1 - Introduction

Survival analysis with tidymodels

Welcome!

Wi-Fi network name

TODO-ADD-LATER

Wi-Fi password

TODO-ADD-LATER

Workshop policies

• Please review the code of conduct: https://events.linuxfoundation.org/user/attend/code-of-conduct/

Who are you?

• You can use the magrittr %>% or base R |> pipe

• You are familiar with functions from dplyr, tidyr, ggplot2

• You have exposure to basic concepts of survival analysis

• You are familiar with the basic predictive modeling workflow

• You do not need intermediate or expert familiarity with modeling or ML

Who are tidymodels?

• Simon Couch
• Hannah Frick
• Emil Hvitfeldt
• Max Kuhn

Many thanks to Davis Vaughan, Julia Silge, David Robinson, Julie Jung, Alison Hill, and Desirée De Leon for their role in creating these materials!

Asking for help

🟪 “I’m stuck and need help!”

🟩 “I finished the exercise”

👀

Plan for this workshop

• Your data budget
• What makes a model
• Evaluating models
• Tuning models

Introduce yourself to your neighbors 👋

Log in to Posit Cloud (free): TODO-ADD-LATER

What is tidymodels?

library(tidymodels)
#> ── Attaching packages ──────────────────────────── tidymodels 1.2.0 ──
#> ✔ broom        1.0.6      ✔ rsample      1.2.1 
#> ✔ dials        1.2.1      ✔ tibble       3.2.1 
#> ✔ dplyr        1.1.4      ✔ tidyr        1.3.1 
#> ✔ infer        1.0.7      ✔ tune         1.2.1 
#> ✔ modeldata    1.4.0      ✔ workflows    1.1.4 
#> ✔ parsnip      1.2.1      ✔ workflowsets 1.1.0 
#> ✔ purrr        1.0.2      ✔ yardstick    1.3.1 
#> ✔ recipes      1.0.10
#> ── Conflicts ─────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()
#> • Use tidymodels_prefer() to resolve common conflicts.

The whole game

• Roadmap for today
• Minimal version of predictive modeling process
• Feature engineering and tuning as iterative extensions

The whole game

The whole game

Stress that we are not fitting a model on the entire training set other than for illustrative purposes in deck 2.

The whole game

The whole game

The whole game

The whole game

The whole game

Let’s install some packages

If you are using your own laptop instead of Posit Cloud:

# Install the packages for the workshop
pkgs <- c("aorsf", "censored", "glmnet", "partykit", "pec", "rpart", "tidymodels")

install.packages(pkgs)

Or log in to Posit Cloud:

TODO-ADD-LATER

Our versions

R version 4.4.0 (2024-04-24), Quarto (1.4.555)

package	version
aorsf	0.1.5
broom	1.0.6
censored	0.3.2
dials	1.2.1
dplyr	1.1.4
ggplot2	3.5.1
glmnet	4.1-8

package	version
libcoin	1.0-10
modeldata	1.4.0
mvtnorm	1.2-5
parsnip	1.2.1
partykit	1.2-20
pec	2023.04.12
prodlim	2023.08.28

package	version
purrr	1.0.2
recipes	1.0.10
rpart	4.1.23
rsample	1.2.1
scales	1.3.0
survival	3.7-0
tibble	3.2.1

package	version
tidymodels	1.2.0
tidyr	1.3.1
tune	1.2.1
workflows	1.1.4
workflowsets	1.1.0
yardstick	1.3.1