Unit 05 · lesson

Evidence Check: Defend the Chart Choice

You have a dataset containing the completion times of 200 robot test runs plus the robot model used for each run.

Your evidence

Choose two visualizations:

one to compare quantities across robot models;
one to show the distribution of completion times.

For each visualization, record:

the chart type;
the variables mapped to each axis or visual channel;
what pattern the chart should make easy to see;
one misleading design choice to avoid; and
one question the chart still cannot answer.

Check your reasoning

A chart is part of the argument. If the visual encoding hides spread, truncates context, or exaggerates a difference, the chart weakens the evidence.

Done when

Each chart has a reason tied to the question and an explicit limitation.

1. Dataset

Fictional robot test runs. No student or personal data.

run	model	sec	battery	°C	done
R-101	Atlas	52	88	22	yes
R-102	Atlas	57	79	23	yes
R-103	Nova	64	74	24	yes
R-104	Nova	71	—	27	no
R-105	Pulse	48	92	21	yes
R-106	Pulse	55	84	25	yes
R-107	Atlas	68	69	29	no
R-107	Atlas	68	69	29	no

2. Quick chart

ViewY

Change the variables before deciding what the data “says.” The same rows can support different questions.

3. Editable Pandas analysis

Runs locally in your browser. First use downloads Pandas and its verified dependencies from Robotnix.

import pandas as pd

rows = [
    {"run_id": "R-101", "model": "Atlas", "duration_s": 52, "battery_pct": 88, "ambient_c": 22, "completed": True},
    {"run_id": "R-102", "model": "Atlas", "duration_s": 57, "battery_pct": 79, "ambient_c": 23, "completed": True},
    {"run_id": "R-103", "model": "Nova", "duration_s": 64, "battery_pct": 74, "ambient_c": 24, "completed": True},
    {"run_id": "R-104", "model": "Nova", "duration_s": 71, "battery_pct": None, "ambient_c": 27, "completed": False},
    {"run_id": "R-105", "model": "Pulse", "duration_s": 48, "battery_pct": 92, "ambient_c": 21, "completed": True},
    {"run_id": "R-106", "model": "Pulse", "duration_s": 55, "battery_pct": 84, "ambient_c": 25, "completed": True},
    {"run_id": "R-107", "model": "Atlas ", "duration_s": 68, "battery_pct": 69, "ambient_c": 29, "completed": False},
    {"run_id": "R-107", "model": "Atlas ", "duration_s": 68, "battery_pct": 69, "ambient_c": 29, "completed": False},
]
df = pd.DataFrame(rows)
df["model"] = df["model"].str.strip()

print(df.groupby("model")["duration_s"].agg(["count", "mean", "median"]).round(1))
print("\nDuration distribution:")
print(df["duration_s"].describe().round(1))

Output

Run the analysis to see evidence.

Ready. Predict before you run.

Runtime boundary: Robotnix serves the core Pyodide runtime and the SHA-256-verified Pandas dependency set. This workbench does not expose pip, micropip, a shell, filesystem projects, cloud credentials, or arbitrary package installation.