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ML-For-Beginners/4-Classification/1-Introduction/solution/R/lesson_10.html

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<style type="text/css">
.tocify {
width: 20%;
max-height: 90%;
overflow: auto;
margin-left: 2%;
position: fixed;
border: 1px solid #ccc;
border-radius: 6px;
}
.tocify ul, .tocify li {
list-style: none;
margin: 0;
padding: 0;
border: none;
line-height: 30px;
}
.tocify-header {
text-indent: 10px;
}
.tocify-subheader {
text-indent: 20px;
display: none;
}
.tocify-subheader li {
font-size: 12px;
}
.tocify-subheader .tocify-subheader {
text-indent: 30px;
}
.tocify-subheader .tocify-subheader .tocify-subheader {
text-indent: 40px;
}
.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader {
text-indent: 50px;
}
.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader {
text-indent: 60px;
}
.tocify .tocify-item > a, .tocify .nav-list .nav-header {
margin: 0px;
}
.tocify .tocify-item a, .tocify .list-group-item {
padding: 5px;
}
.tocify .nav-pills > li {
float: none;
}
</style>
<script>/* jquery Tocify - v1.9.1 - 2013-10-22
* http://www.gregfranko.com/jquery.tocify.js/
* Copyright (c) 2013 Greg Franko; Licensed MIT */
// Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic. I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE.
(function(tocify) {
// ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
"use strict";
// Calls the second IIFE and locally passes in the global jQuery, window, and document objects
tocify(window.jQuery, window, document);
}
// Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable. The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set. All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript.
(function($, window, document, undefined) {
// ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
"use strict";
var tocClassName = "tocify",
tocClass = "." + tocClassName,
tocFocusClassName = "tocify-focus",
tocHoverClassName = "tocify-hover",
hideTocClassName = "tocify-hide",
hideTocClass = "." + hideTocClassName,
headerClassName = "tocify-header",
headerClass = "." + headerClassName,
subheaderClassName = "tocify-subheader",
subheaderClass = "." + subheaderClassName,
itemClassName = "tocify-item",
itemClass = "." + itemClassName,
extendPageClassName = "tocify-extend-page",
extendPageClass = "." + extendPageClassName;
// Calling the jQueryUI Widget Factory Method
$.widget("toc.tocify", {
//Plugin version
version: "1.9.1",
// These options will be used as defaults
options: {
// **context**: Accepts String: Any jQuery selector
// The container element that holds all of the elements used to generate the table of contents
context: "body",
// **ignoreSelector**: Accepts String: Any jQuery selector
// A selector to any element that would be matched by selectors that you wish to be ignored
ignoreSelector: null,
// **selectors**: Accepts an Array of Strings: Any jQuery selectors
// The element's used to generate the table of contents. The order is very important since it will determine the table of content's nesting structure
selectors: "h1, h2, h3",
// **showAndHide**: Accepts a boolean: true or false
// Used to determine if elements should be shown and hidden
showAndHide: true,
// **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown"
// Used to display any of the table of contents nested items
showEffect: "slideDown",
// **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
// The time duration of the show animation
showEffectSpeed: "medium",
// **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp"
// Used to hide any of the table of contents nested items
hideEffect: "slideUp",
// **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
// The time duration of the hide animation
hideEffectSpeed: "medium",
// **smoothScroll**: Accepts a boolean: true or false
// Determines if a jQuery animation should be used to scroll to specific table of contents items on the page
smoothScroll: true,
// **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
// The time duration of the smoothScroll animation
smoothScrollSpeed: "medium",
// **scrollTo**: Accepts Number (pixels)
// The amount of space between the top of page and the selected table of contents item after the page has been scrolled
scrollTo: 0,
// **showAndHideOnScroll**: Accepts a boolean: true or false
// Determines if table of contents nested items should be shown and hidden while scrolling
showAndHideOnScroll: true,
// **highlightOnScroll**: Accepts a boolean: true or false
// Determines if table of contents nested items should be highlighted (set to a different color) while scrolling
highlightOnScroll: true,
// **highlightOffset**: Accepts a number
// The offset distance in pixels to trigger the next active table of contents item
highlightOffset: 40,
// **theme**: Accepts a string: "bootstrap", "jqueryui", or "none"
// Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents
theme: "bootstrap",
// **extendPage**: Accepts a boolean: true or false
// If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased
extendPage: true,
// **extendPageOffset**: Accepts a number: pixels
// How close to the bottom of the page a user must scroll before the page is extended
extendPageOffset: 100,
// **history**: Accepts a boolean: true or false
// Adds a hash to the page url to maintain history
history: true,
// **scrollHistory**: Accepts a boolean: true or false
// Adds a hash to the page url, to maintain history, when scrolling to a TOC item
scrollHistory: false,
// **hashGenerator**: How the hash value (the anchor segment of the URL, following the
// # character) will be generated.
//
// "compact" (default) - #CompressesEverythingTogether
// "pretty" - #looks-like-a-nice-url-and-is-easily-readable
// function(text, element){} - Your own hash generation function that accepts the text as an
// argument, and returns the hash value.
hashGenerator: "compact",
// **highlightDefault**: Accepts a boolean: true or false
// Set's the first TOC item as active if no other TOC item is active.
highlightDefault: true
},
// _Create
// -------
// Constructs the plugin. Only called once.
_create: function() {
var self = this;
self.extendPageScroll = true;
// Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings)
self.items = [];
// Generates the HTML for the dynamic table of contents
self._generateToc();
// Adds CSS classes to the newly generated table of contents HTML
self._addCSSClasses();
self.webkit = (function() {
for (var prop in window) {
if (prop) {
if (prop.toLowerCase().indexOf("webkit") !== -1) {
return true;
}
}
}
return false;
}());
// Adds jQuery event handlers to the newly generated table of contents
self._setEventHandlers();
// Binding to the Window load event to make sure the correct scrollTop is calculated
$(window).on("load", function() {
// Sets the active TOC item
self._setActiveElement(true);
// Once all animations on the page are complete, this callback function will be called
$("html, body").promise().done(function() {
setTimeout(function() {
self.extendPageScroll = false;
}, 0);
});
});
},
// _generateToc
// ------------
// Generates the HTML for the dynamic table of contents
_generateToc: function() {
// _Local variables_
// Stores the plugin context in the self variable
var self = this,
// All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above
firstElem,
// Instantiated variable that will store the top level newly created unordered list DOM element
ul,
ignoreSelector = self.options.ignoreSelector;
// Determine the element to start the toc with
// get all the top level selectors
firstElem = [];
var selectors = this.options.selectors.replace(/ /g, "").split(",");
// find the first set that have at least one non-ignored element
for(var i = 0; i < selectors.length; i++) {
var foundSelectors = $(this.options.context).find(selectors[i]);
for (var s = 0; s < foundSelectors.length; s++) {
if (!$(foundSelectors[s]).is(ignoreSelector)) {
firstElem = foundSelectors;
break;
}
}
if (firstElem.length> 0)
break;
}
if (!firstElem.length) {
self.element.addClass(hideTocClassName);
return;
}
self.element.addClass(tocClassName);
// Loops through each top level selector
firstElem.each(function(index) {
//If the element matches the ignoreSelector then we skip it
if ($(this).is(ignoreSelector)) {
return;
}
// Creates an unordered list HTML element and adds a dynamic ID and standard class name
ul = $("<ul/>", {
"id": headerClassName + index,
"class": headerClassName
}).
