box
ggplot2-book
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box | ggplot2-book | |
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31 | 31 | |
811 | 1,500 | |
- | - | |
7.5 | 3.9 | |
17 days ago | about 2 months ago | |
R | Perl | |
MIT License | - |
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box
- Trying to Replicate Excel financial Functions
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Can someone explain how R project are organized and deployed?
As for organising code within a project, as mentioned packages really don’t allow this beyond collation order. The best solution in this space is the ‘box’ package which implements a fully-featured module system for R. ‘box’ notably gets used by some folks to implement large-scale Shiny applications; if this is what you’re after, I would recommend the ‘rhino’ framework, which builds upon Siny and ‘box’.
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Good practice with long R scripts - any examples?
You can write amazing, clean, modular code with the box package.
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Does anyone feel like R is actually vastly worse for dependency/environment management than Python?
I would look into box https://github.com/klmr/box if you haven’t heard of it already
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"Managing large codebases in R" webinar (Oct. 6, 2022)
Shapeless plug: check out the already mentioned ‘box’, I think it’s strictly superior to ‘import’ (but I’m biased).
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Get tsarted wiht R using this Cheat Sheet - DataCamp
By contrast, R code doesn’t need to change the working directory at all! Having to do so hides other flaws in the code. For instance, when trying to load code or data, use the tools provided by R. That is, write packages and use system.file or, when not writing packages, use ‘box’.
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Which R function do you find somewhat tricky?
‘box’ fixes that.
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Restructuring a large R project. Need advice on how to wire up file paths and associated objects.
I think your use-case is best addressed by the ‘targets’ package. But I would also recommend checking out the ‘box’ package for a more general way of structuring R projects in modules which isn’t supported well natively by R (disclaimer: I wrote that package). Writing R code as modules fundamentally side-steps the issue of having to deal with absolute paths. Instead, all code and data are either contained in the module or can be accessed relative to the working directory.
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[Q] Loading `dplyr` packages within a function but not outside of it
However, using ‘box’, as recommended in another comment, allows you to achieve the same effect with less (and cleaner) code, by declaring your imports locally with the box::use function.
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Is it possible to see what functions are used from what library?
If you're writing you're own code you can use packageName::functionName(), or the box package. Which is definitely useful on larger codebases.
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/
What are some alternatives?
renv - renv: Project environments for R.
r4ds - R for data science: a book
ggplot2 - An implementation of the Grammar of Graphics in R
cheatsheets - Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/.
rnim - A bridge between R and Nim
dtplyr - Data table backend for dplyr
tidytable - Tidy interface to 'data.table'
forcats - 🐈🐈🐈🐈: tools for working with categorical variables (factors)
workflowr - Organize your project into a research website
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.
rspm - RStudio Package Manager
mech - 🦾 Main repository for the Mech programming language. Start here!