https://github.com/ohjho/courseragetdataproject

https://github.com/ohjho/courseragetdataproject

Last synced: 11 days ago
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• Host: GitHub
• URL: https://github.com/ohjho/courseragetdataproject
• Owner: ohjho
• Created: 2015-06-10T00:56:28.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-06-19T14:26:11.000Z (over 9 years ago)
• Last Synced: 2023-03-01T20:34:42.253Z (almost 2 years ago)
• Language: R
• Size: 64.5 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CourseraGetDataProject

this is a description of what the run_analysis.R script does to the data in the UCI_HAR_Dataset directory

## R packpages reqired

These are loaded at the beginning of the script. User is expected to have these packages installed already. 

 * dplyr

 * tidyr

 * stringr

 * sqldf

## Data Files required

A data frame object is created for each of the following file in the UCI_HAR_Dataset directory

 1. test/X_test.txt

 2. test/Y_test.txt

 3. test/subject_test.txt

 4. train/X_train.txt

 5. train/Y_train.txt

 6. train/subject_train.txt

 7. features.txt

 8. activity_labels.txt

## 1. Merging the training and test set together

With respect to the above list, the file 1, 4 are joined together to form the **x_joined** data frame. The file 2, 5 are joined together to form the **y_joined** data frame. And the file 3, 6 above are joined together to form the **subject_joined** data frame.

## 2. Extract only the measurements on the mean and standard deviation (sd) for each measurement

In order to extract the mean and sd measures, **x_joined** is labelled with the column names equal to the data set from the file 7 above.

Some columns in **x_joined** are duplicated, so the duplicates are filtered out first. Then we use select from the dplyr package to extract just the _mean()_ and _std()_ columns. The resulting data frame is called **x_slim**

## 3. Use descriptive activity names to name the activities in the data set

To do this we merge file 8 above with the **x_slim** data frame and name the newly added column activity. At the same time, we also cbind **y_joined** and **subject_joined**. The resulting data frame is called **x_complete**.

## 4. Appropriately label the data set with descriptive variable names

All the data in the **x_complete** data frame has the appropriate column names.

## 5. 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

To make the data tidy, the script creates the **x_tidy** data set by doing the following to the **x_complete** data set:

 1. Have each observative form a row, by transposing the various features measured for each subject and activity pair (originally in columns) into a rows.

 2. Have each variable forms a column by having a column each for the mean and standard deviation (i.e. each feature for each subject-and-activity pair has a mean and standard deviation).

Lastly, to form the summary data set **x_summary**, we group the **x_tidy** data set by _feature_, _activity_, and _subject_ before running the **dplyr** package function _summarize_.