README.Rmd

# GettingAndCleaningData
Course project for the Coursera.org course Getting and Cleaning Data

## Prerequisites
* The HAR data (from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip ) has to be present in the working directory in the folder UCI HAR Dataset.
* The dplyr library has to be installed

## run_analysis.R
The script reads the data from the folder UCI HAR Dataset, merges the training and test data, extracts only the measurements on the mean and standard deviation for each measurement, and returns a tidy data set with the average of each variable for each activity and each subject.

```{r, eval=FALSE}
library(dplyr)

runAnalysis <- function() {

    #read column names
    featureNames <- read.table("UCI HAR Dataset//features.txt")$V2
    
    #read test data
    testSubject <- read.table("UCI HAR Dataset/test/subject_test.txt", col.names="subject")
    testY <- read.table("UCI HAR Dataset/test/y_test.txt", col.names="activity")
    testX <- read.table("UCI HAR Dataset/test/X_test.txt", col.names=featureNames)
    test <- cbind(testSubject, testY, testX)
    
    #read training data
    trainSubject <- read.table("UCI HAR Dataset/train/subject_train.txt", col.names="subject")
    trainY <- read.table("UCI HAR Dataset/train/y_train.txt", col.names="activity")
    trainX <- read.table("UCI HAR Dataset/train/X_train.txt", col.names=featureNames)
    train <- cbind(trainSubject, trainY, trainX)
    
    #combine test and training
    data <- rbind(test, train)  
    
    #replace dots in colum names
    colnames(data) <- gsub(colnames(data), pattern="\\.+", replacement="_")
    colnames(data) <- gsub(colnames(data), pattern="_+$", replacement="")    
    
    #set subject as factor
    data$subject <- as.factor(data$subject)
    
    #set activity as factor and use labels
    data$activity <- as.factor(data$activity)
    activityLabels <- read.table("UCI HAR Dataset//activity_labels.txt")$V2
    levels(data$activity) <- activityLabels  
    
    #extract mean and std
    data <- select(data, subject, activity, contains("_mean"), contains("_std"), -contains("_meanFreq"))
    
    #build second data set with mean values grouped by subject/activity
    data2 <- group_by(data, subject, activity)
    data2 <- summarise_each(data2, funs(mean))
    return(data2)
}
```