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.gitattributes

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.gitignore

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+# pyenv
+.python-version
+
+# python
+__pycache__

.streamlit/config.toml

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+[theme]
+base = 'light'
+textColor = '#000000'
+
+
+[browser]
+gatherUsageStats = false

README.md

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+---
+title: Doj Antitrust Viz
+emoji: 😻
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.25.0
+app_file: app.py
+pinned: false
+license: cc-by-4.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import numpy as np
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import plotly.io as pio
+import streamlit as st
+
+from datetime import datetime
+from pprint import pprint
+
+from scipy.stats import bootstrap
+
+
+# Load data
+with open('data.txt', 'r') as f:
+  cases_data = f.readlines()
+
+monthly_records = []
+annual_records = []
+for case_count in cases_data:
+  data = case_count.split()
+
+  # Annual data
+  if len(data) == 2:
+    data[1] = data[1].replace('(', '').replace(')', '')
+    annual_records.append((int(data[0]), int(data[1])))
+    continue
+
+  # Monthly data
+  data[2] = data[2].replace('(', '').replace(')', '')
+  monthly_records.append((data[0], int(data[1]), int(data[2])))
+
+pres_records = [
+  ('Lyndon B. Johnson', datetime(1963, 11, 22), datetime(1969, 1, 20)),
+  ('Richard Nixon', datetime(1969, 1, 20), datetime(1974, 8, 9)),
+  ('Gerald Ford', datetime(1974, 8, 9), datetime(1977, 1, 20)),
+  ('Jimmy Carter', datetime(1977, 1, 20), datetime(1981, 1, 20)),
+  ('Ronald Reagan', datetime(1981, 1, 20), datetime(1989, 1, 20)),
+  ('George H. W. Bush', datetime(1989, 1, 20), datetime(1993, 1, 20)),
+  ('Bill Clinton', datetime(1993, 1, 20), datetime(2001, 1, 20)),
+  ('George W. Bush', datetime(2001, 1, 20), datetime(2009, 1, 20)),
+  ('Barack Obama', datetime(2009, 1, 20), datetime(2017, 1, 20)),
+  ('Donald Trump', datetime(2017, 1, 20), datetime(2021, 1, 20)),
+  ('Joe Biden', datetime(2021, 1, 20), datetime(2023, 6, 28))  # cut Biden short so that it lines up with our last data point
+]
+pres_df = pd.DataFrame.from_records(pres_records, columns=['name', 'start', 'end'])
+
+
+
+# Clean the data
+
+month2int = {
+  'January': 1,
+  'February': 2,
+  'March': 3,
+  'April': 4,
+  'May': 5,
+  'June': 6,
+  'July': 7,
+  'August': 8,
+  'September': 9,
+  'October': 10,
+  'November': 11,
+  'December': 12
+}
+
+mn_df = pd.DataFrame.from_records(monthly_records, columns=['month', 'year', 'cases'])
+
+dts = []
+for i, r in mn_df.iterrows():
+  dts.append(datetime(year=r['year'], month=month2int[r['month']], day=28))
+
+mn_df['date'] = dts
+
+# This is the first year that has more than 1 case
+clipped_mn_df = mn_df.query('year >= 1964')
+
+
+# add 0s for months that are missing
+# we cut off at 1964 but Johnson started in November of 1963
+# There weren't any cases in 1963 so it's okay to start
+# filling 0s from November of 1963
+cur_yr = 1963
+cur_mn = 11
+new_rows = []
+
+# pandas `in` is busted so we have to pull out the column manually
+# and check against that
+existing_dates = clipped_mn_df['date'].to_numpy(dtype=datetime)
