Hi dear reader!
Hope you’re having a great June so far! 🙂 Welcome to this week’s dose of weekly JS!
For this week, we have:
- Underscore
- Lodash
- Lazy
- Apriori
- Grunt
As a quick warmup for this series of Javascript adventure, I took on something more familiar for Day 1 which is Underscore.js. A JS library which we also saw in a previous blog post early this year:Â Underscore.js: Your Helpful Library.
Underscore.js provides a lot of functional programming helpers. It allows for easy manipulation of collections, arrays, objects, and even functions.
For a quick application of Underscore.JS, we have a simple Text Analyzer that allows word frequency tracking and word highlighting with HTML, CSS, Underscore.js, and jQuery.
For this application, we mostly used uniq, map, and reduce (which is very helpful!!!) functions, as well as Underscore templates.
For Day 2, we have Lodash, a JS library that is very similar to Underscore (in fact Lodash started as a fork of Underscore but was mostly rewritten underneath after).
Lodash presents a whole lot of functional programming helpers as well.
To quickly use Lodash, we have a very simple application that allows the input of students’ name and eventually groups them into the specified input. This app uses HTML, CSS, jQuery, together with Lodash.
To make this application a little different from our Underscore app, this app focused on DOM manipulation (i.e. wrapInTD) in addition to text and data processing.
Day 3. Lazy.js
Woot, 2 days down, we’re on Day 3, the game changer!
Day 3 has become a game changer for this JS series as this is the first time I used Node.js to quickly apply the JS library for the day. Starting out with Node.js, luckily, was not too difficult as npm install commands were already a little bit familiar from projects before.
Lazy.js presents almost the same functionalities as Underscore but as its official site says, it’s lazier. So what does it mean to be lazier?
Recalling from Underscore, if we want to take 5 last names that start with ‘Victoria’, we do:
var results = _.chain(people)
.pluck('lastName')
.filter(function(name) { return name.startsWith('Victoria'); })
.take(5)
.value();
But taking off from procedural code, the following seems to be lazier … and also faster. Why? – ‘Cause we already stop once we complete the length:5 requirement.
var results = [];
for (var i = 0; i < people.length; ++i) {
var lastName = people[i].lastName;
if (lastName.startsWith('Victoria')) {
results.push(value);
if (results.length === 5) {
break;
}
}
}
And the way Lazy.js evaluates the following chain of code is along the lines of the above procedural code.
var result = Lazy(people)
.pluck('lastName')
.filter(function(name) { return name.startsWith('Smith'); })
.take(5);
Inspired by one blog post I found on the web by Adam N England, I started the quick application with a section on benchmarking. From which, I also met for the first time another npm plugin, bench. bench is a JS utility that allows side by side comparison of functions’ performance.
This application was a great learning experience as it also served as a playground for Node.js (i.e. requiring npm packages, own files, using exports, etc).
Moving on from benchmarking, in this app, we were also able to harness some of the capabilities of Lazy.js which includes indefinite sequence generation and asynchronous iteration.
Indefinite Sequence Generation Sample:
var summation = Lazy.generate(function() {
var sum = 0, start = 1;
return function() {
sum += start;
start += 1
return sum;
};
}());
console.log(summation.length());
// undefined
summation.take(10).toArray();
// [1,3,6,10,15,21,28,36,45,55]
Day 4. Apriori.js
Yay, Day 4! My graduate class for this semester just ended and one of our final topics was on Unsupervised Mining methodologies which included Market Basket Analysis.
For work, we also have been looking into the Apriori algorithm for Rails as we already have it for R. Wanting to investigate the Apriori algorithm more, I tried looking for a JS plugin that implements it. And luckily, I found apriori.js!
Documentation was quite limited so I learned to read the repository’s tests and also its main code to get to know the available functions.
For the quick app, we have a Market Basket Analyzer that outputs the associations found with their respective support and confidence values. Input includes the minimum support and minimum confidence but are optional.
Day 5. Grunt
Woot! And finally for Day 5, we have Grunt! Being really new to Grunt, I started Day 5 by reading the book Automating with Grunt. Grunt is almost similar with Rake, a Ruby tool that we use to define and run tasks too.
One of the quick applications I used Grunt with is a weather fetcher on openweathermap. This an example of multitask, a task that can have multiple outputs.
Running grunt tasks is easy, for example, to run the weather app we just need to do:
$ grunt weather
In one of the quick apps too, I was able to discover and incorporate a grunt plugin, grunt-available-tasks which makes viewing available tasks easier and more colorful (literally!)
So there! Yay, that’s it for Week 1 of this Days of JS project! ❤
Stay tuneeeed for more! 🙂
Thanks, reader, and have a great week ahead!!!
