| title | author | date | output |
|---|---|---|---|
README.md |
Lauren Fitch |
Sunday, March 22, 2015 |
html_document |
I started by getting the list of files in the test and train directories.
test_files <- list.files("tidy_data/test",
pattern = "*.txt", full.names = TRUE)
training_files <- list.files("tidy_data/train",
pattern = "*.txt", full.names = TRUE)
Then I read those files into R, which required the stringr package.
install.packages("stringr")
library(stringr)
I made names for the data frames that match the original filenames.
test_file_names <- str_sub(test_files, 16, -5)
train_file_names <- str_sub(training_files, 17, -5)
ldf_test <- lapply(test_files, read.table)
names(ldf_test) <- test_file_names
ldf_train <- lapply(training_files, read.table)
names(ldf_train) <- train_file_names
Read in features
features <- read.table("tidy_data//features.txt")
Just take the second column
features <- features[ ,2]
Remove special characters
features <- str_replace_all(features, "[-(),]", "")
Concatenate together into one data frame
merge_data_test <- data.frame(ldf_test[1], ldf_test[2], ldf_test[3])
names(merge_data_test) <- c("subject", features, "y")
merge_data_train <- data.frame(ldf_train[1], ldf_train[2], ldf_train[3])
names(merge_data_train) <- c("subject", features, "y")
merge_data <- rbind(merge_data_test, merge_data_train)
Extracts only the measurements on the mean and standard deviation for each measurement. I looked for either "mean" or "std" in the variable name. I did not remove any variables from the resulting list.
col_names <- str_detect(names(merge_data), "mean|std")
col_names <- names(merge_data)[col_names]
col_names <- c("subject", col_names, "y")
extract_data <- merge_data[ , col_names]
I use descriptive activity names to name the activities in the data set, by reading in activity labels and matching them to the right row.
activity <- read.table("tidy_data/activity_labels.txt")
names(activity) <- c("id", "name")
extract_data$activity <- activity$name[match(extract_data$y, activity$id)]
I labeled the data set with descriptive variable names
names(extract_data) <- c(names(extract_data)[1:80],
"activity_code", "activity")
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.
Requires the dplyr package.
install.packages("dplyr")
library(dplyr)
Group the data by subject and activity
who <- group_by(extract_data, subject, activity)
tidy <- summarise_each(who, funs(mean))
Remove the activity code column
tidy <- tidy[ ,1:81]
Output the tidy data set
write.table(tidy, "tidy.txt", row.name = FALSE)