03:00
Introduction to Machine Learning in R with tidymodels
Welcome!
Wi-Fi network name
TODO-ADD-LATER
Wi-Fi password
TODO-ADD-LATER
There are gender neutral bathrooms located on floor LL2, next to Chicago A
A meditation/prayer room is located on floor LL2 in Chicago A
A lactation room is located on floor LL2 in Chicago B
Please review the posit::conf code of conduct, which applies to all workshops: https://posit.co/code-of-conduct
CoC site has info on how to report a problem (in person, email, phone)
Please do not photograph people wearing red lanyards
You can use the magrittr %>%
or base R |>
pipe
You are familiar with functions from dplyr, tidyr, ggplot2
You have some exposure to basic statistical concepts like linear models and residuals
You do not need intermediate or expert familiarity with modeling or ML
+ our TA today, Kristin Bott!
Many thanks to Davis Vaughan, Julia Silge, David Robinson, Julie Jung, Alison Hill, and DesirΓ©e De Leon for their role in creating these materials!
πͺ βIβm stuck and need help!β
π© βI finished the exerciseβ
#workshop-tidymodels
Illustration credit: https://vas3k.com/blog/machine_learning/
Illustration credit: https://vas3k.com/blog/machine_learning/
Illustration credit: https://vas3k.com/blog/machine_learning/
How are statistics and machine learning related?
How are they similar? Different?
03:00
library(tidymodels)
#> ββ Attaching packages ββββββββββββββββββββββββββββ tidymodels 1.3.0 ββ
#> β broom 1.0.9 β rsample 1.3.1
#> β dials 1.4.2 β tibble 3.3.0
#> β dplyr 1.1.4 β tidyr 1.3.1
#> β infer 1.0.9 β tune 2.0.0
#> β modeldata 1.5.1 β workflows 1.3.0
#> β parsnip 1.3.3 β workflowsets 1.1.1
#> β purrr 1.1.0 β yardstick 1.3.2
#> β recipes 1.3.1
#> ββ Conflicts βββββββββββββββββββββββββββββββ tidymodels_conflicts() ββ
#> β purrr::discard() masks scales::discard()
#> β dplyr::filter() masks stats::filter()
#> β dplyr::lag() masks stats::lag()
#> β recipes::step() masks stats::step()
If you are using your own laptop instead of Posit Cloud:
# Install the packages for the workshop
pkgs <-
c("bonsai", "Cubist", "doParallel", "earth", "embed", "finetune",
"lightgbm", "lme4", "parallelly", "plumber", "probably",
"ranger", "rpart", "rpart.plot", "rules", "splines2", "stacks",
"text2vec", "textrecipes", "tidymodels", "vetiver")
install.packages(pkgs)
R version 4.5.1 (2025-06-13), Quarto (1.7.32)
package | version |
---|---|
bonsai | 0.4.0 |
broom | 1.0.9 |
Cubist | 0.5.0 |
dials | 1.4.2 |
doParallel | 1.0.17 |
dplyr | 1.1.4 |
earth | 5.3.4 |
embed | 1.1.5 |
finetune | 1.2.1 |
forested | 0.2.0 |
Formula | 1.2-5 |
package | version |
---|---|
ggplot2 | 3.5.2 |
lattice | 0.22-7 |
lightgbm | 4.6.0 |
lme4 | 1.1-37 |
modeldata | 1.5.1 |
parallelly | 1.45.1 |
parsnip | 1.3.3 |
plotmo | 3.6.4 |
plotrix | 3.8-4 |
plumber | 1.3.0 |
probably | 1.1.1 |
package | version |
---|---|
purrr | 1.1.0 |
ranger | 0.17.0 |
recipes | 1.3.1 |
rpart | 4.1.24 |
rpart.plot | 3.1.3 |
rsample | 1.3.1 |
rules | 1.0.2 |
scales | 1.4.0 |
splines2 | 0.5.4 |
stacks | 1.1.1 |
text2vec | 0.6.4 |
package | version |
---|---|
textrecipes | 1.1.0 |
tibble | 3.3.0 |
tidymodels | 1.3.0 |
tidyr | 1.3.1 |
tune | 2.0.0 |
vetiver | 0.2.5 |
workflows | 1.3.0 |
workflowsets | 1.1.1 |
yardstick | 1.3.2 |