// Appends a top level list item HTML element to the previously created HTML header
append(self._nestElements($(this), index));
// Add the created unordered list element to the HTML element calling the plugin
self.element.append(ul);
// Finds all of the HTML tags between the header and subheader elements
$(this).nextUntil(this.nodeName.toLowerCase()).each(function() {
// If there are no nested subheader elemements
if ($(this).find(self.options.selectors).length === 0) {
// Loops through all of the subheader elements
$(this).filter(self.options.selectors).each(function() {
//If the element matches the ignoreSelector then we skip it
if ($(this).is(ignoreSelector)) {
return;
}
self._appendSubheaders.call(this, self, ul);
});
}
// If there are nested subheader elements
else {
// Loops through all of the subheader elements
$(this).find(self.options.selectors).each(function() {
//If the element matches the ignoreSelector then we skip it
if ($(this).is(ignoreSelector)) {
return;
}
self._appendSubheaders.call(this, self, ul);
});
}
});
});
},
_setActiveElement: function(pageload) {
var self = this,
hash = window.location.hash.substring(1),
elem = self.element.find('li[data-unique="' + hash + '"]');
if (hash.length) {
// Removes highlighting from all of the list item's
self.element.find("." + self.focusClass).removeClass(self.focusClass);
// Highlights the current list item that was clicked
elem.addClass(self.focusClass);
// Triggers the click event on the currently focused TOC item
elem.click();
} else {
// Removes highlighting from all of the list item's
self.element.find("." + self.focusClass).removeClass(self.focusClass);
if (!hash.length && pageload && self.options.highlightDefault) {
// Highlights the first TOC item if no other items are highlighted
self.element.find(itemClass).first().addClass(self.focusClass);
}
}
return self;
},
// _nestElements
// -------------
// Helps create the table of contents list by appending nested list items
_nestElements: function(self, index) {
var arr, item, hashValue;
arr = $.grep(this.items, function(item) {
return item === self.text();
});
// If there is already a duplicate TOC item
if (arr.length) {
// Adds the current TOC item text and index (for slight randomization) to the internal array
this.items.push(self.text() + index);
}
// If there not a duplicate TOC item
else {
// Adds the current TOC item text to the internal array
this.items.push(self.text());
}
hashValue = this._generateHashValue(arr, self, index);
// Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
item = $("<li/>", {
// Sets a common class name to the list item
"class": itemClassName,
"data-unique": hashValue
});
if (this.options.theme !== "bootstrap3") {
item.append($("<a/>", {
"html": self.html()
}));
} else {
item.html(self.html());
}
// Adds an HTML anchor tag before the currently traversed HTML element
self.before($("<div/>", {
// Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore)
"name": hashValue,
"data-unique": hashValue
}));
return item;
},
// _generateHashValue
// ------------------
// Generates the hash value that will be used to refer to each item.
_generateHashValue: function(arr, self, index) {
var hashValue = "",
hashGeneratorOption = this.options.hashGenerator;
if (hashGeneratorOption === "pretty") {
// prettify the text
hashValue = self.text().toLowerCase().replace(/\s/g, "-");
// fix double hyphens
while (hashValue.indexOf("--") > -1) {
hashValue = hashValue.replace(/--/g, "-");
}
// fix colon-space instances
while (hashValue.indexOf(":-") > -1) {
hashValue = hashValue.replace(/:-/g, "-");
}
} else if (typeof hashGeneratorOption === "function") {
// call the function
hashValue = hashGeneratorOption(self.text(), self);
} else {
// compact - the default
hashValue = self.text().replace(/\s/g, "");
}
// add the index if we need to
if (arr.length) {
hashValue += "" + index;
}
// return the value
return hashValue;
},
// _appendElements
// ---------------
// Helps create the table of contents list by appending subheader elements
_appendSubheaders: function(self, ul) {
// The current element index
var index = $(this).index(self.options.selectors),
// Finds the previous header DOM element
previousHeader = $(self.options.selectors).eq(index - 1),
currentTagName = +$(this).prop("tagName").charAt(1),
previousTagName = +previousHeader.prop("tagName").charAt(1),
lastSubheader;
// If the current header DOM element is smaller than the previous header DOM element or the first subheader
if (currentTagName < previousTagName) {
// Selects the last unordered list HTML found within the HTML element calling the plugin
self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index));
}
// If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element
else if (currentTagName === previousTagName) {
ul.find(itemClass).last().after(self._nestElements($(this), index));
} else {
// Selects the last unordered list HTML found within the HTML element calling the plugin
ul.find(itemClass).last().
// Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name
after($("<ul/>", {
"class": subheaderClassName,
"data-tag": currentTagName
})).next(subheaderClass).
// Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
append(self._nestElements($(this), index));
}
},
// _setEventHandlers
// ----------------
// Adds jQuery event handlers to the newly generated table of contents
_setEventHandlers: function() {
// _Local variables_
// Stores the plugin context in the self variable
var self = this,
// Instantiates a new variable that will be used to hold a specific element's context
$self,
// Instantiates a new variable that will be used to determine the smoothScroll animation time duration
duration;
// Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin
this.element.on("click.tocify", "li", function(event) {
if (self.options.history) {
window.location.hash = $(this).attr("data-unique");
}
// Removes highlighting from all of the list item's
self.element.find("." + self.focusClass).removeClass(self.focusClass);
// Highlights the current list item that was clicked
$(this).addClass(self.focusClass);
// If the showAndHide option is true
if (self.options.showAndHide) {
var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]');
self._triggerShow(elem);
}
self._scrollTo($(this));
});
// Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin
this.element.find("li").on({
// Mouseenter event handler
"mouseenter.tocify": function() {
// Adds a hover CSS class to the current list item
$(this).addClass(self.hoverClass);
// Makes sure the cursor is set to the pointer icon
$(this).css("cursor", "pointer");
},
// Mouseleave event handler
"mouseleave.tocify": function() {
if (self.options.theme !== "bootstrap") {
// Removes the hover CSS class from the current list item
$(this).removeClass(self.hoverClass);
}
}
});
// only attach handler if needed (expensive in IE)
if (self.options.extendPage || self.options.highlightOnScroll || self.options.scrollHistory || self.options.showAndHideOnScroll) {
// Window scroll event handler
$(window).on("scroll.tocify", function() {
// Once all animations on the page are complete, this callback function will be called
$("html, body").promise().done(function() {
// Local variables
// Stores how far the user has scrolled
var winScrollTop = $(window).scrollTop(),
// Stores the height of the window
winHeight = $(window).height(),
// Stores the height of the document
docHeight = $(document).height(),
scrollHeight = $("body")[0].scrollHeight,
// Instantiates a variable that will be used to hold a selected HTML element
elem,
lastElem,
lastElemOffset,
currentElem;
if (self.options.extendPage) {
// If the user has scrolled to the bottom of the page and the last toc item is not focused
if ((self.webkit && winScrollTop >= scrollHeight - winHeight - self.options.extendPageOffset) || (!self.webkit && winHeight + winScrollTop > docHeight - self.options.extendPageOffset)) {
if (!$(extendPageClass).length) {
lastElem = $('div[data-unique="' + $(itemClass).last().attr("data-unique") + '"]');
if (!lastElem.length) return;
// Gets the top offset of the page header that is linked to the last toc item
lastElemOffset = lastElem.offset().top;
// Appends a div to the bottom of the page and sets the height to the difference of the window scrollTop and the last element's position top offset
$(self.options.context).append($("<div/>", {
"class": extendPageClassName,
"height": Math.abs(lastElemOffset - winScrollTop) + "px",
"data-unique": extendPageClassName
}));
if (self.extendPageScroll) {
currentElem = self.element.find('li.' + self.focusClass);
self._scrollTo($('div[data-unique="' + currentElem.attr("data-unique") + '"]'));
}
}
}
}
// The zero timeout ensures the following code is run after the scroll events
setTimeout(function() {
// _Local variables_
// Stores the distance to the closest anchor
var closestAnchorDistance = null,
// Stores the index of the closest anchor
closestAnchorIdx = null,
// Keeps a reference to all anchors
anchors = $(self.options.context).find("div[data-unique]"),
anchorText;
// Determines the index of the closest anchor
anchors.each(function(idx) {
var distance = Math.abs(($(this).next().length ? $(this).next() : $(this)).offset().top - winScrollTop - self.options.highlightOffset);
if (closestAnchorDistance == null || distance < closestAnchorDistance) {
closestAnchorDistance = distance;
closestAnchorIdx = idx;
} else {
return false;
}
});
anchorText = $(anchors[closestAnchorIdx]).attr("data-unique");
