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Skylight.io
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Skylight.io| Categories |
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|---|---|
| Website | skylight.io |
| Pricing URL | Official Skylight.io Pricing |
| Details $ |
Shiny| Categories |
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| Website | shiny.posit.co |
| Pricing URL | - |
| Details $ |
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Based on our record, Shiny seems to be a lot more popular than Skylight.io. While we know about 32 links to Shiny, we've tracked only 3 mentions of Skylight.io. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Hey, thanks for the follow-up. While traffic on the marketing website has decreased since launch, traffic on the main app has increased. I do some performance monitoring (using https://skylight.io) and have identified some opportunities for speed-up. I'm working on getting those updated. I also just resized the database - it seems that some read queries were being slow. I'm continuing to dig into the postgres... - Source: Hacker News / 7 months ago
Appsignal is pretty great, I use skylight.io as well. Source: almost 2 years ago
I'm using skylight.io for the same. Source: over 2 years ago
A lighterweight alternative to renv is to use Posit Public Package Manage (https://packagemanager.posit.co/) with a pinned date. That doesn't help if you're installing packages from a mix of places, but if you're only using CRAN packages it lets you get everything as of a fixed date. And of course on the web side you have shiny (https://shiny.posit.co), which now also comes in a python flavour. - Source: Hacker News / about 2 months ago
Sometimes the war is lost even before the battle begins. During grad school, I wrote a whole bunch of web apps entirely in R using Shiny. It was clunky as hell, but yeah, it worked. I went looking for what's up with Shiny these days and found this - https://shiny.posit.co/ So yeah, full on pivot into python. Pip install shiny. Alright! "No web development skills required. Develop web apps entirely in R I mean... - Source: Hacker News / 9 months ago
Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: 12 months ago
We work along side bio-statisticians and data analysts, from my experience in this world I recommend to build some plots/graphs in R based on some information you find appealing. After you have some work to show off to potential employers , learn Shiny and publish those graphs online as your portfolio. Source: about 1 year ago
One of the most difficult yet most fun projects I’ve done. Using Shiny to make an app, all coded in R! Source: about 1 year ago
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