Using Plotly in R it is possible to generate interaction between different graphs easily with the help of the highlight_key() function, as can be seen in the link and in the example below:
require(plotly)
# load the `txhousing` dataset
data(txhousing, package = "ggplot2")
# declare `city` as the SQL 'query by' column
tx <- highlight_key(txhousing, ~city)
# initiate a plotly object
base <- plot_ly(tx, color = I("black")) %>%
group_by(city)
# create a time series of median house price
time_series <- base %>%
group_by(city) %>%
add_lines(x = ~date, y = ~median)
# remember, `base` is a plotly object, but we can use dplyr verbs to
# manipulate the input data
# (`txhousing` with `city` as a grouping and querying variable)
dot_plot <- base %>%
summarise(miss = sum(is.na(median))) %>%
filter(miss > 0) %>%
add_markers(
x = ~miss,
y = ~forcats::fct_reorder(city, miss),
hoverinfo = "x+y"
) %>%
layout(
xaxis = list(title = "Number of months missing"),
yaxis = list(title = "")
)
subplot(dot_plot, time_series, widths = c(.2, .8), titleX = TRUE) %>%
layout(showlegend = FALSE) %>%
highlight(on = "plotly_selected", dynamic = TRUE, selectize = TRUE)
With highlight_key() it is possible to create a “new” dataframe which Plotly will use to automatically generate interactivity. Is there something similar for Python?
I saw this old stackoverflow post, but perhaps there is something more optimized and simple nowadays.