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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="author" content="Sarah Haley, slh54@drexel.edu" />
<meta name="author" content="Zach Carlson, zc378@drexel.edu" />
<meta name="author" content="Nancy Melucci, njm99@drexel.edu" />
<meta name="date" content="2021-11-14" />
<title>Superstore Sales Predictor</title>
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pre > code.sourceCode { white-space: pre; position: relative; }
pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
pre > code.sourceCode > span:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode > span { color: inherit; text-decoration: inherit; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
pre > code.sourceCode { white-space: pre-wrap; }
pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
}
pre.numberSource code
{ counter-reset: source-line 0; }
pre.numberSource code > span
{ position: relative; left: -4em; counter-increment: source-line; }
pre.numberSource code > span > a:first-child::before
{ content: counter(source-line);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
color: #aaaaaa;
}
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
div.sourceCode
{ }
@media screen {
pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; } /* Alert */
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
code span.at { color: #7d9029; } /* Attribute */
code span.bn { color: #40a070; } /* BaseN */
code span.bu { } /* BuiltIn */
code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
code span.ch { color: #4070a0; } /* Char */
code span.cn { color: #880000; } /* Constant */
code span.co { color: #60a0b0; font-style: italic; } /* Comment */
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
code span.do { color: #ba2121; font-style: italic; } /* Documentation */
code span.dt { color: #902000; } /* DataType */
code span.dv { color: #40a070; } /* DecVal */
code span.er { color: #ff0000; font-weight: bold; } /* Error */
code span.ex { } /* Extension */
code span.fl { color: #40a070; } /* Float */
code span.fu { color: #06287e; } /* Function */
code span.im { } /* Import */
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
code span.kw { color: #007020; font-weight: bold; } /* Keyword */
code span.op { color: #666666; } /* Operator */
code span.ot { color: #007020; } /* Other */
code span.pp { color: #bc7a00; } /* Preprocessor */
code span.sc { color: #4070a0; } /* SpecialChar */
code span.ss { color: #bb6688; } /* SpecialString */
code span.st { color: #4070a0; } /* String */
code span.va { color: #19177c; } /* Variable */
code span.vs { color: #4070a0; } /* VerbatimString */
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
/* A workaround for https://github.com/jgm/pandoc/issues/4278 */
a.sourceLine {
pointer-events: auto;
}
</style>
<script>
// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
var sheets = document.styleSheets;
for (var i = 0; i < sheets.length; i++) {
if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
try { var rules = sheets[i].cssRules; } catch (e) { continue; }
for (var j = 0; j < rules.length; j++) {
var rule = rules[j];
// check if there is a div.sourceCode rule
if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") continue;
var style = rule.style.cssText;
// check if color or background-color is set
if (rule.style.color === '' && rule.style.backgroundColor === '') continue;
// replace div.sourceCode by a pre.sourceCode rule
sheets[i].deleteRule(j);
sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
}
}
})();
</script>
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WZWHK5LZgl9279229we2OBUX50kuVjv5QDo7PBwnsvrhWJF%2BYDIuVagZDxeFHOF1MEKbsBMEQS%2BKJjOVdXJ1BKw61EH%2BfeqSTzTz3I7ZA3Zuv%2Bwhshy3sDFL2TjctJR6n2SDsfFJ3A0I5ewXfAgugw7s%2B0XQG0SAfFVWHOEsr6TyphSHW5NHFc9J6Wa%2B7B3Dfp42HguHAUINniPlZCpQ%2Fl0CogDIrW%2F8u85iv7sGv8ZzGzYAxjwV%2FMCxTwobJQCTWU8HRPQeruaaXpRqestVdUOXso7dupeF7px4Z8%2Bed3arKFc44AIg51W9ch4kIIiUEocmSk4sBpCcj15oUDRJXYYExl37RmirrkIv55rLASYJJF%2BS3t0nopeptU%2BE%2BmLrLK%2BlPgQyid3mCBU6UP1rVz8R2n770zc%2FXf7x8s%2FNn9fvaFi3rmFHPfmMLWRP4lycho%2FjNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ%2Fhbrd7GuYBwhgAIYtbTx%2F3%2Bd4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB%2FcXYlcQshhyMsZrI6PYLWc3lOG%2FvlA4rHr%2F3uTFD3r38%2Fr%2B3fMKOke9W4oJ9G566u7au84CpOz%2Fct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY%2B0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq%2BBLL299am8j%2Bzv774zj995%2FdgTOZsOfWr3rnTWPj2h8qGbo1%2FM%2F%2FkYYvmxfms7TtPrM54E7ns4vwBw0rFy%2FaNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk%2BhMQ5oJDqhrC%2Fl%2F%2FFxmAUlGYeK5Z6Jl5MDec2yJQdc%2Bl5ViNduL1avoZ805eGll04jy6COKheT8S%2BU6kQwdw%2BlW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M%2BWgUwldSkjbL1HPLrCf51d8MHbv66zu%2FmcGl5Kz0YNZ0%2Bmcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn%2F9i8n08OU3L%2F760UX2E369YuvqVUPrI9VryFR8CXc5V%2FrYefbW7svv%2FYNdxUHv%2FOnFVQ1V8yse2Dde0UcAIY%2FzU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF%2BmpaKOYunHhldNAlZhEyFGpz4R20C%2Bc47Vmu%2B6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf%2BvWjNvnr22b%2BbsfDJR7%2Be%2BcL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg%2F48%2B8C5U0euLuu%2Ff8ozr1xteHTRssdGru8V3kwfeHTMsN937%2FzksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0%2FIwe1eO%2B%2B6%2FRVHpg2mv%2FuPbBuguPMtfKLU%2BtuXfjkIFraEVzg2tlMuZg6O57%2FvXBP1C3kZ3H9od2PPV81RMVE%2FaNAy3HEcaokRS34Ta%2BLAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL%2BY3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj%2FT6FDccpXsmn6Rjlxv%2BknyrTFMR8%2BU%2FcF9%2BDiRwh%2FUCiChwdeXD58cDhSwsRjeikNNcTo83%2F0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up%2Bg5GIBDhGEr2BkRNVlMZTa%2FP3HKVyrMMKrF3H%2FKPYUAWjlGsXaRnXrxTIhrJwqp%2FbMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ%2Baq%2FctSSXgtmD6gf2emV91%2F9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj%2BzqPXn0w4On3e9nZ%2BNJLYFZ1yqkQ2ITFEM5zzwyA%2B1KLJ1kVwpAjsvSTgx3S%2BrQQeiisxv5Ky%2B9kGbnqUmllmSFEhOP6%2FG4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH%2FudKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt%2FDeaWvZt3%2B8wA7swe6Y%2F5cvjv3I1rHJn%2BAyhLM44ODVn14%2F7bBUDpq%2Fhpxb8c388XfdM%2BrU3veu%2BTws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz%2B5W8H0OIlBsz%2FtURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9%2FgxBMbluwe4SAjxpj%2FmcgN0ef3cCt2IAhVVLsR%2F7%2BTIjjZjU9PTeY1ew4I9%2FOvhn8cCeI%2FNf9BnK2Pk3%2FkZ7TF00%2B6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi%2FHK2DL%2B4emRymUNIE3%2BRo3WokKfbtNP37Cs0%2F7rxjQ0X2Cvs2Rex%2FNNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA%2BZuAPZTBx2Az2Uo2CY%2FhIHysic%2F1z59PI%2FdU5CtWz%2BaJB9gi9gKmYebVKZgHgMq89Bc%2Br1GJWSSDAQXQoWAyS%2FreEUlCQsTeEUKRr3B03DZmUZBwxy%2F6S%2FMZmh%2BdTYZHt5OF4oH1LKc%2BeilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4%2B8%2FHaphRyV9%2Brq5lT1xe9nfPc0a2IrDuKQL%2F%2F9bve3DrL%2Fso%2FQj0kbVrGXCYuWZWXjUhzzD7xn%2F%2BD6GvYau8Q%2BZe8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG%2FQbydi5f%2FDYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15%2BJJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC%2BUjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4%2BjCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0%2B0aio0pV%2FbIp9V%2BKIgpPrUZJOFCUev%2FJSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo%2Bbh4oGrVQLPOume7Uev%2FBCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj%2BWvNn%2BtsW4%2FRVB2xkGeEk582NR%2FnE3ZMwaxy2guAqFp99FZ5bu%2BIXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9%2Bv1%2Ffvn5h%2F9Eeb77LHuYa%2B94HIt1bArbxs6yU1iIuRjEAnYqZp%2BE8erqdUBRONnA%2Bc75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW%2FmlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp%2BZp8MeAIEa%2BOqmZtns6x0xC7KTL2yZM%2BMtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf%2FDnN%2BWMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7%2FVXTzl2vfEZGRLeJB94%2Fzf4%2BLjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D%2Bo5jgx%2B6jmBbwy4BEI%2B9d3rHnZ0I%2FGN%2B7usnL1ey%2BxM389WLx%2F1%2BINHRbWXfoDLjz%2B6Z07su%2BYN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij%2F2F%2FcJfFxrfee3ld8QDzf2vsC8wo5nuaa44%2BMabh%2BghQAAA4XW1%2FpMcNqJgMuooCJQqiPLlrxWvQhjgF8%2F%2FSgXTwej3O6M%2FNmF1x8zWHdVaFh%2F5uU3bnwXkmg1yXz6aT6km%2BQwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u%2BnL9p6UYpSa0Nne7yy%2B1EQ%2F7PaW6%2Fdbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj%2FPIY0oTXmKdjuAkfHg%2F60QWROeQZnI4%2Bgq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE%2BqKL%2BGCJdcNEhlrSb%2BQ6T8%2BR887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82%2F0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B%2BdkS6svyKX6HWC0A%2Bi1c2Kd5c2XRy3h0mgYbo%2F4spg%2FKNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA%2BKF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO%2BG5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU%2FLQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ%2Bs1ZoxwDW1OMStBHU83G1fm5MZ0%2B4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz%2BRw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o%2F98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc%2FVveH0F3HR77xCrNs%2FmPDWy89tOWB3js3Y1%2Bb1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0%2BD1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv%2F3%2Bk0l%2F7GwKzGzQ6Wa811i%2FqXFjfb0wlJ1jP%2FDXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9%2B2mlNGRBlVqGU88fB%2BdM97E%2BVvGCx2CV7ht%2FhtgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi%2BJOiG7ST%2F%2Fn2W%2B%2F%2B%2BTCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA%2Frh0V%2B%2Fmfny%2B4Gv3r54%2Bi%2BfxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632%2B%2BJsUuivNbh53Kb%2Bx%2F2JYp%2Fe%2F%2B7qFl8eecf%2FzBk65bfb7WQLstc2AZl1GMH9v3fJxx%2Fp2pttp%2F%2Bc%2FeGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE%2BzrxreeANH3lObN6LH8KHopW83l9G3%2B3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn%2BQzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm%2FJekTY2T7teEU9KnHUa%2Bzj%2F8pXd%2BrzbxD1uragaVBdAqDC%2BjaAUkrJv%2FOXKcGMXmJOnbhQXF%2FF3QsHJVnf87VhB3sSqoa%2Fte5X9jf3r7FdPzMgtC%2FccNOnTtwb3ZPb6ZWdOPLzh7amPD50%2F4z8%2F1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY%2B2PS66ypEhs2ZFOn5IO08%2FZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm%2F%2BGZhgadoWD%2FjBMnyJuLfn%2Fkk%2BjrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04%2F3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv%2FeNGZnLt9g16B6h%2BaqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW%2FXTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz%2BgdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv%2Fzx9HjQmV99dDDg8e8%2BheuMZq2cnxdUBBOApeiri69x23S22xcWW02g%2FV2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ%2Bq8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf%2FwOTZcvobX5lZ3PPffii88%2F10Cy2I%2FswyeR%2FAFNmMfeZ1f%2F8rfzH545p1j5vdyW1apU%2B6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y%2BgRHrThpU8Niry7c%2BPBf%2F%2Bf7yzvryabGFc8%2B6xowcMRg1kUqqh9azT5h%2F1GcNr14%2BGTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz%2F%2BY2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf%2FfJ57P0SQsajObpM%2Fd9mHXp3YunT59birloRDO2a6z%2F9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk%2FfTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq%2Frj%2F5t8pdueG5kbsG25Hfpq50%2Bj%2Fe%2F%2BtjA%2FbXzF82%2BdmN88r%2FevSPL3Z6ftEjj7Yds%2BJ13jSzsaHnpjbt7h4Uvrdr2aAH%2ByzaXLm4R1W3O7p2KO71FCCkX%2FuG7BQrwKPWJlwu3jPioEKS1%2BC0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9%2F83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS%2F4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau%2BHAQ0ms6hHK6jhiJZl%2BNX0NFTicIYQt7ER%2B76ptuiMte%2FtYyP4oI%2F8o0cx9iPtrx6K5UpSgI%2FWinsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY%2B3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR%2FdK5QyUaz6j5l%2B4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m%2FJktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z%2FsVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS%2FtYTsWfl%2FStKu2xq3cXzuCVn9wf%2Bpn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2%2F5cz0oeZ%2FksHR0%2BTL6D5y31Q6eN685sPxrixetlPl5%2FYlJxu9AFbZRbmnpqlpTq09K3F7TdV%2FbpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58%2F49MClCxdM44aRZaRxE%2BaPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN%2BtlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx%2BZ%2F9jjp45OnvHwVFIePIvB49LSPRvZ%2ByPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS%2FXAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK%2FsMJSfuLFn%2FUKvsVinhsvqH%2FRkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1%2FXt0PNKk%2FGszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg%2B07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY%2BtHv7My8sX%2FFdifTO%2Bxlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI%2BdGD1OMS3dlORL6h%2FR%2B3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2%2B99f%2FEEzUL%2B%2F1uGTs397MxS%2B7YtDz%2FxwtzsfO%2BU4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4%2BOLx8%2BeOq7t%2FUVXVgmF14%2BYuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD%2FCHvfj7JZN%2BN2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW%2FYmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg%2B33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd%2BARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p%2FYebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK%2Fn1%2FmCviN%2Btt%2FWK6OGGznh%2Fs4t9I39VVFmLztSUlwuwZdCiRC2l%2FKk33lG0dHD%2FqprTbw5%2FZmTxqMV9Z8yYvelw%2FcCqjf%2F%2B6K9P9H9t4KLl7R%2BcvmJR99W%2Ff6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH%2BklDl%2BfctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI%2FFxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t%2F2dq9X3n575qfMjIXZI%2FQ7b%2Fu6brOGD0zj0rT%2BwD%2F%2BwB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0%2FedPztWvHjM%2BbtnB%2BHauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI%2BCBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1%2Fdk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc%2BCdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS%2Bhp0Bqm51Fnr%2BL2UjHz5YPXLwfRNx36B%2Bl3eeXrwWxYbNVy%2F8n%2BpGrtwd7tNtSfXsNFaLo9jTdPZ89ub%2FpXB47YrkEiRpzW3r%2BoJ09UfBJLnmAoG5dBi5LJ5U83Z%2F2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c%2FfPn39g4Oqa%2Bfun195VPX3qwLunC2vmH9i%2FoGZlTdOCgdOm3l0zdZoiv%2FGASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz%2B0JMt%2Bsjy645vIQH91edGont0XbPj9msiaPXiIVI2%2FNHhk35IePbMLh0yeP6V6%2FZPPA4KflKlzBqAsnGkVRaCONIPUOstxn%2FMhJ%2BnrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd%2F2PpPpA3vsPbRzw4w1sz%2F8snbmA6Or7%2Bw%2BpUPP8mXDl2wVvqx%2BwJu%2F%2FYmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6%2BLeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz%2BZ%2F0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa%2F8RJkG7MortqVTEvILI6Z9PL1rzacn%2F%2Fov0pY1S3t%2FraYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz%2B7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog%2F71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu%2BbHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2%2FhFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V%2F3qF3eu3%2FB8lN07fsKwYRMeGCZM3nHw8LPP7T%2Bw%2FTH%2Bb%2FYjjwCBau4hdsY9BF%2BZRr1AgMrEoJdu5R%2F4fBhELEUxdqM72c5aTGef1%2BIQVnvjPTGxCb3wfhzek01IufGW24c%2BAOIZzq8gnCYLACAbHrsGKMNHNDV6EPR%2FosTBA8ziYuCw7Tjs%2BThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w%2B9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9%2BdbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB%2FyIz70jFvcU%2FeGRvmVKrdoPJ0bltbq9R1v%2FYaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA%2FfAdPM%2BwIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y%2FSAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG%2FAvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC%2FhhXgUA%2BgFip9id54Z5wod3t1glmAKcgCUk%2BrogS11erXC6%2FJJ%2BWL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf%2Bbn%2F07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS%2B3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0%2B0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa%2F3fbaLyfPTsaR%2BCIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0%2Ba32f2945YHTpRoDazQHnjnES1lrm3%2BFq4%2BYgL%2Fygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu%2F9zy%2FNwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO%2FfrHjvH9%2BpMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM%2BzzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq%2FB6ei%2FV%2B5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj%2Bvm0xpaZCBL2vZIQjWCo6Q2%2F2lnOTKUqE%2F1UYJv5ZAOKb36Lxv32p%2BOTCrfUnn27ofnjujZq094yVz2TcPf%2Fv7%2B58IPi6dX3OnPyC0L3b917LZdPTcF8w%2F0mVQxcHZN%2BcTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t%2B7FP1eajFc%2FGz689fe%2BUW3xc%2FvP58whegruiOKsCNGRZehzj%2BcwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF%2BWlJTe3ghi5y4pbYNtKyK%2BAqGgV6RD66BdECyZQU%2BxzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy%2F%2BqHSkOv7bLFExMz5TiAMaaVIb%2Fwg7NmPnUc0VVb4%2Ba%2F3xO8a6Hj%2F0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY%2F%2ByddrEUqp%2Fie%2B4eMYP%2F9%2ByRWGwjyVpav5k5sXH9%2F5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU%2BJFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4%2F9rD7PafGcS1R7PsEQk1d7TaLX%2FgqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0%2B9LqSttY3QmLJw6%2F3b430QyITiIlAqxdlBMcj%2FlHpUk%2B6gRVqnV4kwil39%2Be%2FsK5T%2F9sUYXdkp9n3vr4YN77ll3OW%2Bpzc8v7NpC3vppe0vPUtC7Ev2FzR%2FcQmlWcInr25%2BcGHXgtrefZ6cNHMlm8b%2BtaaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk%2F1HquySb%2Fe8drD6PPN2z4%2Bp45Ngi%2Bd8fu35a9%2Ff4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ%2FQQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP%2Fq3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M%2Bb2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt%2BDw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs%2B5ZLXnE%2FyxtZarrfrYDqw6wr2xGWIjpKsAWu%2BI2t%2BVyXex0jOkFJfNZpfsrQMOsKeYPHqqT%2BNdjB7q5euvRZPnb3oYUWsXUUomXo%2FW9JUVbx7J4HugOKR748Sz333%2Fyd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce%2FZMxo1nF6Nmgn7Y%2FTmxejV%2BpuEyuv9TaJArLfsb%2BIw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj%2BVwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8%2BRfs09fmXXEH%2Becs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs%2FNzup6pu8B%2BROnrBae6djz2%2BInL0aAOq4Y%2Fe8%2BQDVf9G154buPm5xvWCb3mrjKRjN%2B7vp4xEwtQh3q8Y%2Ba0KbPYz19MYDO5tw1mkLIPz3985rOPP%2F10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE%2FIgzx1QzhIE0DR3nLfB89CcmUMWLuFF2u%2BWPJGTu3C%2Bt3TBoiIAgpP5iG2lhdp%2BkEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn%2Bk78TN1N5wPn%2BSzg2gC%2FnKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB%2FTOLAxZRpmn39tucP%2FKjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff%2FFEyCCePMvngykw%2BK%2FeMIh5f8VUtYgffQ49lB7%2BR0HUNTpQenhP6WBBkscHEs5y%2BQZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk%2FbGKDPGARzmLjqmfcouq%2Fe4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1%2Bx3pUgbyrhA%2FvjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe%2Fei9JQJ4ELUkjOsxJ7m6%2BQYbnXvbTY2Ow6D6FHh%2F7lTTBZZSVLOtqB8g4iCCHzeZK%2BdC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD%2FURrIP3Wr6u%2BOqQ9OmDF94qRp5JtZj%2F9u9sx5C%2Ficym8TiHvgB8gGOwAEwU4c%2FM4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ%2B16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4%2BZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb%2F%2BILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT%2FdUDsCHUKOz4AWfRHQvA065Z1snHLxtW7%2FoddaNewgZANO4LY%2Bn9OPN%2BrQSxmD80rC7ed1%2FRm9%2FpuaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y%2BEuJtrjkUwGEVlI650ylKvE%2B5ABA%2FHNTwuf9lc%2BBgItUcf0%2FAgZwQedwuks0ypTyaYjSqY%2BiqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO%2B0ESk9LSjDup6913x%2FKzVKdeX9THFGzb1v5TDDfpQ45bECoJ9%2B43cBcf0nCXXr%2FF8%2F43notvxJ6rVEnqc1TWG05X9cp%2BAAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK%2FV%2BGK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua%2FdUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n%2FqjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786%2B2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d%2FYbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS%2Fohgnt9rG%2BooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc%2F4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8%2FsMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW%2BjOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX%2BV%2F3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr%2BPdo7x4dIHTKe5DFn%2BO%2Fj%2BW2VnE3ooW6isf0LIUENvZs1gf%2FLHojJwdpplCP5gn%2F5gi26FoYa19ZVFOJ6Sxuoz%2Fq2Ti20IKVJdnqvYJwnhfPH%2F2f6YHoQF30aZaK9J8T026RxH5fA%2FWPW%2F8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM%2BhdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe%2FuYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H%2FDXNOTeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznOcY5znOMc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A7%2BtETl5RXdNNZGDm%2BvXYXWjgLDRzEhoLBAYv0%2F0NHAAAAAADBQ8CvAAFAAgFmgUzAAABHwWaBTMAAAPRAGYB%2FAgCAgsIBgMFBAICBOAAAu9AACBbAAAAKAAAAAAxQVNDACAAIP%2F9Bh%2F%2BFACECI0CWCAAAZ8AAAAABF4FtgAAACAAA3gBY2BgYGRgBmIGBh4GFoYDQFqHQYGBBcjzYPBkqGM4zXCe4T%2BjIWMw0zGmW0x3FEQUpBTkFJQU1BSsFFwUShTWKAn9%2Fw%2FUpQBU7cWwgOEMwwWg6iCoamEFCQUZsGpLhOr%2Fjxn6%2Fz%2F6f5CB9%2F%2Fe%2Fz3%2Fc%2F7%2B%2Bvv877MHGx6sfbDmwcoHyx5MedD9IOGByr39QHeRAABARzfieAFjE2EQZ2Bg3QYkS1m3sZ5lQAEscUDxagaG%2F29APAT5TwRIgnSJ%2Fpny%2F%2FW%2F%2Fv8P%2Fu0Bigj9C2MgC3BAqKcM3xgZGLUZLjNsYmQCsoGY4S3DfYZNDAyMIQAKyCHTAAAAeAGNVEd320YQ3oUaqwO66gUpi6wpN9K9V4QEYCquKnxvoTRA7VE5%2BZLemEvKyvkvA%2BtC%2BeRj6m9Iv0VH5%2BrMLEiml1XhzPdNn3n0rj6%2FEKn2%2FNzszO1bN29cv%2FbcdOtqGPjNxrPelcuXLl44f%2B7smdOnjh09crhe279vqrpXPuM%2BPbmzYj%2B2rVws5HMT42OjIxZnNQE8DmCkKiphIgOZtOo1EUx2%2FHotkGEMIhGAH6NTstUykExAxAKmEqSGMFl6aLn6J0svs%2FSGltwWF9lFSiEFfO1L0eMLMwrlT30ZCdgy8g2S0cMoZVRcFz1MVVStCCB8raOD2Md4abHQlM2VQr3G0kIRxSJKsF%2FeSfn%2By9wI1v7gfGqxXBmDUKdBsgy3Z1TgO64b1WvTsE36hmJNExLGmzBhQoo1Kp2ti7T2QN%2Ft2WwxPlRalsvJCwpGEvTVI4HWH0HlEByQPhx468dJ7HwFatIP4BBFvTY7zHPtt5Qcxqq2FPohw3bk1s9%2FRJI%2BMl61HzISwWoCn1UuPSfEWWsdShHqWCe9R91FKWyp01JJ3wlw3Oy2Ao74%2FXUHwrsR2HGHn4%2F6rYez12DHzPMKrGooOgki%2BHtFumcdtzK0uf1PNMOxwDhN2HVpDOs9jy2iAt0ZlemCLTr3mHfkUARWTMyDAbOrTUx3wAzdY%2BniaOaUhtHq9LIMcOLrCXQXQSSv0GKkDdt%2BcVypt1fEuSORsRUwgrZrAsamYJy8fu%2BAd0Mu2iYFhexjy9FIVLaLcxLDUJxABnH%2F97XOJAYQOOjWoewQ5hV4Pgpe0t9YkB49gh5JjAtb880y4Yi8AztlY7hdKitYm1PGpe8GO5vA4qW%2BFxwJfMosAk2X9n9X2cVVfnA36pzHNHJGbbITj75NTwpn4wQ7ySKfAu9u4kVOBVotr8LTsbMMIl4VynHBizBEJNVKBAfMNA9867j0InNX8%2BranLw2s6DOmqIHBIbDfQR%2FCiOVk4XBY4VcNSeU5YxEaGgjIEIUZOMi%2FoeJag4mEB3PUOweCaG4wwbWWAYcEMGKn9mR%2FsegY3R6zdYg2jipGKfZctzINQ%2FvxkJa9BOjR44W0OpTKAskcnjLTcKyuU%2FSVIWSKzKSHQHebYW9mfGYjfSHYfbT3%2Bv877XhsIwGzEUaleEwITyE2u%2F0q0Yfqq0%2F0dMDWuicvDanKbjsB2RY%2BTQwOnfvbMUhiNPFyDCRwhZhdjE69Ty6FjoOoeX0spZz6qKxxu%2Bed523KNd2do1fm2%2FUa6nFGqnkH8%2BkHv94bkFt2oyJj%2BfVPYtbzbgRpXuRU5uCMc%2BgFqEIGkWQQpFmUckZe2fTY6xr2FEDGH2px5nBcgOMs6WelWF2lmiKEiFjITOaMd7AehSxXIZ1DWZeymhkXmHMy3l5r2SVLSflBN1D5D5nLM%2FZRomXuZOi16yBe7yb5j0ns%2BiihRdlFbd%2FS91eUBslhm7mPyZq0MNzmezgspUUgVimQ3kn6ug48mntu3E1%2BMuBy8u4JnkZCxkvQUGuNKAoG4RfIfxKho8TPoEnyndzdO%2Fi7m8Dpwt4XrnSBvH45462t2hTEX4Bafun%2Bq8jIzK%2FAAEAAgAIAAr%2F%2FwAPeAF8egd8lFXW9zn3PmX6PNMnPZNJMRRDMkzmDYgZMRRDCEmMMUPJIgZEepHlRYyIiNhRUdYuS4ksy9reLDYsdOmLLC%2FLy7L2CgKrrCJkLt%2B9T2YyYPl%2BD8804J5zT%2Fn%2FzznPBQKbACSTvAEoqJAdtUhUJpQYjBJVAUrKSkIOJ1ZUOEKOUGkfV8ARiPB7E72m87WJZF58ibzhXPVE6QsAAnMufI4H9XXsUBh1UpOJSJLmQNWqNsasLkKhsrKnA%2FT1HCF9PQzSAPYtD5V5PW4lmFeIK86EcCRbObLp2lGjGxpH4%2Bf0wLkjjU3NDSNGxYSMxbSdDkzomhE1SypQalCISvniob1lDuTL7injC1O%2BMr%2FxmeJtxeRt%2FiJviJ8mmrjFOr0BJCZ3QAbkQFu0ypCZ45HcRqNJQkiT%2FLKsOO02s2Ryudze7CxVUnw%2Bv9%2BtmKTcgEEymzPRlgN2e5rHaeOXyeeiisnJFagMOSsqSkr45kL8Tr450SfM5%2Fy1V66pGvBwTV1BcYcDEX67QjQkbo8cigTplyVI2OHh%2F6zdXHO4%2BiR6SjoxMPzo8O21h2tPx7O2lmylNV%2FtY5Nwubj3fXUA%2F8BuFveBr74CoNB84V6pSnFCLhRCL7g7OijfR7Oy3FalR49AcXYRFBnsQUcgkAYO6H15j6wiAGu%2BI%2BAo6pleFDAWKJZMX%2BaImNunWOpiskIVH796ewAqEzvV9gqX9nQ4Qd8S%2F1V%2FScSM%2FrmsTP9FfNUNIvzuVlRPMFxY5PB6fY6iwsJw3%2FJIOOTx%2BlT%2BWzaR%2BxYWecrR7fWFFanqi%2F33nnn9%2Bv%2BMvXr7mk933%2Fv5Gy3PrN6yZjg7WFV1D5s2oGoh7nx%2Bk2vvTrkeDT0HKlieXvvakkfecj%2F5uKnhm6iNHRk27a6bevTL%2BclH3ulVkX3cBTJUXjip%2FCDvBiO4wQ95PB6qo%2Flen0%2BWTRpofo8nLa04mB3UgpeX5PbMLEzzKz4%2FtapOlXt5a1llpXhN7FF7r8zJ37o%2FiN15Q2XhvsE8RdajOqwFyrwFGETXr%2F0F9u9dNnZsWW9869X1azow9qe%2Fkpc7D52mPRf%2F%2FHcJFrR1npvf9sWX336EO7%2F9x7lqeUMn6frt8y%2B%2F%2FZD%2FJjzecOGEAnxvWdzjpTAzWtHbGjRhlhdMXqvLVZSWnl5kpSoChLJVtcwXSPea8vNLSrT0dEnTegyPaZIUqIlJLnSKhAV%2FpfBuhb9EbE53bYVIM%2F3S45hfiZ%2B7th8IFPHN5QuXcscms1vF8kiAZ2qBsEEEFQX7FnJDeNy%2B8nIF2JLZ7%2F77DPtk3rJhVV9vefPD%2B57CzCF98cr82%2Bs631s4%2FvbxrKPf1XjT0Iqrh%2F%2BuafTMxR%2B9e%2B%2BmxqZnxzzx5l8embstxo7PeX0Ju3DjoqYJA7C611hyd3hAtH%2FzpD5jAAVm4DM6Zjj5C5WIAIu9DuxCIB0kuvEBAKGBbSTz%2BL%2B3Qm7UZjaZqCSBqtrN%2BVQgmAMTua3joeaMhBTicTt9wULS8PSj5x58eNk9Z5c9RUrRiPte3MTKzvyHRd5Yh9vFygP4yq3JlfmyfHG%2Bso1LyP%2F5yqgRNVjuDPclRSGvk7Q%2B%2FejZJY89%2FOA5sTT7ifVb%2Bzru%2FOEM7tv0EisFhErSJGUpbrBBOOo3ms0ypVZUVc0umUyqilarYrDxpN1aJrKQuykJwvwz%2FyPMUOCTXSqlRa6CiEzJy8U4J8DWf%2FjpM%2FeeOMZeLMKpxYqbPTyx088Oz8MKtnMuFqefm4gzAKEZPpUqpG1g5qivGRSjkSKAxWo2giJRKOFCysqS4vjNhQXCAa4Bxz1HEI%2ByNlx0FBextqOk9SjezW49yhaIHbGzuBtOggKe1wgFWVapDCXbdSNt5ghfoNCgMxLA3X1v%2B%2BdV%2Beg%2FvIsdR9MJYWVcS5rISqDg%2BCuVQQLkSiTc7QoHPANIGq49dw6wi7GwgmvujZoUrrSRNsaMLqjsmfjnkYu4aU6SlJZ28xECNyqt0mMrM2pBricBidueiNS5iDcRA0ir4h%2By4yQgGJP%2FDwLVF05IQ%2BW9XLoPLou6LYoTFPCnGT0jYkaV2kfEaBok8y%2B1kkYCeeDQnIEyQI2nUrlDE3kkDT3PzsfZhXMoxZHGw2OmTRl7w%2BSpLeQoW8gexttwNi7C6ewO9hD7%2FusTaELr8eOAMA%2BA1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae%2F8Tw%2F1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD%2BEQLt4TkOo6CU5f1drrhvrrVly%2FdspDayfe%2B8EtQx7fuJG0HcbZLyyc1r%2B5qXbojtE1xa0dt4x%2F5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8%2Fvq4tZ%2FGilfr8pX7VqJm1EzJQGeg3j5%2FxX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j%2Fo2ykPlEmSr335KxvYPvbZydev29P65KNrX58%2Bc92zfxv6%2BKil76PnU1Sl6fe%2Bl694%2F%2FzIweMjUO1ZPnH2TU3fxqa09%2Bl%2F6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5%2B98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4%2FHXxjyKYhkRU9LgYsIJ6e%2BpgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT%2Fn6efcC%2BPLN8xActmMGOhu%2B4bH6EpsV%2FyAgOoO0n9%2F%2BHnR2B5h7hr455LAPJ1%2Bwc%2B1i1AYGhXOs6eQf4IR%2BuigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd%2Fgvy8Q3E6TmsbErv%2B%2BZ2tRuuN%2F7f1X%2BzsNyv%2FvYhoN066sbVlcRuZiq%2FiWvuP7rEb%2F7LuhyPfsFPLMffdxfMnz7%2B1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U%2Bo3%2BQo%2FHnp1ttBtL%2BihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0%2FLqoW8qk7kShFiku3kK9cfCPVHyDedt%2FqpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg%2Bflxtq7zz3ZUzXhrU%2FO6sjqN73mrbXD2iY%2FKzm89vbBp7Y%2F3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR%2BNfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP%2F3%2BJIOl3xOz3v0nmr9Y%2Bf2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r%2B83%2FH0fEE%2B3DTXbdNum1%2BHfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU%2BxG7xcf6mPNQAQX6%2FIUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK%2FVYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304%2FFkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA%2BB2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m%2F7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44%2BjrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o%2BUfT%2Fqt9NdGau79%2FZhf73%2BexCP2T2Pz%2FZefZXez6I%2FgIyv%2FEkRs7Yf3IFpM1FG27n5x%2B%2BNQ9Q%2FotPPTGQSQBH%2FPd%2F9Yf%2Fvjjne1sx152gh0p6f3eKHwYW3%2FEZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik%2BLHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI%2Ft4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn%2FC%2FfPZh2fjx7C84%2FaZ8xev2nXHraxT3vDKpkVrHaacdQ%2B%2B%2FxGdXTuy8Zr4NrZo3PgNgDCXI%2FUBnh9eKI36VZeLN%2BNWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2%2B0Iw4l2BUdoTI%2BZiikBS%2B9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U%2FHmW846xyGi3DSZ3j9azd1FvUDImwoz%2BE2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO%2FxGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt%2Br%2BzlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr%2ByVfg43vTxgZtW%2FGXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu%2FUg004jIzW%2BQJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr%2FNdmE5uRrDZG78Xj5t2EIGAOCFiawBT%2BozgRw%2BbSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS%2BtfxVS0wMJpjIcRkNiOAzUBl2cq%2FUrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6%2FHf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI%2FEnAKwL1KEcM%2FJO9nv43fpSiwh81U7%2BqQGdrQtXseFv4FZvycdQPQ8%2BVKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj%2Bkj3BXubXE3gz1vNr%2FPlDb76Bs9nSNzaSY%2BxxdivejVP5tZCj0mP%2FOYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr%2FTSzoRn%2FZLXHoEyAo4ckJSx%2Bau%2BBBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb%2FgchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe%2BLX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C%2FWaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm%2BPz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n%2BRlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq%2BuhvwVHHWrRpn%2BIvGGoVFl%2FMhDadWMcJP9LZen9cr%2Bdin7JuOx%2FZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f%2Flkz5g4srx3e8%2Borqypveeqmzf8qL%2F13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k%2BdXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6%2B6CYLnlEXbyLfYFex%2FD9zz7BAf0ztqVZ%2B7EwHn6YufCPz33%2FDraBqjXfyHBI2K%2BRonRKAOiVZYkC3BDJ%2Bq9VNpUJOaj%2BsXtVx6h57CC2dmLTMMKdPlKFXO0a4DY%2BdTwvZeN%2FqJLhrqRy8gSsx%2BT0e52yQh%2Bv2ynlszMrKwci9mcnemSzdRvt6NJiOSi%2BEtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy%2Fnm1hKoRixV%2FmpUJxwVT85trLi1EAa6twb%2BaS%2B9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG%2BooUZ3VpjZj8Izex59h6fnXg56wfNmF%2FDGMtC5Pi%2BGHyHdka%2F47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA%2FLwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m%2FRXi%2F0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt%2FZIKYnGpfnBa6sUmAHY9%2FmJhqI4S4fJ%2BQL55xoKIY%2BVYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd%2FUoR8AzFIA%2BR3QIHeUTdBWVYkSTznFd6SVJko0DW%2BxLKLeyTRZYcwiGjADQ%2FjqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl%2F%2FvMAAGKNdIQ%2BqATAwK1gBjVKRVTIdwCUpB%2FrioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ%2Fz7GYIdmjju9pfm6E8Zq6OFTovBQvLy%2FP78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az%2Fy%2BZVYSeXlcUJWdMvMcN9%2B1u8h0omny9N6YT%2BhuGr1r0xzd%2BOr%2F5xbv%2FOn7T8Y9PswO%2FX3znY5MWPHHDsNfXvfono1K6rn7f%2BK3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX%2B7YNsMT%2BhpLLZbJM2ZOJJNvaZk%2BQ5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM%2B%2BlZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf%2FBTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi%2BobTZhqFy7xwIOIyi6fTTdPt5ft4%2BoT4Q%2BecShOXlPGioU%2FBLkji3iOnVPiAnZ9vHnOw9ON%2Fmw7Jv%2B1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y%2BDMEKROSUpzOLKcJlOm3%2BOkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs%2B%2FY3zDt7LdoiJfID985vmNc3Qb61PyZM%2Bd3NmAPdGAahth3Jx%2B789Eel5%2B4rCjB7nSOkgMeuCKa7SZElSn1%2BqwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l%2B9eu3bj%2Fufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz%2Bn4pxIOWz79gRHv0LneqJs2FQ76ewKfPao%2BpSsqEvmsj%2BykQFfCF6ZeRcGFyUQK8v26El%2F4WGzqS33OfxjpXbL2ndc3sTfYvm9%2BvP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5%2Fdrrmura10otT%2FceNqZjJ5Xzew187smt%2F1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D%2FJfU5336yUOPaKqJJEuCQeFQirWX7O%2B6YxfZjqapqE%2F61bQ958LsXt8S%2F40CwpeDekav%2Fvh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ%2FZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd%2FL0JgaDT%2FNcE9PAaBctOk%2FsjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd%2BeJXN6mThXRIWPMH%2FUn%2BNdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7%2BG7krl7%2BOC8ePqbjJDCRIiEfKFykdziVfBd8q%2Bke9n%2B%2BuvnTGL7vy529F437Xwso%2FdL097ZwvbVXz9jOnlw3rz12%2BLfSS1Lh1%2B%2FurZpy%2BF4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV%2FXLRG8TS0ierkyof%2BZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu%2FxUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl%2FI6Xt6%2F70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl%2BRVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p%2FtaZv8wfaPbt58TGf7vePdSx%2Fwsv5K9SPtbB87%2FT%2Fs7H10mU722JDgM67pTN1euaIq8dIsyh%2BTpOUZ%2Bfg6PcNnz%2FZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook%2BLIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9%2B6xqU%2FbcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy%2BJIqpdWewrCD%2B6iu3%2Fsre97yvSbCP7xLR8SXyH1LKxZTYkqp%2F1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX%2BvJ2%2F2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1%2FbDrDtW395oycuG8yJd%2F3%2FXu6MZHa5Zcv2zRrf2wZn1HILfzsvKx%2Bb0rCstHz73%2B8VXN%2F8y%2F%2FJriK%2FqHR%2F%2B30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu%2BTD4aeiNN0jGwgknTn4e%2F%2FyqK4UOT%2FGc4zM%2BcENZ1E8cDrfby3t%2Fj9NoJ7JNtumyPcmJ1sVDgItr7tQYgH%2BgrxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt%2FEpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X%2Fx0SbTfiOtaElf%2FINyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin%2FpGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH%2FPGvvE03jMkcer4XnuKKI5gApOW6bWqi%2BYoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu%2FmD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy%2BDRewhcJLzK17ycs1KTC73ZrXK0koahm%2FJgob%2FpNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu%2BvcfR9RErqqfl%2Bfkfr2%2FYcZNo8ic866XXnR8Z72xNZI450HXce2MIn%2BoKqkIYDYgmvQhAm8c7YR%2FMwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6%2B3xekDI4T38Id7D54ipCHUbcnIcfn%2BuNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm%2BREWRw08Coc18VTQ4xFQ6%2BEJhDmL2m6%2Fc%2FOZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg%2BknbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl%2F3%2F2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2%2B9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG%2FAg4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv%2FRAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii%2Bnphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA%2Bn4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8%2BePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN%2BrL%2FfHcyhjZk77c8arjmflv%2FBn9kZObzbAuFFEB4A0ST%2Bd2BztZXeaidFqTfd6iV%2FzO51ado7Fn%2BavjxnT0sDFqcleG3P6QR7xs%2BNNXUfUIJTSVqjbjT%2BpBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3%2Fmvbhr%2B6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM%2FOyYp9LSycWo2xQPZ4ctYBHz%2FYyHc11H2qb9S%2BiA4oURXyC3SM%2B0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt%2BNn93Y9siC8v1T6%2BqnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo%2FiNtYyYOnz6ttm7aDBHpCoDEp%2BPghZnR%2F7I53U6Plce2UaYyMYkJqxeRED%2FHBp%2FidDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448%2FvsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw%2F0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO%2FObSe%2B8uy39mVlqEzr76oeyi%2BbG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb%2Fbpj257c%2BOS5s4tvmrim7appHXPputbn8kPlVdURssit194%2FxklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0%2FYrQaWIvIdGcQ4pBB7dzU9snCdpLZJF%2FSOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0%2F7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7%2Fc7yzPc%2BbV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul%2Bc91p4c3j55tf%2FfvilPddGFx5b8zJqf5X9DCi9v%2Fm10vvcrj6U09uHsg%2F0Ke%2F29invHSBfX7VJ%2BTAv99nwkcNvfNd82xjlI%2F4%2FSu%2BrLyi3%2FObXaPaLTJb0b6xlBfCX%2BDHKMLqgAOoieZk65HLlmXXU56PLK%2FRmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy%2FKbYvMvxsYvGRLrPu%2FBCWegef0l%2BcNcmpeGP%2FqIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI%2BK5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12%2FoayC49HL1E%2Fs9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW%2BS5kUqBYFHUgcViJEbamxB9uT%2FROLFE8QLBIegdsp5%2BnaSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h%2FcO3K%2Bs89eFTJuceP%2Bdft9utd0xUFqDpyj3kqh3K1%2BH6uhrlzX%2FZctHQEckuSNLhJG8MjPTGCNLRbwWDZH%2BFr%2F6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg%2B7W0766bZL10emd02YWJ7G%2BNFAYSwiCGdcx%2BZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2%2Fh3kG0RVlFY12UNylYiiCAo%2FgZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF%2BJnjK%2F5%2BbriipGXeqiuf3np9%2B4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo%2BIr4APJkRy7enY7QbIl%2FLTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9%2Fn1P7N541ards5oXtJ3REgwFWsOdE%2Fb9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn%2BqLI0JEDyCnCdwXdyjui3uFjR%2FVNMjMIUk6ao6YiGZWHZ0i%2FDX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR%2FdrXNlOGl7yiCq4Y2Z%2BzTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF%2Bvigy%2Byj67YoVdz%2FPrAF7Z8wo%2F9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1%2F72IW5Tjkmn%2F3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr%2F%2B2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1%2F3PjAIiZPQYprQ3RWgnr3Xd88KXuOu%2FGW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD%2BWfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw%2FxTGK3kxc9YlKPgt969IarH3%2FwwP4nFG9dY%2BPEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79%2FOej1B9UBL%2B6NL0aoFonqQehRdg4ip%2FLxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y%2BTuAYvg1fgUA6eAP5Nrjj4L8IMFW%2BuJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as%2BEb9qmXpVwDBeWUH%2BLLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6%2F%2FYLru%2Bj0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2%2F%2FheIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj%2FJFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9%2FghbKIUT%2BhoLlq5jiPvcYqZDnXNrC6WKXZGjNP8%2BVlGYAXOBfY556p5%2BZaodTT0KC89ZE%2BUXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia%2BgnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0%2BBgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9%2Bd%2BCzzzahATIdd1Du%2FG5GoOPWnR9%2BofQoyl1qHsRXeDuriLez36eUA%2BdUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL%2BuB%2BVocqr6Rhetkf5tfL7vfj3qKxH%2BSMavZf%2B%2BVuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW%2BTMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO%2FyqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1%2FZBiOG0%2BWqzyvXWXXHDpANrEwNWGNfM3DSi%2FfHYJ%2Frbsp%2B8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv%2FagMUij5c2Vc18rz5%2FNZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH%2FaTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1%2FRVfoo3fmXTau%2BubIbfnTo2vnNjQ0TVjXsWQjbb4%2BhL9FfuGvkV%2BcNqai1JldVTJn7srmu%2B7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv%2FLO%2FazH5ASbVEBr5VQ%2BUtQfAPb2jbbEazY1vfvCE6Xna%2BkHfxhi6RUj001a%2BkAasPTikemClt4lAX%2B3T%2BGCYcUDmqJ%2FlKrwqwogTCEpQjeUQBBOgS2RydU1JDM%2FP2g3GoNBuabG7%2FGMKZPlsC%2FfW50fjVVXsyDp7OxQNJZtNo6aSoF3p%2BS0NFDHPHgbYiBJgQZGv%2FERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19%2F03NdfPRtr2nHzvKc%2Femdx%2Fd3LDyM4XkaJq%2Bcfm%2FbY8bqFq1fv6FyOvX%2B1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T%2F05acLVrNap952xtZhP0xWx0%2B0iY%2BfnCrjtT1FbQ2389oqStRWanr34n%2BeflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA%2Fy5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ%2BkEY0tOTnr6qEKLpcQNmZh%2BSJ2ImdjppB56CnnSKS02%2BRpiJifBU2MEnYC8izsQ2clwI9I%2B1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5%2F0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n%2Fm5lz4fKh8pLyPrJR8gyV%2Bhdtuva4%2FMv2Lj1ih27%2Blg74MwMf2tPV9%2FaEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS%2BgUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2%2BK1tf%2FPFpXNzVaf2ojO%2BKOwcEvTpva%2FPOG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx%2FC%2BzhmCNsJ9hQWsvOLVn0n5GBm1eUrt%2FzK5jR21o%2FOiJKy9AhwzKa%2F6alefjSoYJlXV2dVyL7IwUqpp%2BQes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np977PkV5qi9zXh%2BBQ8XJOgmziYWsLhqD%2B1vHQZzli2Dxi8VWsCcbXDIRM6dEpOdxEnL%2BCQocxLLTDtnDWdWTT4Wyh0nAU7ot8Herhf%2F%2FuZLf5xv0ulUfvGjOONEDrXMYEgzK%2BCtE9qVsXpQVixvbB7mnLQ8CVqeut5Qc%2F0zNdcJKk9oH6byMk5M5VGJGk2mO108BE7wQmekxuJwGFF%2Bvs6WAeDL0umKLHa6drMgI7HQX0YznaWSNBddcwhCLotpRQ5tBcd%2BThplmiAy%2BBMMx2M6XcOLuERnVGvx%2B3WnH9vn31Wm9Cv3oTPQhPGbvaRDW9Q9dstdd%2FXVrfR7t8jpaBvqQuejTSZZXeCR145%2B8%2B1PDivZbnPyN%2BhT3SphMXhgNARhQWRMoMKEHQ6%2FX19RkWu3V%2BXr9aEchzvgiMYCATCbfxaNmc3YJNDOmfLEZnDT4VwQvFNiQupwHj45Cp00iOdT56kG4bniI7dDo6KTeT2fSk%2BLtyhf7dl5pPfHLSgb4QUvT7nsi2%2BR%2BbhTt2fL%2BU90tDx99FwN5Pu4fbWMBnC3%2FZprdiD9%2FciByqY1XcvYaf26naXlbOCeHGf7BhavuJhFHD0h%2FFXwSAVgZP0Zi5ozAMh6jE0ZWF4vsh39sg5pyx2NKqQzEZ2XGU%2BdFNAgrdc1Ne977elTUafn6kbhr2ed0XJ29tMLqh5sYBENqFX4M4lKD8Q9ehmS1eqmkUWyR8ay7CDxvRTYHVKNZ7qk8YhEdy1YcOklCy%2B67Pqa0tKaiorSGvGlCzavv%2BiCDZu7ykKhsrKqKkDwa%2BHPgkEygQuqIm4KNEUEQjLdBhvobPTrYvM6MzavFyCQ9fpZmoNENQebXw6qkISXvbF5mNVHiE23yjF6xRM27knfvXTUtKZoET%2B%2FfAk7F%2Buray7vKyjOr%2BKHAr4bGHqI3IN7%2BG5S%2BAS7SU0nbeih999Xlbp%2FqtQllG7Sj%2Fp4jIw7kiaIOqTTySBou5KZB5gLq7jGWhvCumKTs7N6sN5L%2Bp1zkG2h8t3HkHQFCVwRmQhIknSCRC8wvD8WUrffQHtNwbWDkz3iI84XlPdRySFI3luLeVIwEfnuWhIEtNuffHstwOzeZBl%2F%2BgzwRczUIGsiggSSZNFlkHRtI0Z%2BoT8E%2BbOoWSnwxY%2FoUzVPdILhSZyRP8ezp2Vz%2BE4SGJn%2FndpNDXwrMFMaMYjsRi%2BqN9Luoz60qB5QH885cqO31JNM8Ua1DBJFgVlJkOt5SRihMGIaeQcIpN7Ap91gROGgt0eWkkvbi2wunXrfKIyCdLA9wszuRplAgHssUq3uc6%2FavnXvvku37cGf9hzou3r%2FLbcAELbTizQXhfm75mXsYF6m6kEvys4gbKuXAofMQuS5LUhtbJnmP9AJy8gdX3yp56m7v%2BAps89kZzPacGPqPmctKUf%2BVkA7vpHbtCsijrgDV9RLQAg9pa0JI9VZmsxW0W%2FVN5vqlE12xKZeO24nRzp2bfoHPRPEf7z2SBs4vvHEBm8ApCxj83oe25YVSSeAEcaCFtqW8B8j5EX48mN%2F%2FIKMjge2AeK7BW0S%2B6EYdkQaJaL3%2BXI8RW5ntmywWIrSafaLika5cnP12dklBpdLzpRy83Knx0heRt66PJxOMvMy82yFPiiEabFCndlkMzXHbNp2YiNNoxZenyxzKUghO%2FCtQOhvro%2FH5DgKdA420DrVfS4oWELdb%2F7qWvq7BuL7XXhXXu9CVyrtGKN5yj0hZNq9ecn93ynPj9q6VMBLtvjQpG%2Be6ps7ebnwys5f3ucNFDzwTXgIxqK0Tx5wFVff9zVyT%2F%2FQ4%2BXsWgfzjp%2B0n6MTYDbdHRriMbs%2FSh7wQyNfQ04lboD45x8nfd7MPgcMBhzF34tPQRpYGbthFXUmWnBEBixim90k62TJikTRaiW6PJLPDTwBLSYu4RpNwn%2B8DhpfWI1CfA%2BzWrZnHP5%2BzefKBrTh0zXKHkmuzliH39q3rwfXHT%2FUN3Nu1gWuZ9Wn05u0pyuGRuJWn14KAMTT4QTpzcPp0q6k3PF0dS8BvtMDAcsjIIiIQGKXQLYPAt8FgTU2uvZ8EQDruB3sL%2FEV7krVDmZIWNNupYoPkxTdQ3NGKoYYgS4mKQ4q76sKS0JxHADfqZupKbq4gq9wuaT6%2FwCVeR0IAAAAAQAAAAEZmiehT9dfDzz1AAkIAAAAAADJQhegAAAAAMnoSqH7DP2oCo0IjQABAAkAAgAAAAAAAHgBY2BkYODo%2FbuCgYGr9zfPv0quXqAIKrgJAJZXBsIAeAFtkQOsGEEQhv%2Fbnd272rZtG0Ft27ZtW1G9dYMiamrbZlgrqN17M89K8uVfTna%2FoRs4AwCUGVBCU0zQl7DAlEIZWoPOfhXUs0BbVQAL1CG0ZepQd9STPdUW9dQ61FGN%2BU5LpOW1pswUpmU0hZj%2BTGOmWnQ2lPNyV2rEoO%2FA%2BmUw0CwATG8cNjkwyXzEYZrG9Of5NUyy%2BXBY7Q4Hm9a8tgCH%2FWU4bOcwPfmsjc7GvDcYPWk7StjU2G8qAf5xwHQE6D%2BzHRXUbqzi96bmrEQNEeim4V965jWnB%2Bho0sNRHnTn7E5H0V3nQAlaAGsawqkxWKfGhDPoO2Ts%2FGdwsk5fIecd011vh9O%2FOaegHO9toBWAfYLM5JBSxvoNquliyEeDvUucbeXvMd55vIqRtTGMJTnzAkP5bdnsXvTX6VGOPkbfYe%2ByRgh%2F6xHoLms6QDmmlvyFPThTB2PEtbczfMbr3XUu1JD7fmqUjaYre68jzpPD3wJIH6QH0RyQ5L6Ui%2FGeGFqDOZLiPj7iXnpkDsKJ5%2BTwO3LmEe8JYecb2fcazoXMC%2FEd4z0J7EFS3MdH3EuPJJX07gom%2Bff4%2FDMcpS1ee85bBLQNGO84cgiqPerpVcghUBEeK%2FS1jzBBfUZbwUv5X%2F7bkOlslqCEwJ5TBw4lBFsBJdRuHA4vYk%2Fown8RLYvLrQAAeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcx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</head>
<body>
<section class="page-header">
<h1 class="title toc-ignore project-name">Superstore Sales Predictor</h1>
<h4 class="author project-author">Sarah Haley, <a href="mailto:slh54@drexel.edu" class="email">slh54@drexel.edu</a></h4>
<h4 class="author project-author">Zach Carlson, <a href="mailto:zc378@drexel.edu" class="email">zc378@drexel.edu</a></h4>
<h4 class="author project-author">Nancy Melucci, <a href="mailto:njm99@drexel.edu" class="email">njm99@drexel.edu</a></h4>
<h4 class="date project-date">November 14, 2021</h4>
</section>
<section class="main-content">
<div id="libraries" class="section level4">
<h4>Libraries</h4>
<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">library</span>(tidyverse) <span class="co">#Tidyverse v3.6.3</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggfortify)</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(knitr)</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(kableExtra)</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(zoo)</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(forecast)</span></code></pre></div>
</div>
<div id="arima-in-r-and-time-series-forecasting" class="section level3">
<h3>ARIMA in R and Time Series Forecasting</h3>
<p>FOr this project we will be using the ARIMA forecasting model which in turn is based on the Box-Jenkins Methodology,with the Autoregressive Integrated Moving Average model(ARIMA). This is comprised of three major steps:</p>
<ol style="list-style-type: decimal">
<li>Conditioning the data and selecting a model (from chapter 8 in Data Science and Big Data Analytics) -identify and account for any trends or seasonality in the time series -examine the remaining time series for any trends or seasonality in the time series</li>
<li>Estimate the model parameters</li>
<li>Assess the model and return to step one if necessary</li>
</ol>
<p><em>Time series data</em> is made up of four possible components: Trend [T], Cycle [C], Seasonality [S], and Random[R]. In our data, there appears to be strong seasonality trends, although the magnitude is hard to tell. It may also have cyclicity but that is also hard to tell with the current graphs. To that end we will employ a few libraries that were developed to deal with these particuar issues in Time Series data.</p>
<p>So to begin, we will start looking at the data and getting a sense of its shape and possibilities.</p>
</div>
<div id="import-and-view-the-data" class="section level2">
<h2>Import and View the Data</h2>
<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>sales_raw <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"./data/superstore_dataset2011-2015.csv"</span>, <span class="at">header =</span> <span class="cn">TRUE</span>, <span class="at">sep =</span> <span class="st">","</span>)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>sales_raw <span class="ot"><-</span> <span class="fu">mutate_at</span>(sales_raw, <span class="fu">vars</span>(<span class="st">"Order.Date"</span>,<span class="st">"Ship.Date"</span>),<span class="fu">funs</span>(dmy))</span></code></pre></div>
<p>The dates need to be converted using the <code>lubridate</code> library. The two date columns are now correctly reading as <code><date></code>.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>sales_raw <span class="sc">%>%</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(Order.Date, Ship.Date) <span class="sc">%>%</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">head</span>(., <span class="dv">6</span>) </span></code></pre></div>
<pre><code>## Order.Date Ship.Date
## 1 2011-01-01 2011-01-06
## 2 2011-01-01 2011-01-08
## 3 2011-01-01 2011-01-05
## 4 2011-01-01 2011-01-05
## 5 2011-01-01 2011-01-08
## 6 2011-01-01 2011-01-08</code></pre>
<div id="initial-observations" class="section level4">
<h4>Initial Observations</h4>
<p>Using the dpylr package’s glimpse function, we see there are</p>
<ul>
<li><code>51,290</code> instances with <code>24</code> features</li>
<li>lubridate dates are now in <code>YYYY-MM-DD</code> format</li>
<li>most features are factored but there are also int and nums where sensible:
<ul>
<li>Quantity is in integers</li>
<li>Sales is num (floating point)</li>
<li>For ease of analysis, we will assume all sales are in USD</li>
<li>Sales column row-wise represents the total amount by Quantity(1-n)</li>
</ul></li>
</ul>
<p>For the superstore data we want to know a number of things that time series can help us understand. Things such as:</p>
<ul>
<li>Are Sales & Profits increasing over time?</li>
<li>What categories of sales are most profitable over time?</li>
<li>What region has the most sales and are the most profitable over time?</li>
<li>Predicted growth rates and declines will help us establish which markets, regions, and products we should be investing in for best profitability.</li>
</ul>
</div>
<div id="exploratory-data-analysis" class="section level4">
<h4>Exploratory Data Analysis</h4>
<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>dplyr<span class="sc">::</span><span class="fu">glimpse</span>(sales_raw, <span class="at">width =</span> <span class="fu">getOption</span>(<span class="st">"width"</span>))</span></code></pre></div>
