Open jfarland opened 2 years ago
When plotting two different mark types on the same plot, the y-axis aren't guaranteed to be the same scale, even by setting the y_min / y_max consistently. One way to do this would be to introduce another parameter to control major tick interval.
Here's some example code:
q.page['timeseries'] = ui.plot_card(box = ui.box('timeseries', height = '1000px'), title = 'My Title', data = ts_plot_data, plot = ui.plot([ ui.mark(type='path', x_scale='time', x='=date', y='=picks', color='=label', y_min=0, y_max = np.max(plot_data["upper_bound"]), color_range="#3399FF #FF9966", y_title="Picks", y_nice=True), ui.mark(type='area', x_scale='time', x='=date', y0='=lower_bound', y='=upper_bound', y_min=0, y_max = np.max(plot_data["upper_bound"]), y_nice = True)]) )
And the result:
in ggplot2, this is controlled explicitly with sec_axis. Ideally, if both marks are plotting the same quantity (or at least in the same units as each other), a secondary axis may not necessary. Here's another example using ggplot: https://www.r-graph-gallery.com/line-chart-dual-Y-axis-ggplot2.html
ggplot2
related to #617
mtanco
commented
2 years ago
When plotting two different mark types on the same plot, the y-axis aren't guaranteed to be the same scale, even by setting the y_min / y_max consistently. One way to do this would be to introduce another parameter to control major tick interval.
Here's some example code:
And the result:
in
ggplot2, this is controlled explicitly with sec_axis. Ideally, if both marks are plotting the same quantity (or at least in the same units as each other), a secondary axis may not necessary. Here's another example using ggplot: https://www.r-graph-gallery.com/line-chart-dual-Y-axis-ggplot2.html