run_analysis.R

# Download and extract data
filename <- "projectfiles_UCI_HAR_Dataset.zip"
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"

if(!file.exists(filename)) {
  download.file(fileURL, destfile = filename)
  unzip(filename)  
}

library("dplyr")
features <- read.table("./UCI HAR Dataset/features.txt", col.names = c("id", "feature"))
activity_labels <- read.table("./UCI HAR Dataset/activity_labels.txt", 
                              col.names = c("id", "activity"))

subject_train <- read.table("./UCI HAR Dataset/train/subject_train.txt", col.names = "subject")
X_train <- read.table("./UCI HAR Dataset/train/X_train.txt", col.names = features$feature)
y_train <- read.table("./UCI HAR Dataset/train/y_train.txt", col.names = "id")

subject_test <- read.table("./UCI HAR Dataset/test/subject_test.txt", col.names = "subject")
X_test <- read.table("./UCI HAR Dataset/test/X_test.txt", col.names = features$feature)
y_test <- read.table("./UCI HAR Dataset/test/y_test.txt", col.names = "id")

# 1. Merges the training and the test sets to create one data set.
X_data <- bind_rows(X_train, X_test)
y_data <- bind_rows(y_train, y_test)
subject_data <- bind_rows(subject_train, subject_test)

# 2. Extracts only the measurements on the mean and standard deviation
# for each measurement. 
X_mean_std <- select(X_data, matches("mean|std"))

# 3. Uses descriptive activity names to name the activities in the data set
y_data <- inner_join(y_data, activity_labels, by = "id") %>% select(activity)

# 4. Appropriately labels the data set with descriptive variable names. 
names(subject_data) <- "subject"
names(X_mean_std) <- names(X_mean_std %>% 
          rename_with(~ gsub("Acc", "Accelerometer", .x)) %>% 
          rename_with(~ gsub("Mag", "Magnitude", .x)) %>% 
          rename_with(~ gsub("Gyro", "Gyroscope", .x)) %>% 
          rename_with(~ gsub("angle", "Angle", .x)) %>% 
          rename_with(~ gsub("gravity", "Gravity", .x)) %>% 
          rename_with(~ gsub("BodyBody", "Body", .x)) %>% 
          rename_with(~ gsub("^t", "Time", .x)) %>% 
          rename_with(~ gsub("\\.t", ".Time", .x)) %>% 
          rename_with(~ gsub("^f|Freq", "Frecuency", .x)) %>% 
          rename_with(~ gsub("\\.$|\\.\\.$", "", .x)))

# 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.
all_data <- bind_cols(X_mean_std, y_data, subject_data)

tidy_data <- all_data %>% group_by(activity, subject) %>%
  summarise(across(.fns = mean))

write.csv(tidy_data, "tidy_data.csv", row.names = FALSE)
write.table(tidy_data, "tidy_data.txt", row.names = FALSE)