selr

See this page for an example.

selr adds functions that can be used in RMarkdown to add interactive functionality to HTML documents. For instance, the creator of an RMarkdown document can add ‘select’ buttons that allow the user of the HTML document to swap the images and text that appear throughout the document.

The advantages of selr over Shiny and other tools is that selr relies entirely on client-side, in-browser processing, so you don’t have to go through the trouble and cost of setting up a client-server relationship. In addition, the interactivity is implemented entirely through HTML, CSS, and plain JavaScript (no JQuery or other plugins) so that the JavaScript is reliable, dependency-free, safe, cross-browser compatible, and transparent. However, compared to Shiny, selr’s functionality is limited, so for intensive web-applications (such as web apps that perform user-specified statistical analyses), you should strongly consider using Shiny instead.

Some of the applications for which selr is a good tool include:

• Interactive reports where the reader wants to view summaries for specific subsets or selections of data.
• Presentation of statistical analyses where the reader wants to see how findings are impacted by an analyst’s choice of parameters (such as a number of k-means clusters or a prior distribution)

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("bschneidr/selr")

Eventually, the package will be available on CRAN.

Example

Here’s a small example that shows how the package can be used to swap the text and plots in an HTML document depending on user input.

# NOTE: When using selr in an RMarkdown chunk, you'll want to set results='asis'
library(selr)
# For a particular HTML document, import the necessary Javascript
add_javascript(in_html_doc = TRUE)

# Add a selector button that the reader of the HTML document can interact with
add_selector(sel_id = 'dataset',
             options = c('Dataset_1' = "The arrests data",
                         'Dataset_2' = "The iris data",
                         'Dataset_3' = "The cars data"),
             label = "Select a dataset:")

# Create three plots which the user can choose
arrests_plot <- ggplot2::qplot(Murder, Assault, data = USArrests, main = "Arrests")
iris_plot <- ggplot2::qplot(Sepal.Length, Sepal.Width, data = iris, main = "Iris")
cars_plot <- ggplot2::qplot(mpg, wt, data = mtcars, colour = I("red"), main = "Cars")
# Place the plots into the RMarkdown document. 
# Only one plot will be shown at a time, depending on the value selected in the selector button.
sel_plot(sel_id = "dataset",
         options = list('Dataset_1' = arrests_plot,
                        'Dataset_2' = iris_plot,
                        'Dataset_3' = cars_plot),
         dev = 'png', fig.width = 6, fig.height = 3.5, dpi = 300,
         embed = TRUE)

# Add textual descriptions of the currently-selected dataset
sel_text(sel_id = "dataset",
                options = list("Dataset_1" = "You've selected the arrests data. Good choice.",
                               "Dataset_2" = "You've selected the iris data- Fisher smiles upon you.",
                               "Dataset_3" = "You've selected the cars data- may you plumb its secrets."))