PCS JS Immersion

AJAX basics

This week we're going to build a Treehouse course recommender system! Everyone here has earned some Treehouse badges (i.e. completed courses), and that data can be downloaded from Treehouse and manipulated with Javascript.

Our goal is to unify all data from all students into one integrated data structure. Then we can calculate the similarity between any two people based on the courses they have each completed. Finally, we can use that measurement to do a form of collaborative filtering: recommending which courses a person might take next, based on what similar people have taken!

Later this week, we'll use jQuery to build a front-end interface for our application. Today we'll focus on the back end, a data-processing module, and also learn the basics of AJAX and how to use it to download treehouse data. But before we work with the Treehouse dataset, which can be huge and confusing, we'll do most of our thinking and code-writing with a "toy" data structure.

Mini-Data: Chores!

Here is a toy data-structure we can use to make sense of the computations we'll need. It represents a set of family members responsible for various household chores.

// people
var mom  = {name:'mom',  jobs:{}},
    dad  = {name:'dad',  jobs:{}},
    billy= {name:'billy',jobs:{}},
    sally= {name:'sally',jobs:{}};

// chores
var wash=   {job:'wash',who:{}},
    dry =   {job:'dry', who:{}},
    mop =   {job:'mop', who:{}},
    cook=   {job:'cook',who:{}};

var people = {

var jobs = {

wash.who = {mom:mom,billy:billy};
dry.who  = {dad:dad,billy:billy,sally:sally};
cook.who = {dad:dad,sally:sally};
mop.who  = {dad:dad,mom:mom};

mom.jobs  = {wash:wash,mop:mop};
dad.jobs  = {dry:dry,cook:cook,mop:mop};
sally.jobs= {dry:dry,cook:cook};
billy.jobs= {wash:wash,dry:dry};

In this implementation, the members of each row or column is an "index" (or "dictionary") object, whose keys are the names of the objects in that row or column, and whose values are the objects with those names.

In all of the challenges below, your code should not contain any reference to particular people or particular jobs. <!--It may use fixed property names like 'job','who','name'.-->


Before coding any components, we'll think in broad terms about what we're trying to accomplish and list some tools we'll need. Then, before writing each tool, we'll practice verbalizing what the solution will require.

Coding Exercises


hasJob(person,job) --> Boolean

Write a function hasJob(personName,jobName) returning true or false. For example:

hasJob('mom','mop') --> true
hasJob('mom','dry') --> false

Now write a variant hasJob(personObj,jobObj) which receives objects instead of strings:

hasJob(mom,mop) --> true
hasJob(mom,dry) --> false

peopleDoing(job) --> array of objects

Write a function peopleDoing(job) which returns an array of people-objects

jobsDoneBy(person) --> array of objects

Write a function jobsDoneBy(person) which returns an array of job-objects

Display functions

maxLength(strings) --> number

Write a function maxLength(strings) which returns the length of the longest string in the array strings

sizeColumns(rowNames, colNames) --> array of ints

Write a function sizeColumns(rowNames, colNames) which returns an array of widths, one per column. The width of the first column should depend on the longest string in rowNames; the width of all other columns depends on the longest string in colNames. There should be one leading column (for all rowNames) plus one column for each of colNames.

For example: var cols = sizeColumns(['wash','dry','cook','mop'],['mom','dad','sally','billy']) should return [4,5,5,5,5].

writeRow(colSizes, strings)

Write a function writeRow(colSizes, strings) which console.logs a single string composed of strings, each padded with space to match the corresponding width in array colSizes.

You may use the functions leftPad(string,totalWidth) and rightPad(string,totalWidth) included here:

function leftPad(str,len) {
    var padding = Array(len+1).join(' ');
    return (padding+str).slice(-len);

function rightPad(str,len) {
    var padding = Array(len+1).join(' ');
    return (str+padding).slice(len);

writeJobsTable(people, jobs)

Write a function writeJobsTable(people,jobs) which will console.log the following series of lines:

       mom   dad   sally billy
wash | X                 X
dry  |       X     X     X
cook |       X     X
mop  | X     X

The functions sizeColumns and writeRow will help you format the table.

Data processing

intersectJobs(nameA,nameB) --> array of strings

Write a function intersectJobs(nameA,nameB) to return an array of names of the jobs shared by the people named nameA and nameB. For example:

intersectJobs('dad','sally') // should return either ['cook','dry'] or ['dry','cook']
intersectJobs('mom','sally') // should return []

similarity(personA,personB) --> number 0..1

Write a function similarity(personA,personB) which receives two person objects and calculates a number from 0 to 1 representing the similarity of their jobs. Use the following measure of similarity: the number of jobs they share as a fraction of the longer job list. Make use of your intersectJobs function.

//person similarity matrix:
    mom dad sal bil
mom 1   .33 0   .50
dad     1   .67 .33
sal         1   .50
bil             1

score(job,person) --> number

Write a function score(job,person) which generates a number representing the "compatibility" of that job and that person. Compatibility is calculated as follows: find all other people (excluding person) who have that job, then sum together the similarity each has to person.

For example, sally is reasonably compatible with mopping because dad does similar jobs to sally but also mops:

score(mop,sally) //--> (0 for mom + .67 for dad)  === .67

But mom is super-compatible with drying, because everyone else does it already!

score(dry,mom) //--> (.33 for dad + 0 for Sally + .50 for Billy) === .83

recommendJobsFor(person) --> array of objects

Write a function recommendJobsFor(person) which returns an array of objects containing possible new jobs for person with compatibility scores for each. The objects in the array should be sorted in descending order of scores, so that the strongest recommendation is first. Omit any jobs which person is already doing or for which they have zero compatibility.

For example, mom could do either of two new jobs, drying or cooking. Recommendations for her would be:

recommendJobsFor(mom) //--> [{job:dry, score:.83},{job:cook, score:.33}]

Intro to AJAX

Before working with the actual Treehouse dataset, we'll explore the basics of ajax with the demo here.

Real-world data: Treehouse Badges!

Now that we've explored the general patterns of the data structure and the basics of AJAX, we're ready for the real thing!

To download treehouse user data, we'll need two files, one HTML and one javascript:

    <script type="text/javascript" src="http://code.jquery.com/jquery-2.1.4.min.js"></script>
    <script type="text/javascript" src="mycode.js"></script>
// mycode.js
var usernames = [ /*list of treehouse names*/ ];

usernames.forEach(function(name) {

function importUser(user) {
    // your code