run_analysis.R does:
- Merges the training and the test sets to create one data set.
- Extracts only the measurements on the mean and standard deviation for each measurement.
- Uses descriptive activity names to name the activities in the data set
- Appropriately labels the data set with descriptive variable names.
- 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.
Returns: Dataset (Step 1) "step5.txt" as a text file.
How to execute:
source("run_analysis.R")
progAssignment()
How it works:
- Step one is done by simple rbind
- Step two is done using select and cbind. At this point, Step 4 is been done
using the custom "trim" function at the beginning of teh code.
- Step three it's a bit tricky, but basically is done by the line:
step3 <- mutate(step2, Activity = (left_join(allActivities,labelss, by="Activity")[,2]))
- Step 5 is just one aggregate, with the following:
preStep5 <- cbind(step3, allSubjects)
step5 <- aggregate(preStep5[,1:24], preStep5[,25:26], FUN=mean)
write.table(step5, file ="step5.txt", row.name = FALSE)
CodeBook:
The configuration section, is made at this way cause I though at the first time, to iterate
the dirs. That is, in case if you want to add an extra set. I didn't have enough time to
do it at this way :S
HAR_dataset : Location of the UCI data
working_dirs : Which directories are we going to parse.
test_dir : Test dir
train_dir : Tran dir
Article about wereable technology: http://www.insideactivitytracking.com/data-science-activity-tracking-and-the-battle-for-the-worlds-top-sports-brand/
Data Set descrition (Samsung Galaxy S) http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
Data Set: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip