dplyr
examples
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dplyr | examples | |
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40 | 142 | |
4,652 | 7,728 | |
0.6% | 1.1% | |
7.4 | 6.2 | |
16 days ago | 16 days ago | |
R | Jupyter Notebook | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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.
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
examples
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
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MLOps in practice: building and deploying a machine learning app
The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
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🔥14 Excellent Open-source Projects for Developers😎
10. TensorFlow - Make Machine Learning Work for You 🤖
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GPU Survival Toolkit for the AI age: The bare minimum every developer must know
AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
#2 TensorFlow
- Are there people out there who still like Sam atlman - AI IS AT DANGER
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Tensorflow help
I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?
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How popular are libraries in each technology
Machine learning is the process of using algorithms and statistical models to enable computers to learn from data. There are many tools and libraries available for machine learning, but the most popular by far is TensorFlow. TensorFlow is an open-source platform for machine learning developed by Google. It has over 176k stars on Github and is used by companies such as Airbnb and Intel.
What are some alternatives?
worldfootballR - A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat and fotmob
cppflow - Run TensorFlow models in C++ without installation and without Bazel
Rustler - Safe Rust bridge for creating Erlang NIF functions
mlpack - mlpack: a fast, header-only C++ machine learning library
ggplot2 - An implementation of the Grammar of Graphics in R
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
nx - Multi-dimensional arrays (tensors) and numerical definitions for Elixir
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
Selenium WebDriver - A browser automation framework and ecosystem.
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
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing