Les 10 concurrents datamentor.io les plus visités

r-graph-gallery.com

The R graph gallery displays hundreds of charts made with R, always providing the reproducible code.

r4ds.had.co.nz

You’re reading the first edition of R4DS; for the latest on this topic see the Joins chapter in the second edition. 13.1 Introduction It’s rare that a data analysis involves only a single table...

bookdown.org

Этот веб учебник поможет вам освоить процесс разработки telegram ботов используя язык программирования R.

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

rdocumentation.org

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

sthda.com

Statistical tools for data analysis and visualization

bjoernwalther.com

Dieser Beitrag zeigt das Vorgehen beim unabhängigen t-Test - Voraussetzung, Berechnung sowie Berichten der Ergebnisse.

stat.ethz.ch

We treat your data as confidential. ETH Zurich endeavours only to collect personal data that is absolutely necessary for carrying out its tasks (data minimisation).

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

intro2r.com

A practical introduction to using R for data analysis