diff --git a/episodes/09-open-and-plot-vector-layers.Rmd b/episodes/09-open-and-plot-vector-layers.Rmd
index 0c385bd3..86b0144f 100644
--- a/episodes/09-open-and-plot-vector-layers.Rmd
+++ b/episodes/09-open-and-plot-vector-layers.Rmd
@@ -6,13 +6,15 @@ exercises: 5
 
 :::::::::::::::::::::::::::::::::::::: questions 
 
-- How can I read, examine and visualize point, line and polygon vector data in R?
+- How can you read, examine and visualize point, line and polygon vector data in R?
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- Know the difference between point, line, and polygon vector data.
+After completing this episode, participants should be able to…
+
+- Differentiate between point, line, and polygon vector data.
 - Load vector data into R.
 - Access the attributes of a vector object in R.
 
@@ -21,11 +23,11 @@ exercises: 5
 :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
 
 Make sure that the `sf` package and its dependencies are installed before the 
-workshop. The installation can take quite some time, so allocate enough extra
-time before the workshop for solving installation problems. We recommend one
-or two installation 'walk-in' hours on a day before the workshop and 15-30 
+workshop. The installation can be lengthy, so allocate enough extra time 
+before the workshop for solving installation problems. We recommend one
+or two installation 'walk-in' hours on a day before the workshop. Also, 15-30 
 minutes at the beginning of the first workshop day should be enough to tackle 
-installation issues.
+last-minute installation issues.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
@@ -38,7 +40,7 @@ If you have not installed the `sf` package yet, run `install.packages("sf")` fir
 First we need to load the packages we will use in this lesson. We will use the package `tidyverse` with which you are already familiar from the previous lesson. In addition, we need to load the [`sf`](https://r-spatial.github.io/sf/) package for working with spatial vector data. 
 
 ```{r lib, message=FALSE}
-library(tidyverse)  # tools for wrangling, reshaping and visualizing data
+library(tidyverse)  # wrangle, reshape and visualize data
 library(sf)         # work with spatial vector data
 ```
 
@@ -46,7 +48,7 @@ library(sf)         # work with spatial vector data
 
 # The `sf` package
 
-`sf` stands for Simple Features which is a standard defined by the Open Geospatial Consortium for storing and accessing geospatial vector data. PostGIS uses the same standard; so those of you who used PostGIS, might find some resemblances with the functions used by the `sf` package. 
+`sf` stands for Simple Features which is a standard defined by the Open Geospatial Consortium for storing and accessing geospatial vector data. PostGIS, for instance, uses the same standard, so PostGIS users might recognise the functions used by the `sf` package. 
 
 :::
 
@@ -54,8 +56,8 @@ library(sf)         # work with spatial vector data
 
 Let's start by opening a shapefile. Shapefiles are a common file format to store spatial vector data used in GIS software. We will read a shapefile with the administrative boundary of Delft with the function `st_read()` from the `sf` package. 
 
-```{r results='hide'}
-boundary_Delft <- st_read("data/delft-boundary.shp")
+```{r}
+boundary_Delft <- st_read("data/delft-boundary.shp", quiet = TRUE)
 ```
 
 ::: callout
@@ -71,13 +73,15 @@ Note that all functions from the `sf` package start with the standard prefix `st
 
 ## Spatial Metadata
 
-The `st_read()` function gave us a message with a summary of metadata about the file that was read in. To examine the metadata in more detail, we can use other, more specialised, functions from the `sf` package. The `st_geometry_type()` function, for instance, gives us information about the geometry type, which in this case is `POLYGON`.
+By default (with `quiet = FALSE`), the `st_read()` function provides a message with a summary of metadata about the file that was read in. To examine the metadata in more detail, we can use other, more specialised, functions from the `sf` package.  
+
+The `st_geometry_type()` function, for instance, gives us information about the geometry type, which in this case is `POLYGON`.
 
 ```{r}
 st_geometry_type(boundary_Delft)
 ```
 
-The `st_crs()` function returns the coordinate reference system (CRS) used by the shapefile, which in this case is `WGS84` and has the unique reference code `EPSG: 4326`. 
+The `st_crs()` function returns the coordinate reference system (CRS) used by the shapefile, which in this case is ``r st_crs(boundary_Delft)$Name`` and has the unique reference code ``r paste0("EPSG: ", st_crs(boundary_Delft)$epsg)``. 
 
