# Mapping

Mappings determine how the data is translated into a plot. Positional mappings correspond to the x, y or z axes of the plot, whereas the keyword arguments correspond to plot attributes that can vary continuously or discretely, such as color or markersize.

Mapping variables are split according to the categorical attributes in it, and then converted to plot attributes using a default palette.

using AlgebraOfGraphics
mapping(:weight_mm => "weight (mm)", :height_mm => "height (mm)", marker = :gender)
Layer(identity, nothing, Any[:weight_mm => "weight (mm)", :height_mm => "height (mm)"], {:marker = :gender})

## Pair syntax

A convenience pair-based syntax can be used to transform variables on-the-fly and rename the respective column.

Let us assume the table df contains a column called bill_length_mm. We can apply an element-wise transformation and rename the column on the fly as follows.

data(df) * mapping(:bill_length_mm => (t -> t / 10) => "bill length (cm)")

A possible alternative, if df is a DataFrame, would be to store a renamed, modified column directly in df, which can be achieved in the following way:

df.var"bill length (cm)" = map(t -> t / 10, df.bill_length_mm)
data(df) * mapping("bill length (cm)") # strings are also accepted for column names

### Row-by-row versus whole-column operations

The pair syntax acts row by row, unlike, e.g., DataFrames.transform. This has several advantages.

• Simpler for the user in most cases.
• Less error prone especially
• with grouped data (should a column operation apply to each group or the whole dataset?)
• when several datasets are used

Naturally, this also incurs some downsides, as whole-column operations, such as z-score standardization, are not supported: they should be done by adding a new column to the underlying dataset beforehand.

### Functions of several arguments

In the case of functions of several arguments, such as isequal, the input variables must be passed as a Tuple.

accuracy = (:species, :predicted_species) => isequal => "accuracy"

### Partial pair syntax

The "triple-pair" syntax is not necessary, one can also only pass the column name, a column name => function pair, or a column name => new label pair.

## Helper functions

Some helper functions are provided, which can be used within the pair syntax to either rename and reorder unique values of a categorical column on the fly or to signal whether a numerical column should be treated as categorical.

The complete API of helper functions is available at Mapping helpers.

### Examples

# column train has two unique values, true and false
:train => renamer([true => "training", false => "testing"]) => "Dataset"
# column price has three unique values, "low", "medium", and "high"
:price => sorter(["low", "medium", "high"])
# column age is expressed in integers and we want to treat it as categorical
:age => nonnumeric
# column labels is expressed in strings and we do not want to treat it as categorical
:labels => verbatim