#Tidy Dataset for Coursera Getting and Cleaning Data class.
link to full description of original dataset: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
link to original dataset: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
#####The dataset for this project can be downloaded with the following code.
(Make sure you are in the working directory you want to be in. This code will un-zip the downloaded file into a sub folder called UCIdataset and set that as the working directory.)
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(url, destfile="UCIdataset.zip",method = "curl")
unzip("UCIdataset.zip", exdir = "UCIdataset")
setwd(paste(getwd(),"/UCIdataset", sep="")) The text version of this table was created with the command:
write.table(tidyUCI, file = "tidyUCI.txt", row.name=FALSE)and can be read into R using the command:
data <- read.table(file_path, header = TRUE)
data$subject <- as.factor(data$subject)(The 'subject' column is a factor when the tidyUCI is created from the script, but must be converted to a factor when reading in as a text file)
To create a tidy dataset containing the mean of all variables that contain mean() or std() data.
This is accomplished by:
- Merging the training and the test sets to create one data set.
- Extracting only the measurements on the mean and standard deviation for each measurement.
- Useing descriptive activity names to name the activities in the data set.
- Appropriately labeling the data set with descriptive variable names.
- Creating an independent tidy data set with the average of each variable for each activity and each subject.
The original data for this project came from:
Human Activity Recognition Using Smartphones Dataset Version 1.0
Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto.
Smartlab - Non Linear Complex Systems Laboratory
DITEN - Universit‡ degli Studi di Genova.
Via Opera Pia 11A, I-16145, Genoa, Italy.
activityrecognition@smartlab.ws
www.smartlab.ws
#####Orginal project summary: A group of 30 volunteers within an age bracket of 19-48 years 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, they captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz.
#####For each record it is provided:
- Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
- Triaxial Angular velocity from the gyroscope.
- A 561-feature vector with time and frequency domain variables.
- Its activity label.
- An identifier of the subject who carried out the experiment.
#####The dataset included the following files:
- features_info.txt: Shows information about the variables used on the feature vector.
- features.txt: List of all features.
- activity_labels.txt: Links the class labels with their activity name.
- train/X_train.txt: Training set.
- train/y_train.txt: Training labels.
- test/X_test.txt: Test set.
- test/y_test.txt: Test labels.
#####The following files are available for the train and test data. Their descriptions are equivalent.
- train/subject_train.txt: Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
- train/Inertial Signals/total_acc_x_train.txt: The acceleration signal from the smartphone accelerometer X axis in standard gravity units g.
Every row shows a 128 element vector. The same description applies for the total_acc_x_train.txt and total_acc_z_train.txt files for the Y and Z axis. - train/Inertial Signals/body_acc_x_train.txt: The body acceleration signal obtained by subtracting the gravity from the total acceleration.
- train/Inertial Signals/body_gyro_x_train.txt: The angular velocity vector measured by the gyroscope for each window sample. The units are radians/second.