How to create an Economist-style chart with plotly

When it comes to static visualizations you can use in print, no publication does a better job creating them than The Economist. That’s because their charts are simple, yet they manage to convey the relevant message contained in the data.

Many people focus on the chart’s aesthetics—how it looks to the human eye. In so doing, they neglect the message contained in the data, which they are supposed to convey to the audience.

I have no idea what software the data visualization analysts at The Economist use to create their charts. Regardless, I thought I should try recreating one of the plots using my favorite plotting library, Plotly.

Here’s the original chart from The Economist.

Since I didn’t have the data to recreate this chart, I put together some fake department data about a company from one of my favorite TV shows, Suits. Below is the dataframe containing the data.

import polars as pl
import plotly.graph_objects as go

data = {
    "Department": ["Production", "Finance", "Customer Service", "Operations", "IT", "Procurement", "Sales", "HR"],
    "2023": [20, 60, 80, 85, 45, 32, 60, 75],
    "2024": [35, 70, 70, 78, 55, 55, 65, 80]
}

df = pl.DataFrame(data).sort('Department', descending=False)
df

shape: (8, 3)

Department	2023	2024
str	i64	i64
"Customer Service"	80	70
"Finance"	60	70
"HR"	75	80
"IT"	45	55
"Operations"	85	78
"Procurement"	32	55
"Production"	20	35
"Sales"	60	65

And now here is the chart designed using Plotly.

BG_COLOR = '#E9EDF0'
RED = '#E3120B'
GREY = '#5e5c64'

fig = go.Figure()

for i in range(df.shape[0]):
    fig.add_trace(go.Scatter(
        x=[df["2023"][i], df["2024"][i]],
        y=[df["Department"][i], df["Department"][i]],
        mode='lines+markers',
        line=dict(color='#598080', width=4),  # Thicker line
        marker=dict(size=16),  # Bigger dots
        showlegend=False
    ))
    fig.add_trace(go.Scatter(
        x=[df["2023"][i]],
        y=[df["Department"][i]],
        mode='markers',
        marker=dict(color=GREY, size=16),  # Bigger dots
        showlegend=False
    ))
    fig.add_trace(go.Scatter(
        x=[df["2024"][i]],
        y=[df["Department"][i]],
        mode='markers',
        marker=dict(color=RED, size=16),  # Bigger dots
        showlegend=False
    ))

# Update layout to customize appearance
fig.update_layout(
    title="<b>Engagement score declined in<br>customer service & operations",
    title_font=dict(size=26),
    title_y=.9,
    plot_bgcolor=BG_COLOR,
    paper_bgcolor=BG_COLOR,
    height=600,
    margin=dict(t=180, b=80),
    xaxis=dict(
        side="top",  # Move x-axis to the top
        tickfont=dict(size=18, family="Inter")  # Increase the font size of the x-axis ticks
    ),
    yaxis=dict(
        tickfont=dict(size=16, family="Inter")
    ),
    shapes=[
        dict(
            type="line",
            xref="paper",
            yref="paper",
            x0=-0.14, y0=1.5,  
            x1=0.022, y1=1.5,  
            line=dict(
                color=RED,  
                width=10  
            )
        )],
    font=dict(family="Inter")  # Set the global font to "Inter"
)

fig.add_annotation(
    dict(
        text="<b>2023",
        x=0.1,  # x position (0 means far left)
        y=0.72,  # y position (adjust as necessary)
        xref="paper",
        yref="paper",
        showarrow=False,  # No arrow
        font=dict(
            family="Inter",  # Font family
            size=22,  # Font size
            color=GREY  # Font color
        ),
        align="left"
    ),
)

fig.add_annotation(
    dict(
        text="<b>2024",
        x=0.6,  # x position (0 means far left)
        y=0.72,  # y position (adjust as necessary)
        xref="paper",
        yref="paper",
        showarrow=False,  # No arrow
        font=dict(
            family="Inter",  # Font family
            size=22,  # Font size
            color=RED  # Font color
        ),
        align="left"
    ),
)

fig.add_layout_image(
    dict(
        source=f"data:image/png;base64,{encoded_image}",
        xref="paper",
        yref="paper",
        x=.94,
        y=-0.18,
        xanchor="right",
        yanchor="bottom",
        sizex=0.22,
        sizey=0.22,
        layer="above"
    )
)

fig.add_annotation(
    dict(
        text="Source: Pearson Specter Litt",
        x=-0.14,  # x position (0 means far left)
        y=-0.175,  # y position (adjust as necessary)
        xref="paper",
        yref="paper",
        showarrow=False,  # No arrow
        font=dict(
            family="Inter",  # Font family
            size=14,  # Font size
            color=GREY  # Font color
        ),
        align="left"
    ),
)

fig.show()

Forget about the differences in the data used in my chart and that by The Economist and pay attention to the style. You’ll notice that they have the same style. This just shows how customizable Plotly is as a graphing library. You can customize any chat to your heart’s content.