R&D Investment vs Patent Count

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

np.random.seed(416)
BG_COLOR = '#ffffff'
TEXT_COLOR = '#1f2937'

n = 55
rd_spend = np.random.uniform(5, 100, n)
patents = 2 + 0.4 * rd_spend + np.random.normal(0, 5, n)
patents = np.clip(patents, 0, None)

df = pd.DataFrame({'R&D Spend ($M)': rd_spend, 'Patents Filed': patents})

fig, ax = plt.subplots(figsize=(10, 6), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)

sns.regplot(
    data=df,
    x='R&D Spend ($M)',
    y='Patents Filed',
    scatter_kws={'color': '#5314E6', 'alpha': 0.6, 's': 55, 'edgecolor': 'white', 'linewidths': 0.5},
    line_kws={'color': '#F5B027', 'linewidth': 2.5},
    ci=95,
    ax=ax
)

for collection in ax.collections[1:]:
    collection.set_facecolor('#F5B027')
    collection.set_alpha(0.15)

corr = np.corrcoef(rd_spend, patents)[0, 1]
ax.text(0.05, 0.95, 'r = %.3f' % corr, transform=ax.transAxes, fontsize=12,
        color='#F5B027', fontweight='bold', va='top',
        bbox=dict(boxstyle='round,pad=0.3', facecolor='#f8fafc', edgecolor='#e5e7eb'))

ax.set_xlabel('R&D Investment ($M)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Patents Filed', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('R&D Spend vs Patent Output', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors='#374151', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#e5e7eb')

plt.tight_layout()
plt.show()