add example

book/in_memory/pitfalls.qmd

@@ -1,12 +1,11 @@
 ---
-title: In-memory interoperability
+title: Pitfalls when using both Python and R
 engine: knitr
 ---
 
-## Pitfalls when mixing Python and R
 Python and R are very different programming languages. Here are some pitfalls you might encounter when mixing both languages.
 
-### Column major vs row major
+## Column major vs row major
 Matrices are stored contiguously in-memory, and are adressed by a single memory addresses, instead of multiple indices along the axis. A translation needs to happen between this single adress and the indices along the axes, and how that translation happens depens on how the matrix is represented in-memory.
 
 ![Different in-memory representations](images/inmemorymatrix.png){#fig-imm-matrix}
@@ -16,13 +15,22 @@ There is usually no issue when converting R matrices to Python matrices: reticul
 
 If you notice something amiss with your matrices, check whether you need to transpose them or change the row/column major attribute.
 
-### Indexing: 0-based or 1-based
+## Indexing: 0-based or 1-based
 Take care to remember that arrays and matrices in Python are indexed starting from 0 (as in, index 0 refers to the first element), while R uses 1-based indexing, where index 1 refers to the first element.
 
-### Dots in variable names
+## Dots in variable names
 In R it is very common to use dots in symbols and variable names. This is invalid in Python: dots are used for function calls.
+
 When using rpy2, these dots are usually translated to underscores `_`. If this does not happen automatically, you can specify mappings for these symbols.
 
-### Integers and floating point numbers
+## Integers and floating point numbers
 In R, unless you explicitly specify, any number is represented as a floating point number.
 Python can be more strict about using integers or floating point numbers.
+
+```{r int_example}
+float_ex <- 12
+int_ex <- 12L
+
+cat(is.integer(float_ex))
+cat(is.integer(int_ex))
+```