Add even more interactivity to interactive charts

Description

This package is largely inspired by the manipulate package from Rstudio. It can be used to easily create graphical interface that let the user modify the data or the parameters of an interactive chart. It also provides the combineWidgets function to easily combine multiple interactive charts in a single view.

Details

manipulateWidget is the main function of the package. It accepts an expression that generates an interactive chart (and more precisely an htmlwidget object. See http://www.htmlwidgets.org/ if you have never heard about it) and a set of controls created with functions mwSlider, mwCheckbox... which are used to dynamically change values within the expression. Each time the user modifies the value of a control, the expression is evaluated again and the chart is updated. Consider the following code:

manipulateWidget(myPlotFun(country), country = mwSelect(c("BE", "DE", "ES", "FR")))

It will generate a graphical interface with a select input on its left with options "BE", "DE", "ES", "FR". By default, at the beginning the value of the variable country will be equal to the first choice of the corresponding input. So the function will first execute myPlotFun("BE") and the result will be displayed in the main panel of the interface. If the user changes the value to "FR", then the expression myPlotFun("FR") is evaluated and the new result is displayed.

The interface also contains a button "Done". When the user clicks on it, the last chart is returned. It can be stored in a variable, be modified by the user, saved as a html file with saveWidget from package htmlwidgets or converted to a static image file with package webshot.

Finally one can easily create complex layouts thanks to function combineWidgets. For instance, assume we want to see a map that displays values of some variable for a given year, but on its right side we also want to see the distributions of three variables. Then we could write:

myPlotFun <- function(year, variable) {
  combineWidgets(
    ncol = 2, colSize = c(3, 1),
    myMap(year, variable),
    combineWidgets(
      ncol = 1,
      myHist(year, "V1"),
      myHist(year, "V2"),
      myHist(year, "V3"),
    )
  )
}

manipulateWidget(
  myPlotFun(year, variable),
  year = mwSlider(2000, 2016, value = 2000),
  variable = mwSelect(c("V1", "V2", "V3"))
)

Of course, combineWidgets can be used outside of manipulateWidget. For instance, it can be used in an Rmarkdown document to easily put together interactive charts.

For more concrete examples of usage, you should look at the documentation and especially the examples of manipulateWidget and combineWidgets.

See Also

manipulateWidget, combineWidgets

Controller object of a manipulateWidget application

Description

MWController is a reference class that is used to manage interaction with data and update of the view created by manipulateWidget. Only users who desire to create automatic tests for applications created with manipulateWidget should care about this object.

Fields

ncharts

Number of charts in the application

nrow

Number of rows.

ncol

Number of columns.

autoUpdate

Boolean indicating if charts should be automatically updated when a value changes. list with value and initBtn (not autoUpdate, if want first charts on init)

Methods

getParams(name, chartId = 1)

Get parameters of an input for a given chart

getValue(name, chartId = 1)

Get the value of a variable for a given chart.

getValues(chartId = 1)

Get all values for a given chart.

isVisible(name, chartId = 1)

Indicates if a given input is visible

returnCharts()

Return all charts.

setValue(name, value, chartId = 1, updateHTML = FALSE, reactive = FALSE)

Update the value of a variable for a given chart.

setValueAll(name, value, updateHTML = TRUE)

Update the value of an input for all charts

updateCharts()

Update all charts.

Testing a manipulateWidget application

When manipulateWidget is used in a test script, it returns a MWController object instead of starting a shiny gadget. This object has methods to modify inputs values and check the state of the application. This can be useful to automatically checks if your application behaves like desired. Here is some sample code that uses package testthat:

library("testthat")

controller <- manipulateWidget(
  x + y,
  x = mwSlider(0, 10, 5),
  y = mwSlider(0, x, 0),
  .compare = "y"
)

test_that("Two charts are created", {
  expect_equal(controller$ncharts, 2)
})

test_that("Parameter 'max' of 'y' is updated when 'x' changes", {
  expect_equal(controller$getParams("y", 1)$max, 5)
  expect_equal(controller$getParams("y", 2)$max, 5)
  controller$setValue("x", 3)
  expect_equal(controller$getParams("y", 1)$max, 3)
  expect_equal(controller$getParams("y", 2)$max, 3)
})

Combine several interactive plots

Description

This function combines different htmlwidgets in a unique view.

