amberv0 / getting-and-cleaning-data

Getting and Cleaning Data course project

In the "data" folder there is all the data needed to run the script run_analysis.R. The data is exactly the same as was provided in the .zip file.

##How run_analysis.R script works

• checks if there is dplyr package installed and installs it if it's not the case.
• loads all data the files into data frames
• merges test and train data, putting test data first
• names variables according to how they are defined in data/activity_labels.txt
• removes columns with duplicated names (as otherwise we'll have problems with dplyr later, and we don't need these columns anyway)
• chooses only columns with names that have "mean" and "std", case insensitive
• adds data about which subject corresponds to each row and which activity type (presented as string)
• makes subject and activity columns the first in the data frame
• saves the data to "dirty.txt"
• generates and saves (to "tidy.txt") a tidy dataset where for each pair (subject, activity) the mean of each variable (names are in data/activity_labels.txt) is calculated.

To explain the last point. Let's say we have following piced of data:

subject	activity	var1
1	ACTIVITY1	VAR1_VALUE1
1	ACTIVITY1	VAR1_VALUE2
1	ACTIVITY2	VAR_VALUE3

Then in the file "dirty.txt" data will be saved as it is. In the file "tidy.txt" following data will be saved:

subject	activity	var1
1	ACTIVITY1	mean(c(VAR1_VALUE1, VAR1_VALUE2))
1	ACTIVITY2	VAR_VALUE3

That is, since the pair subject="1" and activity="ACTIVITY1" was present twice, the mean of corresponding var1 values was taken

There is some more info in CodeBook.md