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In this lesson, we'll look at how we can use Ramda's
constructN functions to take methods of an object and turn them into reusable utility functions that are curried and accept their object as the last argument. We'll convert a dot-chained string of jQuery methods and create a composed function that can be applied to multiple elements.
Sorting an array of objects by multiple fields can be tricky. Even worse, the code can be tough to follow and making changes in the future is complicated by conditional logic in the sorting function. In this lesson we'll see how to simplify this using Ramda's
sortWith along with
descending. We'll sort an array of objects by multiple fields and the code will be readable and dead simple to change.
Promise chains can be a powerful way to handle a series of transformations to the results of an async call. In some cases, additional promises are required along the way. In cases where there are no new promises, function composition can reduce the number of dot chained
thens you need. In this lesson, we'll look at how to take a promise chain, and reduce it down with function composition.
Naming things is hard and arguments in generic utility functions are no exception. Making functions "tacit" or "point free" removes the need for the extra parameter names and can make your code cleaner and more succinct. In this lesson, we'll create a normal, "pointed" function and then use ramda's
useWith function to refactor our way to point-free bliss.
Functional composition is a great way to write declarative code that reads well. But sometimes, you need to get insight into what's happening at different steps in your composition. This is where
tap comes in. With tap, we can run code that generates side-effects, like logging to the console, and our data will continue to be passed along, untouched.
In this lesson, we'll grab arrays of values from other arrays, resulting in a nested array. From there, we'll look at multiple ways to flatten the array structure using composition with
unnest and then refactoring to use
flatMap. Finally, we'll add Ramda's
uniq function to remove duplicate values.
Take the first step in learning how to create native desktop applications with Electron. We'll set up a package.json, create an npm start script to launch our app in development, and create a browser window and load a local HTML file into it.
An important concept to understand about Electron is it's multi-process architecture. The main process creates web pages by creating
BrowserWindow instances, manages application lifecycle events, and more. Each
BrowserWindow instance runs the web page in its own renderer process.
Handling your logic with composable functions makes your code declarative, leading to code that's easy to read and easy to test. Breaking that up to wrap some risky function in a
try/catch block introduces imperative code and makes it harder to maintain that declarative approach. With Ramda's
tryCatch function, you can handle errors right in the middle of a composition and leave your code clean and functional. We'll also see how you can use
propOr to avoid common "cannot find X of undefined" errors.
We often want to check if an array includes a specific item. It's been common to do this with the
Array.prototype.indexOf method, but now we have a simpler way: We can use the
Array.prototype.includes method, which is available starting with ES2016.
Promises are rapidly overtaking callbacks in popularity. In this lesson, I show you how to rewrite a callback using ES6 Promises. After getting a handle on the basics, we'll convert an error-first style callback to use the Resolve and Reject handlers built into Promises. If you aren't familiar with Promises, this lesson can be a great starting point by learning how to duplicate the behavior of a callback with ES6 Promises.
In this lesson we'll take some existing code and refactor it using some functions from the Ramda library, most notably,
converge. When we're done, we'll have taken a function with a couple of local variables and parameter references and converted it into more streamlined "point-free" or "tacit" functions.
When doing comparisons inside of functions, you end of relying heavily on the argument passed into the function. Ramda's
converge allows you to do comparisons in a Point-Free style allowing you more flexibility with composing and constructing functions. This lesson walks through refactoring a function to Point-Free style using Ramda's Converge.
You can really unlock the power of ramda (and functional programming in general) when you combine functions. Counting words in a string may seem like a relatively difficult task, but ramda makes it easy by providing a
countBy function. This lesson walks through using the
countBy to count words in a string.
fromPairs functions, along with the crucial
One of the more powerful functions for creating streams in MostJS is the
In this lesson, we use
As a bonus, we see what it looks like to use
Function.prototype.bind for a quick and dirty way to partially apply arguments to a function.
We don't always control the data we need in our applications, and that means we often find ourselves massaging and transforming our data. In this lesson, we'll learn how to transform objects in a declarative way using ramda's evolve function.
Public Class Fields allow you to add instance properties to the class definition with the assignment operator (
=). In this lesson, we'll look at their use case for simplifying event callbacks and state initialization with a React component.
In this lesson we'll take an array of objects and map it to a new array where each object is a subset of the original. We'll look at multiple ways to accomplish this, refactoring our code into a simple and easy to read function using Ramda's
get "trap" that will allow us to get items starting from the end of the array with ease.
Most of the functions offered by the ramda library are curried by default. Functions you've created or that you've pulled in from another library may not be curried. Ramda's
curryN functions allow you to take a non-curried function and use it as a curried functions. In the case where you have a manually curried function and you want to just call it like a normal function, you can use
uncurryN to get back a function that accepts all of the arguments at once.
Most provides many means for creating streams, the simplest of which is the
of function. In this lesson, we demonstrate the use of
of to lift a single value into a stream. We also show off the
just alias of
of, as well as, a common method for currying a function.
Learn how to use Object.assign to combine multiple objects together. This pattern is helpful when writing libraries that have a set of default options where you want to allow a user to override specific options. You'll also learn how to use a third party library to recursively merge nested objects together.
In Ember.js, functions that are executed through user interaction with your application are called actions, which can be defined at the route, controller, or component level. In this tutorial, we look at a simple example of defining actions on the controller and hooking it up to our template in order to update properties on the controller.
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.
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.
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.
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
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.
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.
ImmutableJS reduce functions can be nested to get data out of complex data structures. In this example, we will create an Immutable List from an Immutable Map that is nested inside of an Immutable List that is itself nested inside of an Immutable Map.
We can create the most basic components of our GraphQL Schema using GraphQL's Object Types. These types allow us to group related fields together under a specific type, such as a Video or a User, and then allows us to fetch these types when we query our schema. In this video, we'll learn how to write GraphQL Object Types in GraphQL's Schema language, as well as how to create resolvers for them, and ultimately how to query them.
In order to support mutations in Relay, there is a requirement that the GraphQL Server exposes mutation fields in a standardized way. This standard includes a way for mutations to accept and emit an identifier string, allowing Relay to track mutations and responses. In this video, we’ll learn how to use a helper available to us through
graphql-relay to create Mutation fields that accept
In order to properly traverse through collections, Relay-compliant servers require a mechanism to page through collections available in a GraphQL Schema. In this video, we’ll create a
Connection type from an existing GraphQL List Type and learn how to access edge information from each collection.
The GraphQL Relay Specification requires that a GraphQL Schema has some kind of mechanism for re-fetching an object. For typical Relay-compliant servers, this is going to be the
Node Interface. In this video, we’ll add in the
Node interface to a GraphQL Schema by using the helpers available in the
As we start building out more complex GraphQL schemas, certain fields start to repeat across different types. This is a perfect use-case for the Interface Type made available to us through GraphQL’s Type System. In this video, we’ll go over how to create an Interface Type and how to add it to an existing type in a GraphQL Schema.