subreddits-plot.py

#!/usr/bin/env python3

"""Plot subreddits creation and relative size."""

__author__ = "Joseph Reagle"
__copyright__ = "Copyright (C) 2024 Joseph Reagle"
__license__ = "GLPv3"
__version__ = "0.2"

import argparse
from pathlib import Path

import matplotlib.dates as mdates
import matplotlib.patheffects as path_effects
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from adjustText import adjust_text

# Create an argument parser
parser = argparse.ArgumentParser(
    description="Plot subreddits creation and relative size."
)
parser.add_argument(
    "-i", "--input", type=Path, required=True, help="Path to the input CSV file"
)
args = parser.parse_args()

# Read in the CSV data
df = pd.read_csv(args.input, comment="#")

# Convert the 'subscribers' column to numeric, replacing non-numeric values with NaN
df["subscribers"] = pd.to_numeric(df["subscribers"], errors="coerce")

# Convert the 'created' column to datetime
df["created"] = pd.to_datetime(df["created"], format="%Y-%m-%d")

# Sort by date
df = df.sort_values("created")

# Calculate the relative size of each subreddit
ADJUST_CIRCUMFERENCE = 4  # Adjustable parameter for bubble size
df["relative_size"] = df["subscribers"] / df["subscribers"].max() * 1000

# Create a dictionary to map categories to colors
category_colors = {
    "general": "magenta",
    "relationship": "blue",
    "legal": "teal",
    "finance": "olive",
    "health": "red",
    "fashion": "pink",
    "gender": "green",
    "disclosure": "purple",
    "judgement": "orange",
}

# Set the threshold values
THRESHOLD_SIZE = 10000  # Ignore subreddits with subscribers less than this value
THRESHOLD_YEAR = 2024  # Ignore subreddits created after this year

# Filter data based on thresholds
df_filtered = df[
    (df["subscribers"] >= THRESHOLD_SIZE) & (df["created"].dt.year <= THRESHOLD_YEAR)
]

BUBBLE_SCALE_FACTOR = 5  # Adjustable factor for overall bubble size

# Create the plot using Seaborn
plt.figure(figsize=(12, 8))
sns.scatterplot(
    data=df_filtered,
    x="created",
    y="subscribers",
    size="relative_size",
    hue="category",
    palette=category_colors,
    sizes=(20 * BUBBLE_SCALE_FACTOR, 2000 * BUBBLE_SCALE_FACTOR),
    alpha=0.7,
    legend=False,
)

# Customize plot aesthetics
plt.yscale("log")
plt.xlabel("Date Created")
plt.ylabel("Number of Subscribers")
plt.title("Creation and Size of Advice Subreddits")

# Create a custom legend for categories only
legend_elements = [
    plt.Line2D(
        [0], [0], marker="o", color="w", label=cat, markerfacecolor=color, markersize=10
    )
    for cat, color in category_colors.items()
]
plt.legend(
    handles=legend_elements,
    title="Category",
    bbox_to_anchor=(1.05, 1),
    loc="upper left",
)

texts = []
for _, row in df_filtered.iterrows():
    texts.append(
        plt.text(
            row["created"],
            row["subscribers"],
            row["subreddit"],
            fontsize=10,
            va="center",
            ha="left",
            path_effects=[path_effects.withStroke(linewidth=3, foreground="white")],
        )
    )

# Adjust the positions of the labels
adjust_text(texts, arrowprops={"arrowstyle": "-", "color": "k", "lw": 0.0})

# Adjust x-axis limits to provide extra space on both sides
x_min, x_max = plt.xlim()
x_min_date = mdates.num2date(x_min)
x_max_date = mdates.num2date(x_max)
plt.xlim(
    mdates.date2num(x_min_date - pd.Timedelta(days=100)),
    mdates.date2num(x_max_date + pd.Timedelta(days=200)),
)

plt.tight_layout()

# Save and show plot
output_file = Path(args.input).with_suffix(".png")
plt.savefig(output_file, dpi=300)
plt.show()