Learn Cypress, Natural and Twit

Imagine the following.

You join a new project and after getting access to the repo you notice that there's no tests.

You decide to ask the team whether they are in different repo or something and unfortunately the answer is:

>We don't have any tests, we didn't have the time.

😐

In this collection we're going to learn how to add e2e tests with cypress.io to an existing React app in order to do two things:

- 🌟 Ensure that our app currently works as intended
- 🌟 Allow us to add features to it with a technique called "Cypress Driven Development" 

In less than 20 minutes we're going to go through:

- ⚡️Adding cypress.io setup to an existing project
- ⚡️Testing search functionality with `data-cy` properties
- ⚡️Using "Cypress Driven Development" to add a new feature to an app
- ⚡️Use Cypress UI to debug an API response
- ⚡️How to use `cy.only` and `cy.skip` in order for us to build our e2e test suite faster

and much more!

Fast, easy and reliable testing for anything that runs in a browser.

cypress

Add e2e tests with cypress to a React application

One of the most important — but most ignored — practices for web developers is performing end-to-end testing on applications before they go live into production. You know: making sure they work like they’re supposed to. But testing can be tedious, and definitely not fun.

In this course, Brett Cassette will show you how you can test all layers of your application stack, simultaneously, with Cypress. When you use Cypress, it’s like having a robot that uses your app the way a real user would. Cypress reports if things work the way you designed them to — and if they don’t — every step of the way. And it’s fun.

After completing this course, you’ll be ready to apply the same E2E testing principles to your own applications. Stop leaning on your QA department (if it exists) to stress-test your application, and ship your app knowing it’s ready. 


Test Production Ready Apps with Cypress

The tools available for unit testing have come a long way. While testing may never be “easy”, the available tools have certainly moved testing closer to the simple end of the spectrum. Full end to end testing hasn’t quite kept the same pace and as a result many applications are testing manually and often by users in production. Cypress provides a testing environment that makes end-to-end testing fast and reliable with a simple API that makes creating automated unit tests for your web applications a no-brainer.


End to End testing with Cypress

In this course we’ll learn how to create Twitter bots. We’ll first learn how to interface with the Twitter API using Twit.js, which allows us to post statuses, search tweets, and interact with followers. We’ll create several example bots that show the diversity of possibilities, using powerful tools such as RiTa.js, Google’s Cloud Vision API, and Tracery. We’ll learn how to tweet data from Google Spreadsheets, and to compose and tweet a song for an audio bot. We’ll also learn how to host our bot logic on Heroku and Docker. 

Twitter API Client for node

Supports both the REST and Streaming API.

twit

Create your own twitter bots

In this course we’ll work through Natural’s API for natural language processing in JavaScript. We’ll look at how to process text: learning how to break up language strings, find the word roots, work with inflectors, find sequences of words, and tag parts of speech. We’ll learn how to find important stats about a body of text: how to compare strings, how to classify text with machine learning, how to use the tf-idf tool to find relevant words. We’ll look at some of the extra tools Natural gives us, including the dictionary/thesaurus of WordNet, a phonetics comparer that lets us see if two words sound the same, and a spellcheck feature. We’ll also look at tries and digraphs, two data structures that help us better analyze bodies of text.

Natural is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural

Natural Language Processing in JavaScript with Natural

Your app can react in various way on different network conditions. Cypress has a great toolset for setting up those conditions, which enable you to test your app thoroughly. That way you can ship your app with confidence.

Cypress’ .intercept() command is one of the most fun command to use. And it’s also one of the most useful.

Especially when you are done with all your happy paths and want to increase your test coverage with some edge cases.

These edge cases can mean different data, headers or server behavior. You can modify all of these, dynamically or with a static fixture prepared beforehand

Test Network Edge Cases with cy.intercept() Command in Cypress

Web accessibility (also referred to as a11y) is the design and creation of websites that can be used by everyone. Accessibility support is necessary to allow assistive technology to interpret web pages.

React fully supports building accessible websites, often by using standard HTML techniques.

Erin Doyle is an expert in creating accessible React applications and has developed a course that will give you a concrete process for auditing react apps for accessibility. In this course, you will go through different tools and libraries to audit components in a React app.

Auditing React Apps for Accessibility 

We’ll get a real-time stream of tweets, and learn how to filter a stream by keyword, location, and user.

Use the Twitter Stream API with Twit.js

We will learn about “stemming,” the process of finding the root of words, often in order to group words by a common base root. We will look at the Porter and Lancaster Stemmers, briefly touch on Natural’s support for Russian and Spanish stemmers, and introduce the function to stem and tokenize at the same time.

Find the roots of words using stemming in Natural

Inflectors are the modifiers of a word that indicate grammatical categories. While Natural’s coverage of inflectors is not comprehensive, we will show how Natural can pluralize/singularize nouns and count numbers. 

Pluralizing nouns and counting numbers with inflectors in Natural

N-grams are sequences of words, where the 'n' stands for the number of words in the sequence. In this lesson, we will see how to find bigrams (2-grams), trigrams (3-grams), and any other length n-gram in a body of text. 

Find sequences of words (n-grams) using Natural

We will learn how to compare how similar two strings are to each other, examining three algorithms: Jaro-Winkler, Levenshtein, and Dice’s Coefficient.  

You should note that none of these algorithms are inherently better than the others. Instead, it's important to choose the one that best fits your text data. 

Compare similarity of strings through string distance in Natural

In this lesson, we will learn how to train a Naive Bayes classifier or a Logistic Regression classifier - basic machine learning algorithms - in order to classify text into categories.

Classify text into categories with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well. 

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization

Classify JSON text data with machine learning in Natural

Tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text, or to search by prefix. 

Search more efficiently with tries using Natural

In this lesson, we’ll see how to use Natural’s probabilistic spell-checker, which uses the trie data structure. 

Include spell-check in text projects using Natural

Tf-idf, or term frequency-inverse document frequency, is a statistic that indicates how important a word is to the entire document. This lesson will explain term frequency and inverse document frequency, and show how we can use tf-idf to identify the most relevant words in a body of text. 

Identify the most important words in a document using tf-idf in Natural

In this lesson, we’ll take a look at Natural’s phonetics feature. We’ll learn how to check whether two words sound alike, looking at both the SoundEx and Metaphone algorithms.

Check if words sound alike using Natural

This lesson introduces WordNet, which is an important resource in natural language processing. With WordNet, we can look up a word’s definition, or find its synonyms.

Find a word’s definition using WordNet in Natural

An important component of many natural language processing projects is being able to identify the grammar of a piece of text. We’ll learn how to do that with Natural’s parts of speech (POS) tagger.  

There are many tags, and it's worth looking them up online (search "POS tag symbols") to become familiar with them all. 

The setup of the tagger may seem a little strange, but it allows you to replace the lexicon or the rules with a different lexicon or rule set of your choice. 

Tag parts of speech using Natural

By this point we've seen that classification can take a long time, and with more data, it would take even longer. Luckily, Natural provides support to save your classifiers. In this lesson, we will learn how to save a classifier and load it into a new project in order to classify new data.