![]() ![]() Using cellWidths allows you to change the size of each individual plot. But splitLayout can also be used with more than two plots. You can also play with the numbers the figure below shows c("60%", "40%")ĮDIT: It is true that Wise new answer's gives some flexibility. Server.R shinyServer(function(input, output) SplitLayout(cellWidths = c("50%", "50%"), plotOutput("plotgraph1"), plotOutput("plotgraph2")) tibble_3.1.6 aws.signature_0.6.0 units_0.8 - 0 ellipsis_0.3.Using Wise example, you can also use splitLayout(cellWidths = c("50%", "50%").to show two plots side by side.ĬheckboxInput("do2", "Make 2 plots", value = T) bslib_0.3.1 shiny_1.6.0 loaded via a namespace ( and not attached) : stats graphics grDevices utils datasets methods base other attached packages : en_US.UTF - 8 / en_US.UTF - 8 / en_US.UTF - 8 / C / en_US.UTF - 8 / en_US.UTF - 8 attached base packages : Running under : macOS Monterey 12.3 Matrix products : default LAPACK : / Library / Frameworks / R.framework / Versions / 4.1 - arm64 / Resources / lib / libRlapack.dylib locale : Platform : aarch64 - apple -darwin20 ( 64 - bit) Using the same data for each map, I used htmlwidgets::saveWidget() to create self-contained versions of the maps, like this: Just wanted to check if anyone had an ideas about how I might be able to publish the river lines as vector?Ĭurrently reworking a map I made that's going to have at least ~50k points in total, with the potential for more layers. ![]() I suspect that the issue is that while I'm trying to plot ~600k river features (less that the 1 million points which the readme suggests should be ok) - that these are slower because there are effectively many more than a million points joined up to form the lines. Currently we rasterize() the vector riverlines in order to get the load times and performance we need to make it useable.įrom the README.md it is apparent that leafglĪllows rendering of a large amount of features on a leaflet map. ![]() Note that the data used in this example is just the river lines data and doesn't include the river water quality attributes.įor context, we regularly publish environmental indicators using Shiny.io (see for example River water quality: clarity and turbidity). I have created a repository in Github to provide an example of the type of map that I'm trying to publish and for people to see the slow load times I'm getting (takes me approximately 2 minutes:20 seconds to load the shiny app. I would really appreciate your thoughts and insights into whether it is possible to speed up the time it takes to load the maps. However, once I try mapping all river segments using leafgl it becomes too slow to load the map. From my testing, I've discovered that leafgl seems to work really fast and nicely for a subset river segments in New Zealand. I've been testing whether we can use leafgl to report on river water quality data for New Zealand.
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