ezknitr - Avoid the typical working directory pain when using 'knitr'
Copyright 2023 Dean Attali. Licensed under the MIT license.
knitr is a popular package for
generating dynamic reports in R using the concept of literate
programming. ezknitr
is an extension of knitr that adds flexibility in several ways and
solves a few issues that are commonly encountered with knitr.
One common source of frustration with knitr is that it assumes the
directory where the source file lives should be the working directory,
which is often not true. ezknitr addresses this problem by giving you
complete control over where all the inputs and outputs are, and adds
several other convenient features. The two main functions are ezknit()
and ezspin(), which are wrappers around knitr's knit() and
spin(), used to make rendering markdown/HTML documents easier.
Table of contents
- Installation
- Overview
- Motivation & simple use case
- Use case: using one script to analyze multiple datasets
- Experiment with ezknitr
- spin() vs knit()
- Using rmarkdown::render()
Installation
`ezknitr` is available through both CRAN and GitHub.To install the CRAN version:
install.packages("ezknitr")
To install the latest developmental version from GitHub:
install.packages("devtools")
devtools::install_github("ropensci/ezknitr")
Overview
If you have a very simple project with a flat directory structure, then `knitr` works great. But even something as simple as trying to knit a document that reads a file from a different directory or placing the output rendered files in a different folder cannot be easily done with `knitr`.ezknitr improves basic knitr functionality in a few ways. You get to
decide:
- What the working directory of the source file is
- Default is your current working directory, which often makes
more sense than the
knitrassumption that the working directory is wherever the input file is
- Default is your current working directory, which often makes
more sense than the
- Where the output files will go
- With
knitr, all the rendered output files will be generated in the folder you're currently in
- With
- Where the figures generated in the markdown document will be stored
knitrmakes it cumbersome to change this directory
- Any parameters to pass to the source file
- Useful if you want to run an identical source file multiple times with different parameters
Motivation & simple use case
Assume you have an Rmarkdown file that reads a data file and produces a short report while also generating a figure. Native `knit()` (or `spin()` if you're starting with an R script instead of an Rmd file) works great if you have a flat directory structure like this:- project/
|- input.csv
|- report.Rmd
But what happens if you have a slightly more complex structure? In a
real project, you rarely have everything just lying around in the same
folder. Here is an example of a more realistic initial directory
structure (assume the working directory/project root is set to
project/):
- project/
|- analysis/
|- report.Rmd
|- data/
|- input.csv
Now if you want knitr to work, you'd have to ensure the path to
input.csv is relative to the analysis/ directory because that's
where the Rmd file is. This is counter-intuitive because most people
expect to create paths relative to the working directory/project root
(project/ in this case), but knitr will use the analysis/ folder
as the working directory. Any code reading the input file needs to use
../data/input.csv instead of data/input.csv.
Other than being confusing, it also means that you cannot naively run
the Rmd code chunks manually because when you run the code in the
console, your working directory is not set to what knitr will use as
the working directory. More specifically, if you try to run the command
that reads the input file, your console will look in
project/../data/input.csv (which doesn't exist).
A similar problem arises when you want to create files in your report:
knitr will create the files relative to where the Rmd file is, rather
than relative to the project root.
Another problem with the flat directory structure is that you may want
to control where the resulting reports get generated. knitr will
create all the outputs in your working directory, and as far as I know
there is no way to control that.
ezknitr addresses these issues, and more. It provides wrappers to
knit() and spin() that allow you to set the working directory for
the input file, and also uses a more sensible default working directory:
the current working directory. ezknitr also lets you decide where the
output files and output figures will be generated, and uses a better
default path for the output files: the directory containing the input
file.
Assuming your working directory is currently set to the project/
directory, you could use the following ezknitr command to do what you
want:
library(ezknitr)
ezknit(file = "analysis/report.Rmd", out_dir = "reports", fig_dir = "myfigs")
- project/
|- analysis/
|- report.Rmd
|- data/
|- input.csv
|- reports/
|- myfigs/
|- fig1.png
|- report.md
|- report.HTML
We didn't explicitly have to set the working direcory, but you can use
the wd argument if you do require a different directory (for example,
if you are running this from some build script or from any arbitrary
directory). After running ezknit(), you can run open_output_dir() to
open the output directory in your file browser if you want to easily see
the resulting report. Getting a similar directory structure with knitr
is not simple, but with ezknitr it's trivial.
