-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsolution.html
545 lines (509 loc) · 142 KB
/
solution.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
<!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="Ashwin Malshe" />
<meta name="date" content="2020-09-09" />
<title>Homework 0</title>
<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
});
</script>
<script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
// v0.0.1
// Written by JooYoung Seo ([email protected]) and Atsushi Yasumoto on June 1st, 2020.
document.addEventListener('DOMContentLoaded', function() {
const codeList = document.getElementsByClassName("sourceCode");
for (var i = 0; i < codeList.length; i++) {
var linkList = codeList[i].getElementsByTagName('a');
for (var j = 0; j < linkList.length; j++) {
if (linkList[j].innerHTML === "") {
linkList[j].setAttribute('aria-hidden', 'true');
}
}
}
});
</script>
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css" data-origin="pandoc">
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; }
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>
<style type="text/css">@font-face{font-family:"Open Sans";font-style:normal;font-weight:400;src:local("Open Sans"),local("OpenSans"),url(data:application/font-woff;base64,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) format("woff")}@font-face{font-family:"Open Sans";font-style:normal;font-weight:700;src:local("Open Sans Bold"),local("OpenSans-Bold"),url(data:application/font-woff;base64,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) format("woff")}*{box-sizing:border-box}body{margin:0;padding:0;border:0}blockquote,q{quotes:none;margin:16px 0}blockquote:after,blockquote:before,q:after,q:before{content:"";content:none}table{border-collapse:collapse;border-spacing:0}body{font-size:16px;line-height:1.5;background-color:#e5e5e5;font-family:"Open Sans",Helvetica,Arial,sans-serif;text-shadow:0 1px 0 rgba(255,255,255,.8);color:#606060}a{color:#d5000d}a:hover{color:#c5000c}header{padding:30px 0;text-align:center}header h1{font-weight:700;letter-spacing:-1px;font-size:2em;color:#303030;line-height:1.2;margin:0 0 .5em}header h2{font-size:1.5em;color:#d5000d;font-weight:700;line-height:1.3;margin:0 0 1em}header h3{font-size:1.2em;color:#6d6d6d;font-weight:400;line-height:1.2;margin:0 0 .5em}header :last-child{margin-bottom:0}#container{background:transparent url(data:image/jpeg;base64,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) 50% 0 no-repeat;min-height:595px}#container .inner img{max-width:100%}.inner{width:840px;margin:0 auto;font-size:1.1em}.toc{width:60%;margin:0 auto;padding:20px;border:solid 1px #bbb}.toc .toc-title{margin:0 0 16px;text-align:center;color:#666}.toc ul{margin:0}code,pre{font-family:Monaco,"Bitstream Vera Sans Mono",Consolas,"Lucida Console",Terminal,Courier,monospace;background-color:#f2f2f2;color:#303030}code{border:solid 1px #ddd;padding:0 3px}pre{padding:20px;text-shadow:none;overflow:auto;font-size:.9em;margin-bottom:16px}pre code{border:none;padding:0}hr{height:1px;line-height:1px;margin-top:1em;margin-bottom:1em;border:none;background:#aaa;background-image:linear-gradient(to right,#eee,#aaa,#eee)}strong{font-weight:700}em{font-style:italic}table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:16px 0}table th{font-weight:700;background-color:#a2000a;color:#fff}table td,table th{border-bottom:1px solid #bbb;text-align:left;padding:10px}table tr:nth-child(odd){background-color:#ddd}table tr:nth-child(even){background-color:#f2f2f2}h1,h2,h3,h4,h5,h6{margin-bottom:16px}h1{font-size:2em}h2{font-size:1.5em;font-weight:700;color:#303030}h3{font-size:1.2em;font-weight:700;color:#d5000d}h4{font-size:1.1em;font-weight:700;color:#303030}h5{font-size:1em;color:#303030}h6{font-size:.9em;color:#303030}a{text-decoration:none}p{font-weight:400;margin-bottom:16px}p a{font-weight:400}blockquote{font-size:1.1em;color:#819198;border-left:10px solid #d0d0d0;margin-bottom:16px;padding:0 0 0 30px}ol,ul{padding-left:2em}.clearfix:after{clear:both;content:".";display:block;visibility:hidden;height:0}.clearfix{display:inline-block}* html .clearfix{height:1%}.clearfix{display:block}@media screen and (max-width:860px){.inner{width:94%;font-size:1em;margin:0 auto}header{padding-top:10px;padding-bottom:10px}header h1{font-size:1.75em}header h2{font-size:1.2em}header h3{font-size:1em}.toc{width:80%}}
