forked from rdpeng/RepData_PeerAssessment1
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathPA1_complete.html
More file actions
462 lines (365 loc) · 128 KB
/
PA1_complete.html
File metadata and controls
462 lines (365 loc) · 128 KB
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
<!DOCTYPE html>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>Reproducible Research: Peer Assessment 1</title>
<!-- Styles for R syntax highlighter -->
<style type="text/css">
pre .operator,
pre .paren {
color: rgb(104, 118, 135)
}
pre .literal {
color: #990073
}
pre .number {
color: #099;
}
pre .comment {
color: #998;
font-style: italic
}
pre .keyword {
color: #900;
font-weight: bold
}
pre .identifier {
color: rgb(0, 0, 0);
}
pre .string {
color: #d14;
}
</style>
<!-- R syntax highlighter -->
<script type="text/javascript">
var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/</gm,"<")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};
hljs.initHighlightingOnLoad();
</script>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 13px;
}
body {
max-width: 800px;
margin: auto;
line-height: 20px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre, img {
max-width: 100%;
}
pre code {
display: block; padding: 0.5em;
}
code {
font-size: 92%;
border: 1px solid #ccc;
}
code[class] {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
</head>
<body>
<h1>Reproducible Research: Peer Assessment 1</h1>
<p>The following document walks through completion of the first peer assessment for the course Reproducible Research on Coursera. </p>
<p>The dataset being used describes the number of steps taken by an unnamed person over a month. Each step count was gathered at five minute intervals. </p>
<h2>Loading and preprocessing the data</h2>
<p>First, load your data into R using the read.csv() function. There are three variables in the data:</p>
<ul>
<li>The number of steps taken per interval</li>
<li>The date that the steps were recorded</li>
<li>The time interval that the steps were recorded</li>
</ul>
<p>Before continuing, use as.Date() to insure that your date variable has been converted from character to dates. </p>
<pre><code class="r">data <- read.csv("activity.csv", header = TRUE, stringsAsFactors = FALSE)
data$date <- as.Date(data$date, format = "%Y-%m-%d")
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>We will first look at the distribution of steps taken per day. The total number of steps taken per day can be seen in the histogram below:</p>
<pre><code class="r">hist(with(data, tapply(steps, date, sum, na.rm = TRUE)), xlab = "Number of steps",
main = "")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-2"/> </p>
<p>As can be seen above, the number of steps taken varies by day. The following table gives the mean and median for each day, further showing this variety. Note that this could have looked very pretty using the package xtable, but I couldn't get R to interpret the html output, so you will have to settle for this less pretty version. </p>
<pre><code class="r">library(plyr)
ddply(data, ~date, summarize, mean = mean(steps, na.rm = TRUE), median = median(steps,
na.rm = TRUE))
</code></pre>
<pre><code>## date mean median
## 1 2012-10-01 NaN NA
## 2 2012-10-02 0.4375 0
## 3 2012-10-03 39.4167 0
## 4 2012-10-04 42.0694 0
## 5 2012-10-05 46.1597 0
## 6 2012-10-06 53.5417 0
## 7 2012-10-07 38.2465 0
## 8 2012-10-08 NaN NA
## 9 2012-10-09 44.4826 0
## 10 2012-10-10 34.3750 0
## 11 2012-10-11 35.7778 0
## 12 2012-10-12 60.3542 0
## 13 2012-10-13 43.1458 0
## 14 2012-10-14 52.4236 0
## 15 2012-10-15 35.2049 0
## 16 2012-10-16 52.3750 0
## 17 2012-10-17 46.7083 0
## 18 2012-10-18 34.9167 0
## 19 2012-10-19 41.0729 0
## 20 2012-10-20 36.0938 0
## 21 2012-10-21 30.6285 0
## 22 2012-10-22 46.7361 0
## 23 2012-10-23 30.9653 0
## 24 2012-10-24 29.0104 0
## 25 2012-10-25 8.6528 0
## 26 2012-10-26 23.5347 0
## 27 2012-10-27 35.1354 0
## 28 2012-10-28 39.7847 0
## 29 2012-10-29 17.4236 0
## 30 2012-10-30 34.0938 0
## 31 2012-10-31 53.5208 0
## 32 2012-11-01 NaN NA
## 33 2012-11-02 36.8056 0
## 34 2012-11-03 36.7049 0
## 35 2012-11-04 NaN NA
## 36 2012-11-05 36.2465 0
## 37 2012-11-06 28.9375 0
## 38 2012-11-07 44.7326 0
## 39 2012-11-08 11.1771 0
## 40 2012-11-09 NaN NA
## 41 2012-11-10 NaN NA
## 42 2012-11-11 43.7778 0
## 43 2012-11-12 37.3785 0
## 44 2012-11-13 25.4722 0
## 45 2012-11-14 NaN NA
## 46 2012-11-15 0.1424 0
## 47 2012-11-16 18.8924 0
## 48 2012-11-17 49.7882 0
## 49 2012-11-18 52.4653 0
## 50 2012-11-19 30.6979 0
## 51 2012-11-20 15.5278 0
## 52 2012-11-21 44.3993 0
## 53 2012-11-22 70.9271 0
## 54 2012-11-23 73.5903 0
## 55 2012-11-24 50.2708 0
## 56 2012-11-25 41.0903 0
## 57 2012-11-26 38.7569 0
## 58 2012-11-27 47.3819 0
## 59 2012-11-28 35.3576 0
## 60 2012-11-29 24.4688 0
## 61 2012-11-30 NaN NA
</code></pre>
