index.Rmd

---
title: "Machine Learning in R"
subtitle: "Modeling Workflows with Tidymodels"
author: "Simon Schölzel"
institute: "*Research Team Berens*"
date: "Winter Term 2021/2022"

output:
  xaringan::moon_reader:
    seal: false
    css: [./custom/xaringan-themer.css, ./custom/custom-theme.css]
    lib_dir: libs
    nature:
      titleSlideClass: [left, middle, inverse]
      highlightStyle: github
      countIncrementalSlides: false
      ratio: '16:9'
---

```{r setup, include=F}

# knitr options
knitr::opts_chunk$set(
  comment = ">",
  collapse = F
)

# package management
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  xaringanthemer, xaringanExtra, metathis,
  tidyverse, tidymodels, tidytuesdayR, skimr, themis, glmnet, doParallel, usemodels
)
pacman::p_load_gh("hadley/emo", "gadenbuie/xaringanExtra")

# xaringan custom setup
xaringanthemer::style_mono_accent(
  base_color = "#000000",
  link_color = "#0000EE",
  header_font_google = google_font("Josefin Sans"),
  text_font_google   = google_font("Montserrat", "700b", "400"),
  code_font_google   = google_font("Fira Mono"),
  colors = c(red = "#f34213", white = "#FFFFFF"),
  code_font_size = "smaller",
  code_inline_background_color = "#F4F4F4",
  outfile = "./custom/xaringan-themer.css"
)

xaringanExtra::use_panelset()
xaringanExtra::use_webcam()

xaringanExtra::use_extra_styles(
  hover_code_line = T,
  mute_unhighlighted_code = T
)

metathis::meta() %>%
  meta_name("github-repo" = "simonschoe/ml-with-tidymodels") %>% 
  meta_social(
    title = "Modeling Workflows with Tidymodels",
    description = "Video series on tidymodels, a unified framework towards modeling and machine learning in R using tidy data principles.",
    url = "https://simonschoe.github.io/ml-with-tidymodels",
    image = "https://simonschoe.github.io/ml-with-tidymodels/img/share-card.png",
    image_alt = "Title slide for Modeling Workflows with Tidymodels",
    og_type = "website",
    og_author = "Simon Schölzel",
    twitter_card_type = "summary_large_image"
  )
```

class: center, middle, hide-count
count: false

# `r rmarkdown::metadata$title`
### `r rmarkdown::metadata$subtitle`

___

**`r rmarkdown::metadata$author`**

`r rmarkdown::metadata$date`  
.small[(updated: `r Sys.Date()`)]

<br><br>

<a href="https://www.wiwi.uni-muenster.de/"><img src="https://www.wiwi.uni-muenster.de/fakultaet/sites/all/themes/wwucd/assets/images/logos/secondary_wiwi_aacsb_german.jpg" alt="fb4-logo" height="45"></a> <a href="https://www.wiwi.uni-muenster.de/ctrl/aktuelles"><img src="https://www.wiwi.uni-muenster.de/ctrl/sites/all/themes/wwucd/assets/images/logos/berenslogo5.jpg" alt="ftb-logo" height="45"></a> <a href="https://www.wiwi.uni-muenster.de/iff2/de/news"><img src="https://www.wiwi.uni-muenster.de/iff2/sites/all/themes/wwucd/assets/images/logos/logo_iff2_en2.jpg" alt="ipb-logo" height="45"></a>

---

name: agenda

## Agenda

**1 Learning Objectives**

**2 Introduction to `tidymodels`**

**3 Himalayan Climbing Expeditions Data**

**4 The Core `tidymodels` Packages**

>4.1 `rsample`: General Resampling Infrastructure  
4.2 `recipes`: Preprocessing Tools to Create Design Matrices  
4.3 `parsnip`: A Common API to Modeling and Analysis Functions  
4.4 `workflows`: Modeling Workflows  
4.5 `dials`: Tools for Creating Tuning Parameter Values  
4.6 `tune`: Tidy Tuning Tools  
4.7 `broom`: Convert Statistical Objects into Tidy Tibbles  
4.8 `yardstick`: Tidy Characterizations of Model Performance

**5 Additions to the `tidymodels` Ecosystem**

---

## 1 Learning Objectives `r emo::ji("idea")`

This workshop introduces `tidymodels`, a unified framework towards modeling and machine learning in `R` using tidy data principles. You will get to know tools that facilitate every step of your machine learning workflow, from resampling, over preprocessing and model building, to model tuning and performance evaluation.

More specifically, after this lecture you will
- be familiar with the core packages of the `tidymodels` ecosystem and hopefully realize the value of a unified modeling framework,<br><br>
- know how to design a full-fledged machine learning pipeline for a particular prediction task,<br><br>
- broaden your technical skill set by learning about declarative programming, hyperparameter scales and parallel processing, and<br><br>
- most importantly, be capable of conducting your own machine learning projects in `R`.

---

```{r child='./01_intro.Rmd', eval=T}
```

---

```{r child='./02_resampling.Rmd', eval=T}
```

---

```{r child='./03_preprocessing.Rmd', eval=T}
```

---

```{r child='./04_model_building.Rmd', eval=T}
```

---

```{r child='./05_workflows.Rmd', eval=T}
```

---

```{r child='./06_tuning.Rmd', eval=T}
```

---

```{r child='./07_model_evaluation.Rmd', eval=T}
```

---

## 5 Additions to the `tidymodels` Ecosystem

Similar to the `tidyverse` ecosystem, there is already a promising supply of complementary packages that further improve the capabilities of `tidymodels`, e.g.:

```{r, echo=F, out.height='40%', out.width='40%', out.extra='style="float:right; padding: 30px"'}
knitr::include_graphics("https://tenor.com/view/shocked-po-kung-fu-panda-gif-4255877.gif")
```

- `textrecipes`: Extra recipes for text processing
- `baguette`: Efficient model functions for bagging
- `stacks`: Tidy model stacking
- `probably`: Tools for post-processing class probability estimates
- `infer`: Statistical inference and hypothesis testing using tidy data principles
- `finetune`: Implementation of additional search algorithms
- `usemodels`: Boilerplate code for `tidymodels` analyses


???
- textrecipes as extension to the recipes package for natural language processing
- baguette as add-on to the parsnip package
- stacking: ensemble technique to integrate the predictions of multiple models into a meta-model
- probably enables the identification of optimal probability thresholds and equivocal zones (uncertain probability regions)
- infer: 

---

## Thank You!

.pull-left[
.center[`r emo::ji("thinking_face")` **Right now**]<br><br>
```{r, echo=F, fig.align='center'}
knitr::include_graphics("https://tenor.com/view/homer-daydreaming-thinking-simpsons-gif-8949118.gif")
```
]
.pull-right[
.center[`r emo::ji("nerd_face")` **After having mastered `tidymodels`**]<br><br>
```{r, echo=F, fig.align='center'}
knitr::include_graphics("https://tenor.com/view/homer-gif-10571731.gif")
```
]

---

## Further Resources

**Kuhn, M./Silge, J. (2021):** Tidy Modeling in R. URL: https://www.tmwr.org (work-in-progress).

[Learn section](https://www.tidymodels.org/learn/) of tidymodels.org.

[TidyTuesday contributions](https://juliasilge.com/blog/) by Julia Silge.

## Credits

`tidymodels` [artworks and illustration](https://github.com/allisonhorst/stats-illustrations) are provided by Allison Horst.