code span.kw { color: #a71d5d; font-weight: normal; }
code span.dt { color: #795da3; }
code span.dv { color: #0086b3; }
code span.bn { color: #0086b3; }
code span.fl { color: #0086b3; }
code span.ch { color: #4070a0; }
code span.st { color: #183691; }
code span.co { color: #969896; font-style: italic; }
code span.ot { color: #007020; }
</style>
</head>
<body>
<div id="container">
<div class="inner">
<header>
<h1 class="title toc-ignore">Homework 0</h1>
<h3 class="author">Ashwin Malshe</h3>
<h3 class="date">09 September 2020</h3>
</header>
<hr>
<p>This homework uses <code>txhousing</code> dataset from <code>ggplot2</code> package, which is a part of the <code>tidyverse</code>. Additionally, it uses Consumer Price Index (CPI) dataset, which I downloaded from the BLS website (<a href="https://data.bls.gov/timeseries/CUUR0000SA0" class="uri">https://data.bls.gov/timeseries/CUUR0000SA0</a>) and cleaned. I will provide the code for cleaning CPI data in the solution to this homework.</p>
<p><code>txhousing</code> consists of the following variables:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">names</span>(txhousing)</span></code></pre></div>
<pre><code>## [1] "city" "year" "month" "sales" "volume" "median"
## [7] "listings" "inventory" "date"</code></pre>
<p>Before you start working on this homework, study the variables in <code>txhousing</code> as well as the structure of the dataset by typing this command in your console.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">help</span>(txhousing)</span></code></pre></div>
<p>Take a peek at the data by typing:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a><span class="kw">head</span>(txhousing)</span></code></pre></div>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a>cpi <-<span class="st"> </span>readxl<span class="op">::</span><span class="kw">read_excel</span>(<span class="st">"cpi.xlsx"</span>, <span class="dt">skip =</span> <span class="dv">11</span>) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb5-2"><a href="#cb5-2"></a><span class="st"> </span><span class="kw">select</span>(<span class="op">-</span><span class="kw">c</span>(<span class="dv">14</span><span class="op">:</span><span class="dv">15</span>)) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb5-3"><a href="#cb5-3"></a><span class="st"> </span>janitor<span class="op">::</span><span class="kw">clean_names</span>() <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb5-4"><a href="#cb5-4"></a><span class="st"> </span><span class="kw">pivot_longer</span>(<span class="dt">cols =</span> <span class="op">!</span>year, <span class="dt">names_to =</span> <span class="st">"month_name"</span>, <span class="dt">values_to =</span> <span class="st">"cpi"</span>)</span>
<span id="cb5-5"><a href="#cb5-5"></a></span>
<span id="cb5-6"><a href="#cb5-6"></a><span class="kw">saveRDS</span>(cpi, <span class="dt">file =</span> <span class="st">"cpi.rds"</span>)</span></code></pre></div>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a>cpi <-<span class="st"> </span><span class="kw">readRDS</span>(<span class="st">"cpi.rds"</span>)</span></code></pre></div>
<p>Save the CPI dataset in your project folder where you have also saved this homework file. Read the CPI dataset into this session. It consists of the following columns:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="kw">names</span>(cpi)</span></code></pre></div>
<pre><code>## [1] "year" "month_name" "cpi"</code></pre>
<p>Take a look at the first few observations by using <code>head()</code> function.</p>
<p style="border:3px; border-style:solid; border-color:#b50000; padding: 1em;">
This homework consists of 10 questions and each carries one point. Your objective is to reproduce the output shown in the HTML file for Q1 through Q9. For Q10 just print the name of the city and the value.