Note that ezknitr produces both a markdown and an HTML file for each
report (you can choose to discard them with the keep_md and
keep_html arguments).
Use case: using one script to analyze multiple datasets
As an example of a more complex realistic scenario where `ezknitr` would be useful, imagine having multiple analysis scripts, with each one needing to be run on multiple datasets. Being the organizer scientist that you are, you want to be able to run each analysis on each dataset, and keep the results neatly organized. I personally was involved in a few projects requiring exactly this, and `ezknitr` was in fact born for solving this exact issue. Assume you have the following files in your project:- project/
|- analysis/
|- calculate.Rmd
|- explore.Rmd
|- data/
|- human.dat
|- mouse.dat
We can easily use ezknitr to run any of the analysis Rmarkdowns on any
of the datasets and assign the results to a unique output. Let's assume
that each analysis script expects there to be a variable named
DATASET_NAME inside the script that tells the script what data to
operate on. The following ezknitr code illustrates how to achieve the
desired output.
library(ezknitr)
ezknit(file = "analysis/explore.Rmd", out_dir = "reports/human",
params = list("DATASET_NAME" = "human.dat"), keep_html = FALSE)
ezknit(file = "analysis/explore.Rmd", out_dir = "reports/mouse",
params = list("DATASET_NAME" = "mouse.dat"), keep_html = FALSE)
ezknit(file = "analysis/calculate.Rmd", out_dir = "reports/mouse",
params = list("DATASET_NAME" = "mouse.dat"), keep_html = FALSE)
- project/
|- analysis/
|- calculate.Rmd
|- explore.Rmd
|- data/
|- human.dat
|- mouse.dat
|- reports/
|- human/
|- explore.md
|- mouse/
|- calculate.md
|- explore.md
Note that this example uses the params = list() argument, which lets
you pass variables to the input Rmarkdown. In this case, I use it to
tell the Rmarkdown what dataset to use, and the Rmarkdown assumes a
DATASET_NAME variable exists. This of course means that the analysis
script has an external dependency by having a variable that is not
defined inside of it. You can use the set_default_params() function
inside the Rmarkdown to ensure the variable uses a default value if none
was provided.
Also note that differentiating the species in the output could also have
been done using the out_suffix argument instead of the out_dir
argument. For example, using out_suffix = "human" would have resulted
in an ouput file named explore-human.md.
Experiment with ezknitr
After installing and loading the package (`library(ezknitr)`), you can experiment with `ezknitr` using the `setup_ezknit_test()` or `setup_ezspin_test()` functions to see their benefits. See `?setup_ezknit_test` for more information.spin() vs knit()
`knit()` is the most popular and well-known function from `knitr`. It lets you create a markdown document from an Rmarkdown file. You can learn more about `knit()` on [the official knitr page](https://yihui.org/knitr/).spin() is similar, but starts one step further back: it takes an R
script as input, creates an Rmarkdown document from the R script, and
then proceeds to create a markdown document from it. spin() can be
useful in situations where you develop a large R script and want to be
able to produce reports from it directly instead of having to copy
chunks into a separate Rmarkdown file. You can read more about why I
like spin() in the blog post "knitr's best hidden gem:
spin".
Using rmarkdown::render()
When the core of this package was developed, none of its functionality was supported in any way by either `knitr` or `rmarkdown`. Over time, `rmarkdown::render()` got some new features that are very similar to features of `ezknitr`. Native support for parameters inside Rmarkdown files using YAML is a big feature which makes the use of `set_default_params()` and the `params` argument of `ezknitr` less important. However, the core problem that `ezknitr` wants to solve is the working directory issue, and this issue has yet to be addressed by `rmarkdown` or `knitr`, which makes `ezknitr` still useful.Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