+
+# our data goes through the end of the previous month (june 2023)
+# we're using 28 as the placeholder "day" for all the months
+while cur_yr < 2023 or cur_mn <= 6:
+  dt = datetime(year=cur_yr, month=cur_mn, day=28)
+  if dt not in existing_dates:
+    new_rows.append((dt.strftime('%B'), dt.year, 0, dt))
+
+  if cur_mn == 12:
+    cur_yr += 1
+    cur_mn = 1
+  else:
+    cur_mn += 1
+
+zero_rows = pd.DataFrame.from_records(new_rows, columns=['month', 'year', 'cases', 'date'])
+clipped_mn_df = pd.concat([clipped_mn_df, zero_rows], ignore_index=True)
+clipped_mn_df = clipped_mn_df.sort_values(by='date', ascending=False).reset_index(drop=True)
+
+
+# add the mean & std for each president
+presidents = []
+for d in clipped_mn_df['date']:
+  for i, r in pres_df.iterrows():
+    if d >= r['start'] and d <= r['end']:
+      presidents.append(str(r['name']))
+
+clipped_mn_df['pres'] = presidents
+tmp = clipped_mn_df[['cases', 'pres']].groupby('pres').agg(['mean', 'std']).reset_index(drop=False)
+tmp.columns = ['name', 'cases_mean', 'cases_std']
+
+pres_df = pd.merge(pres_df, tmp, on='name', how='inner')
+
+
+# bootstrap confidence intervals for the mean
+# the data doesn't really look normal enough for 2 std to be super meaningful
+pres_names = pres_df['name'].unique()
+
+president_cis = []
+for pres in pres_names:
+  cases = clipped_mn_df.query(f'pres == "{pres}"')['cases'].to_numpy()
+  ci = bootstrap(
+      cases.reshape(1,-1),
+      np.mean,
+      vectorized=False,
+      confidence_level=0.95,
+      method='BCa'  # "bias-corrected and accelerated" (shifts the CI bounds if the distribution is skewed)
+  ).confidence_interval
+
+  president_cis.append((pres, ci.low, ci.high))
+
+ci_df = pd.DataFrame.from_records(president_cis, columns=['name', 'ci_low', 'ci_high'])
+
+# add the confidence intervals to pres_df
+pres_df = pd.merge(pres_df, ci_df, on='name')
+
+
+# Utils for converting colors
+def hex2rgb(h):
+  """
+  '#FF44BB' -> 'rgb(255, 68, 187)'
+  """
+  if h[0] == '#':
+    h = h[1:]
+  if len(h) != 6:
+    raise ValueError(f'malformed hex input')
+  values = []
+  for i in range(0, len(h), 2):
+    values.append(int(h[i:i+2], base=16))
+  return f'rgb({values[0]}, {values[1]}, {values[2]})'
+
+def rgb2rgba(c, a=1.0):
+  """
+  'rgb(95, 70, 144)'
+  ->
+  'rgba(95, 70, 144)'
+  ->
+  'rgba(95, 70, 144, 1.0)
+
+  defaults to 100% opacity
+  but you can set it
+  """
+  c = c[:3] + 'a' + c[3:]
+  c = c[:-1] + f', {a})'
+  return c
+
+
+# Draw the plot
+
+# streamlit ignores this but streamlit's theme
+# is pure white so it's okay I guess?
+pio.templates.default = 'plotly_white'
+
+f = go.Figure()
+
+FONT_SIZE = 14
+
+# add the cases as a bar plot
+bar_color = '#bbbbbb'
+f.add_trace(go.Bar(
+    x=clipped_mn_df['date'],
+    y=clipped_mn_df['cases'],
+    name='DOJ Antitrust Cases',
+    marker_color=bar_color,
+    marker_line_color=bar_color,
+    hovertemplate='%{x}: <b>%{y}</b><extra></extra>',
+    hoverlabel={'bgcolor': rgb2rgba(hex2rgb(bar_color), 0.2), 'font': {'size': FONT_SIZE}},
+    legendrank=1000 + 1  # default is 1000. Bigger means closer to the top
+))
+
+
+
+# add the president means + CI
+pres_colors = px.colors.qualitative.Prism
+
+for i, r in pres_df.iterrows():