</p>
<p>Dollar values over a long time series make comparison difficult due to inflation. $100 in January 2000 is worth $154 in July 2020. In <code>txhousing</code>, there are two variables—<code>volume</code> and <code>median</code>—which are specified in unadjusted USD. Questions 1 to 4 are designed to inflation-adjust these two variables to July 2020 dollars.</p>
<div id="q1" class="section level3">
<h3>Q1</h3>
<p>Create a new data frame <code>month_map</code> with 12 rows and 2 columns titled <code>month_name</code> and <code>month</code>. <code>month_name</code> in <code>month_map</code> should have only the unique values from <code>month_name</code> column in CPI data frame. <code>month</code> column should contain the month numbers from 1 to 12.</p>
<p>Try to not type out this data frame manually and instead try to do it algorithmically. This is how it will look:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a>month_map <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">month_name =</span> cpi<span class="op">$</span>month_name[<span class="dv">1</span><span class="op">:</span><span class="dv">12</span>],</span>
<span id="cb9-2"><a href="#cb9-2"></a> <span class="dt">month =</span> <span class="dv">1</span><span class="op">:</span><span class="dv">12</span>)</span>
<span id="cb9-3"><a href="#cb9-3"></a></span>
<span id="cb9-4"><a href="#cb9-4"></a><span class="kw">head</span>(month_map, <span class="dv">4</span>)</span></code></pre></div>
<pre><code>## month_name month
## 1 jan 1
## 2 feb 2
## 3 mar 3
## 4 apr 4</code></pre>
</div>
<div id="q2" class="section level3">
<h3>Q2</h3>
<p>Merge <code>month_map</code> to the CPI data frame. Explicitly identify the common key variable on which you will perform the merge. Store the resulting merged data frame as <code>cpi_merge</code>. <strong>Print the first six rows.</strong></p>
<p>Here are the first six rows of <code>cpi_merge</code>:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>cpi_merge <-<span class="st"> </span>cpi <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb11-2"><a href="#cb11-2"></a><span class="st"> </span><span class="kw">inner_join</span>(month_map, <span class="dt">by =</span> <span class="st">"month_name"</span>)</span>
<span id="cb11-3"><a href="#cb11-3"></a></span>
<span id="cb11-4"><a href="#cb11-4"></a><span class="kw">head</span>(cpi_merge)</span></code></pre></div>
<pre><code>## # A tibble: 6 x 4
## year month_name cpi month
## <dbl> <chr> <dbl> <int>
## 1 1999 jan 164. 1
## 2 1999 feb 164. 2
## 3 1999 mar 165 3
## 4 1999 apr 166. 4
## 5 1999 may 166. 5
## 6 1999 jun 166. 6</code></pre>
</div>
<div id="q3" class="section level3">
<h3>Q3</h3>
<p>We want to add a column to the <code>txhousing</code> data frame that holds the information on the CPI. Rather than altering <code>txhousing</code>, create a new data frame <code>housing</code> by merging <code>txhousing</code> and <code>cpi_merge</code>. The common keys for merging are <code>year</code> and <code>month</code>. Note that the resulting data frame is essentially <code>txhousing</code> with <em>just one more column</em> of CPI.</p>
<p>Here are the top six rows of <code>housing</code>:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a>housing <-<span class="st"> </span>txhousing <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb13-2"><a href="#cb13-2"></a><span class="st"> </span><span class="kw">inner_join</span>(cpi_merge, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">"year"</span>, <span class="st">"month"</span>)) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb13-3"><a href="#cb13-3"></a><span class="st"> </span><span class="kw">select</span>(<span class="op">!</span>month_name)</span>
<span id="cb13-4"><a href="#cb13-4"></a></span>
<span id="cb13-5"><a href="#cb13-5"></a><span class="kw">head</span>(housing)</span></code></pre></div>
<pre><code>## # A tibble: 6 x 10
## city year month sales volume median listings inventory date cpi
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abilene 2000 1 72 5380000 71400 701 6.3 2000 169.