<pre><code>## Observations: 51,290
## Variables: 24
## $ Row.ID <int> 42433, 22253, 48883, 11731, 22255, 22254, 21613, 346...
## $ Order.ID <fct> AG-2011-2040, IN-2011-47883, HU-2011-1220, IT-2011-3...
## $ Order.Date <date> 2011-01-01, 2011-01-01, 2011-01-01, 2011-01-01, 201...
## $ Ship.Date <date> 2011-01-06, 2011-01-08, 2011-01-05, 2011-01-05, 201...
## $ Ship.Mode <fct> Standard Class, Standard Class, Second Class, Second...
## $ Customer.ID <fct> TB-11280, JH-15985, AT-735, EM-14140, JH-15985, JH-1...
## $ Customer.Name <fct> Toby Braunhardt, Joseph Holt, Annie Thurman, Eugene ...
## $ Segment <fct> Consumer, Consumer, Consumer, Home Office, Consumer,...
## $ City <fct> Constantine, Wagga Wagga, Budapest, Stockholm, Wagga...
## $ State <fct> Constantine, New South Wales, Budapest, Stockholm, N...
## $ Country <fct> Algeria, Australia, Hungary, Sweden, Australia, Aust...
## $ Postal.Code <int> NA, NA, NA, NA, NA, NA, NA, 92691, NA, NA, NA, NA, N...
## $ Market <fct> Africa, APAC, EMEA, EU, APAC, APAC, APAC, US, Africa...
## $ Region <fct> Africa, Oceania, EMEA, North, Oceania, Oceania, Cent...
## $ Product.ID <fct> OFF-TEN-10000025, OFF-SU-10000618, OFF-TEN-10001585,...
## $ Category <fct> Office Supplies, Office Supplies, Office Supplies, O...
## $ Sub.Category <fct> Storage, Supplies, Storage, Paper, Furnishings, Pape...
## $ Product.Name <fct> "Tenex Lockers, Blue", "Acme Trimmer, High Speed", "...
## $ Sales <dbl> 408.300, 120.366, 66.120, 44.865, 113.670, 55.242, 2...
## $ Quantity <int> 2, 3, 4, 3, 5, 2, 2, 2, 1, 3, 5, 2, 6, 5, 3, 2, 3, 5...
## $ Discount <dbl> 0.00, 0.10, 0.00, 0.50, 0.10, 0.10, 0.00, 0.15, 0.00...
## $ Profit <dbl> 106.1400, 36.0360, 29.6400, -26.0550, 37.7700, 15.34...
## $ Shipping.Cost <dbl> 35.46, 9.72, 8.17, 4.82, 4.70, 1.80, 57.30, 54.64, 5...
## $ Order.Priority <fct> Medium, Medium, High, High, Medium, Medium, Critical...</code></pre>
<p>Right away we can see there are a lot of NA values for Postal.Code so we will drop that column; we will not be using it for this project.</p>
<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>sales <span class="ot"><-</span> <span class="fu">subset</span>(sales_raw, <span class="at">select =</span> <span class="sc">-</span><span class="fu">c</span>(Postal.Code))</span></code></pre></div>
<p>Additionally, we will also add a few columns to the data set to make it easier for analysis throughout the project. A year column, a YearMonth column, a monthabbr column, and a month date object. Note some of these are not considered actual date parts but can be wrangled to look like dates.</p>
<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>sales <span class="ot"><-</span> sales <span class="sc">%>%</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> <span class="at">YearMonth =</span> <span class="fu">format</span>(Order.Date, <span class="st">"%Y-%m"</span>)</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> ) <span class="co">#character</span></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>sales <span class="ot"><-</span> sales <span class="sc">%>%</span> </span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> <span class="fu">year</span>(Order.Date), <span class="at">monthAbbr =</span> <span class="fu">month</span>(Order.Date, <span class="at">label =</span> <span class="cn">TRUE</span>)</span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> ) <span class="co">#Ordinal</span></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>sales<span class="sc">$</span>month <span class="ot"><-</span> <span class="fu">as.Date</span>(<span class="fu">cut</span>(sales<span class="sc">$</span>Order.Date, <span class="at">breaks =</span> <span class="st">"month"</span>)) <span class="co">#Date</span></span></code></pre></div>
<p>A quick look at the new columns:</p>
<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>sales <span class="sc">%>%</span> </span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(YearMonth, year, month, monthAbbr) <span class="sc">%>%</span></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">tail</span>(., <span class="at">n =</span> <span class="dv">10</span>)</span></code></pre></div>
<pre><code>## YearMonth year month monthAbbr
## 51281 2014-12 2014 2014-12-01 Dec
## 51282 2014-12 2014 2014-12-01 Dec
## 51283 2014-12 2014 2014-12-01 Dec
## 51284 2014-12 2014 2014-12-01 Dec
## 51285 2014-12 2014 2014-12-01 Dec
## 51286 2014-12 2014 2014-12-01 Dec
## 51287 2014-12 2014 2014-12-01 Dec
## 51288 2014-12 2014 2014-12-01 Dec
## 51289 2014-12 2014 2014-12-01 Dec
## 51290 2014-12 2014 2014-12-01 Dec</code></pre>
<p>And here we will look at a few 30,000ft summary statistics around the factors affecting Revenue (Sales, Quantity, Discount Rates, Shipping Costs, and Profits) over the four year lifecycle:</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="fu">kable</span>(sales <span class="sc">%>%</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(Sales, Quantity, Discount, Shipping.Cost, Profit) <span class="sc">%>%</span></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>() </span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> , <span class="at">format =</span> <span class="st">"html"</span>, <span class="at">digits =</span> <span class="dv">2</span>) <span class="sc">%>%</span> </span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable_styling</span>(<span class="at">bootstrap_options=</span> <span class="fu">c</span>(<span class="st">"striped"</span>), <span class="at">full_width =</span> F, <span class="at">font_size =</span> <span class="dv">14</span>)</span></code></pre></div>
<table class="table table-striped" style="font-size: 14px; width: auto !important; margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:left;">
Sales
</th>
<th style="text-align:left;">
Quantity
</th>
<th style="text-align:left;">
Discount
</th>
<th style="text-align:left;">
Shipping.Cost
</th>
<th style="text-align:left;">
Profit
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:left;">
Min. : 0.444
</td>
<td style="text-align:left;">
Min. : 1.000
</td>
<td style="text-align:left;">
Min. :0.0000
</td>
<td style="text-align:left;">
Min. : 0.00
</td>
<td style="text-align:left;">
Min. :-6599.98
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:left;">
1st Qu.: 30.759
</td>
<td style="text-align:left;">
1st Qu.: 2.000
</td>
<td style="text-align:left;">
1st Qu.:0.0000
</td>
<td style="text-align:left;">
1st Qu.: 2.61
</td>
<td style="text-align:left;">
1st Qu.: 0.00
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:left;">
Median : 85.053
</td>
<td style="text-align:left;">
Median : 3.000
</td>
<td style="text-align:left;">
Median :0.0000
</td>
<td style="text-align:left;">
Median : 7.79
</td>
<td style="text-align:left;">
Median : 9.24
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:left;">
Mean : 246.491
</td>
<td style="text-align:left;">
Mean : 3.477
</td>
<td style="text-align:left;">
Mean :0.1429
</td>
<td style="text-align:left;">
Mean : 26.38
</td>
<td style="text-align:left;">
Mean : 28.61
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:left;">
3rd Qu.: 251.053
</td>
<td style="text-align:left;">
3rd Qu.: 5.000
</td>
<td style="text-align:left;">
3rd Qu.:0.2000
</td>
<td style="text-align:left;">
3rd Qu.: 24.45
</td>
<td style="text-align:left;">
3rd Qu.: 36.81
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:left;">
Max. :22638.480
</td>
<td style="text-align:left;">
Max. :14.000
</td>
<td style="text-align:left;">
Max. :0.8500
</td>
<td style="text-align:left;">
Max. :933.57
</td>
<td style="text-align:left;">
Max. : 8399.98
</td>
</tr>
</tbody>
</table>
<p><code>Discount</code> is in a percentage format already, showing an average at about 14% and the average quantity per sale is relatively low with an IQR of 2-5 and a median value of 3 per sale item. We will look more closely at sales volume trends shortly.</p>
<p>Dropping <code>Quantity</code> and <code>Discount</code> data, we can see the main revenue factors in side-by-side box plots. Total revenue, or the Sales plot, is the highest, as it should be, and Profit and Shipping Cost are subsets of that number. Profit is also quite large in the plot but Shipping Costs are not far behind, leading to some questions around efficiency for upper management to contemplate further. Additionally, there are a large number of outliers in the data and it is highly positively skewed, hence the addition of log10 on the y scale.</p>
<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="fu">options</span>(<span class="at">scipen=</span><span class="dv">999</span>)<span class="co"># prevents scientific numbering on axis</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>sales <span class="sc">%>%</span></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(Sales, Shipping.Cost, Profit) <span class="sc">%>%</span></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(<span class="at">cols =</span> <span class="fu">everything</span>()) <span class="sc">%>%</span></span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x=</span> name, <span class="at">y =</span> value, <span class="at">fill=</span>name)) <span class="sc">+</span></span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_boxplot</span>()<span class="sc">+</span></span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">"Factors in Revenue"</span>) <span class="sc">+</span></span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_log10</span>(<span class="st">"Values in $ (Log 10 scale)"</span>) <span class="sc">+</span></span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Boxplot of Profit, Sales, and Shipping Costs"</span>, <span class="at">subtitle =</span> <span class="st">"Y scale in Log 10 Scale"</span>)<span class="sc">+</span></span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Paired"</span>)<span class="sc">+</span></span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Looking a little more closely at Sales by year:</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="fu">kable</span>(sales <span class="sc">%>%</span> </span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(year) <span class="sc">%>%</span></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">total_sales =</span> <span class="fu">sum</span>(Sales), <span class="at">mean_sales =</span> <span class="fu">mean</span>(Sales), <span class="at">median_sales =</span> <span class="fu">median</span>(Sales), <span class="at">max_sales =</span> <span class="fu">max</span>(Sales), <span class="at">min_sales =</span> <span class="fu">min</span>(Sales)), <span class="at">format =</span> <span class="st">"html"</span>, <span class="at">digits =</span> <span class="dv">2</span>) <span class="sc">%>%</span> </span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable_styling</span>(<span class="at">full_width =</span> F, <span class="at">font_size =</span> <span class="dv">12</span>)</span></code></pre></div>
<table class="table" style="font-size: 12px; width: auto !important; margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:right;">
year
</th>
<th style="text-align:right;">
total_sales
</th>
<th style="text-align:right;">
mean_sales
</th>
<th style="text-align:right;">
median_sales
</th>
<th style="text-align:right;">
max_sales
</th>
<th style="text-align:right;">
min_sales
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:right;">
2011
</td>
<td style="text-align:right;">
2259451
</td>
<td style="text-align:right;">
251.11
</td>
<td style="text-align:right;">
84.27
</td>
<td style="text-align:right;">
22638.48
</td>
<td style="text-align:right;">
0.85
</td>
</tr>
<tr>
<td style="text-align:right;">
2012
</td>
<td style="text-align:right;">
2677439
</td>
<td style="text-align:right;">
244.25
</td>
<td style="text-align:right;">
87.14
</td>
<td style="text-align:right;">
6354.95
</td>
<td style="text-align:right;">
0.98
</td>
</tr>
<tr>
<td style="text-align:right;">
2013
</td>
<td style="text-align:right;">
3405746
</td>
<td style="text-align:right;">
246.81
</td>
<td style="text-align:right;">
85.23
</td>
<td style="text-align:right;">
17499.95
</td>
<td style="text-align:right;">
0.84
</td>
</tr>
<tr>
<td style="text-align:right;">
2014
</td>
<td style="text-align:right;">
4299866
</td>
<td style="text-align:right;">
245.27
</td>
<td style="text-align:right;">
84.48
</td>
<td style="text-align:right;">
13999.96
</td>
<td style="text-align:right;">
0.44
</td>
</tr>
</tbody>
</table>
<p>Total sales have grown every year while average sales have not. It looks like a lot of small sales make up the vast majority of the revenue for this company. Quantity sold, (our sales volume), should help us understand if this supposition is true and help us determine if there are visible seasonal trends. Let’s start by looking at monthly sales trends, aggregating the <code>Quantity</code> column by month.</p>
<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>monthly_sales_quantity <span class="ot"><-</span> sales <span class="sc">%>%</span> </span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(year, monthAbbr) <span class="sc">%>%</span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">total.qty =</span> <span class="fu">sum</span>(Quantity))</span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="fu">tail</span>(monthly_sales_quantity, <span class="dv">24</span>)</span></code></pre></div>
<pre><code>## # A tibble: 24 x 3
## # Groups: year [2]
## year monthAbbr total.qty
## <dbl> <ord> <int>
## 1 2013 Jan 2413
## 2 2013 Feb 2102
## 3 2013 Mar 2686
## 4 2013 Apr 2688
## 5 2013 May 3808
## 6 2013 Jun 5327
## 7 2013 Jul 3252
## 8 2013 Aug 4934
## 9 2013 Sep 5793
## 10 2013 Oct 3883
## # ... with 14 more rows</code></pre>
<p>And used to visualize, we get:</p>
<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>monthly_sales_quantity <span class="sc">%>%</span></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> monthAbbr, <span class="at">y =</span> total.qty, <span class="at">group =</span> year)) <span class="sc">+</span></span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_area</span>(<span class="fu">aes</span>(<span class="at">fill=</span>year), <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Volume of Sales by Month"</span>, <span class="at">x=</span> <span class="st">""</span>, <span class="at">y =</span><span class="st">"Total Sales Qty"</span>) <span class="sc">+</span></span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_continuous</span>() <span class="sc">+</span></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"top"</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>There are obvious seasonal trends in terms of quantity sold with slow and steady growth throughout the year capped with three peaks, and a bulk of sales happening in the last quarter of the year. There is a small peak between April through to July, another between July and October, and the third (and largest) trending from October to the end of December.</p>
<p>We will continue to look at total quantity (volume of sales) and total sales (revenue) over the course of this project. Since the per sale dollar amount already reflects the unit by price * quantity, the charts below are representing the <code>volume</code> of sales per month by individual days’ (points) <code>revenue</code>. Each point represents a Sales line item multiplied the quantity sold.</p>
<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="fu">ggplot</span>(sales, <span class="fu">aes</span>(monthAbbr, Sales)) <span class="sc">+</span></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> <span class="co">#geom_point(color= "steelblue")</span></span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="fu">geom_jitter</span>(<span class="at">alpha =</span><span class="fl">0.5</span>, <span class="fu">aes</span>(<span class="at">color=</span>year), <span class="at">position =</span> <span class="fu">position_jitter</span>(<span class="at">width =</span> .<span class="dv">2</span>))<span class="sc">+</span></span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Daily Sales by Month"</span>,</span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">" Sales"</span>, </span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Annual SuperStore Sales by Month"</span>) <span class="sc">+</span></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>()<span class="sc">+</span></span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> <span class="co">#stat_summary(fun.y = sum, geom = "bar")+</span></span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a> <span class="co">#geom_bar(stat = "identity") +</span></span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(<span class="sc">~</span><span class="fu">year</span>(month),<span class="at">ncol =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>,<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>,</span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a> <span class="at">vjust =</span> <span class="fl">0.5</span>,</span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a> <span class="at">hjust =</span> <span class="dv">1</span>)) </span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>Looking at this panel, we see we have a number of outliers in the data and that most of the sales amounts per sale are below $5000 (and often much less than that). Also, it is apparent that there was less variance in the 2012 month to month data points than from the other years. Finally, outside of the obvious connection between sales and quantity sold, a closer look at the relationship between sales and profits can reveal more details.</p>
<p>Let’s look at a few of the ouliers to see if we can determine what is causing them.</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="fu">kable</span>(sales <span class="sc">%>%</span> </span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(year) <span class="sc">%>%</span> <span class="fu">arrange</span>(year) <span class="sc">%>%</span></span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(., <span class="fu">c</span>(Order.Date, Customer.Name, Segment, Product.Name, Sales, Quantity, Discount, Profit)) <span class="sc">%>%</span></span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(Sales <span class="sc">==</span> <span class="fu">max</span>(Sales)),<span class="at">format =</span> <span class="st">"html"</span>, <span class="at">digits =</span> <span class="dv">2</span>, <span class="at">table.attr =</span> <span class="st">"style='width:40%;'"</span>) <span class="sc">%>%</span> </span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable_styling</span>(<span class="at">full_width =</span> F, <span class="at">font_size =</span> <span class="dv">12</span>)</span></code></pre></div>
<table style="width:40%; font-size: 12px; width: auto !important; margin-left: auto; margin-right: auto;" class="table">
<thead>
<tr>
<th style="text-align:right;">
year
</th>
<th style="text-align:left;">
Order.Date
</th>
<th style="text-align:left;">
Customer.Name
</th>
<th style="text-align:left;">
Segment
</th>
<th style="text-align:left;">
Product.Name
</th>
<th style="text-align:right;">
Sales
</th>
<th style="text-align:right;">
Quantity
</th>
<th style="text-align:right;">
Discount
</th>
<th style="text-align:right;">
Profit
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:right;">
2011
</td>
<td style="text-align:left;">
2011-03-18
</td>
<td style="text-align:left;">
Sean Miller
</td>
<td style="text-align:left;">
Home Office
</td>
<td style="text-align:left;">
Cisco TelePresence System EX90 Videoconferencing Unit
</td>
<td style="text-align:right;">
22638.48
</td>
<td style="text-align:right;">
6
</td>
<td style="text-align:right;">
0.5
</td>
<td style="text-align:right;">
-1811.08
</td>
</tr>
<tr>
<td style="text-align:right;">
2012
</td>
<td style="text-align:left;">
2012-03-16
</td>
<td style="text-align:left;">
Christopher Martinez
</td>
<td style="text-align:left;">
Consumer
</td>
<td style="text-align:left;">
Fellowes PB500 Electric Punch Plastic Comb Binding Machine with Manual Bind
</td>
<td style="text-align:right;">
6354.95
</td>
<td style="text-align:right;">
5
</td>
<td style="text-align:right;">
0.0
</td>
<td style="text-align:right;">
3177.47
</td>
</tr>
<tr>
<td style="text-align:right;">
2013
</td>
<td style="text-align:left;">
2013-10-03
</td>
<td style="text-align:left;">
Tamara Chand
</td>
<td style="text-align:left;">
Corporate
</td>
<td style="text-align:left;">
Canon imageCLASS 2200 Advanced Copier
</td>
<td style="text-align:right;">
17499.95
</td>
<td style="text-align:right;">
5
</td>
<td style="text-align:right;">
0.0
</td>
<td style="text-align:right;">
8399.98
</td>
</tr>
<tr>
<td style="text-align:right;">
2014
</td>
<td style="text-align:left;">
2014-03-24
</td>
<td style="text-align:left;">
Raymond Buch
</td>
<td style="text-align:left;">
Consumer
</td>
<td style="text-align:left;">
Canon imageCLASS 2200 Advanced Copier
</td>
<td style="text-align:right;">
13999.96
</td>
<td style="text-align:right;">
4
</td>
<td style="text-align:right;">
0.0
</td>
<td style="text-align:right;">
6719.98
</td>
</tr>
</tbody>
</table>
<p>The above appear to be genuine sales, although they are certainly much larger than the average sales and very rare in the overall dataset. Also, they appear to be strangely segmented - I have yet to meet someone who buys 6 video conferencing units for their <code>Home Office</code> and the Canon sales are each attributed to a different segment for the same product.</p>
</div>
<div id="sales-and-profit-relationships" class="section level4">
<h4>Sales and Profit Relationships</h4>
<p>Now to plot the multiple line plots to look at the relationship between the “Sales” and “Profit” columns by year by month.</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>monthly_sales_profit <span class="ot"><-</span> sales <span class="sc">%>%</span></span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(month) <span class="sc">%>%</span> </span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise_at</span>(<span class="fu">c</span>(<span class="st">"Sales"</span>, <span class="st">"Profit"</span>), sum)</span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a></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>MSP <span class="ot"><-</span>monthly_sales_profit <span class="sc">%>%</span> </span>
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">gather</span>(<span class="at">key =</span> <span class="st">"variable"</span>, <span class="at">value =</span> <span class="st">"values"</span>, <span class="sc">-</span>month)</span>
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a>MSP</span></code></pre></div>
