update

README.md

@@ -12,7 +12,6 @@ The assignment is to create one R script called run_analysis.R that does the fol
     2. Extracts only the measurements on the mean and standard deviation for each measurement. 
     3. Uses descriptive activity names to name the activities in the data set
     4. Appropriately labels the data set with descriptive variable names.
-
     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 dataset
@@ -41,6 +40,6 @@ https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Datas
 
 * README.md - this file
 * run_analysis.R - script to run to get the final tidy data set
-* codebook.md - descriptions of variables in the final tidy set
+* codebook.md - descriptions of variables in the final tidy data set
 
 Please refer to the README.txt in the directory "UCI HAR Dataset" for the full details of the original datasets and description.

codebook.md

@@ -1,8 +1,10 @@
-#DATA DICTIONARY Human Activity Recognition Using Smartphones
+#DATA DICTIONARY for the tidy data set.
 
 ##Subject
+* Number from 1-30 representing the subject/volunteers for the experiment.
 
 ##Activity
+* These are the activities performed by each person/subject
 	* WALKING
 	* WALKING_UPSTAIRS
 	* WALKING_DOWNSTAIRS

run_analysis.R

@@ -1,7 +1,44 @@
+#
+## run_analysis() 
+#
+# 1. Merges the training and the test sets to create one data set.
+# 2. Extracts only the measurements on the mean and standard deviation for each measurement. 
+# 3. Uses descriptive activity names to name the activities in the data set
+# 4. Appropriately labels the data set with descriptive variable names.
+# 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.
+#
+# Here are the data for the project:
+# https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
+#
+## to execute the script in R
+#
+## 1. unzip the source data file
+## from the directory "UCI HAR Dataset"
+## 2. source("run_analysis.R")
+## 3. run_analysis()
+#
+## a text file tidy_data.txt will be created in the directory for where the script was run.
+#
+# to view the tidy data saved in tidy_data.txt
+# data <- read.table("tidy_data.txt", header = TRUE)
+# View(data)
+#
+
+## 1. create temporary tidy data for TEST data set with mean and standard deviation measurements only
+## 2. create temporary tidy data fro TRAINING data set with mean and standard deviation measurements only
+## 3. merge both temporary data sets
+## 4. create tidy data which average of each variable for each activity and each subject.
+## 5. write to file tidy_data.txt
+#
+
 run_analysis <- function() {
 
+        # get the activity labels
         activity_labels <- read.table("./activity_labels.txt")
 
+        # TEST data set
+
+        # read data sets
         test_subject <- read.table("test/subject_test.txt")
         test_labels <- read.table("test/y_test.txt", colClass= "character")
         test_sets <- read.table("test/X_test.txt")
@@ -22,17 +59,15 @@ run_analysis <- function() {
 
         # put in column names in data set
         names(test_sets) <- col_names
-        #ll <- tidy_data[grepl("mean", names(tidy_data))]
-
-        # get all "mean" and "std" columns from data set
+
+        # get all "mean()" and "std()" columns from data set
         test_mean_std_columns <- test_sets[grep("-(mean|std)\\(\\)", names(test_sets))]      
-        #mean_columns <- test_sets[grepl("mean", names(test_sets))]
-        #std_columns <- test_sets[grepl("std", names(test_sets))]
-
+
         test_tidy_data <- cbind(test_subject_activities, test_mean_std_columns)
 
-        # now get the training data
+        # now get the TRAINING data
 
+        # read data sets
         train_subject <- read.table("train/subject_train.txt")
         train_labels <- read.table("train/y_train.txt", colClass= "character")
         train_sets <- read.table("train/X_train.txt")
@@ -48,25 +83,19 @@ run_analysis <- function() {
 
         # put in column names in data set
         names(train_sets) <- col_names
-        #ll <- tidy_data[grepl("mean", names(tidy_data))]
-
+
         # get all "mean" and "std" columns from data set
         train_mean_std_columns <- train_sets[grep("-(mean|std)\\(\\)", names(train_sets))]        
-        #train_mean_columns <- train_sets[grepl("mean", names(train_sets))]
-        #train_std_columns <- train_sets[grepl("std", names(train_sets))]
-
+
         train_tidy_data <- cbind(train_subject_activities, train_mean_std_columns)
-
+
+        # merge TEST and TRAINING data sets        
         tidy_data <- rbind(test_tidy_data, train_tidy_data)
 
+        # get the average
         aggdata <-aggregate(tidy_data[,3:68], by=list(Subject = tidy_data$Subject, Activity = tidy_data$Activity), FUN=mean, na.rm=TRUE)
 
+        # write to file tidy.txt
         write.table(aggdata, "tidy_data.txt", row.names = FALSE )  
 
-        # to view the data saved
-        # data <- read.table("tidy_data.txt", header = TRUE)
-        # View(data)
-
-        # get the column names
-        # write.table(names(aggdata), "column_names.txt", row.names = FALSE)
 }