prob_plot: Plotting probability distributions across granularities

In Sayani07/gravitas: Explore Probability Distributions for Bivariate Temporal Granularities

Plotting probability distributions across granularities

Description

Plot probability distribution of univariate series across bivariate temporal granularities.

Usage

prob_plot(
  .data,
  gran1 = NULL,
  gran2 = NULL,
  hierarchy_tbl = NULL,
  response = NULL,
  plot_type = NULL,
  quantile_prob = c(0.01, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99),
  facet_h = NULL,
  symmetric = TRUE,
  alpha = 0.8,
  threshold_nobs = NULL,
  nrow = NULL,
  ...
)

Arguments

.data	a tsibble
gran1	the granularity which is to be placed across facets. Can be column names if required granularity already exists in the tsibble. For example, a column with public holidays which needs to be treated as granularity, can be included here.
gran2	the granularity to be placed across x-axis. Can be column names if required granularity already exists in the tsibble.
hierarchy_tbl	A hierarchy table specifying the hierarchy of units and their relationships.
response	response variable to be plotted.
plot_type	type of distribution plot. Options include "boxplot", "lv" (letter-value), "quantile", "ridge" or "violin".
quantile_prob	numeric vector of probabilities with value in [0,1] whose sample quantiles are wanted. Default is set to "decile" plot.
facet_h	levels of facet variable for which facetting is allowed while plotting bivariate temporal granularities.
symmetric	If TRUE, symmetic quantile area <- is drawn. If FALSE, only quantile lines are drawn instead of area. If TRUE, length of quantile_prob should be odd and ideally the quantile_prob should be a symmetric vector with median at the middle position.
alpha	level of transperancy for the quantile area
threshold_nobs	the minimum number of observations below which only points would be plotted
nrow	number of rows in facets
...	other arguments to be passed for customising the obtained ggplot object.

Value

a ggplot object which can be customised as usual.

Examples

library(tsibbledata)
library(ggplot2)
library(tsibble)
library(lvplot)
library(dplyr)

smart_meter10 %>%
  dplyr::filter(customer_id %in% c("10017936")) %>%
  prob_plot(
    gran1 = "day_week", gran2 = "hour_day",
    response = "general_supply_kwh", plot_type = "quantile",
    quantile_prob = c(0.1, 0.25, 0.5, 0.75, 0.9),
    symmetric = TRUE,
    outlier.colour = "red",
    outlier.shape = 2, palette = "Dark2"
  ) + scale_y_continuous(breaks = c(0.01, 0.1, 1.00))


cricket_tsibble <- cricket %>%
  mutate(data_index = row_number()) %>%
  as_tsibble(index = data_index)

hierarchy_model <- tibble::tibble(
  units = c("index", "over", "inning", "match"),
  convert_fct = c(1, 20, 2, 1)
)

cricket_tsibble %>%
  prob_plot("inning", "over",
    hierarchy_tbl = hierarchy_model,
    response = "runs_per_over",
    plot_type = "lv"
  )

Sayani07/gravitas documentation built on June 18, 2022, 2:40 a.m.