Usage

combineWidgets(
  ...,
  list = NULL,
  nrow = NULL,
  ncol = NULL,
  title = NULL,
  rowsize = 1,
  colsize = 1,
  byrow = TRUE,
  titleCSS = "",
  header = NULL,
  footer = NULL,
  leftCol = NULL,
  rightCol = NULL,
  width = NULL,
  height = NULL
)

Arguments

Details

The function only allows table like layout : each row has the same number of columns and reciprocally. But it is possible to create more complex layout by nesting combined htmlwidgets. (see examples)

Value

A htmlwidget object of class combineWidget. Individual widgets are stored in element widgets and can be extracted or updated. This is useful when a function returns a combineWidgets object but user wants to keep only one widget or to update one of them (see examples).

Examples

if (require(plotly)) {
  data(iris)

  combineWidgets(title = "The Iris dataset",
    plot_ly(iris, x = ~Sepal.Length, type = "histogram", nbinsx = 20),
    plot_ly(iris, x = ~Sepal.Width, type = "histogram", nbinsx = 20),
    plot_ly(iris, x = ~Petal.Length, type = "histogram", nbinsx = 20),
    plot_ly(iris, x = ~Petal.Width, type = "histogram", nbinsx = 20)
  )

  # Create a more complex layout by nesting combinedWidgets
  combineWidgets(title = "The iris data set: sepals", ncol = 2, colsize = c(2,1),
    plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width, type = "scatter",
            mode = "markers", color = ~Species),
    combineWidgets(
      plot_ly(iris, x = ~Sepal.Length, type = "histogram", nbinsx = 20),
      plot_ly(iris, x = ~Sepal.Width, type = "histogram", nbinsx = 20)
    )
  )

  # combineWidgets can also be used on a single widget to easily add to it a
  # title and a footer.
  require(shiny)
  comments <- tags$div(
    "Wow this plot is so ",
    tags$span("amazing!!", style = "color:red;font-size:36px")
  )

  combineWidgets(
    plot_ly(iris, x = ~Sepal.Length, type = "histogram", nbinsx = 20),
    title = "Distribution of Sepal Length",
    footer = comments
  )

  # It is also possible to combine htmlwidgets with text or other html elements
  myComment <- tags$div(
    style="height:100%;background-color:#eee;padding:10px;box-sizing:border-box",
    tags$h2("Comment"),
    tags$hr(),
    "Here is a very clever comment about the awesome graphics you just saw."
  )
  combineWidgets(
    plot_ly(iris, x = ~Sepal.Length, type = "histogram", nbinsx = 20),
    plot_ly(iris, x = ~Sepal.Width, type = "histogram", nbinsx = 20),
    plot_ly(iris, x = ~Petal.Length, type = "histogram", nbinsx = 20),
    myComment
  )

  # Updating individual widgets.
  myWidget <- combineWidgets(
    plot_ly(iris, x = ~Sepal.Length, type = "histogram", nbinsx = 20),
    plot_ly(iris, x = ~Sepal.Width, type = "histogram", nbinsx = 20),
    ncol = 2
  )
  myWidget


  myWidget$widgets[[1]] <- myWidget$widgets[[1]] %>%
    layout(title = "Histogram of Sepal Length")

  myWidget$widgets[[2]] <- myWidget$widgets[[2]] %>%
    layout(title = "Histogram of Sepal Width")

  myWidget


  # Instead of passing directly htmlwidgets to the function, one can pass
  # a list containing htmlwidgets. This is especially useful when the widgets
  # are generated using a loop function like "lapply" or "replicate".
  #
  # The following code generates a list of 12 histograms and use combineWidgets
  # to display them.
  samples <- replicate(12, plot_ly(x = rnorm(100), type = "histogram", nbinsx = 20),
                       simplify = FALSE)
  combineWidgets(list = samples, title = "12 samples of the same distribution")
}

Shiny bindings for combineWidgets

Description

Output and render functions for using combineWidgets within Shiny applications and interactive Rmd documents.

