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.
sourceData: is the R variable that is produced when run_analysis) function is run. It contains structured lists of the source data, see Transformations section below for a description of functions that process the source data into this dataframe.
transformData: is the R variable that is produced as an intermediary dataframe which contains the dataframe after merging.
tidyData: is the R variable that is produced after the full dataset has been merged. it contains a dataframe that is the result of extracting and summarising the data and summarises the average of each variable for each subject.
Each variable that is a list contains multiple elements, use the $ to navigate in the R console command line
metadataLog.txt: an output file which displays a log that records the process and timestamps for the output.
transformData.txt: an output file which contains the full dataset.
tidyData.txt: an output file which only shows mean and standard deviation values for subject and activity.
The source data was downloaded from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
See the README.txt in the link to the zip folder for a fuller explanation of the dataset source files.
[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012
In addition to the source file variables which can be found within the features.txt and features_info.txt of the source data, 3 additional variable are created as a result of the transformation described below:
group: Defines the dataset from which the record came from {values: test,train}
activity: Displays the written label for the observation rather than the index which was provided in ./group/y_group.txt
subjectID: Displays that the data which was provided in ./[group]/subject_[group].txt
The original labels containing mean and std are filtered and a rename process has occured on them. See Notes for more detail of the renaming process.
subjectID
activity
time.body.acceleration-mean-x
time.body.acceleration-mean-y
time.body.acceleration-mean-z
time.gravity.acceleration-mean-x
time.gravity.acceleration-mean-y
time.gravity.acceleration-mean-z
time.body.accelerationjerk-mean-x
time.body.accelerationjerk-mean-y
time.body.accelerationjerk-mean-z
time.bodygyro-mean-x
time.bodygyro-mean-y
time.bodygyro-mean-z
time.bodygyrojerk-mean-x
time.bodygyrojerk-mean-y
time.bodygyrojerk-mean-z
time.body.acceleration.magnitude-mean
time.gravity.acceleration.magnitude-mean
time.body.accelerationjerk.magnitude-mean
time.bodygyro.magnitude-mean
time.bodygyrojerk.magnitude-mean
frequency.body.acceleration-mean-x
frequency.body.acceleration-mean-y
frequency.body.acceleration-mean-z
frequency.body.acceleration-meanfreq-x
frequency.body.acceleration-meanfreq-y
frequency.body.acceleration-meanfreq-z
frequency.body.accelerationjerk-mean-x
frequency.body.accelerationjerk-mean-y
frequency.body.accelerationjerk-mean-z
frequency.body.accelerationjerk-meanfreq-x
frequency.body.accelerationjerk-meanfreq-y
frequency.body.accelerationjerk-meanfreq-z
frequency.bodygyro-mean-x
frequency.bodygyro-mean-y
frequency.bodygyro-mean-z
frequency.bodygyro-meanfreq-x
frequency.bodygyro-meanfreq-y
frequency.bodygyro-meanfreq-z
frequency.body.acceleration.magnitude-mean
frequency.body.acceleration.magnitude-meanfreq
frequency.bodybody.accelerationjerk.magnitude-mean
frequency.bodybody.accelerationjerk.magnitude-meanfreq
frequency.bodybodygyro.magnitude-mean
frequency.bodybodygyro.magnitude-meanfreq
frequency.bodybodygyrojerk.magnitude-mean
frequency.bodybodygyrojerk.magnitude-meanfreq
angletime.body.accelerationmeangravity
angletime.body.accelerationjerkmeangravitymean
angletime.bodygyromeangravitymean
angletime.bodygyrojerkmeangravitymean
anglexgravitymean
angleygravitymean
anglezgravitymean
time.body.acceleration-std-x
time.body.acceleration-std-y
time.body.acceleration-std-z
time.gravity.acceleration-std-x
time.gravity.acceleration-std-y
time.gravity.acceleration-std-z
time.body.accelerationjerk-std-x
time.body.accelerationjerk-std-y
time.body.accelerationjerk-std-z
time.bodygyro-std-x
time.bodygyro-std-y
time.bodygyro-std-z
time.bodygyrojerk-std-x
time.bodygyrojerk-std-y
time.bodygyrojerk-std-z
time.body.acceleration.magnitude-std
time.gravity.acceleration.magnitude-std
time.body.accelerationjerk.magnitude-std
time.bodygyro.magnitude-std
time.bodygyrojerk.magnitude-std
frequency.body.acceleration-std-x
frequency.body.acceleration-std-y
frequency.body.acceleration-std-z
frequency.body.accelerationjerk-std-x
frequency.body.accelerationjerk-std-y
frequency.body.accelerationjerk-std-z
frequency.bodygyro-std-x
frequency.bodygyro-std-y
frequency.bodygyro-std-z
frequency.body.acceleration.magnitude-std
frequency.bodybody.accelerationjerk.magnitude-std
frequency.bodybodygyro.magnitude-std
frequency.bodybodygyrojerk.magnitude-std
platform x86_64-w64-mingw32
arch x86_64
os mingw32
system x86_64, mingw32
major 3
minor 3.2
year 2016
month 10
day 31
svn rev 71607
language R
version.string R version 3.3.2 (2016-10-31)
`stringr:: 1.1.0`
`dplyr:: 0.5.0`
`library:: 1.6.0`
'run_analysis.R` is the file which contains the assignment function
loadAssignment(): - This performs the datagrab from the source and processes it to produce the assignmentData dataframe. During this function, the fuller data set with all variables is also saved to the variable assignmentData_FullVarSet
it is made up of a number internal functions:
source_Assignment(): obtains the data from the download link
read_Assignment_Data(): reads the observation data from multiple txt files
rename_Label(): defines a function to rename the labels which is used in readlabels()
read_Label_Data(): reads the label data from multiple txt files
map_descriptions(): maps the descriptions to the column headers and the activities
append_Columns(): performs operations adds the subject and activity columns to the observation data for the test and train data set
merge_Datasets(): joins the test and train datasets together into one full dataframe
filter_Variables(): filters the variables down to only observataions that contain mean or std in their description
extract_Data(): - This performs average by subject and activty for each variable and creates a duplicate dataframe called tidyData
For more information read the #comments in the run_analysis.R script
Each function within the script is structured as follows:
Section
0 Dependencies and Input validation
...
Some of the variables in the original source dataset described in features.txt are not unique which conflicts with the dplyr functions.
To overcome this make.unique() is used on the this file after it is loaded and the ~~~ seperator is used followed by a unique number and all alpha-character are transformed to lowercase.
Some characters are replaced with words which are concatenated by .
See the rename_Label() function for more detail or view the metadataLog.txt for a sample.
For more detail of the transformation of the label names see rename_label() function.
Use of this dataset in publications must be acknowledged by referencing the following publication [1]
[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012
This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.
Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.