// Stores the list item HTML element that corresponds to the currently traversed anchor tag
elem = $('li[data-unique="' + anchorText + '"]');
// If the `highlightOnScroll` option is true and a next element is found
if (self.options.highlightOnScroll && elem.length) {
// Removes highlighting from all of the list item's
self.element.find("." + self.focusClass).removeClass(self.focusClass);
// Highlights the corresponding list item
elem.addClass(self.focusClass);
}
if (self.options.scrollHistory) {
if (window.location.hash !== "#" + anchorText) {
window.location.replace("#" + anchorText);
}
}
// If the `showAndHideOnScroll` option is true
if (self.options.showAndHideOnScroll && self.options.showAndHide) {
self._triggerShow(elem, true);
}
}, 0);
});
});
}
},
// Show
// ----
// Opens the current sub-header
show: function(elem, scroll) {
// Stores the plugin context in the `self` variable
var self = this,
element = elem;
// If the sub-header is not already visible
if (!elem.is(":visible")) {
// If the current element does not have any nested subheaders, is not a header, and its parent is not visible
if (!elem.find(subheaderClass).length && !elem.parent().is(headerClass) && !elem.parent().is(":visible")) {
// Sets the current element to all of the subheaders within the current header
elem = elem.parents(subheaderClass).add(elem);
}
// If the current element does not have any nested subheaders and is not a header
else if (!elem.children(subheaderClass).length && !elem.parent().is(headerClass)) {
// Sets the current element to the closest subheader
elem = elem.closest(subheaderClass);
}
//Determines what jQuery effect to use
switch (self.options.showEffect) {
//Uses `no effect`
case "none":
elem.show();
break;
//Uses the jQuery `show` special effect
case "show":
elem.show(self.options.showEffectSpeed);
break;
//Uses the jQuery `slideDown` special effect
case "slideDown":
elem.slideDown(self.options.showEffectSpeed);
break;
//Uses the jQuery `fadeIn` special effect
case "fadeIn":
elem.fadeIn(self.options.showEffectSpeed);
break;
//If none of the above options were passed, then a `jQueryUI show effect` is expected
default:
elem.show();
break;
}
}
// If the current subheader parent element is a header
if (elem.parent().is(headerClass)) {
// Hides all non-active sub-headers
self.hide($(subheaderClass).not(elem));
}
// If the current subheader parent element is not a header
else {
// Hides all non-active sub-headers
self.hide($(subheaderClass).not(elem.closest(headerClass).find(subheaderClass).not(elem.siblings())));
}
// Maintains chainablity
return self;
},
// Hide
// ----
// Closes the current sub-header
hide: function(elem) {
// Stores the plugin context in the `self` variable
var self = this;
//Determines what jQuery effect to use
switch (self.options.hideEffect) {
// Uses `no effect`
case "none":
elem.hide();
break;
// Uses the jQuery `hide` special effect
case "hide":
elem.hide(self.options.hideEffectSpeed);
break;
// Uses the jQuery `slideUp` special effect
case "slideUp":
elem.slideUp(self.options.hideEffectSpeed);
break;
// Uses the jQuery `fadeOut` special effect
case "fadeOut":
elem.fadeOut(self.options.hideEffectSpeed);
break;
// If none of the above options were passed, then a `jqueryUI hide effect` is expected
default:
elem.hide();
break;
}
// Maintains chainablity
return self;
},
// _triggerShow
// ------------
// Determines what elements get shown on scroll and click
_triggerShow: function(elem, scroll) {
var self = this;
// If the current element's parent is a header element or the next element is a nested subheader element
if (elem.parent().is(headerClass) || elem.next().is(subheaderClass)) {
// Shows the next sub-header element
self.show(elem.next(subheaderClass), scroll);
}
// If the current element's parent is a subheader element
else if (elem.parent().is(subheaderClass)) {
// Shows the parent sub-header element
self.show(elem.parent(), scroll);
}
// Maintains chainability
return self;
},
// _addCSSClasses
// --------------
// Adds CSS classes to the newly generated table of contents HTML
_addCSSClasses: function() {
// If the user wants a jqueryUI theme
if (this.options.theme === "jqueryui") {
this.focusClass = "ui-state-default";
this.hoverClass = "ui-state-hover";
//Adds the default styling to the dropdown list
this.element.addClass("ui-widget").find(".toc-title").addClass("ui-widget-header").end().find("li").addClass("ui-widget-content");
}
// If the user wants a twitterBootstrap theme
else if (this.options.theme === "bootstrap") {
this.element.find(headerClass + "," + subheaderClass).addClass("nav nav-list");
this.focusClass = "active";
}
// If the user wants a twitterBootstrap theme
else if (this.options.theme === "bootstrap3") {
this.element.find(headerClass + "," + subheaderClass).addClass("list-group");
this.element.find(itemClass).addClass("list-group-item");
this.focusClass = "active";
}
// If a user does not want a prebuilt theme
else {
// Adds more neutral classes (instead of jqueryui)
this.focusClass = tocFocusClassName;
this.hoverClass = tocHoverClassName;
}
//Maintains chainability
return this;
},
// setOption
// ---------
// Sets a single Tocify option after the plugin is invoked
setOption: function() {
// Calls the jQueryUI Widget Factory setOption method
$.Widget.prototype._setOption.apply(this, arguments);
},
// setOptions
// ----------
// Sets a single or multiple Tocify options after the plugin is invoked
setOptions: function() {
// Calls the jQueryUI Widget Factory setOptions method
$.Widget.prototype._setOptions.apply(this, arguments);
},
// _scrollTo
// ---------
// Scrolls to a specific element
_scrollTo: function(elem) {
var self = this,
duration = self.options.smoothScroll || 0,
scrollTo = self.options.scrollTo,
currentDiv = $('div[data-unique="' + elem.attr("data-unique") + '"]');
if (!currentDiv.length) {
return self;
}
// Once all animations on the page are complete, this callback function will be called
$("html, body").promise().done(function() {
// Animates the html and body element scrolltops
$("html, body").animate({
// Sets the jQuery `scrollTop` to the top offset of the HTML div tag that matches the current list item's `data-unique` tag
"scrollTop": currentDiv.offset().top - ($.isFunction(scrollTo) ? scrollTo.call() : scrollTo) + "px"
}, {
// Sets the smoothScroll animation time duration to the smoothScrollSpeed option
"duration": duration
});
});
// Maintains chainability
return self;
}
});
})); //end of plugin
</script>
<script>
/**
* jQuery Plugin: Sticky Tabs
*
* @author Aidan Lister <aidan@php.net>
* adapted by Ruben Arslan to activate parent tabs too
* http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
*/
(function($) {
"use strict";
$.fn.rmarkdownStickyTabs = function() {
var context = this;
// Show the tab corresponding with the hash in the URL, or the first tab
var showStuffFromHash = function() {
var hash = window.location.hash;
var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
var $selector = $(selector, context);
if($selector.data('toggle') === "tab") {
$selector.tab('show');
// walk up the ancestors of this element, show any hidden tabs
$selector.parents('.section.tabset').each(function(i, elm) {
var link = $('a[href="#' + $(elm).attr('id') + '"]');
if(link.data('toggle') === "tab") {
link.tab("show");
}
});
}
};
// Set the correct tab when the page loads
showStuffFromHash(context);
// Set the correct tab when a user uses their back/forward button
$(window).on('hashchange', function() {
showStuffFromHash(context);
});
// Change the URL when tabs are clicked
$('a', context).on('click', function(e) {
history.pushState(null, null, this.href);
showStuffFromHash(context);
});
return this;
};
}(jQuery));
window.buildTabsets = function(tocID) {
// build a tabset from a section div with the .tabset class
function buildTabset(tabset) {
// check for fade and pills options
var fade = tabset.hasClass("tabset-fade");
var pills = tabset.hasClass("tabset-pills");
var navClass = pills ? "nav-pills" : "nav-tabs";
// determine the heading level of the tabset and tabs
var match = tabset.attr('class').match(/level(\d) /);
if (match === null)
return;
var tabsetLevel = Number(match[1]);
var tabLevel = tabsetLevel + 1;
// find all subheadings immediately below
var tabs = tabset.find("div.section.level" + tabLevel);
if (!tabs.length)
return;
// create tablist and tab-content elements
var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
$(tabs[0]).before(tabList);
var tabContent = $('<div class="tab-content"></div>');
$(tabs[0]).before(tabContent);
// build the tabset
var activeTab = 0;
tabs.each(function(i) {
// get the tab div
var tab = $(tabs[i]);
// get the id then sanitize it for use with bootstrap tabs
var id = tab.attr('id');
// see if this is marked as the active tab
if (tab.hasClass('active'))
activeTab = i;
// remove any table of contents entries associated with
// this ID (since we'll be removing the heading element)
$("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
// sanitize the id for use with bootstrap tabs
id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
tab.attr('id', id);
// get the heading element within it, grab it's text, then remove it
var heading = tab.find('h' + tabLevel + ':first');
var headingText = heading.html();
heading.remove();
// build and append the tab list item
var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
a.attr('href', '#' + id);
a.attr('aria-controls', id);
var li = $('<li role="presentation"></li>');
