rmarkdown
dplyr
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rmarkdown | dplyr | |
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38 | 40 | |
2,782 | 4,634 | |
0.9% | 0.5% | |
7.6 | 7.4 | |
23 days ago | 17 days ago | |
R | R | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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.
rmarkdown
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Pandoc
I'm surprised to see no one has pointed out [RMarkdown + RStudio](https://rmarkdown.rstudio.com) as one way to immediately interface with Pandoc.
I used to write papers and slides in LaTeX (using vim, because who needs render previews), then eventually switched to Pandoc (also vim). I eventually discovered RMarkdown+RStudio. I was looking for a nice way to format a simple table and discovered that rmarkdown had nice extensions of basic markdown (this was many years ago so maybe that is incorporated into vanilla markdown/pandoc).
The RMarkdown page claims:
> R Markdown supports dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.
...which I think is largely due to using pandoc as the core generator.
RStudio shows you the pandoc command it runs to generate your document, which I've used to figure out the pandoc command I want to run when I've switched to using pandoc directly.
This is a bit of a "lazy" way to interact with pandoc. Maybe the "laziest" aspect: when I get a new computer, I can install the entire stack by installing Rstudio, then opening a new rmarkdown document. Rstudio asks whether I'd like to install all the necessary libraries -- click "yes" and that's it. Maybe that sounds silly but it used to be a lot of work to manage your LaTeX install. These days I greatly favor things that save me time, which seems to get more precious every year.
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We’re Washington Post reporters who analyzed Google’s C4 data set to see which websites AI uses to make itself sound smarter. Ask us Anything!
We used R Markdown for cleaning and analysis, creating updateable web pages we could share with everyone involved. Similarweb’s categories were useful, but too niche for us. So we spent a lot of time recategorizing and redefining the groupings. We used the token count for each website — how many words or phrases — to measure it’s importance in the overall training data.
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Generating PDF 📄 with Python 🐍
R Markdown / Quarto https://quarto.org/ https://rmarkdown.rstudio.com/ ; can dynamically generate a document and compile it to HTML, PDF, others
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PYTHON CHARTS: the Python data visualization site with more than 500 different charts with reproducible code and color tools
Hi! At this moment I'm not opening the source code, but I can explain you the tech used. This site is based on another site I created before named https://r-charts.com/ and it was created with blogdown (HUGO + R Markdown). Hence, each tutorials is an R markdown file. For PYTHON CHARTS, in order to run Python within an R markdown file I had to use an R package named reticulate. In addition, the template depends on shuffle.js for filtering and fuse.js for searching
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looking for an "low dependency" or pythonesque way to generate PDF's
What you want is not Python, its R Markdown; https://rmarkdown.rstudio.com/
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LaTex alternative/replacement written in Rust?
not sure what you mean by this exactly but in my experience its far better to use Markdown + pandoc for stuff like this. Actually I use R Markdown which can compile to either HTML or PDF from the same source document, with executable code chunks embedded (to generate the document contents) ; https://rmarkdown.rstudio.com/
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Neovim support for editing Quarto (.qmd) files
Quarto is a relatively new Markdown-based file format. One of its main uses is writing reports that interleave text with code and results; it supports rendering with knitr (an engine widely used in the R community) as well as Jupyter (more popular with Python users). Since I work in data science, I use both languages regularly. For writing R reports, I've switched from R Markdown (Quarto's R-focused predecessor) to Quarto. I'd also like to start writing Python reports in Quarto using Neovim.
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How do you build and send reports to your users?
If you're not already aware of and using RMarkdown, make learning it a priority. I use both R and Python extensively. Although Jupyter Notebooks have utility, RMarkdown is the superior tool for the most flexibility in reporting.
- Ask HN: Markdown/reStructuredText to write a PhD thesis in STEM fields?
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Securing R Markdown Documents
The polished package now supports Rmarkdown documents that use the shiny runtime. This includes flexdashboard!
dplyr
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Show HN: Open-source, browser-local data exploration using DuckDB-WASM and PRQL
That's great feedback, thanks!
