Course Project for Getting and Cleaning Data

This code is part of the course project for Cousera course Getting and Cleaning Data.

How to use the code

Make sure you have all the dependant libraries installed before you run this code.

Library dependencies (required):

1. dplyr

This code use the data from Human Activity Recognition Using Smartphones Data Set. You need to download this data and unzip it into your workspace. By default, the code will read the data from UCI HAR Dataset folder in your workspace. If your data is not in the default folder, you can pass in the folder path as the first argument in command line like this:

Rscript run_analysis.R your_data_path

The code will then output a summary.txt to your workspace.

How the code works

The code generate the analysis in the following steps:

1. Load in the feature data from features.txt, and extracts only the features on the mean and standard deviation. The feature data is stored in variable featureDf
2. Activity data is read from activity_labels.txt and loaded into variable activityDf in order to label the activity name later
3. A function buildDataset is defined for cleaning and merging feature, activity, and observation data. The function take in partiton (train or test) as the first argument along with featureDf and activityDf, and return merged dataframe
4. buildDataset function is called seperately for test and train data, and the results are merged back to finalDf
5. Finally, we use dplyr to group the data by subjectId and activity, and produce the average of each variable
6. Write the result to summary.txt in your workspace

The dataframe form of the output looks like this:

   subjectID         activity tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z tBodyAcc-std()-X
       (int)           (fctr)             (dbl)             (dbl)             (dbl)            (dbl)
1          1          WALKING         0.2656969       -0.01829817        -0.1078457       -0.5457953
2          2          WALKING         0.2731131       -0.01913232        -0.1156500       -0.6055865
3          3          WALKING         0.2734287       -0.01785607        -0.1064926       -0.6234136
4          4          WALKING         0.2770345       -0.01334968        -0.1059161       -0.6838645
5          4 WALKING_UPSTAIRS         0.2696859       -0.01710851        -0.1100534       -0.4811578
6          5          WALKING         0.2791780       -0.01548335        -0.1056617       -0.5076910
7          6          WALKING         0.2694342       -0.01639111        -0.1179007       -0.6476692
8          6 WALKING_UPSTAIRS         0.2801791       -0.02069496        -0.1109400       -0.1270006
9          7 WALKING_UPSTAIRS         0.2702117       -0.01879049        -0.1124924       -0.5775183
10         8 WALKING_UPSTAIRS         0.2707591       -0.01818950        -0.1068096       -0.5829589
..       ...              ...               ...               ...               ...              ...