<pre><code>## # A tibble: 96 x 3
## month variable values
## <date> <chr> <dbl>
## 1 2011-01-01 Sales 98898.
## 2 2011-02-01 Sales 91152.
## 3 2011-03-01 Sales 145729.
## 4 2011-04-01 Sales 116916.
## 5 2011-05-01 Sales 146748.
## 6 2011-06-01 Sales 215207.
## 7 2011-07-01 Sales 115510.
## 8 2011-08-01 Sales 207581.
## 9 2011-09-01 Sales 290214.
## 10 2011-10-01 Sales 199071.
## # ... with 86 more rows</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">scipen=</span><span class="dv">999</span>)<span class="co"># prevents scientific numbering on axis</span></span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(MSP, <span class="fu">aes</span>(<span class="at">x =</span> month, <span class="at">y =</span> values)) <span class="sc">+</span></span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">color =</span> variable), <span class="at">size =</span> <span class="dv">1</span>)<span class="sc">+</span></span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Monthly Totals of Sales and Profits by Year"</span>, <span class="at">y=</span><span class="cn">NULL</span>)<span class="sc">+</span></span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"#96be25"</span>, <span class="st">"#2596be"</span>))<span class="sc">+</span></span>
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>It is really hard to see if anything is changing at all with profits. So we will plot again using a log 10 scale on the Y-axis.</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">scipen=</span><span class="dv">999</span>)<span class="co"># prevents scientific numbering on axis</span></span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(MSP, <span class="fu">aes</span>(<span class="at">x =</span> month, <span class="at">y =</span> values)) <span class="sc">+</span></span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">color =</span> variable), <span class="at">size =</span> <span class="dv">1</span>)<span class="sc">+</span></span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Monthly Totals of Sales and Profits by Year"</span>, <span class="at">subtitle =</span> <span class="st">"Y axis scaled by log 10"</span>, <span class="at">y=</span><span class="cn">NULL</span>)<span class="sc">+</span></span>
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"#96be25"</span>, <span class="st">"#2596be"</span>))<span class="sc">+</span></span>
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()<span class="sc">+</span></span>
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_log10</span>(<span class="at">breaks =</span> scales<span class="sc">::</span><span class="fu">log_breaks</span>(<span class="dv">10</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>Using a <code>log10 scale</code> on the y axis helps us see what is happening with the data. While not moving in complete tandem, the growth of total sales does have an impact on total profits and we appear to have the answer to our first question; sales and profits are increasing, but total profits appear to remain relatively low in relation - never once breaking the $50,000 mark despite total sales growing past $500,000.</p>
<p>Let’s see how correlated the two values are:</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cor</span>(sales<span class="sc">$</span>Sales, sales<span class="sc">$</span>Profit)</span></code></pre></div>
<pre><code>## [1] 0.4849181</code></pre>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(sales, <span class="fu">aes</span>(Sales, Profit)) <span class="sc">+</span></span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>()<span class="sc">+</span></span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">"lm"</span>) <span class="sc">+</span></span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAArlBMVEUAAAAAADoAAGYAOpAAZrYzZv86AAA6ADo6AGY6kNtNTU1NTW5NTY5NbqtNjshmAABmtv9uTU1uTW5uTY5ubo5ubqtuq+SOTU2OTW6OTY6Obk2OyP+QOgCQ27aQ2/+rbk2rbm6rbo6ryKur5OSr5P+2ZgC2///Ijk3I///KysrW1tbbkDrb///kq27k///r6+v/tmb/yI7/25D/29v/5Kv//7b//8j//9v//+T////ZnyBeAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAW9klEQVR4nO2dDXfbNrZF1emkb6rWSTuZOMk4r+68cRpPUltxRnHM///Hnkh9ASRIAgRA3oN77upaimRt3UNzFwRIRllVLJbgWi0dgMUaKgrKEl0UlCW6KChLdFFQluiioCzRlVPQzVIwIgsZeoYNpqBCWMjQFFQPCxmaguphIUNTUD0sZGgKqoeFDE1B9bCQoSmoHhYyNAXVw0KGpqB6WMjQFFQPCxmaguphIUNTUD0sZGgKqoeFDE1B9bCQoSmoHhYyNAXVw0KGpqB62KUar1YRClBQPexCjVerGEMpqB6WgrqLggphKai7KKgQlnNQd1FQISxkaAqqh4UMTUH1sJChKageFjI0BdXDQoZeXtDtr+ufb6vq8fX64r79kChBBhiRhQy9uKCPb26qu4v7p+ur6u55ZT+kSpABRmQhQy8u6PbVffX49nb3X7V92XpIlSADjMhChl5c0MMI2nj65sZ+OLZhsTKUn6CH6ebDRaOk/TAuPwcUhMbCN3jkEP/3m+rh59uhETQ6QQYYkYUMvbigh8GSc9D8LGToxQU9jKBP15f75bv5kCpBBhiRhQy9uKDVw3r90w3Pg87AQoZeXtCo4v5CaCx8gymoEBYyNAXVw0KGpqB6WMjQFFQPCxmaguphIUNTUD0sZGgKqoeFDE1B9bCQoSmoHhYyNAXVw0KGpqB6WMjQFFQPCxmaguphIUNTUD0sZGgKqoeFDE1B9bCQoSmoHhYyNAXVw0KGpqB6WMjQFFQPCxmaguphIUNTUD0sZGgKqoeFDE1B9bCQoSmoHtYH7v03jUrd4IqCimE94P5/Fa7QDa6LggphKai7KKgQloK6i4IKYTkHdRcFFcJChqageljI0BRUDwsZmoLqYSFDU1A9LGRoCqqHhQxNQfWwkKEpqB4WMjQF1cNChqageljI0BRUDwsZmoLqYSFDU1A9LGRoCqqHhQxNQfWwkKERBN2wWBkqmaBD7i4FI7KQoRFG0OgEGWBEFjI0BdXDQoamoHpYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMTUH1sJChKageFjI0BdXDQoamoHpYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMTUH1sJChKageFjI0BdXDQoamoHpYyNAUVA8LGZqC6mEhQ1NQWLb3X4zJ3RiGpaBLsv3/5lbmxjgsBV2SpaCpYAqait0ZSUHTwxQ0EdtSknPQRDAFTcROGDPTNEZlKei8LAXNA1PQVKw1B52zMShLQbFYyNAUVA8LGZqC6mEhQ1NQ6ay9MFKwwelYCjoD21q6l7/BCVkKOgNLQbPDFDSGpaDZYQoaxXIOmhumoEJYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMTUH1sJChKageFjI0BdXDQoamoHpYyNAUVA8LGVqCoE/XV1X1+Hp9cd9+SJQgA4zIQoaWIOjd+qqR9O556yFVggwwIgsZWoCg23/886p6fHtbbV/e2g+pEmSA07Dz/tV2ARs8L5tG0Kff/70bL7ev7qvHNzf2w7FNqdXcSpf049J9mILyFPTusj6gP1w0StoP4/JjDyhpv71m/NMW3+CZ2SQj6G6wfBoZQaMTZIApqHT2466ePXvm8c7hX9rduq5LzkGj+1LQfX3c17N9eQBep5meri/3y3fzYbzABU3LjuouMXRi9mxmXf/5+NGD4XlQISxk6AD2mVX+MK8kCWEhQ/uwH61xs34W1JiCCmEhQw+y9nTz8Cy4MQUVwkKG7mft6eZH13STgkKxkKHdbHe6GdGYggphIUO3WT8zgxpTUCEsZOgTa62EPjqP6BMbU1AhLGToPdteCSVtTEGFsJChN+ZB3V/NkMYUVAiLFtpzJRTdmIIKYWFC2yfeszemoEJYjND2dHOODaagQlj5oR3TTQqqhxUcun+6SUGzsIN3vlHQc40uhChoDnb43mEKWvkvhChoDpaCDrOthVCuvhS0ryhoPxt43p2CZmE5B3Wwk867U1A97GKN/2PON2fsS0Gx2GUaW/PNGfv6wxS0dcjXI2jEfR5RfcNgCtpaNCkQ1D69KXyDKagiQZ0n3oVvsA5BB9ftAYKOfPuCnA12VN8anYLOD7fZke+d8Z6Djn1/jZgNbtfQiXcKOj8cKKh3XzxBPRZCFHR+mIIG3PBOQeeHw+agAX1B5qBBl4Qo6Pzwng3/+sQ0fReADTb4kpDwDS5X0NWEb6BN0XcJuGYnXEpP0jczXLqgwdsnfH85yn+6mbZvJEtBpxkqfH9ZFWfm9L5JWPWCGoZ2Ne03V/j+Opdp5lKhKWgcexC0O5AOjK3C99e+2oMmBc2YIDFci7cxnuQR1E3n3+CeQzoFzZggLbz/57fMp67z67GC9uAZN3h4JURBMyZIC7cEPRnqeFtE35kFHV0JUdCMCdLC3RE0cB0vTVCvNToFzZggGXw+nAcKOuGO+hnmoCFnkChoxgSpYGNBFCaotBuWJ5x4p6AZE6SCjXOe7TmoFzi1r1GRGzz9khAFzZggFWx4tun8xBOc0teoGDbqkhAFzZggGXzWzD7NNHqYnzAHdddUNvJiJQXNmiADbJ+oD1vJz7q/zCM6omQUdKg8zmWKFdQx3USUjIIOlM/VoNPKPmHfeLacy5UIgm4WqdPI2Pvj1h/E1EHNpWOIr2SCDrmbDXYcult/Dr+E5NM3hh07g4Q4CiKMoNEJpsBOP8/Pwmeenn0nsZ6nNxElo6A91bVPoqBhJ94RJaOgfdWRz/JxI0DQ4BPviJJRUJ/ai9i+GrSkoJNOvCNKRkHt6irnHioXE3TipfT4xoBsgYL2zDz3r5o/nF/Q6fd5RDbGZcsT9DxWntQ7C2qNpBvrTWHfMTIhdKSZ0xtDs8UJelbwfH2oNYD2382U6LuZumWZufzNB0BsUYKeZTw8MwbOynzJ2TiLoK4jOgVND5s77uuLX+qHz3/5I6ZvaIJxeGUYaT9vv8PdOK2gA9NNCpoeBhC07Wf7hc7PO40TzUFHF0IUND183nOfTrv5rzFtgxOMwKtVV0DrFccbNiFGeob2WQhR0PSwYwRNVgnirxz6VfZB3vGOoGO6R2jfNToFTQ/LXiTZfoYJOlFRK3TguU0Kmh4+7cXd8Pn1xX7HypmDtgR131DXPwWYHnraiXcKmh4WO4K23TwbZ3ppvNVsHCfo5BPvFDQ9bI+gcuagTj8b6cwjuw2YjacKGnVJiIKmh+EEbXlqvd9sHOpn7KX0Y99FYEQ24jTT8nPQISk7L50Js7G3oK3ppvD9VQ4Le5ppbOx0WFp1BW0NsK5yL4ScoT1tp6DpYWmLpHA97aXTsfGYoL0HdFdoD9t7We9ClGx2QZvD/I8xXcMTWDXFz+5ffB8RdHCqSUFnYqcI+qmefX59kcrQ4Pie46Xp5cr15UuHOWjHKZ+FEAWdiYW7WcR7vLTvDK0c/mzaLwacd+ccdCYWTFBfPVfGYr51SvRs0fEQv38+3+VKCpoelnGID9Cz6+tBTWPI3JwH2eBTm8L3Vzks0CIpRs+TlOYdIrWgU0+8C99f5bAop5n8HBx82f6gafd5hIXOwEJKNrOg397NfqLed5D0ELS1EjJ+kDp0FhZSsuKvJHn62SOouRSyzIy4WUT6/iqHnbRI+v7PmI6hCXz17LHz+BHGQujwsyk3i/iGzsVCSjb7CLrfv/OcZorSsz1sPju+aPtr9EoTOh8LKVnJi6QoOzsLobOXLkMdyk4LnZGFlKxcQdPY2f2885NOt/jQWVlIyeYV9H26g/tIgmg5O75RUDg2WND3uxXSp6SG9iSIMbOx0zGlNP10TDn9/ZS+v8phQwVtToKmPRPad9/FVDONs5uGcavu/UwF769y2FBBm5Og394lu87pTBBvpvE57Y8caTw5tH9R0PTwrIJOsdN+3fyY9kcenvY2nhg6oChoenhGQUPtdP6sshdErh+Zjc23TQqd6ovH8sKIrDRBA8zstXNQ0NNTo3FL3ODQgZdMKWh62BD0tKszXEnyN3NAzoMsPYK6RlDzfeGhrU+YwIYVomSlnKj3tXNE46OIPYYennYaTww94RMoaHp4BkE95PQz0zDR8MYSdP+0MwcNDz3pEyhoeji7oMNmhtu5d/FspMPPifcxGaEXYCElwxd00Mxpcp4M3edv+UhBUVgRgqY38yjoKb+PoCHKCt9f5bAZBX18vb649/lsl52RZrYE7fpYP19qoUNB08MTBH26vqrunnt8tC1nGjNdgnY6U1AANp+gj29vq+3L29FPjlwIOay0/tR393x303ve5S7h+6scNp+g21f31eObm2Obvko+dG42fa+PlefbWIIqQtCHC1PQ3krl5blOt9Y5b2EyynEukyOoNHauEXTgo6dZeLBp0zLRUixQUN6wLJBdfA4aOYQ6zvLbnz2gXcH7qxw25yr+MmgVP7U6n+CdsOD9VQ4r6zzoNEP7BB2TteD9VQ4r4EpSMkEre51j2+qogvdXOWwJghpnMCloaSyuoKNfDkJBS2AFCNpzYj1EVCOosW4f8XOxezopaHpYrqCOgbT1tL+x/dQXc7FBRUHTwwCC9t/82dvYfkpBJbLwgrZ9rShoUSycoCNrqiasp2icgwKwEgQNWMdXp3NKK2v62XmDeyvs1wveX+WwIgQNONXUvm3T1nb4CN/+QcH7qxwWTdD2qn3TtZKClsQKETR8CD2odoxvjKS9c0kKCshKEdTb0Mo29BTf+JFlaN+fq6L3VzmsFEGDBlBjHno8xB9zmiKfXhhqHBN6ARYytCJBjTHSuSKioKWxoIIOfWWI9x0jBe+vclgpgnoaWlljZ9Uj4Kp/3tltHBN6fhYydBGCVj6Odu/83AwNkb6NgVjI0KUIGnKuiYIqYTEFXcUIenp/wfurHBZe0KBbPE5NWo1jQs/LQoYuQdCzdLaI44KGFgWFYoUI6rbRuGhkv1BRUC2sJEHHDulVVVlnmaYJyjkoFIshaJVSUKsxGgsZugBBLUOtm5Edh/5DpOYPiL9zCpoenmEV3xor7aN7108bjmkMxUKGLkNQ+yBuygggaDtWtr5SNng2VpCgh06GoNZLkgXt5srVV8gGz8dKE3TlELTq91PI75yCZmMlC1q1HW2H2r2yCTy89jaOYiloNlaooN0B0+ln/Q3LYXL0No5jOQfNxUoT1HE1yfhJ951CBJ2NhQxdlKC9a6KOiBRUAytQ0M55+tOr3SFUyBx0PhYydGmCNg3dgnYkbU9goxsLZyFDlyio/fczV9YFpT449GBf8P4qhxUraNP17OfK9UWgFQUtnhUtaGUPpK61k3mdlIIWyAoXtGNkj6CdkTW2sVQWMnTkBvvs1gUP8T2G2rDH4Nl9A+j+0sV6/rXIRQRdDQjagsc3wvEOzP2ljJUmqJnFKWhrJNw4X3UUBcVkhQlqhTlNLAcinkfQkS4UFJMVNge1NVqZfxtpCPb5n4xzUEh2rlX8xq8a0zzfG4+x4CuZoEPumk8CzxQFjKAjjUFYyNAII2h0gmF4ip8l769y2EIEVcNChtYs6KShM0Fj4furHBZc0GmTzwSNhe+vclgQQfs0pKClsxiC9npIQUtnxQu6Mr4MtO/HWRrLZCFDlyyocbkzHI5pLJSFDF2+oP0DJeLvnIKmh5cWdBoc01goCxm6ZEHHJpmIv3MKmh5e+jRTFhiRhQxNQfWwkKH1CFrGPZ0UND0sQ9BC7oqnoOlhCiqEhQytUNCTqIi/cwqaHpYhaGX4CfyvxVHQ9LAQQY9FQdWwFBSLhQytT1DOQdWwaIK2l/GIv3MKmh6WImjnRBPi75yCpofRBPW6j7ng/VUOW6Kgg1/mNLGxFBYytCJBfeagw982NrWxEBYytCZBPWAKWhJbrqB5GwvfX+WwBQrq/Tc9C95f5bAlCloyCxmaguphIUNTUD0sZGgKqoeFDK1ZUH71TeEsuKD88rDSWQqKxUKGpqDzNxa+v8phwQXlHLR0Fl1QxN85Nzg9TEGFsJChKageFjK0QkH5l+a0sJiC8q8dq2EpKBYLGZqCztbYxQaf6aKg6WFhgkqag4ZfK6Cg6WFpgiaBKSgAm0rQp+urqnp8vb64bz8kSpABpqAAbCpB79ZXjaR3z1sPqRJkgDkHBWATCbr9xz+vqse3t9X25a39kCpBBhiRhQy9vKBPv/97N15uX91Xj29u7IdjGxYrQ3kKendZH9AfLhol7Ydx+dP9/xV4sC14QCmHjR5BP6zXz3eD5dPICBqdwAMOXa4UvL/KYZMc4u/WdV0uPQeloAWyKU8zPV1f7pfv5kOqBB4wBS2QLeo8KOeg5bG8koTFQoamoHpYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMTUH1sJChKageFjI0BdXDQoamoHpYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMTUH1sJChKageFjI0BdXDQoamoHpYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMTUH1sJChKageFjI0BdXDQoamoHpYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMTUH1sJChKageFjI0BdXDQoamoHpYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMTUH1sJChKageFjI0BdXDQoamoHpYyNAUVA8LGZqC6mEhQ1NQPSxkaAqqh4UMjSDohsXKUMkEHXJ3KRiRhQyNMIJGJ8gAI7KQoSmoHhYyNAXVw0KGpqB6WMjQFFQPCxmaguphIUNTUD0sZGgKqoeFDE1B9bCQoSmoHhYyNAXVw0KGpqB6WMjQFNS7VitjSwreX+WwugRdrUxDC95f5bAUdI6+6VjI0BTUtygoHKtLUM5B4VhlgsKzkKEpqB4WMjQF1cNChqageljI0BRUDwsZmoLqYSFDU1A9LGRoCqqHhQxNQfWwkKEpqB4WMjQF1cNChqageljI0BRUDwsZmoLqYSFDU1A9LGRoCqqHhQwNLiiLFV0UlCW6KChLdFFQluiioCzRRUFZoouCskQXBWWJLgrKEl3ZBH18vb64z/Xh3bpbr9c/3x672g/5avvytt1sntZN49m3efvren018wbnEvTp+qq6e57pwx314croaj/kq4faD2fPzK2bxrNv8+Obm2r795t5NziXoI9vb/f/m89TT7/fGF3th2xNP/z0f7tPd/bM23rfePZtfqgF/HA17wbnEnT76r75P26m2h1g6qPPoav9kLFtvUucPXO3rhsvss19W5qtby5BHy5mFXR35KlHlENX+yFn250nzp65Wzf/ZyywzU/XlzNvcCEjaFMfrlSNoE3Nu82Pry+rmTe4kDloUz3To4wdt8vMQS1BZ2y8/bVelpUxB62PBTOu4usjzNO/bg9d7YeMVe8SZ8/crY9zi1m3ee/nzBtc0HnQn25mPhkp4DzorNtcn3etl2VFnAdlsZIUBWWJLgrKEl0UlCW6KChLdFFQluiioCzRRUHz1afVavXdb+fnX/7nt/43s9xFQbPVp7/8UVWfV7+cXqCgE4qC5qpv7xo133//5/EVCjqhKGiu+vbux+Mfv/ywO9j/2Aj67d1qVY+szUu/DPGspihotvpcS1nX1xe/NAf8naDf3v1198fv/2wG0y8/0NDRoqAZq14l7YT8b32Q3ym5++9zPXruhP3ytz+WDgdSFDRvfX1Rz0E/N+v5naC1ss3h/n2jLmu0KGjm2ln59cV3vx1G0E/nJdPXF81klDVcFDRXHdbs9XG9GUSbEfSzeV60mZuyhouCZqv3tYz1qqi28ssPjaDf3u1k3T1v5qI87eRRFDRffdpPN6t6wvnd/+5WRofTTLW4n1tXmVg9RUFZoouCskQXBWWJLgrKEl0UlCW6KChLdFFQluiioCzRRUFZoouCskTX/wNrr8syR0pZNQAAAABJRU5ErkJggg==" /><!-- --></p>