This tool definitely comes from a place of personal need - beyond just handling large files, I've also never really gelled well with the Excel/Google Sheet model of changing data in place as if you were editing text. I'm a Data Scientist and always preferred the chained data transforms you see in things like dplyr (https://dplyr.tidyverse.org/) or Polars (https://pola.rs/) and I feel this tool maps very closely to the chained model.
Also, thank you for the feature requests! Those would all be very useful - we'll put them on the roadmap.
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PSA: You don't need fancy stuff to do good work.
Before diving into advanced machine learning algorithms or statistical models, we need to start with the basics: collecting and organizing data. Fortunately, both Python and R offer a wealth of libraries that make it easy to collect data from a variety of sources, including web scraping, APIs, and reading from files. Key libraries in Python include requests, BeautifulSoup, and pandas, while R has httr, rvest, and dplyr.
- osdc-2023-assignment1
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Modern Polars: an extensive side-by-side comparison of Polars and Pandas
It really can't be said enough how pandas is a mess. It has way too much surface area and no common thread pulling it all together. This gets obvious when you work with better dataframe libs like dplyr [1] or DataFramesMeta [2]. I've worked on production systems with all of these libs, this is not gratuitous bashing.
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How do I find R code for R functions?
There are two ways you can generally see the source code for packages. The simplest is to look for the github repository for the package (assuming it exists). For dplyr, it's here. Easiest way to find these is to google search "r github" plus the name of the package. Usually it'll be one of the first results. The github repo would also usually be linked on the package's CRAN page. However, be aware that this may be a development version of the package and not the same version that is currently released on CRAN (e.g. dplyr on CRAN is version 1.0.10, but on github it is listed as version 1.0.99.9000, which will probably become version 1.1.0 when it is released onto CRAN).
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People who live near other people vote for Democrats
Tools used: various packages in R (tidycensus, dplyr, ggplot2, sf)
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Used Cars Data Scraping - R & Github Actions & AWS
It came up with the idea of how to combine Data Engineering with Cloud and automation. I needed to find a data source as it would be an automated pipeline, so I needed a dynamic source. At the same time, I wanted to find a site where I thought retrieving data would not be a problem and do practice with both rvest and dplyr. After I had no problems with my experiments with Carvago, I added the necessary data cleaning steps. Another thing I aimed for in the project was to keep the data in different ways in different environments. While raw (daily CSV) and processed data were written to the Github repo, I wrote the processed data to PostgreSQL on AWS RDS. In addition, I sync the raw and processed data to S3 to be able to use it with Athena. However, I have separated some stages for GitHub Actions to be a good practice. For example, in the first stage, I added synchronization with AWS S3 as a separate action while scraping data, cleaning, and printing fundamental analysis to a simple log file. If there is no error after all this, I added a report with RMarkdown and the action that will be published on github.io. Thus, I created an end-to-end data pipeline where the data from the source is made to offer basic reporting with simple processing.
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Quick candlestick summaries with Elixir's Explorer
The API is heavily influenced by Tidy Data and borrows much of its design from dplyr. The philosophy is heavily influenced by this passage from dplyr's documentation:
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tidytable v0.8.1 is on CRAN - it also comes with a new logo! Need data.table speed with tidyverse syntax? Check out tidytable.
Also - I might have been the one that put in the request for .by in dplyr 😅
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ibis-datasette: Query datasette servers without writing a line of SQL
For my day job I work on ibis. ibis lets users write queries using a familiar dataframe-like API, and then execute those queries on a number of SQL (and non-SQL) backends. Think of it like dplyr but for Python.
What are some alternatives?
Pluto.jl - 🎈 Simple reactive notebooks for Julia
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
worldfootballR - A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat and fotmob
Rustler - Safe Rust bridge for creating Erlang NIF functions
here_here - I love the here package. Here's why.
tinytex - A lightweight, cross-platform, portable, and easy-to-maintain LaTeX distribution based on TeX Live
ggplot2 - An implementation of the Grammar of Graphics in R
nx - Multi-dimensional arrays (tensors) and numerical definitions for Elixir
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
TikZ - Complete collection of my PGF/TikZ figures.
codebraid - Live code in Pandoc Markdown
blogdown - Create Blogs and Websites with R Markdown