/coursera-getdata

Primary LanguageR

Getting and Cleaning Data Course Project

This is the course project for the Coursera course Getting and Cleaning Data presented by Johns Hopkins University.

The purpose of this project is to product a tidy data set from raw data collected from accelerometers of a number of Samsung Galaxy S smartphones https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones.

All code for this project is contained within run_analysis.R. To reproduce the tidy data set from the raw data set, set your current working directory to the directory containing run_analysis.R, then run the script with no inputs, which does the following:

  1. Looks for a file named getdata-projectfiles-UCI-HAR-Dataset.zip (containing the raw data) in the current working directory
  2. If there is no such file it does the following: a. Downloads https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip to the file named getdata-projectfiles-UCI-HAR-Dataset.zip in the current working directory a. Unzips the downloaded file into the current working directory, creating a subdirectory named UCI HAR Dataset
  3. Sets the download date to the last modified time of the zip file
  4. Produces a tidy data set in a file named tidy.txt in the current working directory

See CodeBook.md for the details regarding the variables, the data, and the work performed to produce the tidy data set.

The tidy data set was produced with the following:

library(devtools)
devtools::session_info()
#> Session info -----------------------------------------------------------------------------------------
#>  setting  value                       
#>  version  R version 3.2.2 (2015-08-14)
#>  system   x86_64, darwin13.4.0        
#>  ui       RStudio (0.99.484)          
#>  language (EN)                        
#>  collate  en_US.UTF-8                 
#>  tz       America/New_York            
#>  date     2015-10-21                  
#> 
#> Packages ---------------------------------------------------------------------------------------------
#>  package    * version date       source        
#>  assertthat   0.1     2013-12-06 CRAN (R 3.2.0)
#>  chron        2.3-47  2015-06-24 CRAN (R 3.2.0)
#>  data.table * 1.9.6   2015-09-19 CRAN (R 3.2.0)
#>  DBI          0.3.1   2014-09-24 CRAN (R 3.2.0)
#>  devtools     1.9.1   2015-09-11 CRAN (R 3.2.0)
#>  digest       0.6.8   2014-12-31 CRAN (R 3.2.0)
#>  dplyr      * 0.4.3   2015-09-01 CRAN (R 3.2.0)
#>  lazyeval     0.1.10  2015-01-02 CRAN (R 3.2.0)
#>  magrittr     1.5     2014-11-22 CRAN (R 3.2.0)
#>  memoise      0.2.1   2014-04-22 CRAN (R 3.2.0)
#>  plyr         1.8.3   2015-06-12 CRAN (R 3.2.0)
#>  R6           2.1.1   2015-08-19 CRAN (R 3.2.0)
#>  Rcpp         0.12.1  2015-09-10 CRAN (R 3.2.0)
#>  reshape2   * 1.4.1   2014-12-06 CRAN (R 3.2.0)
#>  rstudioapi   0.3.1   2015-04-07 CRAN (R 3.2.0)
#>  stringi      0.5-5   2015-06-29 CRAN (R 3.2.0)
#>  stringr    * 1.0.0   2015-04-30 CRAN (R 3.2.0)