Effortless but Powerful Exception Logging in R: loggit! 1.0.0 Released on CRAN

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Frustrated with the lack of pain-free logging in R, a colleague inspired me to write my first public R package: loggit. What follows is the README that you can find on Github, and verison 1.0.0 of loggit is now available on CRAN. Please feel free to submit requests, bug reports, etc.!
This package is designed for anyone who has written their own package or analysis functions that already use basic exception handling by way of base R’s message()warning(), and stop(). The loggit package masks base R’s versions of these functions, but slots in a logging function (loggit()) just before the exception handler executes. Since these functions maintain identical functionality to their base equivalents, there is no need for any change in your existing code, if you have already written handlers. Just install & load/import loggit, tell R where to store your log file on startup, and instantly have your exceptions recorded.
The included loggit() function is also very flexible on its own, and allows for any number of custom fields to be added to your logs, in addition to maintaining the aforementioned functionality.
This package was inspired by a colleague’s suggestion of how we could log our ETL workflow results in R, as well as the rlogging package. When we looked at how rlogging was doing this, we felt that we could expand upon it and make it more flexible, system-portable, and with even less user effort. As opposed to other packages like log4rfutile.logger and logging, which may allow for more powerful logs in some cases, we wanted to make great logging “out of sight, out of mind”; at least until your boss asks for those ETL logs!


R has a selection of built-in functions for handling different exceptions, or special cases where diagnostic messages are provided, and/or function execution is halted because of an error. However, R itself provides nothing to record this diagnostic post-hoc; useRs are left with what is printed to the console as their only means of analyzing the what-went-wrong of their code. There are some slightly hacky ways of capturing this console output, such as sinking to a text file, repetitively cating identical exception messages that are passed to existing handler calls, etc. But there are two main issues with these approaches:
  1. The console output is not at all easy to parse, so that a user can quickly identify the causes of failure without manually scanning through it
  2. Even if the user tries to structure a text file output, they would likely have to ensure consistency in that output across all their work, and there is still the issue of parsing that text file into a familiar, usable format
Enter: JSON
JSON is a lightweight, portable (standardized) data format that is easy to read and write by both humans and machines. An excerpt from the introduction of the JSON link above:
“JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition – December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.”
Basically, you can think of JSON objects like you would think of lists in R: a set of key-value pairs, with the option to nest lists within other lists. This makes JSON not only as powerful as R’s lists, but it allows this nested-list functionality to be translated to other software, and let them handle them just as easily! As such, JSON is the default log file output type in the loggit package. You can manipulate & review JSON objects in R by way of the jsonlite package.

How to Use

Chances are that if you’ve read this far, you’re familiar with R’s exception handlers. If so, then there are only two other things that you need to know in order to use loggit:
  1. As per CRAN policies, a package cannot write to a user’s “home filespace” without approval. Therefore, you need to set the log file before any logs are written to disk, using setLogFile(logfile) (I recommend in your working directory, and naming it “loggit.json”). If you are using loggit in your own package, you can wrap this in a call to .onLoad(), so that logging is set on package load. If not, then make the set call as soon as possible (e.g. at the top of your script(s), right after your calls to library()); otherwise, no logs will be written to persistent storage!
  2. The logs are output to your log file each time an exception is raised, or when loggit() is called, and you can review it there. The fields supplied by default are:
  • timestamp: when the exception was raised
  • log_lvl: the “level” of the exception (INFO, WARN, ERROR, or custom)
  • log_msg: the diagnostic message generated by R
  • log_detail: additional exception details you can pass to the exception handlers, though not necessary to supply (will be filled with “” if not supplied).
That’s it!

However, if you want more control, or care a bit more about the details:
While this package automatically masks the base handlers, the loggit function that they call is also exported for use, if so desired. This allows you to log arbitrary things as much or as little as you see fit. The loggit function is called as: loggit(log_lvl, log_msg, log_detail, ...). The argument you may not recognize is ..., which is supplied as named vectors (each of length one) providing any additional field names you would like to log:
loggit("INFO", "This is a message", but_maybe = "you want more fields?", sure = "why not?", like = 2, or = 10, what = "ever")
Since loggit() calls dplyr::bind_rows() behind the scenes, you can add as many fields of any type as you want without any append issues or errors. Note, however, that this means that any future calls to loggit() for the same custom fields, require that those fields are spelled the same as originally provided. If not, then the misspelling will cause another field to be created, and (the data frame version of) your log file will look like this:
> loggit("INFO", "Result 1", scientist = "Ryan")
> loggit("INFO", "Result 2", scietist = "Thomas")

            timestamp log_lvl  log_msg  log_detail scientist scietist
1 2018-02-13 18:01:32    INFO Result 1          ""      Ryan     <NA>
2 2018-02-13 18:02:32    INFO Result 2          ""      <NA>   Thomas
Also note that you do not always have to supply a custom field once it is created; thanks, JSON and dplyr!
> loggit("INFO", "Result 1", scientist = "Ryan")
> loggit("INFO", "Result 2", scientist = "Thomas")
> loggit("INFO", "Result 3")

            timestamp log_lvl  log_msg  log_detail     scientist
1 2018-02-13 18:01:32    INFO Result 1          ""          Ryan
2 2018-02-13 18:02:32    INFO Result 2          ""        Thomas
3 2018-02-13 18:03:32    INFO Result 3          ""          <NA>

Also, other control options:
  • You can control the output name & location of the log file using setLogFile(logfile, folder)loggit will not write to disk unless an exception is raised, or unless loggit() is called, but you should specify this change early, if desired. You can see the current log file path at package attachment, or by calling getLogFile().
  • If for any reason you do not want to log the output to a log file, you can set each handler’s .loggit argument to FALSE. This will eventually be a global option that the user can set, and leave the handlers without the argument. If using loggit in your own package, you can just importFrom(loggit, function) the handlers that you do want.
  • You can control the format of the timestamp in the logs; it defaults to ISO format "%Y-%m-%d %H:%M:%S", but you may set it yourself using setTimestampFormat(). Note that this format is passed to format.Date(), so the supplied format needs to be valid.

Note on What Gets Logged

Note that this package does not mask the handler functions included in other packages, including in base R; for example, running the following will throw an error as usual, but will not write to the log file:
> 2 + "a"
Error in 2 + "a" : non-numeric argument to binary operator
> dplyr::left_join(data.frame(a = 1), data.frame(b = 2))
Error: `by` required, because the data sources have no common variables
> # Did loggit write these exception messages to the logfile?
> file.exists(loggit:::.config$logfile)
This is integral to how R works with packages, and is by design; it has to do with namespaces, which is how R looks for what to do when you ask it to do something. Basically, if a package you use doesn’t have loggit in its NAMESPACE file, then its internal exception handlers won’t be masked by loggit.
If you really wish to have all exception messages logged by loggit, please be patient, as this feature is in the works.


You can install the latest CRAN release of loggit via install.packages("loggit").
Or, to get the latest development version from GitHub —
Via devtools:
Or, clone & build from source:
cd /path/to/your/repos
git clone https://github.com/ryapric/loggit.git loggit
To use the most recent development version of loggit in your own package, you can include it in your Remotes: field in your DESCRIPTION file:
Remotes: github::ryapric/loggit
Note that packages being submitted to CRAN cannot have a Remotes field. Refer here for more info.

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