<p>Note that most of the median values are zero. This reflects the large number of intervals in each day that this person was not moving. This further suggests that the number of steps was localized to only a minority of time intervals recorded. </p>
<h2>What is the average daily activity pattern?</h2>
<p>To further investigate this variance in steps taken per time interval, the below plot displays the mean numbers of steps for each time interval over the entire recording period. </p>
<pre><code class="r">intmean <- with(data, tapply(steps, interval, mean, na.rm = TRUE))
plot(intmean, type = "l", xlab = "Five minute interval", ylab = "Mean number of steps taken")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>The periods of inactivity seem to focus on the early and late time intervals for each day, likely when this person was sleeping. During their waking hours, there is at least some activity in each interval, but notice the large spikes in the plot, particular near the 100th interval range.</p>
<p>You can use the following code to retrieve the maximum average number of steps taken and identifying that time interval.</p>
<pre><code class="r">intmean[which.max(abs(intmean))]
</code></pre>
<pre><code>## 835
## 206.2
</code></pre>
<p>Each interval is named as an interpretable time. For this person, it appears the maximum average number of steps taken is 206.2 during the interval between 8:35 and 8:40am. </p>
<h2>Imputing missing values</h2>
<p>Another thing to note is the large number of NA and NaN values in the above calculations. This is due to the fact that there are large amounts of data missing, including full days worth. The following code will output the total number of intervals that are missing data.</p>
<pre><code class="r">nrow(data[!complete.cases(data), ])
</code></pre>
<pre><code>## [1] 2304
</code></pre>
<p>In total, there are 2,304 time intervals where the number of steps taken was not recorded. How much of the entire data set does this constitute? Taking a quick percentage:</p>
<pre><code class="r">nrow(data[!complete.cases(data), ])/nrow(data)
</code></pre>
<pre><code>## [1] 0.1311
</code></pre>
<p>We can see that this is a full 13% of the data! </p>
<p>While analysts vary in how they deal with missing values, for the purposes of this project, we will be filling in the missing values. I have decided to use the average number of steps per interval to fill in the missing values, since every interval has at least one data point. The alternative, using the mean of each day to fill in the missing values, would not help for the days where there is no data. </p>
<p>The following recreates our data set with all the missing values replaced by the interval mean. </p>
<pre><code class="r">dataNA = data.frame(matrix(vector(), nrow(data), 3, dimnames = list(c(), c("steps",
"date", "interval"))), stringsAsFactors = F)
dataNA$steps <- ifelse(is.na(data$steps), intmean[as.character(data$interval)],
data$steps)
dataNA$date <- data$date
dataNA$interval <- data$interval
</code></pre>
<p>We can now recreate the histogram showing the total number of steps taken per day. </p>
<pre><code class="r">hist(with(dataNA, tapply(steps, date, sum)), xlab = "Number of steps", main = " ")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/> </p>
<p>While the histogram still peaks at the same number of steps, the distribution has become more normal. This is due to the fact that many of the days that had a low number of steps only did so due to missing values. </p>
<p>We can also calculate the mean and median number of steps taken per day for this new dataset. </p>
