downsampler: R6 class for down-sampling data
In shinyHugePlot: Efficient Plotting of Large-Sized Data
downsampler R Documentation
R6 class for down-sampling data
Description
A class for down-sampling data with a large number of samples.
An instance contains (the reference of) original data, layout of the figure,
and options for aggregating the original data.
An interactive plot for displaying large-sized data can be obtained using
the figure, down-sampler and its options included in the instance,
while making the plot using shiny_hugeplot function is easier (see examples).
See the super class (plotly_datahandler) to find more members
to handle the data in plotly.
Format
An R6::R6Class object
Super class
shinyHugePlot::plotly_datahandler -> downsampler
Active bindings
downsample_optionsOptions for aggregating (down-sampling) data
registered in this instance.
n_out_defaultDefault sample size.
aggregator_defaultDefault aggregator instance.
Methods
Public methods
-
-
-
-
-
Inherited methods
Method new()
To construct an instance, original data, layout of the figure, and options
for aggregating the original data are necessary.
The original data and the layout of the figure can be given by providing
a plotly object (figure argument).
The options for aggregating the original data can be given by providing
an aggregator (aggregator argument) and the number of samples
(n_out argument).
See the constructor of the plotly_datahandler class for more
information on other arguments.
Usage
downsampler$new(
figure = NULL,
formula = NULL,
srcs = NULL,
srcs_ext = list(),
n_out = 1000L,
aggregator = min_max_aggregator$new(),
tz = Sys.timezone(),
use_light_build = TRUE,
legend_options = list(name_prefix = "<b style=\"color:sandybrown\">[S]</b> ",
name_suffix = "", xdiff_prefix = "<i style=\"color:#fc9944\"> ~", xdiff_suffix =
"</i>"),
verbose = F
)
Arguments
figure, srcs, srcs_ext, formula, legend_options, tz, use_light_buildArguments passed to plotly_datahandler$new.
n_outInteger or numeric.
The number of samples shown after down-sampling. By default 1000.
aggregatorAn instance of an R6 class for aggregation.
Select an aggregation function. The list of the functions are obtained
using list_aggregators.
By default, min_max_aggregator$new().
verboseBoolean.
Whether detailed messages to check the procedures are shown. By default, FALSE.
Method add_trace()
Add a new series to the data registered in the instance.
If a data frame (traces_df argument) compliant with
self$orig_data is given, it will be added to self$orig_data.
If attributes to construct a plotly object (... argument)
are given, a data frame is constructed and added.
Options for aggregating data can be set using
aggregator and n_out arguments.
It is a wrapper of self$set_trace_data and
self$set_downsample_options. See these methods for more information.
Note that the traces of the figure are not updated with this method and
self$update_trace is necessary.
Usage
downsampler$add_trace(..., traces_df = NULL, n_out = NULL, aggregator = NULL)
Arguments
..., traces_dfArguments passed to self$set_trace_data
(see the super class of plotly_datahandler)
n_out, aggregatorArguments passed to self$set_downsample_options.
Method update_trace()
Update traces of the figure registered in the instance
(self$figure$x$data) according to
re-layout order (relayout_order argument).
Using reset and reload arguments, traces are updated
without re-layout orders.
It just registers the new traces and returns nothing by default.
It returns the new traces if send_trace is TRUE.
Usage
downsampler$update_trace(
relayout_order = list(NULL),
reset = FALSE,
reload = FALSE,
send_trace = FALSE
)
Arguments
relayout_orderNamed list.
A list generated by plotlyjs_relayout,
which is obtained using plotly::event_data.
e.g.,
If you would like set the range of the 2nd x axis to [10.0, 21.5],
list(`xaxis2.range[0]` = 10.0, `xaxis2.range[1]` = 21.5).
If you would like reset the range of the 1st x axis,
list(xaxis.autorange = TRUE, xaxis.showspike = TRUE).
resetBoolean.
If it is TRUE, all other arguments are neglected and
the figure will be reset (all the ranges of x axes are initialized).
By default, FALSE.
reloadBoolean.
If it is TRUE, the ranges of the figure are preserved but
the aggregation will be conducted with the current settings.
By default, FALSE.
send_traceBoolean.
If it is TRUE, a named list will be returned,
which contains the indexes of the traces that will be updated
(trace_idx_update) and the updated traces (new_trace).
By default, FALSE.
Method set_downsample_options()
In the instance, options for aggregating data are registered as data frame.
