diff --git a/episodes/01-intro-to-r.Rmd b/episodes/01-intro-to-r.Rmd
index 9593a135..cccdbb1d 100644
--- a/episodes/01-intro-to-r.Rmd
+++ b/episodes/01-intro-to-r.Rmd
@@ -148,8 +148,8 @@ Each of the modes o interactions has its advantages and drawbacks.
     
 |        | Console | R script|
 |--------|---------|---------|
-|**Pros**|Immediate results|Work lost once you close RStudio |
-|**Cons**|Complete record of your work |Messy if you just want to print things out|
+|**Pros**|Immediate results| Complete record of your work |
+|**Cons**| Work lost once you close RStudio  | Messy if you just want to print things out|
  
 
 
@@ -312,7 +312,7 @@ In the script, we will write:
 ```{r download-files}
 # Download the data
 download.file('https://bit.ly/geospatial_data', 
-              here('episodes', 'data','gapminder_data.csv'))
+              here('data','gapminder_data.csv'))
 
 ```
 
diff --git a/episodes/02-data-structures.Rmd b/episodes/02-data-structures.Rmd
index bee71477..6071354e 100644
--- a/episodes/02-data-structures.Rmd
+++ b/episodes/02-data-structures.Rmd
@@ -65,13 +65,16 @@ You can create a vector with a `c()` function.
 
 ```{r vectors}
 
-numeric_vector <- c(2, 6, 3) # vector of numbers - numeric data type.
+# vector of numbers - numeric data type.
+numeric_vector <- c(2, 6, 3) 
 numeric_vector
 
-character_vector <- c('banana', 'apple', 'orange') # vector of words - or strings of characters- character data type
+# vector of words - or strings of characters- character data type
+character_vector <- c('banana', 'apple', 'orange') 
 character_vector
 
-logical_vector <- c(TRUE, FALSE, TRUE) # vector of logical values (is something true or false?)- logical data type.
+# vector of logical values (is something true or false?)- logical data type.
+logical_vector <- c(TRUE, FALSE, TRUE) 
 logical_vector
 
 ```
@@ -121,7 +124,9 @@ First, let's try to calculate mean for the values in this vector
 ```{r remove-na1}
 mean(with_na) # mean() function cannot interpret the missing values
 
-mean(with_na, na.rm = T) # You can add the argument na.rm=TRUE to calculate the result while ignoring the missing values.
+# You can add the argument na.rm=TRUE to calculate the result while
+# ignoring the missing values.
+mean(with_na, na.rm = T) 
 ```
 
 However, sometimes, you would like to have the `NA` 
@@ -130,9 +135,11 @@ For this you need to identify which elements of the vector hold missing values
 with `is.na()` function. 
 
 ```{r remove-na2}
-is.na(with_na) #  This will produce a vector of logical values, stating if a statement 'This element of the vector is a missing value' is true or not
+is.na(with_na) # This will produce a vector of logical values, 
+# stating if a statement 'This element of the vector is a missing value'
+# is true or not
 
-!is.na(with_na) # # The ! operator means negation ,i.e. not is.na(with_na)
+!is.na(with_na) # The ! operator means negation, i.e. not is.na(with_na)
 
 ```
 
@@ -142,7 +149,8 @@ Sub-setting in `R` is done with square brackets`[ ]`.
 
 ```{r remove-na3}
 
-without_na <- with_na[ !is.na(with_na) ] # this notation will return only the elements that have TRUE on their respective positions
+without_na <- with_na[ !is.na(with_na) ] # this notation will return only
+# the elements that have TRUE on their respective positions
 
 without_na
 
@@ -170,7 +178,8 @@ known as levels.
 nordic_str <- c('Norway', 'Sweden', 'Norway', 'Denmark', 'Sweden')
 nordic_str # regular character vectors printed out
 
-nordic_cat <- factor(nordic_str) # factor() function converts a vector to factor data type
+# factor() function converts a vector to factor data type
+nordic_cat <- factor(nordic_str)
 nordic_cat # With factors, R prints out additional information - 'Levels'
 
