[Doc] Add integers to four digit year format example

doc/user_guide/encodings/index.rst

@@ -198,34 +198,42 @@ 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 ``quantitative`` , ``ordinal`` and ``temporal`` scale for an column
 that contains integers specifying a year:
 
 .. altair-plot::
 
-    pop = data.population.url
+    pop = data.population()
+    # Convert interger to string data type
+    pop.year = pop.year.astype(str)
 
-    base = alt.Chart(pop).mark_bar().encode(
+    base = alt.Chart(pop).mark_line().encode(
         alt.Y('mean(people):Q').title('total population')
     ).properties(
-        width=200,
+        width=150,
         height=200
     )
 
     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='year=ordinal'),
+        base.encode(x='year:T').properties(title='year=temporal')
     )
 
 Because quantitative values 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.
 
+To plot the year data as four digit format; i.e. without thousand separator,
+we recommend converting integer to string data type first, then storing the data as temporal,
+since directly convert integer to temporal data type would result in an error.
+
+
 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:
@@ -526,6 +534,18 @@ While the above examples show sorting of axes by specifying ``sort`` in the
 Here the y-axis is sorted reverse-alphabetically, while the color legend is
 sorted in the specified order, beginning with ``'Morris'``.
 
+Here is another example using :class:`EncodingSortField` class to sort color legend.
+By specifying ``field``, ``op`` and ``order``, the legend can be sorted based on chosen data field.
+
+.. altair-plot::
+
+    alt.Chart(barley).mark_rect().encode(
+    alt.X('mean(yield):Q').sort('ascending'),
+    alt.Y('site:N').sort('x'),
+    color=alt.Color('site',
+            sort=alt.EncodingSortField(field='yield', op='mean', order='ascending'))
+    )
+
 Datum and Value
 ~~~~~~~~~~~~~~~
 

doc/user_guide/times_and_dates.rst

@@ -52,7 +52,12 @@ example, we'll limit ourselves to the first two weeks of data:
 
 (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 to plot integers as four digit year format stored as temporal data, 
+please see the :ref:`type-axis-scale`).
+
+
 
 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.).