diff --git a/README.md b/README.md
index b8b99ed..04b742b 100644
--- a/README.md
+++ b/README.md
@@ -34,22 +34,26 @@ getExampleData("DensePatchA")
 LASfile = system.file("extdata", "DensePatchA.laz", package="spanner")
 las = readTLSLAS(LASfile, select = "xyzc", "-filter_with_voxel 0.01")
 # Don't forget to make sure the las object has a projection
-# projection(las) <- sp::CRS("+init=epsg:26912")
+# projection(las) = sp::CRS("+init=epsg:26912")
 
 # pre-process the example lidar dataset by classifying the ground points
 # using lidR::csf(), normalizing it, and removing outlier points 
 # using lidR::ivf()
-las <- classify_ground(las, csf(sloop_smooth = FALSE, 
+las = classify_ground(las, csf(sloop_smooth = FALSE, 
                                 class_threshold = 0.5,
                                 cloth_resolution = 0.5, rigidness = 1L, 
                                 iterations = 500L, time_step = 0.65))
-las <- normalize_height(las, tin())
-las <- classify_noise(las, ivf(0.25, 3))
-las <- filter_poi(las, Classification != LASNOISE)
+las = normalize_height(las, tin())
+las = classify_noise(las, ivf(0.25, 3))
+las = filter_poi(las, Classification != LASNOISE)
 
 # plot the non-ground points, colored by height
 plot(filter_poi(las, Classification!=2), color="Z", trim=30)
 
+# perform a deep inspection of the las object. If you see any 
+# red text, you may have issues!
+las_check(las)
+
 # find individual tree locations and attribute data
 myTreeLocs = get_raster_eigen_treelocs(las = las, res = 0.05, 
                                        pt_spacing = 0.0254, 
@@ -78,5 +82,5 @@ myTreeGraph = segment_graph(las = las, tree.locations = myTreeLocs, k = 50,
                              output_location = getwd())
 
 # plot it in 3d colored by treeID
-plot(myTreeGraph, color="treeID")
+plot(myTreeGraph, color = "treeID")
 ```