<p>and looking at a scatter plot is not very helpful. The raw values of the data show a poor alignment between the two values.</p>
<p>Let’s standardize for easier readability. To do that we will first look at the percentage change month over month for Sales and Profits.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>MSP<span class="ot"><-</span>MSP <span class="sc">%>%</span></span>
<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">pct_change =</span> (values<span class="sc">/</span><span class="fu">lag</span>(values)<span class="sc">-</span><span class="dv">1</span>)<span class="sc">*</span><span class="dv">100</span>)</span>
<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a><span class="fu">tail</span>(MSP, <span class="dv">5</span>)</span></code></pre></div>
<pre><code>## # A tibble: 5 x 4
## month variable values pct_change
## <date> <chr> <dbl> <dbl>
## 1 2014-08-01 Profit 53543. 91.0
## 2 2014-09-01 Profit 67979. 27.0
## 3 2014-10-01 Profit 58210. -14.4
## 4 2014-11-01 Profit 62857. 7.98
## 5 2014-12-01 Profit 46917. -25.4</code></pre>
<p>Charting the data we get:</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">scipen=</span><span class="dv">999</span>)<span class="co"># prevents scientific numbering on axis</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(MSP, <span class="fu">aes</span>(<span class="at">x =</span> month, <span class="at">y =</span> pct_change)) <span class="sc">+</span></span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">color =</span> variable), <span class="at">size =</span> <span class="dv">1</span>)<span class="sc">+</span></span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Monthly % Change of Sales and Profits by Year"</span>, <span class="at">y=</span><span class="cn">NULL</span>)<span class="sc">+</span></span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"#96be25"</span>, <span class="st">"#2596be"</span>))<span class="sc">+</span></span>
<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>This makes things a little clearer and shows that sales and profits are both trending at similar rates, with profits being much less stable and more variable than sales.</p>
</div>
<div id="rolling-averages" class="section level3">
<h3>Rolling Averages</h3>
<p>Further standardizing, we will next look a the rolling means:</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>daily_sales <span class="ot"><-</span> sales <span class="sc">%>%</span></span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(Order.Date, Sales, Quantity, Profit, Shipping.Cost) <span class="sc">%>%</span></span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(Order.Date) <span class="sc">%>%</span> </span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(</span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a> <span class="at">daSales =</span> <span class="fu">sum</span>(Sales),</span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a> <span class="at">daProfits =</span> <span class="fu">sum</span>(Profit), </span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a> <span class="at">daShipping =</span> <span class="fu">sum</span>(Shipping.Cost))</span>
<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a>rolling <span class="ot"><-</span>daily_sales <span class="sc">%>%</span></span>
<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">sales_01da =</span> <span class="fu">rollmean</span>(daSales, <span class="at">k =</span> <span class="dv">1</span>, <span class="at">fill =</span> <span class="cn">NA</span>),</span>
<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a> <span class="at">sales_07da =</span> <span class="fu">rollmean</span>(daSales, <span class="at">k =</span> <span class="dv">7</span>, <span class="at">fill =</span> <span class="cn">NA</span>),</span>
<span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a> <span class="at">sales_30da =</span> <span class="fu">rollmean</span>(daSales, <span class="at">k =</span> <span class="dv">30</span>, <span class="at">fill =</span> <span class="cn">NA</span>),</span>
<span id="cb29-12"><a href="#cb29-12" aria-hidden="true" tabindex="-1"></a> <span class="at">sales_360da =</span> <span class="fu">rollmean</span>(daSales, <span class="at">k =</span> <span class="dv">360</span>, <span class="at">fill =</span> <span class="cn">NA</span>), </span>
<span id="cb29-13"><a href="#cb29-13" aria-hidden="true" tabindex="-1"></a> <span class="at">profits_01da =</span> <span class="fu">rollmean</span>(daProfits, <span class="at">k =</span> <span class="dv">1</span>, <span class="at">fill =</span> <span class="cn">NA</span>),</span>
<span id="cb29-14"><a href="#cb29-14" aria-hidden="true" tabindex="-1"></a> <span class="at">profits_07da =</span> <span class="fu">rollmean</span>(daProfits, <span class="at">k =</span> <span class="dv">7</span>, <span class="at">fill =</span> <span class="cn">NA</span>),</span>
<span id="cb29-15"><a href="#cb29-15" aria-hidden="true" tabindex="-1"></a> <span class="at">profits_30da =</span> <span class="fu">rollmean</span>(daProfits, <span class="at">k =</span> <span class="dv">30</span>, <span class="at">fill =</span> <span class="cn">NA</span>),</span>
<span id="cb29-16"><a href="#cb29-16" aria-hidden="true" tabindex="-1"></a> <span class="at">profits_360da =</span> <span class="fu">rollmean</span>(daProfits, <span class="at">k =</span> <span class="dv">360</span>, <span class="at">fill =</span> <span class="cn">NA</span>)) </span>
<span id="cb29-17"><a href="#cb29-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-18"><a href="#cb29-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-19"><a href="#cb29-19" aria-hidden="true" tabindex="-1"></a>rolling <span class="ot"><-</span> <span class="fu">subset</span>(rolling, <span class="at">select =</span> <span class="sc">-</span><span class="fu">c</span>(daSales, daProfits, daShipping))</span>
<span id="cb29-20"><a href="#cb29-20" aria-hidden="true" tabindex="-1"></a>rolling</span></code></pre></div>
<pre><code>## # A tibble: 1,430 x 9
## Order.Date sales_01da sales_07da sales_30da sales_360da profits_01da
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 809. NA NA NA 199.
## 2 2011-01-02 314. NA NA NA 3.12
## 3 2011-01-03 4504. NA NA NA 185.
## 4 2011-01-04 2809. 2835. NA NA 635.
## 5 2011-01-05 3662. 3618. NA NA 1053.
## 6 2011-01-06 623. 3690. NA NA 86.1
## 7 2011-01-07 7123. 4017. NA NA 1541.
## 8 2011-01-08 6293. 4537. NA NA -631.
## 9 2011-01-09 814. 4390. NA NA -118.
## 10 2011-01-10 6794. 4670. NA NA 1444.
## # ... with 1,420 more rows, and 3 more variables: profits_07da <dbl>,
## # profits_30da <dbl>, profits_360da <dbl></code></pre>
<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>rs <span class="ot"><-</span> <span class="fu">ggplot</span>(rolling, <span class="fu">aes</span>(<span class="at">x =</span> Order.Date, <span class="at">y =</span> sales_01da)) <span class="sc">+</span></span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="at">color=</span><span class="st">"#2596be"</span>) <span class="sc">+</span></span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> sales_07da), <span class="at">color =</span> <span class="st">"dark blue"</span>, <span class="at">size =</span> <span class="fl">0.75</span>)<span class="sc">+</span></span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span> (<span class="at">x =</span> <span class="st">"Order Date"</span>, <span class="at">y =</span> <span class="st">"Daily Sales"</span>, <span class="at">title =</span> <span class="st">"Daily Sales with 7 day Rolling Average"</span>) </span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>rs</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAABPlBMVEUAAAAAADoAAGYAAIsAOjoAOmYAOpAAZpAAZrYllr4zMzM6AAA6ADo6AGY6OgA6Ojo6OmY6ZmY6ZpA6ZrY6kLY6kNtNTU1NTW5NTY5NbqtNjshmAABmADpmOgBmOjpmOmZmZmZmkJBmkLZmkNtmtpBmtrZmtttmtv9uTU1uTW5uTY5ubo5ubqtuq+SOTU2OTW6OTY6Obk2OyP+QOgCQZgCQZjqQZmaQkDqQkGaQkLaQtraQttuQ27aQ2/+rbk2rbm6rbo6ryKur5OSr5P+2ZgC2Zjq2kDq2kGa2tpC2ttu225C229u22/+2/7a2/9u2///Ijk3I///bkDrbkGbbtmbbtpDb27bb29vb2//b/7bb/9vb///kq27k///r6+v/tmb/yI7/25D/27b/29v/5Kv//7b//8j//9v//+T///8UjpLZAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO3di3/dRnYf8CvN2uExN9skW3FXVt1Hmqwcx7IdqWnTJtYmMZOmzaaymN0kG0lDcUOJwv//DxSPOfMAZoABMADm4v7Ox9YlAdwzZzDfO3jwkvdQIBAZx2HrAhCIvgBQRNYBoIisA0ARWQeAIrIOAEVkHQCKyDoAFJF1JAF6dajjd37hLL179vGr6v/21nf/+0eHww9+/1V726im6g1v/tOL9jNuzpsa7n3rbHw2WHSrai56sKDrw+OoghHzIiXQgztmgXG+e9Zs66wYB/TqfkKghzJZt+jBgi4PPbkRySIN0HqM7/7x3OFRhWecrw8fl3PWb545mqOB6va8z7g+PHBb7wPawLz7G+c5UTiL6gXx7zqdRSwQCYGyj1+WR/B7/9WMdXMwfPdIObhqBvbdo7PWtqXaz9QR9+5Py8U/a7ZvVl1VqUtw5Xf/Wk3BD8ovfv1Z/VwT7x4ZWb88P3z098/sNtp1qKKbBVXTH/3CnUHL///uR6qJMt2937e8X93/v+cPdJa6M1x9WeXV4f4vdLv2k3UPEZGRFGhz/OWjPY9zQ/GaJxx7lnO2VYfpcjt1FqCm2Fp0fUS9OX9gAf3os/ZZxaWZ1OrMH312ZrXRqkMV/W+XVT3XpmkbqD5xUUk00DJX88K5OX9cNPat6n9wXp7AXHmerLdBxEZSoMXl/Rd3z8rJoxyJM3O0vKxWX+rJ7W8O9373f/1L9VV722rS/FX53c35j1/Vi+uoDLx7VJ0qlrisc9DqLPYf7TNB/YwK0L0/r04jzqw2WnXY56Blsp9VS5zZswJ6Vr3gzqx0KiqR9YzcMK37zdWXT3tg982uhbdBxEZyoOW///x3//38YAG9rqcvM2/+05+e6yO4te1Nfcyskt2c/+C//L3evJr5bn74Z+W8c6kSNkCrtppZUYU1gTa5lFjVRqsOBfTej1/pDS+bF4D5n5toZn3rBVB1tGm7mt/rCnX1zdNMu+bJZpv5+/xUIj1QdT1tAa2H2D2u3f3qP1bD5mx7fdAX4pfV4+/xuVrp8vr+Pzx6XOFqXcXbl0E21uZ8s16r22jVURf9y/PfqybU5kDNM7R7JV8lac6bTWMqZ5WhQtfMp1y9usbS7ZonWz1EREbyc9DyYPy7//P//PqRBbRye9m+MO5uaw/frz6z7v+Ubi7LU76zilEPUPvOpAFq2mjV0RR9fTBnkvFA+fTgQdOP6nXZBmraBdA5kfwqvhlr5xy0XP6H+siqr7Qvq0O5va1SwvFP/43B3Zz/4bMHxdXHf8su/UAvrSNnczBtDr3chlsHF33ZzOT+Qzw30TrEqzmy6crVvb94dKaN67WmXfsQj5v7YyPtfdD6CF9eWPzms4M7xtZNw8v62H33y/OKpLtteTFRXk3cf3F9+Peviru/5eeUl8U//Lac3n50xjcK1OW2A9S5e1lemPysulo5s+pp1aGAvnvUc5HETbQukvhGRH3Se3P+n5urJa6egXK7zkWS2ibFXj+NSP6TJPODIuue95X145ry2MfHOXdbPgI+4OXmaHxozm35qvmquQ/qAnWul5pc1W0m5wdXdh087V/V10/+20y6Cfc2E1+NXR/ql8xBnSzo6pun6XbNk/U2iNhICPRec1VT3/L+88tmJtEXDBae+mfxzcbOtuVm5bc/qK7u6xv1P9YTooJQOWiuuj47nP1rG6i6ROYoz2I/+n/Pzqw2WnWYE+cK/o3/Rr1uorrX/gfqJWAd6qscV8otV6/6bLVrnqx7iIiMdd7NlMs7K+bVYd8qW/fJJxyrAP3NZ2N+0r5cTK7j5vy3ypPiy2m8Zz355GMFoNVJZw6zx4w62ifF6z355GMFoOUA/Xj5VoZjTh2tk+IVn3zqgXfUI7IOAEVkHQCKyDoAFJF1ACgi60gBVIajb11sIMduc/SkAFDk2D4HgCJH1jkAFDmyzgGgyJF1DgBFjqxzAChyZJ0DQJEj6xwAihxZ5wBQ5Mg6B4AiR9Y55gL98PxpUbz/6uLhm8ADgCLHQimigL6+eFojff2p/wFAkWOpFDFAb//4T54W7795Wdx+8dL7AKDIsVSKCKAffv7X5UR5++Wb4v3X33kfyo0+KaNvEkYgZkUP0NdPqiP524e1Re+D2nDZVxly7DfHrBm0nCU/DM+gAIoci6QYBvr6ooonOAdFjsVypLjN9OH5k+a63fMAoMgxKig+RTRQ3AdFjmQ5UgONjLSdQI795gBQ5Mg6B4AiR9Y5ABQ5ss4BoMiRdQ4ARY6scwAocmSdA0CRI+scAIocWecAUOTIOgeAIkfWOQAUObLOAaDIkXUOAEWOrHMAKHJknQNAkSPrHACKHFnnAFDkyDoHgCJH1jkAFDmyzgGgyJF1DgBFjqxzAChyZJ0DQJEj6xwnBrTd3a3qQI7YANBN6kCO2ADQTepAjtgA0E3qOLYcBKBzOhGfA0CnBYDO6kR8DgCdFgA6qxPxOQB0WgDorE7E5wDQaQGgszoRnwNApwWAzupEfA4AnRYAOqsT8TkAdFoA6KxOxOcA0GkBoLM6EZ8DQKcFgM7qRHwOAJ0WADqrE/E5