Usage

combineWidgetsOutput(outputId, width = "100%", height = "400px")

renderCombineWidgets(expr, env = parent.frame(), quoted = FALSE)

Arguments

Options for comparison mode

Description

This function generates a list of options that are used by manipulateWidget to compare multiple charts.

Usage

compareOptions(ncharts = NULL, nrow = NULL, ncol = NULL, allowCompare = TRUE)

Arguments

Value

List of options

Examples

if (require(dygraphs)) {

  mydata <- data.frame(
    year = 2000+1:100,
    series1 = rnorm(100),
    series2 = rnorm(100),
    series3 = rnorm(100)
  )
  manipulateWidget(
    dygraph(mydata[range[1]:range[2] - 2000, c("year", series)], main = title),
    range = mwSlider(2001, 2100, c(2001, 2100)),
    series = mwSelect(c("series1", "series2", "series3")),
    title = mwText("Fictive time series"),
    .compare = list(title = NULL, series = NULL),
    .compareOpts = compareOptions(ncharts = 4)
  )

  manipulateWidget(
    dygraph(mydata[range[1]:range[2] - 2000, c("year", series)], main = title),
    range = mwSlider(2001, 2100, c(2001, 2100)),
    series = mwSelect(c("series1", "series2", "series3")),
    title = mwText("Fictive time series"),
    .compare = list(title = NULL, series = NULL),
    .compareOpts = compareOptions(ncharts = 3, nrow = 3)
  )
}

knit_print method for MWController object

Description

knit_print method for MWController object

Usage

knit_print.MWController(x, ...)

Arguments

Add Controls to Interactive Plots

Description

This function permits to add controls to an interactive plot created with packages like dygraphs, highcharter or plotly in order to change the input data or the parameters of the plot.

Technically, the function starts a shiny gadget. The R session is bloqued until the user clicks on "cancel" or "done". If he clicks on "done", then the the function returns the last displayed plot so the user can modify it and/or save it.

Usage

manipulateWidget(
  .expr,
  ...,
  .updateBtn = FALSE,
  .saveBtn = TRUE,
  .exportBtn = TRUE,
  .exportType = c("html2canvas", "webshot"),
  .viewer = c("pane", "window", "browser"),
  .compare = NULL,
  .compareOpts = compareOptions(),
  .translations = mwTranslations(),
  .return = function(widget, envs) {     widget },
  .width = NULL,
  .height = NULL,
  .runApp = TRUE
)

Arguments

Value

The result of the expression evaluated with the last values of the controls. It should be an object of class htmlWidget.

Advanced Usage

The "normal" use of the function is to provide an expression that always return an htmlwidget. In such case, every time the user changes the value of an input, the current widget is destroyed and a new one is created and rendered.

Some packages provide functions to update a widget that has already been rendered. This is the case for instance for package leaflet with the function leafletProxy. To use such functions, manipulateWidget evaluates the parameter .expr with four extra variables:

• .initial: TRUE if the expression is evaluated for the first time and then the widget has not been rendered yet, FALSE if the widget has already been rendered.

• .session: A shiny session object.

• .output: ID of the output in the shiny interface.

• .id: Id of the chart. It can be used in comparison mode to make further customization without the need to create additional input controls.

You can take a look at the last example to see how to use these two variables to update a leaflet widget.

Modify the returned widget

In some specific situations, a developer may want to use manipulateWidget in a function that waits the user to click on the "Done" button and modifies the widget returned by manipulateWidget. In such situation, parameter .return should be used so that manipulateWidget is the last function called. Indeed, if other code is present after, the custom function will act very weird in a Rmarkdown document with "runtime: shiny".

