Julia Silge
Engineering Manager
I am a data scientist and engineering manager at Posit PBC
where I work on tools for data science like Positron
, vetiver
, and others. My last name is pronounced SILL-GHEE (two syllables, short i, hard g). I love making beautiful charts, the statistical programming language R, Jane Austen, black coffee, and red wine.
In school, I studied physics and astronomy; I worked in academia (teaching and doing research) and ed tech before moving into data science in 2015 and discovering R. I am an author, an international speaker, and a real-world practitioner focusing on data analysis and machine learning. I have written books with my collaborators about text mining , supervised machine learning for text , and modeling with tidy data principles in R.
I live in Salt Lake City, UT, with my husband, three kids, and two cats.
Software by Julia Silge
devtools
Tools to make an R developer's life easier
positron
Positron, a next-generation data science IDE
tidyr
Tidy Messy Data
usethis
Set up commonly used 📦 components
air
R formatter and language server
applicable
Quantify extrapolation of new samples given a training set
ark
Ark, an R kernel
broom
Convert statistical analysis objects from R into tidy format
brulee
High-Level Modeling Functions with 'torch'
bundle
Prepare objects for serialization with a consistent interface
butcher
Reduce the size of model objects saved to disk
censored
Parsnip wrappers for survival models
corrr
Explore correlations in R
dials
Tools for creating tuning parameter values
discrim
Wrappers for discriminant analysis and naive Bayes models for use with the parsnip package
education.rstudio.com
embed
Extra recipes for predictor embeddings
finetune
Additional functions for model tuning
hardhat
Construct Modeling Packages
hex-stickers
RStudio hex stickers
infer
An R package for tidyverse-friendly statistical inference
learntidymodels
Learn tidymodels with interactive learnr primers
model-implementation-principles
recommendations for creating R modeling packages
modeldb
Run models inside a database using R
multilevelmod
Parsnip wrappers for mixed-level and hierarchical models
parsnip
A tidy unified interface to models
pins-python
pins-r
Pin, discover, and share resources
planning
Documents to plan and discuss future development
probably
Tools for post-processing class probability estimates
recipes
Pipeable steps for feature engineering and data preprocessing to prepare for modeling
reprex
Render bits of R code for sharing, e.g., on GitHub or StackOverflow
rsample
Classes and functions to create and summarize resampling objects
rstudio-conf
Materials for rstudio::conf
rstudio-conf-2022-program
rstudio::conf(2022, "program")
rules
parsnip extension for rule-based models
shiny-vscode
Shiny VS Code Extension
shinymodels
spatialsample
Create and summarize spatial resampling objects 🗺
textrecipes
Extra recipes for Text Processing
themis
Extra recipes steps for dealing with unbalanced data
tidymodels
Easily install and load the tidymodels packages
tidyposterior
Bayesian comparisons of models using resampled statistics
tidypredict
Run predictions inside the database
tidyverse.org
Source of tidyverse.org
TMwR
Code and content for "Tidy Modeling with R"
tune
Tools for tidy parameter tuning
usemodels
Boilerplate Code for tidymodels
vetiver-python
Version, share, deploy, and monitor models
vetiver-r
Version, share, deploy, and monitor models
workflows
Modeling Workflows
workflowsets
Create a collection of modeling workflows
workshops
Website and materials for tidymodels workshops
yardstick
Tidy methods for measuring model performance