Seattle Bike Counts
NA
Notable topics: NA
Recorded on: 2019-04-04
Timestamps by: Alex Cookson
Screencast
Timestamps
Using summarise_all / summarise_at function to aggregate multiple variables at the same time
Using magnitude instead of absolute numbers to see trends in time of day
Dividing time into categories (four categories for times of day, e.g., morning commute, night) using between function
Looking for systematically missing data (which would bias the results of the analysis)
Summarising using a filter in the arguments based on whether the time window is during a commute time
Combining day of week and hour using functions in the lubridate package and as.difftime function (but then he uses facetting as an easier method)
Normalizing day of week data to percent of weekly traffic
Starting analysis of directions of travel by time of day (commute vs. reverse-commute)
Filtering out weekend days using wday function from lubridate package
Using spread function to create new variable of ratio of bike counts at different commute times
Visualizing ratio of bike counts by time of day
Visualizing ratio by hour instead of time of day
Ordering crossing in graph by when the average trip happened using mean of hour weighted by bike count
Quick and dirty filter when creating a new variable within a mutate function