knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

logVisualizer

Lifecycle: experimental Travis build status

Foreword

The goal of logVisualizer is to create a simple application that allows data scientists to visualize production logs.

As a data scientist in a small team, I commonly find myself navigating through the file-systems to look at daily production logs. Sometimes, I am troubleshooting an error that my application encountered; other times, I just want to make sure that everything executed as intended, regardless of whether a fatal error occurred.

Another thing I noticed is that while my intentions of logging were great, my logs lacked consistency. Aside from throwing the occasional error, it's unclear that my current logs provided useful information.

As projects scale up and new projects start up, I would like to 1) automate as much as possible, and 2) ensure that all relevant information is readily available for inspection. The latter speaks to doing a deep-dive on what should be logged (and how they should be logged), which is outside of the scope of this project. The former, however, was the motivation behind this project.

logVisualizer - the application

logVisualizer is a Shiny application that allows data scientists to easily visualize production logs. Rather than navigating to each log-file individually, one can point the paths of log directories that they would like to monitor. logVisualizer is a central hub for logged information, displayed in an easily digestible manner.

Installation

You can install the released version of logVisualizer from CRAN with:

devtools::install_github("dwhdai/logVisualizer")


dwhdai/logVisualizer documentation built on March 25, 2020, 9:17 a.m.