Examples

# Basic example with fake data
if (require(dygraphs)) {
  mydata <- data.frame(period = 1:100, value = rnorm(100))
  manipulateWidget(dygraph(mydata[range[1]:range[2], ], main = title),
                   range = mwSlider(1, 100, c(1, 100)),
                   title = mwText("Fictive time series"))
}

# Let use manipulateWidget to explore the evolution of energy consumption in
# the world
data("worldEnergyUse")

if (require(plotly)) {
  # Function that generates a chart representing the evolution of energy
  # consumption per country. Creating a function is not necessary. We do it
  # for clarity and reuse in the different examples.
  plotEnergyUse <- function(Country, Period, lwd = 2, col = "gray") {
    dataset <- subset(
      worldEnergyUse,
      country == Country & year >= Period[1] & year <= Period[2]
    )
    plot_ly(dataset) %>%
      add_lines(~year, ~energy_used, line = list(width = lwd, color = col)) %>%
      layout(title = paste("Energy used in", Country))
  }

  # Launch the interactive visualisation
  manipulateWidget(
    plotEnergyUse(Country, Period),
    Period = mwSlider(1960, 2014, c(1960, 2014)),
    Country = mwSelect(sort(unique(worldEnergyUse$country)), "United States")
  )

  # Directly start comparison mode
  manipulateWidget(
    plotEnergyUse(Country, Period),
    Period = mwSlider(1960, 2014, c(1960, 2014)),
    Country = mwSelect(sort(unique(worldEnergyUse$country))),
    .compare = list(Country = c("United States", "China")),
    .compareOpts = compareOptions(ncol = 2)
  )

  # Dynamic input parameters
  #-------------------------
  # The arguments of an input can depend on the values of other inputs.
  # In this example, when the user changes the region, the choices of input
  # "Country" are updated with the countries of that region.

  # First we create a list that contains for each region the countries in that
  # retion
  refRegions <- by(worldEnergyUse$country, worldEnergyUse$region,
                   function(x) as.character(sort(unique(x))))

  manipulateWidget(
    plotEnergyUse(Country, Period),
    Period = mwSlider(1960, 2014, c(1960, 2014)),
    Region = mwSelect(sort(unique(worldEnergyUse$region))),
    Country = mwSelect(choices = refRegions[[Region]])
  )

  # Grouping inputs
  #----------------
  # Inputs can be visually grouped with function mwGroup()
  manipulateWidget(
    plotEnergyUse(Country, Period, lwd, col),
    Period = mwSlider(1960, 2014, c(1960, 2014)),
    Country = mwSelect(sort(unique(worldEnergyUse$country)), "United States"),
    `Graphical Parameters` = mwGroup(
      lwd = mwSlider(1,10, 2, label = "Line Width"),
      col = mwSelect(choices = c("gray", "black", "red"))
    )
  )

  # Conditional inputs
  #-------------------
  # Inputs can be displayed or hidden depending on the state of other inputs.
  # In this example, user can choose to display the level of aggregation
  # (region or country). Depending on the choixe, the application displays
  # input Region or input Country.
  plotEnergyUseRegion <- function(Region, Period, lwd = 2, col = "gray") {
    dataset <- subset(
      worldEnergyUse,
      region == Region & year >= Period[1] & year <= Period[2]
    )
    dataset <- aggregate(energy_used ~ year, sum, data = dataset)

    plot_ly(dataset) %>%
      add_lines(~year, ~energy_used, line = list(width = lwd, color = col)) %>%
      layout(title = paste("Energy used in", Region))
  }

  manipulateWidget(
    {
      if (Level == "Region") {
        plotEnergyUseRegion(Region, Period)
      } else {
        plotEnergyUse(Country, Period)
      }
    },
    Period = mwSlider(1960, 2014, c(1960, 2014)),
    Level = mwSelect(c("Region", "Country")),
    Region = mwSelect(sort(unique(worldEnergyUse$region)),
                      .display = Level == "Region"),
    Country = mwSelect(sort(unique(worldEnergyUse$country)),
                       .display = Level == "Country")
  )

}

# Advanced Usage
# --------------
# When .expr is evaluated with tehnical variables:
# .initial: is it the first evaluation?
# .outputId: integer representing the id of the chart
# .output: shiny output id
# .session: shiny session
# They can be used to update an already rendered widget instead of replacing
# it each time an input value is modified.
#
# In this example, we represent on a map, the energy use of countries.
# When the user changes an input, the map is not redrawn. Only the circle
# markers are updated.
if (require(leaflet)) {
  plotMap <- function(Year, MaxRadius = 30, .initial, .session, .output) {
    dataset <- subset(worldEnergyUse, year == Year)
    radius <- sqrt(dataset$energy_used) /
      max(sqrt(worldEnergyUse$energy_used), na.rm = TRUE) * MaxRadius

    if (.initial) { # map has not been rendered yet
      map <- leaflet() %>% addTiles()
    } else { # map already rendered
      map <- leafletProxy(.output, .session) %>% clearMarkers()
    }

    map %>% addCircleMarkers(dataset$long, dataset$lat, radius = radius,
                             color = "gray", weight = 0, fillOpacity = 0.7)
  }

  manipulateWidget(
    plotMap(Year, MaxRadius, .initial, .session, .output),
    Year = mwSlider(1960, 2014, 2014),
    MaxRadius = mwSlider(10, 50, 20)
  )
}

Add a checkbox to a manipulateWidget gadget

Description

Add a checkbox to a manipulateWidget gadget

Usage

mwCheckbox(value = FALSE, label = NULL, ..., .display = TRUE)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if(require(plotly)) {
 manipulateWidget(
   {
       plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width,
               color = ~Species, type = "scatter", mode = "markers") %>%
         layout(showlegend = legend)
   },
   legend = mwCheckbox(TRUE, "Show legend")
 )
}

Add a group of checkboxes to a manipulateWidget gadget

Description

Add a group of checkboxes to a manipulateWidget gadget

Usage

mwCheckboxGroup(choices, value = c(), label = NULL, ..., .display = TRUE)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if (require(plotly)) {
  manipulateWidget(
    {
      if (length(species) == 0) mydata <- iris
      else mydata <- iris[iris$Species %in% species,]

      plot_ly(mydata, x = ~Sepal.Length, y = ~Sepal.Width,
              color = ~droplevels(Species), type = "scatter", mode = "markers")
    },
    species = mwCheckboxGroup(levels(iris$Species))
  )
}

Add a date picker to a manipulateWidget gadget

Description

Add a date picker to a manipulateWidget gadget

Usage

mwDate(value = NULL, label = NULL, ..., .display = TRUE)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if (require(dygraphs) && require(xts)) {
  mydata <- xts(rnorm(365), order.by = as.Date("2017-01-01") + 0:364)

  manipulateWidget(
    dygraph(mydata) %>% dyEvent(date, "Your birthday"),
    date = mwDate("2017-03-27", label = "Your birthday date",
                  min = "2017-01-01", max = "2017-12-31")
  )
}

Add a date range picker to a manipulateWidget gadget

Description

Add a date range picker to a manipulateWidget gadget

Usage

mwDateRange(
  value = c(Sys.Date(), Sys.Date() + 1),
  label = NULL,
  ...,
  .display = TRUE
)

Arguments

Value

An Input object

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if (require(dygraphs) && require(xts)) {
  mydata <- xts(rnorm(365), order.by = as.Date("2017-01-01") + 0:364)

  manipulateWidget(
    dygraph(mydata) %>% dyShading(from=period[1], to = period[2], color = "#CCEBD6"),
    period = mwDateRange(c("2017-03-01", "2017-04-01"),
                  min = "2017-01-01", max = "2017-12-31")
  )
}

Group inputs in a collapsible box

Description

This function generates a collapsible box containing inputs. It can be useful when there are a lot of inputs and one wants to group them.

Usage

mwGroup(..., label = NULL, .display = TRUE)

Arguments

Value

Input of type "group".

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if(require(dygraphs)) {
  mydata <- data.frame(x = 1:100, y = rnorm(100))
  manipulateWidget(
    dygraph(mydata[range[1]:range[2], ],
            main = title, xlab = xlab, ylab = ylab),
    range = mwSlider(1, 100, c(1, 100)),
    "Graphical parameters" = mwGroup(
      title = mwText("Fictive time series"),
      xlab = mwText("X axis label"),
      ylab = mwText("Y axis label")
    )
  )
}

Add a manipulateWidget to a shiny application

Description

These two functions can be used to include a manipulateWidget object in a shiny application. mwModuleUI must be used in the UI to generate the required HTML elements and add javascript and css dependencies. mwModule must be called once in the server function of the application.

Usage

mwModule(id, controller, fillPage = FALSE, ...)

mwModuleUI(
  id,
  border = TRUE,
  okBtn = FALSE,
  saveBtn = TRUE,
  exportBtn = TRUE,
  updateBtn = FALSE,
  allowCompare = TRUE,
  margin = 0,
  width = "100%",
  height = 400,
  header = NULL,
  footer = NULL
)

Arguments

Value

mwModuleUI returns the required HTML elements for the module. mwModule is only used for its side effects.

Examples

if (interactive() & require("dygraphs")) {
  require("shiny")
  ui <- fillPage(
  fillRow(
    flex = c(NA, 1),
    div(
      textInput("title", label = "Title", value = "glop"),
      selectInput("series", "series", choices = c("series1", "series2", "series3"))
    ),
    mwModuleUI("ui", height = "100%")
  ))

  server <- function(input, output, session) {
    mydata <- data.frame(
      year = 2000+1:100,
      series1 = rnorm(100),
      series2 = rnorm(100),
      series3 = rnorm(100)
    )

    c <- manipulateWidget(
      {
        dygraph(mydata[range[1]:range[2] - 2000, c("year", series)], main = title)
      },
      range = mwSlider(2001, 2100, c(2001, 2050)),
      series = mwSharedValue(),
      title = mwSharedValue(), .runApp = FALSE,
      .compare = "range"
    )
    #
    mwModule("ui", c, title = reactive(input$title), series = reactive(input$series))
  }

  shinyApp(ui, server)


}

Add a numeric input to a manipulateWidget gadget

Description

Add a numeric input to a manipulateWidget gadget

Usage

mwNumeric(value, label = NULL, ..., .display = TRUE)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if (require(plotly)) {
  manipulateWidget({
      plot_ly(data.frame(x = 1:10, y = rnorm(10, mean, sd)), x=~x, y=~y,
              type = "scatter", mode = "markers")
    },
    mean = mwNumeric(0),
    sd = mwNumeric(1, min = 0, step = 0.1)
  )
}

Add a password to a manipulateWidget gadget

Description

Add a password to a manipulateWidget gadget

Usage

mwPassword(value = "", label = NULL, ..., .display = TRUE)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if (require(plotly)) {
  manipulateWidget(
    {
      if (passwd != 'abc123') {
        plot_ly(type = "scatter", mode="markers") %>%
          layout(title = "Wrong password. True password is 'abc123'")
      } else {
        plot_ly(data.frame(x = 1:10, y = rnorm(10)), x=~x, y=~y, type = "scatter", mode = "markers")
      }
    },
    user = mwText(label = "Username"),
    passwd = mwPassword(label = "Password")
  )
}

Add radio buttons to a manipulateWidget gadget

Description

Add radio buttons to a manipulateWidget gadget

Usage

mwRadio(choices, value = NULL, label = NULL, ..., .display = TRUE)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if (require(plotly)) {
  mydata <- data.frame(x = 1:100, y = rnorm(100))

  manipulateWidget(
    {
      mode <- switch(type, points = "markers", lines = "lines", both = "markers+lines")
      plot_ly(mydata, x=~x, y=~y, type = "scatter", mode = mode)
    },
    type = mwRadio(c("points", "lines", "both"))
  )
}

Add a Select list input to a manipulateWidget gadget

Description

Add a Select list input to a manipulateWidget gadget

Usage

mwSelect(
  choices = value,
  value = NULL,
  label = NULL,
  ...,
  multiple = FALSE,
  .display = TRUE
)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSharedValue(), mwSlider(), mwText()

Examples

if (require(plotly)) {
  mydata <- data.frame(x = 1:100, y = rnorm(100))

  manipulateWidget(
    {
      mode <- switch(type, points = "markers", lines = "lines", both = "markers+lines")
      plot_ly(mydata, x=~x, y=~y, type = "scatter", mode = mode)
    },
    type = mwSelect(c("points", "lines", "both"))
  )

  Sys.sleep(0.5)

  # Select multiple values
  manipulateWidget(
    {
      if (length(species) == 0) mydata <- iris
      else mydata <- iris[iris$Species %in% species,]

      plot_ly(mydata, x = ~Sepal.Length, y = ~Sepal.Width,
              color = ~droplevels(Species), type = "scatter", mode = "markers")
    },
    species = mwSelect(levels(iris$Species), multiple = TRUE)
  )
}

Add a Select list input to a manipulateWidget gadget

Description

Add a Select list input to a manipulateWidget gadget

Usage

mwSelectize(
  choices = value,
  value = NULL,
  label = NULL,
  ...,
  multiple = FALSE,
  options = NULL,
  .display = TRUE
)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelect(), mwSharedValue(), mwSlider(), mwText()

Examples

if (require(plotly)) {
  mydata <- data.frame(x = 1:100, y = rnorm(100))

  # Select multiple values
  manipulateWidget(
    {
      if (length(species) == 0) mydata <- iris
      else mydata <- iris[iris$Species %in% species,]

      plot_ly(mydata, x = ~Sepal.Length, y = ~Sepal.Width,
              color = ~droplevels(Species), type = "scatter", mode = "markers")
    },
    species = mwSelectize(c("Select one or two species : " = "", levels(iris$Species)),
        multiple = TRUE, options = list(maxItems = 2))
  )
}

Shared Value

Description

This function creates a virtual input that can be used to store a dynamic shared variable that is accessible in inputs as well as in output.

Usage

mwSharedValue(expr = NULL)

Arguments

Value

An Input object of type "sharedValue".

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSlider(), mwText()

Examples

if (require(plotly)) {
  # Plot the characteristics of a car and compare with the average values for
  # cars with same number of cylinders.
  # The shared variable 'subsetCars' is used to avoid subsetting multiple times
  # the data: this value is updated only when input 'cylinders' changes.
  colMax <- apply(mtcars, 2, max)

  plotCar <- function(cardata, carName) {
    carValues <- unlist(cardata[carName, ])
    carValuesRel <- carValues / colMax

    avgValues <- round(colMeans(cardata), 2)
    avgValuesRel <- avgValues / colMax

    plot_ly() %>%
      add_bars(x = names(cardata), y = carValuesRel, text = carValues,
               hoverinfo = c("x+text"), name = carName) %>%
      add_bars(x = names(cardata), y = avgValuesRel, text = avgValues,
               hoverinfo = c("x+text"), name = "average") %>%
      layout(barmode = 'group')
  }

  c <- manipulateWidget(
    plotCar(subsetCars, car),
    cylinders = mwSelect(c("4", "6", "8")),
    subsetCars = mwSharedValue(subset(mtcars, cylinders == cyl)),
    car = mwSelect(choices = row.names(subsetCars))
  )
}

Add a Slider to a manipulateWidget gadget

Description

Add a Slider to a manipulateWidget gadget

Usage

mwSlider(min, max, value, label = NULL, ..., .display = TRUE)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwText()

Examples

if (require(plotly)) {

  myWidget <- manipulateWidget(
    plot_ly(data.frame(x = 1:n, y = rnorm(n)), x=~x, y=~y, type = "scatter", mode = "markers"),
    n = mwSlider(1, 100, 10, label = "Number of values")
  )

  Sys.sleep(0.5)

  # Create a double ended slider to choose a range instead of a single value
  mydata <- data.frame(x = 1:100, y = rnorm(100))

  manipulateWidget(
    plot_ly(mydata[n[1]:n[2], ], x=~x, y=~y, type = "scatter", mode = "markers"),
    n = mwSlider(1, 100, c(1, 10), label = "Number of values")
  )

}

Add a text input to a manipulateWidget gadget

Description

Add a text input to a manipulateWidget gadget

Usage

mwText(value = "", label = NULL, ..., .display = TRUE)

Arguments

Value

A function that will generate the input control.

