zhuoyizhang / Getting-and-Cleaning-Data

Course Project

Getting-and-Cleaning-Data

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title: "README.md" author: "Zhuoyi Zhang" date: "Sunday, March 22, 2015" output: html_document

Requirement

dplyr package should be installed in Rstudio

How to run run_analysis.R

1. download raw data from the following link https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
2. unzip it to any local folder
3. copy run_analysis.R to "UCI HAR Dataset" under your chosen local folder Note: Make sure run_analysis.R is located in folder "UCI HAR Dataset" If your type dir() in Rstudio, you should see run_analysis.R are located in UCI HAR Dataset

dir() [1] "activity_labels.txt" "features.txt" "features_info.txt" "README.md" "README.txt"
[6] "run_analysis.R" "test" "train"

4. Open RStudio and set your workspace to \UCI HAR Dataset 1)in Rstudio 2)Session>set working directory->choose directory 3)to folder "UCI HAR Dataset" and click "select folder" button you should see "> setwd("/UCI HAR Dataset")" in the console
5. Open run_analysis.R in Rstudio and compile it
6. Run run_analysis.R in the console by typing run_analysis()
7. The tidydata file will be generated in "UCI HAR Dataset" you will see a message in console like the following. [1] "tidydata.txt is successfully generated in /UCI HAR Dataset"

What does run_analysis.R does

step 1 --line 24-44 Merges the training and the test sets to create one data set I merge the following 3 files in folder "Train" ##'train/X_train.txt': Training set. ##'train/y_train.txt': Train Activity code ##'train/subject_train.txt': Each row identifies the subject who performed the activity ##'for each window sample.

I merge the following 3 files in folder "test"

##'test/X_test.txt': Test set. ##'test/y_test.txt': Test activity code. ##'test/subject_test.txt': Each row identifies the subject who performed the activity ##'for each window sample.

step 2 --line 48 merge the train set and test set into one data frame

step 3 --line 52-54 load the activity id and activity discription table from activity_labels.txt

step 4 --line 58-60 Extracts only the measurements on the mean and standard deviation for each measurement. I search all the column names that contain "mean" or "std"

step 5 --line 61 merge activity name to the one data frame with matching activity id

step 6 --line 71 Summarise all the measurements group by activity id, activity name and subject. Activity id and activity name are 1 to 1 mapping, so it is correct to include both columns into group by function

step 7 --line 74-75 Write the result data frame into tidydata.txt and print the successful message