x_train:
Data frame of measured data for the training set. Ecah row is a 561-element vector of measured corresponding to a study subject and an activity that that subject performed
y_train:
Data frame of activities performed by training set subjects corresponding to data values in x_train. Values 1-6 corresponding to factor levels of activities that were performed:
1 WALKING
2 WALKING_UPSTAIRS
3 WALKING_DOWNSTAIRS
4 SITTING
5 STANDING
6 LAYING
subject_train:
Data frame of training set subject identification numbers that performed activities in y_train corresponding to measured values in x_train, values 1-30.
x_test:
Data frame of measured data for the test set. Ecah row is a 561-element vector of measured corresponding to a study subject and an activity that that subject performed
y_test:
Data frame of activities performed by test set subjects corresponding to data values in x_test. Values 1-6 corresponding to factor levels of activities that were performed:
1 WALKING
2 WALKING_UPSTAIRS
3 WALKING_DOWNSTAIRS
4 SITTING
5 STANDING
6 LAYING
subject_test:
Data frame of test set subject identification numbers that performed activities in y_test corresponding to measured values in x_test, values 1-30.
activities:
A list of names of activities that subjects performed. Six activities were included
"WALKING" "WALKING_UPSTAIRS" "WALKING_DOWNSTAIRS" "SITTING" "STANDING" "LAYING"
features:
A list of 561 features describing measured variables in x_train and x_test data frames.
Variables were derived from a list of signals that were measured. Variables are as follows:
mean(): Mean value
std(): Standard deviation
mad(): Median absolute deviation
max(): Largest value in array
min(): Smallest value in array
sma(): Signal magnitude area
energy(): Energy measure. Sum of the squares divided by the number of values
iqr(): Interquartile range
entropy(): Signal entropy
arCoeff(): Autorregresion coefficients with Burg order equal to 4
correlation(): correlation coefficient between two signals
maxInds(): index of the frequency component with largest magnitude
meanFreq(): Weighted average of the frequency components to obtain a mean frequency
skewness(): skewness of the frequency domain signal
kurtosis(): kurtosis of the frequency domain signal
bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window
angle(): Angle between to vectors
The list of signals they were derived from are as follows:
tBodyAcc-XYZ
tGravityAcc-XYZ
tBodyAccJerk-XYZ
tBodyGyro-XYZ
tBodyGyroJerk-XYZ
tBodyAccMag
tGravityAccMag
tBodyAccJerkMag
tBodyGyroMag
tBodyGyroJerkMag
fBodyAcc-XYZ
fBodyAccJerk-XYZ
fBodyGyro-XYZ
fBodyAccMag
fBodyAccJerkMag
fBodyGyroMag
fBodyGyroJerkMag
The angle() variable was further vectorized by averaging following signals:
gravityMean
tBodyAccMean
tBodyAccJerkMean
tBodyGyroMean
tBodyGyroJerkMean
train:
Merged data frame of training data set with the subject and activity data.
test:
Merged data frame of test data set with the subject and activity data.
full_df:
Merged data frame contaiing both train and test data.
df_mean_std:
Data frame containing extracted measurements on only the mean and standard deviation for each measurement.
The data frame contains
subject: Subject ID of the individual that performed the activity
activity: Descriptive activity name of the activity performed by the subject
a 66-element vector of variables that contain mean() or std() on each measurement
df_tidy:
The final, tidy-format, data frame containing averages of variables from df_mean_std grouped by the subject and the activity type.
The data frame contains
subject: Subject ID of the individual that performed the activity
activity: Descriptive activity name of the activity performed by the subject
a 66-element vector of averages of variables that contain mean() or std() on each measurement