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codebook.txt
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This readme file describes the data set in output.txt generated using run_analysis.R <https://github.com/rahulsden/cleaningdatacourseproject>
==================================================================
Generated by processing 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 - Università degli Studi di Genova.
Via Opera Pia 11A, I-16145, Genoa, Italy.
www.smartlab.ws
==================================================================
Problem statement:
One of the most exciting areas in all of data science right now is wearable computing - see for example this article . Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained:
http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
Here are the data for the project:
https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
You should create one R script called run_analysis.R that does the following.
Merges the training and the test sets to create one data set.
Extracts only the measurements on the mean and standard deviation for each measurement.
Uses descriptive activity names to name the activities in the data set
Appropriately labels the data set with descriptive variable names.
From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
======================================================================
'data.frame': 180 obs. of 68 variables:
$ Subject : num 1 1 1 1 1 1 2 2 2 2 ...
$ ActivityDescription : cha WALKING WALKING_UPSTAIRS WALKING_DOWNSTAIRS SITTING STANDING LAYING WALKING...
$ tBodyAcc.mean...X : num 0.277 0.255 0.289 0.261 0.279 ...
$ tBodyAcc.mean...Y : num -0.01738 -0.02395 -0.00992 -0.00131 -0.01614 ...
$ tBodyAcc.mean...Z : num -0.1111 -0.0973 -0.1076 -0.1045 -0.1106 ...
$ tBodyAcc.std...X : num -0.284 -0.355 0.03 -0.977 -0.996 ...
$ tBodyAcc.std...Y : num 0.11446 -0.00232 -0.03194 -0.92262 -0.97319 ...
$ tBodyAcc.std...Z : num -0.26 -0.0195 -0.2304 -0.9396 -0.9798 ...
$ tGravityAcc.mean...X : num 0.935 0.893 0.932 0.832 0.943 ...
$ tGravityAcc.mean...Y : num -0.282 -0.362 -0.267 0.204 -0.273 ...
$ tGravityAcc.mean...Z : num -0.0681 -0.0754 -0.0621 0.332 0.0135 ...
$ tGravityAcc.std...X : num -0.977 -0.956 -0.951 -0.968 -0.994 ...
$ tGravityAcc.std...Y : num -0.971 -0.953 -0.937 -0.936 -0.981 ...
$ tGravityAcc.std...Z : num -0.948 -0.912 -0.896 -0.949 -0.976 ...
$ tBodyAccJerk.mean...X : num 0.074 0.1014 0.0542 0.0775 0.0754 ...
$ tBodyAccJerk.mean...Y : num 0.028272 0.019486 0.02965 -0.000619 0.007976 ...
$ tBodyAccJerk.mean...Z : num -0.00417 -0.04556 -0.01097 -0.00337 -0.00369 ...
$ tBodyAccJerk.std...X : num -0.1136 -0.4468 -0.0123 -0.9864 -0.9946 ...
$ tBodyAccJerk.std...Y : num 0.067 -0.378 -0.102 -0.981 -0.986 ...
$ tBodyAccJerk.std...Z : num -0.503 -0.707 -0.346 -0.988 -0.992 ...
$ tBodyGyro.mean...X : num -0.0418 0.0505 -0.0351 -0.0454 -0.024 ...
$ tBodyGyro.mean...Y : num -0.0695 -0.1662 -0.0909 -0.0919 -0.0594 ...
$ tBodyGyro.mean...Z : num 0.0849 0.0584 0.0901 0.0629 0.0748 ...
$ tBodyGyro.std...X : num -0.474 -0.545 -0.458 -0.977 -0.987 ...
$ tBodyGyro.std...Y : num -0.05461 0.00411 -0.12635 -0.96647 -0.98773 ...
$ tBodyGyro.std...Z : num -0.344 -0.507 -0.125 -0.941 -0.981 ...
$ tBodyGyroJerk.mean...X : num -0.09 -0.1222 -0.074 -0.0937 -0.0996 ...
$ tBodyGyroJerk.mean...Y : num -0.0398 -0.0421 -0.044 -0.0402 -0.0441 ...
$ tBodyGyroJerk.mean...Z : num -0.0461 -0.0407 -0.027 -0.0467 -0.049 ...
$ tBodyGyroJerk.std...X : num -0.207 -0.615 -0.487 -0.992 -0.993 ...
$ tBodyGyroJerk.std...Y : num -0.304 -0.602 -0.239 -0.99 -0.995 ...
