| title | author | date | output |
|---|---|---|---|
Getting and cleanind data (Project) |
Juan Fernandez Martin |
Friday, January 23, 2015 |
html_document |
source('run_analysis.R')
myprocess <- create_process()
result <- myprocess$full_process()
The result is a list with three elements:
- The raw dataset (raw)
- The data set with averages and standard deviations (tidy1)
- The data set with the averages by subject and activity (tidy2).
Some problems related to the file transfer may be solved by passing the parameter method='curl' to the full_process function.
source('run_analysis.R')
myprocess <- create_process('base')
result <- myprocess$full_process(method='curl')
create_process(basedir='assignement')
Create an object with the following functions:
- get_data: check data is ready for the process in the basedir. If necessary it will download the data and install the environment in the basedir.
- load_data: load and merge the data into one big data set. It fulfills the requirements 1 and 4 (raw data set).
- extract_mean_and_std: extract variables related to mean and standard deviation from the given data set. It fulfills the requirement 2 (tidy1 data set).
- name_the_activities: add an extra row to the dataset with the activity name. It fulfills the requirement 3 (raw data set).
- set_descriptive_variable_names: set proper names to the data set columns. This is actually done on load_data.
- averages_by_activity_and_subject: calculate a new data set with the mean of each measurement by subject and activity (tidy2 data set). It fulfills the requirement 4.
- full_process: call the functions listed before to build up a result list with the three described data sets (raw, tidy1, tidy2). It is just a convenience function.