Les 10 concurrents rpubs.com les plus visités

cran.r-project.org

Provides tools for working with multiple related tables, stored as data frames or in a relational database. Multiple tables (data and metadata) are stored in a compound object, which can then be manipulated with a pipe-friendly syntax.

bookdown.org

Investment databook containing business financials, industry and market environments, and global economy

rdocumentation.org

easily search the documentation for every version of every r package on cran and bioconductor.

r-bloggers.com

sentiment.aiMore powerful sentiment analysis using deep learning models made simple..See github.io page herehttps://benwiseman.github.io/sentiment.ai/OverviewKorn Ferry Institute's AITMI team made sentiment.ai for researchers and tinkerers who want a straight-forward way to use powerful, open source deep learning models to improve their sentiment analyses. Our approach is relatively simple and out performs the current best offerings on CRAN and even Microsoft's Azure Cognitive Services. Given that we felt the current norm for sentiment analysis isn't quite good enough, we decided to open-source our simplified interface to turn Universal Sentence Encoder embedding vectors into sentiment scores.We've wrapped a lot of the underlying hassle up to make the process as simple as possible. In addition to just being cool, this approach solves several problems with traditional sentiment analysis, namely:More robust, can handle spelling mitsakes and mixed case, and can be applied to dieciséis (16) languages!Doesn't need a ridged lexicon, rather it matches to an embedding vector (reduces language to a vector of numbers that capture the information, kind of like a PCA). This means you can get scores for words that are not in the lexicon but are similar to existing words!Choose the context for what negative and positive mean using the sentiment_match() function. For example, you could set positive to mean "high quality" and negative to mean "low quality" when looking at product reviews.Power Because it draws from language embedding models trained on billions of texts, news articles, and wikipedia entries, it is able to detect things such as "I learned so much on my trip to Hiroshima museum last year!" is associated with something positive and that "What happeded to the people of Hiroshima in 1945" is associated with something negative.The power is yours We've designed sentiment.ai such that the community can contribute sentiment models via github. This way, it's easier for the co

stats.stackexchange.com

Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization

r-graph-gallery.com

This post describes how to build a hexbin map, with input data available at geojson format. It provides a step-by-step explanation and reproducible code.

posit.co

Posit offers product support for customers of our professional products, including Post Workbench, Posit Connect, Posit Package Manager, and Posit Team.

kaggle.com

Discover what actually works in AI. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons.

r-coder.com

▷ Learn how to use the vectorized sapply function in R, the difference between the lapply function, how to use additional arguments and much more