What does HackerNews think of vnlog?

Miller: Like Awk, sed, cut, join, and sort for CSV, TSV, and tabular JSON | Mar 2023

There's also https://github.com/dkogan/vnlog/ which is a wrapper around the existing coreutils, so all the options work, and there's nothing to learn

Learn to Process Text in Linux Using Grep, Sed, and Awk | Jan 2023

Expand Context ↕

I sorta, kinda agree. Tools written in AWK (and friends) are indeed somewhat unmaintainable, but they're really close to being just right for a LOT of applications. The vnlog toolkit (https://github.com/dkogan/vnlog) adds just a little bit of syntactic sugar to the usual commandline tools to make processing scripts robust and easy to read and write. This was not my intent initially, but I now do most of my data processing with the shell and vnl-wrapped awk (and sort and join, ...) It's really nice. If you write stuff in awk, you should check it out. (Disclaimer: I'm the author)

Miller – tool for querying, shaping, reformatting data in CSV, TSV, and JSON | Dec 2021

Expand Context ↕

Lots of them out there. Here's one that repurposes the extends the standard unix tools to know about comments and column labels: https://github.com/dkogan/vnlog/

Understanding Awk | Sep 2021

Expand Context ↕

If you want to do that, use vnlog instead. You're 90% there already. It formalizes the idea of a header to label columns, and allows normal unix data-processing tools to just work with those labels. Your awk use case is quite literally "vnl-filter --eval"

https://github.com/dkogan/vnlog/

Miller CLI – Like Awk, sed, cut, join, and sort for CSV, TSV and JSON | Aug 2021

Similar, but using the ACTUAL awk, sed, join, sort tools you already have and know about: https://github.com/dkogan/vnlog/

A straightforward way to extend CSV with metadata | Aug 2021

vnlog: https://github.com/dkogan/vnlog/

Magic comment with field labels is required, and you can then use the usual unix tools for processing. It's magical.

Feedgnuplot – plotting standard input in the shell | Jul 2021

I use https://github.com/dkogan/vnlog/ and feedgnuplot for $WORK and it strikes a wonderful balance of simplicity and effectiveness for both quick data exploration and shell scripted data analysis and report generation.

What took me a bit to grok at the start was how feedgnuplot's different flags change how it assigns semantic meaning to different data columns. So by default the first column is simply treated as data (y) while the implicit row index becomes the x axis. If you add --domain the first column becomes your domain (x) and the second column your data (y) allowing you to create a scatter plot. If you have multiple curves you can add --dataid with --domain which makes the first column x, the second column the id of the curve that point belongs to, and the third column the data itself (y). You can throw in more dimensions of visualization by mapping columns to colors, point size, or 3d.

Typing it out it sounds more complicated than it really is. Combined with vnlog to easily select columns and filter rows it becomes a really powerful command line tool set.

Feedgnuplot – plotting standard input in the shell | Jul 2021

Expand Context ↕

If you're massaging and plotting tabular text data, take a look at vnlog too: https://github.com/dkogan/vnlog/

Works wonderfully in the shell, in conjunction with feedgnuplot for plotting.

Q – Run SQL Directly on CSV or TSV Files | Jun 2021

Expand Context ↕

And yet another set of tools is vnlog (https://github.com/dkogan/vnlog). It assumes you have whitespace-separated data, with a legend comment, then you can do the above thing with

  < data.vnl vnl-filter -p thiscolumn,thatcolumn 'thiscolumn != "foo"'

If you don't strictly need csv, those tools are quite nice, and have a really friendly learning curve. Disclaimer: I'm the author

Ask HN: What data visualization tools do you use? | Mar 2021

Outputting vnlog, using those tools to filter, and then plotting using feedgnuplot have covered most of my visualization needs.

https://github.com/dkogan/vnlog

https://github.com/dkogan/feedgnuplot

Setting up personal OKR (objectives and key-results) | Jan 2021

Expand Context ↕

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: https://github.com/jodavaho/bashlog#git-integration

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

The text file is structured with space-delimited text, like this: https://github.com/dkogan/vnlog

My current structure for tracking a contribution to an OKR item (e.g. "meditate") by date (e.g., 2020-01-15) and amount (e.g., "1") is simply:

"# 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"

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

Another example is "workout-mapping.vnl" with structure: "# item muscle-group factor"

etc etc.

vnlog has nice vnl-join commands that quickly build the table and all linked metadata and output formatted text (one command). Then reading that in R is easy ...

``` data <- read.table('../2020-Q4-OKR.vnl',skip=2,stringsAsFactors = FALSE) ```

Aggregating by date is easy:

``` data.agg<- aggregate(x=data$Amount,by=list(data$Date),FUN=sum) ```

Then plot using ggplot2.

Examples from 2020 Q4 (just ran the numbers yesterday!)

https://josh.vanderhook.info/media/okr.png

https://josh.vanderhook.info/media/workouts.png

https://josh.vanderhook.info/media/mood.png