## 2 Abilene 2000 2 98 6505000 58700 746 6.6 2000. 170.
## 3 Abilene 2000 3 130 9285000 58100 784 6.8 2000. 171.
## 4 Abilene 2000 4 98 9730000 68600 785 6.9 2000. 171.
## 5 Abilene 2000 5 141 10590000 67300 794 6.8 2000. 172.
## 6 Abilene 2000 6 156 13910000 66900 780 6.6 2000. 172.</code></pre>
</div>
<div id="q4" class="section level3">
<h3>Q4</h3>
<p>Modify <code>housing</code> by adding these new columns:</p>
<ol style="list-style-type: decimal">
<li><p><code>cpi_latest</code> - Contains the CPI of July 2020. This entire column will have the same value appearing in each cell.</p></li>
<li><p><code>multiplier</code> - Ratio of <code>cpi_latest</code> and <code>cpi</code></p></li>
<li><p><code>volume_adj</code> - Adjusted volume as a product of <code>volume</code> and <code>multiplier</code></p></li>
<li><p><code>median_adj</code> - Adjusted median sale price as a product of <code>median</code> and <code>multiplier</code></p></li>
</ol>
<p>Here I show the top six rows with only a select few columns to help you ensure your output resembles this:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a>july_<span class="dv">2020</span>_cpi <-<span class="st"> </span>cpi_merge <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb15-2"><a href="#cb15-2"></a><span class="st"> </span><span class="kw">filter</span>(year <span class="op">==</span><span class="st"> </span><span class="dv">2020</span>, month <span class="op">==</span><span class="st"> </span><span class="dv">7</span>) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb15-3"><a href="#cb15-3"></a><span class="st"> </span><span class="kw">pull</span>(cpi)</span>
<span id="cb15-4"><a href="#cb15-4"></a></span>
<span id="cb15-5"><a href="#cb15-5"></a>housing <-<span class="st"> </span>housing <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb15-6"><a href="#cb15-6"></a><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">cpi_latest =</span> july_<span class="dv">2020</span>_cpi,</span>
<span id="cb15-7"><a href="#cb15-7"></a> <span class="dt">multiplier =</span> cpi_latest <span class="op">/</span><span class="st"> </span>cpi,</span>
<span id="cb15-8"><a href="#cb15-8"></a> <span class="dt">volume_adj =</span> volume <span class="op">*</span><span class="st"> </span>multiplier,</span>
<span id="cb15-9"><a href="#cb15-9"></a> <span class="dt">median_adj =</span> median <span class="op">*</span><span class="st"> </span>multiplier)</span>
<span id="cb15-10"><a href="#cb15-10"></a></span>
<span id="cb15-11"><a href="#cb15-11"></a>housing <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb15-12"><a href="#cb15-12"></a><span class="st"> </span><span class="kw">select</span>(city, year, month, volume, median, </span>
<span id="cb15-13"><a href="#cb15-13"></a> cpi_latest, multiplier, volume_adj, median_adj) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb15-14"><a href="#cb15-14"></a><span class="st"> </span><span class="kw">head</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 9
## city year month volume median cpi_latest multiplier volume_adj median_adj
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abilene 2000 1 5.38e6 71400 259. 1.53 8258077. 109596.
