sample_expand

dibble_to_tibble

Simulates outcomes from all distributions in the dataset to make an "expanded" data
set that can be intepreted by ggplot2. This can be used to debug ggdibbler plots, or
used to make an uncertainty visualisation for a geom that doesn't exist. If
(and only if) you are implementing a ggdibbler version of a ggplot stat
extension, you should use dibble_to_tibble instead.

A 'ggplot2' extension for visualising uncertainty with the goal
of signal suppression. Usually, uncertainty visualisation focuses on
expressing uncertainty as a distribution or probability, whereas
'ggdibbler' differentiates itself by viewing an uncertainty
visualisation as an adjustment to an existing graphic that
incorporates the inherent uncertainty in the estimates.  You provide
the code for an existing plot, but replace any of the variables with a
vector of distributions, and it will convert the visualisation into
it's signal suppression counterpart.

Harriet Mason

ggdibbler

Add Uncertainty to Data Visualisations

Dianne Cook

Sarah Goodwin

Susan VanderPlas

sample_expand function

<dl><dt>data</dt>
<dd>Distribution dataset to expand into samples</dd>
<dt>times</dt>
<dd>A parameter used to control the number of values sampled from 
each distribution.</dd>
<dt>seed</dt>
<dd>Set the seed for the layers random draw, allows you to get the
same draw from repeated sample_expand calls</dd>
<dt>params</dt>
<dd>the params argument for the stat function
sample_expand(uncertain_mpg, times=10)</dd></dl>

Arguments

Simulate outcomes from dibble to make a tibble — sample_expand

<dl>

<dt>data</dt>
<dd>Distribution dataset to expand into samples</dd>


<dt>times</dt>
<dd>A parameter used to control the number of values sampled from 
each distribution.</dd>


<dt>seed</dt>
<dd>Set the seed for the layers random draw, allows you to get the
same draw from repeated sample_expand calls</dd>


<dt>params</dt>
<dd>the params argument for the stat function
sample_expand(uncertain_mpg, times=10)</dd>

</dl>

sample_expand: Simulate outcomes from dibble to make a tibble

Description

Usage

Value

Arguments