diff --git a/README.md b/README.md
index 29c8d3289ac..9db569a52f4 100644
--- a/README.md
+++ b/README.md
@@ -107,7 +107,7 @@ take the average GEMPAK script for a weather map, you need to:
 
 One of the benefits hoped to achieve over GEMPAK is to make it easier to use these routines for
 any meteorological Python application; this means making it easy to pull out the LCL
-calculation and just use that, or re-use the Skew-T with your own data code. MetPy also prides
+calculation and just use that, or reuse the Skew-T with your own data code. MetPy also prides
 itself on being well-documented and well-tested, so that on-going maintenance is easily
 manageable.
 
diff --git a/examples/calculations/Vorticity.py b/examples/calculations/Vorticity.py
index 1cd256a34e8..d99025d2cc4 100644
--- a/examples/calculations/Vorticity.py
+++ b/examples/calculations/Vorticity.py
@@ -20,13 +20,13 @@
 ds = example_data()
 
 # Calculate the vertical vorticity of the flow
-vor = mpcalc.vorticity(ds.uwind, ds.vwind)
+vort = mpcalc.vorticity(ds.uwind, ds.vwind)
 
 # start figure and set axis
 fig, ax = plt.subplots(figsize=(5, 5))
 
 # scale vorticity by 1e5 for plotting purposes
-cf = ax.contourf(ds.lon, ds.lat, vor * 1e5, range(-80, 81, 1), cmap=plt.cm.PuOr_r)
+cf = ax.contourf(ds.lon, ds.lat, vort * 1e5, range(-80, 81, 1), cmap=plt.cm.PuOr_r)
 plt.colorbar(cf, pad=0, aspect=50)
 ax.barbs(ds.lon.values, ds.lat.values, ds.uwind, ds.vwind, color='black', length=5, alpha=0.5)
 ax.set(xlim=(260, 270), ylim=(30, 40))
diff --git a/examples/gridding/Natural_Neighbor_Verification.py b/examples/gridding/Natural_Neighbor_Verification.py
index 24d8fff332b..ae314665708 100644
--- a/examples/gridding/Natural_Neighbor_Verification.py
+++ b/examples/gridding/Natural_Neighbor_Verification.py
@@ -162,11 +162,11 @@ def draw_circle(ax, x, y, r, m, label):
 # spatial data structure that we use here simply to show areal ratios.
 # Notice that the two natural neighbor triangle circumcenters are also vertices
 # in the Voronoi plot (green dots), and the observations are in the polygons (blue dots).
-vor = Voronoi(list(zip(xp, yp)))
+vort = Voronoi(list(zip(xp, yp)))
 
 fig, ax = plt.subplots(1, 1, figsize=(15, 10))
 ax.ishold = lambda: True  # Work-around for Matplotlib 3.0.0 incompatibility
-voronoi_plot_2d(vor, ax=ax)
+voronoi_plot_2d(vort, ax=ax)
 
 nn_ind = np.array([0, 5, 7, 8])
 z_0 = zp[nn_ind]
diff --git a/examples/plots/Station_Plot_with_Layout.py b/examples/plots/Station_Plot_with_Layout.py
index e98fd701727..af884491703 100644
--- a/examples/plots/Station_Plot_with_Layout.py
+++ b/examples/plots/Station_Plot_with_Layout.py
@@ -15,7 +15,7 @@
 
 The `StationPlotLayout` class is used to standardize the plotting various parameters
 (i.e. temperature), keeping track of the location, formatting, and even the units for use in
-the station plot. This makes it easy (if using standardized names) to re-use a given layout
+the station plot. This makes it easy (if using standardized names) to reuse a given layout
 of a station plot.
 """
 import cartopy.crs as ccrs