Tracking-Data

This is a repository for the course project of Getting and Cleaning Data (Coursera). The project required to run certain analysis on the Human Activity Recognition Dataset.

Dataset

Human Activity Recognition Using Smartphones

Files

1. CodeBook.md This enlists all the steps, variables and the transformation done in the scripts attached.

2. getting_data.R Gets the data from the net so that it could be applied for further operations. I made this so that you may not need to go through the hassle of getting the data yourself.

3. run_analysis.R Performs the steps given in the instructions, which are as follows:

   • 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.

4. final_data.txt This is the data set mentioned in the last instruction.

List of Identifiers

Following is the list of all the variables used in the two scripts I have made.

Identifier	Description
file_name	Stores the name of the downloaded zip file
file_url	Stores the url
features	Reads features.txt into R
activity_labels	Reads activiy_labels.txt into R
subject_train	Reads train/subject_train.txt into R
x_train	Reads train/X_train.txt into R
y_train	Reads train/y_train.txt into R
subject_test	Reads test/subject_test.txt into R
x_test	Reads test/X_test.txt into R
y_test	Reads test/y_test.txt into R
train_merged	Merges all the data related to train using cbind()
test_merged	Merges all the data related to test using cbind()
merged_data	Merges train_merged and test_merged using rbind()
req_data	Using the dplyr function select() it extracts the columns as stated in the instructions
final_data	Dataframe which stores the average of each variable for each activity and each subject

Steps to follow

1. Download the Dataset

   • Run getting_data.R to get the dataset.

   • It also reads data into R as mentioned in the table.

2. Run run_analysis.R which performs the following steps:

   • Merges the training and the test sets to create one data set. merged_data 10299 rows, 563 cols is made by merging train_merged 7352 rows, 563 cols and test_merged 2947 rows, 563 cols using rbind().

   • Extracts only the measurements on the mean and standard deviation for each measurement. This is done using select() function in dplyr library and is stored in req_data 10299 rows, 88 cols.

   • Uses descriptive activity names to name the activities in the data set. This is done by replacing the contents of the code column in req_data with the second column of activity_labels.

   • Appropriately labels the data set with descriptive variable names. Here the function gsub() is used to get desired result.

   • From the data set in the step above, creates a second, independent tidy data set with the average of each variable for each activity and each subject. final_data 180 rows, 88 cols stores the required content.

   • Writes final_data.

NOTE: Please be careful to change the working directory before trying to source run_analysis.