forked from rdpeng/RepData_PeerAssessment1
-
Notifications
You must be signed in to change notification settings - Fork 0
/
PA1_template.html
442 lines (328 loc) · 178 KB
/
PA1_template.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
<!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;
padding: 1em;
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>
<pre><code class="r">setwd("~/RepData_PeerAssessment1")
library(ggplot2)
</code></pre>
<h2>Loading and preprocessing the data</h2>
<p>1) Read the zipped data</p>
<pre><code class="r">data <- read.csv(unz("activity.zip", "activity.csv"), header=TRUE)
str(data)
</code></pre>
<pre><code>## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
</code></pre>
<p>2)
Make the date variable into a date</p>
<pre><code class="r">data$date <- as.Date(data$date)
str(data)
</code></pre>
<pre><code>## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Date, format: "2012-10-01" "2012-10-01" ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
</code></pre>
<pre><code class="r">summary(data)
</code></pre>
<pre><code>## steps date interval
## Min. : 0.0 Min. :2012-10-01 Min. : 0
## 1st Qu.: 0.0 1st Qu.:2012-10-16 1st Qu.: 589
## Median : 0.0 Median :2012-10-31 Median :1178
## Mean : 37.4 Mean :2012-10-31 Mean :1178
## 3rd Qu.: 12.0 3rd Qu.:2012-11-15 3rd Qu.:1766
## Max. :806.0 Max. :2012-11-30 Max. :2355
## NA's :2304
</code></pre>
<p>Make a new data frame for total daily steps.</p>
<pre><code class="r">dailysteps <- aggregate(steps ~ date, data, sum)
str(dailysteps)
</code></pre>
<pre><code>## 'data.frame': 53 obs. of 2 variables:
## $ date : Date, format: "2012-10-02" "2012-10-03" ...
## $ steps: int 126 11352 12116 13294 15420 11015 12811 9900 10304 17382 ...
</code></pre>
<pre><code class="r">summary(dailysteps)
</code></pre>
<pre><code>## date steps
## Min. :2012-10-02 Min. : 41
## 1st Qu.:2012-10-16 1st Qu.: 8841
## Median :2012-10-29 Median :10765
## Mean :2012-10-30 Mean :10766
## 3rd Qu.:2012-11-16 3rd Qu.:13294
## Max. :2012-11-29 Max. :21194
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>1) Make a histogram of steps each per day.</p>
<pre><code class="r">hist(dailysteps$steps, main = "Histogram of steps per day", xlab="Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p>
<p>2) Calculate the mean and median total steps per day.</p>
<pre><code class="r">dailysteps.mean <- mean(dailysteps$steps, na.rm=T)
dailysteps.median <- median(dailysteps$steps, na.rm=T)
</code></pre>
<p>The mean number of steps is <strong>10766</strong></p>
<p>The median number of steps is <strong>10765</strong></p>
<h2>What is the average daily activity pattern?</h2>
<p>Summarise the data </p>
<pre><code class="r">activity <- aggregate(steps ~ interval, data, mean)
str(activity)
</code></pre>
<pre><code>## 'data.frame': 288 obs. of 2 variables:
