This document describes the coding style used within the package. Having a consistent style enhances the readability and "understandability" of the code and make it easier for users and developers to work with this package and with other, related packages from Mazama Science.
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 embraces lowerCamelCase
for object names.
With the casing settled, we use an ornithologist’s sensibility for how to identify things:
bird
blackBird
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:
count
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:
patList
— a list
of pat
objectsSPDF
— a SpatialPolygonsDataFrame
statsTbl
— a tibble
of statistical resultsWe occasionally use ‘_’ to create classes of similar variables that are otherwise hard to name, e.g.:
SoH_sub SoH_tbl SoH_tidy
Most functions should strive to be atomic in nature and should do one thing really well. Think of them as individual 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:
pas_createNew() pas_filter() pas_filterArea() pas_filterNear() pas_getColumn() pas_getDeviceDeploymentIDs() pas_getIDs() ...
All of these functions begin with pas_
because they are for creating or working
with pas
objects. Many of these functions accept a pas
object as their first
argument and return a modified pas
. This means that they can be used with the
"pipe" operator and chained together as in:
Nipomo_labels <- pas_load() %>% pas_filterNear( longitude = -120.476, latitude = 35.043, radius = "10 km" ) %>% pas_getLabels()
Sometimes functions are named after the object they return. The initial
create
in these function names is understood. This is especially true with
plotting functions. Examples include:
pat_dygraph() pat_leaflet() pat_multiPlot() pat_scatterPlotMatrix()
Each file should contain a single function of the same name. Thus, the function
named createAPIList()
is defined in createAPIList.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-outliers.R utils-paSynoptic.R utils-paTimeseries.R utils-pipe.R utils-plot.R utils-sampling.R utils-sensor.R
We generally adhere to the Wickham Style Guide for syntax with a few exceptions:
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.
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:
diamond_bar <- pas_load() %>% pas_filterNear( longitude = -117.820833, latitude = 34.001667, radius = "20 km" )
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 need 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.
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