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Scraped_txt_files_2013_to_2005
capitolwords
fms_soundsystem Project
music
sample_csv
.Rapp.history
.gitignore
brian.r
capitolwords-download.sh
capitolwords-insert.py
chords.wav.asd
chords_final.wav.asd
clean_plot.pdf
clean_plot.png
data-capitolwords.r
data.r
debt_ceiling_final.wav
debt_ceiling_final1.wav
debt_ceiling_final1.wav.asd
debt_ceiling_final_up.mp3
debt_ceiling_final_up.wav
debt_ceiling_final_up.wav.asd
debt_ceiling_test.wav
download_fms_fixies.py
exploratory.plots.pdf
fed_rate.csv
fms.day.csv
fms_soundsystem_melody.mp3
fms_soundsystem_melody.wav
fms_soundsystem_melody.wav.asd
for-michael.csv
for-michael.db
for-michael.sh
horn_stab_down.wav
horn_stab_down.wav.asd
horn_stab_down1.wav
horn_stab_down1.wav.asd
horn_stab_down2.wav
horn_stab_down2.wav.asd
horn_stab_up2.wav
horn_stab_up2.wav.asd
main.r
melody.wav.asd
melody1.wav.asd
melody2.wav
melody2.wav.asd
melody_final.wav
melody_final.wav.asd
music_data.csv
output_csv_columns_to_json.py
output_program_jsons.ipynb
output_program_jsons.py
parse_fms_fixies.py
parse_fms_fixies_2.ipynb
parse_fms_fixies_2.py
plot.r
readme.md
slideshow.html
song.r
song2.wav.asd
soundsystem.R
soundsystem.v2.r
summary.fixie
table1.csv
table2-std.csv
table2.csv
table3a.csv
table3cdata_subject_ceiling_net.csv

readme.md

Given some data created by fms-treasury-statements, make something cool.

Load data from capitol words

./capitolwords-download.sh spending
./capitolwords-insert.py spending

Questions

What spending shoots up in the middle of the month?

Why do we have this bimodal distribution? Why does spending stop at $50 billion, and what causes it to jump past that?

For the wiki

We used various R sonification, processing and visualization libraries to produce our multisensory information interactive. These libraries include

  • plyr
  • reshape2
  • csvsoundsystem
  • aplpack

Me sought to produce an interactive that simultaneously displayed highly dimensional data. We started with a 55-dimensional dataset

  • 1 date
  • 52 daily line items
  • 1 daily interest rate
  • 1 debt ceiling

We added a few variables to assist with our analyses.

  • Day of week
  • Day of month
  • Rolling mean
  • Rolling z-score
  • Daily balance
  • Variance of the 52 line items

And then reduced the now-61-dimensional dataset to an interactive.

We used principal component analysis to rotate the 52 line-items, and we plotted the 15 highest-loaded components as Chernoff faces. We plotted interest rate and balance, with an error width.

We represented similar data in audio. We selected chords based on the derivative of cash balance, and we composed a melody based on the interest rate.

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