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README.Rmd
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---
output:
github_document:
pandoc_args: --webtex=https://ibm.codecogs.com/png.latex?
always_allow_html: yes
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
```{css, echo=FALSE}
.console-out {
background-color: black;
border: 3px solid white;
}
```
# dyn.log
# Overview <img src="man/figures/hex.png" width = "175" height = "200" align="right" />
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[](https://cran.r-project.org/web/licenses/MIT)
[](https://app.codecov.io/gh/bmoretz/dyn.log?branch=master)
[](https://cran.r-project.org/package=dyn.log)
[](https://bmoretz.github.io/dyn.log/)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
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The goal of `dyn.log` is to be a comprehensive, dynamic, configuration-based logging package for R. While there are several excellent logging solutions already in the R ecosystem, I found none of them were both robust functionality-wise, and completely generic i.e., they are designed exclusively for use within a specific environment (shiny/plumber/etc).
This is the raison d'être for `dyn.log`: a flexible logging solution that:
* Integrates seamlessly
+ set an R option or call `init_logging` in your .Rprofile and your logger is attached (and will re-attach when you `rm(list = ls())`).
* Agnostics of project type, so it can be deployed across the entire application stack
+ [shiny](https://github.com/rstudio/shiny)
+ [plumber](https://github.com/rstudio/plumber)
+ stand-alone prototypes/scripts
+ etc.
* Reusable
+ log layouts, levels, associations, etc. can be highly-customized for both shiny apps and plumber services, then reused across packages running in different environments (docker containers).
### Demos
In order to keep the package as light as possible dependency-wise, there are no demos that come integrated with the package. However, separate repositories are **coming soon** to demonstrate the above scenarios and will be linked here.
## Installation
#### GitHub
You can install the development version of dyn.log from [GitHub](https://github.com/) with:
```r
remotes::install_github("bmoretz/dyn.log")
```
#### CRAN
You can install the latest stable version of dyn.log from CRAN:
```r
install.packages("dyn.log")
```
### Overview
### Basic Usage
For basic/most common usage simply install the package from one of the above sources, load the package, initialize the logger, and a logging instance will show up in your global environment (by default, named 'Logger'):
```{r, eval=F}
library(dyn.log)
init_logger()
var1 <- "abc"; var2 <- 123; var3 <- runif(1)
Logger$debug("my log message - var1: {var1}, var2: {var2}, var3: {var3}")
```
You can also skip the call to *init_logger* by setting a global option that specifies the configuration you wish to use, i.e., placing:
```{r}
options("dyn.log.config" = "default")
```
In your .Rprofile will automatically configure the default logger and the global logging instance will be attached when you call:
```{r}
library(dyn.log)
```
The **"dyn.log.config"** variable can be either a predefined configuration (name) in the package, or a path to a local file that you have pre-customized. This is useful for sharing a single bespoke log configuration across multiple packages or projects.
### Logging
There are three main components of a log message, each of them are covered in detail in their respective vignettes. For more detail about how logging works and how you can customize it, please see the [package site](https://bmoretz.github.io/dyn.log/):
* Levels
+ The levels you want to have accessible in your logger.
* Formats
+ The types that define contextual information to be logged in a message.
* Layouts
+ Containers for format objects that define the rendering specifications for a log message.
The logging functionality is exposed by a R6 class, *LogDispatch*, is accessible as a global variable called, by default, **Logger**. The Logger will have methods that correspond to the *log levels* that are defined in its yaml configuration, which makes logging intuitive. Log messages are automatically assumed to be in standard [glue](https://github.com/tidyverse/glue) format so local environment variables are captured in messages.
#### Simple Example
The "out of the box" (OTB) configuration specifies a default vanilla log format that displays the
level that was logged, the current time-stamp (with the default TS format), and the log message:
```{r simple_example, eval=F}
var1 <- "abc"; var2 <- 123; var3 <- runif(1)
Logger$debug("my log message - var1: {var1}, var2: {var2}, var3: {var3}")
```
#### Configuration
Everything about dyn.log is configuration driven, the package comes with a basic configuration **default.yaml**, show below it its entirety and broken down in the sections that follow:
*For a detailed look at customizing these settings please see [Configurations](https://bmoretz.github.io/dyn.log/articles/Configuration.html) vignette online.*
```yaml
`r xfun::file_string('inst/default.yaml')`
```
### Logger Variable
The first setting, *variable*, defines the name of the global variable you want to access the logger with. The default is **Logger**, but you can easily change it to: *log*, *my_log*, *msg* or any other value (as long as it's a *valid* R variable name). The **LogDispatch** object is also a [singleton](https://en.wikipedia.org/wiki/Singleton), so you always access the logger directly:
```{r, eval=F}
nums <- paste0(round(rnorm(25, 0, 5), digits = 2), collapse = ", ")
LogDispatch$new()$warn("These numbers '{nums}' are out of the expected range.")
