gravitas v0.1.2

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Explore Probability Distributions for Bivariate Temporal Granularities

Provides tools for systematically exploring large quantities of temporal data across nonlinear temporal granularities (deconstructions of time) by visualizing probability distributions. Nonlinear time granularities can be circular, quasi-circular or aperiodic. 'gravitas' computes nonlinear single-order-up or multiple-order-up granularities, check the feasibility of creating plots for any two nonlinear granularities and recommend probability distributions plots for exploring periodicity in the data.

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gravitas

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Overview

Package gravitas provides a tool to examine the probability distribution of univariate time series across bivariate temporal granularities using a range of graphics in ggplot2 through the following:

  • create multiple-order-up circular or aperiodic temporal granularities.

  • categorize pairs of granularities as either harmony or clash, where harmonies are pairs of granularities that aid exploratory data analysis, and clashes are pairs that are incompatible with each other for exploratory analysis.

  • recommending appropriate probability distribution plots of the time series variable across the bivariate granularities based on the levels of the bivariate granularities and their interaction.

gravitas is not restricted to temporal data. It can be utilized in non-temporal cases for which a hierarchical structure can be construed similar to time. The hierarchical structure of time creates a natural nested ordering. For example, hours are nested within days, days within weeks, weeks within months, and so on. Similarly, if a periodic nesting exists for a non-temporal application, gravitas can be used to explore the probability distribution of a continuous random variable.

Installation

You can install gravitas from CRAN:

install.packages("gravitas")

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Sayani07/gravitas")

Quick look

gravitas comes with an interactive webpage, which lets you go through the different functionalities of this package. To try it, simply use gravitas::run_app().

Features

  • Search for a set of all possible temporal granularities with search_gran()

  • Build any temporal granularity with create_gran()

  • Check if two temporal granularities are harmonies with is_harmony()

  • Get all possible harmonies with harmony()

  • Get recommendations on choosing more appropriate distribution plots and advice on the interaction between granularities, number of observations available for drawing probability distributions for chosen granularities with gran_advice()

  • Validate if the created granularity matches your already existing column with validate_gran()

  • Explore probability distribution across bivariate temporal granularities with prob_plot()

Example: temporal case

The probability distribution of energy consumption for ten households from customer trials can be explored as follows:

Search for granularities

library(gravitas)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 3.5.2
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 3.5.2
library(lvplot)

 smart_meter10 %>%
   search_gran(highest_unit = "week")
#> [1] "hhour_hour" "hhour_day"  "hhour_week" "hour_day"   "hour_week" 
#> [6] "day_week"

Screen harmonies from the search list

 smart_meter10 %>%
   harmony(
     ugran = "day",
     filter_in = "wknd_wday"
   )
#> # A tibble: 7 x 4
#>   facet_variable x_variable facet_levels x_levels
#>   <chr>          <chr>             <int>    <int>
#> 1 hour_day       hhour_hour           24        2
#> 2 wknd_wday      hhour_hour            2        2
#> 3 wknd_wday      hhour_day             2       48
#> 4 hhour_hour     hour_day              2       24
#> 5 wknd_wday      hour_day              2       24
#> 6 hhour_hour     wknd_wday             2        2
#> 7 hour_day       wknd_wday            24        2

Visualize probability distribution of the harmony pair (wknd_wday, hour_day)

Box plots are drawn across hours of the day faceted by weekend/weekday for a household in the data smart_meter10 in the package. It is interesting to see the difference in energy consumption in the early morning hours (1 am - 7 am) for weekdays and weekends. The interquartile range is the same in these hours, however, median energy consumption for these hours is higher for weekends compared to weekdays. The distribution for the rest of the day is not significantly different for weekdays and weekends, implying their energy behavior after 7 am remains more or less similar.

smart_meter10 %>%
 filter(customer_id == 10017936) %>%
   prob_plot(
     gran1 = "wknd_wday",
     gran2 = "hour_day",
     response = "general_supply_kwh",
     plot_type = "boxplot") +
   scale_y_sqrt() + scale_x_discrete(breaks = seq(0, 23, 3))

