Process labelled tabular ASCII data using normal UNIX tools (by dkogan)

Vnlog Alternatives

Similar projects and alternatives to vnlog

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better vnlog alternative or higher similarity.

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Reviews and mentions

Posts with mentions or reviews of vnlog. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-24.
  • Vnlog: Manipulate labeled tabular data with standard Unix tools | 2021-09-17
  • Miller CLI – Like Awk, sed, cut, join, and sort for CSV, TSV and JSON | 2021-08-24
    Similar, but using the ACTUAL awk, sed, join, sort tools you already have and know about:
  • A Straightforward Way to Extend CSV with Metadata | 2021-08-19
  • Best scientific graphing library? | 2021-08-12
    Write space delimited tabular data (ideally in vnlog format) and plot it using feedgnuplot. Also helps decouple concerns (data generating application focuses on generating data).
  • Ask HN: What data visualization tools do you use? | 2021-03-05
  • plotpipe: plot data from a pipe
    I use feedgnuplot in conjunction with vnlog all the time at work! It’s a great pipeline.
  • Setting up personal OKR (objectives and key-results) | 2021-01-02
    I have a text file in a known location. It's backed up on git.

    I can open and edit that file quickly using bash. From within VIM I can quickly commit to reduce the # keystrokes. More details:

    (Really though, you can just save as an environment variable your file location)

    The text file is structured with space-delimited text, like this:

    My current structure:

    "# date item amount"

    Easy to open, easy to plop down today's date and the item / amount. Save multiple date/item entries and add them inscripts ... Easy to save. Done.

    The work came in building stuff to plot it:

    I have other files that link "items" to goals or other metadata like categories, etc. Like a relational database, but easily edited in text.

    For example, to make a link from daily items to Key Results

    "# item okr"

    okr is structured like this: ...

    For example:

    2020.Q4.01.01 is workout 90x (1/day)

    2020.Q4.02.02 is get through 26 textbook chapters

    2020.Q4.02.03 is 100 h studying literature

    The categories are usually Fighting (I box), Tech, Mental health, and Social, but YMMV

    Another example is "workout-mapping.vnl" with structure: | 2021-01-02
    I've used OKR for 7 years, on a quarterly basis. I had simple markdown files at first, then vnl-log files, and now R notebooks ( to read / plot.

    It may seem like overhead, and there's some snark in this thread about how it's project / team management without the project and team.

    I completely disagree. If you set up your KR's so they are 1) quantitative, 2) daily measurable, 3) simple to log ( a few keystrokes while journalling and 4) completely under your control.

    At the end of the day, I mark down my progress on all my OKRs. I can quickly plot them, look back at progress, and look back at goals and concerns by seeing the types of objectives I had. It's a 10,000 foot journal that I otherwise wouldn't have.

    There's more to this than simply quantifying yourself. We like #'s because they are representations of complex systems. The self and your personal history are absolutely a complex system worth tracking.

    Looking back at my OKRs when I was dating my (now) wife, comparing the ways I put effort into our relationship and our changing priorities. Seeing over time my running distances, weight lifting activity, meditation record, and seeing how I consistently attempt to over-achieve by setting KR values too high ... Having those points of reference has made today more enjoyable, and been a constant reminder that progress comes slowly and missing on any particular attempt at something is irrelevant. It's so completely a part of my life now that I can't imagine setting goals or daily priorities without it.

    Think of it like quantitative journalling.


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