diff --git a/doc/user_guide/encodings/index.rst b/doc/user_guide/encodings/index.rst
index 4de8616fc..87fa7c3e7 100644
--- a/doc/user_guide/encodings/index.rst
+++ b/doc/user_guide/encodings/index.rst
@@ -170,7 +170,7 @@ Effect of Data Type on Color Scales
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 As an example of this, here we will represent the same data three different ways,
 with the color encoded as a *quantitative*, *ordinal*, and *nominal* type,
-using three vertically-concatenated charts (see :ref:`vconcat-chart`):
+using three horizontally-concatenated charts (see :ref:`hconcat-chart`):
 
 .. altair-plot::
 
@@ -178,11 +178,11 @@ using three vertically-concatenated charts (see :ref:`vconcat-chart`):
        x='Horsepower:Q',
        y='Miles_per_Gallon:Q',
    ).properties(
-       width=150,
-       height=150
+       width=140,
+       height=140
    )
 
-   alt.vconcat(
+   alt.hconcat(
       base.encode(color='Cylinders:Q').properties(title='quantitative'),
       base.encode(color='Cylinders:O').properties(title='ordinal'),
       base.encode(color='Cylinders:N').properties(title='nominal'),
@@ -198,35 +198,49 @@ Effect of Data Type on Axis Scales
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 Similarly, for x and y axis encodings, the type used for the data will affect
 the scales used and the characteristics of the mark. For example, here is the
-difference between a ``quantitative`` and ``ordinal`` scale for an column
+difference between a ``ordinal``, ``quantitative``, and ``temporal`` scale for an column
 that contains integers specifying a year:
 
 .. altair-plot::
 
-    pop = data.population.url
+    pop = data.population()
 
     base = alt.Chart(pop).mark_bar().encode(
-        alt.Y('mean(people):Q').title('total population')
+        alt.Y('mean(people):Q').title('Total population')
     ).properties(
-        width=200,
-        height=200
+        width=140,
+        height=140
     )
 
     alt.hconcat(
-        base.encode(x='year:Q').properties(title='year=quantitative'),
-        base.encode(x='year:O').properties(title='year=ordinal')
+        base.encode(x='year:O').properties(title='ordinal'),
+        base.encode(x='year:Q').properties(title='quantitative'),
+        base.encode(x='year:T').properties(title='temporal')
     )
 
-Because quantitative values do not have an inherent width, the bars do not
+Because values on quantitative and temporal scales do not have an inherent width, the bars do not
 fill the entire space between the values.
-This view also makes clear the missing year of data that was not immediately
-apparent when we treated the years as categories.
+These scales clearly show the missing year of data that was not immediately
+apparent when we treated the years as ordinal data,
+but the axis formatting is undesirable in both cases.
+
+To plot four digit integers as years with proper axis formatting,
+i.e. without thousands separator,
+we recommend converting the integers to strings first,
+and the specifying a temporal data type in Altair.
+While it is also possible to change the axis format with ``.axis(format='i')``,
+it is preferred to specify the appropriate data type to Altair.
+
+.. altair-plot::
+
+    pop['year'] = pop['year'].astype(str)
+
+    base.mark_bar().encode(x='year:T').properties(title='temporal')
 
 This kind of behavior is sometimes surprising to new users, but it emphasizes
 the importance of thinking carefully about your data types when visualizing
 data: a visual encoding that is suitable for categorical data may not be
-suitable for quantitative data, and vice versa.
-
+suitable for quantitative data or temporal data, and vice versa.
 
 .. _shorthand-description:
 
diff --git a/doc/user_guide/times_and_dates.rst b/doc/user_guide/times_and_dates.rst
index 8f62b061c..066e4d032 100644
--- a/doc/user_guide/times_and_dates.rst
+++ b/doc/user_guide/times_and_dates.rst
@@ -50,9 +50,12 @@ example, we'll limit ourselves to the first two weeks of data:
         y='temp:Q'
     )
 
-(notice that for date/time values we use the ``T`` to indicate a temporal
+Notice that for date/time values we use the ``T`` to indicate a temporal
 encoding: while this is optional for pandas datetime input, it is good practice
-to specify a type explicitly; see :ref:`encoding-data-types` for more discussion).
+to specify a type explicitly; see :ref:`encoding-data-types` for more discussion.
+If you want Altair to plot four digit integers as years,
+you need to cast them as strings before changing the data type to temporal,
+please see the :ref:`type-axis-scale` for details.
 
 For date-time inputs like these, it can sometimes be useful to extract particular
 time units (e.g. hours of the day, dates of the month, etc.).