li.append(a);
tabList.append(li);
// set it's attributes
tab.attr('role', 'tabpanel');
tab.addClass('tab-pane');
tab.addClass('tabbed-pane');
if (fade)
tab.addClass('fade');
// move it into the tab content div
tab.detach().appendTo(tabContent);
});
// set active tab
$(tabList.children('li')[activeTab]).addClass('active');
var active = $(tabContent.children('div.section')[activeTab]);
active.addClass('active');
if (fade)
active.addClass('in');
if (tabset.hasClass("tabset-sticky"))
tabset.rmarkdownStickyTabs();
}
// convert section divs with the .tabset class to tabsets
var tabsets = $("div.section.tabset");
tabsets.each(function(i) {
buildTabset($(tabsets[i]));
});
};
</script>
<script>
window.initializeCodeFolding = function(show) {
// handlers for show-all and hide all
$("#rmd-show-all-code").click(function() {
$('div.r-code-collapse').each(function() {
$(this).collapse('show');
});
});
$("#rmd-hide-all-code").click(function() {
$('div.r-code-collapse').each(function() {
$(this).collapse('hide');
});
});
// index for unique code element ids
var currentIndex = 1;
// select all R code blocks
var rCodeBlocks = $('pre.r, pre.python, pre.bash, pre.sql, pre.cpp, pre.stan, pre.julia, pre.foldable');
rCodeBlocks.each(function() {
// skip if the block has fold-none class
if ($(this).hasClass('fold-none')) return;
// create a collapsable div to wrap the code in
var div = $('<div class="collapse r-code-collapse"></div>');
var showThis = (show || $(this).hasClass('fold-show')) && !$(this).hasClass('fold-hide');
var id = 'rcode-643E0F36' + currentIndex++;
div.attr('id', id);
$(this).before(div);
$(this).detach().appendTo(div);
// add a show code button right above
var showCodeText = $('<span>' + (showThis ? 'Hide' : 'Code') + '</span>');
var showCodeButton = $('<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm code-folding-btn pull-right float-right"></button>');
showCodeButton.append(showCodeText);
showCodeButton
.attr('data-toggle', 'collapse')
.attr('data-bs-toggle', 'collapse') // BS5
.attr('data-target', '#' + id)
.attr('data-bs-target', '#' + id) // BS5
.attr('aria-expanded', showThis)
.attr('aria-controls', id);
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<h1 class="title toc-ignore">Build a classification model: Delicious
Asian and Indian Cuisines</h1>
</div>
<div id="introduction-to-classification-clean-prep-and-visualize-your-data" class="section level2">
<h2>Introduction to classification: Clean, prep, and visualize your
data</h2>
<p>In these four lessons, you will explore a fundamental focus of
classic machine learning - <em>classification</em>. We will walk through
using various classification algorithms with a dataset about all the
brilliant cuisines of Asia and India. Hope you’re hungry!</p>
<div class="figure">
<img src="data:image/png;base64,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
<p class="caption">Celebrate pan-Asian cuisines in these lessons! Image
by Jen Looper</p>
</div>
<p>Classification is a form of <a href="https://wikipedia.org/wiki/Supervised_learning">supervised
learning</a> that bears a lot in common with regression techniques. In
classification, you train a model to predict which <code>category</code>
an item belongs to. If machine learning is all about predicting values
or names to things by using datasets, then classification generally
falls into two groups: <em>binary classification</em> and <em>multiclass
classification</em>.</p>
<p>Remember:</p>
<ul>
<li><p><strong>Linear regression</strong> helped you predict
relationships between variables and make accurate predictions on where a
new datapoint would fall in relationship to that line. So, you could
predict a numeric values such as <em>what price a pumpkin would be in
September vs. December</em>, for example.</p></li>
<li><p><strong>Logistic regression</strong> helped you discover “binary
categories”: at this price point, <em>is this pumpkin orange or
not-orange</em>?</p></li>
</ul>
<p>Classification uses various algorithms to determine other ways of
determining a data point’s label or class. Let’s work with this cuisine
data to see whether, by observing a group of ingredients, we can
determine its cuisine of origin.</p>
<div id="pre-lecture-quiz" class="section level3">
<h3><a href="https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/"><strong>Pre-lecture
quiz</strong></a></h3>
</div>
<div id="introduction" class="section level3">
<h3><strong>Introduction</strong></h3>
<p>Classification is one of the fundamental activities of the machine
learning researcher and data scientist. From basic classification of a
binary value (“is this email spam or not?”), to complex image
classification and segmentation using computer vision, it’s always
useful to be able to sort data into classes and ask questions of it.</p>
<p>To state the process in a more scientific way, your classification
method creates a predictive model that enables you to map the
relationship between input variables to output variables.</p>
<div class="figure">
<img src="data:image/png;base64,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
<p class="caption">Binary vs. multiclass problems for classification
algorithms to handle. Infographic by Jen Looper</p>
</div>
<p>Before starting the process of cleaning our data, visualizing it, and
prepping it for our ML tasks, let’s learn a bit about the various ways
machine learning can be leveraged to classify data.</p>
<p>Derived from <a href="https://wikipedia.org/wiki/Statistical_classification">statistics</a>,
classification using classic machine learning uses features, such as
<code>smoker</code>, <code>weight</code>, and <code>age</code> to
determine <em>likelihood of developing X disease</em>. As a supervised
learning technique similar to the regression exercises you performed
earlier, your data is labeled and the ML algorithms use those labels to
classify and predict classes (or ‘features’) of a dataset and assign
them to a group or outcome.</p>
<p>✅ Take a moment to imagine a dataset about cuisines. What would a
multiclass model be able to answer? What would a binary model be able to
answer? What if you wanted to determine whether a given cuisine was
likely to use fenugreek? What if you wanted to see if, given a present
of a grocery bag full of star anise, artichokes, cauliflower, and
horseradish, you could create a typical Indian dish?</p>
</div>
<div id="hello-classifier" class="section level3">
<h3><strong>Hello ‘classifier’</strong></h3>
<p>The question we want to ask of this cuisine dataset is actually a
<strong>multiclass question</strong>, as we have several potential
national cuisines to work with. Given a batch of ingredients, which of
these many classes will the data fit?</p>
<p>Tidymodels offers several different algorithms to use to classify
data, depending on the kind of problem you want to solve. In the next
two lessons, you’ll learn about several of these algorithms.</p>
<div id="prerequisite" class="section level4">
<h4><strong>Prerequisite</strong></h4>
<p>For this lesson, we’ll require the following packages to clean, prep
and visualize our data:</p>
<ul>
<li><p><code>tidyverse</code>: The <a href="https://www.tidyverse.org/">tidyverse</a> is a <a href="https://www.tidyverse.org/packages">collection of R packages</a>
designed to makes data science faster, easier and more fun!</p></li>
<li><p><code>tidymodels</code>: The <a href="https://www.tidymodels.org/">tidymodels</a> framework is a <a href="https://www.tidymodels.org/packages/">collection of packages</a>
for modeling and machine learning.</p></li>
<li><p><code>DataExplorer</code>: The <a href="https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html">DataExplorer
package</a> is meant to simplify and automate EDA process and report
generation.</p></li>
<li><p><code>themis</code>: The <a href="https://themis.tidymodels.org/">themis package</a> provides Extra
Recipes Steps for Dealing with Unbalanced Data.</p></li>
</ul>
<p>You can have them installed as:</p>
<p><code>install.packages(c(&quot;tidyverse&quot;, &quot;tidymodels&quot;, &quot;DataExplorer&quot;, &quot;here&quot;))</code></p>
<p>Alternately, the script below checks whether you have the packages
required to complete this module and installs them for you in case they
are missing.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">suppressWarnings</span>(<span class="cf">if</span> (<span class="sc">!</span><span class="fu">require</span>(<span class="st">&quot;pacman&quot;</span>))<span class="fu">install.packages</span>(<span class="st">&quot;pacman&quot;</span>))</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(tidyverse, tidymodels, DataExplorer, themis, here)</span></code></pre></div>
<p>We’ll later load these awesome packages and make them available in
our current R session. (This is for mere illustration,
<code>pacman::p_load()</code> already did that for you)</p>
</div>
</div>
</div>
<div id="exercise---clean-and-balance-your-data" class="section level2">
<h2>Exercise - clean and balance your data</h2>
<p>The first task at hand, before starting this project, is to clean and
<strong>balance</strong> your data to get better results</p>
<p>Let’s meet the data!🕵️</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import data</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="at">file =</span> <span class="st">&quot;https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv&quot;</span>)</span></code></pre></div>
<pre><code>## New names:
