Adding a slider
A slider filter allows you to easily update the data values plotted by restricting a numeric variable to a specific range. In this exercise, your task is to include two slider filters for the scatterplot of the housing price index against homeownership in 2017: one for each axis.
plotly and crosstalk have already been loaded for you, and the data are stored in us2017.
Note: You may need to scroll down on or pop out the HTML Viewer to see the sliders.
This exercise is part of the course
Intermediate Interactive Data Visualization with plotly in R
Exercise instructions
- Place two slider filters below the scatterplot stored in
p17. The first slider should correspond to the housing price index (house_price), and the second slider should correspond to the percentage of home ownership (home_owners). - Add slider labels that match the axis titles in
p17,"HPI"and"Home ownership (%)".
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
shared_us <- SharedData$new(us2017)
p17 <- shared_us %>%
plot_ly(x = ~home_owners, y = ~house_price,
color = ~region, height = 400) %>%
add_markers() %>%
layout(xaxis = list(title = "Home ownership (%)"),
yaxis = list(title = "HPI"))
# add a slider filter for each axis below the scatterplot
___(
___(p17,
___(id = "price", label = ___, sharedData = ___, column = ___),
___(id = "owners", label = ___, sharedData = ___, column = ___)
)
)