代写JSON file作业、代做Python程序语言作业

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Data

For this exam you will be working with a data from the 2018 Formula 1 season. The data was downloaded from ergast.com in the form of a single large JSON file which contains information on the results of all 21 races from the 2018 season. Your repo should contain both the original json file (f1_2018.json) as well as an RDS binary file (f1_2018.rds) which can be read in using

f1 = readRDS(file="data/f1_2018.rds")

The data is structured as a list of lists of lists of lists and so on, it is up to you to look at the data and figure out how it is structured and how best to get at the information you want.


Task 1 - Tidy the data (20 marks)

Starting from the f1 object create a tidy data frame from these data including the following columns:

name - The name of the race (character type)

round - Round of the race (integer type, between 1 and 21)

date - Date of the race (date class)

driver - Name of a driver, including first and last name (character type)

constructor - Name of a driver's constructor, i.e. team (character type)

position - Position (place) driver finished in for the race (integer type, NA if they did not finish for any reason)

points - Number of points the driver earned for the race (integer type)

Print out at least 10 rows of this data frame, clearly showing the format and column types of your answer.


Task 2 - Drivers' Championship (30 marks)

Using the data frame from Task 1, construct a table showing the World Drivers' Championship standings for this F1 season. This table should resemble but not be identical to the results available on Wikipedia. Your data frame should also have 23 columns: Driver name, finishing position for all 21 races, and finally their overall points total for the season. Failure to finish for any reason (did not start, did not finish, disqualified, retired, etc.) should be coded as an NA. Race finishes and points total should all have an integer type. The order of the race columns should follow the chronological order in which the races occured. Finally, your data frame should be sorted by points total, but you do not need to include any additional logic to handle ties.

Print out a nicely formatted version of the complete table in your rendered md document.


Task 3 - Cumulative Constructors (30 marks)

Using the data frame from Task 1 (as a starting point), construct a table that contains the cumulative points earned by each of the 10 teams at the end of each of the 21 races of the 2018 season. For example Mercedes earned 22 points from the Australian Grand Prix, 33 from the Bahrain Grand Prix, and 30 from the China Grand Prix. Therefore the row for Mercedes in this data frame would contain the values 22, 55, 85 for the first three columns, corresponding to these races. Note - there is no need to take into account the exclusion, sale, and rentry of the Force India team, this can be treated as a single team for the purpose of this task.

Your final data frame should have 22 columns: Constructor name and one column for each of the 21 grand prix races. You results should be ordered by the constructors total points at the end of the season.

Print out a nicely formatted version of the complete table in your rendered md document.


Task 4 - Visualization (20 marks)

Design a visualization that shows the performance of both drivers and teams over the course of the 2018 F1 season in terms of the points earned toward the drivers' and constructors' Championship standings. This exercise is meant to be purposefully open ended, come up with a visualization that tells a compelling story about this season and use your write up to justify your design choices. Your write up must include a discussion of what you are trying to convey as well as how your visualization achieves those goals.

You may use any visualization tools and/or packages you would like as long as your results are reproducible (e.g. I should be able to change the source data and that change would be reflected in the visualization).