tidymodels

Overview

tidymodels is a “meta-package” for modeling and statistical analysis that shares the underlying design philosophy, grammar, and data structures of the tidyverse.

It includes a core set of packages that are loaded on startup:

broom takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy data frames.
dials has tools to create and manage values of tuning parameters.
dplyr contains a grammar for data manipulation.
ggplot2 implements a grammar of graphics.
infer is a modern approach to statistical inference.
parsnip is a tidy, unified interface to creating models.
purrr is a functional programming toolkit.
recipes is a general data preprocessor with a modern interface. It can create model matrices that incorporate feature engineering, imputation, and other help tools.
rsample has infrastructure for resampling data so that models can be assessed and empirically validated.
tibble has a modern re-imagining of the data frame.
tune contains the functions to optimize model hyper-parameters.
workflows has methods to combine pre-processing steps and models into a single object.
yardstick contains tools for evaluating models (e.g. accuracy, RMSE, etc.).

A list of all tidymodels functions across different CRAN packages can be found at https://www.tidymodels.org/find/.

You can install the released version of tidymodels from CRAN with:

install.packages("tidymodels")

Install the development version from GitHub with:

# install.packages("pak")
pak::pak("tidymodels/tidymodels")

When loading the package, the versions and conflicts are listed:

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
#> ✔ broom        1.0.5      ✔ recipes      1.0.10
#> ✔ dials        1.2.1      ✔ rsample      1.2.0 
#> ✔ dplyr        1.1.4      ✔ tibble       3.2.1 
#> ✔ ggplot2      3.5.0      ✔ tidyr        1.3.1 
#> ✔ infer        1.0.6      ✔ tune         1.2.0 
#> ✔ modeldata    1.3.0      ✔ workflows    1.1.4 
#> ✔ parsnip      1.2.1      ✔ workflowsets 1.1.0 
#> ✔ purrr        1.0.2      ✔ yardstick    1.3.1
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()
#> • Learn how to get started at https://www.tidymodels.org/start/

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
Most issues will likely belong on the GitHub repo of an individual package. If you think you have encountered a bug with the tidymodels metapackage itself, please submit an issue.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
Check out further details on contributing guidelines for tidymodels packages and how to get help.

variable names in the chicago taxi data

Following up on #10.

The variable names in the Chicago taxi data are:

dplyr::glimpse(
  pins::pin_read(modeldatatoo:::modeldatatoo_board, "chicago_taxi")
)
#> Rows: 10,000
#> Columns: 14
#> $ tip              <fct> yes, no, yes, no, yes, yes, yes, yes, yes, no, yes, y…
#> $ trip_id          <fct> 9fee331b5a4b19daa19a149cfdfbeea91eb4dff9, b1dbc452aeb…
#> $ trip_seconds     <dbl> 333, 2692, 1076, 1599, 346, 1790, 885, 720, 2190, 216…
#> $ trip_miles       <dbl> 1.24, 5.39, 3.01, 18.38, 1.76, 13.65, 3.71, 4.80, 18.…
#> $ fare             <dbl> 6.50, 25.50, 11.23, 45.25, 7.50, 34.25, 11.25, 14.75,…
#> $ tolls            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ extras           <dbl> 0.0, 0.0, 0.0, 0.0, 1.0, 4.5, 0.0, 1.5, 4.0, 4.0, 5.0…
#> $ trip_total       <dbl> 8.00, 25.50, 14.10, 45.25, 11.00, 49.06, 12.62, 19.60…
#> $ payment_type     <fct> Credit Card, Prcard, Mobile, Prcard, Credit Card, Cre…
#> $ company          <fct> Sun Taxi, Flash Cab, City Service, Sun Taxi, Sun Taxi…
#> $ local_trip       <fct> no, no, no, no, no, no, no, no, no, yes, no, NA, no, …
#> $ trip_start_dow   <fct> Thu, Sat, Wed, Sat, Sun, Mon, Mon, Tue, Fri, Thu, Tue…
#> $ trip_start_month <fct> Feb, Mar, Feb, Apr, Jan, Feb, Mar, Mar, Jan, Apr, Apr…
#> $ trip_start_hour  <int> 13, 12, 17, 6, 15, 17, 21, 9, 19, 12, 20, 22, 10, 10,…

^{Created on 2023-06-30 with reprex v2.0.2}

The unit of observation in the data is a trip; would we be up for removing the trip_ prefix from those variables that have it? It might make sense in that case to rename total to total_cost.

I proposed in #14 that we document trip_start_dow, trip_start_month, and trip_start_hour in such a way that mentions these are the values at the start of the trip; would we be up for removing the start_ prefix from those variables that have it?

Altogether, these changes should make for more concise variable names.

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