reddit-top-2.5-million
cheatsheets
reddit-top-2.5-million | cheatsheets | |
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3 | 60 | |
608 | 5,612 | |
- | 0.9% | |
0.0 | 7.6 | |
about 4 years ago | 5 days ago | |
TeX | ||
GNU General Public License v3.0 or later | Creative Commons Attribution 4.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
reddit-top-2.5-million
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Since Everyone liked the Brave New World load screens, I pulled all the relevant artwork I could find out of the game files and put them in one album including the G&K load screens, wonder screens, and victory screens. (plus /u/IAMA_Ghost_Boo 's touchups of the BNW load screens. Thanks!) Enjoy!
google couldn't even find it, but searching for various parts of the title eventually pointed me to a github csv file with the top 1000 posts as of 2013: https://github.com/umbrae/reddit-top-2.5-million/blob/master/data/civ.csv
- reddit-top-2.5-million/madisonwi.csv at master ยท umbrae/reddit-top-2.5-million
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Looking for packages full of datasets
Reddit top dataset
cheatsheets
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Tools a Data Scientist should know:
If you're an R user, stringr + its cheatsheet gets you very close to remembering what to do without needing to look further!
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JSON to PDF Magic: Harnessing LaTeX and JSON for Effortless Customization and Dynamic PDF Generation
For more information on how to use ggplot2 and create charts consult the ggplot2 official page or the ggplot2 cheat graphic.
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Best packages to learn?
I'd suggest you have a look at cheatsheets (or download them from GitHub) if you want to get to know your way around a package or set if functions, it saves you a lot of time.
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How do I make these shapes (pictured below) in ggplot?
You could use geom_hline and geom_vline, geom_abline, or geom_segment for this. (The ggplot cheat sheet is very useful for answering these kinds of questions, BTW.)
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Why does my scatter plot look like this?
I can't say for sure because I don't know what your ultimate aim is for your visualization. Check out the cheat sheet for ggplot2 here.
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Import from Excel
Finally just do your analysis. You should also should give a try and see the cheat sheet for data importing on the tidyverse package.
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[Request] How to best visualize percentages with R?
That said, when Iโm trying to come up with an interesting way to visualize data, I find the ggplot cheat sheet very helpful: https://github.com/rstudio/cheatsheets/raw/main/data-visualization-2.1.pdf
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Need help with variables
Here's a cheat sheet: https://github.com/rstudio/cheatsheets/blob/main/strings.pdf
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Data manipulation in R
The cheat sheet of the stringr package should give you good overview of string manipulation/ regex in R.
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I'm trying to recreate this plot but I keep failing
I would very highly recommend that rather than trying to get started by translating an existing graph, you check out some documentation about ggplot first. If nothing else, the ggplot cheat sheet from RStudio should help explain what the component parts of the code are, and that might help you figure out what you actually want to do.
What are some alternatives?
awesome-public-datasets - A topic-centric list of HQ open datasets.
tidytuesday - Official repo for the #tidytuesday project
dataRetrieval - This R package is designed to obtain USGS or EPA water quality sample data, streamflow data, and metadata directly from web services.
forcats - ๐๐๐๐: tools for working with categorical variables (factors)
mostly-adequate-guide - Mostly adequate guide to FP (in javascript)
rnoaa - R interface to many NOAA data APIs
ggplot2-book - ggplot2: elegant graphics for data analysis
mech - ๐ฆพ Main repository for the Mech programming language. Start here!
ggplot2 - An implementation of the Grammar of Graphics in R
reveal.js - The HTML Presentation Framework
nbdev - Create delightful software with Jupyter Notebooks