City Organizer

You are tasked with converting a csv into a markdown file. Reading in US Census data which contains the population for a 1000 cities, grouping the data by Region and State.

Rules

Not allowed to use for loops.
Avoid using the foreach method

In order to get familiar with using Array methods, the only rule is that you are not allowed to use traditional for loops. The more you use Array methods like .reduce and .filter instead of .forEach the better.

Utils

You have a couple of utility functions provided for you

read( `csvFileName` )

Will read in and parse the given file. Returning something that looks like this

[{ 
    Region:"Northeast",
    State:"New York",
    City:"New York, NY",
    Population:"8622698",
}, ... ]

write( `markdownFileName` , `regions` )

Creates the markdown file at the specified location, expecting regions to be in a format like this:

[{
  name: 'Northeast',
  states:[{
    name: 'New York',
    cities:[{
      name: 'New York, NY',
      population: 8622698
    }]
  }]
}]

prettyprint( `{ Object }` )

To help with debugging, will console.log an object after passing it through beautify

Getting Started

Open the terminal
Navigate to your Documents folder then run the following to clone the repository

git clone https://github.com/byuitechops/city-organizer.git
cd city-organizer
npm i

Open the created folder in vscode
Install the Markdown Preview plugin in vscode to see how your output will be rendered
Write some awesome code
Run your code by running node main.js in the console
To run the test cases install jest globaly with

npm install -g jest

Challenge #1 Create

Testing: run jest create.test.js

Outcome: Be comfortable with using array methods to manipulate data into different formats

In main.js add the nessesary code to transform the flat csv data into the nested format that the write function is expecting. If everything goes good the write function should create a file that looks like this:

# Midwest
## Illinois
- Chicago, IL ( `pop. 2,716,450` )

# Mountain
## Arizona
- Phoenix, AZ ( `pop. 1,626,078` )

# Northeast
## New York
- New York, NY ( `pop. 8,622,698` )
## Pennsylvania
- Philadelphia, PA ( `pop. 1,580,863` )

# Pacific
## California
- San Jose, CA ( `pop. 1,035,317` )
- San Diego, CA ( `pop. 1,419,516` )
- Los Angeles, CA ( `pop. 3,999,759` )

# South
## Texas
- Dallas, TX ( `pop. 1,341,075` )
- San Antonio, TX ( `pop. 1,511,946` )
- Houston, TX ( `pop. 2,312,717` )

Challenge #2 Sorting

Testing: run jest sort.test.js

Outcome: Become comfortable with using the sort function with multiple different data formats and targets

Complete the following sorting challenges

Sorting States Alphabetically

Either add code or change your existing code so that states under a REGION are sorted alphabetically

# Pacific
## California
- Oxnard, CA ( `pop. 210,037` )
## Oregon
- Portland, OR ( `pop. 647,805` )
## Washington
- Tacoma, WA ( `pop. 213,418` )
- Spokane, WA ( `pop. 217,108` )
- Seattle, WA ( `pop. 724,745` )

# Mountain
## Colorado
- Aurora, CO ( `pop. 366,623` )
- Colorado Springs, CO ( `pop. 464,474` )
- Denver, CO ( `pop. 704,621` )
## Idaho
- Boise City, ID ( `pop. 226,570` )
## Nevada
- North Las Vegas, NV ( `pop. 242,975` )
- Reno, NV ( `pop. 248,853` )
- Henderson, NV ( `pop. 302,539` )
- Las Vegas, NV ( `pop. 641,676` )

Sorting Cities by Population

Either add code or change your existing code so that cities under a STATE are sorted by their population

