Project

The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set.

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.
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 activity names.
5. Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

#Load the dplyr library. library(dplyr)

#Step1: Merges the training and the test sets to create one data set.

• Load the data of the subject_train and subject_test.
• Load the data of the x_train and x_test.
• Load the data of the y_train and y_test.

train.temp1 <- read.table("train/X_train.txt") test.temp1 <- read.table("test/X_test.txt")

train.temp2 <- read.table("train/subject_train.txt") test.temp2 <- read.table("test/subject_test.txt")

train.temp3 <- read.table("train/y_train.txt") test.temp3 <- read.table("test/y_test.txt")

• Merge the data. x <- rbind(train.temp1, test.temp1) subject <- rbind(train.temp2, test.temp2) y <- rbind(train.temp3, test.temp3)

data <- cbind(x, y, subject)

#Step 2 Extracts only the measurements on the mean and standard deviation for each measurement.

• Load de features data and find only the rows that contains mean() or std(), then load only the data of the columns that exists in the features dataframe, in a dataframe.

data_features<- read.table("features.txt")

features<-data_features[grep("mean\(\)|std\(\)", data_features$V2),] features.data <- x[,features$V1]

#Step 3 Uses descriptive activity names to name the activities in the data set.

• Load the data of activity_labels.

data_activities <- read.table("activity_labels.txt")

activities <- inner_join(y, data_activities, by="V1")

activities.data <- activities$V2

##Step 4 Appropriately labels the data set with descriptive variable names.

• Fix the names of the dataset, setting the names of features, activity and subject columns.

data <- cbind(features.data , activities.data, subject ) names(data) <- c(as.character(features$V2), "activity", "subject");

#Step 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.

• Create the final data, grupping the data by activity and subject, and calculating the mean with the summarise function. data2 <- data; final_data <- data2 %>% group_by(activity,subject) %>% summarise_each(funs(mean)) write.table(final_data, file="tidy_data.txt", row.names=FALSE)