diff --git a/ci/deps/travis-36-doc.yaml b/ci/deps/travis-36-doc.yaml
index f79fcb11c179f..fb54c784d6fac 100644
--- a/ci/deps/travis-36-doc.yaml
+++ b/ci/deps/travis-36-doc.yaml
@@ -2,7 +2,6 @@ name: pandas
 channels:
   - defaults
   - conda-forge
-  - r
 dependencies:
   - beautifulsoup4
   - bottleneck
@@ -31,14 +30,11 @@ dependencies:
   - python-snappy
   - python=3.6*
   - pytz
-  - r
-  - rpy2
   - scipy
   - seaborn
   - sphinx
   - sqlalchemy
   - statsmodels
-  - tzlocal
   - xarray
   - xlrd
   - xlsxwriter
diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst
index 88634d7f75c63..d0b2601668069 100644
--- a/doc/source/r_interface.rst
+++ b/doc/source/r_interface.rst
@@ -33,10 +33,10 @@ See also the documentation of the `rpy2 <http://rpy2.bitbucket.org/>`__ project:
 
 In the remainder of this page, a few examples of explicit conversion is given. The pandas conversion of rpy2 needs first to be activated:
 
-.. ipython:: python
+.. code-block:: python
 
-   from rpy2.robjects import r, pandas2ri
-   pandas2ri.activate()
+    >>> from rpy2.robjects import pandas2ri  # doctest: +SKIP
+    >>> pandas2ri.activate()  # doctest: +SKIP
 
 Transferring R data sets into Python
 ------------------------------------
@@ -44,10 +44,17 @@ Transferring R data sets into Python
 Once the pandas conversion is activated (``pandas2ri.activate()``), many conversions
 of R to pandas objects will be done automatically. For example, to obtain the 'iris' dataset as a pandas DataFrame:
 
-.. ipython:: python
+.. code-block:: python
 
-    r.data('iris')
-    r['iris'].head()
+    >>> from rpy2.robjects import r  # doctest: +SKIP
+    >>> r.data('iris')  # doctest: +SKIP
+    >>> r['iris'].head()  # doctest: +SKIP
+        Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species
+    0           5.1          3.5           1.4          0.2  setosa
+    1           4.9          3.0           1.4          0.2  setosa
+    2           4.7          3.2           1.3          0.2  setosa
+    3           4.6          3.1           1.5          0.2  setosa
+    4           5.0          3.6           1.4          0.2  setosa
 
 If the pandas conversion was not activated, the above could also be accomplished
 by explicitly converting it with the ``pandas2ri.ri2py`` function
@@ -59,13 +66,19 @@ Converting DataFrames into R objects
 The ``pandas2ri.py2ri`` function support the reverse operation to convert
 DataFrames into the equivalent R object (that is, **data.frame**):
 
-.. ipython:: python
+.. code-block:: python
+
+   >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]},
+   ...                   index=["one", "two", "three"])  # doctest: +SKIP
+   >>> r_dataframe = pandas2ri.py2ri(df)  # doctest: +SKIP
+   >>> print(type(r_dataframe))  # doctest: +SKIP
+   <class 'rpy2.robjects.vectors.DataFrame'>
+   >>> print(r_dataframe)  # doctest: +SKIP
+         A B C
+   one   1 4 7
+   two   2 5 8
+   three 3 6 9
 
-   df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C':[7,8,9]},
-                     index=["one", "two", "three"])
-   r_dataframe = pandas2ri.py2ri(df)
-   print(type(r_dataframe))
-   print(r_dataframe)
 
 The DataFrame's index is stored as the ``rownames`` attribute of the
 data.frame instance.