Update README links

README.md

@@ -18,7 +18,8 @@ Not all are used in webinar
   * **Leave your data in that big, beautiful data frame.** [`ex01_leave-it-in-the-data-frame`](ex01_leave-it-in-the-data-frame.md) Show the evil of creating copies of certain rows of certain variables, using Magic Numbers and cryptic names, just to save some typing.
   * **Adding or modifying variables.** [`ex02_create-or-mutate-in-place`](ex02_create-or-mutate-in-place.md) `df$var <- ...` versus `dplyr::mutate()`. Recycling/safety, `df`'s as data mask, aesthetics.
   * **Are you SURE you need to iterate over rows?** [`ex03_row-wise-iteration-are-you-sure`](ex03_row-wise-iteration-are-you-sure.md) Don't fixate on most obvious generalization of your pilot example and risk overlooking a vectorized solution. Features a `paste()` example, then goes out with some glue glory.
-  * **Row-wise thinking vs. column-wise thinking.** [`ex04_attack-via-rows-or-columns`](ex04_attack-via-rows-or-columns.md) Data rectangling example. Both are possible, but I find building a tibble column-by-column is less aggravating than building rows, then row binding.
+  * **Working with non-vectorized functions.** [`ex04_map-example`](ex04_map-example.md) Small example using `purrr::map()` to apply `nrow()` to list of data frames.
+  * **Row-wise thinking vs. column-wise thinking.** [`ex05_attack-via-rows-or-columns`](ex05_attack-via-rows-or-columns.md) Data rectangling example. Both are possible, but I find building a tibble column-by-column is less aggravating than building rows, then row binding.
   * **Iterate over rows of a data frame.** [`iterate-over-rows`](iterate-over-rows.md) Empirical study of reshaping a data frame into this form: a list with one component per row. Revisiting a study originally done by Winston Chang. Run times for different number of [rows](row-benchmark.png) or [columns](col-benchmark.png).
   * **Split-apply-combine.** Nesting vs splitting.
     - Downside of `split()`: First-class grouping variable(s) --> character vector of names --> variable is a big drag. Integer-y numerics must be coerced back, factors must be recreated, with original levels. Transitting data through attributes is an anti-pattern.