Horizon Chart Data Visualization

Data Visualization: Shaping Data for a Horizon Chart

Problem Analysis:

• Identify the key metrics or variables to display in the horizon chart.
• Ensure the data is structured in a way that allows for clear visualization of trends and patterns over time.

Expert Guidance:

• Transform the data into a long format with columns for time periods and values to be displayed.
• Consider using a dual-axis horizon chart to compare positive and negative values effectively.
• Normalize the data to a consistent scale for accurate comparison.

Detailed Explanation:

• Utilize tools like Power Query in Excel or Python's pandas library to reshape the data.
• Use a line chart as a basis, segmenting the data into bands based on positive and negative values.
• Apply color gradients to differentiate between segments for improved visualization.

Code-Based Solutions:

• Excel (Power Query):

let
    Source = // input data source,
    Transposed = Table.Transpose(Source),
    PromotedHeaders = Table.PromoteHeaders(Transposed, [PromoteAllScalars=true]),
    MeltedData = Table.TransformColumns(PromotedHeaders, 
        {"Column1", each Text.BeforeDelimiter(_, "-"), type text}
    ),
    RenamedColumns = Table.RenameColumns(MeltedData,{{"Column1", "Category"}, {"Column2", "Value"}})
in
    RenamedColumns

• Python (Pandas):

df_long = df.melt(id_vars='Date', var_name='Category', value_name='Value')

Promoting Best Practices:

• Regularly review and refine the visualization to ensure it effectively communicates insights.
• Seek feedback from stakeholders to enhance the clarity and impact of the horizon chart.
• Document the data transformation process for future reference and reproducibility.