## Rows: 2448 Columns: 385
## -- Column specification
## -------------------------------------------------------- Delimiter: &quot;,&quot; chr
## (1): cuisine dbl (384): ...1, almond, angelica, anise, anise_seed, apple,
## apple_brandy, a...
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -&gt; `...1`</code></pre>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># View the first 5 rows</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>df <span class="sc">%&gt;%</span> </span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">5</span>)</span></code></pre></div>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":["...1"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["cuisine"],"name":[2],"type":["chr"],"align":["left"]},{"label":["almond"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["angelica"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["anise"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["anise_seed"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["apple"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["apple_brandy"],"name":[8],"type":["dbl"],"align":["right"]},{"label":["apricot"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["armagnac"],"name":[10],"type":["dbl"],"align":["right"]},{"label":["artemisia"],"name":[11],"type":["dbl"],"align":["right"]},{"label":["artichoke"],"name":[12],"type":["dbl"],"align":["right"]},{"label":["asparagus"],"name":[13],"type":["dbl"],"align":["right"]},{"label":["avocado"],"name":[14],"type":["dbl"],"align":["right"]},{"label":["bacon"],"name":[15],"type":["dbl"],"align":["right"]},{"label":["baked_potato"],"name":[16],"type":["dbl"],"align":["right"]},{"label":["balm"],"name":[17],"type":["dbl"],"align":["right"]},{"label":["banana"],"name":[18],"type":["dbl"],"align":["right"]},{"label":["barley"],"name":[19],"type":["dbl"],"align":["right"]},{"label":["bartlett_pear"],"name":[20],"type":["dbl"],"align":["right"]},{"label":["basil"],"name":[21],"type":["dbl"],"align":["right"]},{"label":["bay"],"name":[22],"type":["dbl"],"align":["right"]},{"label":["bean"],"name":[23],"type":["dbl"],"align":["right"]},{"label":["beech"],"name":[24],"type":["dbl"],"align":["right"]},{"label":["beef"],"name":[25],"type":["dbl"],"align":["right"]},{"label":["beef_broth"],"name":[26],"type":["dbl"],"align":["right"]},{"label":["beef_liver"],"name":[27],"type":["dbl"],"align":["right"]},{"label":["beer"],"name":[28],"type":["dbl"],"align":["right"]},{"label":["beet"],"name":[29],"type":["dbl"],"align":["right"]},{"label":["bell_pepper"],"name":[30],"type":["dbl"],"align":["right"]},{"label":["bergamot"],"name":[31],"type":["dbl"],"align":["right"]},{"label":["berry"],"name":[32],"type":["dbl"],"align":["right"]},{"label":["bitter_orange"],"name":[33],"type":["dbl"],"align":["right"]},{"label":["black_bean"],"name":[34],"type":["dbl"],"align":["right"]},{"label":["black_currant"],"name":[35],"type":["dbl"],"align":["right"]},{"label":["black_mustard_seed_oil"],"name":[36],"type":["dbl"],"align":["right"]},{"label":["black_pepper"],"name":[37],"type":["dbl"],"align":["right"]},{"label":["black_raspberry"],"name":[38],"type":["dbl"],"align":["right"]},{"label":["black_sesame_seed"],"name":[39],"type":["dbl"],"align":["right"]},{"label":["black_tea"],"name":[40],"type":["dbl"],"align":["right"]},{"label":["blackberry"],"name":[41],"type":["dbl"],"align":["right"]},{"label":["blackberry_brandy"],"name":[42],"type":["dbl"],"align":["right"]},{"label":["blue_cheese"],"name":[43],"type":["dbl"],"align":["right"]},{"label":["blueberry"],"name":[44],"type":["dbl"],"align":["right"]},{"label":["bone_oil"],"name":[45],"type":["dbl"],"align":["right"]},{"label":["bourbon_whiskey"],"name":[46],"type":["dbl"],"align":["right"]},{"label":["brandy"],"name":[47],"type":["dbl"],"align":["right"]},{"label":["brassica"],"name":[48],"type":["dbl"],"align":["right"]},{"label":["bread"],"name":[49],"type":["dbl"],"align":["right"]},{"label":["broccoli"],"name":[50],"type":["dbl"],"align":["right"]},{"label":["brown_rice"],"name":[51],"type":["dbl"],"align":["right"]},{"label":["brussels_sprout"],"name":[52],"type":["dbl"],"align":["right"]},{"label":["buckwheat"],"name":[53],"type":["dbl"],"align":["right"]},{"label":["butter"],"name":[54],"type":["dbl"],"align":["right"]},{"label":["buttermilk"],"name":[55],"type":["dbl"],"align":["right"]},{"label":["cabbage"],"name":[56],"type":["dbl"],"align":["right"]},{"label":["cabernet_sauvignon_wine"],"name":[57],"type":["dbl"],"align":["right"]},{"label":["cacao"],"name":[58],"type":["dbl"],"align":["right"]},{"label":["camembert_cheese"],"name":[59],"type":["dbl"],"align":["right"]},{"label":["cane_mol
</script>
</div>
<p>Interesting! From the looks of it, the first column is a kind of
<code>id</code> column. Let’s get a little more information about the
data.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Basic information about the data</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>df <span class="sc">%&gt;%</span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">introduce</span>()</span></code></pre></div>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":["rows"],"name":[1],"type":["int"],"align":["right"]},{"label":["columns"],"name":[2],"type":["int"],"align":["right"]},{"label":["discrete_columns"],"name":[3],"type":["int"],"align":["right"]},{"label":["continuous_columns"],"name":[4],"type":["int"],"align":["right"]},{"label":["all_missing_columns"],"name":[5],"type":["int"],"align":["right"]},{"label":["total_missing_values"],"name":[6],"type":["int"],"align":["right"]},{"label":["complete_rows"],"name":[7],"type":["int"],"align":["right"]},{"label":["total_observations"],"name":[8],"type":["int"],"align":["right"]},{"label":["memory_usage"],"name":[9],"type":["dbl"],"align":["right"]}],"data":[{"1":"2448","2":"385","3":"1","4":"384","5":"0","6":"0","7":"2448","8":"942480","9":"7615584"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize basic information above</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>df <span class="sc">%&gt;%</span> </span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot_intro</span>(<span class="at">ggtheme =</span> <span class="fu">theme_light</span>())</span></code></pre></div>
<p><img src="data:image/png;base64,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
<p>From the output, we can immediately see that we have
<code>2448</code> rows and <code>385</code> columns and <code>0</code>
missing values. We also have 1 discrete column, <em>cuisine</em>.</p>
</div>
<div id="exercise---learning-about-cuisines" class="section level2">
<h2>Exercise - learning about cuisines</h2>
<ol style="list-style-type: decimal">
<li>Now the work starts to become more interesting. Let’s discover the
distribution of data, per cuisine.</li>
</ol>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Count observations per cuisine</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>df <span class="sc">%&gt;%</span> </span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">count</span>(cuisine) <span class="sc">%&gt;%</span> </span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(n)</span></code></pre></div>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":["cuisine"],"name":[1],"type":["chr"],"align":["left"]},{"label":["n"],"name":[2],"type":["int"],"align":["right"]}],"data":[{"1":"thai","2":"289"},{"1":"japanese","2":"320"},{"1":"chinese","2":"442"},{"1":"indian","2":"598"},{"1":"korean","2":"799"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the distribution</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_light</span>())</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>df <span class="sc">%&gt;%</span> </span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">count</span>(cuisine) <span class="sc">%&gt;%</span> </span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> n, <span class="at">y =</span> <span class="fu">reorder</span>(cuisine, <span class="sc">-</span>n))) <span class="sc">+</span></span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>(<span class="at">fill =</span> <span class="st">&quot;midnightblue&quot;</span>, <span class="at">alpha =</span> <span class="fl">0.7</span>) <span class="sc">+</span></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">ylab</span>(<span class="st">&quot;cuisine&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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
<p>There are a finite number of cuisines, but the distribution of data
is uneven. You can fix that! Before doing so, explore a little more.</p>
<ol start="2" style="list-style-type: decimal">
<li>Next, let’s assign each cuisine into it’s individual tibble and find
out how much data is available (rows, columns) per cuisine.</li>
</ol>
<blockquote>
<p>A tibble, or tbl_df, is a modern reimagining of the data.frame,
keeping what time has proven to be effective, and throwing out what is
not.</p>
</blockquote>
<div class="figure">
<img src="data:image/jpeg;base64,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
<p class="caption">Artwork by @allison_horst</p>
