---
title: "Developer Style Guide"
author: "Jonathan Callahan"
date: "2022-02-15"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Developer Style Guide}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# R Style Guide

This document describes the coding style used within the package.  Having a
consistent style enhances the readability and "understandability" of the code 
and makes it easier for users and developers to work with this package and
with other, related [Mazama Science packages](https://github.com/MazamaScience).

## Naming Objects

Naming variables is one of the most important things to get right to make your 
code readable and understandable to future readers of the code. _(Perhaps even 
yourself!)_. Having a system for creating names also makes it easier to come up 
with new ones.

Mazama Science packages embrace **`lowerCamelCase`** for object names.

With the casing settled, we use an ornithologist’s sensibility for how to 
identify things:

* What is it? — a `bird`
* What kind of bird  is it? — a `blackBird`
* What kind of blackBird  is it? — a `redwingedBlackBird`

It’s a simple system:  start with a noun and prefix it with descriptors until 
it is uniquely identified.

In this system we would never have a variable called: `num_hours`. Instead we go 
through our process:

* What is it? — _(Hmm. What noun describes this? Ah yes!)_ — a `count`
* What kind of count  is it? — _(It's not a "head count" or a "body count".)_ 
It's an `hourCount`.

For complex objects it is often helpful to give readers of the code a hint as to 
what type of object it is so they will know how to work with it. We often use 
variable names like:

* `monitor`  — a _mts_monitor_ object

We occasionally use ‘_’ to create classes of similar variables that are 
otherwise hard to name, _e.g._:

```
QC_negativeValues
```

## Naming Functions

Most functions should strive to be atomic in nature and should do one thing 
really well. Think of them as functional _Lego_ bricks that we click together
to achieve more advanced functionality. Where objects are _**well described nouns**_, 
functions are _**well described verbs**_ that describe what they do as in:

```
monitor_collapse()
monitor_combine()
monitor_dropEmpty()
monitor_filter()
monitor_filterDate()
monitor_select()
...
```

All of these functions begin with `monitor_` because they are for creating or working
with _mts_monitor_ objects. Many of these functions accept a _mts_monitor_ object as their first 
argument and return a modified _mts_monitor_. This means that they can be used with the
`%>%` "pipe" operator and chained together as in:

```
# Create a daily average dataframe (aka tibble)
Sacramento_area_daily_avg <-

  # get all data for 2018
  monitor_loadAnnual(2018) %>%
  
  # filter to a 2-week period
  monitor_filterDate(
    startdate = 20181108,
    enddate = 20181123,
    timezone = "America/Los_Angeles"
  ) %>%
  
  # filter to monitors near Sacramento
  monitor_filterByDistance(
    longitude = -121.4931,
    latitude = 38.56844 ,
    radius = 50000
  )  %>%
  
  # combine by averaging hourly values from different monitors
  monitor_collapse() %>%
  
  # calculate local time daily averages
  monitor_dailyStatistic() %>%
  
  # extract daily average values from 'monitor' object
  monitor_getData()
```  

## Naming Files

Each file should contain a single function of the same name. Thus, the function 
named `monitor_filterDate()` is defined in `monitor_filterDate.R`. An exception is made
for small, mostly internal functions used in conjunction with a particular type
of object or activity. These can be stored together in a file named `utils-~`:

```
utils-monitor.R
```

## Syntax

We generally adhere to the [Wickham Style Guide](http://adv-r.had.co.nz/Style.html) 
for syntax with a few exceptions:

### Spacing

**Do** place spaces around code in parentheses if it is an `if` test:

```
if ( <logical expression part1> && <logical expression part2> ) {
  ...
}
```

When debugging, this makes it much easier to select the logical test with a 
cursor and paste it into the RStudio console.

### Lists

We generally like to specify R lists with each `parameter = value` pair on a 
separate line. This goes for regular lists and for named argument lists passed 
to a function:

```
  # Filter to a 2-week period
  monitor_filterDate(
    startdate = 20181108,
    enddate = 20181123,
    timezone = "America/Los_Angeles"
  ) %>%
```

Coding this way makes it easy to see which function arguments are being passed.
It also eases future refactoring of the code when additional arguments need to
be added or the order of arguments needs to be changed.

-----

It is our belief that good code should be both readable and understandable and
should inspire others to copy and innovate on their own.
