ggplot2-book
cheatsheets
ggplot2-book | cheatsheets | |
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31 | 61 | |
1,588 | 5,939 | |
0.5% | 0.6% | |
0.0 | 8.7 | |
6 months ago | 8 days ago | |
Perl | TeX | |
- | Creative Commons Attribution 4.0 |
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ggplot2-book
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Does anyone else absolutely love plotting their data
I also only recently started using ggplot after doing most of my graphs with base R‘s plot() function. I started by reading ggplot2 by Hadley Wickham which is also available as a free ebook. Reading the first few chapters is enough to enable you to plot many basic plots. I can’t imagine going back to any other visualization tool ever again. Absolutely love the freedom ggplot gives you.
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I am starting to learn R and I love it. I would like to learn at least 1 another simmilar language. Which one(s) should I learn?
His ggplot book will teach you all you need to know about R plotting, and is probably right at your current level. It is likewise pretty great, ggplot
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What are your favorite softwares for data visualization?
The OG book is still the best in my opinion! https://ggplot2-book.org/
- Data analysis skills before/in lieu of master’s program
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How can I do this graph?
You could use base R, see ?plot but a lot of people would use ggplot2. However, looking at your data it won’t look very good because there’s going to be very few points per country.
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Can someone explain how R project are organized and deployed?
If you included DESCRIPTION to your repository (like in ggplot2-book - https://github.com/hadley/ggplot2-book/blob/master/DESCRIPTION ) devtools::install_deps() and renv::install() will install dependencies listed there as would pip with requirements.txt , you can trigger this from your R script, from command line or from whatever deployment / automation tool you are using.
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[Q] is majoring in stats a bad choice if i suck at programming?
Chapters 1-8 of https://adv-r.hadley.nz/, https://r4ds.had.co.nz/ , and https://ggplot2-book.org/ were covered in my statistical computing courses. I don't think it gets much more advanced than that at the undergrad level.
- How to add color?
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How can I make a line graph!?
You can check out more about Ggplot2 here: https://ggplot2-book.org/
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Ask HN: How would you spatialize higher dimensional data?
* "ggplot2: Elegant graphics for data analysis" : https://ggplot2-book.org/
cheatsheets
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KlongPy: High-Performance Array Programming in Python
I 100% agree, I think the functional features that have been added across all the popular languages (map, reduce, fold etc.) has been a positive. Nothing demonstrates it better (imo) than purrr in R: https://github.com/rstudio/cheatsheets/blob/main/purrr.pdf
I also think there is some merit to “high syntactical density” clearly if you can see the entire code in one place instead of having to navigate through many files or sections that’s beneficial. (Heavily discussed in the last big HN thread: https://news.ycombinator.com/item?id=38981639)
I also think JQ has proven the merit of tacit functional languages in that you can concisely write arbitrary transforms/queries on json that can be more expressive than SQL (many SQL engines have added JSONPath anyway). And I also think postfix is great for processing pipelines.
But I am not totally convinced in the approach of APL/J/Q/KDB for the combination of terse style + prefix + tacit because it makes the code so difficult to read. I think if you took an approach similar to JQ where instead of relying on symbols operators were just human readable words it would be easier to get us half way there to trying out the verb, adverbs etc. approach of the APL family. The problem with making it human readable text is that you lose the conciseness which is part of the draw of the APL family as they want to have a high syntax density and analogous code to mathematical expressions.
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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 I’m 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.
What are some alternatives?
r4ds - R for data science: a book
forcats - 🐈🐈🐈🐈: tools for working with categorical variables (factors)
mech - 🦾 Mech is a programming language for building data-driven systems like robots, games, and interfaces. Start here!
mostly-adequate-guide - Mostly adequate guide to FP (in javascript)
tidytuesday - Official repo for the #tidytuesday project
handson-ml2 - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
tidyr - Tidy Messy Data
stringr - A fresh approach to string manipulation in R
dtplyr - Data table backend for dplyr
Frustration-One-Year-With-R - An extremely long review of R.