Shayne Easterwood
This purpose of this project is to get and clean data obtained from Smartlabs, for experiments done using activity/movement measurements taken from a smartphone. The final data set shows averages of the means and standard deviations for 79 different measurements provided in the data, for each of 30 participants for each of 6 different activities.
The data for this project was obtained here: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
A full description of the experiments and data is available here: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones.
The directory includes the following files:
- run_analysis.R script as described above
- README.md file
- codebook.md file
- tidy data output txt file as described above.
run_analysis <- function() {
subject_test <- read.table("subject_test.txt") colnames(subject_test) <- "subject"
subject_train <- read.table("subject_train.txt") colnames(subject_train) <- "subject"
X_test <- read.table("X_test.txt") features <- read.table("features.txt") colnames(X_test) <- features[, 2]
X_train <- read.table("X_train.txt") colnames(X_train) <- features[, 2]
y_test <- read.table("y_test.txt") colnames(y_test) <- "activity"
y_train <- read.table("y_train.txt") colnames(y_train) <- "activity"
test_data <- cbind(subject_test, y_test, X_test) train_data <- cbind(subject_train, y_train, X_train) complete_data <- rbind(test_data, train_data)
library(dplyr) subset_data <- complete_data[,grepl("mean|std|subject|activity", colnames(complete_data))]
subset_data$activity[subset_data$activity == '1'] <- 'walking' subset_data$activity[subset_data$activity == '2'] <- 'walking_upstairs' subset_data$activity[subset_data$activity == '3'] <- 'walking_downstairs' subset_data$activity[subset_data$activity == '4'] <- 'sitting' subset_data$activity[subset_data$activity == '5'] <- 'standing' subset_data$activity[subset_data$activity == '6'] <- 'laying'
tidy_data <- group_by(subset_data, subject, activity) tidy_data <- summarize_each(tidy_data, funs(mean)) write.table(tidy_data, "tidy_data.txt", row.names = FALSE) }