What has to be considered when creating a radar chart?

I’m currently analyzing a WhatsApp chat history. One thing I’m interested in is the time when the two people communicated. I thought this is a perfect use case for a radar chart (aka spider chart, star chart). So here is one example:

I find this super hard to read. In contrast, have a look at the following bar chart with exactly the same information:

Here it is way easier for me to see patterns:

• 0 – 5: No activity (sleeping)
• 6 – 9: A morning peak (messages after waking up)
• 10 – 16: Little activity (work times)
• 17 – 19: Increased activity (work is done)
• 20 – 21: High activity
• 22 – 23: Sinking activity (going to bed)

Question

What are typical use-cases for radar charts? Are there specific requirements on the data? When are they better than bar charts? How should I set the ticks for radar charts?

Before people starting to close-vote: I could imagine very well that there are similar studies like for color maps – how many errors are done in analysis? How fast can people pick up main insights?

Example code

In case you want to improve the existing visualizations:

# core modules
from math import pi

# 3rd party modules
import matplotlib.pyplot as plt
import pandas as pd


def main():
    df = pd.DataFrame({'date': [209, 13, 1, 2, 1, 25, 809, 3571, 1952, 1448, 942, 1007, 1531, 1132, 981, 864, 975, 2502, 2786, 2717, 3985, 4991, 2872, 761]},
                      index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])
    create_bar_chart(df)
    create_radar_chart(df)


def create_bar_chart(df, output_path='bar_chart.png'):
    df.plot(kind='bar')
    plt.savefig(output_path)


def create_radar_chart(df, output_path='radar_chart.png'):
    """
    Create a radar chart.

    Parameters
    ----------
    df : pandas.DataFrame
        Has a column 'date'
    """
    values = df['date'].tolist()

    df = df.T.reset_index(drop=True)
    df.insert(0, 'group', 'A')

    # number of variable
    categories = list(df)[1:]
    N = len(categories)

    # What will be the angle of each axis in the plot?
    # (we divide the plot / number of variable)
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]

    # Initialise the spider plot
    ax = plt.subplot(111, polar=True)

    # Draw one axe per variable + add labels labels yet
    plt.xticks(angles[:-1], categories, color='grey', size=8)

    # We are going to plot the first line of the data frame.
    # But we need to repeat the first value to close the circular graph:
    values = df.loc[0].drop('group').values.flatten().tolist()
    values += values[:1]
    values

    # Plot data
    ax.plot(angles, values, linewidth=1, linestyle='solid')

    # Fill area
    ax.fill(angles, values, 'b', alpha=0.1)
    plt.savefig(output_path)


if __name__ == '__main__':
    main()