logger

A lightweight, modern and flexibly logging utility for R -- heavily inspired by the futile.logger R package and logging Python module.

Installation

install.packages('logger')

The most recent, development version of logger can also be installed from GitHub:

remotes::install_github('daroczig/logger')

Quick example

Setting the log level threshold to something low and logging various messages in ad-hoc and programmatic ways:

library(logger)
log_threshold(DEBUG)
log_info('Script starting up...')
#> INFO [2018-20-11 22:49:36] Script starting up...

pkgs <- available.packages()
log_info('There are {nrow(pkgs)} R packages hosted on CRAN!')
#> INFO [2018-20-11 22:49:37] There are 13433 R packages hosted on CRAN!

for (letter in letters) {
    lpkgs <- sum(grepl(letter, pkgs[, 'Package'], ignore.case = TRUE))
    log_level(if (lpkgs < 5000) TRACE else DEBUG,
              '{lpkgs} R packages including the {shQuote(letter)} letter')
}
#> DEBUG [2018-20-11 22:49:38] 6300 R packages including the 'a' letter
#> DEBUG [2018-20-11 22:49:38] 6772 R packages including the 'e' letter
#> DEBUG [2018-20-11 22:49:38] 5412 R packages including the 'i' letter
#> DEBUG [2018-20-11 22:49:38] 7014 R packages including the 'r' letter
#> DEBUG [2018-20-11 22:49:38] 6402 R packages including the 's' letter
#> DEBUG [2018-20-11 22:49:38] 5864 R packages including the 't' letter

log_warn('There might be many, like {1:2} or more warnings!!!')
#> WARN [2018-20-11 22:49:39] There might be many, like 1 or more warnings!!!
#> WARN [2018-20-11 22:49:39] There might be many, like 2 or more warnings!!!

Setting a custom log layout to render the log records with colors:

library(logger)
log_layout(layout_glue_colors)
log_threshold(TRACE)
log_info('Starting the script...')
log_debug('This is the second log line')
log_trace('Note that the 2nd line is being placed right after the 1st one.')
log_success('Doing pretty well so far!')
log_warn('But beware, as some errors might come :/')
log_error('This is a problem')
log_debug('Note that getting an error is usually bad')
log_error('This is another problem')
log_fatal('The last problem')

Or simply run the related demo:

demo(colors, package = 'logger', echo = FALSE)

But you could set up any custom colors and layout, eg using custom colors only for the log levels, make it grayscale, include the calling function or R package namespace with specific colors etc. For more details, see the related vignettes.

Why yet another logging R package?

Although there are multiple pretty good options already hosted on CRAN when it comes to logging in R, such as

futile.logger: probably the most popular log4j variant (and I'm a big fan)
logging: just like Python's logging package
loggit: capture message, warning and stop function messages in a JSON file
log4r: log4j-based, object-oriented logger
rsyslog: logging to syslog on 'POSIX'-compatible operating systems
lumberjack: provides a special operator to log changes in data

Also many more work-in-progress R packages hosted on eg GitHub, such as

But some/most of these packages are

not actively maintained any more, and/or maintainers are not being open for new features / patches
not being modular enough for extensions
prone to scoping issues
using strange syntax elements, eg dots in function names or object-oriented approaches not being very familiar to most R users
requires a lot of typing and code repetitions

So based on all the above subjective opinions, I decided to write the n+1th extensible log4j logger that fits my liking -- and hopefully yours as well -- with the focus being on:

keep it close to log4j
respect the most recent function / variable naming conventions and general R coding style
by default, rely on glue when it comes to formatting / rendering log messages, but keep it flexible if others prefer sprintf (eg for performance reasons) or other functions
support vectorization (eg passing a vector to be logged on multiple lines)
make it easy to extend with new features (eg custom layouts, message formats and output)
prepare for writing to various services, streams etc
provide support for namespaces, preferably automatically finding and creating a custom namespace for all R packages writing log messages, each with optionally configurable log level threshold, message and output formats
allow stacking loggers to implement logger hierarchy -- even within a namespace, so that the very same log call can write all the TRACE log messages to the console, while only pushing ERRORs to DataDog and eg INFO messages to CloudWatch
optionally colorize log message based on the log level
make logging fun

Welcome to the Bazaar, and if you have happened to already use any of the above mentioned R packages for logging, you might find useful the Migration Guide.

Interested in more details?

Check out the main documentation site at https://daroczig.github.io/logger or the vignettes on the below topics:

Polkas/logger

logger

Installation

Quick example

Why yet another logging R package?

Interested in more details?