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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<title>Touring tidyverse</title>
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class: center, middle, inverse, title-slide
# Touring tidyverse
## tidymodels
---
# Plan
1. Brief intro to `tidymodels`.
1. Demo of `parsnip`/`dials`.
1. Demo of `recipes` (?).
---
background-image: url(https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/tidymodels.png)
background-size: 100px
background-position: 90% 6%
# tidymodels
tidymodels is a "meta-package" for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse.
1. `broom` takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy data frames.
1. `infer` is a modern approach to statistical inference.
1. `recipes` is a general data preprocessor with a modern interface.
1. `rsample` has infrastructure for resampling data so that models can be assessed and empirically validated.
1. `yardstick` contains tools for evaluating models (e.g. accuracy, RMSE, etc.)
1. `tidypredict` translates some model prediction equations to SQL for high-performance computing.
1. `tidyposterior` can be used to compare models using resampling and Bayesian analysis.
1. `tidytext` contains tidy tools for quantitative text analysis, including basic text summarization, sentiment analysis, and text modeling.
1. `dials` contains tools to create and manage values of tuning parameters and is designed to integrate well with the parsnip package.
---
# Current state
1. Current version - 0.0.2.
1. https://github.com/tidymodels/
1. Developed by __Max Kuhn__, Hadley Wickham, Davis Vaughan.
1. Very early stage.
```
## # A tibble: 10 x 2
## package version
## <chr> <chr>
## 1 broom 0.5.2
## 2 infer 0.4.0.1
## 3 recipes 0.1.5
## 4 rsample 0.0.4
## 5 yardstick 0.0.3
## 6 tidypredict 0.3.0
## 7 tidyposterior 0.0.2
## 8 tidytext 0.2.0
## 9 parsnip 0.0.2
## 10 dials 0.0.2
```
---
# Prior art
1. [`h2o`](https://github.com/h2oai/h2o-3)
1. [`caret`](https://topepo.github.io/caret/index.html)
1. [`mlr`](https://github.com/mlr-org/mlr)
1. [`scikit-learn`](https://scikit-learn.org/stable/index.html)
1. Apache Spark with, e.g., [`sparklyr`](https://spark.rstudio.com/)
1. [MLflow](https://mlflow.org/docs/latest/tutorial.html)
1. [CRAN Machine Learning taskview](https://cran.r-project.org/web/views/MachineLearning.html)
1. More?
---
# Demo
We will have a (very) condensed walkthrough of 2-day workshop "Applied Machine Learning" by Max Kuhn that he had during `rstudio::conf(2019L)`.
This should give you an idea about what `tidymodels` is planning to be and what is possible already now.
---
# Resources
1. https://tidymodels.github.io/model-implementation-principles/index.html
1. Applied Machine Learning workshop - https://github.com/topepo/rstudio-conf-2019
1. https://github.com/tidymodels/tidymodels
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