The data was collected from the accelerometers from the Samsung Galaxy S smartphone. A full description of data is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
Data was downloaded from here: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
In the project, the following files were used:
- features.txt : A 561-feature vector with time and frequency domain variables.
- activity_labels.txt : The correspondance between activity ids and activity names.
- test/subject_test.txt : Each row identifies the subject who performed the activity for each window sample. Its range is between 1 and 30.
- test/X_test.txt : Test set
- test/y_test.txt : Test labels
- train/subject_train.txt : Each row identifies the subject who performed the activity for each window sample. Its range is between 1 and 30.
- train/X_train.txt : Training set
- train/y_train.txt : Training labels
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y_train : data.frame that stores data from y_train.txt. Consists of 7352 obs of 1 variable (V1). All values are integers between 1 and 6 and correspond to the activities listed in activity_labels.txt. The rows correspond to the subjects described in subject_train.txt.
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X_train : data.frame that stores data from X_train.txt. Consists of 7352 obs of 561 variable (V1,...,V561). The column corresponds to the measuremens obtained for features listed in features.txt. The rows correspond to the subjects described in subject_train.txt. The values are between -1 and 1.
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subject_train : data.frame that stores data from subject_train.txt. Consists of 7352 obs of 1 variable(V1). All values are integers between 1 and 30 and correspond to the subjects performed activity.
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y_test : data.frame that stores data from y_test.txt. Consists of 2947 obs of 1 variable(V1). All values are integers between 1 and 6 and correspond to the activities listed in activity_labels.txt. The rows correspond to the subjects described in subject_test.txt.
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X_test : data.frame that stores data from X_test.txt. Consists of 2947 obs of 561 variable(V1,...,V561). The columns correspond to the measuremens obtained for features listed in features.txt. The rows correspond to the subjects described in subject_test.txt. The values are between -1 and 1.
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subject_test : data.frame that stores data from subject_test.txt. Consists of 2947 obs of 1 variable (V1). All values are integers between 1 and 30 and correspond to the subjects performed activity.
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features - A data.frame that stores data from features.txt. Consists of 561 obs of 2 variables(V1,V2). The first variable V1 is index field. The second variable V2 contains names of features. See features_info.txt for the detailes information about feature names.
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The subject_train,y_train and X_train data.frames were merged together by the columns. The result data.frame is train_data. It consists of 7352 obs. of 563 variables (V1-the subject id,V1.1-the activity id,V1.2,V2..V561- 561 features).
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The subject_test,y_test,X_test data.frames were merged together by the columns. The result data.frame is test_data. It consists of 2947 obs. of 563 variables (V1-the subject id,V1.1-the activity id,V1.2,V2..V561- 561 features).
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The rows from test_data were added to train_data. The result data.frame is all_data consisting of 10299 obs. of 563 variables (V1-the subject id,V1.1-the activity id,V1.2,V2..V561- 561 features).
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The data for the measurements on the mean and standard deviation(feature names with mean() and std() measurement) was extracted from all_data. The result data.frame is all_data_subset consisting of 10299 obs. of 68 variables(V1 - the subject id; V1.1 - the activity id; V1.2, V2-V6, V41-V46, V81-V86, V121-V126, V161-V166, V201, V202, V214, V215, V227, V228, V240, V241, V253, V254, V266-V271, V345-350, V424-V429, V503, V504, V516, V517, V52, V530, V542, V543 - 66 features).
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The activity ids (V1.1) were substituted by their real names in all_data_subset: 1 -> walking 2 -> walking_upstairs 3 -> walking_downstairs 4 -> sitting 5 -> standing 6 -> laying
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Column names in all_data_subset were changed to the vector consisting of "subject", "activity" and 66 feature names with mean() and std() measurement. The new names are:
1.subject
2.activity
3.tBodyAcc-mean()-X
4.tBodyAcc-mean()-Y
5.tBodyAcc-mean()-Z
6.tBodyAcc-std()-X
7.tBodyAcc-std()-Y
8.tBodyAcc-std()-Z
9.tGravityAcc-mean()-X
10.tGravityAcc-mean()-Y
11.tGravityAcc-mean()-Z
12.tGravityAcc-std()-X
13.tGravityAcc-std()-Y
14.tGravityAcc-std()-Z
15.tBodyAccJerk-mean()-X
16.tBodyAccJerk-mean()-Y
17.tBodyAccJerk-mean()-Z
18.tBodyAccJerk-std()-X
19.tBodyAccJerk-std()-Y
20.tBodyAccJerk-std()-Z
21.tBodyGyro-mean()-X
22.tBodyGyro-mean()-Y
23.tBodyGyro-mean()-Z
24.tBodyGyro-std()-X
25.tBodyGyro-std()-Y
26.tBodyGyro-std()-Z
27.tBodyGyroJerk-mean()-X
28.tBodyGyroJerk-mean()-Y
29.tBodyGyroJerk-mean()-Z
30.tBodyGyroJerk-std()-X
31.tBodyGyroJerk-std()-Y
32.tBodyGyroJerk-std()-Z
33.tBodyAccMag-mean()
34.tBodyAccMag-std()
35.tGravityAccMag-mean()
36.tGravityAccMag-std()
37.tBodyAccJerkMag-mean()
38.tBodyAccJerkMag-std()
39.tBodyGyroMag-mean()
40.tBodyGyroMag-std()
41.tBodyGyroJerkMag-mean()
42.tBodyGyroJerkMag-std()
43.fBodyAcc-mean()-X
44.fBodyAcc-mean()-Y
45.fBodyAcc-mean()-Z
46.fBodyAcc-std()-X
47.fBodyAcc-std()-Y
48.fBodyAcc-std()-Z
49.fBodyAccJerk-mean()-X
50.fBodyAccJerk-mean()-Y
51.fBodyAccJerk-mean()-Z
52.fBodyAccJerk-std()-X
53.fBodyAccJerk-std()-Y
54.fBodyAccJerk-std()-Z
55.fBodyGyro-mean()-X
56.fBodyGyro-mean()-Y
57.fBodyGyro-mean()-Z
58.fBodyGyro-std()-X
59.fBodyGyro-std()-Y
60.fBodyGyro-std()-Z
61.fBodyAccMag-mean()
62.fBodyAccMag-std()
63.fBodyBodyAccJerkMag-mean()
64.fBodyBodyAccJerkMag-std()
65.fBodyBodyGyroMag-mean()
66.fBodyBodyGyroMag-std()
67.fBodyBodyGyroJerkMag-mean()
68.fBodyBodyGyroJerkMag-std()
- A new data.frame aver_data with the average of each variable for each activity and each subject was created from all_data_subset. It consists of 180 obs of 68 variables (the variables names are similar to the variables names in previous section for all_data_subset).