Example: non-temporal case

cricket data set in the package can be explored by explicitly defining a hierarchy table as follows:

library(tsibble)
#> Warning: package 'tsibble' was built under R version 3.5.2
#> 
#> Attaching package: 'tsibble'
#> The following object is masked from 'package:dplyr':
#> 
#>     id
cricket_tsibble <- cricket %>%
 mutate(data_index = row_number()) %>%
 as_tsibble(index = data_index)

 hierarchy_model <- tibble::tibble(
   units = c("index", "ball", "over", "inning", "match"),
   convert_fct = c(1, 6, 20, 2, 1)
 )
 cricket_tsibble %>%
   create_gran(
     "over_inning",
     hierarchy_model
   )
#> # A tsibble: 8,560 x 12 [1]
#>    season match_id batting_team bowling_team inning  over wicket dot_balls
#>     <dbl>    <dbl> <chr>        <chr>         <dbl> <dbl>  <dbl>     <dbl>
#>  1   2008        2 Chennai Sup… Kings XI Pu…      1     1      0         4
#>  2   2008        2 Chennai Sup… Kings XI Pu…      1     2      0         2
#>  3   2008        2 Chennai Sup… Kings XI Pu…      1     3      1         4
#>  4   2008        2 Chennai Sup… Kings XI Pu…      1     4      0         3
#>  5   2008        2 Chennai Sup… Kings XI Pu…      1     5      0         3
#>  6   2008        2 Chennai Sup… Kings XI Pu…      1     6      0         3
#>  7   2008        2 Chennai Sup… Kings XI Pu…      1     7      1         1
#>  8   2008        2 Chennai Sup… Kings XI Pu…      1     8      1         3
#>  9   2008        2 Chennai Sup… Kings XI Pu…      1     9      0         1
#> 10   2008        2 Chennai Sup… Kings XI Pu…      1    10      0         2
#> # … with 8,550 more rows, and 4 more variables: runs_per_over <dbl>,
#> #   run_rate <dbl>, data_index <int>, over_inning <fct>

Visualize granularities for non-temporal data

Letter value plot of total runs per over is shown overs of the innings (x-axis) and innings of the match (facet). It can be observed that there is no clear upward shift in runs in the second innings as compared to the first innings. The variability in runs increases as the teams approach towards the end of the innings, as observed through the longer and more distinct letter values.


   cricket_tsibble %>%
   filter(batting_team %in% c("Mumbai Indians",
                              "Chennai Super Kings"))%>%
   prob_plot("inning", "over",
   hierarchy_model,
   response = "runs_per_over",
   plot_type = "lv")

More information

View the vignette to get started!

This package takes tsibble as the data input. Tsibble provides a data class of tbl_ts to represent tidy temporal data. It consists of a time index, key and other measured variables in a data-centric format, which makes it easier to work with temporal data. To learn more about it, please visit https://tsibble.tidyverts.org/

Acknowledgements

Thanks to PhD supervisors Prof. Rob J Hyndman, Prof. Dianne Cook and Google Summer of Code 2019 mentor Prof. Antony Unwin for their support and always leading by example. The fine balance of encouraging me to work on my ideas and stepping in to help when I need has made the development of this package a great learning experience for me.

Moreover, I want to thank my cohort at NUMBATS, Monash University, especially Mitchell O’Hara-Wild and Nicholas Spyrison for always lending an ear and sharing their wisdom and experience of developing R packages, with such kindness.

Reporting and issues

Please submit all bug reports, errors, and feature requests to https://github.com/Sayani07/gravitas/issues

Functions in gravitas

Name Description
print.gran_advice Advice summaries for granularities
prob_plot Plotting probability distributions across granularities
search_gran Search for granularities
create_gran Build dynamic temporal granularities
smart_meter10 Smart meter data for ten households
cricket Cricket data set for different seasons of Indian Premier League
is_harmony Check if two temporal granularities are harmonies
harmony Get possible set of harmonies for a given tsibble
run_app Runs a shiny app demonstrating functionalities of gravitas
reexports Objects exported from other packages
validate_gran Validate created granularities with existing columns
gran_advice Advice summaries for granularities
gran_obs Cross tabulation of granularities useful for validating if number of observations are sufficient to compute probability distributions
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Vignettes of gravitas

Name
cricket.Rmd
gravitas_vignette.Rmd
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Details

Type Package
VignetteBuilder knitr
BugReports https://github.com/Sayani07/gravitas/issues
License GPL-3
URL https://github.com/Sayani07/gravitas/
Encoding UTF-8
LazyData true
ByteCompile true
RoxygenNote 7.0.2
NeedsCompilation no
Packaged 2020-02-16 13:12:48 UTC; sgup0008
Repository CRAN
Date/Publication 2020-02-17 09:20:02 UTC

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