Closing out our year with a Q4 2021 tidymodels update

  2021/12/16

  tidymodels, survey, rsample, shinymodels

  Julia Silge

The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles.

library(tidymodels)
#> ── Attaching packages ──────────────────────────── tidymodels 0.1.4 ──
#> ✓ broom        0.7.10     ✓ rsample      0.1.1 
#> ✓ dials        0.0.10     ✓ tibble       3.1.6 
#> ✓ dplyr        1.0.7      ✓ tidyr        1.1.4 
#> ✓ infer        1.0.0      ✓ tune         0.1.6 
#> ✓ modeldata    0.1.1      ✓ workflows    0.2.4 
#> ✓ parsnip      0.1.7      ✓ workflowsets 0.1.0 
#> ✓ purrr        0.3.4      ✓ yardstick    0.0.9 
#> ✓ recipes      0.1.17
#> ── Conflicts ─────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter()  masks stats::filter()
#> x dplyr::lag()     masks stats::lag()
#> x recipes::step()  masks stats::step()
#> • Dig deeper into tidy modeling with R at https://www.tmwr.org

Starting at the beginning of this year, we now publish regular updates here on the tidyverse blog summarizing what’s new in the tidymodels ecosystem. You can check out the tidymodels tag to find all tidymodels blog posts here, including our roundup posts as well as those that are more focused. The purpose of these quarterly posts is to share useful new features and any updates you may have missed.

Since our last roundup post, there have been seven CRAN releases of tidymodels packages. You can install these updates from CRAN with:

install.packages(c("broom", "embed", "rsample", "shinymodels",
                   "tidymodels", "workflows", "yardstick"))

The NEWS files are linked here for each package; you’ll notice that some of these releases involve small bug fixes or internal changes that are not user-facing. We write code in these smaller, modular packages that we can release frequently both to make models easier to deploy and to keep our software easier to maintain. We know it may feel like a lot of moving parts to keep up with if you are a tidymodels user, so we want to highlight a couple of the more useful updates in these releases.

Tools for tidymodels analyses

Several of these releases incorporate tools to reduce the overhead for getting started with your tidymodels analysis or for understanding your results more deeply. The new release of the tidymodels metapackage itself provides an R Markdown template. To use the tidymodels analysis template from RStudio, access through File -> New File -> R Markdown. This will open the dialog box where you can select from one of the available templates:

R Markdown template dialog box with three choices, including the tidymodels Model Analysis template

If you are not using RStudio, you’ll also need to install Pandoc. Then, use the rmarkdown::draft() function to create the model card:

rmarkdown::draft(
    "my_model_analysis.Rmd", 
    template = "model-analysis", 
    package = "tidymodels"
)

This template offers an opinionated guide on how to structure a basic modeling analysis from exploratory data analysis through evaluating your models. Your individual modeling analysis may require you to add to, subtract from, or otherwise change this structure, but you can consider this a general framework to start from.

This quarter, the package shinymodels had its first CRAN release. This package was the focus of our tidymodels summer intern Shisham Adhikari in 2021, and it provides support for launching a Shiny app to interactively explore tuning or resampling results.

Screenshot of shinymodels app exploring a regression model

Make your own rsample split objects

The data resampling infrastructure provided by the rsample package has always worked well when you start off with a dataset to split into training and testing. However, we heard from users that in some situations they have training and testing sets determined by other processes, or need to create their data split using more complex conditions. The latest release of rsample provides more fluent and flexible support for custom rsplit creation that sets you up for the rest of your tidymodels analysis. For example, you can create a split object from two dataframes.

library(gapminder)

year_split <-
  make_splits(
    gapminder %>% filter(year <= 2000),
    gapminder %>% filter(year > 2000)
  )

year_split
#> <Analysis/Assess/Total>
#> <1420/284/1704>
testing(year_split)
#> # A tibble: 284 × 6
#>    country     continent  year lifeExp      pop gdpPercap
#>    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#>  1 Afghanistan Asia       2002    42.1 25268405      727.
#>  2 Afghanistan Asia       2007    43.8 31889923      975.
#>  3 Albania     Europe     2002    75.7  3508512     4604.
#>  4 Albania     Europe     2007    76.4  3600523     5937.
#>  5 Algeria     Africa     2002    71.0 31287142     5288.
#>  6 Algeria     Africa     2007    72.3 33333216     6223.
#>  7 Angola      Africa     2002    41.0 10866106     2773.
#>  8 Angola      Africa     2007    42.7 12420476     4797.
#>  9 Argentina   Americas   2002    74.3 38331121     8798.
#> 10 Argentina   Americas   2007    75.3 40301927    12779.
#> # … with 274 more rows

You can alternatively create a split using a list of indices; this make_splits() flexibility is good for when the defaults in initial_split() and initial_time_split() are not appropriate. We also added a new function validation_time_split() to create a single validation resample, much like validation_split(), but taking the first prop samples for training.

Survey says…

This fall, we launched our second tidymodels survey to gather community input on our priorities for 2022. Thank you so much to everyone who shared their opinion! Over 600 people completed the survey, a significant increase from last year, and the top three requested features overall are:

  • Supervised feature selection: This includes basic supervised filtering methods as well as techniques such as recursive feature elimination.

  • Model fairness analysis and metrics: Techniques to measure if there are biases in model predictions that treat groups or individuals unfairly.

  • Post modeling probability calibration: Methods to characterize (and correct) probability predictions to make sure that probability estimates reflect the observed event rate(s).

You can also check out our full analysis of the survey results.

Bar chart broken out by role showing that all groups (students, academics, industry, hobbyists) rate supervised feature selection highest

Acknowledgements

We’d like to extend our thanks to all of the contributors who helped make these releases during Q4 possible!

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