Package: tidyfun 0.1.2

Fabian Scheipl

tidyfun: Tidy Functional Data Wrangling and Visualization

Represent, visualize, describe and wrangle functional data in tidy data frames, building on the 'tf' package. Provides data types for functional observations that work as columns in data frames, enabling manipulation with 'dplyr' verbs and visualization with 'ggplot2' geoms designed for functional data.

Authors:Fabian Scheipl [aut, cre, cph], Jeff Goldsmith [aut], Julia Wrobel [aut], Maximilian Mücke [ctb]

tidyfun_0.1.2.tar.gz
tidyfun_0.1.2.zip(r-4.7)tidyfun_0.1.2.zip(r-4.6)tidyfun_0.1.2.zip(r-4.5)
tidyfun_0.1.2.tgz(r-4.6-any)tidyfun_0.1.2.tgz(r-4.5-any)
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tidyfun_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tidyfun/json (API)

# Install 'tidyfun' in R:
install.packages('tidyfun', repos = c('https://tidyfun.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidyfun/tidyfun/issues

Pkgdown/docs site:https://tidyfun.github.io

Datasets:
  • chf_df - Congestive heart failure accelerometry data
  • dti_df - Diffusion tensor imaging data

On CRAN:

Conda:

8.14 score 39 stars 74 scripts 533 downloads 31 exports 47 dependencies

Last updated from:d21a98b87d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK229
source / vignettesOK305
linux-release-x86_64OK216
macos-release-arm64OK168
macos-oldrel-arm64OK117
windows-develOK185
windows-releaseOK176
windows-oldrelOK152
wasm-releaseOK155

Exports:autolayerautoplotgeom_capelinigeom_capellinigeom_cappelinigeom_cappellinigeom_errorbandgeom_fboxplotgeom_meatballsgeom_spaghettiGeomCapelliniGeomErrorbandGeomFboxplotGeomMeatballsGeomSpaghettigglasagnais_tf_ggplotparse_tf_aestheticsstat_capellinistat_errorbandstat_fboxplotstat_tfStatCapelliniStatErrorbandStatFboxplotStatTftf_gathertf_ggplottf_nesttf_spreadtf_unnest

Dependencies:backportscheckmateclicpp11crayondplyrfarverforcatsgenericsGGallyggplot2ggstatsgluegtablehmsisobandlabelinglatticelifecyclemagrittrMatrixmgcvmvtnormnlmepatchworkpillarpkgconfigpracmaprettyunitsprogresspurrrR6RColorBrewerrlangS7scalesstringistringrtftibbletidyrtidyselectutf8vctrsviridisLitewithrzoo

Converting to & from tf
Conversion from matrices | Conversion to tf from a data frame | ... in "long" format | ... in "wide" format | Changing representation with tf_rebase | Splitting and combining functions | Conversion from fda objects | Reversing the conversion | Conversion from tf to data frames | Converting back to a matrix or data frame
Rendered fromx02_Conversion.Rmdusingknitr::rmarkdown

Last update: 2026-04-24
Started: 2019-06-01

Curve Registration: Practical Guide and Pitfalls
Registration: Template, Warp, Domain | What Is the Template? | What is a Warping Function? | Domain-Preserving vs Non-domain-preserving Warps | Example: Shift Registration with Explicit Template | Choosing a Registration Method | Evidence-Based Method Selection | Pitfalls and Likely Failure Modes | Structured shape variation | Noise dominates phase signal | Sparse or irregular grids | Boundary and overlap artifacts | Over-warping | Lambda penalization | Unsuitable template | Practical Diagnostics Workflow | Step 1: Register and inspect summary | Step 2: Visual inspection via plot() | Step 3: Quantify alignment of specific features | Step 4: Compare with an alternative method | Theoretical Background 101 | SRVF Framework | CC (Continuous Registration Criterion) | Affine (Shift / Scale) | Landmark | Worked Examples | Pinch Data: Cross-method Comparison | Growth Data: Deeper Real-data Workflow | Data representation matters | Penalization | Landmark registration | References
Rendered fromx06_Registration.Rmdusingknitr::rmarkdown

Last update: 2026-04-24
Started: 2026-03-17

Data Wrangling
Data manipulation using tidyfun | Example datasets | Existing tidyverse functions | tf helper functions in tidy workflows | Working with data.table
Rendered fromx03_Data_Wrangling.Rmdusingknitr::rmarkdown

Last update: 2026-04-24
Started: 2019-06-01

Developer Notes
Why use tidyfun | Thoughts on design | Using tidyfun in new functions | Keep us informed!
Rendered fromx05_Advice.Rmdusingknitr::rmarkdown

Last update: 2026-04-24
Started: 2020-04-21

tf Vectors and Operations
tf-Class: Definition | tf-class | Example Data | tf subclass: tfd | tf subclass: tfb | tfb_spline: spline basis | Penalization: | tfb FPC-based | tf-Class: Methods | subset & subassign: | compare & compute: | summarize across a vector of functions: | summarize each function over its domain: | Methods for "functional" operations | evaluate: | (simple, local) smoothing | differentiate & integrate: | query | zoom & query
Rendered fromx01_tf_Vectors.Rmdusingknitr::rmarkdown

Last update: 2026-04-24
Started: 2019-06-01

Visualization
Plotting with ggplot2 | tf_ggplot with standard geoms | Using with other ggplot2 features | Functional data boxplots with geom_fboxplot | Heatmaps for functional data: gglasagna | geom_capellini | Plotting with base R
Rendered fromx04_Visualization.Rmdusingknitr::rmarkdown

Last update: 2026-04-24
Started: 2019-06-01

Readme and manuals

Help Manual

Help pageTopics
Add layers to tf_ggplot objects+.tf_ggplot
Autoplot and autolayer methods for 'tf' objectsautolayer.tf autoplot.tf
Congestive heart failure accelerometry datachf_df
Diffusion tensor imaging datadti_df
Glyph plots for 'tf' objectsGeomCapellini geom_capelini geom_capellini geom_cappelini geom_cappellini ggcapellini StatCapellini stat_capellini
Error bands using 'tf' objects as boundsGeomErrorband geom_errorband ggerrorband StatErrorband stat_errorband
Functional boxplots for 'tf' objectsGeomFboxplot geom_fboxplot ggfboxplot StatFboxplot stat_fboxplot
Lasagna plots for 'tf's using 'ggplot2'gglasagna
ggplot_build method for tf_ggplotggplot_build.tf_ggplot
Spaghetti plots for 'tf' objectsGeomMeatballs GeomSpaghetti geom_meatballs geom_spaghetti ggspaghetti StatTf stat_tf
Check if object is a tf_ggplotis_tf_ggplot
Parse aesthetic mappings to separate tf and regular aestheticsparse_tf_aesthetics
Print method for tf_ggplotprint.tf_ggplot
Evaluate 'tf's inside a 'data.frame'tf_evaluate.data.frame
Gather all columns representing functional measurements into a 'tfd'-objecttf_gather
Create a tf-aware ggplottf_ggplot
Turn "long" tables into tidy data frames with 'tf'-objectstf_nest
Spread a 'tf'-column into many columns representing the function evaluations.tf_spread
Turn (data frames with) 'tf'-objects / list columns into "long" tables.tf_unnest tf_unnest.data.frame tf_unnest.tf
Format tidy functional data for tibblesobj_sum.tf pillar_shaft.tf type_sum.tf