tidyfun

tidyfun is a tidyverse-oriented interface layer around the core tf package. It makes functional data analysis in R easier by focusing on data wrangling, exploratory analysis, and graphics for functional data stored in ordinary data frames.

The underlying tf package defines the tf vector type and the core functional-data methods. tidyfun builds on those objects with helpers for working inside data frames, plus plotting and workflow tools that fit naturally into the tidyverse.

Crucially, vectors of class tf can be included in data frames alongside other variables, enabling data manipulation with standard tidyverse tools. This matches the functional-data view that complete functions are the unit of observation; with tidyfun, full curves sit alongside numeric, factor, and other observations on the same subject.

Installation

Install the released version from CRAN:

install.packages("tidyfun")

Or install the current development version from GitHub:

install.packages("pak")
pak::pak("tidyfun/tidyfun")

Overview

tidyfun provides:

• tidyverse-friendly wrangling tools for functional data inside data frames
• tidyfun-level graphics helpers for tf vectors and tidy functional data frames
• documentation and examples that sit on top of the core tf data structures and methods

For detailed information on the features of tidyfun, check out articles on the following topics:

• Representing functional data as tf vectors, and operating on those vectors with core tf methods and tidyfun conveniences
• Converting non-tidy functional data (matrices, “long” and “wide” data frames) to tidy functional data using tidyfun workflows built on tf
• Wrangling data frames that include functional data using tidyverse and tidyfun tools
• Visualizing tidy functional data with tidyfun graphics helpers
• Registering functional data with the tf registration methods documented for use in the tidyfun ecosystem

The result is a package that enables exploratory data analysis like the following, which computes group-specific mean curves in the dti_df dataset, and plots the result:

library("tidyfun")
data(dti_df, package = "tidyfun")

dti_df |>
  group_by(case, sex) |>
  summarize(mean_cca = mean(cca, na.rm = TRUE)) |>
  tf_ggplot(aes(tf = mean_cca, color = case)) +
  geom_line(linewidth = 2) +
  facet_grid(~sex)

What does it do?

Core tf data types and methods

The underlying {tf} package defines the tf vector classes and related methods, including raw functional data (tfd) and basis representations (tfb). For an overview, see the tf vectors article and the reference index.

Such tf-objects can be subsetted or subassigned, computed on and summarized.

Almost all

• operators like ==, + or *
• math functions like sum, log or abs
• and statistics functions like mean or sd

are defined for these tf data structures, as illustrated in the tf vectors article and the reference index.

The tf objects are basically glorified lists, so they work well as columns in data frames. That makes it a lot easier to keep your other data and functional measurements together in one object for preprocessing, exploratory analysis and description. At the same time, these objects actually behave like vectors of functions to some extent, i.e., they can be evaluated on any point in their domain, they can be integrated or differentiated, etc.

See here for more information on the operations defined for tf vectors.

Methods for converting existing data to tf

The underlying tf package provides the tfd and tfb constructors for converting matrices, data frames, etc. to tf vectors. tidyfun builds on those objects with tf_gather & tf_nest to reshape tables with functional data, by going from wide to narrow or from long to short; functions like as.matrix, tf_spread & tf_unnest can reverse these data conversions.

See here for details on getting data into (and out of) the tf format.

tidyverse verbs for dealing with functional data inside data frames

All dplyr verbs work on tf-columns, so you can filter, mutate, group_by & summarize, etc., functional data pretty much like conventional “tidy” data. tidyfun workflows also work naturally with tf helpers such as tf_anywhere and tf_smooth.

See here to see how you can wrangle functional data.

New ggplot2 geoms and stats for functional data

tidyfun provides tf_ggplot(), which lets you use standard ggplot2 geoms (geom_line, geom_point, geom_ribbon, …) with functional data via the tf aesthetic. It also includes specialized pasta-themed geoms:

• gglasagna for lasagna plots (heatmaps), with an order-aesthetic to sort the lasagna layers,
• geom_capellini for glyphs plots (i.e., sparklines),
• geom_fboxplot for functional boxplots based on data depth,

as well as methods for base R graphics functions plot, lines and points for quick and easy visualizations of functional data.

See here for the documentation of the visualization approaches, or browse the reference index.

Curve registration

tidyfun documents multiple tf methods for aligning curves in time via tf_register: elastic SRVF alignment, continuous registration (CC), affine shift/scale models, and landmark registration. All methods return tf_registration objects with a consistent interface for extracting aligned curves, inverse warping functions, and diagnostic summaries.

See here for a practical guide covering method selection, diagnostics, and worked examples on real data.

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