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by jerzyt 1273 days ago
100% in agreement on Excel. Even when coding in Python I frequently save an intermediate file as xlsx to explore/debug, or even load into Tableau for viz.
3 comments

110% agreement on Excel

The ability create a relational database in Excel with vlookups and hlookups, then capture it all into a macro is amazing.

I've really enjoyed using Excel as a Postgres frontend, with a real Postgres DB instance handling data, and then using the report functionality to dump to Word.

While a pro reporting engine and cutting out MS Office altogether would be a better longterm solution, it is hard to beat for quick & dirty results.

> The ability create a relational database in Excel with vlookups and hlookups,

Do yourself a favour and ditch vlookup and hlookup in favour of the recently introduced xlookup, which even obsoletes index/match !

I try to keep my exploratory joins out of Excel, but I admit that I often don't resist the immediacy of Excel's poor man's joins located right where I need them.

Took them long enough to add it.
I'm curious, how did you establish the Postgres connection?
I used ODBC [1] out of the big list of options [2] which gets a bad rep but worked for my use-cases.

The commercial devart plugin [3] looks pretty neat too but I haven't used it yet

I've also tried the JDBC connectivity option too [4], but with some different use-cases in mind for Postgres (not about Excel)

[1] https://datacornering.com/how-to-connect-to-postgresql-datab...

[2] https://www.postgresql.org/download/products/2-drivers-and-i...

[3] https://www.devart.com/excel-addins/postgresql/

[4] https://jdbc.postgresql.org/

You should learn R and dplyr ;)
Tell us more - in the context of them being replacements for Excel for his use case.
I love R but can't use it at work :(

With just the tidyverse library (which includes dplyr), R can be very useful in a data analysis pipeline. It is great for data cleaning and aggregation, especially when a process needs to be done multiple times. It is much faster than excel/power query. I am an accountant in SaaS and spend a lot of my day waiting for excel/powerbi automations to refresh. Similar solutions in R/sql/python would be nearly instant. Also excel/powerbi automations are a bitch to troubleshoot, and are unnecessarily complex.

When following tidy principles, a framework designed by the tidyverse dev Hadley Wickham, R code can be very easy to interpret, similar to SQL. Additionally the R community has made libraries for everything, and I consider R a great general purpose language as well.

Note that different flavours of R have very different performance. 'Base R' is quite slow. But R + data.table is blinding fast. Power Query perforamnce is awful, even compared to base R. Some benchmarks of these plus other data wrangling software (including my own product) at:

https://www.easydatatransform.com/data_wrangling_etl_tools.h...

"Manipulating hundreds of thousands of rows" is exactly where R, dplyr and data.table are great at. I do that on a daily basis.
I got to play around with Tableau when I was helping my wife in a collage programming course and though I don't have a current use to justify the significant cost, I must say that the tool was amazingly flexible and easy to use. I'd highly recommend it.