Coursera_GaCD

Note: RStudio helps in building this script because of Golbal Environment tab details about all variables extracted in this package.

Required Packages - dplyr & reshape2

The data anlysis is carried around extract taken from "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"

A full description of the data is found at:

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Steps involved in data extraction and preparing tidy data.

Step 1. Extract "TEST" & "TRAINING" data from 3 files

Extract TEST Data

Test set

x_test <- read.table("UCI HAR Dataset/test/X_test.txt")

Test label

y_test <- read.table("UCI HAR Dataset/test/y_test.txt")

Subject range who performed the activity

subject_test <- read.table("UCI HAR Dataset/test/subject_test.txt")

Extract TRAINING Data

Training set

x_train <- read.table("UCI HAR Dataset/train/X_train.txt")

Training label

y_train <- read.table("UCI HAR Dataset/train/y_train.txt")

Subject range who performed the activity

subject_train <- read.table("UCI HAR Dataset/train/subject_train.txt")

Step 2. Assign column names (looks at README.TXT & features_info.txt files how they can assigned names)

Extract the column names(variables) from features.txt

features <- read.table("UCI HAR Dataset/features.txt") names(x_test) <- features$V2 names(x_train) <- features$V2

names(y_test) <- "activity_id" names(y_train) <- "activity_id" names(subject_test) <- "volunteer_id" names(subject_train) <- "volunteer_id"

Step 3. Merge all TEST & TRAINING data by observing its rows(observations) and columns(variables) numbers.

Merge the data

col_merge_test <- cbind(y_test,subject_test) %>% cbind(x_test) col_merge_train <- cbind(y_train,subject_train) %>% cbind(x_train) test_train <- rbind(col_merge_test, col_merge_train)

Step 4. Filter data for mean() and std()

Filter only mean and standard deviation columns by keeping activity_id and volunteer id intact

mean_std <- test_train[grepl('activity_id|volunteer_id|mean()|std()',names(test_train))]

Step 5. Extract activity names from activity_labels.txt

Use descriptive activity names to name the activities in the data set

activity_data <- read.table("UCI HAR Dataset/activity_labels.txt") names(activity_data) <- c("activity_id","activity_name")

Step 6. Join it with filtered data in step 4 mean_std_2 <- left_join(activity_data,mean_std)

Step 7. Remove duplicate column (activity_id was removed as activity_name was found in mean_std_2)

mean_std_3 <- subset(updated_data2,select=-c(1))

Step 8. Find the group average - "volunteer_id","activity_name

tidy data set with the average of each variable for each activity and each subject.

var_extract <- melt(mean_std_3,(id.vars=c("volunteer_id","activity_name"))) mean_extract <- dcast(var_extract, volunteer_id+activity_name ~ variable , mean)

add mean as prefix on all columns except volunteer_id and activity_name

names(mean_extract)[-c(1:2)] <- paste("[Mean of ]" , names(mean_extract)[-c(1:2)] )

Step 9. Extract the tidy data to csv file

tidy data

write.csv(mean_extract, "tidy_data.csv",row.name=FALSE)