Intent

run_analysis.R produces a tidy data set that contains averages of means and standard deviation measurements of Human Activity Recognition Using Smartphones Dataset Version 1.0 (http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) downloaded from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

##Instructions Download the zip file above and extract it. Copy run_analysis.R to that folder and source it from R (after chaging working directory to unzipped file folder). tidy.txt will be produced after sourcing is complete

Methodology

The tidy data set is produced with the following steps:

• Convert feature names (in features.txt) to descriptive names as follows (see name mappings below):
  • Convert to lower case
  • Characters '(', ')', '-' and ',' were converted to dot '.'
  • Wrap terms of mean, std, body, gyro, acc, jerk, mag, min, max, sma, mean and gravity within dots "." e.g "word"" --> ".word."
  • Remove consequtive and trailing dots
  • "bandEnergy" variales actually represent ranges, therefore change "X-Y" to "XtoY"
  • Rename duplicated "bandEnergy" variables to contain X,Y,Z coordinates
• Produce a merged data set
  • Merge the training and the test sets while adding respecitve subjects for each row and using activity names instead of codes (loaded from activity_labels.txt)
• From the merged data set, extract columns that contain the mean and standard deviation for each measurement (see name mappings below)
  • Columns are selected by grep-ing "mean" and "std" from the feature names
• Produce the tidy data set with the average of each feature for each activity and each subject

Feature name mapping

Please look at the feature_info.txt in the downloaded zip file for information on raw names. As indicated above, the raw names were converted into descriptive names. Following table contains the conversion info.

