Perl Language

Good comparisons provide the insight we need to make well-informed decisions.

So our first task is to decide what comparison to make. As we outline our research question, we outline the statistical analysis that we plan to make.

Then, to assemble the dataset for our comparison, we read and write text files, placing the information that we read from an input file into data structures that allow us to identify, edit and collect patterns in text.

Finally, we write the assembled dataset to a text file for use in our statistical analysis.

Here, we will create the datasets that we used to explore the questions:

  • Has New York City's Vision Zero initiative reduced traffic injuries and fatalities?
  • Does increasing the minimum wage increase the odds that an American adult will be employed?

To create the Vision Zero dataset, we will use crash data from the NYC Dept. of Transportation.

To create the minimum wage dataset, we will combine the minimum wage data from the Washington Center for Equitable Growth with the data on employment status, average annual pay and consumer prices from the US Bureau of Labor Statistics.

Both cases provide examples of how to read and write text files, how to collect information into data structures and how to identify and edit patterns in text.


Vision Zero

Minimum Wage


Finally, we must thank the Perl Foundation and the Perl Community for the development of the Perl language. Without their work, this analysis would not be possible.