(see self$downsample_options.)
Using this method, the options can be set.
Usage
downsampler$set_downsample_options(uid = NULL, n_out = NULL, aggregator = NULL)
Arguments
uidCharacter, optional.
The unique id of the trace.
If NULL, all the options registered in this instance are updated.
By default, NULL.
n_outNumeric or integer, optional.
The number of samples output by the aggregator.
If NULL, the default value registered in this instance is used.
By default, NULL.
aggregatoraggregator object, optional.
An instance that aggregate the data.
If NULL, the default value registered in this instance is used.
Method clone()
The objects of this class are cloneable with this method.
Usage
downsampler$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
data(noise_fluct)
# example 1 : Easy method using shiny_hugeplot
shiny_hugeplot(noise_fluct$time, noise_fluct$f500)
# example 2 : Manual method using a downsampler object
fig <- plot_ly(
x = noise_fluct$time,
y = noise_fluct$f500,
type = "scatter",
mode = "lines"
) %>%
layout(xaxis = list(type = "date")) %>%
shinyHugePlot::plotly_build_light()
ds <- downsampler$new(
figure = fig,
aggregator = min_max_aggregator$new(interleave_gaps = TRUE)
)
ui <- fluidPage(
plotlyOutput(outputId = "hp", width = "800px", height = "600px")
)
server <- function(input, output, session) {
output$hp <- renderPlotly(ds$figure)
observeEvent(plotly::event_data("plotly_relayout"),{
updatePlotlyH(session, "hp", plotly::event_data("plotly_relayout"), ds)
})
}
shinyApp(ui = ui, server = server)
# example 3 : Add another series of which aggregator is different
noise_events <- tibble(
time = c("2022-11-09 12:25:50", "2022-11-09 12:26:14"),
level = c(60, 60)
)
ds$add_trace(
x = noise_events$time, y = noise_events$level, name = "event",
type = "scatter", mode = "markers",
aggregator = null_aggregator$new()
)
ds$update_trace(reset = TRUE)
server <- function(input, output, session) {
output$hp <- renderPlotly(ds$figure)
observeEvent(plotly::event_data("plotly_relayout"),{
updatePlotlyH(session, "hp", plotly::event_data("plotly_relayout"), ds)
})
}
shinyApp(ui = ui, server = server)
shinyHugePlot documentation built on Oct. 1, 2024, 5:08 p.m.
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| downsampler | R Documentation |
R6 class for down-sampling data
Description
A class for down-sampling data with a large number of samples.
An instance contains (the reference of) original data, layout of the figure,
and options for aggregating the original data.
An interactive plot for displaying large-sized data can be obtained using
the figure, down-sampler and its options included in the instance,
while making the plot using shiny_hugeplot function is easier (see examples).
See the super class (plotly_datahandler) to find more members
to handle the data in plotly.
Format
An R6::R6Class object
Super class
shinyHugePlot::plotly_datahandler -> downsampler
Active bindings
downsample_optionsOptions for aggregating (down-sampling) data registered in this instance.
n_out_defaultDefault sample size.
aggregator_defaultDefault aggregator instance.
Methods
Public methods
Inherited methods
Method new()
To construct an instance, original data, layout of the figure, and options
for aggregating the original data are necessary.
The original data and the layout of the figure can be given by providing
a plotly object (figure argument).
The options for aggregating the original data can be given by providing
an aggregator (aggregator argument) and the number of samples
(n_out argument).
See the constructor of the plotly_datahandler class for more
information on other arguments.
Usage
downsampler$new(
figure = NULL,
formula = NULL,
srcs = NULL,
srcs_ext = list(),
n_out = 1000L,
aggregator = min_max_aggregator$new(),
tz = Sys.timezone(),
use_light_build = TRUE,
legend_options = list(name_prefix = "<b style=\"color:sandybrown\">[S]</b> ",
name_suffix = "", xdiff_prefix = "<i style=\"color:#fc9944\"> ~", xdiff_suffix =
"</i>"),
verbose = F
)Arguments
figure, srcs, srcs_ext, formula, legend_options, tz, use_light_buildArguments passed to
plotly_datahandler$new.n_outInteger or numeric. The number of samples shown after down-sampling. By default 1000.
aggregatorAn instance of an R6 class for aggregation. Select an aggregation function. The list of the functions are obtained using
list_aggregators. By default,min_max_aggregator$new().verboseBoolean. Whether detailed messages to check the procedures are shown. By default,
FALSE.
