plots for rq2

scripts/plotsforrq2.py

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+import os
+import matplotlib.pyplot as plt
+
+# -----------------------------
+# Raw counts
+# -----------------------------
+data = {
+    "Data Collection Settings": {
+        "blank or not specified": 16,
+        "clinical": 75,
+        "mix": 6,
+        "lab": 28,
+        "at-home / naturalistic": 11,
+        "educational": 1,
+    },
+    "Existing vs. Newly Collected Datasets": {
+        "no data collection": 13,
+        "yes data collection": 107,
+        "not specified or blank": 17,
+    },
+    "Cross-sectional vs. Longitudinal Design": {
+        "not specified or blank": 16,
+        "mix": 1,
+        "single session": 110,
+        "longitudinal": 10,
+    },
+    "Dataset Access Availability": {
+        "not specified or blank": 11,
+        "on request": 4,
+        "no": 94,
+        "yes": 28,
+    },
+    "Open Source": {
+        "blank": 12,
+        "yes": 7,
+        "no": 118,
+    },
+}
+
+# -----------------------------
+# Standardize totals
+# Add missing amount to blank category
+# -----------------------------
+def total_count(d):
+    return sum(d.values())
+
+max_total = max(total_count(d) for d in data.values())
+print(f"Standardizing all plots to total N = {max_total}")
+
+def blank_key_for_dict(d):
+    for k in d:
+        if "blank" in k.lower() or "not specified" in k.lower():
+            return k
+    return None
+
+standardized_data = {}
+
+for rq_name, counts in data.items():
+    counts = counts.copy()
+    current_total = total_count(counts)
+    diff = max_total - current_total
+
+    blank_key = blank_key_for_dict(counts)
+
+    if diff > 0:
+        if blank_key is not None:
+            counts[blank_key] += diff
+        else:
+            counts["blank or not specified"] = diff
+
+    standardized_data[rq_name] = counts
+
+# -----------------------------
+# Plotting function
+# -----------------------------
+def make_bar_plot(title, counts, output_dir="barplots"):
+    os.makedirs(output_dir, exist_ok=True)
+
+    labels = list(counts.keys())
+    values = list(counts.values())
+
+    # Size scales a bit with number of categories
+    fig_width = max(8, len(labels) * 1.3)
+    plt.figure(figsize=(fig_width, 6))
+    bars = plt.bar(labels, values)
+
+    plt.title(title.replace("_", " "), fontsize=13)
+    plt.ylabel("Count")
+    plt.xlabel("Category")
+    plt.xticks(rotation=30, ha="right")
+    plt.ylim(0, max(values) * 1.15)
+
+    # Add value labels above bars
+    for bar, val in zip(bars, values):
+        plt.text(
+            bar.get_x() + bar.get_width() / 2,
+            bar.get_height() + max(values) * 0.02,
+            str(val),
+            ha="center",
+            va="bottom",
+            fontsize=10,
+        )
+
+    plt.tight_layout()
+
+    filename = os.path.join(output_dir, f"{title}.png")
+    plt.savefig(filename, dpi=300, bbox_inches="tight")
+    plt.close()
+    print(f"Saved: {filename}")
+
+# -----------------------------
+# Create all plots
+# -----------------------------
+for rq_name, counts in standardized_data.items():
+    make_bar_plot(rq_name, counts)
+
+# -----------------------------
+# Print standardized totals
+# -----------------------------
+print("\nStandardized totals:")
+for rq_name, counts in standardized_data.items():
+    print(f"{rq_name}: total = {sum(counts.values())}, counts = {counts}")