## 2 Abilene 2000 2 6.50e6 58700 259. 1.53 9926101. 89571.
## 3 Abilene 2000 3 9.28e6 58100 259. 1.51 14052294. 87931.
## 4 Abilene 2000 4 9.73e6 68600 259. 1.51 14717179. 103761.
## 5 Abilene 2000 5 1.06e7 67300 259. 1.51 15999298. 101676.
## 6 Abilene 2000 6 1.39e7 66900 259. 1.50 20905423. 100544.</code></pre>
</div>
<div id="q5" class="section level3">
<h3>Q5</h3>
<p>Using <code>housing</code> from Q4, create a new data frame <code>housing_sum1</code> with this information for each <code>city</code> <em>across all years and months</em>:</p>
<ol style="list-style-type: decimal">
<li>Maximum and minimum <code>volume_adj</code></li>
<li>Maximum and minimum <code>median_adj</code> sale price</li>
</ol>
<p><em>Hint: If you group by <code>city</code>, you will get the summary across all the years and months. </em></p>
<p>Merge <code>housing_sum1</code> into <code>housing</code> by <code>city</code> and save it as a new dataset <code>housing_1</code>.</p>
<p>Here I show first six rows of <code>housing_1</code> and only a select columns:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a>housing_sum1 <-<span class="st"> </span>housing <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb17-2"><a href="#cb17-2"></a><span class="st"> </span><span class="kw">group_by</span>(city) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb17-3"><a href="#cb17-3"></a><span class="st"> </span><span class="kw">summarize</span>(<span class="dt">volume_adj_max =</span> <span class="kw">max</span>(volume_adj),</span>
<span id="cb17-4"><a href="#cb17-4"></a> <span class="dt">volume_adj_min =</span> <span class="kw">min</span>(volume_adj),</span>
<span id="cb17-5"><a href="#cb17-5"></a> <span class="dt">median_adj_max =</span> <span class="kw">max</span>(median_adj),</span>
<span id="cb17-6"><a href="#cb17-6"></a> <span class="dt">median_adj_min =</span> <span class="kw">min</span>(median_adj),</span>
<span id="cb17-7"><a href="#cb17-7"></a> <span class="dt">.groups =</span> <span class="st">"drop"</span>)</span>
<span id="cb17-8"><a href="#cb17-8"></a></span>
<span id="cb17-9"><a href="#cb17-9"></a>housing_<span class="dv">1</span> <-<span class="st"> </span>housing <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb17-10"><a href="#cb17-10"></a><span class="st"> </span><span class="kw">inner_join</span>(housing_sum1, <span class="dt">by =</span> <span class="st">"city"</span>)</span>
<span id="cb17-11"><a href="#cb17-11"></a></span>
<span id="cb17-12"><a href="#cb17-12"></a></span>
<span id="cb17-13"><a href="#cb17-13"></a>housing_<span class="dv">1</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb17-14"><a href="#cb17-14"></a><span class="st"> </span><span class="kw">select</span>(city, year, month, volume_adj_max, </span>
<span id="cb17-15"><a href="#cb17-15"></a> volume_adj_min, median_adj_max, median_adj_min) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb17-16"><a href="#cb17-16"></a><span class="st"> </span><span class="kw">head</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 7
## city year month volume_adj_max volume_adj_min median_adj_max median_adj_min
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Abile… 2000 1 49773624. 7678915. 161440. 84890.
## 2 Abile… 2000 2 49773624. 7678915. 161440. 84890.
## 3 Abile… 2000 3 49773624. 7678915. 161440. 84890.
## 4 Abile… 2000 4 49773624. 7678915. 161440. 84890.
## 5 Abile… 2000 5 49773624. 7678915. 161440. 84890.
## 6 Abile… 2000 6 49773624. 7678915. 161440. 84890.</code></pre>
<p>Here I show <em>last</em> six rows of <code>housing_1</code> and only a select columns:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a>housing_<span class="dv">1</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb19-2"><a href="#cb19-2"></a><span class="st"> </span><span class="kw">select</span>(city, year, month, volume_adj_max, volume_adj_min, median_adj_max, median_adj_min) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb19-3"><a href="#cb19-3"></a><span class="st"> </span><span class="kw">tail</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 7
## city year month volume_adj_max volume_adj_min median_adj_max median_adj_min
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Wichi… 2015 2 33832205. 8337061. 147084. 84452.
## 2 Wichi… 2015 3 33832205. 8337061. 147084. 84452.
## 3 Wichi… 2015 4 33832205. 8337061. 147084. 84452.
## 4 Wichi… 2015 5 33832205. 8337061. 147084. 84452.
## 5 Wichi… 2015 6 33832205. 8337061. 147084. 84452.
## 6 Wichi… 2015 7 33832205. 8337061. 147084. 84452.</code></pre>
</div>
<div id="q6" class="section level3">
<h3>Q6</h3>
<ol style="list-style-type: decimal">
<li>Using <code>housing_1</code> from Q5, create a new data frame <code>housing_min</code> which will retain only the rows of <code>housing_1</code> where <code>volume_adj</code> of a <code>city</code> was equal to the minimum adjusted volume.</li>
</ol>
<p>Here I show first six rows of <code>housing_min</code> and only a select columns:</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a>housing_min <-<span class="st"> </span>housing_<span class="dv">1</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb21-2"><a href="#cb21-2"></a><span class="st"> </span><span class="kw">filter</span>(volume_adj <span class="op">==</span><span class="st"> </span>volume_adj_min)</span>
<span id="cb21-3"><a href="#cb21-3"></a></span>
<span id="cb21-4"><a href="#cb21-4"></a>housing_min <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb21-5"><a href="#cb21-5"></a><span class="st"> </span><span class="kw">select</span>(city, year, month, volume_adj, </span>
<span id="cb21-6"><a href="#cb21-6"></a> volume_adj_min, volume_adj_max) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb21-7"><a href="#cb21-7"></a><span class="st"> </span><span class="kw">head</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 6
