A brief overview of lumberjack

Lumberjack separates concerns between data processing and monitoring the process by allowing R programmers (analysts) to declare what objects to track, and how to track them.

Add logging capabilities to existing analyses scripts

Start tracking changes by adding a single line of code to an existing script.

# contents of 'script.R'

mydata <- read.csv("path/to/my/data.csv")

# add this line after reading the data:
start_log(mydata, logger=simple$new())

# Existing data analyses code here...

Next, run the script using lumberjack::run(), and read the logging info.

library(lumberjack)
run("script.R")

read.csv("mydata_simple.csv")

Every aspect of the logging process can be customized, including output file locations and the logger.

Interactive logging with the lumberjack not-a-pipe operator.

out <- mydata %L>%
  start_log(logger = simple$new()) %L>%
  transform(z = 2*sqrt(x)) %L>%
  dump_log(file="mylog.csv")
read.csv("mylog.csv")

Loggers included with lumberjack

logger description
simple Record whether data has changed or not
cellwise Record every change in every cell
expression_logger Record the value of user-defined expressions
filedump Dump data to file after each change.

Extend with your own loggers

A logger is a reference object (either R6 or Reference Class) with the following mandatory elements.

There is also an optional element:

More information

install.packages("lumberjack")
library(lumberjack)
vignette("using_lumberjack", package="lumberjack")