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.
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"
)No return value. This function is called for the side effect of launching a Shiny application.
An exposed_df object.
A character vector of independent variables in dat to
include in the Shiny app.
A character vector of expected values in dat to include
in the Shiny app.
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.
Optional. Title of the Shiny app. If no title is provided,
a descriptive title will be generated based on attributes of dat.
If TRUE, the output will include partial credibility
weights and credibility-weighted termination rates.
Confidence level used for the Limited Fluctuation credibility method and confidence intervals
Error tolerance under the Limited Fluctuation credibility method
The name of a theme passed to the preset argument of
bslib::bs_theme(). Alternatively, a complete Bootstrap theme created using
bslib::bs_theme().
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().
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.
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.
This box includes a toggle to switch between termination studies and transaction studies (if available). Different options are available for each study type.
The expected values checkboxes are used to activate and deactivate expected
values passed to the expected argument. This impacts the table output
directly and the available "y" variables for the plot. If there are no
expected values available, this widget will not appear. The "Weight by"
widget is used to specify which column, if any, contains weights for
summarizing experience.
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.
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.
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
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.
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)
}
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