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now that you can use the bokeh python package to create data visualizations, try the following challenge to apply your skills to a real-world case. anycompany airlines has collected sample data for flight departures. they asked you to analyze the data to determine if there is an association between departure times and flight delays. you can access the sample data from amazon s3. develop a visualization that will describe this association.

Sagot :

Display any relationship between the total delay and the flight's distance (between the two airports of origin and destination) graphically. Make sense of the display.

smoothScatter(winterDelays$DISTANCE, winterDelays$ARR_DELAY, nbin = 200, ylim = c(-30,300), xlab = "Flight Distance (Miles)," ylab = "Arrival Delay (Minutes)," and main = "13" Connection Between Disturbance and Delay

There does not appear to be a significant relationship between flight distance and delay time in this smoothed scatterplot. Outliers in the data are any major delays. We "zoom in" on this scatter plot to better see any relationship between the two variables because of the high variance in arrival delays.

1. What is the total number of flights in the data set?

dim(winterDelays) This data set contains 1,961,489 flights.

2. Which airline flies the most?

[1] Southwest Airlines (WN) has the most flights in this data set, with 354,963 of them. sort(table(winterDelays$UNIQUE_CARRIER), decreasing = TRUE)

3. Create a two-way frequency table by calculating the number of flights for each airline carrier and originating airport. Only the rows and columns for the 20 airports and ten airline carriers with the most flights should be displayed. Display all of the calculations?

Yes, if mean(winterDelays[winterDelays$MONTH == 12,'ARR_DELAY'], na.rm = TRUE) or mean(winterDelays[winterDelays$MONTH == 11,'ARR_DELAY'). November's average arrival time was -0.1247 minutes, while December's average was 6.893 minutes.

To learn more about flight distance here

https://brainly.com/question/13334218

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