Seq	Raw Name	Descriptive Name
1	<from subject_XXX.txt>	subject
2	<from activity_label.txt>	activity
3	tBodyAcc-mean()-X	t.body.acc.mean.x
4	tBodyAcc-mean()-Y	t.body.acc.mean.y
5	tBodyAcc-mean()-Z	t.body.acc.mean.z
6	tBodyAcc-std()-X	t.body.acc.std.x
7	tBodyAcc-std()-Y	t.body.acc.std.y
8	tBodyAcc-std()-Z	t.body.acc.std.z
9	tGravityAcc-mean()-X	t.gravity.acc.mean.x
10	tGravityAcc-mean()-Y	t.gravity.acc.mean.y
11	tGravityAcc-mean()-Z	t.gravity.acc.mean.z
12	tGravityAcc-std()-X	t.gravity.acc.std.x
13	tGravityAcc-std()-Y	t.gravity.acc.std.y
14	tGravityAcc-std()-Z	t.gravity.acc.std.z
15	tBodyAccJerk-mean()-X	t.body.acc.jerk.mean.x
16	tBodyAccJerk-mean()-Y	t.body.acc.jerk.mean.y
17	tBodyAccJerk-mean()-Z	t.body.acc.jerk.mean.z
18	tBodyAccJerk-std()-X	t.body.acc.jerk.std.x
19	tBodyAccJerk-std()-Y	t.body.acc.jerk.std.y
20	tBodyAccJerk-std()-Z	t.body.acc.jerk.std.z
21	tBodyGyro-mean()-X	t.body.gyro.mean.x
22	tBodyGyro-mean()-Y	t.body.gyro.mean.y
23	tBodyGyro-mean()-Z	t.body.gyro.mean.z
24	tBodyGyro-std()-X	t.body.gyro.std.x
25	tBodyGyro-std()-Y	t.body.gyro.std.y
26	tBodyGyro-std()-Z	t.body.gyro.std.z
27	tBodyGyroJerk-mean()-X	t.body.gyro.jerk.mean.x
28	tBodyGyroJerk-mean()-Y	t.body.gyro.jerk.mean.y
29	tBodyGyroJerk-mean()-Z	t.body.gyro.jerk.mean.z
30	tBodyGyroJerk-std()-X	t.body.gyro.jerk.std.x
31	tBodyGyroJerk-std()-Y	t.body.gyro.jerk.std.y
32	tBodyGyroJerk-std()-Z	t.body.gyro.jerk.std.z
33	tBodyAccMag-mean()	t.body.acc.mag.mean
34	tBodyAccMag-std()	t.body.acc.mag.std
35	tGravityAccMag-mean()	t.gravity.acc.mag.mean
36	tGravityAccMag-std()	t.gravity.acc.mag.std
37	tBodyAccJerkMag-mean()	t.body.acc.jerk.mag.mean
38	tBodyAccJerkMag-std()	t.body.acc.jerk.mag.std
39	tBodyGyroMag-mean()	t.body.gyro.mag.mean
40	tBodyGyroMag-std()	t.body.gyro.mag.std
41	tBodyGyroJerkMag-mean()	t.body.gyro.jerk.mag.mean
42	tBodyGyroJerkMag-std()	t.body.gyro.jerk.mag.std
43	fBodyAcc-mean()-X	f.body.acc.mean.x
44	fBodyAcc-mean()-Y	f.body.acc.mean.y
45	fBodyAcc-mean()-Z	f.body.acc.mean.z
46	fBodyAcc-std()-X	f.body.acc.std.x
47	fBodyAcc-std()-Y	f.body.acc.std.y
48	fBodyAcc-std()-Z	f.body.acc.std.z
49	fBodyAcc-meanFreq()-X	f.body.acc.mean.freq.x
50	fBodyAcc-meanFreq()-Y	f.body.acc.mean.freq.y
51	fBodyAcc-meanFreq()-Z	f.body.acc.mean.freq.z
52	fBodyAccJerk-mean()-X	f.body.acc.jerk.mean.x
53	fBodyAccJerk-mean()-Y	f.body.acc.jerk.mean.y
54	fBodyAccJerk-mean()-Z	f.body.acc.jerk.mean.z
55	fBodyAccJerk-std()-X	f.body.acc.jerk.std.x
56	fBodyAccJerk-std()-Y	f.body.acc.jerk.std.y
57	fBodyAccJerk-std()-Z	f.body.acc.jerk.std.z
58	fBodyAccJerk-meanFreq()-X	f.body.acc.jerk.mean.freq.x
59	fBodyAccJerk-meanFreq()-Y	f.body.acc.jerk.mean.freq.y
60	fBodyAccJerk-meanFreq()-Z	f.body.acc.jerk.mean.freq.z
61	fBodyGyro-mean()-X	f.body.gyro.mean.x
62	fBodyGyro-mean()-Y	f.body.gyro.mean.y
63	fBodyGyro-mean()-Z	f.body.gyro.mean.z
64	fBodyGyro-std()-X	f.body.gyro.std.x
65	fBodyGyro-std()-Y	f.body.gyro.std.y
66	fBodyGyro-std()-Z	f.body.gyro.std.z
67	fBodyGyro-meanFreq()-X	f.body.gyro.mean.freq.x
68	fBodyGyro-meanFreq()-Y	f.body.gyro.mean.freq.y
69	fBodyGyro-meanFreq()-Z	f.body.gyro.mean.freq.z
70	fBodyAccMag-mean()	f.body.acc.mag.mean
71	fBodyAccMag-std()	f.body.acc.mag.std
72	fBodyAccMag-meanFreq()	f.body.acc.mag.mean.freq
73	fBodyBodyAccJerkMag-mean()	f.body.body.acc.jerk.mag.mean
74	fBodyBodyAccJerkMag-std()	f.body.body.acc.jerk.mag.std
75	fBodyBodyAccJerkMag-meanFreq()	f.body.body.acc.jerk.mag.mean.freq
76	fBodyBodyGyroMag-mean()	f.body.body.gyro.mag.mean
77	fBodyBodyGyroMag-std()	f.body.body.gyro.mag.std
78	fBodyBodyGyroMag-meanFreq()	f.body.body.gyro.mag.mean.freq
79	fBodyBodyGyroJerkMag-mean()	f.body.body.gyro.jerk.mag.mean
80	fBodyBodyGyroJerkMag-std()	f.body.body.gyro.jerk.mag.std
81	fBodyBodyGyroJerkMag-meanFreq()	f.body.body.gyro.jerk.mag.mean.freq
82	angle(tBodyAccMean,gravity)	angle.t.body.acc.mean.gravity
83	angle(tBodyAccJerkMean),gravityMean)	angle.t.body.acc.jerk.mean.gravity.mean
84	angle(tBodyGyroMean,gravityMean)	angle.t.body.gyro.mean.gravity.mean
85	angle(tBodyGyroJerkMean,gravityMean)	angle.t.body.gyro.jerk.mean.gravity.mean
86	angle(X,gravityMean)	angle.x.gravity.mean
87	angle(Y,gravityMean)	angle.y.gravity.mean
88	angle(Z,gravityMean)	angle.z.gravity.mean