## city year month volume_adj volume_adj_min volume_adj_max
## <chr> <dbl> <int> <dbl> <dbl> <dbl>
## 1 Abilene 2003 1 7678915. 7678915. 49773624.
## 2 Amarillo 2005 10 9397614. 9397614. 67596289.
## 3 Arlington 2011 1 30008080. 30008080. 137867140.
## 4 Austin 2009 1 252191554. 252191554. 1248942028.
## 5 Bay Area 2000 1 45009066. 45009066. 214278169.
## 6 Beaumont 2001 1 13117820. 13117820. 57413784.</code></pre>
<ol start="2" style="list-style-type: decimal">
<li>Using <code>housing_1</code> from Q5, create a new data frame <code>housing_max</code> which will retain only the rows of <code>housing_1</code> where <code>median_adj</code> of a <code>city</code> was equal to the maximum adjusted median sale price.</li>
</ol>
<p>Here I show first six rows of <code>housing_max</code> and only a select columns:</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a>housing_max <-<span class="st"> </span>housing_<span class="dv">1</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb23-2"><a href="#cb23-2"></a><span class="st"> </span><span class="kw">filter</span>(median_adj <span class="op">==</span><span class="st"> </span>median_adj_max)</span>
<span id="cb23-3"><a href="#cb23-3"></a></span>
<span id="cb23-4"><a href="#cb23-4"></a>housing_max <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb23-5"><a href="#cb23-5"></a><span class="st"> </span><span class="kw">select</span>(city, year, month, median_adj, </span>
<span id="cb23-6"><a href="#cb23-6"></a> median_adj_max, median_adj_min) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb23-7"><a href="#cb23-7"></a><span class="st"> </span><span class="kw">head</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 6
## city year month median_adj median_adj_max median_adj_min
## <chr> <dbl> <int> <dbl> <dbl> <dbl>
## 1 Abilene 2015 7 161440. 161440. 84890.
## 2 Amarillo 2015 5 172040. 172040. 113162.
## 3 Arlington 2015 6 195435. 195435. 131800.
## 4 Austin 2015 4 296007. 296007. 200091.
## 5 Bay Area 2015 7 218004. 218004. 154570.
## 6 Beaumont 2010 1 195862. 195862. 108773.</code></pre>
</div>
<div id="q7" class="section level3">
<h3>Q7</h3>
<p>Using <code>housing_1</code> from Q5, create a new data frame <code>housing_sum2</code> with this information for each <code>year</code> and <code>month</code> pair <em>across all cities</em>:</p>
<ol style="list-style-type: decimal">
<li>Median listings</li>
<li>Median sales</li>
</ol>
<p><em>Hint: If even a single value for <code>listings</code> or <code>sales</code> of a <code>city</code> is <code>NA</code>, the median of that variable will be <code>NA</code>. In order to avoid this, use <code>na.rm = TRUE</code> argument in <code>median()</code>. </em></p>
<p>Here I show first six rows of <code>housing_sum2</code> and all the columns:</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a>housing_sum2 <-<span class="st"> </span>housing_<span class="dv">1</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb25-2"><a href="#cb25-2"></a><span class="st"> </span><span class="kw">group_by</span>(year, month) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb25-3"><a href="#cb25-3"></a><span class="st"> </span><span class="kw">summarize</span>(<span class="dt">listings_med =</span> <span class="kw">median</span>(listings, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>),</span>
<span id="cb25-4"><a href="#cb25-4"></a> <span class="dt">sales_med =</span> <span class="kw">median</span>(sales, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>),</span>
<span id="cb25-5"><a href="#cb25-5"></a> <span class="dt">.groups =</span> <span class="st">"drop"</span>) </span>
<span id="cb25-6"><a href="#cb25-6"></a></span>
<span id="cb25-7"><a href="#cb25-7"></a><span class="kw">head</span>(housing_sum2)</span></code></pre></div>
<pre><code>## # A tibble: 6 x 4
## year month listings_med sales_med
## <dbl> <int> <dbl> <dbl>
## 1 2000 1 972 99
## 2 2000 2 916. 134
## 3 2000 3 946. 167
## 4 2000 4 985 153
## 5 2000 5 978. 165
## 6 2000 6 864. 188</code></pre>
<p>Merge <code>housing_sum2</code> into <code>housing_1</code> and save a new data frame <code>housing_2</code>.</p>
