This repository contains R code to analyze UCI smartphones data set.
- Merges the training and the test sets to create one data set.
# read test & train X data with column names and add activity col
trainX <- read.csv(file.path(getwd(), "UCI HAR Dataset/train/X_train.txt"),
sep="",
header = FALSE)
testX <- read.csv(file.path(getwd(), "UCI HAR Dataset/test/X_test.txt"),
sep="",
header = FALSE)
# Merges the training and the test sets to create one data set.
mergedX <- rbind(trainX, testX)- Extracts only the measurements on the mean and standard deviation for each measurement.
# extract features names from file
features <-
readLines(file.path(getwd(), "UCI HAR Dataset/features.txt")) %>%
lapply(function(x) strsplit(x, split=" ")[[1]][[2]]) %>%
unlist
# extract only mean / std measurements
extractedX <- mergedX[, grep("-(std|mean)\\(\\)", features)]- Uses descriptive activity names to name the activities in the data set
# extract activity factors from file
activities <-
readLines(file.path(getwd(), "UCI HAR Dataset/activity_labels.txt")) %>%
lapply(function(x) strsplit(x, split=" ")[[1]][[2]]) %>%
unlist %>%
factor
# parse activity labels as factors
trainY <- readLines(file.path(getwd(), "UCI HAR Dataset/train/y_train.txt"))
testY <- readLines(file.path(getwd(), "UCI HAR Dataset/test/y_test.txt"))
mergedY <- c(trainY, testY) %>% sapply(function(x) activities[as.numeric(x)])
- Appropriately labels the data set with descriptive variable names.
names(extractedX) <- features[grep("-(std|mean)\\(\\)", features)]- 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.
extractedX %>%
mutate(activity=mergedY, subject=mergedSub, .before="tBodyAcc-mean()-X") %>%
group_by(activity, subject) %>%
summarise_all(mean) %>%
write.table(file.path(getwd(), "analysis-output.txt"), row.name = FALSE)
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