Is there any standard logging package for R?
LoggingRLogging Problem Overview
I am looking for the standard (if any) logging package for R, and some sample usage?
I also don't see any among the packages listed: http://cran.r-project.org/web/packages/
Logging Solutions
Solution 1 - Logging
I just submitted a logging package to CRAN. it is based on some parts of an older version of the 'futile' package (by Brian Lee Yung Rowe).
You find the logging package:
It mimics the standard python logging package, but please be careful if you decide to use it. I also attempted to document it by example, the package home page on R-Forge points to a couple of possible usage sessions.
Any feedback will be read with interest!
Solution 2 - Logging
At the moment, there is still no native library for logging. But there are four of them available on CRAN:
- logging
- simple & log4j-like
- resembles standard Python library (uses that documentation as guideline)
- author started it in 2010, 'mature' by 2012
- adopted by WLOGSolutions
- actively maintained
- futile.logger (recommended! I am also using it)
- actively maintaining
- supports json error logging
- similar semantics to Python’s logging as well as log4j-like
- may be complicated
- log4r
- easy and log4j-like
- not being maintained since 2014
- luzlogr
- supersimple - (open, write, close file)
Solution 3 - Logging
I suggest the futile.logger package, it implements multiple hierarchical loggers with formatted output strings and you can send the output different ways. It also implements per-package loggers naturally.
Solution 4 - Logging
The in-built (package base) functions are "warning", "message", "stop". These functions support multiple languages. If you want to log to a file, maybe you could use these functions together with "sink".
Searching with RSeek brought up the package futile with a logger functionality.
Solution 5 - Logging
I've started logR project in June 2014. Initially it was a R process logger with exception handling capable to log to csv and DBI/RODBDC/RJDBC databases.
Starting from 2.1 version I've switched to support only PostgreSQL as backend for logs.
If you are able to arrange single table in postgres database then you can easily use logR.
Upstream repo, and github mirror.
Some of logR features:
- transactional logging: insert log, evaluate expression, update log
- log to postgres database
- records errors, warnings, messages, interrupts
- log process metadata: in/out nrow, flexible list of custom metadata
- high precision timing with optional microbenchmarkCore
- support parallel processing
- hierarchical logs - logging parent log id (new in 2.1.5)
It requires RPostgreSQL and data.table packages.
Usage:
# install logR
install.packages("logR", repos = c("https://jangorecki.github.io/logR", "https://cran.rstudio.com"))
# attach logR
library(logR)
# setup connection, default to env vars: `POSTGRES_DB`, etc.
# if you have docker then: docker run --rm -p 127.0.0.1:5432:5432 -e POSTGRES_PASSWORD=postgres --name pg-logr postgres:9.5
logR_connect()
# [1] TRUE
# create logr table
logR_schema()
# make some logging and calls
logR(1+2) # OK
#[1] 3
logR(log(-1)) # warning
#[1] NaN
f = function() stop("an error")
logR(r <- f()) # stop
#NULL
g = function(n) data.frame(a=sample(letters, n, TRUE))
logR(df <- g(4)) # out rows
# a
#1 u
#2 c
#3 w
#4 p
# try CTRL+C / 'stop' button to interrupt
logR(Sys.sleep(15))
# wrapper to: dbReadTable(conn = getOption("logR.conn"), name = "logr")
logR_dump()
# logr_id logr_start expr status alert logr_end timing in_rows out_rows mail message cond_call cond_message
#1: 1 2016-02-08 16:35:00.148 1 + 2 success FALSE 2016-02-08 16:35:00.157 0.000049163 NA NA FALSE NA NA NA
#2: 2 2016-02-08 16:35:00.164 log(-1) warning TRUE 2016-02-08 16:35:00.171 0.000170801 NA NA FALSE NA log(-1) NaNs produced
#3: 3 2016-02-08 16:35:00.180 r <- f() error TRUE 2016-02-08 16:35:00.187 0.000136896 NA NA FALSE NA f() an error
#4: 4 2016-02-08 16:35:00.197 df <- g(4) success FALSE 2016-02-08 16:35:00.213 0.000696145 NA 4 FALSE NA NA NA
#5: 5 2016-02-08 16:35:00.223 Sys.sleep(15) interrupt TRUE 2016-02-08 16:35:05.434 5.202319000 NA NA FALSE NA NA NA
More examples can be found in logR unit tests.
Solution 6 - Logging
I'm not aware of any, so I was about to release a wrapper for log4j in the next few days (I've been testing it for a while now). I'll let you know when it's available.
Solution 7 - Logging
A simpler alternative compared to logging and futile.logger:
> The log4r package is meant to provide a clean, lightweight object-oriented approach to logging in R based roughly on the widely emulated log4j API. The example code below shows how the logger is used in practice to print output to a simple plaintext log file.