# A tibble: 10 × 4
awarding_agency recipient award_date total_amount
<chr> <chr> <date> <dbl>
1 Department of Defense NEW RIVER SYSTEMS CORPORATION 2017-03-31 408352.
2 Department of Defense TELECOMMUNICATION SOLUTIONS GR… 2017-03-31 285132.
3 Department of Defense CHENEGA DECISION SCIENCES LLC 2017-08-01 2970511.
4 Department of Defense ICF INCORPORATED, L.L.C. 2018-02-01 16398456.
5 Department of Defense CHENEGA DECISION SCIENCES LLC 2017-08-14 5527781.
6 Department of Defense KBR WYLE SERVICES, LLC 2015-12-24 19313398.
7 Department of Defense CHENEGA DECISION SCIENCES LLC 2017-12-04 1969678.
8 Department of Defense BOOZ ALLEN HAMILTON INC 2014-06-26 9872792.
9 Department of Defense THE UNIVERSITY OF TEXAS AT EL … 2017-04-27 490700.
10 Department of Defense TORCH TECHNOLOGIES INC 2014-09-24 12884028.
Code Base: Federal Cyber/Preparedness Contract Spending
Introduction
This analysis examines federal cybersecurity and preparedness-related contract spending to understand how different administrations prioritized cybersecurity.
Overview
This analysis explores federal contract spending related to cybersecurity and preparedness using USAspending.gov contract award data. The goal is to understand which agencies receive the largest share of funding and how spending has evolved across fiscal years. This serves as an initial exploratory step toward comparing federal priorities over time.
Data Source
The dataset contains over 800 federal prime contract awards with hundreds of variables. To make the analysis interpretable, I subset the data to a small number of columns capturing the awarding agency, fiscal year, recipient, obligated amount, and contract description.
Data Selection, Subset, and Motivation
Each row in the dataset represents a single prime federal contract award.
For this analysis, I keep all rows, but subset to a small set of columns to focus on:
- where the money came from (awarding agency)
- where the money went (recipient)
- fiscal year
- total obligated amount
- award description
Data Cleaning and Subsetting
This code loads the dataset from GitHub, keeps only five interpretable columns, creates a year field from the award date, and outputs a cleaned dataset.
Conclusions / Next Steps
In a future iteration, I would (1) verify which awards are truly cyber/preparedness-related by filtering on keywords in the description, (2) summarize spending by year and agency, and (3) compare trends across administrations by grouping years into administration periods and checking whether patterns persist when using obligated vs outlayed amounts.