$ tBodyGyroJerk.std...Z : num -0.404 -0.606 -0.269 -0.988 -0.992 ...
$ tBodyAccMag.mean.. : num -0.137 -0.1299 0.0272 -0.9485 -0.9843 ...
$ tBodyAccMag.std.. : num -0.2197 -0.325 0.0199 -0.9271 -0.9819 ...
$ tGravityAccMag.mean.. : num -0.137 -0.1299 0.0272 -0.9485 -0.9843 ...
$ tGravityAccMag.std.. : num -0.2197 -0.325 0.0199 -0.9271 -0.9819 ...
$ tBodyAccJerkMag.mean.. : num -0.1414 -0.4665 -0.0894 -0.9874 -0.9924 ...
$ tBodyAccJerkMag.std.. : num -0.0745 -0.479 -0.0258 -0.9841 -0.9931 ...
$ tBodyGyroMag.mean.. : num -0.161 -0.1267 -0.0757 -0.9309 -0.9765 ...
$ tBodyGyroMag.std.. : num -0.187 -0.149 -0.226 -0.935 -0.979 ...
$ tBodyGyroJerkMag.mean.. : num -0.299 -0.595 -0.295 -0.992 -0.995 ...
$ tBodyGyroJerkMag.std.. : num -0.325 -0.649 -0.307 -0.988 -0.995 ...
$ fBodyAcc.mean...X : num -0.2028 -0.4043 0.0382 -0.9796 -0.9952 ...
$ fBodyAcc.mean...Y : num 0.08971 -0.19098 0.00155 -0.94408 -0.97707 ...
$ fBodyAcc.mean...Z : num -0.332 -0.433 -0.226 -0.959 -0.985 ...
$ fBodyAcc.std...X : num -0.3191 -0.3374 0.0243 -0.9764 -0.996 ...
$ fBodyAcc.std...Y : num 0.056 0.0218 -0.113 -0.9173 -0.9723 ...
$ fBodyAcc.std...Z : num -0.28 0.086 -0.298 -0.934 -0.978 ...
$ fBodyAccJerk.mean...X : num -0.1705 -0.4799 -0.0277 -0.9866 -0.9946 ...
$ fBodyAccJerk.mean...Y : num -0.0352 -0.4134 -0.1287 -0.9816 -0.9854 ...
$ fBodyAccJerk.mean...Z : num -0.469 -0.685 -0.288 -0.986 -0.991 ...
$ fBodyAccJerk.std...X : num -0.1336 -0.4619 -0.0863 -0.9875 -0.9951 ...
$ fBodyAccJerk.std...Y : num 0.107 -0.382 -0.135 -0.983 -0.987 ...
$ fBodyAccJerk.std...Z : num -0.535 -0.726 -0.402 -0.988 -0.992 ...
$ fBodyGyro.mean...X : num -0.339 -0.493 -0.352 -0.976 -0.986 ...
$ fBodyGyro.mean...Y : num -0.1031 -0.3195 -0.0557 -0.9758 -0.989 ...
$ fBodyGyro.mean...Z : num -0.2559 -0.4536 -0.0319 -0.9513 -0.9808 ...
$ fBodyGyro.std...X : num -0.517 -0.566 -0.495 -0.978 -0.987 ...
$ fBodyGyro.std...Y : num -0.0335 0.1515 -0.1814 -0.9623 -0.9871 ...
$ fBodyGyro.std...Z : num -0.437 -0.572 -0.238 -0.944 -0.982 ...
$ fBodyAccMag.mean.. : num -0.1286 -0.3524 0.0966 -0.9478 -0.9854 ...
$ fBodyAccMag.std.. : num -0.398 -0.416 -0.187 -0.928 -0.982 ...
$ fBodyBodyAccJerkMag.mean.. : num -0.0571 -0.4427 0.0262 -0.9853 -0.9925 ...
$ fBodyBodyAccJerkMag.std.. : num -0.103 -0.533 -0.104 -0.982 -0.993 ...
$ fBodyBodyGyroMag.mean.. : num -0.199 -0.326 -0.186 -0.958 -0.985 ...
$ fBodyBodyGyroMag.std.. : num -0.321 -0.183 -0.398 -0.932 -0.978 ...
$ fBodyBodyGyroJerkMag.mean..: num -0.319 -0.635 -0.282 -0.99 -0.995 ...
$ fBodyBodyGyroJerkMag.std.. : num -0.382 -0.694 -0.392 -0.987 -0.995 ...
License:
========
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.