DOC: expanding comparison with R section

doc/source/comparison_with_r.rst

@@ -31,6 +31,79 @@ For transfer of ``DataFrame`` objects from ``pandas`` to R, one option is to
 use HDF5 files, see :ref:`io.external_compatibility` for an
 example.
 
+
+Quick Reference
+---------------
+
+We'll start off with a quick reference guide pairing some common R
+operations using `dplyr
+<http://cran.r-project.org/web/packages/dplyr/index.html>`__ with
+pandas equivalents.
+
+
+Querying, Filtering, Sampling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+===========================================  ===========================================
+R                                            pandas
+===========================================  ===========================================
+``dim(df)``                                  ``df.shape``
+``head(df)``                                 ``df.head()``
+``slice(df, 1:10)``                          ``df.iloc[:9]``
+``filter(df, col1 == 1, col2 == 1)``         ``df.query('col1 == 1 & col2 == 1')``
+``df[df$col1 == 1 & df$col2 == 1,]``         ``df[(df.col1 == 1) & (df.col2 == 1)]``
+``select(df, col1, col2)``                   ``df[['col1', 'col2']]``
+``select(df, col1:col3)``                    ``df.loc[:, 'col1':'col3']``
+``select(df, -(col1:col3))``                 ``df.drop(cols_to_drop, axis=1)`` but see [#select_range]_
+``distinct(select(df, col1))``               ``df[['col1']].drop_duplicates()``
+``distinct(select(df, col1, col2))``         ``df[['col1', 'col2']].drop_duplicates()``
+``sample_n(df, 10)``                         ``df.sample(n=10)``
+``sample_frac(df, 0.01)``                    ``df.sample(frac=0.01)``
+===========================================  ===========================================
+
+.. [#select_range] R's shorthand for a subrange of columns
+                   (``select(df, col1:col3)``) can be approached
+                   cleanly in pandas, if you have the list of columns,
+                   for example ``df[cols[1:3]]`` or
+                   ``df.drop(cols[1:3])``, but doing this by column
+                   name is a bit messy.
+
+
+Sorting
+~~~~~~~
+
+===========================================  ===========================================
+R                                            pandas
+===========================================  ===========================================
+``arrange(df, col1, col2)``                  ``df.sort_values(['col1', 'col2'])``
+``arrange(df, desc(col1))``                  ``df.sort_values('col1', ascending=False)``
+===========================================  ===========================================
+
+Transforming
+~~~~~~~~~~~~
+
+===========================================  ===========================================
+R                                            pandas
+===========================================  ===========================================
+``select(df, col_one = col1)``               ``df.rename(columns={'col1': 'col_one'})['col_one']``
+``rename(df, col_one = col1)``               ``df.rename(columns={'col1': 'col_one'})``
+``mutate(df, c=a-b)``                        ``df.assign(c=df.a-df.b)``
+===========================================  ===========================================
+
+
+Grouping and Summarizing
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+==============================================  ===========================================
+R                                               pandas
+==============================================  ===========================================
+``summary(df)``                                 ``df.describe()``
+``gdf <- group_by(df, col1)``                   ``gdf = df.groupby('col1')``
+``summarise(gdf, avg=mean(col1, na.rm=TRUE))``  ``df.groupby('col1').agg({'col1': 'mean'})``
+``summarise(gdf, total=sum(col1))``             ``df.groupby('col1').sum()``
+==============================================  ===========================================
+
+
 Base R
 ------