 ```
@@ -201,8 +210,14 @@ displayed in a plot or which category is taken as a baseline in a statistical mo
 You can reorder the categories using `factor()` function. This can be useful, for instance, to select a reference category (first level) in a regression model or for ordering legend items in a plot, rather than using the default category systematically (i.e. based on alphabetical order).
 
 ```{r factor-reorder1}
-nordic_cat <- factor(nordic_cat, levels = c('Norway' , 'Denmark', 'Sweden'))  # now Norway should be the first category, Denmark second and Sweden third
-
+nordic_cat <- factor(
+  nordic_cat, levels = c(
+    'Norway', 
+    'Denmark', 
+    'Sweden'
+  )) 
+
+# now Norway will be the first category, Denmark second and Sweden third
 nordic_cat
 ```
 
@@ -212,7 +227,15 @@ There is more than one way to reorder factors. Later in the lesson,
 we will use `fct_relevel()` function from `forcats` package to do the reordering.
 
 ```{r factor-reorder2}
-# nordic_cat <- fct_relevel(nordic_cat, 'Norway' , 'Denmark', 'Sweden') # now Norway should be the first category, Denmark second and Sweden third
+library(forcats)
+
+nordic_cat <- fct_relevel(
+  nordic_cat, 
+  'Norway' , 
+  'Denmark', 
+  'Sweden'
+  ) # With this, Norway will be  first category, 
+    # Denmark second and Sweden third
 
 nordic_cat
 ```
@@ -239,8 +262,14 @@ outside of this set, it will become an unknown/missing value detonated by
 
 ```{r factor-missing-level}
 nordic_str
-nordic_cat2 <- factor(nordic_str, levels = c('Norway', 'Denmark'))
-nordic_cat2 # since we have not included Sweden in the list of factor levels, it has become NA.
+nordic_cat2 <- factor(
+  nordic_str, 
+  levels = c('Norway', 'Denmark')
+  )
+
+# because we did not include Sweden in the list of 
+# factor levels, it has become NA.
+nordic_cat2 
 ```
 ::::::::::::::::::::::::::::::::::::::::::::::::::::
 
diff --git a/episodes/03-explore-data.Rmd b/episodes/03-explore-data.Rmd
index 5fab2d56..489b0826 100644
--- a/episodes/03-explore-data.Rmd
+++ b/episodes/03-explore-data.Rmd
@@ -59,7 +59,7 @@ Because columns are vectors, each column must contain a **single type of data**
 For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.
 
 ![](fig/data-frame.svg)
-<br><font size="3">*Source*:[Data Carpentry R for Social Scientists ](https://datacarpentry.org/r-socialsci/02-starting-with-data/index.html#what-are-data-frames-and-tibbles)</font>
+<br><font size="3">*Source*: [Data Carpentry R for Social Scientists ](https://datacarpentry.org/r-socialsci/02-starting-with-data/index.html#what-are-data-frames-and-tibbles)</font>
 
 
 ## Reading data
@@ -68,7 +68,7 @@ For example, here is a figure depicting a data frame comprising a numeric, a cha
 We're gonna read in the `gapminder` data set with information about countries' size, GDP and average life expectancy in different years.
 
 ```{r reading-data}
-gapminder <- read_csv("data/gapminder_data.csv")
+gapminder <- read.csv("data/gapminder_data.csv")
 
 ```
 
@@ -92,9 +92,11 @@ There are multiple ways to explore a data set. Here are just a few examples:
 