# Pacific
## California
- Los Angeles, CA ( `pop. 3,999,759` )
- San Diego, CA ( `pop. 1,419,516` )
- San Jose, CA ( `pop. 1,035,317` )
- San Francisco, CA ( `pop. 884,363` )
- Fresno, CA ( `pop. 527,438` )
- Sacramento, CA ( `pop. 501,901` )
- Long Beach, CA ( `pop. 469,450` )
- Oakland, CA ( `pop. 425,195` )
- Bakersfield, CA ( `pop. 380,874` )
- Anaheim, CA ( `pop. 352,497` )
- Santa Ana, CA ( `pop. 334,136` )
- Riverside, CA ( `pop. 327,728` )
- Stockton, CA ( `pop. 310,496` )
- Irvine, CA ( `pop. 277,453` )
- Chula Vista, CA ( `pop. 270,471` )
- Fremont, CA ( `pop. 234,962` )
- San Bernardino, CA ( `pop. 216,995` )
- Modesto, CA ( `pop. 214,221` )
- Fontana, CA ( `pop. 211,815` )
- Santa Clarita, CA ( `pop. 210,888` )
- Oxnard, CA ( `pop. 210,037` )

Sort Regions West to East

Either add code or change your existing code so that the regions are in order of West to East. So they follow as Pacific, Mountain, Midwest, South, Northeast,

# Pacific
## California
- Los Angeles, CA ( `pop. 3,999,759` )
- San Diego, CA ( `pop. 1,419,516` )
- San Jose, CA ( `pop. 1,035,317` )

# Mountain
## Arizona
- Phoenix, AZ ( `pop. 1,626,078` )

# Midwest
## Illinois
- Chicago, IL ( `pop. 2,716,450` )

# South
## Texas
- Houston, TX ( `pop. 2,312,717` )
- San Antonio, TX ( `pop. 1,511,946` )
- Dallas, TX ( `pop. 1,341,075` )

# Northeast
## New York
- New York, NY ( `pop. 8,622,698` )
## Pennsylvania
- Philadelphia, PA ( `pop. 1,580,863` )

Challenge #3 Unique City Names

Testing: run jest unique.test.js

Outcome: Become better at noticing your assumptions

Use the nonunique.csv csv instead of cities.csv. The only thing that has changed is that city names no longer contain the state name acronym. So there are now 5 cities with the name Springfield all in different states. Just making sure that you are not relying on the state names to be unique.

Challenge #4 Writing

Testing: run jest write.test.js

Outcome: Become more comfortable with researching documentation

Research how to use fs.writeFileSync, fs.readFileSync and d3.csvParse to read, write, and parse the CSV. You are not allowed to use the provided utils functions in this challenge. You will need to read the csv and write to the file on your own, with the fs and d3-dsv libraries.

Output

Using the sorts from the previous challenge. Write out just the first 10 rows to the output.md file. That file will be checked that it matches this output exactly.

# Pacific
## California
- Los Angeles, CA `pop. 3999759`
- San Diego, CA `pop. 1419516`
- San Jose, CA `pop. 1035317`

# Mountain
## Arizona
- Phoenix, AZ `pop. 1626078`

# Midwest
## Illinois
- Chicago, IL `pop. 2716450`

# South
## Texas
- Houston, TX `pop. 2312717`
- San Antonio, TX `pop. 1511946`
- Dallas, TX `pop. 1341075`

# Northeast
## New York
- New York, NY `pop. 8622698`
## Pennsylvania
- Philadelphia, PA `pop. 1580863`

Challenge #5 Summing

Testing: run jest sum.test.js

Outcome: Become better at designing code that has taken into account things that might need to be accessable in the future.

Add the population property to Region and States, which contains the sum of the populations beneath them.

[{
  name: 'Northeast',
  population: 8622698
  states:[{
    name: 'New York',
    population: 8622698
    cities:[{
      name: 'New York, NY',
      population: 8622698
    }]
  }]
}]

Challenge #6 Optimization (just for fun)

Testing: run jest optimize.test.js

Outcome: Become more aware of the costs that come from a specific implementation

Run your code against all.csv to see how long it takes. It contains almost 8,500 cities across america.

matthewwyndham/city-organizer