
take:
data
main:
knit_report( data, report_rmd )
emit:
knit_report.out
}
By exposing our reporting logic as a
workflow that manages
its own template, we create a truly
modular component. The template
becomes an implementation detail
hidden from users of the module -
they only need to provide their
data, and the module handles
everything else. This approach makes
adding reports to pipelines nearly
effortless, which encourages their
use across projects. The result?
Faster time-to-insight and more
consistent analysis interpretation
across your entire workflow
collection.
Summary
While combining Nextflow’s workflow management capabilities with R’s sophisticated reporting tools might seem challenging at first, the integration is not only possible but powerful. This combination offers the best of both worlds: Nextflow’s robust pipeline orchestration and R’s exceptional data visualization and reporting capabilities. The result is a system that can automatically generate rich, informative reports as part of your bioinformatics workflows.
I hope this guide helps you incorporate automated R-powered reporting into your Nextflow pipelines. By following these patterns, you can create maintainable, reproducible workflows that automatically generate professional reports - saving time and reducing the risk of manual reporting errors. If you encounter any challenges implementing these patterns or have suggestions for improvements, please share your experiences in the comments below.
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