 
 ```{r}
-head(gapminder) # see first 6  rows of the data set
 
-summary(gapminder) # gives basic statistical information about each column. Information format differes by data type.
+head(gapminder) # shows first 6  rows of the data set
+
+summary(gapminder) # basic statistical information about each column. 
+# Information format differes by data type.
 
 nrow(gapminder) # returns number of rows in a dataset
 
@@ -108,7 +110,9 @@ When you're analyzing a data set, you often need to access its specific columns.
 
 One handy way to access a column is using it's name and a dollar sign `$`: 
 ```{r subset-dollar-sign}
-country_vec <- gapminder$country  # Notation means: From dataset gapminder, give me column country. You can see that the column accessed in this way is just a vector of characters. 
+# This notation means: From dataset gapminder, give me column country. You can 
+# see that the column accessed in this way is just a vector of characters. 
+country_vec <- gapminder$country 
 
 head(country_vec)
 
@@ -157,8 +161,9 @@ We already know how to select only the needed columns. But now, we also want to
 In the `gapminder` data set, we want to see the results from outside of Europe for the 21st century. 
 ```{r}
 year_country_gdp_euro <- gapminder %>% 
-  filter(continent != "Europe" & year >= 2000) %>% # & operator (AND) - both conditions must be met
+  filter(continent != "Europe" & year >= 2000) %>% 
   select(year, country, gdpPercap)
+# '&' operator (AND) - both conditions must be met
 
 head(year_country_gdp_euro)
 ```
@@ -177,8 +182,9 @@ Write a single command (which can span multiple lines and includes pipes) that w
 
 ```{r ex5, class.source="bg-info"}
 year_country_gdp_eurasia <- gapminder %>% 
-  filter(continent == "Europe" | continent == "Asia") %>% # | operator (OR) - one of the conditions must be met
-  select(year, country, gdpPercap)
+  filter(continent == "Europe" | continent == "Asia") %>% 
+  select(year, country, gdpPercap) 
+# '|' operator (OR) - one of the conditions must be met
 
 nrow(year_country_gdp_eurasia)
 ``` 
@@ -191,7 +197,7 @@ So far, we have provided summary statistics on the whole dataset, selected colum
 ```{r dplyr-group}
 gapminder %>% # select the dataset
   group_by(continent) %>% # group by continent
-  summarize(avg_gdpPercap = mean(gdpPercap)) # summarize function creates statistics for the data set 
+  summarize(avg_gdpPercap = mean(gdpPercap)) # create basic stats
 
 ```
 
@@ -211,7 +217,8 @@ Calculate the average life expectancy per country. Which country has the longest
 gapminder %>%
    group_by(country) %>%
    summarize(avg_lifeExp=mean(lifeExp)) %>%
-   filter(avg_lifeExp == min(avg_lifeExp) | avg_lifeExp == max(avg_lifeExp))
+   filter(avg_lifeExp == min(avg_lifeExp) | 
+            avg_lifeExp == max(avg_lifeExp) )
 ```
 
 ### Multiple groups and summary variables
diff --git a/episodes/04-intro-to-visualisation.Rmd b/episodes/04-intro-to-visualisation.Rmd
index 60f9b941..188b74ac 100644
--- a/episodes/04-intro-to-visualisation.Rmd
+++ b/episodes/04-intro-to-visualisation.Rmd
@@ -48,24 +48,30 @@ After completing this episode, participants should be able to…
 
 # [Introduction to Visualisation](https://datacarpentry.org/r-intro-geospatial/07-plot-ggplot2/index.html)
 
-The package `ggplot2` is a powerful plotting system. We will start with an introduction of key
-features of `ggplot2`. In the following parts of this workshop, you will
-use this package to visualize geospatial data. `gg` stands for grammar
-of graphics, the idea that three components are needed to create a graph: 
+The package `ggplot2` is a powerful plotting system. We will start with an introduction of key features of `ggplot2`.  `gg` stands for grammar of graphics. The idea idea behind it is that the following three components are needed to create a graph: 
 
 - data,
 - aesthetics - a coordinate system on which we map the data
 (what is represented on x axis, what on y axis), and
 - geometries - visual representation of the data (points, bars, etc.)
 
-Fun part about `ggplot2` is that you can add layers to
-the plot to provide more information and to make it more beautiful.
+A fun part about `ggplot2` is that you can add layers to the plot to provide more information and to make it more beautiful.
 
-First, lets plot the distribution of life expectancy in the `gapminder` dataset:
+In the following parts of this workshop, you will use this package to visualize geospatial data. First, make sure that you have the following packages loaded.
+
+```{r load-pkgs, eval=FALSE}
+library(tidyverse)
+library(terra)
+```
+
+Now, lets plot the distribution of life expectancy in the `gapminder` dataset:
 