</div>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create individual tibbles for the cuisines</span></span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>thai_df <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span> </span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(cuisine <span class="sc">==</span> <span class="st">&quot;thai&quot;</span>)</span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>japanese_df <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span> </span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(cuisine <span class="sc">==</span> <span class="st">&quot;japanese&quot;</span>)</span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>chinese_df <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span> </span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(cuisine <span class="sc">==</span> <span class="st">&quot;chinese&quot;</span>)</span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>indian_df <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span> </span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(cuisine <span class="sc">==</span> <span class="st">&quot;indian&quot;</span>)</span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a>korean_df <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span> </span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(cuisine <span class="sc">==</span> <span class="st">&quot;korean&quot;</span>)</span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a><span class="co"># Find out how much data is available per cuisine</span></span>
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot; thai df:&quot;</span>, <span class="fu">dim</span>(thai_df), <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>,</span>
<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;japanese df:&quot;</span>, <span class="fu">dim</span>(japanese_df), <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>,</span>
<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;chinese_df:&quot;</span>, <span class="fu">dim</span>(chinese_df), <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>,</span>
<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;indian_df:&quot;</span>, <span class="fu">dim</span>(indian_df), <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>,</span>
<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;korean_df:&quot;</span>, <span class="fu">dim</span>(korean_df))</span></code></pre></div>
<pre><code>## thai df: 289 385
## japanese df: 320 385
## chinese_df: 442 385
## indian_df: 598 385
## korean_df: 799 385</code></pre>
<p>Perfect!😋</p>
</div>
<div id="exercise---discovering-top-ingredients-by-cuisine-using-dplyr" class="section level2">
<h2><strong>Exercise - Discovering top ingredients by cuisine using
dplyr</strong></h2>
<p>Now you can dig deeper into the data and learn what are the typical
ingredients per cuisine. You should clean out recurrent data that
creates confusion between cuisines, so let’s learn about this
problem.</p>
<ol style="list-style-type: decimal">
<li>Create a function <code>create_ingredient()</code> in R that returns
an ingredient dataframe. This function will start by dropping an
unhelpful column and sort through ingredients by their count.</li>
</ol>
<p>The basic structure of a function in R is:</p>
<p><code>myFunction &lt;- function(arglist){</code></p>
<p><strong><code>...</code></strong></p>
<p><strong><code>return</code></strong><code>(value)</code></p>
<p><code>}</code></p>
<p>A tidy introduction to R functions can be found <a href="https://skirmer.github.io/presentations/functions_with_r.html#1">here</a>.</p>
<p>Let’s get right into it! We’ll make use of <a href="https://dplyr.tidyverse.org/">dplyr verbs</a> which we have been
learning in our previous lessons. As a recap:</p>
<ul>
<li><p><code>dplyr::select()</code>: help you pick which
<strong>columns</strong> to keep or exclude.</p></li>
<li><p><code>dplyr::pivot_longer()</code>: helps you to “lengthen” data,
increasing the number of rows and decreasing the number of
columns.</p></li>
<li><p><code>dplyr::group_by()</code> and
<code>dplyr::summarise()</code>: helps you to find find summary
statistics for different groups, and put them in a nice table.</p></li>
<li><p><code>dplyr::filter()</code>: creates a subset of the data only
containing rows that satisfy your conditions.</p></li>
<li><p><code>dplyr::mutate()</code>: helps you to create or modify
columns.</p></li>
</ul>
<p>Check out this <a href="https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome"><em>art</em>-filled
learnr tutorial</a> by Allison Horst, that introduces some useful data
wrangling functions in dplyr <em>(part of the Tidyverse)</em></p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Creates a functions that returns the top ingredients by class</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>create_ingredient <span class="ot">&lt;-</span> <span class="cf">function</span>(df){</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> <span class="co"># Drop the id column which is the first colum</span></span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> ingredient_df <span class="ot">=</span> df <span class="sc">%&gt;%</span> <span class="fu">select</span>(<span class="sc">-</span><span class="dv">1</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> <span class="co"># Transpose data to a long format</span></span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(<span class="sc">!</span>cuisine, <span class="at">names_to =</span> <span class="st">&quot;ingredients&quot;</span>, <span class="at">values_to =</span> <span class="st">&quot;count&quot;</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a> <span class="co"># Find the top most ingredients for a particular cuisine</span></span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(ingredients) <span class="sc">%&gt;%</span> </span>
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">n_instances =</span> <span class="fu">sum</span>(count)) <span class="sc">%&gt;%</span> </span>
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(n_instances <span class="sc">!=</span> <span class="dv">0</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a> <span class="co"># Arrange by descending order</span></span>
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(n_instances)) <span class="sc">%&gt;%</span> </span>
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">ingredients =</span> <span class="fu">factor</span>(ingredients) <span class="sc">%&gt;%</span> <span class="fu">fct_inorder</span>())</span>
<span id="cb11-16"><a href="#cb11-16" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb11-17"><a href="#cb11-17" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb11-18"><a href="#cb11-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(ingredient_df)</span>
<span id="cb11-19"><a href="#cb11-19" aria-hidden="true" tabindex="-1"></a>} <span class="co"># End of function</span></span></code></pre></div>
<ol start="2" style="list-style-type: decimal">
<li>Now we can use the function to get an idea of top ten most popular
ingredient by cuisine. Let’s take it out for a spin with
<code>thai_df</code></li>
</ol>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Call create_ingredient and display popular ingredients</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>thai_ingredient_df <span class="ot">&lt;-</span> <span class="fu">create_ingredient</span>(<span class="at">df =</span> thai_df)</span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>thai_ingredient_df <span class="sc">%&gt;%</span> </span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">10</span>)</span></code></pre></div>
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<p>In the previous section, we used <code>geom_col()</code>, let’s see
how you can use <code>geom_bar</code> too, to create bar charts. Use
<code>?geom_bar</code> for further reading.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Make a bar chart for popular thai cuisines</span></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>thai_ingredient_df <span class="sc">%&gt;%</span> </span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">10</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> n_instances, <span class="at">y =</span> ingredients)) <span class="sc">+</span></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">width =</span> <span class="fl">0.5</span>, <span class="at">fill =</span> <span class="st">&quot;steelblue&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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
<ol start="3" style="list-style-type: decimal">
<li>Let’s do the same for the Japanese data</li>