Method add_trace()
Add a new series to the data registered in the instance.
If a data frame (traces_df argument) compliant with
self$orig_data is given, it will be added to self$orig_data.
If attributes to construct a plotly object (... argument)
are given, a data frame is constructed and added.
Options for aggregating data can be set using
aggregator and n_out arguments.
It is a wrapper of self$set_trace_data and
self$set_downsample_options. See these methods for more information.
Note that the traces of the figure are not updated with this method and
self$update_trace is necessary.
Usage
downsampler$add_trace(..., traces_df = NULL, n_out = NULL, aggregator = NULL)
Arguments
..., traces_dfArguments passed to
self$set_trace_data(see the super class ofplotly_datahandler)n_out, aggregatorArguments passed to
self$set_downsample_options.
Method update_trace()
Update traces of the figure registered in the instance
(self$figure$x$data) according to
re-layout order (relayout_order argument).
Using reset and reload arguments, traces are updated
without re-layout orders.
It just registers the new traces and returns nothing by default.
It returns the new traces if send_trace is TRUE.
Usage
downsampler$update_trace( relayout_order = list(NULL), reset = FALSE, reload = FALSE, send_trace = FALSE )
Arguments
relayout_orderNamed list. A list generated by
plotlyjs_relayout, which is obtained usingplotly::event_data. e.g., If you would like set the range of the 2nd x axis to [10.0, 21.5],list(`xaxis2.range[0]` = 10.0, `xaxis2.range[1]` = 21.5). If you would like reset the range of the 1st x axis,list(xaxis.autorange = TRUE, xaxis.showspike = TRUE).resetBoolean. If it is
TRUE, all other arguments are neglected and the figure will be reset (all the ranges of x axes are initialized). By default,FALSE.reloadBoolean. If it is
TRUE, the ranges of the figure are preserved but the aggregation will be conducted with the current settings. By default,FALSE.send_traceBoolean. If it is
TRUE, a named list will be returned, which contains the indexes of the traces that will be updated (trace_idx_update) and the updated traces (new_trace). By default,FALSE.
Method set_downsample_options()
In the instance, options for aggregating data are registered as data frame.
(see self$downsample_options.)
Using this method, the options can be set.
Usage
downsampler$set_downsample_options(uid = NULL, n_out = NULL, aggregator = NULL)
Arguments
uidCharacter, optional. The unique id of the trace. If
NULL, all the options registered in this instance are updated. By default,NULL.n_outNumeric or integer, optional. The number of samples output by the aggregator. If
NULL, the default value registered in this instance is used. By default,NULL.aggregatoraggregatorobject, optional. An instance that aggregate the data. IfNULL, the default value registered in this instance is used.
Method clone()
The objects of this class are cloneable with this method.
Usage
downsampler$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
data(noise_fluct)
# example 1 : Easy method using shiny_hugeplot
shiny_hugeplot(noise_fluct$time, noise_fluct$f500)
# example 2 : Manual method using a downsampler object
fig <- plot_ly(
x = noise_fluct$time,
y = noise_fluct$f500,
type = "scatter",
mode = "lines"
) %>%
layout(xaxis = list(type = "date")) %>%
shinyHugePlot::plotly_build_light()
ds <- downsampler$new(
figure = fig,
aggregator = min_max_aggregator$new(interleave_gaps = TRUE)
)
ui <- fluidPage(
plotlyOutput(outputId = "hp", width = "800px", height = "600px")
)
server <- function(input, output, session) {
output$hp <- renderPlotly(ds$figure)
observeEvent(plotly::event_data("plotly_relayout"),{
updatePlotlyH(session, "hp", plotly::event_data("plotly_relayout"), ds)
})
}
shinyApp(ui = ui, server = server)
# example 3 : Add another series of which aggregator is different
noise_events <- tibble(
time = c("2022-11-09 12:25:50", "2022-11-09 12:26:14"),
level = c(60, 60)
)
ds$add_trace(
x = noise_events$time, y = noise_events$level, name = "event",
type = "scatter", mode = "markers",
aggregator = null_aggregator$new()
)
ds$update_trace(reset = TRUE)
server <- function(input, output, session) {
output$hp <- renderPlotly(ds$figure)
observeEvent(plotly::event_data("plotly_relayout"),{
updatePlotlyH(session, "hp", plotly::event_data("plotly_relayout"), ds)
})
}
shinyApp(ui = ui, server = server)
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