def plot_correlation(self, on, x_col=None, plot_type="jointplot", stat_func=pearsonr, show_stat_func=True, plot_kwargs={}, **kwargs):
"""Plot the correlation between two variables.
Parameters
----------
on : list or dict of functions or strings
See `cohort.load.as_dataframe`
x_col : str, optional
If `on` is a dict, this guarantees we have the expected ordering.
plot_type : str, optional
Specify "jointplot", "regplot", "boxplot", or "barplot".
stat_func : function, optional.
Specify which function to use for the statistical test.
show_stat_func : bool, optional
Whether or not to show the stat_func result in the plot itself.
plot_kwargs : dict, optional
kwargs to pass through to plotting functions.
"""
if plot_type not in ["boxplot", "barplot", "jointplot", "regplot"]:
raise ValueError("Invalid plot_type %s" % plot_type)
plot_cols, df = self.as_dataframe(on, return_cols=True, **kwargs)
if len(plot_cols) != 2:
raise ValueError("Must be comparing two columns, but there are %d columns" % len(plot_cols))
for plot_col in plot_cols:
df = filter_not_null(df, plot_col)
if x_col is None:
x_col = plot_cols[0]
y_col = plot_cols[1]
else:
if x_col == plot_cols[0]:
y_col = plot_cols[1]
else:
y_col = plot_cols[0]
series_x = df[x_col]
series_y = df[y_col]
coeff, p_value = stat_func(series_x, series_y)
if plot_type == "jointplot":
plot = sb.jointplot(data=df, x=x_col, y=y_col,
stat_func=stat_func if show_stat_func else None,
**plot_kwargs)
elif plot_type == "regplot":
plot = sb.regplot(data=df, x=x_col, y=y_col,
**plot_kwargs)
elif plot_type == "boxplot":
plot = stripboxplot(data=df, x=x_col, y=y_col, **plot_kwargs)
else:
plot = sb.barplot(data=df, x=x_col, y=y_col, **plot_kwargs)
return CorrelationResults(coeff=coeff, p_value=p_value, stat_func=stat_func,
series_x=series_x, series_y=series_y, plot=plot)
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