When working with data visualizations, dealing with missing data is a common challenge. In Vega-Lite, we can handle this efficiently using the impute transform. Let's dive into how you can tackle missing values, either through encoding field definitions or a transform array.

What Is Impute in Vega-Lite?

The impute transform helps fill in missing values in your dataset. This is done by grouping data and determining the missing values of a key field within each group. You can impute these values either by using a constant value or by applying a method such as calculating the mean among the group.

How to Impute Data Using Encoding Field Definition?

Basic Example

Let's say you have a visualization defined like this:

{
  "mark": "line",
  "encoding": {
    "x": {
      "field": "a",
      "type": "quantitative",
      "impute": {
        "value": 0
      }
    },
    "y": {
      "field": "b",
      "type": "quantitative"
    }
  }
}

Here, any missing values in the x field will be replaced with 0. Simple, right?

Grouping and Key Fields

In more complex scenarios, you might want to group your data and impute values based on those groups. For example, imagine you want to impute missing y values ("b") based on groups identified by the x field ("a") and a color field ("c"):

{
  "mark": "line",
  "encoding": {
    "x": {
      "field": "a",
      "type": "quantitative",
      "impute": {
        "value": 0
      }
    },
    "y": {
      "field": "b",
      "type": "quantitative"
    },
    "color": {
      "field": "c",
      "type": "ordinal"
    }
  }
}

In this example, the impute transform will fill in any missing y values for each unique combination of a and c.

Using Statistical Methods

You can also use methods like mean to impute missing data:

{
  "mark": "line",
  "encoding": {
    "x": {
      "field": "a",
      "type": "quantitative",
      "impute": {
        "method": "mean"
      }
    },
    "y": {
      "field": "b",
      "type": "quantitative"
    }
  }
}

This example will fill in missing values with the mean of the available data points.

How to Impute Data Using a Transform Array?

Basic Transform Example

Instead of including imputation within an encoding, you can use a transform array:

{
  "transform": [
    {
      "impute": "b",
      "key": "a",
      "keyvals": {"start": 1, "stop": 10, "step": 1},
      "groupby": ["c"],
      "frame": [-2, 2],
      "method": "mean"
    }
  ],
  "mark": "line",
  "encoding": {
    "x": {"field": "a", "type": "quantitative"},
    "y": {"field": "b", "type": "quantitative"},
    "color": {"field": "c", "type": "ordinal"}
  }
}

Here, the missing values in b are imputed by taking the mean, within a window of 2 preceding and 2 following values grouped by c.

Custom Key Values

If you need more control over which values are considered for imputation, you can specify keyvals:

{
  "transform": [
    {
      "impute": "b",
      "key": "a",
      "keyvals": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
      "groupby": ["c"],
      "method": "value",
      "value": 0
    }
  ],
  "mark": "line",
  "encoding": {
    "x": {"field": "a", "type": "quantitative"},
    "y": {"field": "b", "type": "quantitative"},
    "color": {"field": "c", "type": "ordinal"}
  }
}

Using Sequences

The keyvals property can also be a sequence for more dynamic scenarios:

{
  "transform": [
    {
      "impute": "b",
      "key": "a",
      "keyvals": {"start": 1, "stop": 10, "step": 1},
      "groupby": ["c"],
      "method": "value",
      "value": 0
    }
  ],
  "mark": "line",
  "encoding": {
    "x": {"field": "a", "type": "quantitative"},
    "y": {"field": "b", "type": "quantitative"},
    "color": {"field": "c", "type": "ordinal"}
  }
}

FAQ

What does the method property do in impute?

The method property defines how to calculate the imputed values. You can use statistical methods like "mean", "max", or "min", or you can specify a constant value using the value property.

Can I use impute for non-numeric data?

While imputation is most commonly used for numeric data, you can technically use it for categorical data by grouping and imputing values using appropriate methods or constants.

Do I always need to specify keyvals?

Not necessarily. If there are grouping fields present, keyvals is optional as the impute transform will use all unique values of the key field. If no grouping fields are present, you must specify keyvals to tell Vega-Lite which values to consider for imputation.

Feel free to experiment with these techniques to handle missing data effectively in your visualizations!