 ```{r ggplot}
-  ggplot(data = gapminder,  aes(x = lifeExp) ) + # aesthetics layer 
-  geom_histogram() # geometry layer
+
+ggplot(data = gapminder,  # data
+  aes(x = lifeExp) # aesthetics layer 
+  ) +  
+geom_histogram() # geometry layer
 
 ```
 
@@ -78,10 +84,9 @@ Let's create another plot, this time only on a subset of observations:
 
 ```{r ggplot-col}
 gapminder %>%  # we select a data set
-  filter(year == 2007 & 
-         continent == 'Americas') %>%  # and filter it to keep only one year and one continent
+  filter(year == 2007 & continent == 'Americas') %>%  # filter to keep one year and one continent
   ggplot(aes(x = country, y = gdpPercap)) +  # the x and y axes represent values of columns
-  geom_col()  # we select a column graph as a geometry
+    geom_col()  # we select a column graph as a geometry
 ```
 
 Now, you can iteratively improve how the plot looks like. For example,
@@ -92,7 +97,7 @@ gapminder %>%
   filter(year == 2007, 
          continent == 'Americas') %>% 
   ggplot(aes(x = country, y = gdpPercap)) + 
-  geom_col()+ 
+  geom_col() + 
   coord_flip()  # flip axes
 ```
 
@@ -123,11 +128,14 @@ gapminder %>%
   filter(year == 2007, 
          continent == 'Americas') %>% 
   mutate(country = fct_reorder(country, gdpPercap )) %>%
-  ggplot(aes(x = country, y = gdpPercap, fill = lifeExp  )) +  # fill argument for colouring surfaces, colour for points and lines
-  geom_col()+ 
+  ggplot(aes(
+    x = country, 
+    y = gdpPercap, 
+    fill = lifeExp # use 'fill' for surfaces; 'colour' for points and lines
+  )) +
+  geom_col() + 
   coord_flip()
 
-
 ```
 
 We can also adapt the colour scale. Common choice that is used for its
@@ -138,11 +146,11 @@ readability and colorblind-proofness are the palettes available in the
 gapminder %>%  
   filter(year == 2007, 
          continent == 'Americas') %>% 
-  mutate(country = fct_reorder(country, gdpPercap )) %>%
-  ggplot(aes(x = country, y = gdpPercap, fill = lifeExp   )) + 
-  geom_col()+ 
-  coord_flip()+
-  scale_fill_viridis_c()  # _c stands for continuous scale
+  mutate(country = fct_reorder(country, gdpPercap)) %>%
+  ggplot(aes(x = country, y = gdpPercap, fill = lifeExp)) + 
+    geom_col() + 
+    coord_flip() +
+    scale_fill_viridis_c()  # _c stands for continuous scale
 
