Package: brolgar 1.0.2

brolgar: Browse Over Longitudinal Data Graphically and Analytically in R

Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.

Authors:Nicholas Tierney [aut, cre], Di Cook [aut], Tania Prvan [aut], Stuart Lee [ctb], Earo Wang [ctb]

brolgar_1.0.2.tar.gz
brolgar_1.0.2.zip(r-4.7)brolgar_1.0.2.zip(r-4.6)brolgar_1.0.2.zip(r-4.5)
brolgar_1.0.2.tgz(r-4.6-any)brolgar_1.0.2.tgz(r-4.5-any)
brolgar_1.0.2.tar.gz(r-4.7-any)brolgar_1.0.2.tar.gz(r-4.6-any)
brolgar_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
brolgar/json (API)

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

Bug tracker:https://github.com/njtierney/brolgar/issues

Pkgdown/docs site:https://brolgar.njtierney.com

Datasets:
  • heights - World Height Data
  • pisa - Student data from 2000-2018 PISA OECD data
  • wages - Wages data from National Longitudinal Survey of Youth

On CRAN:

Conda:

8.59 score 115 stars 161 scripts 432 downloads 59 exports 42 dependencies

Last updated from:1b5e558042. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK187
source / vignettesOK266
linux-release-x86_64OK255
macos-release-arm64OK136
macos-oldrel-arm64OK112
windows-develOK148
windows-releaseOK158
windows-oldrelOK145
wasm-releaseOK168

Exports:%>%add_key_slopeadd_key_slope.defaultadd_n_obsas_tsibbleb_diff_iqrb_diff_maxb_diff_meanb_diff_medianb_diff_minb_diff_q25b_diff_q75b_diff_sdb_diff_varb_iqrb_madb_maxb_meanb_medianb_minb_q25b_q75b_rangeb_range_diffb_sdb_vardecreasingfacet_samplefacet_stratafeat_brolgarfeat_diff_summaryfeat_five_numfeat_monotonicfeat_rangesfeat_spreadfeat_three_numfeaturesfeatures_allfeatures_atfeatures_ifincreasingindex_regularindex_summarykey_slopekeys_nearl_five_numl_three_nummonotonicn_keysn_obsnear_betweennear_middlenear_quantilenearest_lglnearest_qt_lglsample_frac_keyssample_n_keysstratify_keysunvarying

Dependencies:anytimeBHclicpp11digestdistributionaldplyrfabletoolsfarvergenericsggdistggplot2gluegtableisobandlabelinglifecyclelubridatemagrittrnumDerivpillarpkgconfigprogressrpurrrquadprogR6RColorBrewerRcpprlangS7scalesstringistringrtibbletidyrtidyselecttimechangetsibbleutf8vctrsviridisLitewithr

Finding Features in Data
Calculating features | Creating your own Features | Accessing sets of features | Registering a feature in a package
Rendered fromfinding-features.Rmdusingknitr::rmarkdown

Last update: 2023-02-06
Started: 2019-08-13

Identify Interesting Observations
Specify your own summaries for keys_near | Implementation of keys_near
Rendered fromid-interesting-obs.Rmdusingknitr::rmarkdown

Last update: 2023-02-06
Started: 2019-08-13

Longitudinal Data Structures
Defining longitudinal data as a tsibble | Converting your longitudinal data to a time series | example data: wages | example: heights data | example: gapminder | example: PISA data | Conclusion
Rendered fromlongitudinal-data-structures.Rmdusingknitr::rmarkdown

Last update: 2023-02-06
Started: 2019-07-11

Using brolgar to understand Mixed Effects Models
Rendered frommixed-effects-models.Rmdusingknitr::rmarkdown

Last update: 2023-02-06
Started: 2019-09-08

Visualisation Gallery
Exploring raw data | Select a sample of individuals | Filter only those with certain number of observations | Clever facets: facet_strata | Clever facets: facet_sample | Clever facets with number of observations | Exploring data using features | Plot monotonic individual series | Plot individuals with negative slope | Move along features with facet_strata | Visualise along slope
Rendered fromvisualisation-gallery.Rmdusingknitr::rmarkdown

Last update: 2023-02-06
Started: 2019-04-29

Exploratory Modelling
Find keys near other summaries with keys_near()
Rendered fromexploratory-modelling.Rmdusingknitr::rmarkdown

Last update: 2023-02-06
Started: 2019-08-13

Getting Started
Setting up your data | Basic summaries of the data | How many observations are there? | add_n_obs() | Efficiently exploring longitudinal data | sample_n_keys() | Filtering observations | Clever facets: facet_strata | Clever facets: facet_sample | Exploratory modelling | Find keys near other summaries with keys_near | Finding features in longitudinal data | Linking individuals back to the data
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2020-12-15
Started: 2019-07-19

Readme and manuals

Help Manual

Help pageTopics
Add the number of observations for each key in a 'tsibble'add_n_obs
Brolgar summaries (b_summaries)b_diff_iqr b_diff_max b_diff_mean b_diff_median b_diff_min b_diff_q25 b_diff_q75 b_diff_sd b_diff_var b_iqr b_mad b_max b_mean b_median b_min b_q25 b_q75 b_range b_range_diff b_sd b_summaries b_var
Calculate features of a 'tsibble' object in conjunction with 'features()'brolgar-features feat_brolgar feat_diff_summary feat_five_num feat_monotonic feat_ranges feat_spread feat_three_num
Facet data into groups to facilitate explorationfacet_sample
Facet data into groups to facilitate explorationfacet_strata
World Height Dataheights
Index summariesindex_regular index_regular.data.frame index_regular.tbl_ts index_summary index_summary.data.frame index_summary.tbl_ts
Fit linear model for each keyadd_key_slope add_key_slope.default key_slope
Return keys nearest to a given statistics or summary.keys_near keys_near.default
Return keys nearest to a given statistics or summary.keys_near.data.frame
Return keys nearest to a given statistics or summary.keys_near.tbl_ts
A named list of the five number summaryl_five_num l_funs l_three_num
Are values monotonic? Always increasing, decreasing, or unvarying?decreasing increasing monotonic unvarying
Return the number of observationsn_obs
Return x percent to y percent of valuesnear_between
Return the middle x percent of valuesnear_middle
Which values are nearest to any given quantilesnear_quantile
Is x nearest to y?nearests nearest_lgl nearest_qt_lgl
Student data from 2000-2018 PISA OECD datapisa
Sample a number or fraction of keys to exploresample-n-frac-keys sample_frac_keys sample_n_keys
Stratify the keys into groups to facilitate explorationstratify_keys
Wages data from National Longitudinal Survey of Youth (NLSY)wages