Functional Reactive Programming (FRP) is a programming paradigm that combines functional programming and reactive programming. It aims to simplify the development of reactive systems by providing a declarative and composable way to handle changes in data over time.

The problem with traditional approaches to handling changes in data is that they often involve mutable state and imperative programming techniques. This can lead to code that is difficult to reason about, prone to bugs, and hard to maintain. FRP offers a solution to these issues by providing a higher-level abstraction for dealing with changes in data.

One of the main reasons for the emergence of FRP in Scala is the paper "Deprecating the Observer Pattern" by Odersky et al. This paper introduces the library Scala.React, which was developed to demonstrate the concepts of FRP in Scala. The paper explains the motivation behind FRP and how it can be applied to solve common problems in reactive programming.

The paper provides insights into the benefits of using FRP, such as improved code modularity, better separation of concerns, and easier testing. It also discusses the challenges and limitations of FRP, such as the complexity of the programming model and the learning curve for developers.

In addition to the paper, there are other resources available for learning about FRP in Scala. The Stack Overflow question "What's the status of Scala.React?" provides an overview of the current state of the Scala.React library and its development. The answer to the question also includes a link to the GitHub repository for Scala.React, where the latest version of the library can be found.

Another Stack Overflow answer by Sobral provides an example of how to use Scala's delimited continuations to invert the control of state. This goes beyond the inversion-of-control of caller/callee that the paper and Scala.React focus on. The example demonstrates the flexibility and power of Scala's programming model for handling changes in data.

In conclusion, the emergence of FRP in Scala is driven by the need for a more declarative and composable way to handle changes in data. The paper "Deprecating the Observer Pattern" and the Scala.React library provide a foundation for understanding and implementing FRP in Scala. Additional resources, such as the Stack Overflow question and answer, further contribute to the knowledge and adoption of FRP in Scala.

Functional Reactive Programming (FRP) is a programming paradigm that allows for the declarative and reactive programming of user interfaces and other time-varying values. In Scala, there is a library called reactive-core that provides FRP functionalities. However, the biggest pain when using reactive-core is the language-related issue - the syntax for applicatives in Scala could be improved.

An applicative is a type class that provides a way to apply functions to arguments within a context. In the case of reactive-core, the Signal class is an applicative. The pure function is used to create a constant signal, while the ap function performs a zip operation followed by a map operation. For example, if we have two signals x and y and we want a signal representing their sum, we can use the expression x zip y map { case (x, y) => x + y }, which is similar to the scalaz syntax (x |@| y)(_ + _).

To address the issue with the syntax for applicatives in Scala, the user suggested adding applicative syntax to reactive-core. They opened an issue on GitHub and provided a commit that adds the desired syntax. This improvement would make it easier and more pleasant to use reactive-core in Scala applications.

Overall, the issue with the syntax for applicatives in Scala was identified when using the reactive-core library for FRP. To solve this issue, the user suggested adding applicative syntax to the library, which would improve the code readability and usability. This improvement would make it easier for developers to work with FRP in Scala applications.

“Functional Reactive Programming in Scala”（以下简称FRP）是Netflix开发的面向JVM的函数式响应式编程库。

Rx（Reactive Extensions）在FRP的意义上并不是“函数式响应式编程”，尽管你可以说它在一般意义上既是函数式的又是响应式的。

FRP的出现原因是为了解决传统的事件驱动编程模型的问题。在传统的事件驱动编程中，我们需要手动地处理事件的注册、派发和响应，这会导致代码变得复杂且难以维护。而FRP通过引入一种新的编程范式，将事件和数据流抽象为数据流，使得我们可以通过声明式的方式来描述事件和数据之间的关系，从而简化了代码的编写和维护过程。

FRP的解决方法是使用响应式扩展库RxJava。RxJava是一个基于观察者模式和迭代器模式的库，它提供了丰富的操作符和函数，用于处理数据流的转换、过滤和合并等操作。通过使用RxJava，我们可以方便地构建响应式的数据流和事件处理链，从而实现函数式响应式编程的目标。

下面是使用RxJava进行函数式响应式编程的示例代码：

import rx.lang.scala.Observable
val stream: Observable[Int] = Observable.just(1, 2, 3, 4, 5)
val filteredStream: Observable[Int] = stream.filter(_ % 2 == 0)
val transformedStream: Observable[String] = filteredStream.map(_.toString)
transformedStream.subscribe(println(_))

在上面的代码中，我们首先创建了一个包含整数的数据流`stream`，然后使用`filter`操作符过滤出其中的偶数，接着使用`map`操作符将偶数转换为字符串类型，最后通过`subscribe`方法订阅数据流并输出结果。

通过使用RxJava库，我们可以以简洁、清晰的方式编写函数式响应式的代码，将复杂的事件处理逻辑变得简单易懂。同时，RxJava还提供了丰富的并发和错误处理机制，使得我们可以更好地处理异步和异常情况。

总结起来，FRP的出现是为了解决传统的事件驱动编程模型的问题，使用RxJava作为解决方法，通过引入函数式响应式编程的思想和操作符，简化了事件处理的代码编写和维护过程。通过使用RxJava，我们可以以简洁、清晰的方式构建函数式响应式的数据流和事件处理链，提高代码的可读性和可维护性。