Data Trivia: POJO and Advanced Array Methods Practice

In this practice, you will practice using many of the concepts you've learned so far, including:

• working with a variety of data types
• destructuring data
• accessing keys and values, and manipulating objects
• using advanced array methods to work with data

All of the problems in this practice focus on a large data set related to world climate. The data set is in the climate-data.js file.

The data set is structured as an array, including 105 objects that take up over 9500 lines of code. Each object represents a summary of average monthly climate data for a single city. The example below shows the data structure for each object:

// climate-data.js file
const climateData = [   // array of 105 objects, each representing a city
  {
    "id": 1,
    "city": "Amsterdam",
    "country": "Netherlands",
    "monthlyAvg": [     // nested array with average monthly data for that city
      {                     // climateData[0].monthlyAvg[0] = January data
        "high": 7,
        "low": 3,
        "dryDays": 19,
        "snowDays": 4,
        "rainfall": 68
      },
      {                     // climateData[0].monthlyAvg[1] = February data
        "high": 6,
        "low": 3,
        "dryDays": 13,
        "snowDays": 2,
        "rainfall": 47
      },
      {                     // climateData[0].monthlyAvg[2] = March data
        "high": 10,
        "low": 6,
        "dryDays": 16,
        "snowDays": 1,
        "rainfall": 65
      },
      {                     // climateData[0].monthlyAvg[3] = April data
        "high": 11,
        "low": 7,
        "dryDays": 12,
        "snowDays": 0,
        "rainfall": 52
      },
      {                     // climateData[0].monthlyAvg[4] = May data
        "high": 16,
        "low": 11,
        "dryDays": 15,
        "snowDays": 0,
        "rainfall": 59
      },
      {                     // climateData[0].monthlyAvg[5] = June data
        "high": 17,
        "low": 11,
        "dryDays": 14,
        "snowDays": 0,
        "rainfall": 70
      },
      {                     // climateData[0].monthlyAvg[6] = July data
        "high": 20,
        "low": 12,
        "dryDays": 14,
        "snowDays": 0,
        "rainfall": 74
      },
      {                     // climateData[0].monthlyAvg[7] = August data
        "high": 20,
        "low": 12,
        "dryDays": 15,
        "snowDays": 0,
        "rainfall": 69
      },
      {                     // climateData[0].monthlyAvg[8] = September data
        "high": 17,
        "low": 10,
        "dryDays": 14,
        "snowDays": 0,
        "rainfall": 64
      },
      {                     // climateData[0].monthlyAvg[9] = October data
        "high": 14,
        "low": 9,
        "dryDays": 16,
        "snowDays": 0,
        "rainfall": 70
      },
      {                    // climateData[0].monthlyAvg[10] = November data
        "high": 9,
        "low": 6,
        "dryDays": 20,
        "snowDays": 1,
        "rainfall": 82
      },
      {                     // climateData[0].monthlyAvg[11] = December data
        "high": 7,
        "low": 1,
        "dryDays": 19,
        "snowDays": 1,
        "rainfall": 85
      }
    ]
  },
  // ...followed by 104 other objects with data for additional cities
];

Some notes about the data:

• high' and low temperatures are the monthly average temperature, measured in degrees Celsius (c).
• rainfall is the average total rainfall for the entire month, measured in millimeters (mm).
• Each monthlyAvg array includes an object for each month, but they are not labeled by month names. To access data for a specific month, use the month's number (1 - 12) and subtract 1 to get the index in the array. For example:
  • monthlyAvg[0] will access January data
  • monthlyAvg[11] will access December data

You will need to use many of the POJO methods and advanced array methods that you have already learned to complete each task.

Phase 0: Setup

To get started, use the following commands:

1. Clone the starter repository
2. cd into the project directory
3. npm install to install dependencies

The npm test command will run all the tests.

To test only one of the test files at a time, you can run the command, npm test test/<test file name> (e.g. npm test test/01-clean-climate-data-spec.js), where test is the name of the test folder. You must run this command one directory above the test directory.

You may notice the package.json, package-lock.json files and node_modules directory. You do not need to edit any of those contents. Those files are used to package the project and create the test cases.

Phase 1: Clean the Climate Data (Basic to Intermediate)

In phase 1, you will complete a few tasks that are common anytime you start exploring a new large data set. You need to know what data is included (or not included), and whether there are any errors in the data. If any errors are found, you need to correct them. This process is also known as "cleaning" the data.

Start in the problems/01-clean-climate-data.js file. Write out functions according to the directions. The first four functions will serve as queries to pull out specific information from the large data set. The last function requires you to correct a mistake in a piece of data.

Complete each function in order, because you may want to use some of your earlier functions as helper functions in later problems.

Run npm test test/01-clean-climate-data-spec.js to run all the test specs for this phase. Run the tests frequently, because the error messages on your failed tests will help you debug your code.

When all of the tests pass, you can move on to Phase 2.

Phase 2: Climate Trivia! (Advanced)

In this phase, you will answer some trivia questions about world climate in the following files:

• Most Snow Days: problems/02-most-snow-days.js

  • Run tests: npm test test/02-most-snow-days-spec.js

• Least Rainfall: problems/03-least-rainfall.js

  • Run tests: npm test test/03-least-rainfall-spec.js

• Greatest Temperature Difference: problems/04-temperature-range.js

  • Run tests: npm test test/04-temperature-range-spec.js

To answer each question, write a function (or a set of related functions) that will return the specific data from the data set. It is important to use the exact function name and arguments that are specified in the directions, otherwise the tests will not run properly. However, feel free to write multiple functions and call helper functions to help you solve each problem.

Only move on to the Bonus phase after you have successfully solved all Phase 2 problems and passed all of the test specs.

Phase 3: Choose Your Own Adventure! (Bonus)

In this phase, let your curiosity guide you to further explore the data!

First, come up with your own question that could be answered with the data set in climate-data.js.

For example, imagine you are planning a vacation. You might ask the following example questions to help you choose your destination:

• If you enjoy skiing: Which city/cities have the most snow in December? In March?
• If you want to avoid rain: Which city and month has the highest number of dry days?

What other types of questions could be answered by the data?

Next, write a function (or set of functions) that query the data set and return the answer to your question.

There are no test specs to guide you for this phase. Instead, use a console.log() to wrap your function call, and run the file using the command node problems/05-choose-your-own-adventure.js.

Congratulations!

Congratulations on completing this practice!

As you continue your programming journey, you will find that many projects require the skills you have practiced here:

• Working with large data sets, often in the form of arrays of objects, or arrays nested within an object
• Using advanced array methods and POJO methods to access and manipulate data