bioawk VS OpenRefine

Data wrangling is actually what awk excels at, and it's generally much more concise than R for that sort of thing. I'm aware that a lot of awk one liners look like gibberish to the uninitiated, but it actually makes a lot of sense when you understand the pattern-action structure of awk programs. It is also installed on any *nix system, there's no need to worry about installing dependencies or setting up virtual environments. And it's several times faster than R. Also Bioawk is glorious.

A few months ago, I learned about this utility known as bioawk, written by Heng Li of samtools fame. Apparently, it is essentially a tweaked version of awk, with some extra goodies added for parsing and processing of bioinformatics file formats. While the functionality seems cool, I was wondering whether it is worth installing on my server, and incorporating into our workflows, because it seems so niche. I have not seen many references to it. Or is it better if we stick to Python scripts for this sort of work? Are there any computational speed advantages, etc. that bioawk offers over regular Python scripts for processing of, let's say, BED files or VCF files?

Beside that their core lectures to non-computer scientists are public (survey), workshops by software carpentry move around the globe. Maybe your intent to seed hands-on knowledge is in similar tune before heading for biopython, bioperl, bioawk. It doesn't hurt to tap into resources initially written for non-labrats either, e.g. about regular expressions by programming historian.

On the other hand, some of the techniques to set the ground for data analysis are equally valuable in other situations. The two installments about regular expressions on programming historian Understanding Regular Expressions and Cleaning OCR’d text with Regular Expressions, for example. They have no relevance to handling chemicals in the lab, yet since then, I find myself working with data files more efficiently, than earlier because of grep, an utility in Linux to crawl across data files. Or AWK, actually picking up theses "regexes", which I find generally useful since Benjamin Porter's "Hack the planet's text" (presentation video, and exercise video) with its link back to chem/bio e.g., to bioawk (btw, there equally is biopython, too).

Well, what language do you want to learn? What is your background so far? Assuming it is more on the side of biology, software carpentry's Python may eventually lead to biopython? Though there equally is a chance for AWK (Hack the planet's text! and bioawk...

There's even a version of awk specifically designed for bioinformatics that natively knows how to handle fasta, fastq, and bam files, among other formats.

https://github.com/lh3/bioawk

"OpenRefine is a powerful free, open source tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data." https://openrefine.org/

Yes, let's hope! The strategy has worked out sometimes - Google shut down 'Google Refine' 10 years ago, it got turned into 'Open Refine', last update 2 months ago. https://github.com/OpenRefine/OpenRefine

It's a hugely useful tool if you're working with messy Excel-scale data, i.e., most biologists or social scientists.

It seems to be pure JS with jQuery: https://github.com/OpenRefine/OpenRefine/blob/master/main/we...

EDIT: trim() does not remove unicode 0x200b (unicode character for zero width space). https://github.com/OpenRefine/OpenRefine/issues/5105 is worth a read.

There were six homework assignments. In order they were Regular Expressions, OpenRefine, Datalog, SQL, Provenance, and Python. None of these assignments took more than two to three hours to complete. They all were basic implementation and programming assignments with autograders.

Open Refine will get you about 70% there. It's FOSS

I’ve heard good things about https://openrefine.org/

It's not suited to SQL, use Open Refine or python fuzzywuzzy.