add slides

slides/slides.qmd

@@ -52,7 +52,7 @@ While interoperability is currently possible developers continue to improve the
 1. Package-based interoperability
 2. Best practices
 
-# Package-based interoperability
+## Package-based interoperability
 or: the question of reimplementation.
 
 - Consider the pros:
@@ -68,47 +68,55 @@ or: the question of reimplementation.
   3. How will you keep it up to date?
   4. How will you ensure parity?
 
-# Package-based interoperability
+## Package-based interoperability
 
 Please learn both R & Python
 
-# Best practices
+## Best practices
 1. Work with the standards
 2. Work with matrices, arrays and dataframes
 3. Provide vignettes on interoperability
 
 # In-memory interoperability
 ![](../book/in_memory/images/imm_overview.png)
 
-# Overview
+## Overview
 
 1. Advantages & disadvantages
 2. Pitfalls when using Python & R
 2. Rpy2
 3. Reticulate
 
-# in-memory interoperability advantages
-- no need to write & read results
-- useful when you need a limited amount of functions in another language
+## in-memory interoperability advantages and disadvantages
+- advantages
+  - no need to write & read results
+  - useful when you need a limited amount of functions in another language
 
-# in-memory interoperability drawbacks
-- not always access to all classes
-- data duplication
-- you need to manage the environments
+- disadvantages
+  - not always access to all classes
+  - data duplication
+  - you need to manage the environments
 
-# Pitfalls when using Python and R
-**Column major vs row major matrices**
+---
+
+## Pitfalls when using Python and R
+
+---
+
+### Column major vs row major matrices
 In R: every dense matrix is stored as column major
 
 ![](../book/in_memory/images/inmemorymatrix.png)
 
-# Pitfalls when using Python and R
-**Indexing**
+---
+
+### Indexing
 
 ![](../book/in_memory/images/indexing.png)
 
-# Pitfalls when using Python and R
-**dots and underscores**
+---
+
+### Dots and underscores
 
 - mapping in rpy2
 
@@ -120,8 +128,9 @@ d = {'package.dependencies': 'package_dot_dependencies',
 tools = importr('tools', robject_translations = d)
 ```
 
-# Pitfalls when using Python and R
-**Integers**
+---
+
+### Integers
 
 ```{r} 
 library(reticulate)
@@ -139,7 +148,9 @@ bi$list(bi$range(0L, 5L))
 # [1] 0 1 2 3 4
 ```
 
-# Rpy2: basics
+---
+
+## Rpy2
 - Accessing R from Python
   - `rpy2.rinterface`, the low-level interface
   - `rpy2.robjects`, the high-level interface
@@ -155,7 +166,9 @@ rsum = robjects.r['sum']
 rsum(vector)
 ```
 
-# Rpy2: basics
+---
+
+### Rpy2: basics
 
 ```{python}
 #| echo: true
@@ -166,7 +179,9 @@ mtx = robjects.r.matrix(robjects.IntVector(range(10)), nrow=5)
 print(mtx)
 ```
 
-# Rpy2: numpy
+---
+
+### Rpy2: numpy
 
 ```{python}
 #| echo: true
@@ -182,7 +197,9 @@ with (default_converter + numpy2ri.converter).context():
     print(mtx)
 ```
 
-# Rpy2: pandas
+---
+
+### Rpy2: pandas
 ```{python}
 #| echo: true
 import pandas as pd
@@ -197,7 +214,9 @@ with (default_converter + pandas2ri.converter).context():
     print(pd_df_r)
 ```
 
-# Rpy2: sparse matrices
+---
+
+### Rpy2: sparse matrices
 
 ```{python}
 #| echo: true
@@ -212,7 +231,9 @@ with (default_converter + scipy2ri.converter).context():
     print(sp_r)
 ```
 
-# Rpy2: anndata
+---
+
+### Rpy2: anndata
 
 ```python
 import anndata as ad
@@ -227,7 +248,9 @@ with anndata2ri.converter.context():
     ad2 = anndata2ri.rpy2py(sce)
 ```
 
-# Rpy2: interactivity
+---
+
+### Rpy2: interactivity
 
 ```python
 %load_ext rpy2.ipython  # line magic that loads the rpy2 ipython extension.
@@ -240,11 +263,15 @@ with anndata2ri.converter.context():
                         # the rest of the cell will be run as R code
 ```
 
-# Reticulate
+---
+
+## Reticulate
 
 ![](images/reticulate_table.png)
 
-# Reticulate
+---
+
+### Reticulate: basics
 
 ```r
 library(reticulate)
@@ -261,7 +288,9 @@ cat(bi$list(bi$reversed(example)))
 # [1] 3 2 1
 ```
 
-# Reticulate numpy
+---
+
+### Reticulate numpy
 
 ```r
 np <- reticulate::import("numpy")
@@ -277,7 +306,9 @@ np$concatenate(tuple(a, b), axis=0L)
 # [3,]    5    6
 ```
 
-# Reticulate conversion
+---
+
+### Reticulate conversion
 
 ```r
 np <- reticulate::import("numpy", convert = FALSE)
@@ -309,7 +340,9 @@ r_to_py(result_r)
 #        [5., 6.]])
 ```
 
-# Reticulate scanpy
+---
+
+### Reticulate scanpy
 
 ```r
 library(anndata)
@@ -338,6 +371,8 @@ adata
 #     obsp: 'connectivities', 'distances'
 ```
 
+---
+
 # Disk-based interoperability
 
 Disk-based interoperability is a strategy for achieving interoperability between tools written in different programming languages by **storing intermediate results in standardized, language-agnostic file formats**.