+  # set up colors for this president
+  pres_color = pres_colors[i]
+  if pres_color[0] == '#':
+    pres_color = hex2rgb(pres_color)
+
+  ci_color = rgb2rgba(pres_color, 0.5)
+  hover_color = rgb2rgba(pres_color, 0.2)
+
+  hover_str = f"<b>{r['name']}</b><br>Mean: <b>{r['cases_mean']:.2f}</b><br>95% CI: <b>({r['ci_low']:.2f}–{r['ci_high']:.2f})</b><extra></extra>"
+  hover_label_fmt = {'bgcolor': hover_color, 'font': {'size': FONT_SIZE}}
+
+
+  # add this president's confidence interval
+  #
+  # draw two lines like this
+  #
+  # o------------------o
+  #
+  # o------------------o
+  #
+  # make the lines transparent,
+  # fill in the area between them
+  upper = r['ci_high']
+  lower = r['ci_low']
+  f.add_trace(go.Scatter(
+    x = [r['start'], r['end'], r['end'], r['start']],
+    y = [upper, upper, lower, lower],
+    fill='toself',
+    fillcolor=ci_color,
+    line_color=rgb2rgba(pres_color, 0),
+    # I have to set `name` for it to show up when I hover over any part of the fill
+    # otherwise the hover only comes up when I hover over the corners where the points are
+    # but `name` doesn't do the <extra></extra> thing to remove the extra hover box
+    name=hover_str.replace('<extra></extra>',''),
+    showlegend=False,
+    hovertemplate=hover_str,
+    hoverlabel=hover_label_fmt
+  ))
+
+  # add this president's mean
+  f.add_trace(go.Scatter(
+    x=[r['start'], r['end']],
+    y=[r['cases_mean'],r['cases_mean']],
+    name=r['name'],
+    line_color=pres_color,
+    # I used to have vertical bars at the ends of the mean line
+    # but I like it more without them
+    # so just set the width to 0
+    marker={'symbol': 'line-ns', 'line': {'width': 0, 'color':pres_color}},
+    hovertemplate=hover_str,
+    hoverlabel=hover_label_fmt
+  ))
+
+
+# Trim the top of the plot a bit because there are a few outliers
+# that make it hard to see the president aggregations
+MAX_HEIGHT = 16
+f.update_yaxes(range=[0, MAX_HEIGHT])
+
+# add hashing over any bars taller than MAX_HEIGHT
+# since we're cutting them off
+too_tall = clipped_mn_df[clipped_mn_df['cases'] > MAX_HEIGHT]['date']
+f.add_trace(go.Bar(
+    x=too_tall,
+    y=[MAX_HEIGHT * 0.25] * len(too_tall),
+    base = [MAX_HEIGHT - MAX_HEIGHT*0.1] * len(too_tall),
+    marker_color='#fff',
+    marker_line_color='rgba(255,255,255,0)',
+    marker_line_width=0,
+    # I think I remember plotly uses milliseconds if the axis is a datetime
+    # so the width has to be huge to cover a whole month
+    # yep 1 month is 2.6 * 10**9 milliseconds
+    width=3e9,
+    # these are the options ['', '/', '\\', 'x', '-', '|', '+', '.']
+    marker_pattern_shape='-',
+    marker_pattern_fillmode='replace',
+    showlegend=False
+))
+f.update_layout(barmode='stack')
+
+f.update_layout(title="<b>What does the DOJ's Antitrust Division look like?</b>")
+
+# since streamlit doesn't respect the Plotly theme,
+# we can instead make the background transparent
+f.update_layout({
+'plot_bgcolor': 'rgba(0, 0, 0, 0)',
+'paper_bgcolor': 'rgba(0, 0, 0, 0)',
+})
+
+st.set_page_config(layout='wide')
+st.plotly_chart(f, use_container_width=True, theme=None)

data.txt

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+June 2023 (2)
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