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[CS209A-24Fall] Assignment 1 (100 points)
This summer, we've enjoyed the Olympic Games Paris 2024. Many of us are still reliving the exciting moments
of the summer Olympics, and many of us may be interested in the event of past Olympics and the past
performance of our favorite athletes.
In this assignment, we provide a large dataset of Olympic games from 1896 to 2022. The dataset contains
athlete bio and athlete-event data, including both team and individual sports, from all Winter/Summer
Olympics (1896-2022). The dataset contains 6 csv, with the following relation:
The data should be self-explanatory. Please check the column name and the above diagram carefully to
understand the data.
Analysis of the Olympic Historical Dataset
In this assignment, you will design an OlympicsAnalyzer class, which could read the dataset and perform
various useful analyses. You are encouraged to use the Java features we recently introduced, such as
Collections, Lambda, and Streams, whenever possible.
The OlympicsAnalyzer class has one constructor that reads a dataset file from a given path.
public OlympicsAnalyzer(String datasetPath)
This class also has 5 other methods that perform data analyses. You'll implement these methods in the
OlympicsAnalyzer class. Method details are described below.
1. Top 10 Performant Female Athletes in Individual Sport
public Map<String, Integer> topPerformantFemale()A1.md 2024-09-29
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This method returns a <name, count> map of size 10, where:
the key is the name of the female athlete.
the value is the total gold medals acquired by that female athlete in individual sport (i.e., not team
sport).
The map should be sorted by the descending order of count (i.e., from most to the least). If two athletes
have the same gold medal count, then they should be sorted by the alphabetical order of the name.
2. BMI by Sports
public Map<String, Float> bmiBySports()
This method returns a <sport, BMI> map, where:
the key is the sport name (e.g., Athletics, Boxing, Diving, etc.)
the value is the average BMI of the athletes of that sport. BMI is computed as weight/(height x
height) where the unit of height is the meter. The average BMI should be rounded to one decimal
place.
You should consider all athletes with valid height and weight in our data. The athlete should also have
participated in any Olympic events so that we know the sport type of that athlete.
The map should be sorted by the descending order of average BMI (i.e., from highest to lowest). If two
sports have the same average BMI, then they should be sorted by the alphabetical order of the sport name.
3. Top 10 Least Appeared Summer Sports
public Map<String, Set<Integer>> leastAppearedSport()
This method returns a <sport, set of years> map with a size of 10, where:
the key is the sport name (e.g., Athletics, Boxing, Diving, etc.)
the value is set of summer Olympics years in which the sport appears.
The map should be sorted by the ascending order of the size of the set (i.e., from least appeared to most
appeared). If two sports have the same occurrences, then they should be sorted by the alphabetical order of
the sport name.
4. Top 10 Countries by Medals in Winter Olympics since the year 2000
public Map<String, Integer> winterMedalsByCountry()
This method returns a <country, count> map with a size of 10, where:
the key is the country code.A1.md 2024-09-29
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the value is the sum of all medals, including gold, silver and bronze, acquired by that country in all
Winter Olympics since the year 2000 (including 2000).
The map should be sorted by the descending order of count (i.e., from most to the least). If two countries
have the same medal count, then they should be sorted by the alphabetical order of the country code.
5. Top 10 Countries with Young Athletes in 2020 Summer Olympic
public Map<String, Integer> topCountryWithYoungAthletes()
This method returns a <country, age> map with a size of 10, where:
the key is the country name (not country code).
the value is the average age of the athletes of that country that participate in 2020 Summer Olympic.
For simplicity, you may ignore the exact month and day and only use the year of birth when computing
the age. For example, an athlete born in Dec 31 2000 can be considered as of age 20 in 2020. The
average age should be rounding to the nearest integer.
Athletes with no born information should be ignored.
The map should be sorted by the ascending order of average age (i.e., from youngest to oldest). If two
countries have the same average age, then they should be sorted by the alphabetical order of the country
name.
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