diff --git a/README.md b/README.md
index f689e2d..378fd56 100644
--- a/README.md
+++ b/README.md
@@ -17,12 +17,12 @@ under construction.
 
 # Requirements
 
-Install the Python requirements from requirements.txt:
+Install the Python requirements from `requirements.txt`:
 ```
 $ python -m pip install -r requirements.txt
 ```
 
-You will also need a working R installation with reticulate and irkernel installed.
+You will also need a working R installation with `reticulate` and `irkernel` installed.
 This command should do the trick:
 ```
 $ R -e 'install.packages(c("reticulate", "IRkernel")); IRkernel::installspec()'
@@ -35,8 +35,8 @@ $ R -e 'install.packages(c("reticulate", "IRkernel")); IRkernel::installspec()'
   [jupytext](https://github.com/mwouts/jupytext) to convert the notebook into
   the right format.
 - To build locally, run ``make``. The output tells you where to find the
-  built HTML.
-- Pages rendered at https://tskit.dev/tutorials
+  built `HTML` file(s).
+- Pages rendered at https://tskit.dev/tutorials.
 - Pages might take a while to be updated after a new tutorial is merged.
 
 If you have an idea for a tutorial, please open an issue to discuss.
diff --git a/tskitr.md b/tskitr.md
index 4e27f4c..cc878dc 100644
--- a/tskitr.md
+++ b/tskitr.md
@@ -19,13 +19,12 @@ kernelspec:
 
 # Tskit and R
 
-To interface with `tskit` in R, we can use the [reticulate](https://rstudio.github.io/reticulate/) R package, which lets you call Python functions within an R session. In this tutorial, we'll go through a couple of examples to show you how to get started. If you haven't done so already, you'll need to install `reticulate` in your R session via `install.packages("reticulate")`. 
+To interface with `tskit` in R, we can use the [reticulate](https://rstudio.github.io/reticulate) R package, which lets you call `tskit`'s Python API within an R session. In this tutorial, we'll go through a couple of examples to show you how to get started. You'll need to install `reticulate` in your R session via `install.packages("reticulate")` and at a minimum state the required Python packages via `reticulate::py_require(c("tskit", "msprime"))`. This setting will use `reticulate`'s isolated Python virtual environment, but you can also use an alternative Python environment as described at https://rstudio.github.io/reticulate.
 
 We'll begin by simulating a small tree sequence using `msprime`.
 
 ```{code-cell}
 msprime <- reticulate::import("msprime")
-
 ts <- msprime$sim_ancestry(80, sequence_length=1e4, recombination_rate=1e-4, random_seed=42)
 ts  # See "Jupyter notebook tips", below for how to render this nicely
 ```
@@ -33,15 +32,16 @@ ts  # See "Jupyter notebook tips", below for how to render this nicely
 ## Attributes and methods
 
 `reticulate` allows us to access a Python object's attributes via
-the `$` operator. For example, we can access (and assign to a variable) the number of
-samples in the tree sequence:
+the `$` operator. For example, we can access (and assign to a native R object)
+the number of samples in the tree sequence:
 
 ```{code-cell}
 n <- ts$num_samples
 n
+is(n)  # Show the type of the object
 ```
 
-The `$` operator can also be used to call methods, for example, the 
+The `$` operator can also be used to call methods, for example, the
 {meth}`~TreeSequence.simplify` method associated with the tree sequence.
 The method parameters are given as native R objects
 (but note that object IDs still use tskit's 0-based indexing system).
@@ -58,33 +58,44 @@ paste(
 ### IDs and indexes
 
 Note that if a bare digit is provided to one of these methods, it will be treated as a
-floating point number. This is useful to know when calling `tskit` methods that
-require integers (e.g. object IDs). For example, the following will not work:
+floating point number (numeric in R). This is useful to know when calling `tskit` methods that
+require integers (such as object IDs). For example, the following will not work:
 
 ```{code-cell}
 :tags: [raises-exception, remove-output]
-ts$node(0)  # Will raise an error
+ts$node(0)  # Will raise a TypeError
+is(0)
 ```
 
 In this case, to force the `0` to be passed as an integer, you can either coerce it
-using `as.integer` or simply prepend the letter `L`:
+using `as.integer` or simply append the letter `L`:
 