<pre><code class="r">ddply(dataNA, ~date, summarize, mean = mean(steps, na.rm = TRUE), median = median(steps,
na.rm = TRUE))
</code></pre>
<pre><code>## date mean median
## 1 2012-10-01 37.3826 34.11
## 2 2012-10-02 0.4375 0.00
## 3 2012-10-03 39.4167 0.00
## 4 2012-10-04 42.0694 0.00
## 5 2012-10-05 46.1597 0.00
## 6 2012-10-06 53.5417 0.00
## 7 2012-10-07 38.2465 0.00
## 8 2012-10-08 37.3826 34.11
## 9 2012-10-09 44.4826 0.00
## 10 2012-10-10 34.3750 0.00
## 11 2012-10-11 35.7778 0.00
## 12 2012-10-12 60.3542 0.00
## 13 2012-10-13 43.1458 0.00
## 14 2012-10-14 52.4236 0.00
## 15 2012-10-15 35.2049 0.00
## 16 2012-10-16 52.3750 0.00
## 17 2012-10-17 46.7083 0.00
## 18 2012-10-18 34.9167 0.00
## 19 2012-10-19 41.0729 0.00
## 20 2012-10-20 36.0938 0.00
## 21 2012-10-21 30.6285 0.00
## 22 2012-10-22 46.7361 0.00
## 23 2012-10-23 30.9653 0.00
## 24 2012-10-24 29.0104 0.00
## 25 2012-10-25 8.6528 0.00
## 26 2012-10-26 23.5347 0.00
## 27 2012-10-27 35.1354 0.00
## 28 2012-10-28 39.7847 0.00
## 29 2012-10-29 17.4236 0.00
## 30 2012-10-30 34.0938 0.00
## 31 2012-10-31 53.5208 0.00
## 32 2012-11-01 37.3826 34.11
## 33 2012-11-02 36.8056 0.00
## 34 2012-11-03 36.7049 0.00
## 35 2012-11-04 37.3826 34.11
## 36 2012-11-05 36.2465 0.00
## 37 2012-11-06 28.9375 0.00
## 38 2012-11-07 44.7326 0.00
## 39 2012-11-08 11.1771 0.00
## 40 2012-11-09 37.3826 34.11
## 41 2012-11-10 37.3826 34.11
## 42 2012-11-11 43.7778 0.00
## 43 2012-11-12 37.3785 0.00
## 44 2012-11-13 25.4722 0.00
## 45 2012-11-14 37.3826 34.11
## 46 2012-11-15 0.1424 0.00
## 47 2012-11-16 18.8924 0.00
## 48 2012-11-17 49.7882 0.00
## 49 2012-11-18 52.4653 0.00
## 50 2012-11-19 30.6979 0.00
## 51 2012-11-20 15.5278 0.00
## 52 2012-11-21 44.3993 0.00
## 53 2012-11-22 70.9271 0.00
## 54 2012-11-23 73.5903 0.00
## 55 2012-11-24 50.2708 0.00
## 56 2012-11-25 41.0903 0.00
## 57 2012-11-26 38.7569 0.00
## 58 2012-11-27 47.3819 0.00
## 59 2012-11-28 35.3576 0.00
## 60 2012-11-29 24.4688 0.00
## 61 2012-11-30 37.3826 34.11
</code></pre>
<p>Overall, most of the values for days with data remain unchanged. For days that had only missing values, there is a large change in the data, since there is now actually data there! This is especially apparent for the median values, since these are the only days that have a median that is not zero. </p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>Overall, we have found that the number of steps taken by a person depends greatly on the time of day that the number of steps was recorded. This makes intuitive sense: people generally have a daily routine that involves more activity at certain hours and less (or no!) activity at others. </p>
<p>Another part of a person routine is the difference between weekends and weekdays. For most people, the weekends involve a very different schedule than weekdays. Can we see these differences reflected in the number of steps this particular person took during the time their steps were recorded? </p>
<p>In order to investigate this, we must first create a factor that will split our data between weekdays and weekends. I did this by first creating a factor that named the day of the week for each row. A second factor was created based on this one that separated weekdays from weekends.</p>
<pre><code class="r">dataNA$day <- weekdays(dataNA$date)
dataNA$dayType <- ifelse(dataNA$day == "Saturday" | dataNA$day == "Sunday",
"Weekend", "Weekday")
</code></pre>
<p>Using this new factor, we can now look at the mean number of steps taken for each time interval per type of day. </p>
<pre><code class="r">intmeanNA <- with(dataNA, tapply(steps, list(interval, dayType), mean))
par(mfrow = c(2, 1))
plot(intmeanNA[, 1], type = "l", ylab = "Mean number of steps", xlab = "Five minute interval",
main = "Weekday")
plot(intmeanNA[, 2], type = "l", ylab = "Mean number of steps", xlab = "Five minute interval",
main = "Weekend")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p>
<p>Overall, it appears that this person is much more active on the weekends than they are during the work week. Instead of just a few peaks of activity during the day, there are many peaks of activity with fewer periods of inactivity. </p>
</body>
</html>