FIX incorrect output in the example; minor rewording

README.md

@@ -12,7 +12,7 @@ language](https://www.r-project.org/), which streamlines dataframe and vector/ma
 transformations.
 
 
-## Example
+## the Essential Example
 
 Below we provide an example, which, we hope, illustrates the cases `plyr` might be useful in.
 
@@ -25,11 +25,11 @@ nt = namedtuple('nt', 'u,v')
 # add the leaf data in a pair of nested objects
 plyr.apply(
     lambda u, v: u + v,
-    [{'a': (1, 2, 3), 'z': 3.1415}, nt(1, 'abc')],
-    [{'a': (4, 6, 8), 'z': 2.7128}, nt(4, 'xyz')],
+    [{'a': (1, 2, 3), 'z': 3.1415}, nt([1, 'u'], 'abc')],
+    [{'a': (4, 6, 8), 'z': 2.7128}, nt([4, 'v'], 'xyz')],
     # _star=True,  # (default) call fn(d1, d2, **kwargs)
 )
-# output: [{'a': [5, 8, 11]}, {'b': 5, 'z': 'abcxyz'}]
+# output: [{'a': (5, 8, 11), 'z': 5.8543}, nt(u=[5, 'uv'], v='abcxyz')]
 
 # join strings in a pair of tuples
 plyr.apply(
@@ -47,7 +47,7 @@ these integrity checks may be turned off by specifying `_safe=False`. Please ref
 to the docs of `plyr.apply`.
 
 
-## Other examples
+## Other Examples
 
 Below we perform something fancy with `numpy`. Specifically, we stack outputs from some
 experiments (dicts of arrays), to get the standard deviation between the results.
@@ -79,5 +79,6 @@ shapes = plyr.apply(lambda x: x.shape, res)
 plyr.apply(np.std, res, axis=0)
 ```
 
-You may notice that `.apply` is very _straightforward_: it applies the specified function
-regardless of the leaf data type.
+You may notice that `.apply` is very _unsophisticated_: it applies the specified function
+to the leaf data regardless of its type, and every dict, list, or tuple is _always_ treated
+as a nested container.