After reading the data and descriptive labels I used sapply to label the data
xtest=read.csv("test/X_test.txt", header=FALSE, sep="") xtrain=read.csv("train/X_train.txt", header=FALSE, sep="")
ytest=read.csv("test/y_test.txt", col.names=c("activity"), header=FALSE, sep="") ytrain=read.csv("train/y_train.txt",col.names=c("activity"), header=FALSE, sep="")
subjecttrain=read.csv("train/subject_train.txt", col.names=c("subject"), header=FALSE, sep="") subjecttest=read.csv("test/subject_test.txt", col.names=c("subject"), header=FALSE, sep="")
xall = rbind(xtrain, xtest) yall = rbind(ytrain, ytest) subjectall = rbind(subjecttrain, subjecttest) #as the extraction simplifies the data set I decided to merge test and train without adding them the subject and the activity #2 Extracts only the measurements on the mean and standard deviation for each measurement.
features=read.csv("features.txt", header=FALSE, sep="") names(features)=c("id","feature")
vectExtract = grep("mean\(\)|std\(\)",features$feature)
library("dplyr") xallExtract = select(xall, one_of(paste0("V",as.character(vectExtract)))) #3 Uses descriptive activity names to name the activities in the data set
activity_labels=read.csv("activity_labels.txt", col.names=c("id","label"),header=FALSE, sep="") yall$activity = sapply(yall$activity, function(x){as.character(activity_labels$label[x])})
xallExtract$activity = yall$activity xallExtract$subject = subjectall$subject #4 Appropriately labels the data set with descriptive variable names.
#as features have labels containing the characters ()- I suppress them using the gsub function. names(xallExtract) = c(gsub("[-\(\)\,]","",grep("mean\(\)|std\(\)",features$feature,value=TRUE)),"activity","subject") #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.
tidySet = summarise_each(group_by(xallExtract, activity,subject),funs(mean), vars=-c(activity,subject))
names(tidySet)[3:length(tidySet)] = paste(names(tidySet)[3:length(tidySet)],"mean", sep=".") write.table(tidySet, file="tidyset.txt", row.name=FALSE)