Scheme vs Ruby

Scheme syntax is extremely regular and easy to pick up. A formal specification of the syntax fits onto just a few pages; it can be introduced informally in a paragraph or two. Students are not distracted by remembering how to write if statements or loops or even operator precedence because every syntactic follows the same pattern.

Ultimately, everything looks something like this:

(func a b c)

This includes not only user-defined functions but even control flow:

(if cond then-clause else-clause)

or even primitive operations like define and set:

(define foo 10)
(set! foo 11)

This means that nothing really has special syntactic treatment in the language. There are essentially no weird edge-cases to memorize, and different concepts are given a more equal weight in the language. (Unlike Algol-like languages which tend to given undue weight to loops and assignment statements, for example.)

Scheme has far less built into the language itself, helping students see that things like OOP are not magical: they are just patterns for organizing code. Everything in Scheme is built up from a very small set of primitives which compose in a natural and intuitive fashion.

Having a language that does not accord many things special status helps keep students open minded. This will help students later go between different languages and paradigms from procedural to object-oriented to functional.

Scheme teaches the important, fundamental ideas immediately without the distraction of unnecessary syntax or language features.

GNU/Linux, OS X, and Windows versions available.

There is a set of very strong textbooks introducing CS and programming using Scheme. These books are available for free online.

The most famous example--and one of the most famous CS books full stop--is Structure and Interpretation of Computer Programs usually known as SICP. This book introduces fundamental ideas in computer science and covers an incredible amount of material quickly and clearly without requiring any prior knowledge.

However, some people find SICP a bit challenging as a first text. Happily, there are other more introductory texts as well. Simply Scheme is a book designed to be read before SICP, easing beginners into the language as well as CS and programming in general. How to Design Programs is another text used in introductory college courses.

The quotation functionality of Lisp allow for extremely powerful, yet syntactically straightforward metaprogramming via macros. This is more powerful than the C preprocessor while being less involved than something like Template Haskell or F# quotations.

Using macros to properly decompose a problem domain teaches new developers good habits, improving composibility and reliability when tackling large programs. Scheme metaprogramming also serves as a gentle introduction to domain specific languages.

Unlike most languages, Scheme actually accords both functional programming and imperative programming roughly equal status. Many other languages like Python and Java are staunchly imperative while SML and Haskell are primarily functional; Scheme is a nice middle ground.

Additionally, since Scheme syntax is extremely flexible, it can easily be re-purposed for teaching non-deterministic and logic programming. There is no need to learn a new language like Prolog when the same ideas can easily be expressed with Scheme syntax.

This gives students a good perspective on different ways to think about and organize programs, which makes it much easier to move forward to other languages and technologies.

Ruby is one of the most popular languages for developing web sites. As a result, there's an abundant amount of documentation, sample code, and libraries available for learning the language and getting your project up and running. The most popular features are just 'gem install' away. Additionally, it is easier to find Ruby jobs because of this.

Ruby has a very clean syntax that makes code easier to both read and write than more traditional Object Oriented languages, such as Java. For beginning programmers, this means the focus is on the meaning of the program, where it should be, rather than trying to figure out the meaning of obscure characters.

presidents = ["Ford", "Carter", "Reagan", "Bush1", "Clinton", "Bush2"]
for ss in 0...presidents.length
    print ss, ": ", presidents[presidents.length - ss - 1], "\n";
end

Ruby has a large ecosystem of tools and libraries for just about every use. Such as ORMs (Active Record, DatabMapper), Web Application Frameworks(Rails, Sinatra, Volt), Virtualization Orchestration(docker-api, drelict), CLI tools(Thor, Commando), GUI Frameworks(Shoes, FXRuby) and the list goes on. If you can think of it, there is probably a gem for that ( and if not you can create your own and share with the community).

The Ruby language is equipped with the necessary features to learn the essence of algorithms.

In online playground environments like ideone.com, measures have been taken to prevent beginners from going astray by restricting the use of external libraries such as Python's NumPy and SymPy.
Even in such constrained Ruby execution environments, the required features for learning algorithms are fully available.

Many of the algorithms that should be learned are documented in the book "Hello Ruby: Adventures in Coding." For example, the cake serving problem in the book leads to topological sorting, which is a graph theory concept useful in project management for creating Gantt charts.