</ol>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Get popular ingredients for Japanese cuisines and make bar chart</span></span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="fu">create_ingredient</span>(<span class="at">df =</span> japanese_df) <span class="sc">%&gt;%</span> </span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">10</span>) <span class="sc">%&gt;%</span></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> n_instances, <span class="at">y =</span> ingredients)) <span class="sc">+</span></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">width =</span> <span class="fl">0.5</span>, <span class="at">fill =</span> <span class="st">&quot;darkorange&quot;</span>, <span class="at">alpha =</span> <span class="fl">0.8</span>) <span class="sc">+</span></span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAAAyVBMVEVNTU1NTW5NTY5Nbm5Nbo5NbqtNjshuTU1uTW5ubk1ubm5ubo5ujo5ujqtujshuq8huq+SOTU2Obk2Obm6Ojk2Ojm6Ojo6Oq6uOq8iOyOSOyP+rbk2rjm6rq46rq8iryOSr5Mir5OSr5P+zs7PIjk3Ijm7Iq27Iq47Iq6vIyI7IyKvI5OTI5P/I/8jI///e3t7kq27kq47kyI7kyKvkyMjk5Kvk5P/k/8jk/+Tk///4nCz/ozP/yI7/5Kv/5Mj/5OT//8j//+T///8+OMuYAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO2dD3/bZpZeQccSO8nEYzmdzkR21q2iuLuN6qjy7LSyJUfi9/9QBS5IiZR8M8IVRD/Ec85vk1Din5zggmdeEIS3WQAAQInmawsAAOwqBBQAoAgBBQAoQkABAIoQUACAIgQUAKAIAQUAKEJAAQCKEFAAgCIEFACgCAEFAChCQAEAihBQAIAiUwjozwAA22EzPpMI6PCn/Da+xSOQspGSwSZHSsbGhoAGLuMuICWDTY6UjI0NAQ1cxl1ASgabHCkZGxsCGriMu4CUDDY5UjI2NgQ0cBl3ASkZbHKkZGxsCGjgMu4CUjLY5EjJ2NgQ0MBl3AWkZLDJkZKxsSGggcu4C0jJYJMjJWNjQ0ADl3EXkJLBJkdKxsaGgAYu4y4gJYNNjpSMjQ0BDVzGXUBKBpscKRkbGwIauIy7gJQMNjlSMjY2BDRwGXcBKRlscqRkbGwIaOAy7gJSMtjkSMnY2BDQwGXcBaRksMmRkrGxIaCBy7gLSMlgkyMlY2NDQAOXcReQksEmR0rGxoaABi7jLiAlg02OlIyNDQENXMZdQEoGmxwpGRsbAhq4jLuAlAw2OVIyNjYENHAZdwEpGWxypGRsbAho4DLuAlIy2ORIydjYENDAZdwFpGSwyZGSsbEhoIHLuAtIyWCTIyVjY0NAA5dxF5CSwSZHSsbGhoAGLuMuICWDTY6UjI0NAQ1cxl1ASgabHCkZGxsCGriMu4CUDDY5UjI2NgQ0cBl3ASkZbHKkZGxsCGjw8ZMSUjZjyzxuti7vywJSMjY2BDSQShYBzXF5XxaQkrGxIaCBVLIIaI7L+7KAlIyNDQENpJJFQHNc3pcFpGRsbAhoIJUsAprj8r4sICVjY0NAA6lkEdAcl/dlASkZGxsCGkgli4DmuLwvC0jJ2NgQ0EAqWQQ0x+V9WUBKxsaGgAZSySKgOS7vywJSMjY2BDSQShYBzXF5XxaQkrGxIaCBVLIIaI7L+7KAlIyNDQENpJJFQHNc3pcFpGRsbAhoIJUsAprj8r4sICVjY0NAA6lkEdAcl/dlASkZG5udCehp8/KhDyWgY0JAc6RspGRsbAhoIJUsAprj8r4sICVjY7MzAR0AAR0TApojZSMlY2NDQAOpZBHQHJf3ZQEpGRsb1YBeHcyOFt2B+37798v53nkcwh83h2d/aprvT+Ixn9/Mm+a7k3vPJaBjQkBzpGykZGxsVAPatTL+vnfeZ3QZ0G+bjmfvF11W4/bs8O5TCeiYENAcKRspGRsb2YBGMK8OopVdTJcBbfbPF2fzLq7tfS/OF9fv+pquQ0DHhIDmSNlIydjYyAb0cr7f/e3b2VGbyraRy4B2C9L2Hy9XR/frp+d/XvHbYD7Cthg+HABZZAN6/Tqq+bf5YXwEugpoRLNr5/Xr2VE8MO7dgBXomLACzZGykZKxsZEN6OJ4drQ4fva/D1727VwGNFabfUCbFXeP4QnomBDQHCkbKRkbG92AXjSHVwd7/+/1fr/WvBvQqwMCuhUIaI6UjZSMjY1uQC/nL9v/Wxzv/d/uI9AvBHR2lDyTgI4JAc2RspGSsbHRDej1672z5nBx8ew/4oPPLxzC3/v+0hICOiYENEfKRkrGxkY3oIvjZ/91drS4/C9/jVLeDWj7t/7k0UXDSaSnhIDmSNlIydjYCAf0ounSeHXQzI4WXwhoe8feyWL5pdANCOiYENAcKRspGRsb4YBezrtOtofqscK8F9DFRX8lUvPi7hMJ6JgQ0BwpGykZGxvhgLbp7HJ5E827AV1eC//23hMJ6JgQ0BwpGykZGxvhgJYhoGNCQHOkbKRkbGwIaCCVLAKa4/K+LCAlY2NDQAOpZBHQHJf3ZQEpGRsbAhpIJYuA5ri8LwtIydjYENBAKlkENMflfVlASsbGhoAGUskioDku78sCUjI2NgQ0kEoWAc1xeV8WkJKxsSGggVSyCGiOy/uygJSMjQ0BDaSSRUBzXN6XBaRkbGwIaCCVLAKa4/K+LCAlY2NDQAOpZBHQHJf3ZQEpGRsbAhq4jLuAlAw2OVIyNjYENHAZdwEpGWxypGRsbAho4DLuAlIy2ORIydjYENDAZdwFpGSwyZGSsbEhoIHLuAtIyWCTIyVjY0NAA5dxF5CSwSZHSsbGhoAGLuMuICWDTY6UjI0NAQ1cxl1ASgabHCkZGxsCGriMu4CUDDY5UjI2NgQ0cBl3ASkZbHKkZGxsCGjgMu4CUjLY5EjJ2NgQ0MBl3AWkZLDJkZKxsSGggcu4C0jJYJMjJWNjQ0ADl3EXkJLBJkdKxsaGgAYu4y4gJYNNjpSMjQ0BDVzGXUBKBpscKRkbGwIauIy7gJQMNjlSMjY2BDRwGXcBKRlscqRkbGwIaOAy7gJSMtjkSMnY2BDQwGXcBaRksMmRkrGxIaCBy7gLSMlgkyMlY2NDQAOXcReQksEmR0rGxoaABi7jLiAlg02OlIyNDQENXMZdQEoGmxwpGRsbAhq4jLuAlAw2OVIyNjYENHAZdwEpGWxypGRsbAho4DLuAlIy2ORIydjYENDAZdwFpGSwyZGSsbEhoIHLuAtIyWCTIyVjY0NAA5dxF5CSwSZHSsbGhoAGLuMuICWDTY6UjI0NAQ1cxl1ASgabHCkZGxsCGriMu4CUDDY5UjI2NgQ0cBl3ASkZbHKkZGxsCGjgMu4CUjLY5EjJ2NgQ0MBl3AWkZLDJkZKxsSGggcu4C0jJYJMjJWNjQ0ADl3EXkJLBJkdKxsaGgAYu4y4gJYNNjpSMjQ0BDVzGXUBKBpscKRkbGwIauIy7gJQMNjlSMjY2BDRwGXcBKRlscqRkbGwIaPDxkxJSNl9JJhmUy/uygJSMjQ0BDaSSRUA/EdDhSMnY2BDQQCpZBPQTAR2OlIyNDQENpJJFQD8R0OFIydjYENBAKlkE9BMBHY6UjI0NAQ2kkkVAPxHQ4UjJ2NgQ0EAqWQT0EwEdjpSMjQ0BDaSSRUA/EdDhSMnY2BDQQCpZBPQTAR2OlIyNDQENpJJFQD8R0OFIydjYENBAKlkE9BMBHY6UjI0NAQ2kkkVAPxHQ4UjJ2NgQ0EAqWQT0EwEdjpSMjQ0BDaSSRUA/EdDhSMnY2OxcQK8Onr1fnDYv/+AhBHRMCGiOlI2UjI0NAQ2kkkVAPxHQ4UjJ2NjsZkD/GAI6JgQ0R8pGSsbGhoAGUskioJ8I6HCkZGxs9AJ6/UvTPD9ZHqR/+KFpmm/+ft7evGhens2b5/997RD+w6v2zh/vvQIBHRMCmiNlIyVjYyMX0OvXbTKb2V8jkb80PfuLLqB/njfN3v+9Dehpf+e9j0MJ6JgQ0BwpGykZGxu5gJ42s7eLz6+iixfd7cXirJkddT80e+frJ5Eu581fzts77x3SE9AxIaA5UjZSMjY2agFtF6CHi4hjm8jjfnXZ/66t6dFiPaCnsTBtH3S4fO7PK34bzEcQY/gMAbaPWkAv590y86adi8Xv//ipXY5GQOOem4AuU/sFWIGOCSvQHCkbKRkbG7WAXvTLyuWnnN2hfPCFgF4dzI6+/BoEdEwIaI6UjZSMjY10QNsD+Wb23d9+XR7Cxz1rAc2+z0RAx4SA5kjZSMnY2OgFtD+EXx6k73c/XCcBnR19+TUI6JgQ0BwpGykZGxu1gK5/BrpK5NXBlwLKZ6DbgYDmSNlIydjYqAV0mcW2mbcBvVh+p2kzoKuz8Mt/rEFAx4SA5kjZSMnY
<ol start="4" style="list-style-type: decimal">
<li>What about the Chinese cuisines?</li>
</ol>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Get popular ingredients for Chinese cuisines and make bar chart</span></span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">create_ingredient</span>(<span class="at">df =</span> chinese_df) <span class="sc">%&gt;%</span> </span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">10</span>) <span class="sc">%&gt;%</span></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> n_instances, <span class="at">y =</span> ingredients)) <span class="sc">+</span></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">width =</span> <span class="fl">0.5</span>, <span class="at">fill =</span> <span class="st">&quot;cyan4&quot;</span>, <span class="at">alpha =</span> <span class="fl">0.8</span>) <span class="sc">+</span></span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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
<ol start="5" style="list-style-type: decimal">
<li>Let’s take a look at the Indian cuisines 🌶️.</li>
</ol>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Get popular ingredients for Indian cuisines and make bar chart</span></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="fu">create_ingredient</span>(<span class="at">df =</span> indian_df) <span class="sc">%&gt;%</span> </span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">10</span>) <span class="sc">%&gt;%</span></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> n_instances, <span class="at">y =</span> ingredients)) <span class="sc">+</span></span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">width =</span> <span class="fl">0.5</span>, <span class="at">fill =</span> <span class="st">&quot;#041E42FF&quot;</span>, <span class="at">alpha =</span> <span class="fl">0.8</span>) <span class="sc">+</span></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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
<ol start="6" style="list-style-type: decimal">
<li>Finally, plot the Korean ingredients.</li>
</ol>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Get popular ingredients for Korean cuisines and make bar chart</span></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="fu">create_ingredient</span>(<span class="at">df =</span> korean_df) <span class="sc">%&gt;%</span> </span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">10</span>) <span class="sc">%&gt;%</span></span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> n_instances, <span class="at">y =</span> ingredients)) <span class="sc">+</span></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">width =</span> <span class="fl">0.5</span>, <span class="at">fill =</span> <span class="st">&quot;#852419FF&quot;</span>, <span class="at">alpha =</span> <span class="fl">0.8</span>) <span class="sc">+</span></span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">&quot;&quot;</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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
<ol start="7" style="list-style-type: decimal">
<li>From the data visualizations, we can now drop the most common
ingredients that create confusion between distinct cuisines, using
<code>dplyr::select()</code>.</li>
</ol>
<p>Everyone loves rice, garlic and ginger!</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Drop rice, garlic and ginger from our original data set</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>df_select <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span> </span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span><span class="fu">c</span>(<span class="dv">1</span>, rice, garlic, ginger))</span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Display new data set</span></span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>df_select <span class="sc">%&gt;%</span> </span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">5</span>)</span></code></pre></div>
<div data-pagedtable="false">
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</div>
</div>
<div id="preprocessing-data-using-recipes---dealing-with-imbalanced-data" class="section level2">
<h2>Preprocessing data using recipes 👩‍🍳👨‍🍳 - Dealing with imbalanced data
⚖️</h2>
<div class="figure">
<img src="data:image/png;base64,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
<p class="caption">Artwork by @allison_horst</p>
</div>
<p>Given that this lesson is about cuisines, we have to put
<code>recipes</code> into context .</p>
<p>Tidymodels provides yet another neat package: <code>recipes</code>- a
package for preprocessing data.</p>
<p>Now we are on the same page 😅.</p>
<p>Let’s take a look at the distribution of our cuisines again.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Distribution of cuisines</span></span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>old_label_count <span class="ot">&lt;-</span> df_select <span class="sc">%&gt;%</span> </span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">count</span>(cuisine) <span class="sc">%&gt;%</span> </span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(n))</span>
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>old_label_count</span></code></pre></div>
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<p>As you can see, there is quite an unequal distribution in the number
of cuisines. Korean cuisines are almost 3 times Thai cuisines.
