diff --git a/DESCRIPTION b/DESCRIPTION
index 515c245..d66bbd1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -12,7 +12,6 @@ Imports:
   broom,
   dplyr,
   DSR,
-  feather,
   gapminder,
   ggplot2,
   hexbin,
diff --git a/import.Rmd b/import.Rmd
index c8201df..0d291f7 100644
--- a/import.Rmd
+++ b/import.Rmd
@@ -567,10 +567,20 @@ This makes csvs a little unreliable for caching interim results - you need to re
 1.  The feather package implements a fast binary file format that can
     be shared across programming languages:
     
-    ```{r}
+    ```{r, eval = FALSE}
     library(feather)
     write_feather(challenge, "challenge.feather")
     read_feather("challenge.feather")
+    #> # A tibble: 2,000 x 2
+    #>       x      y
+    #>   <dbl> <date>
+    #> 1   404   <NA>
+    #> 2  4172   <NA>
+    #> 3  3004   <NA>
+    #> 4   787   <NA>
+    #> 5    37   <NA>
+    #> 6  2332   <NA>
+    #> # ... with 1,994 more rows
     ```
 
 feather tends to be faster than rds and is usable outside of R. `rds` supports list-columns (which you'll learn about in [[Many models]]), which feather does not yet.
@@ -578,19 +588,24 @@ feather tends to be faster than rds and is usable outside of R. `rds` supports l
 ```{r, include = FALSE}
 file.remove("challenge-2.csv")
 file.remove("challenge.rds")
-file.remove("challenge.feather")
 ```
 
 ## Other types of data
 
-We have worked on a number of packages to make importing data into R as easy as possible. These packages are certainly not perfect, but they are the best place to start because they behave as similar as possible to readr. 
+To get other types of data into R, we recommend starting with the packages listed below. They're certainly not perfect, but they are a good place to start as they are fully fledged members of the tidyverse.
 
-Two packages helper
+For rectanuglar data:
 
-* haven reads files from other SPSS, Stata, and SAS files.
+* haven reads SPSS, Stata, and SAS files.
 
 * readxl reads excel files (both `.xls` and `.xlsx`).
 
-There are two common forms of hierarchical data: XML and json. We recommend using xml2 and jsonlite respectively. These packages are performant, safe, and (relatively) easy to use. To work with these effectively in R, you'll need to x
+* DBI, along with a database specific backend (e.g. RMySQL, RSQLite,
+  RPostgreSQL etc) allows you to run SQL queries against a database
+  and return a data frame.
 
-If your data lives in a database, you'll need to use the DBI package. DBI provides a common interface that works with many different types of database. R's support is particularly good for open source databases (e.g. RPostgres, RMySQL, RSQLite, MonetDBLite).
+For hierarchical data:
+
+* jsonlite (by Jeroen Ooms) reads json
+
+* xml2 reads XML.