 ```
 
@@ -154,16 +162,23 @@ p <-  # this time let's save the plot in an object
   gapminder %>%  
   filter(year == 2007 & 
          continent == 'Americas') %>% 
-  mutate(country = fct_reorder(country, gdpPercap ),
-         lifeExpCat = if_else(lifeExp >= mean(lifeExp), 'high', 'low')) %>%
+  mutate(country = fct_reorder(country, gdpPercap),
+         lifeExpCat = if_else(
+           lifeExp >= mean(lifeExp), 
+           'high', 
+           'low')) %>%
   ggplot(aes(x = country, y = gdpPercap, fill = lifeExpCat)) + 
-  geom_col()+ 
-  coord_flip()+
-  scale_fill_manual(values = c('light blue', 'orange'))  # customize the colours of the fill aesthetic
+  geom_col() + 
+  coord_flip() +
+  scale_fill_manual(values = c(
+    'light blue', 
+    'orange'
+    ) # customize the colors
+  )  
 
 ```
 
-Since we saved a plot as an object, nothing has been printed out. Just
+Since we saved a plot as an object `p`, nothing has been printed out. Just
 like with any other object in `R`, if you want to see it, you need to
 call it.
 
@@ -177,12 +192,12 @@ Now we can make use of the saved object and add things to it.
 Let's also give it a title and name the axes:
 
 ```{r ggplot-titles}
-p <- 
-  p +
+p <- p +
   ggtitle('GDP per capita in Americas', subtitle = 'Year 2007') +
   xlab('Country')+
   ylab('GDP per capita')
 
+# show plot
 p
 ```
 
@@ -193,9 +208,12 @@ p
 Once we are happy with our plot we can save it in a format of our
 choice. Remember to save it in the dedicated folder.
 
-```{r save-plot}
+```{r save-plot, eval=FALSE}
 ggsave(plot = p, 
-       filename = here('fig_output','plot_americas_2007.pdf'))  # By default, ggsave() saves the last displayed plot, but you can also explicitly name the plot you want to save
+       filename = here('fig_output','plot_americas_2007.pdf')
+      )  
+# By default, ggsave() saves the last displayed plot, but 
+# you can also explicitly name the plot you want to save
 
 ```
 
@@ -220,13 +238,17 @@ Another output of your work you want to save is a cleaned data set. In
 your analysis, you can then load directly that data set. Let's say we want to
 save the data only for Americas:
 
-```{r writing-data}
+```{r writing-data, eval=FALSE}
 gapminder_amr_2007 <- gapminder %>%
   filter(year == 2007 & continent == 'Americas') %>%
-  mutate(country_reordered = fct_reorder(country, gdpPercap ), 
-         lifeExpCat = if_else(lifeExp >= mean(lifeExp), 'high', 'low'))
-
-write.csv(gapminder_amr_2007, here('data_output', 'gapminder_americas_2007.csv'), row.names=FALSE)
+  mutate(country_reordered = fct_reorder(country, gdpPercap), 
+         lifeExpCat = if_else(lifeExp >= mean(lifeExp), 'high', 'low')
+        )
+
+write.csv(gapminder_amr_2007, 
+          here('data_output', 'gapminder_americas_2007.csv'), 
+          row.names=FALSE
+         )
 ```
 
 ::::::::::::::::::::::::::::::::::::: keypoints 
diff --git a/episodes/09-open-and-plot-vector-layers.Rmd b/episodes/09-open-and-plot-vector-layers.Rmd
index 3b8bcc40..22e97d18 100644
--- a/episodes/09-open-and-plot-vector-layers.Rmd
+++ b/episodes/09-open-and-plot-vector-layers.Rmd
@@ -141,7 +141,7 @@ Now, let's plot this shapefile. You are already familiar with the `ggplot2` pack
 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
+  coord_sf(datum = st_crs(28992))  # displays the axes in meters
 ```
 
 ::::::::::::::::::::::::::::::::::::: challenge 
diff --git a/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd b/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd
index ca18aa96..bf947df7 100644
--- a/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd
+++ b/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd
@@ -128,8 +128,8 @@ head(point_Delft)
 We can increase the number of rows with the n argument (e.g., `head(n = 10)` to show 10 rows) until we see at least three distinct values in the leisure column. Note that printing an `sf` object will also display the first 10 rows.
 
 ```{r}
-head(point_Delft, 10)  # you might be lucky to see three distinct values
-# point_Delft
+head(point_Delft, 10)  
+# you might be lucky to see three distinct values
 ```
 