Imbalanced data often has negative effects on the model performance.
Think about a binary classification. If most of your data is one class,
a ML model is going to predict that class more frequently, just because
there is more data for it. Balancing the data takes any skewed data and
helps remove this imbalance. Many models perform best when the number of
observations is equal and, thus, tend to struggle with unbalanced
data.</p>
<p>There are majorly two ways of dealing with imbalanced data sets:</p>
<ul>
<li><p>adding observations to the minority class:
<code>Over-sampling</code> e.g using a SMOTE algorithm</p></li>
<li><p>removing observations from majority class:
<code>Under-sampling</code></p></li>
</ul>
<p>Let’s now demonstrate how to deal with imbalanced data sets using a
<code>recipe</code>. A recipe can be thought of as a blueprint that
describes what steps should be applied to a data set in order to get it
ready for data analysis.</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load themis package for dealing with imbalanced data</span></span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(themis)</span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a recipe for preprocessing data</span></span>
<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>cuisines_recipe <span class="ot">&lt;-</span> <span class="fu">recipe</span>(cuisine <span class="sc">~</span> ., <span class="at">data =</span> df_select) <span class="sc">%&gt;%</span> </span>
<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">step_smote</span>(cuisine)</span>
<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a>cuisines_recipe</span></code></pre></div>
<pre><code>## </code></pre>
<pre><code>## -- Recipe ----------------------------------------------------------------------</code></pre>
<pre><code>## </code></pre>
<pre><code>## -- Inputs</code></pre>
<pre><code>## Number of variables by role</code></pre>
<pre><code>## outcome: 1
## predictor: 380</code></pre>
<pre><code>## </code></pre>
<pre><code>## -- Operations</code></pre>
<pre><code>## * SMOTE based on: cuisine</code></pre>
<p>Let’s break down our preprocessing steps.</p>
<ul>
<li><p>The call to <code>recipe()</code> with a formula tells the recipe
the <em>roles</em> of the variables using <code>df_select</code> data as
the reference. For instance the <code>cuisine</code> column has been
assigned an <code>outcome</code> role while the rest of the columns have
been assigned a <code>predictor</code> role.</p></li>
<li><p><a href="https://themis.tidymodels.org/reference/step_smote.html"><code>step_smote(cuisine)</code></a>
creates a <em>specification</em> of a recipe step that synthetically
generates new examples of the minority class using nearest neighbors of
these cases.</p></li>
</ul>
<p>Now, if we wanted to see the preprocessed data, we’d have to <a href="https://recipes.tidymodels.org/reference/prep.html"><strong><code>prep()</code></strong></a>
and <a href="https://recipes.tidymodels.org/reference/bake.html"><strong><code>bake()</code></strong></a>
our recipe.</p>
<p><code>prep()</code>: estimates the required parameters from a
training set that can be later applied to other data sets.</p>
<p><code>bake()</code>: takes a prepped recipe and applies the
operations to any data set.</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Prep and bake the recipe</span></span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>preprocessed_df <span class="ot">&lt;-</span> cuisines_recipe <span class="sc">%&gt;%</span> </span>
<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">prep</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">bake</span>(<span class="at">new_data =</span> <span class="cn">NULL</span>) <span class="sc">%&gt;%</span> </span>
<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(cuisine)</span>
<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Display data</span></span>
<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a>preprocessed_df <span class="sc">%&gt;%</span> </span>
<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">5</span>)</span></code></pre></div>
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<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Quick summary stats</span></span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>preprocessed_df <span class="sc">%&gt;%</span> </span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">introduce</span>()</span></code></pre></div>
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<p>Let’s now check the distribution of our cuisines and compare them
with the imbalanced data.</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Distribution of cuisines</span></span>
<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>new_label_count <span class="ot">&lt;-</span> preprocessed_df <span class="sc">%&gt;%</span> </span>
<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">count</span>(cuisine) <span class="sc">%&gt;%</span> </span>
<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(n))</span>
<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a><span class="fu">list</span>(<span class="at">new_label_count =</span> new_label_count,</span>
<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a> <span class="at">old_label_count =</span> old_label_count)</span></code></pre></div>
<pre><code>## $new_label_count
## # A tibble: 5 x 2
## cuisine n
## &lt;fct&gt; &lt;int&gt;
## 1 chinese 799
## 2 indian 799
## 3 japanese 799
## 4 korean 799
## 5 thai 799
##
## $old_label_count
## # A tibble: 5 x 2
## cuisine n
## &lt;chr&gt; &lt;int&gt;
## 1 korean 799
## 2 indian 598
## 3 chinese 442
## 4 japanese 320
## 5 thai 289</code></pre>
<p>Yum! The data is nice and clean, balanced, and very delicious 😋!</p>
<blockquote>
<p>Normally, a recipe is usually used as a preprocessor for modelling
where it defines what steps should be applied to a data set in order to
get it ready for modelling. In that case, a <code>workflow()</code> is
typically used (as we have already seen in our previous lessons) instead
of manually estimating a recipe</p>
<p>As such, you don’t typically need to
<strong><code>prep()</code></strong> and
<strong><code>bake()</code></strong> recipes when you use tidymodels,
but they are helpful functions to have in your toolkit for confirming
that recipes are doing what you expect like in our case.</p>
<p>When you <strong><code>bake()</code></strong> a prepped recipe with
<strong><code>new_data = NULL</code></strong>, you get the data that you
provided when defining the recipe back, but having undergone the
preprocessing steps.</p>
</blockquote>
<p>Let’s now save a copy of this data for use in future lessons:</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Save preprocessed data</span></span>
<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a><span class="fu">write_csv</span>(preprocessed_df, <span class="st">&quot;../../../data/cleaned_cuisines_R.csv&quot;</span>)</span></code></pre></div>
<p>This fresh CSV can now be found in the root data folder.</p>
<p><strong>🚀Challenge</strong></p>
<p>This curriculum contains several interesting datasets. Dig through
the <code>data</code> folders and see if any contain datasets that would
be appropriate for binary or multi-class classification? What questions
would you ask of this dataset?</p>
</div>
<div id="post-lecture-quiz" class="section level2">
<h2><a href="https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/"><strong>Post-lecture
quiz</strong></a></h2>
</div>
<div id="review-self-study" class="section level2">
<h2><strong>Review &amp; Self Study</strong></h2>
<ul>
<li><p>Check out <a href="https://github.com/tidymodels/themis">package
themis</a>. What other techniques could we use to deal with imbalanced
data?</p></li>
<li><p>Tidy models <a href="https://www.tidymodels.org/start/">reference
website</a>.</p></li>
<li><p>H. Wickham and G. Grolemund, <a href="https://r4ds.had.co.nz/"><em>R for Data Science: Visualize, Model,
Transform, Tidy, and Import Data</em></a>.</p></li>
</ul>
<div id="thank-you-to" class="section level4">
<h4>THANK YOU TO:</h4>
<p><a href="https://twitter.com/allison_horst/"><code>Allison Horst</code></a>
for creating the amazing illustrations that make R more welcoming and
engaging. Find more illustrations at her <a href="https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&amp;sa=D&amp;source=editors&amp;ust=1626380772530000&amp;usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM">gallery</a>.</p>
<p><a href="https://www.twitter.com/cassieview">Cassie Breviu</a> and <a href="https://www.twitter.com/jenlooper">Jen Looper</a> for creating the
original Python version of this module ♥️</p>
<div class="figure">
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<p class="caption">Artwork by @allison_horst</p>
</div>
</div>
</div>
<div id="rmd-source-code">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