Interactively explore experience data — exp

Launch a Shiny application to interactively explore drivers of experience.

dat must be an exposed_df object. An error will be thrown is any other object type is passed. If dat has transactions attached, the app will contain features for both termination and transaction studies. Otherwise, the app will only support termination studies.

If nothing is passed to predictors, all columns names in dat will be used (excluding the policy number, status, termination date, exposure, transaction counts, and transaction amounts columns).

The expected argument is optional. As a default, any column names containing the word "expected" are used.

Usage

exp_shiny(
  dat,
  predictors = names(dat),
  expected = names(dat)[grepl("expected", names(dat))],
  distinct_max = 25L,
  title,
  credibility = TRUE,
  conf_level = 0.95,
  cred_r = 0.05,
  theme = "shiny",
  col_exposure = "exposure"
)

Arguments

dat: An exposed_df object.
predictors: A character vector of independent variables in dat to include in the Shiny app.
expected: A character vector of expected values in dat to include in the Shiny app.
distinct_max: Maximum number of distinct values allowed for predictors to be included as "Color" and "Facets" grouping variables. This input prevents the drawing of overly complex plots. Default value = 25.
title: Optional. Title of the Shiny app. If no title is provided, a descriptive title will be generated based on attributes of dat.
credibility: If TRUE, the output will include partial credibility weights and credibility-weighted termination rates.
conf_level: Confidence level used for the Limited Fluctuation credibility method and confidence intervals
cred_r: Error tolerance under the Limited Fluctuation credibility method
theme: The name of a theme passed to the preset argument of bslib::bs_theme(). Alternatively, a complete Bootstrap theme created using bslib::bs_theme().
col_exposure: Name of the column in dat containing exposures. This input is only used to clarify the exposure basis when dat is a split_exposed_df object. For more information on split exposures, see expose_split().

Value

No return value. This function is called for the side effect of launching a Shiny application.

Layout

Filters

The sidebar contains filtering widgets organized by data type for all variables passed to the predictors argument.

At the top of the sidebar, information is shown on the percentage of records remaining after applying filters. A description of all active filters is also provided.

The top of the sidebar also includes a "play / pause" switch that can pause reactivity of the application. Pausing is a good option when multiple changes are made in quick succession, especially when the underlying data set is large.

Grouping variables

This box includes widgets to select grouping variables for summarizing experience. The "x" widget determines the x variable in the plot output. Similarly, the "Color" and "Facets" widgets are used for color and facets. Multiple faceting variable selections are allowed. For the table output, "x", "Color", and "Facets" have no particular meaning beyond the order in which grouping variables are displayed.

Study type

This box includes a toggle to switch between termination studies and transaction studies (if available). Different options are available for each study type.

Termination studies

The expected values checkboxes are used to activate and deactivate expected values passed to the expected argument. These checkboxes also include a a "control" item for expected values derived using control variables. These boxes impact the table output directly and the available "y" variables for the plot. The "Weight by" widget is used to specify which column, if any, contains weights for summarizing experience. The "Control variables" widget is used to specify which columns, if any, are used as control variables ( see exp_stats() for more information).

Transaction studies

The transaction types checkboxes are used to activate and deactivate transaction types that appear in the plot and table outputs. The available transaction types are taken from the trx_types attribute of dat. In the plot output, transaction type will always appear as a faceting variable. The "Transactions as % of" selector will expand the list of available "y" variables for the plot and impact the table output directly. Lastly, a toggle exists that allows for all transaction types to be aggregated into a single group.

Output

Plot

This tab includes a plot and various options for customization:

y: y variable
Geometry: plotting geometry
Second y-axis: activate to enable a second y-axis
Second axis y: y variable to plot on the second axis
Add Smoothing: activate to plot loess curves
Confidence intervals: If available, add error bars for confidence intervals around the selected y variable
Free y Scales: activate to enable separate y scales in each plot
Log y-axis: activate to plot all y-axes on a log-10 scale

The gear icon above the plot contains a pop-up menu that can be used to change the size of the plot for exporting.

Table

This tab includes a data table.

The gear icon above the table contains a pop-up menu that can be used to change the appearance of the table:

The "Confidence intervals" and "Credibility-weighted termination rates" switches add these outputs to the table. These values are hidden as a default to prevent over-crowding.
The "Include color scales" switch disables or re-enables conditional color formatting.
The "Decimals" slider controls the number of decimals displayed for percentage fields.
The "Font size multiple" slider impacts the table's font size

Export

This pop-up menu contains options for saving summarized experience data, the plot, or the table. Data is saved as a CSV file. The plot and table are saved as png files.

Examples


if (interactive()) {
  study_py <- expose_py(census_dat, "2019-12-31", target_status = "Surrender")
  expected_table <- c(seq(0.005, 0.03, length.out = 10),
                      0.2, 0.15, rep(0.05, 3))

  study_py <- study_py |>
    mutate(expected_1 = expected_table[pol_yr],
           expected_2 = ifelse(inc_guar, 0.015, 0.03)) |>
    add_transactions(withdrawals) |>
    left_join(account_vals, by = c("pol_num", "pol_date_yr"))

  exp_shiny(study_py)
}