rlang
dplyr
rlang | dplyr | |
---|---|---|
2 | 40 | |
482 | 4,665 | |
1.2% | 0.7% | |
7.1 | 7.1 | |
1 day ago | 7 days ago | |
R | R | |
GNU General Public License v3.0 or later | 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.
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rlang
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Any responses to this article titled "Why R is the new Perl"? I personally had some responses to a few of the points and the overall message, but I was curious as to how others thought.
Granted, but these weird filenames are admittedly a very common solution, including in fairly high-profile packages from R experts (here’s ‘rlang’).
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Base R functionality vs purrr from tidyverse
Frankly, I haven't ever needed purrr. If I want to use purrr's interface, I just grab the "compat-purrr" from rlang. There are some functions that require rlang internals, but unless you care about tidyverse closure syntax (e.g. ~ .x^2) you can rewrite everything in base R.
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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IS it possible for a R package to set an R option that only affects that package?
There's an example of how to use zzz.R with a .onload() function to set options in the dplyr code base: https://github.com/tidyverse/dplyr/blob/bbcfe99e29fe737d456b0d7adc33d3c445a32d9d/R/zzz.r
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Calculation within a data table by calling on specific values in two columns
Look at the tidyverse, especially the case_when or mutate functions.
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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.
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Creating data frame
It looks like your syntax is wrong. I think you’re trying to calculate a new variables in your data frame, or alter an existing column in a data frame. Have a look at the select() function in this reference for the proper syntax to use. https://dplyr.tidyverse.org/ Does that help?
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I'm designing a shirt for a friend, it has 4 embroidered images of things they like/do. One thing is coding, they use R... I'm wondering two things. 1) What's a good image or piece of code or something that I should use? and 2) should I even add it to the design the shirt?
A lot of populat libraries have their own logos. Maybe one of them would be good. Check out dplyr for example: https://dplyr.tidyverse.org/
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Anyone use Python for statistics, particularly DOE or QA/QC? What are your thoughts?
I hope you give it a try when you get a chance: https://dplyr.tidyverse.org/
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Rstudio tidyverse help!
You can read up on the dplyr-verbs here, which I strongly suggest for your exam! In the code examples, you can simply click on any function you don't understand and it will take you directly to the documentation. Good Luck!
- Beginner question
- osdc-2023-assignment1
What are some alternatives?
DataScienceR - a curated list of R tutorials for Data Science, NLP and Machine Learning
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awesome-R - A curated list of awesome R packages, frameworks and software.
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rmarkdown - Dynamic Documents for R
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r4ds - R for data science: a book
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more