To evaluate the effectiveness of algorithms with a level of complexity comparable to topological sorting,
it is necessary to be able to solve mathematical computation problems up to the high school level easily.
As shown in the table below, using only Ruby's standard library, it is possible to solve high school-level math problems effortlessly.
However, other programming languages may not be able to perform such computations in online playground environments.

To experience the superior performance of algorithms, it is important to challenge oneself by reimplementing good algorithms. Ruby's standard library includes implementations of excellent algorithms. For instance, the algorithm for solving linear equations, which has been widely known since the era of Fortran, is used within the code of SolvingLinearEquations through the "/" operator. Reimplementing code from Ruby's standard library serves as an excellent learning resource with high reusability and efficiency.

The SolvingLinearEquations function mentioned above demonstrates the benefits of duck typing and forced type conversion between objects of different types in arithmetic operations. While Rust also has features like duck typing, the implementation of "forced type conversion" is still far from being realized.

# Title: "(1) Cake Serving Procedure Problem"
require 'tsort'

class Hash
  include TSort
  alias tsort_each_node each_key
  def tsort_each_child(node, &block)
    fetch(node).each(&block)
  end
end

puts 'Tasks'
task_names = {
  'A' => 'Arrange the plates.',
  'B' => 'Set the spoons.',
  'C' => 'Place the birthday cake on the table.',
  'D' => 'Spread the tablecloth.'
}
p task_names

puts 'Preceding Tasks'
preceding_tasks = {
  'A' => ['D'],
  'B' => ['C', 'A'],
  'C' => ['A', 'D'],
  'D' => []
}

steps = preceding_tasks.strongly_connected_components
puts 'The appropriate steps are as follows:'
steps.each do |task_candidates|
  p task_candidates.map { |task| [task, task_names[task]] }
end

p "#(2) Equation Solving Rule"
def SolvingLinearEquations(y, a, b)
   x = (y - b) / a
end

p "(2-1) Real Solution", SolvingLinearEquations(1.0, 5, 0.5)
# => 0.1

p "(2-2) Fraction Solution", SolvingLinearEquations(Rational(1, 1), Rational(5, 1), Rational(1, 2))
# =>  (1/10)

p "(2-3) Imaginary Solution", SolvingLinearEquations(1 + 1i, 5, 1.0 / (2 + 2i))
# => (0.15+0.25i)

p "(2-4) Complex & Fraction Solution", SolvingLinearEquations(Rational(1 + 1i, 1), Rational(5, 1), Rational(1, 2 + 2i))
# => ((3/20)+(1/4)*i)

p "(2-5) Matrix Solution with Large Integers", 
   SolvingLinearEquations(Matrix[[Rational(1234567890123456789890, 1), Rational(0, 1)]],
                                                Matrix[[Rational(1234567890123456789890, 1), Rational(1234567890123456789890 * 2, 1)],
                                                             [Rational(1234567890123456789890, 1), Rational(1234567890123456789890 * 3, 1)]],
                                                Matrix[[Rational(1234567890, 1), Rational(123456789, 1)]] )
# => Matrix[[(3703703670366790122789/1234567890123456789890), (-2469135780244567900789/1234567890123456789890)]]

p "(2-7) Matrix Solution with Large Integers, Complex Numbers, and Fractions", 
     SolvingLinearEquations(Matrix[[Rational(1234567890123456789890, 1i), Rational(0, 1)]],
                                                 Matrix[[Rational(1234567890123456789890, 1), Rational(1234567890123456789890 * 2, 1i)],
                                                               [Rational(1234567890123456789890, 1), Rational(1234567890123456789890 * 3, 1i)]],
                                                Matrix[[1234567890, 0 + 1i]] )
# => Matrix[[((-3703703671/1234567890123456789890)-(3/1)*i), ((2469135781/1234567890123456789890)+(2/1)*i)]]

Lays out an easy to follow and opinionated MVC pattern that teaches best practices through necessity.

It's the fore-runner and trend setter for TDD.

Object oriented programming is one of the most important concepts in programming.

Meta-programming provides efficiency and freedom.

No indentation increase development efficiency.