forked from rdpeng/RepData_PeerAssessment1
-
Notifications
You must be signed in to change notification settings - Fork 0
/
PA1_template.html
309 lines (229 loc) · 43.6 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
<!DOCTYPE html>
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<meta http-equiv="x-ua-compatible" content="IE=9" >
<title>Reproducible Research: Peer Assessment 1</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
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 {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
}
pre code {
display: block; padding: 0.5em;
}
code.r, code.cpp {
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>
<!-- Styles for R syntax highlighter -->
<style type="text/css">
pre .operator,
pre .paren {
color: rgb(104, 118, 135)
}
pre .literal {
color: rgb(88, 72, 246)
}
pre .number {
color: rgb(0, 0, 205);
}
pre .comment {
color: rgb(76, 136, 107);
}
pre .keyword {
color: rgb(0, 0, 255);
}
pre .identifier {
color: rgb(0, 0, 0);
}
pre .string {
color: rgb(3, 106, 7);
}
</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>
</head>
<body>
<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<pre><code class="r">act <- read.csv("activity.csv")
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<pre><code class="r">library("ggplot2")
library("reshape2")
act.melt <- melt(act, id=c("date"), measure.vars=c("steps"),na.rm=TRUE)
steps.aggr <- dcast(act.melt, date ~ variable, sum)
qplot(steps, data=steps.aggr, binwidth = 500)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">mean.steps <- mean(steps.aggr$steps)
median.steps <- median(steps.aggr$steps)
</code></pre>
<p>The mean of the total number of steps taken each day is 10766.19 and the mediam is 10765.00.</p>
<h2>What is the average daily activity pattern?</h2>
<pre><code class="r">act.melt.inter <- melt(act, id=c("interval"), measure.vars=c("steps"), na.rm=TRUE)
steps.inter.aggr <- dcast(act.melt.inter, interval ~ variable, mean)
qplot(interval, steps, data = steps.inter.aggr, geom="line")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-3"/> </p>
<pre><code class="r">max.inter <- steps.inter.aggr[which.max(steps.inter.aggr$steps),"interval"]
</code></pre>
<p>The interval 835.00 contains the maximum number of steps</p>
<h2>Imputing missing values</h2>
<pre><code class="r">mis.val <- sum(is.na(act$steps))
</code></pre>
<p>1.The total number of rows with missing values is 2304</p>
<p>2.Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.
Strategy: To use the mean for the 5-minute interval.</p>
<pre><code class="r">act.melt.inter <- melt(act, id=c("interval"), measure.vars=c("steps"), na.rm=TRUE)
steps.inter.aggr <- dcast(act.melt.inter, interval ~ variable, mean)
head(steps.inter.aggr)
</code></pre>
<pre><code>## interval steps
## 1 0 1.71698
## 2 5 0.33962
## 3 10 0.13208
## 4 15 0.15094
## 5 20 0.07547
## 6 25 2.09434
</code></pre>
<p>3.Create a new dataset that is equal to the original dataset but with the missing data filled in.
Implementing strategy: Loping through the na values in the original dataset and finding for each interval the correpsonfing mean value in the dataset calculated in the previous point</p>
<pre><code class="r">act.imp <- act
act.imp.na <- which(is.na(act.imp$steps))
for (i in act.imp.na) {
int.na <- act.imp[i, "interval" ]
tmp <- steps.inter.aggr[steps.inter.aggr$interval == int.na,]
act.imp[i,1] <- tmp[1,2]
}
head(act.imp)
</code></pre>
<pre><code>## steps date interval
## 1 1.71698 2012-10-01 0
## 2 0.33962 2012-10-01 5
## 3 0.13208 2012-10-01 10
## 4 0.15094 2012-10-01 15
## 5 0.07547 2012-10-01 20
## 6 2.09434 2012-10-01 25
</code></pre>
<p>4.Make a histogram of the total number of steps taken each day and 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>
<pre><code class="r">library("ggplot2")
library("reshape2")
act.melt.imp <- melt(act.imp, id=c("date"), measure.vars=c("steps"),na.rm=TRUE)
steps.aggr.imp <- dcast(act.melt.imp, date ~ variable, sum)
qplot(steps, data=steps.aggr.imp, binwidth = 500)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-8"/> </p>
<p>The mean of the total number of steps taken each day is 10766.19 and the mediam is 10766.19</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>Yes, there are some differences in activity. During weekdays, there is just one peak, while on weekends there are several peaks. </p>
<pre><code class="r">act.imp$date <- as.Date(act.imp$date, "%Y-%m-%d")
act.imp$weekd <- sapply(weekdays(act.imp$date),switch,
Monday="weekday",
Tuesday="weekday",
Wednesday="weekday",
Thursday="weekday",
Friday="weekday",
Saturday="weekend",
Sunday="weekend")
act.week <- melt(act.imp, id=c("interval","weekd"), measure.vars=c("steps"), na.rm=TRUE)
steps.week.aggr <- dcast(act.week, interval+weekd ~ variable, mean)
qplot(interval, steps, data = steps.week.aggr, geom="line") + facet_wrap(~ weekd, nrow=2, ncol=1)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/> </p>
</body>
</html>