Shiny app choose color ui how to#
Shiny app choose color ui zip#

Instead of selecting a zone from a long list of 129 zones, this tool offers an intuitive way to select a zone directly from the map where users simultaneously get the zone information of location, size and boundary.įreight Analysis Framework Zones (Image by Author) Users can explore the freight movement data between any origin and destination zone pair of their choices. In other words, there are 129×129 possible combinations of the origin-destination zone pairs. region and every zone can be either an origin zone or a destination zone. There are in total 129 FAF zones in the continental U.S. The geographic information associated with the FAF data is defined as zones. FAF is a public data source that provides estimates and forecasts of freight movements among states and major metropolitan areas by all modes of transportation. We choose to use the Freight Analysis Framework (FAF) data for this demonstration.

Shiny app choose color ui how to#

With this application, we demonstrate how to create a free and open source (FOSS) solution using R and R Shiny, that allows users to interact with the geospatial data, and quickly find what they want to see using an interactive map.

Shiny app choose color ui zip#

Geospatial data combines the location information such as address, ZIP code, or zone with the corresponding attribute information associated with the location.įor most people, it is really hard to explore geospatial data without using a map.

Geospatial data (or spatial data, also known as Geo Data) refers to any data that is indicated by or related to a geographic location ( REF). Scale_x_date(breaks = seq(as.Date(' UTC'), as.The important stories that numbers can tell often involve locations. Geom_tile(aes(fill = n), colour = 'white') + Ggplot(dataInput(), aes(localdate, localhour2)) +

'Mobile' = local %>% filter(device = 'mobile')) 'Desktop' = local %>% filter(device = 'desktop'), We have shown how to build a simple dashboard like this before. Inspecting the peak hours by device types, it is apparent that the majority accessed the online resources via desktop devices, while very few accessed the resources via mobile devices. Clearly, when the due dates approached, there were more students accessing online resources, ususally lasting from evening to midnight. The tick marks on the X axis indicate the due dates. head(local, 15) # localdate localhour n device We have added the device data to the data of local hours. Here we add the feature that enables people to interactively view the heatmap by device types. We showed how to make a static heatmap previously. Labs(title = 'Time Spent Online Learning', x = 'Date', y = 'Total Minutes per Day')įinally, we would like to create a heatmap to reveal the peak hours of users accessing the online resources. Scale_colour_manual(values = color, name = 'Resource') + Geom_vline(xintercept = due, alpha = 0.6, size = 0.65, colour = 'grey55') + Scale_x_datetime(breaks = seq(as.POSIXct(' UTC'), as.POSIXct(' UTC'), '7 days'), date_labels = '%b %d') + Ggplot(aes(date, sum, group = resource, colour = resource)) + library(shiny)įilter(date >= input$trajectory & date % On the ui side, same as what we have done in the section above, we need to set the sliderInput() for display range with arguments min and max. head(resource_day_sum) # date resource sum To explore how users accessed the platform within a date range, we customize the slider so that we can extract data within a period of time.īelow is the sample data, same as what we used to create the animated line graph. Not only can we input single values in the slider, but also ranges.

Geom_point(aes(x = longitude, y = latitude, size = n), library(shiny)īorders('world', colour = 'gray90', fill = 'gray85') + value sets the default value to show in the slider bar when the app is first loaded.

We set the step to be 1 so that the increment between the values will be one day and in the animation the map will reflect the change daily. With the animate argument and the interval option, we set the pace of the iterations in the animation. To create something as shown above, on the ui side, for the slider, we need to specify the display range for the sliderInput(): min sets the minimum value to use in the slider bar, and max sets the maximum value both should be date objects in our case. Here we want to (1) view the map on a selected date, and (2) run the animation sequences once we hit the play button, as shown below. head(map) # date latitude longitude city n We use the same data as in the earlier post, where we have created an animated graph with gganimate.