getting_cleaning_data_assignment

A repository for the course assignment

The data files analysed in this project were downloaded from the internet. The source adress is: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip Only a subset of the unzipped files were used in the analysis. These are the files in the directory "UCI HAR Dataset" and the two directories below this "Test" and "Train" The R code will show which files were used.

The following procedure was used for merging the dataset:

Step 1) Combine X_test dataset with column names from features.txt using names() command

Step 2) Use names() to name y_test column as activity_code

Step 3) Combine X_test and y_test using cbind()

Step 4) Combine the file from step 3 with subject_test using cbind()

Step 5) Do step 1 to step 4 for the corresponding train datasets. The order of columns must be identical in the two datasets from Test and Train data.

Step 6) Combine the two datasets from step 1-4 using rbind()

Step 7: Extract columns with mean or standard deviation and identifiers for activity and subject

Step 8) Combine the new file with activity names using left_join by activity code

The R code follows and indicates the steps of this procedure.

Finally a new dataset is established with mean values for all variables and grouped by activity and subject. The new dataset is established using the following tidyverse code: grpmeans <- cfile %>% group_by(activity_code, activity_name, subject) %>% summarise_all(mean)

I comment here on the 5 parts on the assignment: You should create one R script called run_analysis.R that does the following.

1 Merges the training and the test sets to create one data set: This is done in steps 1-6 in the procedure. 2 Extracts only the measurements on the mean and standard deviation for each measurement: This is done in step seven using the select order 3 Uses descriptive activity names to name the activities in the data set: This is done in step 8 4 Appropriately labels the data set with descriptive variable names: This is done in step 1 in the procudure. I use the variable names in the original dataset. 5 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: The new dataset is established using the following tidyverse code: grpmeans <- cfile %>% group_by(activity_code, activity_name, subject) %>% summarise_all(mean)