US Federal Spending Analysis

11 February 2019 in TidyTuesday

This week's #TidyTuesday is about federal spending.

I was interested in understanding if spending had shifted towards clean energy sub-agencies in the last 20 years. Spoiler alert: unfortunately no! 😰#Rstats #tidyverse cc @thomas_mock @R4DScommunity pic.twitter.com/AbQzheSagU
— Davide Magno (@DavideMagno) February 12, 2019

Davide Magno

An Italian coding, tech, financial math professional and running lover

Head of Financial Risk Management

Dublin, Ireland

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US Federal Spending Analysis

Feb 2, 2019

This week's #TidyTuesday is about federal spending. I was interested in understanding if spending had shifted towards clean energy sub-agencies in the last 20 years. Spoiler alert: unfortunately no! 😰#Rstats #tidyverse cc @thomas_mock @R4DScommunity pic.twitter.com/AbQzheSagU — Davide Magno (@DavideMagno) February 12, 2019

Geospatial Analysis on Housing Price Index

Feb 2, 2019

Here's my #TidyTuesday submission.I studied the total increase in the housing index for each US State compared to the US federal index. No surprise in California house price has increased much more than the rest of US.The #mapdata library was 👍🏼@thomas_mock @R4DScommunity #rstats pic.twitter.com/sYI1zKhIpC — Davide Magno (@DavideMagno) February 10, 2019

My First #TidyTuesday Submission

Feb 2, 2019

Finally my first #TidyTuesday submission! 🎊🎉I am starting from week 1 2019 but I will catch up soon😉When do people submit their visualisations?Mostly on Tuesday and Wednesday afternoon, but also on Monday before the new set is released #rstats #ggplot @rstats4ds @thomas_mock pic.twitter.com/pKZAyL8vp1 — Davide Magno (@DavideMagno) February 3, 2019

Convert foreign currency valuations

Feb 2, 2018

One of the most common problems when dealign with financial data is to have assets (or liabilities) denominated in a currency that is different from the domestic one. I propose a tidy solution to this problem that involves no use of for cycles. The principle of the solution is that converting each currency can be done in parallel using the map function while the consolidation of the results will be done using the reduce logic.

Generate scenarios correlated to existing ones

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In quantitative finance we often look at simulations of some market risk factors like equity returns or interest rate changes. There are many third party companies who specialize in the historical calibration of such variables and provide simulations of future expected outcomes to the companies who require them. For example, let’s suppose that we receive the expected returns of the Google shares as per the following distribution # This modelling is given by the third party and in theory we don't know it google <- rnorm(10000, mean = 0.