test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Scala Coding Standards and Practices\n",
    "\n",
    "## Introduction\n",
    "\n",
    "This Scala notebook documents coding standards and best practices that should be adopted by the Home Office RMP team.\n",
    "\n",
    "This document is best viewed as a [Jupyter Notebook](https://jupyter.org/index.html) where you can edit and run the code.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some imports for the Libraries used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[32mimport \u001b[39m\u001b[36m$ivy.$                           \n",
       "\u001b[39m"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import $ivy.`org.vegas-viz::vegas:0.3.9`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naming Standards\n",
    "\n",
    "The [Scala Documentation](https://docs.scala-lang.org/style/naming-conventions.html) has an excellent guide for naming conventions and this should be adhered to.\n",
    "\n",
    "One point worth empahsing is on naming convention for [constants](https://docs.scala-lang.org/style/naming-conventions.html#constants-values-variable-and-methods) with regards to pattern matching. A constant pattern only matches itself however if the constant does not begin with an uppdercase letter then the compiler treats it as a variable pattern, as can be seen in the example below the const **_anotherConst_** is treated as a variable. In fact the normal Scala compiler would not even compile because patterns after a variable pattern can never match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[36mSomeConst\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m7\u001b[39m\n",
       "\u001b[36manotherConst\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m8\u001b[39m\n",
       "defined \u001b[32mfunction\u001b[39m \u001b[36mmatchInt\u001b[39m\n",
       "\u001b[36ms1\u001b[39m: \u001b[32mString\u001b[39m = \u001b[32m\"matched to 5\"\u001b[39m\n",
       "\u001b[36ms2\u001b[39m: \u001b[32mString\u001b[39m = \u001b[32m\"7 matched to 7\"\u001b[39m\n",
       "\u001b[36ms3\u001b[39m: \u001b[32mString\u001b[39m = \u001b[32m\"9 matched to 8\"\u001b[39m"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final val SomeConst = 7\n",
    "final val anotherConst = 8\n",
    "\n",
    "def matchInt(i: Int) = i match {\n",
    "    case 5 => \"matched to 5\"\n",
    "    case SomeConst => s\"$SomeConst matched to 7\"\n",
    "    case anotherConst => s\"$anotherConst matched to 8\"\n",
    "    case _ => \"no match\"\n",
    "}\n",
    "\n",
    "val s1 = matchInt(5)\n",
    "val s2 = matchInt(7)\n",
    "val s3 = matchInt(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Type Inference\n",
    "\n",
    "With Scala type inference you get the benefits of a static type system which feels like it is dynamically typed.\n",
    "\n",
    "### Avoid unnecessary type annotations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[36ml\u001b[39m: \u001b[32mList\u001b[39m[\u001b[32mInt\u001b[39m] = \u001b[33mList\u001b[39m(\u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m, \u001b[32m3\u001b[39m)\n",
       "\u001b[36ml2\u001b[39m: \u001b[32mList\u001b[39m[\u001b[32mInt\u001b[39m] = \u001b[33mList\u001b[39m(\u001b[32m123\u001b[39m)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val l: List[Int] = List[Int](1,2,3)\n",
    "val l2 = List(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use curried functions to help the inferencer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cmd9.sc:2: missing parameter type for expanded function ((x$1) => x$1.$greater$eq(2))\n",
      "val res9_1 = exists1(List(1,2,3), _ >= 2)\n",
      "                                  ^"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "Compilation Failed"
     ]
    }
   ],
   "source": [
    "def exists1[T](l: List[T], f: T => Boolean) = l.exists(f)\n",
    "exists1(List(1,2,3), _ >= 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defined \u001b[32mfunction\u001b[39m \u001b[36mexists1\u001b[39m\n",
       "\u001b[36mres10_1\u001b[39m: \u001b[32mBoolean\u001b[39m = \u001b[32mtrue\u001b[39m\n",
       "defined \u001b[32mfunction\u001b[39m \u001b[36mexists2\u001b[39m\n",
       "\u001b[36mres10_3\u001b[39m: \u001b[32mBoolean\u001b[39m = \u001b[32mtrue\u001b[39m"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def exists1[T](l: List[T], f: T => Boolean) = l.exists(f)\n",
    "exists1(List(1,2,3), (i: Int) => i >= 2)\n",
    "\n",
    "def exists2[T](l: List[T])(f: T => Boolean) = l.exists(f)\n",
    "exists2(List(1,2,3))(_ >= 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotate methods with the return type if it is not apparent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Don't use return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Don't use null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avoid throwing exceptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Futures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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