https://github.com/casualcomputer/table-summarizer
This RShiny app summarizes large tables stored in database servers
https://github.com/casualcomputer/table-summarizer
Last synced: 4 months ago
JSON representation
This RShiny app summarizes large tables stored in database servers
- Host: GitHub
- URL: https://github.com/casualcomputer/table-summarizer
- Owner: casualcomputer
- License: gpl-3.0
- Created: 2021-08-31T02:16:59.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-04T02:36:28.000Z (about 2 years ago)
- Last Synced: 2024-08-13T07:11:59.470Z (8 months ago)
- Language: R
- Size: 40 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - casualcomputer/table-summarizer - This RShiny app summarizes large tables stored in database servers (R)
README
Table summarizer for large tables in databases
================
Henry Luan
## About this web app
This RShiny app summarizes large tables stored in database servers. It
allows large, high-dimensional datasets to be summarized in R (size of
local RAM \<\<\< size of tables) in a way that R can’t handle with your
computer’s local RAM.
If you want to understand the core functions used to create this web
app, you can go to my other [github
page](https://github.com/casualcomputer/sql.mechanic) for understanding
the package `sql.mechanic` that I created.
To use the RShiny app, just download this folder and run
“table-summarizer.R”.
## Limitations
- You probably want to understand how [**Example
2**](https://github.com/casualcomputer/sql.mechanic#example-2-automatically-summarize-tables-in-your-databases)
(part of my package called “sql.mechanic”) affects the CPU and disk
usage of your database server, to avoid bad surprises on your server’s
resource usage.
- If you use the setup of [**Example
2**](https://github.com/casualcomputer/sql.mechanic#example-2-automatically-summarize-tables-in-your-databases)
(part of my package called “sql.mechanic”) on a cloud database, you
**MUST** do some testing, to understand how the example affects the
CPU and disk usage of your cloud resource. Please avoid potentially
expensive mistakes.
- Currently, I designed the app to work only with Microsoft SQL Server
and Netezza databases. Feel free to contribute to the codes, if
interested.