The original data set is available at the UCI Machine Learning
Repository as the Human Activity Recognition Using Smartphones Data
Set
(data
folder).
Full details are available in the README.txt
in the download archive
or on the web at UCI HAR
Dataset.names
file. This package contains two tidy data sets:
ucihar
-- mean and standard deviation measurements (10299 obs. of
68 variables)ucihar_avgs
-- summarized (mean) values for each measurement in
ucihar
by subject and activity (180 obs. of 68 variables)The source data is across multiple files. The following are the steps
taken to create the ucihar
data set:
features.txt
test/X_test.txt
, using feature names
as column names, adding activity IDs from test/y_test.txt
, and
adding subject IDs from test/subject_test.txt
.activity_labels.txt
The following are the steps taken to clean field and activity labels:
_
instead of
camelCase, recode f
and t
to freq
and time
, remove
non-alphanumeric characteris (
, )
, and -
(e.g., from
tBodyAcc-mean()-X
to time_body_acc_mean_x
)WALKING_UPSTAIRS
to walking_upstairs
)The following are the steps taken to create the ucihar_avgs
data set:
ucihar
data by subject_id
and activity
mean()
Implementations for both data sets are in get_ucihar()
and
get_ucihar_avgs()
in
data-raw/run_analysis.R
.
ucihar
data setThe following maps variables from the source details document to the new variable names:
subject_id
Subject identifier
activity
Type of activity being performed (standing, sitting, laying, walking,
walking_downstairs, walking_upstairs)
time_body_acc_{mean,std}_{x,y,z}
, time_body_acc_mag_{mean,std}
, time_body_acc_jerk_{mean,std}_{x,y,z}
, time_body_acc_jerk_mag_{mean,std}
Mean and standard deviation body acceleration, in time domain; units are
g's; ref. tBodyAcc-XYZ
, tBodyAccMag
, tBodyAccJerk-XYZ
,
tBodyAccJerkMag-XYZ
time_body_gyro_{mean,std}_{x,y,z}
, time_body_gyro_mag_{mean,std}
, time_body_gyro_jerk_{mean,std}_{x,y,z}
, time_body_gyro_jerk_mag_{mean,std}
Mean and standard deviation body angulary velocity, in time domain;
units are rad/seg; ref. tBodyGyro-XYZ
, tBodyGyroMag
,
tBodyGyroJerk-XYZ
, tBodyGyroJerkMag-XYZ
time_gravity_acc_{mean,std}_{x,y,z}
, time_gravity_acc_mag_{mean,std}
Mean and standard deviation gravity in x, y, and z directions, in time
domain; units are g's; ref. tGravityAcc-XYZ
, tGravityAccMag
freq_body_acc_{mean,std}_{x,y,z}
, freq_body_acc_mag_{mean,std}
, freq_body_acc_jerk_{mean,std}_{x,y,z}
, freq_body_body_acc_jerk_mag_{mean,std}
Mean and standard deviation body acceleration, in frequency domain;
units are g's; ref. fBodyAcc-XYZ
, fBodyAccMag
, fBodyAccJerk-XYZ
,
fBodyBodyAccJerkMag
freq_body_gyro_{mean,std}_{x,y,z}
, freq_body_body_gyro_mag_{mean,std}
, freq_body_body_gyro_jerk_mag_{mean,std}
Mean and standard deviation body angulary velocity, in frequency domain;
units are rad/seg; ref. fBodyGyro-XYZ
, fBodyBodyGyroMag
,
fBodyBodyGyroJerkMag
ucihar_avgs
data setThis data set summarizes measurements from ucihar
, grouped by subject
and activity. In all cases, the measurements are averaged with mean()
.
See the above ucihar
code book (above) for more details.
subject_id
Subject identifier
activity
Type of activity being performed (standing, sitting, laying, walking,
walking_downstairs, walking_upstairs)
time_body_acc_{mean,std}_{x,y,z}
, time_body_acc_mag_{mean,std}
, time_body_acc_jerk_{mean,std}_{x,y,z}
, time_body_acc_jerk_mag_{mean,std}
Mean by subject_id
and activity
time_body_gyro_{mean,std}_{x,y,z}
, time_body_gyro_mag_{mean,std}
, time_body_gyro_jerk_{mean,std}_{x,y,z}
, time_body_gyro_jerk_mag_{mean,std}
Mean by subject_id
and activity
time_gravity_acc_{mean,std}_{x,y,z}
, time_gravity_acc_mag_{mean,std}
Mean by subject_id
and activity
freq_body_acc_{mean,std}_{x,y,z}
, freq_body_acc_mag_{mean,std}
, freq_body_acc_jerk_{mean,std}_{x,y,z}
, freq_body_body_acc_jerk_mag_{mean,std}
Mean by subject_id
and activity
freq_body_gyro_{mean,std}_{x,y,z}
, freq_body_body_gyro_mag_{mean,std}
, freq_body_body_gyro_jerk_mag_{mean,std}
Mean by subject_id
and activity
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