Run Analysis R, a Script to Clean and Tidy Activity Data

The script performs the following tasks:

1. merges the training and test sets to create one data set.
2. extracts the measurements on the mean and std dev for each measurement.
3. uses descriptive activity names to name the activities in the data set
4. appropriately labels the data set with descriptive variable names
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.

Information about the dataset can be found at the repository:

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Merging the Data Sets

Merge the training and test sets to create one data set and #3, use descriptive activity names to name the activities in the data set, and #4 appropriately labels the data set with descriptive variable names. The following script below accomplishes this task.

X_train <- read.table("~/Downloads/UCI HAR Dataset/train/X_train.txt", quote="\"", stringsAsFactors=FALSE)

X_test <- read.table("~/Downloads/UCI HAR Dataset/test/X_test.txt", quote="\"", stringsAsFactors=FALSE)

library(tidyr)
library(dplyr)

## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(reshape2)
library(data.table)

## 
## Attaching package: 'data.table'
## 
## The following objects are masked from 'package:dplyr':
## 
##     between, last

X_all <- bind_rows(X_train, X_test) #add all the training and test data together
View(X_all)

Now I name the columns something reasonable that indicates the data type.

features <- read.table("~/Downloads/UCI HAR Dataset/features.txt", quote="\"", stringsAsFactors=FALSE)
View(features)
names(X_all) = features$V2

subject_train <- read.table("~/Downloads/UCI HAR Dataset/train/subject_train.txt", quote="\"", stringsAsFactors=FALSE)
subject_test <- read.table("~/Downloads/UCI HAR Dataset/test/subject_test.txt", quote="\"")

subject_all <- bind_rows(subject_train, subject_test) #combine as before in the x train and test sets

activity_labels <- read.table("~/Downloads/UCI HAR Dataset/activity_labels.txt", quote="\"", stringsAsFactors=FALSE)

View(activity_labels)

subject_activity <- full_join(subject_all, activity_labels) #descriptive activity names for all the activities in the data set on each subject

## Joining by: "V1"

subject.no <- subject_activity$V1
activity <- subject_activity$V2
subject.activity <- data.frame(subject.no, activity)
all_data <- cbind(as.data.table(X_all),(subject_activity))

There are now two complete, corresponding data frames, one with all the X data and one with all the subject activity data by number and label

The next task is accomplished by joining the data to make one large data set.

y_train <- read.table("~/Downloads/UCI HAR Dataset/train/y_train.txt", quote="\"", stringsAsFactors=FALSE)
y_test <- read.table("~/Downloads/UCI HAR Dataset/train/y_train.txt", quote="\"", stringsAsFactors=FALSE)

y_all <- bind_rows(y_train, y_test) #add all the training and test data together
View(y_all)

As a precautionary step, just for good measure, I create .csv output files to save and check data or to send to those who prefer other data analysis methods

write.csv(all_data, "all_data.csv")
write.csv(subject_activity, "subject_activity.csv")
write.csv(y_all, "all_y_data.csv")

The next few scripts extracts measurements on the mean and std for each measurement; labels correctly with descriptive name. The first 6 columns in the all_data set have the mean and std, the rest are other measurements or quantiles. Below is a new data set giving just these measurements.

mean <- select(all_data, contains("mean"))
std <- select(all_data, contains("std"))
mean.std <- cbind(as.data.table(mean),(std))
mean.std.subject.activity <- cbind(as.data.table(mean.std), (subject.activity))

write.csv(mean.std.subject.activity, "mean.std.subject.activity.csv")

Create a tidy data set with average of each variable for each activity by each subject and save it as a text file. Now you have a data file ready for further analysis.

tidy <- na.omit(mean.std.subject.activity) #data set has no missing variables and contains all the means (averages) and std for each subject by activity in which they participated

write.table(tidy, row.name=FALSE, "tidy.txt")