This is my submission for the project of the Coursera course Getting and Cleaning Data by Jeff Leek, PhD, Roger D. Peng, PhD, Brian Caffo, PhD.
The course website is found on Coursera.
In this repository, you will find the following files:
- get_data.R: Downloads the project data. After running this, you'll find the zip file "getdata-projectfiles-UCI HAR Dataset.zip" in the current folder.
- run_analysis.R: This script analyses the raw data and create a new file "feature_means.txt" which contains the analysis results.
- getdata-projectfiles-UCI HAR Dataset.zip: the [raw data file][1].
- features_means.txt: the result of running the analyse_data.R script on the data obtained with the get_data.R script. This is the wide form as mentioned in the rubric as either long or wide form is acceptable, see https://class.coursera.org/getdata-030/forum/thread?thread_id=107 for discussion.
- feature_means_info.md: the codebook explaining the content and structure of the feature_means.txt file.
You can read the feature_means.txt file with the following R snippet.
data_filename <- "feature_means.txt"
data <- read.table(data_filename, header = TRUE, check.names = FALSE)
View(data)
Use of the raw dataset in publications must be acknowledged by referencing the [following publication][1]
[1]: Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012
This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.