Statistical Inference via Data Science A ModernDive into R and the Tidyverse (Second Edition)

Another useful tool for viewing linegraphs is the dygraph function in the dygraphs package in combination with the dyRangeSelector function. This allows us to zoom in on a selected range and get an interactive plot for us to work with:

library(dygraphs)
library(nycflights23)
flights_day <- mutate(flights, date = as.Date(time_hour))
flights_summarized <- flights_day |>
  group_by(date) |>
  summarize(median_arr_delay = median(arr_delay, na.rm = TRUE)) |>
  as.data.frame()
rownames(flights_summarized) <- flights_summarized$date
flights_summarized <- select(flights_summarized, -date)
dyRangeSelector(dygraph(flights_summarized))

The syntax here is a little different than what we have covered so far. The dygraph function is expecting for the dates to be given as the rownames of the object. We convert our data to a data frame (since tibbles don’t allow for easy row name manipulation), and then remove the date variable from the flights_summarized data frame since it is accounted for in the rownames. Lastly, we run the dygraph function on the new data frame that only contains the median arrival delay as a column and then provide the ability to have a selector to zoom in on the interactive plot via dyRangeSelector. (Note that this plot will only be interactive in the HTML version of this book.)