---
title: "Extending riskmetric"
author: "R Validation Hub"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Extending riskmetric}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
library(riskmetric)
library(dplyr)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

options(repos = "https://cran.rstudio.com")

print_riskmetric_function <- function(names) {
  for (name in names) {
    fprint <- capture.output(getNamespace("riskmetric")[[name]])
    fprint <- fprint[!startsWith(fprint, "<")]
    cat(name, ' <- ', sep = '')
    cat(paste(fprint, collapse = '\n'), '\n\n')
  }
}
```

`riskmetric` is designed to be readily extensible. This is done through use of
the S3 method dispatch system and a conscious acknowledgement of the varying
needs that someone may have when assessing package risk. With this in mind,
every user-facing function is designed first and foremost to be flexible.

Here we'll walk through a trivial example where we'll extend `riskmetric` to add
a new assessment, scoring and risk summary function to determine the risk
associated with a package given its name starts with the letter "r".


# Package Classes

Before we can assess a package we first need to represent a package as data.
We refer to this collection of package metadata as a "package reference" - a
way of referring to the related information we've been able to pull together
about a package. This is represented as a `pkg_ref` class object. As dimensions
of risk are assessed, the data needed to evaluate it in those terms is cached
within this object, building up a small store of information about the package
which other assessments can refer to or build off of. 

Importantly, not all references to packages are equal. We can collect
information given the source code, a locally installed package or by scraping
data about a package from the web. There's a hierarchy of subclasses that
encapsulate these disparate use cases.

```
pkg_ref
 ├─ pkg_source
 ├─ pkg_install
 └─ pkg_remote
     ├─ pkg_cran_remote
     └─ pkg_bioc_remote
```

These subclasses direct the behavior of all downstream operations and provides
flexibility about how to bucket the implementations of how similar data can be
collected from a number of potential sources. For example, to determine the
author of a package it would be easiest to look at a package's `DESCRIPTION`
file where this content is maintained. However, without access to the source or
installed files, one could find the same information on the CRAN package
webpage. Using these subclasses, the appropriate method of collecting this data
can be selected.


# Adding an Assessment

Assessments are the atomic unit of the `riskmetric` package, and are used to
kick off an individual metric evaluation. Each assessment is a generic function
starting with an `assess_` prefix, which can dispatch based on the subclass of
the `pkg_ref` object.

## Assessment Example

As an example, take a look at how `assess_has_news` has been implemented. We'll
focus on just the generic and the `pkg_install` functions:

```{r, echo = FALSE}
print_riskmetric_function(c("assess_has_news", "assess_has_news.pkg_install"))
```

There are a couple things to note. First, the S3 system is used to dispatch
functionality for the appropriate package reference class. Since the way we'd
assess the inclusion of a NEWS file might be different for an installed package
or remotely sourced metadata, we may have separate functions to process these
datatypes in distinct ways.

Second, for each assessment, a `pkg_metric` object is returned. This stores the
atomic data pertaining to the metric and importantly adopts a unique subclass
for the assessment function.

Finally, a cosmetic `"column_name"` attribute is tagged to the function. This
is used when calling the `assess` function. The `assess` verb is a convenience
function which steps through all available assessments returning a `tibble`
of assessment outputs. The `"column_name"` provides a more user-friendly
label for the assessment in this `tibble`. 

## Writing a New Assessment

Now we'll write our assessment. Eventually we want to consider a package high
risk if the name does not start with "r". We'll need to make a `pkg_metric`
object containing the first letter of the name.

```{r, results = 'hide'}
assess_name_first_letter <- function(x, ...) {
  UseMethod("assess_name_first_letter")
}
attr(assess_name_first_letter, "column_name") <- "name_first_letter"

assess_name_first_letter.pkg_ref <- function(x, ...) {
  pkg_metric(substr(x$name, 0, 1), class = "pkg_metric_name_first_letter")
} 
```


# Adding `pkg_ref` Metadata

Perhaps we want to reuse metadata used when assessing the first letter so that
it can be reused by other assessments. For particularly taxing metadata, such
as metadata that requires a query against a public API, scraping a web page or
a large data download, it's important to store it for other assessment
functions to reuse.

To handle this, we define a function for `pkg_ref_cache` to dispatch to.

## Example Metadata Caching

This is how the `riskmetric` package handles parsing the DESCRIPTION file so
that it can feed into all downstream assessments without having to re-parse the
file each time or copy the code to do so.

