Package: sweep 0.2.7.9000

Matt Dancho

sweep: Tidy Tools for Forecasting

Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to "tidy" data frames with 'sw_sweep'.

Authors:Matt Dancho [aut, cre], Davis Vaughan [aut]

sweep_0.2.7.9000.tar.gz
sweep_0.2.7.9000.zip(r-4.7)sweep_0.2.7.9000.zip(r-4.6)sweep_0.2.7.9000.zip(r-4.5)
sweep_0.2.7.9000.tgz(r-4.6-any)sweep_0.2.7.9000.tgz(r-4.5-any)
sweep_0.2.7.9000.tar.gz(r-4.7-any)sweep_0.2.7.9000.tar.gz(r-4.6-any)
sweep_0.2.7.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sweep/json (API)

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

Bug tracker:https://github.com/business-science/sweep/issues

Pkgdown/docs site:https://business-science.github.io

Datasets:
  • bike_sales - Fictional sales data for bike shops purchasing Cannondale bikes

On CRAN:

Conda:

broomforecastforecasting-modelspredictiontidytidyversetimetime-seriestimeseries

9.24 score 154 stars 1 packages 294 scripts 1.4k downloads 6 exports 130 dependencies

Last updated from:098ff3ec79. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK218
source / vignettesOK308
linux-release-x86_64OK219
macos-release-arm64OK170
macos-oldrel-arm64OK161
windows-develOK165
windows-releaseOK173
windows-oldrelOK167
wasm-releaseOK169

Exports:%>%sw_augmentsw_glancesw_sweepsw_tidysw_tidy_decomp

Dependencies:anytimeaskpassbackportsbase64encBHbitbit64broombslibcachemclassclicliprclockcodetoolscolorspacecpp11crayoncrosstalkcurldata.tablediagramdigestdplyrevaluatefarverfastmapfontawesomeforcatsforecastfracdifffsfurrrfuturefuture.applygenericsggplot2globalsgluegowergtablehardhathighrhmshtmltoolshtmlwidgetshttripredisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevallifecyclelistenvlmtestlubridatemagrittrMASSMatrixmemoisemimenlmennetnumDerivopensslotelpadrparallellypillarpkgconfigplotlyprettyunitsprodlimprogressprogressrpromisespurrrquadprogquantmodR6rappdirsRColorBrewerRcppRcppArmadilloRcppRollreadrrecipesrlangrmarkdownrpartrsampleS7sassscalesshapeslidersparsevctrsSQUAREMstringistringrsurvivalsystibbletidyrtidyselecttimechangetimeDatetimetktinytextseriestsfeaturesTTRtzdburcautf8vctrsviridisLitevroomwarpwithrxfunxtsyamlzoo

Forecasting Using Multiple Models
Prerequisites | Forecasting Bike Sales Revenue | Performing Forecasts Using Multiple Models | Multiple Models Concept | Multiple Model Implementation | Inspecting the Model Fit | sw_tidy | sw_glance | sw_augment | Forecasting the model | Tidying the forecast | Recap
Rendered fromSW02_Forecasting_Multiple_Models.Rmdusingknitr::rmarkdown

Last update: 2026-03-16
Started: 2017-04-13

Introduction to sweep
Prerequisites | Forecasting Monthly Bike Sales Revenue | Forecasting Workflow | Step 1: Coerce to a ts object class | Step 2: Modeling a time series | sw_tidy | sw_glance | sw_augment | sw_tidy_decomp | Step 3: Forecasting the model | Step 4: Tidy the forecast object | Recap
Rendered fromSW00_Introduction_to_sweep.Rmdusingknitr::rmarkdown

Last update: 2026-03-16
Started: 2017-04-11

Forecasting Time Series Groups in the tidyverse
Prerequisites | Bike Sales | Performing Forecasts on Groups | Forecasting Workflow | Step 1: Coerce to a ts object class | mutate and map | Step 2: Modeling a time series | sw_tidy | sw_glance | sw_augment | sw_tidy_decomp | Step 3: Forecasting the model | Step 4: Tidy the forecast | Recap
Rendered fromSW01_Forecasting_Time_Series_Groups.Rmdusingknitr::rmarkdown

Last update: 2023-12-08
Started: 2017-04-12

Readme and manuals

Help Manual

Help pageTopics
Adds a sequential index column to a data frameadd_index
Print the ARIMA model parametersarima_string
Print the BATS model parametersbats_string
Fictional sales data for bike shops purchasing Cannondale bikesbike_sales
Augment data according to a tidied modelsw_augment
Augments datasw_augment_columns
Default augment methodsw_augment.default
Construct a single row summary "glance" of a model, fit, or other objectsw_glance
Default glance methodsw_glance.default
Tidy forecast objectssw_sweep
Tidy the result of a time-series model into a summary tibblesw_tidy
Coerces decomposed time-series objects to tibble format.sw_tidy_decomp
Default tidying methodsw_tidy.default
Print the TBATS model parameterstbats_string
Tidying methods for ARIMA modeling of time seriessw_augment.Arima sw_glance.Arima sw_tidy.Arima sw_tidy.stlm tidiers_arima
Tidying methods for BATS and TBATS modeling of time seriessw_augment.bats sw_glance.bats sw_tidy.bats sw_tidy_decomp.bats tidiers_bats
Tidying methods for decomposed time seriessw_tidy_decomp.decomposed.ts tidiers_decomposed_ts
Tidying methods for ETS (Error, Trend, Seasonal) exponential smoothing modeling of time seriessw_augment.ets sw_glance.ets sw_tidy.ets sw_tidy_decomp.ets tidiers_ets
Tidying methods for HoltWinters modeling of time seriessw_augment.HoltWinters sw_glance.HoltWinters sw_tidy.HoltWinters sw_tidy_decomp.HoltWinters tidiers_HoltWinters
Tidying methods for Nural Network Time Series modelssw_augment.nnetar sw_glance.nnetar sw_tidy.nnetar tidiers_nnetar
Tidying methods for STL (Seasonal, Trend, Level) decomposition of time seriessw_augment.stlm sw_glance.stlm sw_tidy.stl sw_tidy_decomp.stl sw_tidy_decomp.stlm tidiers_stl
Tidying methods for StructTS (Error, Trend, Seasonal) / exponential smoothing modeling of time seriessw_augment.StructTS sw_glance.StructTS sw_tidy.StructTS tidiers_StructTS
Validates data frame has column named the same name as variable rename_indexvalidate_index