A Fully Featured Logging Framework

A flexible, feature-rich yet light-weight logging framework based on 'R6' classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, 'JSON', memory buffers, and databases, as well as email and push notifications. For a full list of features with examples please refer to the package vignette.

lgr is a logging package for R built on the back of R6 classes. It is designed to be flexible, performant and extensible. The package vignette contains a comprehensive description of the features of lgr (some of them unique among R logging packages) along with many code examples.

Users that have not worked with R6 classes before, will find configuring Loggers a bit strange and verbose, but care was taken to keep the syntax for common logging tasks and interactive usage simple and concise. User that have experience with shiny, plumber, python logging or Apache Log4j will feel at home. User that are proficient with R6 classes will also find it easy to extend and customise lgr, for example with their own appenders Loggers or Appenders.

Features

• Hierarchical loggers like in log4j and python logging. This is useful if you want to be able to configure logging on a per-package basis.
• An arbitrary number of appenders for each logger. A single logger can write to the console, a logfile, a database, etc... .
• Allow for custom fields in log events. As opposed to many other logging packages for R a log event is not just a message with a timestamp, but can contain arbitrary data fields. This is very helpful if you want to produce logs that are machine readable and easy to analyze.
• Vectorized logging (so lgr$fatal(capture.output(iris)) works)
• Lightning fast in-memory log based in data.table included for interactive use.
• Comes with a wide range of appenders, for example for:
  • Appending to Databases (buffered or directly)
  • Sending notifications via email or pushbullet
  • writing JSON with arbitrary data fields
  • In memory buffers
  • colored console output
• Optional support to use glue instead of sprintf() for composing log messages.

Usage

To log an event with with lgr we call lgr$<logging function>(). Unnamed arguments to the logging function are interpreted by sprintf(). For a way to create loggers that glue instead please refer to the vignette.

lgr$fatal("A critical error")
lgr$error("A less severe error")
#> ERROR [21:55:34.254] A less severe error
lgr$warn("A potentially bad situation")
#> WARN  [21:55:34.293] A potentially bad situation
lgr$info("iris has %s rows", nrow(iris))
#> INFO  [21:55:34.295] iris has 150 rows
 
# the following log levels are hidden by default
lgr$debug("A debug message")
lgr$trace("A finer grained debug message")

A Logger can have several Appenders. For example, we can add a JSON appender to log to a file with little effort.

tf <- tempfile()
lgr$add_appender(AppenderJson$new(tf))
lgr$info("cars has %s rows", nrow(cars))
#> INFO  [21:55:34.430] cars has 50 rows
cat(readLines(tf))
#> {"level":400,"timestamp":"2019-01-30 21:55:34","logger":"","caller":"eval","msg":"cars has 50 rows"}

JSON naturally supports custom fields. Named arguments passed to info(), warn(), etc... are interpreted as custom fields.

lgr$info("loading cars", "cars", rows = nrow(cars), cols = ncol(cars))
#> INFO  [21:55:34.455] loading cars {rows: 50, cols: 2}
cat(readLines(tf), sep = "\n")
#> {"level":400,"timestamp":"2019-01-30 21:55:34","logger":"","caller":"eval","msg":"cars has 50 rows"}
#> {"level":400,"timestamp":"2019-01-30 21:55:34","logger":"","caller":"eval","msg":"loading cars","rows":50,"cols":2}

For more examples please see the package vignette and documentation

See lgr in Action

lgr is used to govern console output in my shiny based csv editor shed

# install.packages("remotes")
 
remotes::install_github("s-fleck/shed")
library(shed)
library(lgr)
 
# the root loggers threshold is NA (= log everything), but the console appender
# only displays `info` level messages by default. Let's set it to NA/"all" so
# that we get more exciting output when running shed
console_threshold(NA)
 
# you also have to set the threshold of the logger of shed which is "info" by
# default
shed:::lg$set_threshold(NA)
 
# edit away and watch the rstudio console!
shed(iris)  

Development Status

The api of lgr is stable and safe for use. The internal implementation of the database logging features still needs some refinement, and if you are using lgr with a database, I would be grateful for any kind of feedback.[1]

lgr is currently very actively developed, and feature requests are encouraged.

Dependencies

R6: The R6 class system prevents the framework on which lgr is built and the only Package lgr will ever depend on.

Optional Dependencies

lgr comes with a long list of optional dependencies. These are not necessary to use lgr, but that are required for some extra functions. Most of these dependencies are tied to specific Appenders, though crayon and data.table are also relevant to interactive use of lgr.

Care was taken to choose packages that are slim, stable, have minimal dependencies, and are well maintained :

• crayon for colored console output.

• glue for a more flexible formatting syntax via LoggerGlue and LayoutGlue.

• data.table for fast in-memory logging with AppenderDt, and also by all database / DBI Appenders.

• jsonlite for JSON logging via LayoutJson. JSON is a popular plaintext based file format that is easy to read for humans and machines alike.

• DBI for logging to databases. Logging with lgr has been tested with the following backends:

  • RSQLite,
  • RMySQL for MariaDB and MySQL (RMariaDB is currently broken, see this issue),
  • RPostgres,
  • RJDBC for DB2.

  In theory all DBI compliant database packages should work. If you are using lgr with a database backend, please report your (positive and negative) experiences, as database support is still somewhat experimental.

• gmailr or sendmailR for email notifications.

• RPushbullet for push notifications.

• whoami for guessing the user name from various sources. You can also set the user name manually if you want to use it for logging.

• desc for the package development convenince function use_logger()

Other optional dependencies (future, future.apply) do not provide any extra functionallity but had to be included for some of the automated unit tests run by lgr.

Installation

You can install lgr from CRAN

install.packages("lgr")

Or you can install the current development version directly from github

#install.packages("remotes")
remotes::install_github("s-fleck/lgr")

Outlook

The long term goal is to support (nearly) all features of the python logging module. If you have experience with python logging or Log4j and are missing features/appenders that you'd like to see, please feel free to post a feature request on the issue tracker.

Acknowledgement

• Inkscape for the hex sticker
• draw.io for the flow chart in the vignette

[1] The only database logging I can currently test extensively is DB2 via RJDBC. I do not recommend this setup if you have other options.