GettingAndCleaningData
Repository for Cousera: https://class.coursera.org/getdata-008
run_analysis.R should do the following:
- Merges the training and the test sets to create one data set.
- Extracts only the measurements on the mean and standard deviation for each measurement.
- Uses descriptive activity names to name the activities in the data set
- Appropriately labels the data set with descriptive variable names.
- 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.
Please upload the tidy data set created in step 5 of the instructions.
Please upload your data set as a txt file created with write.table() using row.name=FALSE (do not cut and paste a dataset directly into the text box, as this may cause errors saving your submission).
Repository structure
-directory /project_data, contains all files needed for evaluation by run_analysis.R
-README.md, this file.
-codebook.md, contains the description of variables produced by run_analysis.R
-run_analysis.R, is the R script that produces the tidyData.txt file
-tidyData.txt, is the file generated by run_analysis.R, which contains the reshaped data set processed from /project_data
Running the run_analysis.R script
- create a directory on your local machine where you would like to clone the repository
- change directory into what you created in #1
- clone this repository : git clone https://github.com/dholtz/GettingAndCleaningData
- change directory into the GettingAndCleaningData directory
- start R from the command line
- source("run_analysis.R")
How the, run_analysis.R script works
Review the run_analysis.R script in the root of the cloned repository.
This script is heavily commented and explains step-wise what is happening. DRY Principle (http://en.wikipedia.org/wiki/Don't_repeat_yourself)