<p>Here I show first six rows of <code>housing_2</code> and some of the columns:</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a>housing_<span class="dv">2</span> <-<span class="st"> </span>housing_<span class="dv">1</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb27-2"><a href="#cb27-2"></a><span class="st"> </span><span class="kw">inner_join</span>(housing_sum2, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">"year"</span>, <span class="st">"month"</span>))</span>
<span id="cb27-3"><a href="#cb27-3"></a></span>
<span id="cb27-4"><a href="#cb27-4"></a>housing_<span class="dv">2</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb27-5"><a href="#cb27-5"></a><span class="st"> </span><span class="kw">select</span>(city, year, month, listings, </span>
<span id="cb27-6"><a href="#cb27-6"></a> sales, listings_med, sales_med) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb27-7"><a href="#cb27-7"></a><span class="st"> </span><span class="kw">head</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 7
## city year month listings sales listings_med sales_med
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Abilene 2000 1 701 72 972 99
## 2 Abilene 2000 2 746 98 916. 134
## 3 Abilene 2000 3 784 130 946. 167
## 4 Abilene 2000 4 785 98 985 153
## 5 Abilene 2000 5 794 141 978. 165
## 6 Abilene 2000 6 780 156 864. 188</code></pre>
</div>
<div id="q8" class="section level3">
<h3>Q8</h3>
<p>Modify <code>housing_2</code> from Q7 to add these indicator variables (also called dummy variables):</p>
<ol style="list-style-type: decimal">
<li><p><code>listings_ind</code> - If a city’s <code>listings</code> is less than or equal to the median listings for that <code>year</code> and <code>month</code> across all the cities, the value should be <code>0</code> else it should be <code>1</code>.</p></li>
<li><p><code>sales_ind</code> - If a city’s <code>sales</code> is less than or equal to the median sales for that <code>year</code> and <code>month</code> across all the cities, the value should be <code>0</code> else it should be <code>1</code>.</p></li>
</ol>
<p><em>Hint: This can be achieved using <code>ifelse()</code> function from R along with <code>mutate()</code> from <code>dplyr</code></em></p>
<p>Here I show first six rows of <code>housing_2</code> and some of the columns:</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1"></a>housing_<span class="dv">2</span> <-<span class="st"> </span>housing_<span class="dv">2</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb29-2"><a href="#cb29-2"></a><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">listings_ind =</span> <span class="kw">ifelse</span>(listings <span class="op"><=</span><span class="st"> </span>listings_med, <span class="dv">0</span>, <span class="dv">1</span>),</span>
<span id="cb29-3"><a href="#cb29-3"></a> <span class="dt">sales_ind =</span> <span class="kw">ifelse</span>(sales <span class="op"><=</span><span class="st"> </span>sales_med, <span class="dv">0</span>, <span class="dv">1</span>))</span>
<span id="cb29-4"><a href="#cb29-4"></a></span>
<span id="cb29-5"><a href="#cb29-5"></a>housing_<span class="dv">2</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb29-6"><a href="#cb29-6"></a><span class="st"> </span><span class="kw">select</span>(city, year, month, listings, </span>
<span id="cb29-7"><a href="#cb29-7"></a> listings_med, listings_ind,</span>
<span id="cb29-8"><a href="#cb29-8"></a> sales, sales_med, sales_ind) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb29-9"><a href="#cb29-9"></a><span class="st"> </span><span class="kw">head</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 9
## city year month listings listings_med listings_ind sales sales_med sales_ind
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abil… 2000 1 701 972 0 72 99 0
## 2 Abil… 2000 2 746 916. 0 98 134 0
## 3 Abil… 2000 3 784 946. 0 130 167 0
## 4 Abil… 2000 4 785 985 0 98 153 0
## 5 Abil… 2000 5 794 978. 0 141 165 0
## 6 Abil… 2000 6 780 864. 0 156 188 0</code></pre>
</div>
<div id="q9" class="section level3">
<h3>Q9</h3>
<p>Using <code>housing_2</code> from Q8, add a new variable <code>market_hotness</code> as follows:</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1"></a>hot_map <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">listings_ind =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">1</span>),</span>
