library("dplyr")

features <- read.csv("features.txt", header=FALSE, sep=" ", col.names = c("FeatureID", "FeatureName"))

activities <- read.csv("activity_labels.txt", header=FALSE, sep=" ", col.names = c("ActivityID", "ActivityName"))

subject_test <- read.csv("test/subject_test.txt", header=FALSE, sep=" ", col.names = c("SubjectID"))

subject_train <- read.csv("train/subject_train.txt", header=FALSE, sep=" ", col.names = c("SubjectID"))

y_test <- read.csv("test/y_test.txt", header=FALSE, sep=" ", col.names = c("ActivityID"))

y_train <- read.csv("train/y_train.txt", header=FALSE, sep=" ", col.names = c("ActivityID"))

x_test <- read.table("test/x_test.txt", header=FALSE, col.names=features$FeatureName)

x_train <- read.table("train/x_train.txt", header=FALSE, col.names=features$FeatureName)

x_test["ActivityID"] <- y_test

x_train["ActivityID"] <- y_train

x_test["SubjectID"] <- subject_test

x_train["SubjectID"] <- subject_train

x_test <- merge(x_test, activities, by.x = "ActivityID", by.y = "ActivityID")

x_train <- merge(x_train, activities, by.x = "ActivityID", by.y = "ActivityID")

merged <- rbind(x_test, x_train)

merged <- select(merged, ActivityName, SubjectID, contains("std"), contains("mean"))

tidy <- merged %>%

group_by(SubjectID, ActivityName) %>%

summarise_all(funs(mean))

rm("features")

rm("activities")

rm("subject_test")

rm("subject_train")

rm("y_test")

rm("y_train")

rm("x_test")

rm("x_train")