asampath1/getting-and-cleaning-data

Getting-and-Cleaning-Data

DataSource for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Creater this R script called run_analysis.R that does 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 activity names.
• Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

output files

• MergedCleanData.txt file - Merged Clean data.txt output file will have features.txt file and activity_labels.txt cleaned and merged together.

• DataSet_Averages.txt file - independent tidy data set with the average of each variable for each activity and each subject.

Scripts logic

• Firstly read the training and test file for X
• Merge using rbind function
• Read the subject_train and subject Test files - Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30
• The extract the measurements on the mean and standard deviation for each measurement using grep and gsub function

(grep search for matches to argument pattern within each element of a character vector. gsub perform replacement of the first and all matches respectively. tolower () - Translate characters in character vectors, in particular from upper to lower case or vice versa)

• Read the activity_labels.txt file that links the class labels with their activity name.

• Create clean and completly merged data set

• Finally create independent tidy data set with the average of each variable for each activity and each subject. using unique function and by using for loop

• We may check the results by executing the following in the R console

• res2 <- read.table("DataSet_Averages.txt")

• result[4,4]

• [1] -0.001308288

• res2[4,4]

• [1] -0.001308288

• res2[4,4]==result[4,4]

• [1] FALSE

• result[6,4]

• [1] -0.04051395

• res2[6,4]

• [1] -0.04051395

• res2[6,4]==result[6,4]

• [1] TRUE