- author: Group-20 DSCI-522
- contributors: Jacob McFarlane, Chirag Rank, Steffen Pentelow, Anita Li
Unidentified flying objects (UFOs) have different shapes and could potentially be linked with the duration of sightings. In this analysis we use Kruskal-Wallis test to answer whether the duration of sightings of UFOs of different shapes are significantly different. The result indicates that there are differences in duration among these shapes. Then, we selected the Dunn test for Post-Hoc analysis with Bonferroni’s correction to identify pairs of groups whose median duration are significantly different.
We have selected the National UFO Reporting Center (NUFORC) maintained
database of sightings of unidentified flying objects (UFO). We will deal
specifically with reports made in British Columbia, Canada and
Washington State, USA before 11/18/2020. While NURFORC does curate
reports and remove obvious hoaxes, the data is submitted by users and in
the majority of cases presented in the users’ own words. The data set
for BC can be found here
and the data set for Washington can be found
here. Each row contains
information on a single reported UFO sighting. Our analysis focuses on
the shape and duration columns of the data set.
The final report can be found here.
There are two suggested ways to run this analysis:
Clone or download this repository, navigate to the root of this project, and then use the following command (PATH_ON_YOUR_COMPUTER is the absolute path to the root of this project on your computer)
docker run --rm -v PATH_ON_YOUR_COMPUTER:/home/out_of_the_world chiragrank/python-r:latest make -C '/home/out_of_the_world' all
To clean up the analysis type:
docker run --rm -v PATH_ON_YOUR_COMPUTER:/home/out_of_the_world chiragrank/python-r:latest make -C '/home/out_of_the_world' clean
Clone this GitHub repository, install the dependencies listed below, and run the following commands at the terminal from the root directory of this project:
make all
To reset the repo to a clean state, with no intermediate or result files, run the following command at the terminal from the root directory of this preoject:
make clean
-
Python 3.8.5 and Python packages:
- docopt==0.6.2
- feather-format==0.4.1
- pandas==1.1.3
- lxml==4.6.1
-
R 4.0.2 and R packages:
- tidyverse==1.3.0
- feather==0.3.5
- ggplot2==3.3.2
- knitr==1.29
- readr==1.3.1
- docopt==0.7.1
- testthat==3.0.0
- here==1.0.0
- DescTools==0.99.38
- reshape2==1.4.4
- broom==0.7.0
- infer==0.5.3
- bookdown==0.21
-
GNU make 4.2.1
de Jonge, Edwin. 2018. Docopt: Command-Line Interface Specification Language. https://CRAN.R-project.org/package=docopt.
Keleshev, Vladimir. 2014. Docopt: Command-Line Interface Description Language. https://github.com/docopt/docopt.
R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
The National UFO Reporting Center. 2020. “The National UFO Reporting Center Report Database.” http://www.nuforc.org/.
Van Rossum, Guido, and Fred L. Drake. 2009. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace.
Wickham, Hadley. 2017. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595.