theScore "the Rush" Interview Challenge

At theScore, we are always looking for intelligent, resourceful, full-stack developers to join our growing team. To help us evaluate new talent, we have created this take-home interview question. This question should take you no more than a few hours.

All candidates must complete this before the possibility of an in-person interview. During the in-person interview, your submitted project will be used as the base for further extensions.

Why a take-home challenge?

In-person coding interviews can be stressful and can hide some people's full potential. A take-home gives you a chance work in a less stressful environment and showcase your talent.

We want you to be at your best and most comfortable.

A bit about our tech stack

As outlined in our job description, you will come across technologies which include a server-side web framework (like Elixir/Phoenix, Ruby on Rails or a modern Javascript framework) and a front-end Javascript framework (like ReactJS)

Challenge Background

We have sets of records representing football players' rushing statistics. All records have the following attributes:

Player (Player's name)
Team (Player's team abbreviation)
Pos (Player's postion)
Att/G (Rushing Attempts Per Game Average)
Att (Rushing Attempts)
Yds (Total Rushing Yards)
Avg (Rushing Average Yards Per Attempt)
Yds/G (Rushing Yards Per Game)
TD (Total Rushing Touchdowns)
Lng (Longest Rush -- a T represents a touchdown occurred)
1st (Rushing First Downs)
1st% (Rushing First Down Percentage)
20+ (Rushing 20+ Yards Each)
40+ (Rushing 40+ Yards Each)
FUM (Rushing Fumbles)

In this repo is a sample data file rushing.json.

Challenge Requirements

Create a web app. This must be able to do the following steps
1. Create a webpage which displays a table with the contents of rushing.json
2. The user should be able to sort the players by Total Rushing Yards, Longest Rush and Total Rushing Touchdowns
3. The user should be able to filter by the player's name
4. The user should be able to download the sorted data as a CSV, as well as a filtered subset
The system should be able to potentially support larger sets of data on the order of 10k records.
Update the section Installation and running this solution in the README file explaining how to run your code

Submitting a solution

Download this repo
Complete the problem outlined in the Requirements section
In your personal public GitHub repo, create a new public repo with this implementation
Provide this link to your contact at theScore

We will evaluate you on your ability to solve the problem defined in the requirements section as well as your choice of frameworks, and general coding style.

Help

If you have any questions regarding requirements, do not hesitate to email your contact at theScore for clarification.

Installation and running this solution

Solution explanation

First of all, I am not a Frontend developer so please don't get crazy with my UI :)

I implemented a REST API with only one endpoint and leveraging the client to render the data (in this case my awsome UI, but in a real environment it can be a React app for example). The endpoint will reply JSON or CSV depending to what the client put in the Accept header.

I also added Pagination so we can scale the amount of data without affecting the bandwidth.

I added some tests and checks in order to have 100% coverage and making Credo/Dialyzer happy. You can check it running:

$ mix check

Installation

This solution requires having installed (or dockerized) a Postgres up and running in the standard port and localhost, if not we will need to change the config file.

In order to fetch the dependencies

$ mix deps.get

If you want to run tests and see the 100% coverage (optional)

$ mix check

In order to create/migrate and fill the DB we will run:

$ mix ecto.setup

once the DB is migrated we can run the service with this command:

$ iex -S mix phx.server

Now the service is running at http://localhost:4000

ferigis/nfl_rushing