AHRaAOisTsTnANBpcXJAtwrauoAjDTqmljGDnl6Ok5tB03YiPgeATgsAndWJ+BwAOi0AdFYn4nMA6LQA0FmdiM8BoNMCQGd1Ij4HgE4LAJ3VifgcADotAHRWJ+JzAOi0ANBZnYjPAaDTAkBndSI+B4BOCALQmZ2IzwGgEwJA53YiPgeATggAnduJ+BwAOiEAdG4n4nMA6IQA0LmdiM8BoBMCQOd2Ij4HgE4IAJ3bifgcADohAHRuJ+JzAOiEANC5nYjPAaATAkDndiI+B4BOCACd24n4HAA6IQB0bificwDohADQuZ2IzwGgEwJA53YiPgeATggAnduJ+BwAOiEAdG4n4nMA6IRIAZSm1gGgm9RxVDkANLaC6ADQlDkANLaC6ADQlDkANLaC6ADQlDkANLaC6ADQlDkANLaC6ADQlDkANLaC6ADQlDkANLaC6ADQlDm2BdoaMgBdpY6jygGgsRVEB4CmzAGgsRVEB4CmzAGgsRVEB4CmzAGgsRVEB4CmzAGgsRVEB4CmzAGgsRVEB4CmzAGgsRVEB4CmzAGgsRVEB4CmzLEjoG8vLn76sijef3Xx8E3gAUCPLsd+gN5+8bJ4/Wnx4fnT4AOAHl+OtYE6g5T8EF8iff/Ny+DDPKCxvgA0ZY59AS1nydsv3xTvv/7O+1Bu8UkZvSnCQROfly7BSQXpB0qUKm5bCn0TF31Abz//yXfF24e1Re+D2q7n9YMZNJscpB9WmEHN0DiTZvIZNDR1mhkUQI8lxy6BFt8/xTnoTnLsDqg6jH94/qS5bvc8AOgR5dgd0OL1xUV5DrrcfVAAXTXH/oBGR0+hAJpNDgD1NhPVif4A0CQ5ANTbTFQn+gNAk+QAUG8zUZ3oDwBNkgNAvc1EdaI/ADRJDgD1NhPVif4A0CQ5ANTbTFQn+gNAk+QAUG8zUZ3oj2MF6isPQJ0A0Jg6lspxkkDJeZBdoM5eAdCYOpbKAaD1VwA6K8eIAFA3FYAO5QDQMbECUBoFtL8MAB0dAOqmAtChHAA6JgDU28xgN4cDQJPkWAsoWY1JAJ2bY0QAqJsqCJQAdFyCgTqWygGg9TIAnZVjRACoSUXeOgB0WoKBOpbKAaD1MgCdlWNEAKhJBaAxOQB0TACot5nebsYFgCbJAaDeZnq7GRcAmiTH9kAJQCfVsVSOEwfK7QHorBwjAkBNKgCNyQGgYwJAvc30djMuADRJjvyAEoBG1bFUDgCtlwHorBwj4niAencMgHqb6e1mXADo2BwACqARAaD1MgCdlWNEAKhJNQuoWQOgMXUslQNA62UAOivHiABQkyoBUALQyDqWykGeAgEUQEfXsVQOAK2X+YCSBND4OpbKAaD1MgCdlWNEAKhJBaAxOQA02HJ44eZACUBH1rFUDgCtl/UApZWATgzaPMGyQZsV6G2X9IN39bj8/hTUrCK7FesDjqn6htQWRQ10ODCDLpYDM2i9LIMZtKcrALpFHZkBJfM0AJ1Qx1I5ALReBqCzcoyIYwZKABpoprebcQGgY3P0AqVtgBKA9i0HUAD1N9PbzbgA0LE58gRKpwc0kGHPQKNwASiARgSAmvYAdCDxHoDyqdxxAKU2UDpVoN7RGFNHbBwF0PAuGQF0aGQANC4HgIY36i7eA9B3jx6/e3S4/wJAR9URCAA17SUCenlWXN1/cXUGoKPqCASAmvYaj0LMA1pOoHfPzorrsVNoT1cANF0dRwWU/EDFXKDvHj0A0LF1BCJfoL1vZfc+v71+G6B3zx5c3/u2OtAD6Jg6AnFSQLndRYEWN+eHs+Ly41cAOqqOQJwuUFoK6MTo6QqASqceAAXQvQIlAC2Kq8Ph8RUO8SPrCEQyoM3zAbQoTz9/3dxpAtAxdQQCQLm95jHRbabHuM00to5AnBhQAtBOgqMCSpPriANqtbJXoMVVdYiv7tUD6Jg6ArEjoK0Fjb4IoNQBKkhtMe0i6fpQxlifAOqP0wRKFlBioJQK6LTo6SqASgAl9gigwcQAevRA3z06cOzrImk6jNg6AgGgmEH9aQDUtxJAvc2M2Q1jcwCoZyvzZWvlHoDenOMQP6GOQHSAhnENJar/zRkojQBKk4HePXtw96y+Vw+gY+oIBICmBlrRvHxQXI99t0hPVwFUrgKU1gZKHqA0AihNBXq1u99JAlCZAqi9bCug1S97lDpHv9+up6sAKg0wAJ0LtDwJLS4P974d5xNA/QGgyYFOjJ6uAqhcAygFgeoSKDOgTYRrANDR4QxIxPbpgdIQUOrsEgcoyaMGelUe26sfeO7s3UwAKvcBtPqV+OrXPfb2ftCdAKWdAJVTgVYXSMXN+ePqbcu4ih+qY89AaSmgYhbQ+gdI1Syq74Pefn5x8bQo3n918fBN4AFAewJAfUDFLKD15KmAvv/6u+L2j7778Pxp8frTwvsAoH2RD1BaASgtDfTu2eOi+Y1j9bdv3lYAv3/6/puXxe0XL70PANoXANoFKqcDrWbP+hT0+mDeLFLOordfvgk+lFt8Usa4M1YdNPF5JgH501DPd4kiJqn+TODmgaI+3DfYGBF/jLBvC7WGOruEuBKzSTFQiOezjO2GyfdBxfUSIlUmqS3U/+rrCmhRAy000N5C6rBuM11Wd5junlk/SPrw/Enx9mFt0fugNut5NWIGlXubQWnGDErODCrHzaDdeP/Vk/JSqX8GPXGgfX0wQLUwAE0J9Pbzp5VSnIN66jgqoHyJTVkAFQy0/ioYw0Abn/Vhvr5u9zwAKIByAQYoDQJt1ldfhqsYBvr6ooqnuA/qqyNzoOQ8dxGg1AdUo+wApXRA46OnswAq9wrUfq8cWUAlgOoEPqAEoBkCpSRA3z0a/V7lpYFSX47tgGoB44AOvUkjmEj2AaWTAVrfCh3995UBNBDbACW5Z6DFpDeE9nQWQOX+gKrbRNsAbYiO+73Ons4CqARQXpsE6NXhcFaM/Kykns7mAXQijFZsCpSOAij1AK3fyTQT6N2zQ/NOkXG/Gt/T2dWBEgFo5kDFZKDvHo3+JG4ADRYnibeKAtqzN5geTQZK+QCVM4BOjZ7OAqgE0DRA8/wDtgAqxwKlnQLFDDoUyYFS8Jt2qqMBSkGgAkD1Mnfx3oFqCbxVH1DaCqiYDbT+FK8dHeL3CrTTF0vChkBrfIsCPeEZNKYQ6QPqGUor6YkCNTc6ARRAJQuwgPLJpvPchYHyn2RogKqv0wPN72/UDwA1A2GWbQS0re2ogbaLMUD5Wn0AqFgEaIZ/o/5ogLpjCqBdoDQfaIZ/ox5A5XECFYNAxTSgmf2N+hRACUD9QANNxgK1NuoFapbSTKAZ/o36kUAJQHcNNL+/UR8L1Np9ALoR0ObEcwTQbjtWeIFOjHArRw/UXrUhUIoDSjp0wlWBCgB1VwKozA1o/a8FlFICva7vgo69yXTaQMldYp62JFD3iSsBNV2OAtr8aMkGygWoHzqNBnr3rL56f/do9O91hluZA1QzA9B8gJIfqPABFUGgasuRQPnivf5j9QDaXZU1UFoFKG0ItPoLy65UAE0IlE4YaLN8LlDzE85MbtRHAqWcgJJ5WjZAKQOgAkD5OXkBJb2OgdoJtwCq3AAoN9OzLglQ2hAoAagKAPVvkT9Q3i4F0PongysCJXtRDkAz+63OvIBSCCg5S8zWaYESvzvtdIHOiHArAMrr1gCqft0CQAE0F6CkqzDbbAlUAKizxZZAKRooJQEqJgClbYFaVbFWDVSoH4UGA0CjCokDyuM3EWibQ7cIBko2UJoIVMwGyjOijg5Q2QPU+gWlzIH2C40Gao5n2wAlPX6TgdJAEQmAiqRABYAeBVBSQK2plLdeAqgYB5QsoExkHFC7PwDa2U8yG6A0GijlB1QwUJEKqE7gAhUAygkigNJmQJsRSgdUzgUqFgRKPqDq47c3BzoxBj8Vugh9+rROwJ/7bD74mj91Wj+TyPPx0p6Wele1U6iPxq6j4M+fVpsT11FjURUU+hOqPe32fM62+iTselCrhIXVW7I+X1v9o4FyDeqTuNlUwY99/TZPtCosdFcMUJNAM2Sghfqm0HNroT6nuylWVRPqtBV7n0HVK32dGVSXknAG5eeqGbR8mDOD8tw1PIM6RTkzKB/N3RmU58XODKrY2jOo/mOhOMTzb8CsApRWBco9HQCq19DSQG2iANokiAEq9gRU9AIVeQEVuwdKawPtVDQFKGUAlPS+cIASgLab6VmXGCjPGrQ6UFoaKC0E1N/mMFAJoHIYKEUApW2AkgFKiYA2x+cEQJvz1GlAKR4oVwOgALouUBoA2uADUB5yP1CzgwG0nvPigdJUoJKBEoCq9RsD5dYBFED96wHUBaqugGygNBUojQEqBAGoZ30PUK1UnRBtCJS51EO6AlABoBsCJfvL7IHq9/PouXwLoDQHKJkixgKlLlB9JCEAPUmglBFQ87sdPqB09EApDJR32lEBpTFAndfhNKC6pABQigCqDUYD5dkRQG2gtCxQLiQ7oDQMtPmOgTKWpYCSHyidFFBKDtSqIwCUMgeqTui8QIUHqFgKKI0E2jMsAOqrY49AxWZA6dSB8j2/TIDSIFAxGagYBGoQpgDq7sYEQOk0gVp3pdWOrPu9O6ByFlARBtodgolAnf3Bf54eQH1AxT6B0mSgog20/G8GUDEMlM8SAPQYgNZDmRCocIDSykCF2qEGKO0QKPEo90QOQHmKmglUJAKqBjwEVKQAanoGoL1AaQ5Qd+zSAKVeoBwyCFQooEalHt8xQCk5ULfPo4FSDFDqAq3/2xdQdbg9baCyDZRVGqB8fjgCqLUHIoCq3kUBVT9e4sUA2gJKiYDSeKAspa5gSaAWTfOWuzBQORMo8ctvGCgBqA+oZrM1UDJA+a09KwFlMB6gsq6lH6h1+6EFVACo3lFDQPn0hrfsAVoNSF9TrcKsby2g9t5cF6j6E0fZAqUTBKrZyQGgzQ7rByqWB2peKsmB8o+D4oDqA/kkoDQMlH35gBp8/A0dGVCyHxIBFX1A+TezVwOq930LqNgWaL0oFqgIAhUdoASgRwpU2EBpdaBkA+VrbwDtNtMeceshFVAxEmi72TWB0kSgNBKosIGam0NtoASg7RG3Ho4DKMUAFQmAktMwN58aKGmghoppnF9to4GybQ9Qs5sAVBoqSYE2OWcCpRRAaR5Qmgq0yiTNr7oBaEKgtC5Q3v1doNQCShsAlQqoXb0E0DaA5YCKNEDNePUDZQhOs+sBJQco8e0sByh1gcqVgVqL9weUMgfatDYWqKm4ByjPdwZoQy4IlLxASbXM4GKBUjRQMRKoOQ8C0PrNd4NAqb1KF5gJUGly81ToAyq9QF0mHqCkGyedwAtU6Pcy8vsaAXQCUD3dTAZKqYGKWKDWDmJOcUBV6sWAigaowQWgKYCKAaB8rGzXlwQoJQXKZ2/mYG0BJRcopQZaL+oBSjsF6heqRmc6UH2K37zuJW0HlH/VbUmgtChQAtB2zAcqDFAppwFtRvs4gJINlM8tmgXRQNWQWHvQAio8QOmEgVJ+QMUwUIeCAUqzgdIgULKB0lJAKT+gt1+8LIr3X108fBN4GA/UGobZQCkWqHq08thfRgEVIaDswweU5gOl8UCteiYCpWlAZRgow0wL9O3FT18WH54/LV5/6n9IArSjdAAo78xUQHVCy0ckUD3ZJARK1i7oAJXLAVXNC/0rcUmAtpakBfr9T/6qnEHff/Oymkm9DysDJQZaPyUAlNIDVb6svqjhcqMLVE4CSomAcgFBoGo/DQHV6XID2hzib798U7z/+jvvQ7nJJ2X0pXCD7E8iJrK+sTei5oOE+XN3neX1mnqhBVRvaYAWar82T2wedRHOl5yvMJ+/TOoDf6n5sGAGaqJu1MFpA62fWD/DB1S1y30j/mBh4h1E1icYF40PVUr1jxDWpxurdvRZctECqooqrH8Kk0PtJ/4E4qJJLwoLaGGnq3utPtu5qKtqBpFHoOB2eCPzD1df2Ku7Q++JQaBvH9YWvQ9qs/DLIDyDUtQMShNmULVI7dfmiZEzKH8xfgZ1DvJJZtAm0awZVJ9L2pNZzwxq0vtn0Ga/8ix4JDNoHkCpDVT/Ff/miTFANZJRQCkAVLhAxRBQvWIaUFIZRgLlv6fQBWpltbJ1gHIvNwS60DnoTKDUA5R6gNIAUD47NkVEAqUYoM2clRSoCAKV6wAVWwP98PxJc93uedgeqAgBVbt5AlAe5qMA6lJKBlSfkRwB0EXug5LmsCxQbs4CSgmACg9QCgAVbaC0DFBTckKg+uNk9AnDdKByAaCRsQBQ0XTaA7QZ4xWBkgaqn+gF6k5mgm9D6rNiD1DzJTd9VEBFH1BpN7oToKQcZgZURALVWGygYj5Qi1EbaJ2YVRqgMglQnTsItP6FL3XeAqBhoPwxey2gYiWgfDdcX7BIfdtnJlBtfRGg5rUdB1QA6CSgZAE1B9tmbTRQDbMfKI0ESgYoqcKSA+VNFwFqdqcXqN43PUC58GMFShOBUgcoSf2TPd6f6wAlZiD4SlcDpfFAKQ6oczKzEFCKASo6QL1aAXRBoBQEqq5gpgI1I2fNNjZQ6gCV5rgbC9Q52s4BaviFgDqTZQ9QeVpAqQ2Ud5wfqNsi6XlhPFDyA2WQALpXoHW3m7EYC1SOBcrbeYEyTg9Qja4DlAczGij1AJVdoHx0TwdUDAPlKzyNy3R1D0ApU6A0CFR0gJq3iTCh8UC5B7pZezKTVlI/UNEBKnXJLaDSANU2DFD9oZoTgOoROBGgPD85QIXVCwYqJgLl3RgCyrOXHmaTJz1QIn0akwqoVXkkUOoDqndtAKjzG89hoHrNCQF174R4gfL4pwMqaqBkAyWzXQ9QDZO/TwFULAKUXKC0CFB7y8yBihlAtRt1c9780C0hUGsARPX+XAPUTIoDQFVYN0hdoOxR2L94lBSoZcUDlPTrpw+oFBQCak+/OwMqNJvJQHkAZwIVyYDykLWB6i19QJvKQ0DFNKDUD1T/R+T0JQBUlxQEKq01+wAqRgJVoxAFVKYAap9rqnfIRwHl54wAKjRQaiVtrgr9QO13w3qBiqlAzYluFFB5gkCbA0sHqBod96+ECb7tMhdoXVEHqHkZjAPK06GjzgAlGyhtC5SP9eZc+BSBkg8oDQGleKDuAU8ZSQ6Uy6MpQIseoG3LM4BSBFD9/AGgzf7xAdU17AMo+5gLlMYDlaOAGnEajgdo11I0UPWd7kEIaJ1nCKi70qpI7aNBoGI6UL4kPBmgNBUo7+txQCkIVJ+bMZx4oLqQEFCeeCOBWgZlGyi/eAJAKQzUTuIFqi/CeoHSjoEKDZT6gAo/UD06TD0KKC0MVA8oCdJjyOulC9TqgXZoJ21Nxm2g5jlzgPJLKgSU92oQKBGAdoDymgGgvH8YKA+SBZQMULKBCj06I4GayydrDB2glDdQ3ZgDVCccB9T9BkBzAEqUBCj3cCZQVXAHqDkNYYHTgcpjB0pxQI2cbYEKA5TTbgLUVRgCahKnBioBtP5JcCZAeVgsoELfHHCAUhuotL6aDVRHJNC2mVigpvUeoKozNACU9O3mPQGlSKDVVy5QPbZ8Wk/unDIHqOgCpZFApbkeV4PAWwwCNUJZTf2C8QDVk/t8oPyV/s4FSs2qXqBmNx8pUBoDlCKB6sGMBtqs7APKk5IDtGnEAWqOxvZIjQNqtRAESn1AKRFQfT7TAmqy7B0ojQdKAaBm2EYCJf1zU90gA+XhtkZN2m9H50GJAaoG314TB5TMXdFBoMR/wHMuUN5WmITSB5RmA5X7A6o3GAVUhoDKIFBKAtQZO2qtGQIqRgLVJQi33SlAmxcv8cmmH6j9JYA2EL1AZRCoGlLpwPECJQ9Q2hQo98YHVPYCbaHbBKjcA1AaCZQvIB2gzXDpP6toMxD8PuFYoBQCSmGgxoQDVEqnsbFArbNPflXypD4WqJY4CSgBaA9Q8gMVzRvogkB5F/BRmsdkLlCh3+3rAUrzgaoTyC5QfYQYAmpamQyUOkAFgPYDVRuS8AM1p5heoNaYxAPVWtYEqm1GACVaDKhOYAGtdz2A2kCb3a9HNiVQORKoiAfaNjkPKGe11tBUoDaleKASQC2g5AFq9gTfLV8RqLSBmkXLALWEeoGK9YBKPmItAFTt4D0AFTZQ6gMqVgTK8/hSQIMzqOqMNHdTdbaEQE2/xgCVdo93CVQID1Bhdsc0oPXP44aACs7rBSo6QGlhoMokhYCaLB2g1s5KAZRsoGannihQEQRqD1Q/UNkGSnqPxwMl6bz9ufuWkxZDI2kKUP7KBUpBoNLKsgzQNrZTB0oRQM0dQgWULKBGTjxQs3MYqGpDnXD0ArV5SGk0qfpmA7VSukKHgBZLAXVe2KcFlPdVLFCzxwR/lLYayyZVEqA0CSjPxCOB8nfTlCYAAAsPSURBVIh0gErrzIGP71sAlaOBFl2gjskhoDJboML8JHMQKNVAiVygUj+OAEpC6Lk3BJQsPW2g1uVNSqCamz7waxc9QBlOCCjvoXigbtXSygKgXqCyBygTmwLU+pGTOZ6yGp7HFgZKawOV7rZJgcrsgfZG1YuC1OcHVwfswgJaLxHNp9/y+NSPhfqo4KLeSH2nVjT/F+YjdDVQXsnLC/3hykoFN9JsyFQKZkl2+2R9ZG/TDQPUWWNVUlB3ja5EbaZmav68YfMEG2ihH6z+tBKR227BK52PMObFhVujnZLXWE+0S9fltDqphrZQ/eN9bXZ7YcrSFdi9MJv1xeozKLkzKAl+V6M9ha0zg1pzGeOQpv1xM6gzuThrvDOoqsGaQU0vrO/0SmfaiZ9BzWK5xQzqjkFrJs31EE++Q7wLVG1tgNIiQEVeQB2hDtD2qG4BtNvJGqjcAVCjynwIKxkwfJ9FMp9BoHImUPan65Dm3lYYqEwFlCygdNxA5W6BCgMmIdDOju4A5ZanAVVPSgnUaiQfoHrbUwBqH5dbThRQcoEKP1BFew5Q/croB2p+aO8HSvOBdg0aoLK70h1VuRhQJxuA2kAbfQGg/P56kyYEVB4DUC53NtA2qblApZPNzn9yQKkLVISAEt98CgE1x8YhoOQAFS2gOj0B6MJA9ZpjASocoK4NP1DeFT6gLgsNlLpAyQ+UzD8doHTSQO2u7ASoiAUqwkD5RNXs8jDQDgsfUPNmlPFAKTVQBgOgGwGl+uC6MdCCgZJuon6QRw3UJdUuZiWgXNZRA1VjGgRKwjpJdEbGTHUJgMokQOVsoKaJAFDrmxBQx0oHqGFoA3XXAKgDVI2sDZR/qGKAWm/1dIA2G4aA6jlpElA5Gmh3TRuoDH2zLlBr8ZJAzW4OA7U2yh2omAbUqAkB1f0PA+VsDVCeDZt2yYyLf2RSAuUEDlDrpZEYqLQTJAAqo4HK7jdHCZRWA0p+oBQEao9qeqDSw8LRugzQAkBHACWhj7HmzfKLATVXZXU28yt76YA6enqBSul9Th5AO1D3C9SarlpAjbsuUNkFKoNArf73AZWWULJ3/iJAO2DGAu2sbAGVA+21tMwAKtvt7Apo44GBkgOUfZlhHwaqd8UcoNKknATUJRUE6mx2fEC9XQkD7TR5FEBNWEBpUaD2nooFKs0WNlCd9RiBukAANBIo6V8/MoNgAbX2lNRAnZ0/GijZLDhhL1B3iC2j/qOeKbhvVF2geqy8QCV1e3P0QM3CTIDWHPR7lPV5JzFQs194ODpAC6E+BUHvfDkFqMVvQ6C2iAlAJYAuAVQHmZAOUNkHlJyxSgC0uallgMoEQMMDmRyolVV2m1gYqEm0J6D6/0GgchWg1XuSpgP1N5EKqHnOSkCdRnxA3f7tH6jU/8r1gJo1+hXSAmp2Ww5AuSYAXR6oDAO1d1IsUA7X1QpAC0V0baAtHkcHVLa+yRIoMVAWcFpA9RYxQKW1eALQFrqlgModAeWzvrWAumPmASoNUGuEpP3NjoHKKKDB/u0VKO0SqDU4Y4DKCKByYaCebU8YqBwGanYKD6D+fhioJHvfRQKVSYA6gx8GKvMEKu1t1wFqbXZ0QHm4WkBlGqCyB6i20g9Urgk0kKiQplbpb28AqAwCDVVvPRdAfUDtPebs7+MCKn1Apa/gQNZ6n64GVLqPewdK+QI1+3gYqDUI+QF1KgDQZEDd/Q2g3axWe/sBKrMBKl2gchioPcQ9QNu7LzRm0UC5giBQtzanmPYQe4ZrHFDZWjgaqLSzLQjUasfpseyut/uVN1DZBqorXxCoHAXUGcNdA/VUf4JA1S3OdEBlazcOApWpgDqNSmm3Nwy0KcNd0wvUNFHv04lA7Z08C6jcG1A5Dqi1/9scUgC1Jq8QUHe4YoDK1nOGgUp3TRRQtU+3AtqKsUBbkRlQJmoBlb1ApbtmXaCt9hYAapfbKbid1dk2EqicC1SalacD1OyHLIC6lNr/tEdqAaCt584E2u5HAqDWE/cJlPqAyuWAOnuqBVTuC2i3UAAdBVTq/eUFKu196uyKAaDWXgkD5W8L+4mrAHUX2jlGAu18lRiobFXt3c2yW9nOgfJq3bOJQLvPMc9YFqj1KN2nrQXU6X4XqLtPnC99QNtd6NbYil0ALewXNAM1vU0EVLYGozuAAaByVaCyB6hn21YTKwDtZvNWouuI2iwU2QGVHqBtFnIAaLj/fUDbe6IF1JtqR0A92RYEGh3LAn3/1cXDNyOByhDQ9pIW0MLeIgpo+8vOniDvELTyjQRqnkatLYaBqm3jxtsHVLrfDCYqZKtzToJVgHIZoVWzgH54/rR4/WkMULtvSwLt5PPVYTaLwdAeqTFAQ/UcB9BAed0c2QJ9/83L4vaLl7FAVZB3QIMxBWhUHfHRmoUjgfbWMXgaPRj9QONSFeGNovfwxH0amWIe0Nsv3xTvv/6u/OqTMqKfRpMa2zBo6wIQ04C+fchAq1j2VYYc+82xwgwKoMixSIp5QKedg6bvBXIcd44Fr+KfjLmKX6wXyHHcORYDGn8fdH4nkGO/OZYD6sSynUCO/eYAUOTIOgeAIkfWOQAUObLOAaDIkXUOAEWOrHMAKHJknQNAkSPrHACKHFnnAFDkyDoHgCJH1jkAFDmyzrES0J6If7f9soE63MikjogyAHTVQB1OACgH6nAjkzoAlAN1uJFJHdsDRSDmBYAisg4ARWQdAIrIOgAUkXWkB3r7+cXFU+v3PtXvz5tfo18pvHWohZvX8fbi4qc57I/m78BtXsbri+D+SA60+oMjt3/0nf77d2+blt+uPSDeOtTCzeuoRsX8XYHt6igqG6sC9ZfxfbiG5EDfVs1+/5T/9sj3P/mr6iWiHlYMbx1q4eZ1VLHyISVQx+0f/8n2u+PDz8NzxiLnoOXLxPz1pq0O8YE6rL8ptW0dK8+g/jo+/PyvVz7E+8ooj/fBM68lgFZ/GMf8/bvtgHrrqBZmUMft5z9Z/XXiqeP1k7XPQX1lVGddoVl0AaDvv3pi//27zYB666gXZlDHBjO5p47yu9WBBnZH6Mxriav4qiXz9++2u4r31NEs3L6OKtY9F/bWUV09X1ys+pIduzuSA1UEzN+/2wiot47NfLbqcP8A8HZ1FKvfZgrujg9/udJtpuZF+XTz+6DeOnjh1nVUS1c+Bw2Ny8pAR+8O/CQJkXUAKCLrAFBE1gGgiKwDQBFZB4Aisg4ARWQdAJowrg9lPHYX3X/R2ezuWbXd4cxe9ptfLFva0QaApovLCuO7Rx+/spb5gT6o/7U2vPnht8vXd5QBoMniqgHX8OMIAy0pm8kWQEMBoKni7pnyVpq8+e3/cbj/4t2jw70/K4FWh/TyoVlYGMJX5UH+5rw82D+o/n3A2yHsANBUoSfB8oub87NqhnxQ/n//xd2zs3p6rRdWwUCvP35Vz6JXpejyybzdRvVnGgCaKjTQEt3N+WN1dC/xXTenps3CKjTQ+y/+7VXzzOrJvN025ecaAJoqDND/UHtrJsOb335xVV+zl8dx3sCaQZsr/3v1E3i7jerPNAA0VTjnoA5QddTuAL08KyfMe9/yDIqjuy8ANFlcNRc4Fb/aYn3ILv+5vtfAbAOtjubNJNrMoLwdwg4ATRfmPmht8d2jM3WRVC5RCOuw7oNWKG/O731bYdULEFYAaMLQP0lqLNq3mZojebOZ+klSPY1elqv+otR5eTjj7RB2ACgi6wBQRNYBoIisA0ARWQeAIrIOAEVkHQCKyDoAFJF1ACgi6wBQRNbx/wEO50J7YKjM2QAAAABJRU5ErkJggg==" /><!-- --></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>sp7 <span class="ot"><-</span> <span class="fu">ggplot</span>(rolling, <span class="fu">aes</span>(<span class="at">x =</span> Order.Date)) <span class="sc">+</span></span>
