https://github.com/fluffynuts/getting-and-cleaning-data

Course project form the Getting And Cleaning Data Coursera course
https://github.com/fluffynuts/getting-and-cleaning-data

Last synced: 5 days ago
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Course project form the Getting And Cleaning Data Coursera course

• Host: GitHub
• URL: https://github.com/fluffynuts/getting-and-cleaning-data
• Owner: fluffynuts
• License: bsd-2-clause
• Created: 2015-08-23T07:20:27.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-08-23T20:06:22.000Z (over 9 years ago)
• Last Synced: 2024-11-17T05:45:52.716Z (about 2 months ago)
• Language: R
• Size: 129 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
This is the course project for part 3 of the Data Science Track

on Coursera: "Getting and cleaning data".

The required artifact is run_analysis.R which manipulates the data

provided at the url:

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

This data is provided in two sets: "train" and "test", which were created

by an arbitrary split selection. The data from both sets relates to telemetry

recorded by a smartphone whilst subjects performed certain activities.

The load is attempted by the following logic:

1. Load the test data

    1. Load the test data from test/X_test.txt

    2. Load the test labels from test/y_test.txt

    3. Load the subjects (identified by number only) from test/subject_test.txt

    4. Load the activity descriptors from activity_labels.txt

    5. combine the above into one large data frame linking subject, activity and recorded values

2. Load the train data

    1. Load the train data from test/X_test.txt

    2. Load the train labels from test/y_test.txt

    3. Load the subjects (identified by number only) from train/subject_test.txt

    4. Load the activity descriptors from activity_labels.txt

    5. combine the above into one large data frame linking subject, activity and recorded values

3. Merge the two data sets together and order by subject and activity

4. Produce a reduced version of the data which has means and standard deviations for the 561 columns provided in the raw data instead of the prior wide table

5. Persist that slimmer table to disk, in the file merged-data.csv

6. Produce a summary of this data with an average of all recorded values per subject and activity and persist that to summary-data.csv