greatkai / cleaningdatacourseproject

CleaningDataCourseProject

README

Overview

As required, there are one tidy data sets, a codebook (CodeBook.md) and a run_analysis.R script.

Each dataset fit the standards:

• The first row contains column names. So it have headings.
• Variables are in different columns
• No duplicated columns

Therefore each dataset can be read into R by

read.table(FILE, header = TRUE)

As I read from the discussion forum, interial folder is not required. Therefore I just used the general x and y data.

Dataset

​ The dataset is "processedTidyData.txt". It takes measurements of mean value and standard deviation. The data also include measurements of mean frequent value.

run_analysis.R

Data downloaded from the web should be in the same path as the run_analysis.R script.

Variable "workDir" can be changed to the work directory of the user.

There are notes explaining each line of code. Here I will give some general idea.

The script contains 3 functions: activityName, readData, and processData.

• activityName: This function is called in readData(). It takes a label in test_y.txt or train_y.txt, and returns the activity name.
• readData: This function is coded for requirement 1~4. It reads raw data from the file, process it and return the tidy dataset. For details please check the notes in the script.
• processData: This function is coded for requirement 5. It takes the tidy dataset from readData() and returns the dataset which fits the requirement. Again, for details please check the notes in the script.

2 dataframes will be generate by

activity_data <- readData()
processed_data <- processData(activity_data)

activity_data is the tidy dataset descripted in requirement 1~4.

processed_data is the final dataset.

later, the processed dataframe is written into txt files by

write.table(processed_data,file = "./processedTidyData.txt",row.names = FALSE)