Madhu Rajan

07/25/2015

run_Analysis.r

File Description:

This script will perform the following steps on the UCI HAR Dataset downloaded from

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

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Step 1

• Combine the values from the subject_test and subject_train files to create a single TestSubject column that identifies the study participant.

• Combine the values from the Y_test and Y_train data to create a single Activity column that indicates that activity class (for instance, walking or sitting).

• Combine the values from the X_test and X_train files to create additional variable columns, one column for each measurement and calculation included in the data set (561 variable columns total, in the initial combined data set; 563 columns including the TestSubject and Activity columns).

Step 2

• Read the features from features.txt and filter it to only leave features that are either means ("mean()") or standard deviations ("std()").

• Read the activity from activity_labels.txt.

• Assign friendly names to both feature and activity data tables

Step 3

• Combine activity, subject and feature data tables into one data set.

Step 4

• Calculate the average of each feature variable for each activity and each subject and store it in a data set. ######################################################################################################################################################## =======

Step 1

• Combine the values from the subject_test and subject_train files to create a single TestSubject column that identifies the study participant.
  • Combine the values from the Y_test and Y_train data to create a single Activity column that indicates that activity class (for instance, walking or sitting).
  • Combine the values from the X_test and X_train files to create additional variable columns, one column for each measurement and calculation included in the data set (561 variable columns total, in the initial combined data set; 563 columns including the TestSubject and Activity columns).

Step 2

• Read the features from features.txt and filter it to only leave features that are either means ("mean()") or standard deviations ("std()").
• Read the activity from activity_labels.txt.
• Assign friendly names to both feature and activity data tables

Step 3

• Combine activity, subject and feature data tables into one data set

Step 4

• Calculate the average of each feature variable for each activity and each subject and store it in a data set. ##########################################################################################################

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