Development

formattable Another nice table-making package is formattable. The cute heatmap-style colour formatting and the easy-to-use formatter functions make formattable very appealing. color_tile() fills the cells with a colour gradient corresponding to the values color_bar() adds a colour bar to each cell, where the length is proportional to the value The true_false_formatter() defined below demonstrates how to define your own formatting function, in this case formatting TRUE, FALSE and NA as green, red and black.

If you want the features of both DT and formattable, you can combine them by converting the formattable() output to as.datatable(), and much of the formattable features will be preserved.

However, one problem I had was that when using DT::datatable, missing values (NA) are left blank in the display (which I prefer), but in the converted from formattable() version, NA’s are printed. Also, color_bar columns seem to be converted to character, which can no longer be sorted numerically.

reactable Next I tried reactable, a package based on the React Table library.

Columns are customised via the columns argument, which takes a named list of column definitions defined using colDef(). These include format definitions created using colFormat.

In the end, I used DT::datatable() in my Shiny app, because I found it the easiest, fastest, and most comprehensive. I’ve been able to achieve most of the features I wanted using just DT.

Heatmap-like fill effect:

apply the formatStyle() function to the output of datatable() to set the backgroundColor for selected columns:

Abbreviate long cells

Sometimes some cells have a large amount of text that would mess up the table layout if I showed it all. In these cases, I like to abbreviate long values and show the full text in a tooltip. To do this, you can use JavaScript to format the column to show a substring with “…” and the full string in a tooltip (<span title="Long string">Substring...</span) when values are longer than N characters (in this case 10 characters). You can do this using columnDefs and pass JavaScript with the JS() function:

Really plain table Sometimes I don’t need any of the faff. Here’s how to get rid of it all:

headerCallbackRemoveHeaderFooter <- c( "function(thead, data, start, end, display){", " $('th', thead).css('display', 'none');", "}" )

datatable( my_pic_villagers, options = list( dom = "t", ordering = FALSE, paging = FALSE, searching = FALSE, headerCallback = JS(headerCallbackRemoveHeaderFooter) ), selection = 'none', callback = JS( "$('table.dataTable.no-footer').css('border-bottom', 'none');" ), class = 'row-border', escape = FALSE, rownames = FALSE, filter = "none", width = 500 )

Compared to event-based programming, reactivity allows Shiny to do the minimum amount of work when input(s) change, and allows humans to more easily reason about complex MVC logic.

An attractive default look based on Bootstrap which can also be easily customized with the bslib package or avoided entirely with more direct R bindings to HTML/CSS/JavaScript.

Tools for improving and monitoring performance, including native support for async programming, caching, load testing, and more.

observeEvent() is used to perform an action in response to an event eventReactive() is used to create a calculated value that only updates in response to an event

observe() and reactive() functions automatically trigger on whatever they access observeEvent() and eventReactive() functions need to be explicitly told what triggers them

And where does isolate fit in all this? isolate() is used to stop a reaction observeEvent() is used to perform an action in response to an event eventReactive() is used to create a calculated value that only updates in response to an event

Using Crosstalk to Add User-Interactivity

The goal is to link the reactable table I created to a plotly chart and provide additional filter options that control both the table and the chart.

An important note: in order to use crosstalk, you must create a shared dataset and call that dataset within both plotly and reactable. Otherwise, your dataset will not communicate and filter with eachother. The code to do this is SharedData$new(dataset).

If you expand the code below, you’ll see that the code to build a table in reactable is quite extensive. I will not go into the details in this post, but do recommend a couple great tutorials that I used to create the interactive table such as this tutorial from Greg Lin, and this from Tom Mock which really helped me understand how to use CSS and Google fonts to enhance the visual appeal of the table (see the “Additional CSS Used for Table” section below for more info).

If you have ever built something in Shiny before, you’ll notice that the crosstalk filters are very similar. You can add a filter to any existing column in the dataset. As you can see in the code below, I used a mixture of filter_checkbox and filter_select depending on how many unique options were available in the column you’re filtering. My rule of thumb is if there are more than five options to choose from it’s probably better to put them into a list in filter_select like I did with the Division filtering as to not take up too much space on the page.

For the layout of the data visualization, I used bscols to place the crosstalk filters side-by-side with the interactive plotly chart. I then placed the reactable table underneath and added a legend to the table using tags from the htmltools package. The final result is shown below. Feel free to click around and the filters and you will notice that both the plot and the table will filter accordingly. Another option is to drag and click on the plot and you will see the table underneath mimic the teams shown.