 ```{r}
 st_crs(boundary_Delft)
@@ -91,7 +95,7 @@ As the output of `st_crs()` can be long, you can use `$Name` and `$epsg` after t
 
 :::
 
-The `st_bbox()` function shows the extent of the layer. As `WGS84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees.
+The `st_bbox()` function shows the extent of the layer. As `WGS 84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees.
 
 ```{r}
 st_bbox(boundary_Delft)
@@ -101,13 +105,15 @@ We need a **projected CRS**, which in the case of the Netherlands is typically t
 
 ```{r}
 boundary_Delft <- st_transform(boundary_Delft, 28992)
-st_crs(boundary_Delft)
+st_crs(boundary_Delft)$Name
+st_crs(boundary_Delft)$epsg
 ```
 
 Notice that the bounding box is measured in meters after the transformation.
 
 ```{r}
 st_bbox(boundary_Delft)
+st_crs(boundary_Delft)$units_gdal
 ```
 
 We confirm the transformation by examining the reprojected shapefile.
@@ -118,7 +124,7 @@ boundary_Delft
 
 ::: callout
 
-More about CRS in [Handling Spatial Projection & CRS]().
+Read more about Coordinate Reference Systems [here](https://datacarpentry.org/organization-geospatial/03-crs.html). We will also practice transformation between CRS in [Handling Spatial Projection & CRS](../episodes/12-handling-spatial-projection-and-crs.Rmd).
 
 :::
 
@@ -126,22 +132,20 @@ More about CRS in [Handling Spatial Projection & CRS]().
 
 ## Plot a vector layer
 
-Now, let's plot this shapefile. You are already familiar with the `ggplot2` package from [Introduction to Visualisation](). `ggplot2` has special `geom_` functions for spatial data. We will use the `geom_sf()` function for `sf` data.
+Now, let's plot this shapefile. You are already familiar with the `ggplot2` package from [Introduction to Visualisation](../episodes/04-intro-to-visualisation.Rmd). `ggplot2` has special `geom_` functions for spatial data. We will use the `geom_sf()` function for `sf` data.
 
 ```{r}
-
 ggplot(data = boundary_Delft) +
   geom_sf(size = 3, color = "black", fill = "cyan1") +
   labs(title = "Delft Administrative Boundary") +
   coord_sf(datum = st_crs(28992))  # this is needed to display the axes in meters
-
 ```
 
 ::::::::::::::::::::::::::::::::::::: challenge 
 
 ### Challenge 1: Import line and point vector layers
 
-Read in `delft-streets.shp` and `delft-leisure.shp` and assign them to `lines_Delft` and `point_Delft` respectively. Answer the following questions:
+Read in `delft-streets.shp` and `delft-leisure.shp` and assign them to `lines_Delft` and `points_Delft` respectively. Answer the following questions:
 
 1. What type of R spatial object is created when you import each layer?
 2. What is the CRS and extent for each object?
@@ -152,28 +156,28 @@ Read in `delft-streets.shp` and `delft-leisure.shp` and assign them to `lines_De
 
 ```{r results='hide'}
 lines_Delft <- st_read("data/delft-streets.shp")
-point_Delft <- st_read("data/delft-leisure.shp")
+points_Delft <- st_read("data/delft-leisure.shp")
 ```
 
-We can check the type of data with the `class()` function from base R. Both `lines_Delft` and `point_Delft` are objects of class `"sf"`, which extends the `"data.frame"` class. 
+We can check the type of data with the `class()` function from base R. Both `lines_Delft` and `points_Delft` are objects of class `"sf"`, which extends the `"data.frame"` class. 
 
 ```{r}
 class(lines_Delft)
-class(point_Delft)
+class(points_Delft)
 ```
 
-`lines_Delft` and `point_Delft` are in the correct CRS.
+`lines_Delft` and `points_Delft` are in the correct CRS.
 
 ```{r}
 st_crs(lines_Delft)$epsg
-st_crs(point_Delft)$epsg
+st_crs(points_Delft)$epsg
 ```
 
-When looking at the bounding boxes with the `st_bbox()` function, we see the spatial extent of the two objects in a projected CRS using meters as units. `lines_Delft()` and `point_Delft` have similar extents.
+When looking at the bounding boxes with the `st_bbox()` function, we see the spatial extent of the two objects in a projected CRS using meters as units. `lines_Delft()` and `points_Delft` have similar extents.
 
 ```{r}
 st_bbox(lines_Delft)
-st_bbox(point_Delft)
+st_bbox(points_Delft)
 ```
 
 :::::::::::::::::::::::::::::::::
@@ -186,6 +190,6 @@ st_bbox(point_Delft)
 
 - Metadata for vector layers include geometry type, CRS, and extent.
 - Load spatial objects into R with the `st_read()` function.
-- Spatial objects can be plotted directly with `ggplot` using the `geom_sf()` function. No need to convert to a data frame.
+- Spatial objects can be plotted directly with `ggplot2` using the `geom_sf()` function. No need to convert to a data frame.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::