run_analysis.R

# Authour: Jakob Berg
# June 2021

library(dplyr)

# Import #
data_dir = "./getdata_projectfiles_UCI HAR Dataset/UCI HAR Dataset/"
features <- read.table(paste0(data_dir, "features.txt"), col.names = c("row", "functions"))
activity_labels <- read.table(paste0(data_dir, "activity_labels.txt"), col.names = c("class", "activity"))

test_data_dir = "./getdata_projectfiles_UCI HAR Dataset/UCI HAR Dataset/test/"
subject_test <- read.table(paste0(test_data_dir, "subject_test.txt"), col.names = "subject")
x_test <- read.table(paste0(test_data_dir, "X_test.txt"), col.names = features$functions)
y_test <- read.table(paste0(test_data_dir, "y_test.txt"), col.names = "class")

train_data_dir = "./getdata_projectfiles_UCI HAR Dataset/UCI HAR Dataset/train/"
subject_train <- read.table(paste0(train_data_dir, "subject_train.txt"), col.names = "subject")
x_train <- read.table(paste0(train_data_dir, "X_train.txt"), col.names = features$functions)
y_train <- read.table(paste0(train_data_dir, "y_train.txt"), col.names = "class")


# Tidy #

## 1) Merge the training and the test sets to create one data set.

x_df <- rbind(x_test, x_train)
y_df <- rbind(y_test, y_train)
subject_df <- rbind(subject_test, subject_train)

HAR_df <- cbind(x_df, y_df, subject_df)


## 2) Extract only the measurements on the mean and standard deviation for each 
#     measurement.

HAR_tidy_df <- HAR_df %>%
  select(subject, class, contains("mean"), contains("std"))


## 3) Use descriptive activity names to name the activities in the data set.

HAR_tidy_df <- inner_join(HAR_tidy_df, activity_labels, by = "class") %>%
  select(-class) %>%  # Drop class var
  relocate(subject, activity)  # Reorder vars to front


## 4) Appropriately label the data set with descriptive variable names.

names(HAR_tidy_df) <- gsub("Acc", "Accelerometer", names(HAR_tidy_df))
names(HAR_tidy_df) <- gsub("Gyro", "Gyroscope", names(HAR_tidy_df))
names(HAR_tidy_df) <- gsub("BodyBody", "Body", names(HAR_tidy_df))


## 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.

HAR_agg_df <- HAR_tidy_df %>% 
  group_by(activity, subject) %>%
  summarize_all(funs(mean))