See Also

Other controls: mwCheckboxGroup(), mwCheckbox(), mwDateRange(), mwDate(), mwGroup(), mwNumeric(), mwPassword(), mwRadio(), mwSelectize(), mwSelect(), mwSharedValue(), mwSlider()

Examples

if (require(plotly)) {
  mydata <- data.frame(x = 1:100, y = rnorm(100))
  manipulateWidget({
      plot_ly(mydata, x=~x, y=~y, type = "scatter", mode = "markers") %>%
        layout(title = mytitle)
    },
    mytitle = mwText("Awesome title !")
  )
}

Translate UI titles and labels

Description

Creates a list of translation strings that can be passed to function manipulateWidget to translate some UI elements.

Usage

mwTranslations(
  settings = "Settings",
  chart = "Chart",
  compare = "Compare",
  compareVars = "Variables to compare",
  ncol = "Nb Columns",
  ncharts = "Nb Charts"
)

Arguments

Value

Named list of translation strings.

Examples

translations <- mwTranslations(
  settings = "Parametres", chart = "Graphique", compare = "Comparaison",
  compareVars = "Variable de comparaison", ncharts = "Nb graph.", ncol = "Nb col."
)

if (require(dygraphs)) {
  mydata <- data.frame(year = 2000+1:100, value = rnorm(100))
  manipulateWidget(dygraph(mydata[range[1]:range[2] - 2000, ], main = title),
                   range = mwSlider(2001, 2100, c(2001, 2100)),
                   title = mwText("Fictive time series"),
                   .translations = translations)
}

Include a static image in a combinedWidgets

Description

staticPlot is a function that generates a static plot and then return the HTML code needed to include the plot in a combinedWidgets. staticImage is a more general function that generates the HTML code necessary to include any image file.

Usage

staticPlot(expr, width = 600, height = 400)

staticImage(file, style = "max-width:100%%;max-height:100%%")

Arguments

Value

a shiny.tag object containing the HTML code required to include the image or the plot in a combinedWidgets object.

Examples

staticPlot(hist(rnorm(100)))

if (require(plotly)) {
  data(iris)

  combineWidgets(
    plot_ly(iris, x = ~Sepal.Length, type = "histogram", nbinsx = 20),
    staticPlot(hist(iris$Sepal.Length, breaks = 20), height = 300)
  )

  # You can also embed static images in the header, footer, left or right
  # columns of a combinedWidgets. The advantage is that the space allocated
  # to the static plot will be constant when the window is resized.

  combineWidgets(
    plot_ly(iris, x = ~Sepal.Length, type = "histogram", nbinsx = 20),
    footer = staticPlot(hist(iris$Sepal.Length, breaks = 20), height = 300)
  )
}

summary method for MWController object

Description

summary method for MWController object

Usage

## S3 method for class 'MWController'
summary(object, ...)

Arguments

Evolution of energy use per country

Description

Data.frame containing energy consumption per country from 1960 to 2014. The data comes from the World Bank website. It contains one line per couple(country, year) and has the following columns:

Usage

worldEnergyUse

Format

An object of class data.frame with 9375 rows and 15 columns.

Details

• country Country name

• iso2c Country code in two characters

• year Year

• population Population of the country

• energy_used_per_capita Energy used per capita in kg of oil equivalent (EG.USE.PCAP.KG.OE)

• energy_imported_prop Proportion of energy used that has been imported (EG.IMP.CONS.ZS)

• energy_fossil_prop Fossil fuel energy consumption in proportion of total consumption (EG.USE.COMM.FO.ZS)

• energy_used Energy consumption in kg of oil equivalent

• energy_fossil Fossil fuel energy consumption in kg of oil equivalent

• prop_world_energy_used Share of the country in the world energy consumption

• prop_world_energy_fossil Share of the country in the world fossil energy consumption

• prop_world_population Share of the country in the world population

• long Longitude of the country

• lat Lattitude of the country

• region Region of the country

Author(s)

François Guillem guillem.francois@gmail.com

References

https://data.worldbank.org/indicator