Faceting

def facet_null():
    """
    No faceting (single panel).
    
    This is the default faceting behavior - creates a single plot panel.
    Useful for explicitly specifying no faceting when needed.
    """

def facet_wrap(facets, nrow=None, ncol=None, scales='fixed', shrink=True,
               labeller='label_value', as_table=True, drop=True, dir='h'):
    """
    Wrap 1D ribbon of panels into 2D layout.
    
    Creates subplots arranged in a rectangular grid based on levels of 
    one or more variables.
    
    Parameters:
    - facets: str or list, variables to facet by (e.g., 'var' or ['var1', 'var2'])
    - nrow: int, number of rows in grid
    - ncol: int, number of columns in grid  
    - scales: str, whether scales are shared across panels
              ('fixed', 'free', 'free_x', 'free_y')
    - shrink: bool, whether to shrink scales to fit data in each panel
    - labeller: str or function, how to format facet labels
    - as_table: bool, whether to fill panels like a table (top-to-bottom)
    - drop: bool, whether to drop unused factor levels
    - dir: str, direction to fill panels ('h' for horizontal, 'v' for vertical)
    """

def facet_grid(rows=None, cols=None, scales='fixed', shrink=True,
               labeller='label_value', as_table=True, drop=True):
    """
    Create 2D grid of panels based on row and column variables.
    
    Creates a matrix of subplots where rows represent levels of one variable
    and columns represent levels of another variable.
    
    Parameters:
    - rows: str or list, variable(s) for panel rows
    - cols: str or list, variable(s) for panel columns
    - scales: str, scale sharing ('fixed', 'free', 'free_x', 'free_y')
    - shrink: bool, whether to shrink scales to fit data
    - labeller: str or function, label formatting
    - as_table: bool, whether to arrange like table (TRUE: top-to-bottom)
    - drop: bool, whether to drop unused factor levels
    """

def labeller(**kwargs):
    """
    Create custom labelling functions for facets.
    
    Parameters:
    - **kwargs: variable-specific labellers or general labelling options
    
    Usage:
    - labeller(variable=label_both) for specific variable
    - labeller(.default=label_both) for all variables
    """

def as_labeller(x, default=None):
    """
    Convert various objects to labeller functions.
    
    Parameters:
    - x: dict, function, or other object to convert
    - default: default labeller if conversion fails
    
    Usage:
    - as_labeller({'A': 'Group A', 'B': 'Group B'})
    - as_labeller(lambda x: f'Value: {x}')
    """

def label_value(x):
    """
    Default labeller that shows variable values only.
    
    Displays just the factor level (e.g., 'A', 'B', 'C').
    """

def label_both(x):
    """
    Labeller that shows both variable names and values.
    
    Displays variable name and value (e.g., 'group: A', 'group: B').
    """

def label_context(x):
    """
    Context-aware labelling.
    
    Chooses appropriate labelling based on the faceting context.
    """

# Facet by single variable
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category')

# Facet by multiple variables
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap(['category', 'group'])

# Control layout dimensions
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', ncol=3)

# Rows and columns for different variables
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_grid(rows='treatment', cols='gender')

# Facet by rows only (creates vertical strip)
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_grid(rows='category')

# Facet by columns only (creates horizontal strip)
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_grid(cols='group')

# Free scales for each panel
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', scales='free')

# Free x-axis only
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', scales='free_x')

# Free y-axis only  
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', scales='free_y')

# Fixed scales (default)
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', scales='fixed')

# Custom label dictionary
label_dict = {'A': 'Treatment A', 'B': 'Treatment B', 'C': 'Control'}

ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('treatment', labeller=as_labeller(label_dict))

# Show both variable name and value
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', labeller=label_both)

# Custom labeller function
def custom_labeller(x):
    return f'Group {x} Data'

ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('group', labeller=as_labeller(custom_labeller))

# Multiple variables with custom layout
ggplot(data, aes(x='value', fill='category')) + \
    geom_histogram(bins=20) + \
    facet_wrap(['region', 'year'], ncol=4, scales='free_y') + \
    theme(strip_text=element_text(size=8))

# Grid with different aspects
ggplot(data, aes(x='date', y='sales')) + \
    geom_line() + \
    facet_grid(rows='product', cols='quarter', scales='free') + \
    theme(axis_text_x=element_text(angle=45))

# Time series by group
ggplot(time_data, aes(x='date', y='value', color='metric')) + \
    geom_line() + \
    facet_wrap('country', scales='free_y', ncol=3) + \
    theme_minimal() + \
    theme(legend_position='bottom')

# Faceted plot with smoothing
ggplot(data, aes(x='x', y='y', color='treatment')) + \
    geom_point(alpha=0.6) + \
    geom_smooth(method='lm', se=False) + \
    facet_wrap('site', scales='free') + \
    scale_color_brewer(type='qual') + \
    theme_bw()

# Box plots across multiple grouping variables
ggplot(data, aes(x='treatment', y='response', fill='treatment')) + \
    geom_boxplot() + \
    facet_grid(rows='timepoint', cols='gender') + \
    scale_fill_manual(values=['lightblue', 'lightcoral']) + \
    theme(legend_position='none')

# Drop unused factor levels (default)
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', drop=True)

# Keep unused factor levels (creates empty panels)
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', drop=False)

# Fill panels horizontally (default)
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', nrow=2, dir='h')

# Fill panels vertically
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', nrow=2, dir='v')

# Table-style layout (top-to-bottom, left-to-right)
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', as_table=True)

# Non-table layout (bottom-to-top, left-to-right)  
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('category', as_table=False)

# Density plots by group
ggplot(data, aes(x='value', fill='category')) + \
    geom_density(alpha=0.7) + \
    facet_wrap('region') + \
    scale_fill_brewer(type='qual') + \
    theme_minimal()

# Regression by groups
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    geom_smooth(method='lm', se=True) + \
    facet_grid(rows='treatment', cols='timepoint') + \
    theme_bw()

# Create faceting variable on-the-fly
ggplot(data, aes(x='x', y='y')) + \
    geom_point() + \
    facet_wrap('value > median(value)', labeller=as_labeller({
        'True': 'Above Median', 'False': 'Below Median'
    }))

# Different geoms highlight different aspects in each panel
ggplot(data, aes(x='x', y='y')) + \
    geom_point(data=data.query('category == "A"')) + \
    geom_line(data=data.query('category == "B"')) + \
    facet_wrap('category', scales='free')