ezknitr - Avoid the typical working directory pain when using 'knitr'
=====================================================================

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> *Copyright 2023 [Dean Attali](https://deanattali.com). Licensed under
> the MIT license.*

[`knitr`](https://github.com/yihui/knitr) is a popular package for
generating dynamic reports in R using the concept of [literate
programming](http://www.literateprogramming.com/knuthweb.pdf). `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](#installation)
-   [Overview](#overview)
-   [Motivation & simple use case](#motivation-simple-usecase)
-   [Use case: using one script to analyze multiple
    datasets](#usecase-advanced)
-   [Experiment with ezknitr](#experiment)
-   [spin() vs knit()](#spin-vs-knit)
-   [Using rmarkdown::render()](#using-render)

<h2 id="installation">
Installation
</h2>
`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")

<h2 id="overview">
Overview
</h2>
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 `knitr` assumption that the working
        directory is wherever the input file is
-   Where the output files will go
    -   With `knitr`, all the rendered output files will be generated in
        the folder you're currently in
-   Where the figures generated in the markdown document will be stored
    -   `knitr` makes 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

<h2 id="motivation-simple-usecase">
Motivation & simple use case
</h2>
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).

<h2 id="usecase-advanced">
Use case: using one script to analyze multiple datasets
</h2>
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`.

<h2 id="experiment">
Experiment with ezknitr
</h2>
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.

<h2 id="spin-vs-knit">
spin() vs knit()
</h2>
`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"](https://deanattali.com/2015/03/24/knitrs-best-hidden-gem-spin/).

<h2 id="using-render">
Using rmarkdown::render()
</h2>
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](https://ropensci.org/code-of-conduct/). By participating in this project you agree to
abide by its terms.

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