 ```{code-cell}
+is(as.integer(0))
 ts$node(as.integer(0))
 # or
+is(0L)
 ts$node(0L)
 ```
 
 Coercing in this way is only necessary when passing parameters to those underlying
-`tskit` methods that expect integers. It is not needed e.g. to index into numeric arrays.
+`tskit` methods that expect integers. It is not needed to index into numeric arrays.
 _However_, when using arrays, very careful attention must be paid to the fact that
 `tskit` IDs start at zero, whereas R indexes start at one:
 
 ```{code-cell}
-root_id <- ts$first()$root
-paste("Root time via tskit method:", ts$node(root_id)$time)
-# When indexing into tskit arrays in R, add 1 to the ID
-paste("Root time via array access:", ts$nodes_time[root_id + 1])
+root_id_int <- ts$first()$root
+is(root_id_int)
+root_id_num <- as.numeric(root_id_int)
+
+# Using integer ID will work
+paste("Root time via tskit method:", ts$node(root_id_int)$time)
+# Using numeric ID will not work
+# paste("Root time via tskit method:", ts$node(root_id_num)$time)
+
+# When indexing into tskit arrays in R, add 1 to the ID (integer or numeric)
+paste("Root time via array access:", ts$nodes_time[root_id_int + 1])
+paste("Root time via array access:", ts$nodes_time[root_id_num + 1])
 ```
 
 ## Analysis
@@ -98,7 +109,6 @@ for each of the tree sequence's sample nodes:
 
 ```{code-cell}
 ts_mut = msprime$sim_mutations(reduced_ts, rate=1e-4, random_seed=321)
-
 paste(ts_mut$num_mutations, "mutations, genetic diversity is", ts_mut$diversity())
 ```
 
@@ -109,6 +119,7 @@ the tree sequence as a matrix object in R.
 ```{code-cell}
 G = ts_mut$genotype_matrix()
 G
+is(G)
 ```
 
 We can then use R functions directly on the genotype matrix:
@@ -142,10 +153,9 @@ It also allows trees and tree sequences to be plotted inline:
 ts_mut$draw_svg(y_axis=TRUE, y_ticks=0:10)
 ```
 
-
 ## Interaction with R libraries
 
-R has a number of libraries to deal with genomic data and trees. Below we focus on the
+R has a number of libraries to deal with genomic data and trees. Below we demo the
 phylogenetic tree representation defined in the the popular
 [ape](http://ape-package.ird.fr) package, taking all the trees
 {meth}`exported in Nexus format<TreeSequence.write_nexus>`, or
@@ -165,14 +175,24 @@ plot(tree, direction="downward", srt=90, adj=0.5)  # or equivalently use trees[[
 ```
 
 Note that nodes are labelled with the prefix `n`, so that nodes `0`, `1`, `2`, ...
-become `n0`, `n1`, `n2` ... etc. This helps to avoid
+become `n0`, `n1`, `n2`, ... This helps to avoid
 confusion between the the zero-based counting system used natively
 by `tskit`, and the one-based counting system used in `R`.
 
 ## Further information
 
-Be sure to check out the [reticulate](https://rstudio.github.io/reticulate/)
+Be sure to check out the [reticulate](https://rstudio.github.io/reticulate)
 documentation, in particular on
 [Calling Python from R](https://rstudio.github.io/reticulate/articles/calling_python.html),
 which includes important information on how R data types are converted to their
-equivalent Python types. 
+equivalent Python types.
+
+## Advanced usage through the tskit's C API
+
+While the approach with `reticulate` is very powerful and will be useful for
+most analyses, it does have an overhead due to calling Python and conversion of objects.
+This overhead will be minimal for some use cases but could be significant for others.
+An alternative approach is to use the `tskit` C API from R,
+via the standard [R Extensions](https://cran.r-project.org/doc/manuals/R-exts.html) or
+via [Rcpp](https://www.rcpp.org),
+but these are advanced topics beyond the scope of this tutorial.