```{r, echo = FALSE}
print_riskmetric_function(c("pkg_ref_cache.description", "pkg_ref_cache.description.pkg_install"))
```

Once these are defined, they'll be automatically called when the field is first
accessed by the `pkg_ref` object, and then stored for any downstream uses.

```{r, eval = FALSE}
library(riskmetric)
package <- pkg_ref("riskmetric")
```

```{r, echo = FALSE}
package_real <- pkg_ref("riskmetric")
```

```{r, echo = FALSE}
rver <- gsub("\\.\\d+$", "", paste0(R.version$major, ".", R.version$minor))
package <- pkg_ref("riskmetric")

# hack in order to mutate package environment directly so nobody accidentally
# publishes any personal info in their library path
invisible(riskmetric:::bare_env(package, {
  package$path <- sprintf("/home/user/username/R/%s/Resources/library/riskmetric", rver)
}))

package
```

Notice that upon initialization, the `description` field indicates that it
hasn't yet been evaluated with a trailing `...` in the name. When accessed, the
object will call a caching function to go out and grab the package metadata and
return the newly derived value.

```{r, eval = FALSE}
package$description
```

Because the `pkg_ref` object stores an environment, caching this value once
makes them available for any future attempts to access the field. This is
helpful because we, as developers of the package, don't need to think critically
about the order that assessments are performed, and allows users to redefine the
order of assessments without worry about how metadata is acquired.

## Writing a Metadata Cache

Now, for our new metric, we want to cache the package name's first letter. We
need to add a new `pkg_ref_cache` function for our field. Thankfully, any
subclass of `pkg_ref` can access the first letter the same way, so we just need
the one function.

```{r}
pkg_ref_cache.name_first_letter <- function(x, name, ...) {
  substr(x$name, 0, 1)
}
```

After adding this caching function, we need to make a small modification to
`assess_name_first_letter.pkg_ref` in order use our newly cached value.

```{r}
assess_name_first_letter.pkg_ref <- function(x, ...) {
  pkg_metric(x$name_first_letter, class = "pkg_metric_name_first_letter")
} 
```

Let's try it out!

```{r}
package$name
package$name_first_letter
```


# Defining an Assessment Scoring Function

Next, we need a function for scoring our assessment output. In this case, our
output is a `pkg_metric` object whose data is the first letter of the package
name.

We'll add a dispatched function for the `score` function. As a convention, these
functions return a numeric value representing how well the package conforms to
best practices with values between 0 (poor practice) and 1 (best practice).

```{r}
metric_score.pkg_metric_name_first_letter <- function(x, ...) {
  as.numeric(x == "r")
}
```


# Adding our Assessment to the `pkg_assess()` Verb

The `assess` function accepts a list of functions to apply. `riskmetric`
provides a shorthand, `all_assessments()`, to collect all the included
assessment functions, and you're free to add to that list to customize your own
assessment toolkit.

```{r}
library(dplyr)
pkg_ref(c("riskmetric", "utils", "tools")) %>%
  as_tibble() %>%
  pkg_assess(c(all_assessments(), assess_name_first_letter))
```

Our scoring function will automatically get picked up and used by the score
method.

```{r, warning = FALSE}
pkg_ref(c("riskmetric", "utils", "tools")) %>%
  as_tibble() %>%
  pkg_assess(c(all_assessments(), assess_name_first_letter)) %>%
  pkg_score()
```

and we can define our own summarizing weights by passing a named list to `pkg_score`.

```{r, warning = FALSE}
pkg_ref(c("riskmetric", "utils", "tools")) %>%
  as_tibble() %>%
  pkg_assess(c(all_assessments(), assess_name_first_letter)) %>%
  pkg_score(weights = c(has_news = 1, name_first_letter = 1))
```

Of course you can do any downstream processing of the resulting `tibble` if
you'd like to further fine-tune your summarization using a nonlinear function.

# How you can help...

The `riskmetric` package was designed to be easily extensible. You can develop
dispatched functions in your development environment, hone them into well formed
assessments and contribute them back to the core `riskmetric` package once
you're done.

If you'd like feedback before embarking on developing a new metric, please feel
free to [file an issue on the riskmetric GitHub](https://github.com/pharmaR/riskmetric/issues/new?labels=Metric%20Proposal). 
