GACDProject

Getting and Cleaning Data Course Project

The script contained within this repository is intended to satisfy project requirements for Getting and Cleaning Data, 2015 June. In short: we've been given some messy data spread out over several files; our task is to create a new, tidy dataset by merging and summarizing.

The data in question involve measurements of three-dimensional motion for thirty individuals. Multiple pieces of information must be aggregated:

1. An X dataset, where the (i, j)th entry records motion type j for some individual;
2. A Y dataset, where the entry in the ith row indicates the type of activity taking place when the motion was recorded in the corresponding row of X;
3. A subject dataset, where the entry in the ith row indicates the individual in question from the corresponding row of X;
4. A dataset of labels describing the motion recordings;
5. A dataset of labels describing the activity taking place;
6. ... where the X, Y, and subject datasets come in 'train' and 'test' varieties.

My end goal is a dataset that looks something like this (for the first six rows):

subject_id	activity	motion_label	mean
1	WALKING	tBodyAcc-mean()-X	0.27733076
1	WALKING	tBodyAcc-mean()-Y	-0.01738382
1	WALKING	tBodyAcc-mean()-Z	-0.11114810
1	WALKING	tBodyAcc-std()-X	-0.28374026
1	WALKING	tBodyAcc-std()-Y	0.11446134
1	WALKING	tBodyAcc-std()-Z	-0.26002790

which fits my interpretation of 'tidy': the mean variable gives the mean value of a motion measurement x (motion_label) for individual i (subject_id) undertaking activity j (activity).

All of this is much less complicated than it sounds. Because the raw data files are all of the same dimension, they can be very simply appended together. Then it's a matter of using a few dplyr functions to construct the tidy dataset:

1. Select only the mean and standard deviation values;
2. Note that the 'wide' dataset represents four variables (subject_id, activity, motion_label, and value), but the motion values are spread out in long rows. So we melt them into a longer / thinner dataset with the gather function;
3. Take the mean over motion types for each subject / activity by grouping and then summarizing.

Check the source in run_analysis.r for more details / comments.