add 2019 dairy plot

2019/2019-01-29/20190129.py

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+import pandas as pd
+import plotnine as gg
+
+# Read CSV data
+url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-01-29/clean_cheese.csv'
+cheese = pd.read_csv(url)
+
+# Select columns and compute differences
+plot_data = cheese[['Year', 'Cheddar']].copy()
+plot_data['diff'] = plot_data['Cheddar'].diff()
+plot_data.loc[plot_data.index[0], 'diff'] = plot_data['Cheddar'].iloc[0]
+plot_data['start'] = plot_data['Cheddar'] - plot_data['diff']
+plot_data['fill'] = plot_data['diff'] > 0
+
+# Define text for annotation
+annot_text = 'Data from the United States\nDepartment of Agriculture shows\nthat pounds of cheese consumed\nper person has been steadily rising\nsince the 1970s.\n\nGraphic: Nicola Rennie'
+
+# Create the plot
+p = (
+    gg.ggplot(plot_data) +
+    gg.geom_rect(mapping=gg.aes(xmin='Year-0.45', xmax='Year+0.45', ymin='start', ymax='Cheddar', fill='fill')) +
+    gg.annotate('text', x=2002, y = 4, label=annot_text, size=8) +
+    gg.labs(title='Who ate all the cheese?',
+            x='',
+            y='Pounds of cheese per person') +
+    gg.scale_fill_manual(values=['#CC3F0C', '#09814A']) +
+    gg.theme_xkcd() +
+    gg.theme(legend_position='none',
+             plot_title=gg.element_text(face='bold'))
+)
+
+# Save the plot
+gg.ggsave(p, filename="2024/viz/05_diverging.png", height=4, width=6, dpi=300)

2019/2019-01-29/README.md

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+<h1 align="center"> Dairy Production and Consumption </h1>
+
+<p align="center">
+  <img src="/2019/2019-03-05/20190129.png" width="60%">
+</p>