```
#### Settings
The **settings** node contains the core settings of the log dispatcher, by attribute. These
are covered in detail in the [Configuration](https://bmoretz.github.io/dyn.log/articles/Configuration.html) section of the manual.
#### Levels
The **levels** node contains the log levels you want available in your environment. When a log
level is defined in the configuration, it automatically becomes accessible via a first-class
function on the dispatcher, e.g.:
```{r, eval = F}
Logger$info("This will be logged with 'INFO' severity level")
```
You can view all configured log levels, and get a quick summary about them by calling *display_log_levels():*
```{r, eval = F}
display_log_levels()
```
The default logging configuration closely resembles the fairly ubiquitous [log4j](https://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html) scheme. For a detailed look at log levels refer to the [Levels](https://bmoretz.github.io/dyn.log/articles/Levels.html) vignette online.
#### Layouts
Every log message needs to have a format so the dispatcher knows what to render on a log call. Formats are defined in the yaml config and comes with some basic ones pre-configured.
The default log layout is a standard format: {LEVEL} - {TIMESTAMP} - {MSG}, with space as a separator between format objects.
### Customizing a Log Message
Log message layouts are exposed as an S3 type in the package called *log_layout*. Layouts are
composed from a series of objects that inherit from *fmt_layout*.
```{r, customized_layout, eval=F}
new_log_layout(
format = list(
new_fmt_metric(crayon::green$bold, "sysname"),
new_fmt_metric(crayon::red$bold, "release"),
new_fmt_line_break(),
new_fmt_log_level(),
new_fmt_timestamp(crayon::silver$italic),
new_fmt_log_msg()
),
seperator = '-',
association = "custom"
)
Logger$info("my log message - var1: {var1}, var2: {var2}, var3: {var3}", layout = "custom")
```
For a detailed look at layouts refer to the [Layouts](https://bmoretz.github.io/dyn.log/articles/Layouts.html) vignette online.
### Logging Associations
One thing you may have noticed about the previous log layout definition was the *association* parameter. Associations are a useful way to build a customized log layout for your custom R6 types. This can be especially useful in larger applications, such as [plumber](https://github.com/rstudio/plumber/) services or [shiny](https://github.com/rstudio/shiny) dashboards.
A TestObject is defined as below, who's primary responsibly is to assign a randomly generated identifier to
the instance via the constructor. There is also a method on the object that will call the logger with some
local scope variables that will be logged as well.
```{r, test_obj_def, eval=F}
TestObject <- R6::R6Class(
classname = "TestObject",
public = list(
id = NULL,
initialize = function() {
self$id <- private$generate_id()
},
test_method = function() {
a <- "test"; b <- 123; c <- runif(1)
Logger$info("these are some variables: {a} - {b} - {c}")
}
),
private = list(
generate_id = function(n = 15) {
paste0(sample(LETTERS, n, TRUE), collapse = '')
}
)
)
obj <- TestObject$new()
```
With the above class defined, we can create a custom log layout that associated with this R6 type with
a new log layout:
```{r, cls_assocation, eval=F}
new_log_layout(
format = list(
new_fmt_literal(crayon::cyan$bold, "Object Id:"),
new_fmt_cls_field(crayon::bgCyan$silver$bold, "id"),
new_fmt_line_break(),
new_fmt_log_level(),
new_fmt_timestamp(crayon::silver$italic),
new_fmt_log_msg(),
new_fmt_line_break(),
new_fmt_metric(crayon::green$bold, "sysname"),
new_fmt_metric(crayon::red$bold, "nodename"),
new_fmt_literal(crayon::blue$bold, "R Version:"),
new_fmt_metric(crayon::blue$italic$bold, "r_ver"),
new_fmt_line_break()
),
association = "TestObject"
)
# notice above, "Logger$info" is called inside the context of the Test Object,
# and the variables are scoped to inside the function.
obj$test_method()
Logger$debug("this is a normal log msg")
```
As you can see, only when the logger is invoked from inside the class that has a custom layout associated with it does the custom layout get used. The follow-up log call (outside the class scope) reverts back to the standard layout settings.
*For a detailed look at customizing a layout for a specific type, please see [Configurations](https://bmoretz.github.io/dyn.log/articles/Configuration.html) vignette online for an example.*
## Acknowledgments
* R Core for developing and maintaining such an amazing language.
* R Studio for building an incredible open-source ecosystem.
* [Hadley Wickham](https://github.com/hadley) for being super-human.
* [Jim Hester](https://github.com/jimhester) for all the fantastic r-lib/actions (covr, lintr & build, pkgdown, etc).
* [Brody Gaslam](https://github.com/brodieG/fansi) for developing the [fansi](https://github.com/brodieG/fansi) package which is responsible for all the the pretty logger output in the documentation.
* Everyone in the [#rstats](https://twitter.com/search?q=%23rstats) community for being inclusive, welcoming and incredibly knowledgeable.