diff --git a/docs/user-guide/plotting.qmd b/docs/user-guide/plotting.qmd
index 4ded728d7..fa7598206 100644
--- a/docs/user-guide/plotting.qmd
+++ b/docs/user-guide/plotting.qmd
@@ -135,12 +135,12 @@ The radial distance from the point to the observation point is the standard devi
 
 ## Directional data (e.g. wind or currents)
 
-Directional data can be plotted using the [`wind_rose`](`modelskill.plotting.wind_rose`). The function takes an array-like structure with speed and direction as columns (from one or two sources) and plots a wind rose:
+Directional data can be plotted using the [`wind_rose`](`modelskill.plotting.wind_rose`). The function takes an array-like structure with speed and direction as columns (from one or two sources) and plots a wind rose. With two sources, the first pair is labelled "Measurement" and the second "Model" (see `labels`):
 
 ```{python}
 import mikeio
 ds = mikeio.read("../data/wave_dir.dfs0")
-df = ds[[0,2,1,3]].to_dataframe()
+df = ds[[1,3,0,2]].to_dataframe()  # measured speed, direction, then model
 df.round(2).head()
 ```
 
diff --git a/src/modelskill/metrics.py b/src/modelskill/metrics.py
index 5d0652666..0a8bcb74d 100644
--- a/src/modelskill/metrics.py
+++ b/src/modelskill/metrics.py
@@ -311,7 +311,9 @@ def kge(obs: ArrayLike, model: ArrayLike) -> Any:
 def r2(obs: ArrayLike, model: ArrayLike) -> Any:
     r"""Coefficient of determination (R2)
 
-    Pronounced 'R-squared'; the proportion of the variation in the dependent variable that is predictable from the independent variable(s), i.e. the proportion of explained variance.
+    Same value as Nash-Sutcliffe efficiency and scikit-learn's `r2_score`.
+    Not squared Pearson correlation (`cc**2`): r2 penalises bias and amplitude
+    errors, and is negative when the model is worse than the observed mean.
 
     $$
     R^2 = 1 - \frac{\sum_{i=1}^n (model_i - obs_i)^2}
@@ -320,9 +322,10 @@ def r2(obs: ArrayLike, model: ArrayLike) -> Any:
 
     Range: $(-\infty, 1]$; Best: 1
 
-    Notes
-    -----
-    r2 = nash_sutcliffe_efficiency(nse)
+    See Also
+    --------
+    nash_sutcliffe_efficiency
+    cc
 
     Examples
     --------