-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathcodebook.html
More file actions
204 lines (153 loc) · 5.03 KB
/
codebook.html
File metadata and controls
204 lines (153 loc) · 5.03 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
<!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>Code Book for Course Project</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>
</head>
<body>
<h1>Code Book for Course Project</h1>
<h2>Source Data</h2>
<p>Original source data can be found here <a href="https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip">https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip</a></p>
<p>The README contained within the data describes fully the original intent</p>
<p>From the README:</p>
<blockquote>
<p>The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities >(WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its >embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The >experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the >volunteers was selected for generating the training data and 30% the test data. </p>
<p>The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of >2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated >using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, >therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time >and frequency domain. See 'features_info.txt' for more details. </p>
</blockquote>
<h2>Variables</h2>
<p>Only the set of variables that calculated mean and standard deviation are included in this tidy data set. More information on these variables can be found in the features_info.txt file included in the original data.</p>
<p>Variables tracked here include:</p>
<ul>
<li>tBodyAcc-XYZ</li>
<li>tGravityAcc-XYZ</li>
<li>tBodyAccJerk-XYZ</li>
<li>tBodyGyro-XYZ</li>
<li>tBodyGyroJerk-XYZ</li>
<li>tBodyAccMag</li>
<li>tGravityAccMag</li>
<li>tBodyAccJerkMag</li>
<li>tBodyGyroMag</li>
<li>tBodyGyroJerkMag</li>
<li>fBodyAcc-XYZ</li>
<li>fBodyAccJerk-XYZ</li>
<li>fBodyGyro-XYZ</li>
<li>fBodyAccMag</li>
<li>fBodyAccJerkMag</li>
<li>fBodyGyroMag</li>
<li>fBodyGyroJerkMag</li>
</ul>
<p>Subject remain unchanged from the original data</p>
<p>Activity is now descriptively labeled.</p>
<h2>Transformations</h2>
<ul>
<li>The training and test data from the original dataset was combined into one large dataset</li>
<li>Data was subset to include only columns that track mean and standard deviation</li>
<li>Activity and Subject data was added to the dataset</li>
<li>Data was transformed to calculate the average of the mean and standard deviation for each tracked variable broken out by Activity and Subject</li>
<li>A file, 'tidy_data.txt' is generated from the resulting transformation</li>
</ul>
</body>
</html>