<span id="cb31-2"><a href="#cb31-2"></a> <span class="dt">sales_ind =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">1</span>), </span>
<span id="cb31-3"><a href="#cb31-3"></a> <span class="dt">market_hotness =</span> <span class="kw">c</span>(<span class="st">"Low"</span>, <span class="st">"High"</span>, <span class="st">"Very Low"</span>, <span class="st">"Average"</span>)) </span>
<span id="cb31-4"><a href="#cb31-4"></a>hot_map <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb31-5"><a href="#cb31-5"></a><span class="st"> </span>knitr<span class="op">::</span><span class="kw">kable</span>(<span class="dt">align =</span> <span class="kw">c</span>(<span class="st">"c"</span>, <span class="st">"c"</span>, <span class="st">"c"</span>))</span></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="center">listings_ind</th>
<th align="center">sales_ind</th>
<th align="center">market_hotness</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">0</td>
<td align="center">0</td>
<td align="center">Low</td>
</tr>
<tr class="even">
<td align="center">0</td>
<td align="center">1</td>
<td align="center">High</td>
</tr>
<tr class="odd">
<td align="center">1</td>
<td align="center">0</td>
<td align="center">Very Low</td>
</tr>
<tr class="even">
<td align="center">1</td>
<td align="center">1</td>
<td align="center">Average</td>
</tr>
</tbody>
</table>
<p>Here I show first six rows of <code>housing_2</code> and some of the columns:</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1"></a>housing_<span class="dv">2</span> <-<span class="st"> </span>housing_<span class="dv">2</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb32-2"><a href="#cb32-2"></a><span class="st"> </span><span class="kw">inner_join</span>(hot_map, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">"listings_ind"</span>, <span class="st">"sales_ind"</span>))</span>
<span id="cb32-3"><a href="#cb32-3"></a></span>
<span id="cb32-4"><a href="#cb32-4"></a>housing_<span class="dv">2</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb32-5"><a href="#cb32-5"></a><span class="st"> </span><span class="kw">select</span>(city, year, month, listings, sales,</span>
<span id="cb32-6"><a href="#cb32-6"></a> listings_ind, sales_ind, market_hotness) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb32-7"><a href="#cb32-7"></a><span class="st"> </span><span class="kw">head</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 8
## city year month listings sales listings_ind sales_ind market_hotness
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Abilene 2000 1 701 72 0 0 Low
## 2 Abilene 2000 2 746 98 0 0 Low
## 3 Abilene 2000 3 784 130 0 0 Low
## 4 Abilene 2000 4 785 98 0 0 Low
## 5 Abilene 2000 5 794 141 0 0 Low
## 6 Abilene 2000 6 780 156 0 0 Low</code></pre>
</div>
<div id="q10" class="section level3">
<h3>Q10</h3>
<p>Which city has the highest <em>average</em> <code>median_adj</code> sale price and what is that price?</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1"></a>housing_<span class="dv">2</span> <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb34-2"><a href="#cb34-2"></a><span class="st"> </span><span class="kw">group_by</span>(city) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb34-3"><a href="#cb34-3"></a><span class="st"> </span><span class="kw">summarize</span>(<span class="dt">median_adj_avg =</span> <span class="kw">mean</span>(median_adj, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>),</span>
<span id="cb34-4"><a href="#cb34-4"></a> <span class="dt">.groups =</span> <span class="st">"drop"</span>) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb34-5"><a href="#cb34-5"></a><span class="st"> </span><span class="kw">arrange</span>(<span class="op">-</span>median_adj_avg) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb34-6"><a href="#cb34-6"></a><span class="st"> </span><span class="kw">head</span>(<span class="dv">1</span>)</span></code></pre></div>
<pre><code>## # A tibble: 1 x 2
## city median_adj_avg
## <chr> <dbl>
## 1 Collin County 252324.</code></pre>
<p><br> <br> <br></p>
<hr style="border: 2px solid#5b9aa0;" />
</div>
</div>
</div>
<!-- code folding -->
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>