<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> profits_07da), <span class="at">color=</span><span class="st">"#96be25"</span>) <span class="sc">+</span></span>
<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> sales_07da), <span class="at">color =</span> <span class="st">"#2596be"</span>, <span class="at">size =</span> <span class="fl">0.75</span>)<span class="sc">+</span></span>
<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span> (<span class="at">x =</span> <span class="st">"Order Date"</span>, <span class="at">y =</span> <span class="st">"Daily Sales"</span>, <span class="at">title =</span> <span class="st">"Rolling 7-day Averages of Sales and Profit"</span>)</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>sp7</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(rolling, <span class="fu">aes</span>(<span class="at">x =</span> Order.Date))</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="arima-in-r-and-time-series-forecasting-1" class="section level3">
<h3>ARIMA in R and Time Series Forecasting</h3>
<p>Now we come to the heart of the matter, using the Box-Jenkins Methodology,with the Autoregressive Integrated Moving Average model(ARIMA). This is comprised of three major steps:</p>
<ol style="list-style-type: decimal">
<li>Conditioning the data and selecting a model (from chapter 8 in Data Science and Big Data Analytics) -identify and account for any trends or seasonality in the time series -examine the remaining time series for any trends or seasonality in the time series</li>
<li>Estimate the model parameters</li>
<li>Assess the model and return to step one if necessary</li>
</ol>
<p><em>Time series data</em> is made up of four possible components: Trend [T], Cycle [C], Seasonality [S], and Random[R]. In our data, there appears to be strong seasonality trends, although the magnitude is hard to tell. It may also have cyclicity but that is also hard to tell with the current graphs. To that end we will employ a few libraries that were developed to deal with these particuar issues in Time Series data.</p>
<p>In R this is done by converting the information to a timeseries object and then use the forecast or other similar libraries to perform some decomposition on the data. Decomposition is a process of seperating out different time series peices, rendering the data in a more “predictable” format.</p>
<p>For the ARIMA model we must also ensure the data is in a <code>stationary</code> format. - the mean of Y(t) is a constant for all values of t - the variance of Y(t) is finite - the covariance of Y(t) and Y(t + h) depends only on the value of h = 0,1,2,…for all of t.</p>
<div id="stationary-data-test" class="section level4">
<h4>Stationary Data Test</h4>
</div>
<div id="using-the-time-series-object-in-r" class="section level4">
<h4>Using the Time Series Object in R</h4>
<p>Converting the monthly sales information into a ts (time series) object in R, we can use its native library and the stats package to decompose the monthly sales data to see if we are on the right track. This will also confirm which parts of the data are due to randomness, which is something we cannot gleen from the earlier charts.</p>
<p>I am going to plot the STL (seasonal and trend decomposition using Loess) for <code>Sales</code> and then we will look at some of the other factors seperately.</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>monthly_revenue_factors <span class="ot"><-</span> sales <span class="sc">%>%</span> </span>
<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(month) <span class="sc">%>%</span></span>
<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">total.sales =</span> <span class="fu">sum</span>(Sales), <span class="at">total.profit =</span> <span class="fu">sum</span>(Profit), <span class="at">total.shipping =</span> <span class="fu">sum</span>(Shipping.Cost))</span>
<span id="cb34-4"><a href="#cb34-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-5"><a href="#cb34-5" aria-hidden="true" tabindex="-1"></a>monthly_ts <span class="ot"><-</span><span class="fu">ts</span>(monthly_revenue_factors[,<span class="dv">2</span><span class="sc">:</span><span class="dv">4</span>], <span class="at">start =</span> <span class="dv">2011</span>, <span class="at">end =</span> <span class="dv">2014</span>, <span class="at">freq =</span> <span class="dv">12</span>)</span>
<span id="cb34-6"><a href="#cb34-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-7"><a href="#cb34-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-8"><a href="#cb34-8" aria-hidden="true" tabindex="-1"></a>monthly_sales <span class="ot"><-</span> sales <span class="sc">%>%</span> </span>
<span id="cb34-9"><a href="#cb34-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(month) <span class="sc">%>%</span></span>
<span id="cb34-10"><a href="#cb34-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">total.sales =</span> <span class="fu">sum</span>(Sales)) </span>
<span id="cb34-11"><a href="#cb34-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-12"><a href="#cb34-12" aria-hidden="true" tabindex="-1"></a>sales_ts <span class="ot"><-</span> <span class="fu">ts</span>(monthly_sales[,<span class="dv">2</span>], <span class="at">start =</span> <span class="dv">2011</span>, <span class="at">end =</span> <span class="dv">2014</span>, <span class="at">frequency =</span> <span class="dv">12</span>)</span>
<span id="cb34-13"><a href="#cb34-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-14"><a href="#cb34-14" aria-hidden="true" tabindex="-1"></a>sales_ts <span class="sc">%>%</span></span>
<span id="cb34-15"><a href="#cb34-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">stl</span>( <span class="at">s.window =</span> <span class="st">"period"</span>) <span class="sc">%>%</span></span>
<span id="cb34-16"><a href="#cb34-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">autoplot</span>() <span class="sc">+</span></span>
<span id="cb34-17"><a href="#cb34-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>With everything split out, it is possible to see the trend and seasonal data very clearly. Sales are growing and seasonality cannot be denied. The <code>Random</code> or reaminder data is also often used for prediction as it can contain underpinning structure within the ‘noise’ letting us know whether or not we have achieved our goals with the data.</p>
<p>Since we know there is seasonality in the data, it means the series is not stationary and for the ARIMA models of prediction, we will need to do a litle work.</p>
<p>Another chart that is a part of the Box Jenkins Methodology is the ACF or Autocorrelation Function. This function provides an autocorrelation with previous lags or time periods of a dataset with itself. The ACF will peak around seasonal lags or at the average cycle length.</p>
<p>Below is an ACF plot on just the monthly sales data. The dashed lines are there to show when a correlation is significantly above or below zero. The lags at 1 and 3 and 12 show that the data is likley not happening by random chance. This is data we can use to build a forcasting model.</p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggAcf</span>(sales_ts) <span class="sc">+</span></span>
<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Here, we will start with some basic predictions of the data.</p>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(caret)</span>
<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a><span class="co"># training and test data</span></span>
<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">42</span>)</span>
<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>index <span class="ot">=</span> <span class="fu">createDataPartition</span>(sales<span class="sc">$</span>Sales, <span class="at">p =</span> <span class="fl">0.70</span>, <span class="at">list =</span> <span class="cn">FALSE</span>)</span>
<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>train <span class="ot">=</span> sales[index, ]</span>
<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>test <span class="ot">=</span> sales[<span class="sc">-</span>index, ]</span></code></pre></div>
<p>we are going to try out the</p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>nf_sales <span class="ot"><-</span> <span class="fu">naive</span>(sales_ts, <span class="at">h =</span> <span class="dv">4</span>)</span>
<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a><span class="fu">autoplot</span>(nf_sales) <span class="sc">+</span></span>
<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(nf_sales)</span></code></pre></div>
<pre><code>##
## Forecast method: Naive method
##
## Model Information:
## Call: naive(y = sales_ts, h = 4)
##
## Residual sd: 87418.4309
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 3954.724 87418.43 72274.79 -5.393697 34.19143 1.518341 -0.3165428
##
## Forecasts:
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Feb 2014 241268.6 129237.33 353299.8 69931.58 412605.5
## Mar 2014 241268.6 82832.48 399704.6 -1038.52 483575.6
## Apr 2014 241268.6 47224.78 435312.3 -55495.79 538032.9
## May 2014 241268.6 17206.10 465331.0 -101405.40 583942.5</code></pre>
<p>We added <code>Shipping Cost</code> back into the equation as well, to look at it in relationship to to the sales and profits side of the equation, as it is a variable of interest.</p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="fu">autoplot</span>(monthly_ts) <span class="sc">+</span> <span class="co">#without facets </span></span>
<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Here we see that the sales appears multiplicative in nature, but with the scale being so narrow and small in comparison for profits and shipping, it is harder to tell. Placing this in a facet could help.</p>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a><span class="fu">autoplot</span>(monthly_ts, <span class="at">facets =</span> <span class="cn">TRUE</span>) <span class="sc">+</span> <span class="co">#without facets </span></span>
<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>monthly_ts_stl <span class="ot"><-</span><span class="fu">stl</span>(sales_ts, <span class="at">s.window =</span> <span class="st">"period"</span>)</span>
<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(monthly_ts_stl)</span></code></pre></div>
<p><img src="data:image/png;base64,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" 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