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
## $ steps : num 1.717 0.3396 0.1321 0.1509 0.0755 ...
</code></pre>
<pre><code class="r">summary(activity)
</code></pre>
<pre><code>## interval steps
## Min. : 0 Min. : 0.00
## 1st Qu.: 589 1st Qu.: 2.49
## Median :1178 Median : 34.11
## Mean :1178 Mean : 37.38
## 3rd Qu.:1766 3rd Qu.: 52.83
## Max. :2355 Max. :206.17
</code></pre>
<p>1) Make a time series plot of the 5-minute interval (x-axis)
and the average number of steps taken, averaged across all days (y-axis).</p>
<pre><code class="r">myplot <- ggplot(activity, aes(x=interval, y=steps)) + geom_line() +
labs(x="5min interval", y = "Average number of steps", title = "Number of steps taken in 5-minute intervals, averaged across all days")
myplot
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-8"/> </p>
<p>2) Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?</p>
<pre><code class="r">maxsteps <- which.max(activity$steps)
activity$steps[maxsteps]
</code></pre>
<pre><code>## [1] 206.2
</code></pre>
<pre><code class="r">maxint <- activity$interval[maxsteps]
</code></pre>
<p>The 5-minute interval with the maximum number of steps is <strong>835</strong></p>
<h2>Imputing missing values</h2>
<p>1) Calculate and report the total number of missing values in the dataset</p>
<pre><code class="r">summary(data)
</code></pre>
<pre><code>## steps date interval
## Min. : 0.0 Min. :2012-10-01 Min. : 0
## 1st Qu.: 0.0 1st Qu.:2012-10-16 1st Qu.: 589
## Median : 0.0 Median :2012-10-31 Median :1178
## Mean : 37.4 Mean :2012-10-31 Mean :1178
## 3rd Qu.: 12.0 3rd Qu.:2012-11-15 3rd Qu.:1766
## Max. :806.0 Max. :2012-11-30 Max. :2355
## NA's :2304
</code></pre>
<p>There are <strong>2304</strong> missing values in the steps column</p>
<p>2)
Devise a strategy for filling in all of the missing values in the dataset. </p>
<p>I calculate the mean of all intervals with data and use this to fill all missing values. This is the value <strong>37.3826</strong> </p>
<p>3)
Create a new dataset that is equal to the original dataset but with the missing data filled in.</p>
<pre><code class="r">fulldata <- data
fulldata[is.na(fulldata)] = mean(data$steps, na.rm=T)
</code></pre>
<p>4)
Make a histogram of the total number of steps taken each day, calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?</p>
<p>Make a new data frame for total daily steps with imputed values.</p>
<pre><code class="r">dailysteps2 <- aggregate(steps ~ date, fulldata, sum)
summary(dailysteps2)
</code></pre>
<pre><code>## date steps
## Min. :2012-10-01 Min. : 41
## 1st Qu.:2012-10-16 1st Qu.: 9819
## Median :2012-10-31 Median :10766
## Mean :2012-10-31 Mean :10766
## 3rd Qu.:2012-11-15 3rd Qu.:12811
## Max. :2012-11-30 Max. :21194
</code></pre>
<pre><code class="r">hist(dailysteps2$steps, main = "Histogram of steps per day (imputed vals)", xlab="Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p>
<p>Calculate the mean and median total steps per day.</p>
<pre><code class="r">dailysteps2.mean <- mean(dailysteps2$steps, na.rm=T)
dailysteps2.median <- median(dailysteps2$steps, na.rm=T)
</code></pre>
<p>Do these values differ from the estimates from the first part of the assignment? </p>
<p>The mean number of steps is <strong>10766</strong> (earlier: 10766)<br/>
The median number of steps is <strong>10766</strong> (earlier 10765 )</p>
<p>There are almost no differences between the earlier values for mean/median and the values after imputing.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>1)
Create a new factor variable in the dataset with two levels – “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.</p>
<pre><code class="r">fulldata$wd <- weekdays(fulldata$date)
weekendDays <- c("Saturday", "Sunday")
fulldata$wd[fulldata$wd %in% weekendDays] <- 'weekend'
fulldata$wd[fulldata$wd != 'weekend'] <- 'weekday'
fullactivity <- aggregate(steps ~ interval + wd, fulldata, mean)
str(activity)
</code></pre>
<pre><code>## 'data.frame': 288 obs. of 2 variables:
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
## $ steps : num 1.717 0.3396 0.1321 0.1509 0.0755 ...
</code></pre>
<pre><code class="r">summary(activity)
</code></pre>
<pre><code>## interval steps
## Min. : 0 Min. : 0.00
## 1st Qu.: 589 1st Qu.: 2.49
## Median :1178 Median : 34.11
## Mean :1178 Mean : 37.38
## 3rd Qu.:1766 3rd Qu.: 52.83
## Max. :2355 Max. :206.17
</code></pre>
<p>2)
Make a panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).</p>
<pre><code class="r">myplot <- ggplot(fullactivity, aes(x=interval, y=steps)) + geom_line() +
labs(x="5min interval", y = "Average number of steps", title = "Steps per 5-min interval, averaged across weekday or weekend days")
myplot + facet_grid(wd ~ .)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-15"/> </p>
</body>
</html>