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Top 23 R R Projects
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Project mention: Create custom `ggplot2` candlesticks `geom` based on two other `geom`s | reddit.com/r/Rlanguage | 2022-11-18
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Zigi
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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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Project mention: PYTHON CHARTS: the Python data visualization site with more than 500 different charts with reproducible code and color tools | reddit.com/r/Python | 2022-10-18
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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Project mention: Charging Progress bar that changes color with percentage and when charging | reddit.com/r/kustom | 2022-10-31
The gradient comes from an R library of colour palettes derived from Wes Anderson's movies. This one is from the Life Aquatic called "Zissou1": https://github.com/karthik/wesanderson
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Project mention: DiagrammeR: Graph and network visualization using tabular data in R. | reddit.com/r/coolgithubprojects | 2022-08-02
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Project mention: blogdown VS Camlog - a user suggested alternative | libhunt.com/r/blogdown | 2022-07-21
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Project mention: esquisse: RStudio add-in to make plots interactively with ggplot2 {R} | reddit.com/r/Sciatro | 2022-06-05
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Project mention: Made an app where you can search for money diaries by location or income | reddit.com/r/MoneyDiariesACTIVE | 2022-11-07
To get the data from the website, I need to use the package (a set of R code someone created and shared that's designed for a certain task) rvest, then I did a bunch of data munging in R to pull out the location/salary/age/etc. I saved that in a dataset and then used another package flexdashboard to make a webpage which I can essentially "one-click" publish using a free tool called RPubs.
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drake
An R-focused pipeline toolkit for reproducibility and high-performance computing (by ropensci)
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What’s the original? Tidyverse, as it exists now, had it’s initial release in 2016. Pandas initial release was in 2009. AFAIK, ggplot2 and reshape are the only individual Tidyverse packages older than that.
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This was fun to play around with. I made some very minor changes and posted at https://gist.github.com/hadley/d54895557fbb0fe0402d2277b9011....
It revealed to me that there's a buglet in `forcats::last()` (https://github.com/tidyverse/forcats/issues/303) and made me wonder if `pivot_longer()` should be able to rename the columns as you pivot them (https://github.com/tidyverse/tidyr/issues/1338)
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Project mention: Working with columns names that are numbers (in this case, years) | reddit.com/r/RStudio | 2022-10-15
I would just clean the names and work with those. Then there is no need to use backticks. Read about the function clean_names in the janitor vignette: https://github.com/sfirke/janitor
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If you've been learning R with the Tidyverse style, tidytext may be useful, too.
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It might be helpful to explore the purrr package as well: https://purrr.tidyverse.org/
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Project mention: People who live near other people vote for Democrats | reddit.com/r/dataisbeautiful | 2022-11-09
Tools used: various packages in R (tidycensus, dplyr, ggplot2, sf)
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Looks like they're close to 3.0.0. https://github.com/r-lib/lintr/milestone/2
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Project mention: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting? | news.ycombinator.com | 2022-11-03
As a few other people have mentioned, I find R to be the easiest tool for this job, specifically the forecast package [0]. I had to use this package for an applied econometrics course in college a few years ago, and I have been using it ever since. I find the syntax to be more straightforward than comparable libraries in Python. I also assume that this library (and other libraries in R) offer higher quality models and results than their counterparts in Python, but this is just an assumption.
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R (yes, the statistics language) has exactly this.
You can literally extract the body of a function as a list of "call" objects (which are themselves just dressed-up lists of symbols), inject/delete/modify individual statements, and then re-cast your new list to a new function object.
I don't know why the original devs thought this was necessary or even desirable in a statistics package, but it turns out to be a lot of fun to program with. It has also made possible a wide variety of clever and elegant custom syntaxes, such as a pipe infix operator implemented as a 3rd-party library without any custom language extensions [0]. The pipe infix operator got so popular that it was eventually made part of the language core syntax in version 4.1 [1].
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Project mention: How many ways to munge data into R? Which is the quickest, or most effective, what is reproducable? | reddit.com/r/RStudio | 2022-05-04
I think you are looking for datapasta package. https://github.com/MilesMcBain/datapasta
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2022-11-30.
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A note from our sponsor - SonarLint
www.sonarlint.org | 3 Dec 2022
Index
What are some of the best open-source R projects in R? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | ggplot2 | 5,690 |
| 2 | awesome-R | 5,265 |
| 3 | dplyr | 4,271 |
| 4 | rmarkdown | 2,566 |
| 5 | DataScienceR | 1,845 |
| 6 | wesanderson | 1,646 |
| 7 | ggstatsplot | 1,606 |
| 8 | DiagrammeR | 1,589 |
| 9 | blogdown | 1,575 |
| 10 | esquisse | 1,557 |
| 11 | rvest | 1,374 |
| 12 | drake | 1,322 |
| 13 | tidyverse | 1,314 |
| 14 | tidyr | 1,196 |
| 15 | janitor | 1,183 |
| 16 | tidytext | 1,099 |
| 17 | purrr | 1,088 |
| 18 | sf | 1,072 |
| 19 | lintr | 1,030 |
| 20 | forecast | 1,009 |
| 21 | magrittr | 913 |
| 22 | future | 868 |
| 23 | datapasta | 842 |
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