index.Rmd

---
title: "Predictive modeling with text using tidy data principles"
author:
- name: Emil Hvitfeldt
  url: https://www.hvitfeldt.me/
  affiliation: University of Southern California
  affiliation_url: https://www.usc.edu/
  orcid_id: 0000-0002-0679-1945
date: "2020-10-09"
description: |
  1 day workshop at R/Pharma
site: distill::distill_website
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

```{r meta, echo=FALSE}
library(metathis)
meta() %>%
 meta_general(
   description = "Predictive modeling with text using tidy data principles",
   generator = "xaringan and remark.js"
 ) %>%
 meta_name("github-repo" = "EmilHvitfeldt/textmodels4pharma") %>%
 meta_social(
   title = "Predictive modeling with text using tidy data principles",
   url = "https://textmodels4pharma.netlify.app/",
   image = "https://textmodels4pharma.netlify.app/slides/03-modeling/images/tidymodels-textrecipes.png",
   image_alt = "tidymodels workflow with textrecipes",
   og_type = "website",
   twitter_card_type = "summary_large_image"
 )
```

## Welcome  

These are the materials for the [R/Pharma](https://rinpharma.com/) workshop Predictive modeling with text using tidy data principles, by Emil Hvitfeldt. In this workshop will you learn:

- Basics of text mining with [tidytext](https://github.com/juliasilge/tidytext)
- The effect of text preprocessing
- How to fit a predictive model in [tidymodels](https://www.tidymodels.org/)
- Use [textrecipes](https://textrecipes.tidymodels.org/) to construct features form text

## Slides

+ [01: Introduction](slides/01-introduction/index.html)

+ [02: Text Mining](slides/02-text-mining/index.html)

+ [03: Modeling](slides/03-modeling/index.html)

## Links

+ Link to this website: <https://textmodels4pharma.netlify.app/>

+ Link to Supervised Machine Learning for Text Analysis in R book: <https://smltar.com/>

+ Link to Text Mining with R book: <https://www.tidytextmining.com/>

## Acknowledgments {.appendix}

This is website made with the [distill package](https://rstudio.github.io/distill/).