 We have our answer (`sports_centre` is the third value), but in general this is not a good approach as the first rows might still have many `NA`s and three distinct values might still not be present in the first `n` rows of the data frame. To remove `NA`s, we can use the function `na.omit()` on the leisure column to remove `NA`s completely. Note that we use the `$` operator to examine the content of a single variable.
@@ -141,7 +141,8 @@ head(na.omit(point_Delft$leisure))  # this is better
 To show only unique values, we can use the `levels()` function on a factor to only see the first occurrence of each distinct value. Note `NA`s are dropped in this case and that we get the first three of the unique alphabetically ordered values.
 
 ```{r}
-head(levels(factor(point_Delft$leisure)), n = 3)   # this is even better
+head(levels(factor(point_Delft$leisure)), n = 3)   
+# this is even better
 ```
 
 3. To see a list of all attribute names, we can use the `names()` function.
@@ -186,7 +187,9 @@ Now we can plot only the cycleways.
 ```{r fig.cap="Map of cycleways in Delft."}
 ggplot(data = cycleway_Delft) +
   geom_sf() +
-  labs(title = "Slow mobility network in Delft", subtitle = "Cycleways") +
+  labs(title = "Slow mobility network in Delft", 
+       subtitle = "Cycleways"
+      ) +
   coord_sf(datum = st_crs(28992))
 ```
 
@@ -237,7 +240,9 @@ nrow(motorway_Delft)
 ```{r}
 ggplot(data = motorway_Delft) + 
   geom_sf(linewidth = 1.5) +
-  labs(title = "Fast mobility network", subtitle = "Motorways") + 
+  labs(title = "Fast mobility network", 
+       subtitle = "Motorways"
+       ) + 
   coord_sf(datum = st_crs(28992))
 ```
 
@@ -258,7 +263,9 @@ nrow(pedestrian_Delft)
 ```{r}
 ggplot() +
   geom_sf(data = pedestrian_Delft) +
-  labs(title = "Slow mobility network", subtitle = "Pedestrian") + 
+  labs(title = "Slow mobility network", 
+       subtitle = "Pedestrian"
+       ) + 
   coord_sf(datum = st_crs(28992))
 ```
 
@@ -350,7 +357,8 @@ ggplot(data = lines_Delft_selection) +
   geom_sf(aes(linewidth = highway)) +
   scale_linewidth_manual(values = line_width) +
   labs(title = "Mobility network of Delft",
-       subtitle = "Roads & Cycleways - Line width varies") + 
+       subtitle = "Roads & Cycleways - Line width varies"
+       ) + 
   coord_sf(datum = st_crs(28992))
 ```
 
@@ -371,13 +379,17 @@ p1 <- ggplot(data = lines_Delft_selection) +
   labs(title = "Mobility network of Delft", 
        subtitle = "Roads & Cycleways - Default Legend") + 
   coord_sf(datum = st_crs(28992))
+
+# show plot
 p1
 ```
 
 ```{r fig.cap="Map of the mobility network in Delft with large-font and border around the legend."}
 p2 <- p1 +
   theme(legend.text = element_text(size = 20), 
-        legend.box.background = element_rect(size = 1))
+        legend.box.background = element_rect(linewidth = 1))
+
+# show plot
 p2
 ```
 
@@ -399,16 +411,22 @@ levels(factor(lines_Delft$highway))
 ```
 
 ```{r}
-# First, create a data frame with only those roads where bicycles are allowed
+# First, create a data frame with only roads where bicycles 
+# are allowed
 lines_Delft_bicycle <- lines_Delft %>% 
   filter(highway == "cycleway")
 
-# Next, visualise using ggplot
+# Next, visualise it using ggplot
 ggplot(data = lines_Delft) +
   geom_sf() +
-  geom_sf(data = lines_Delft_bicycle, aes(color = highway), linewidth = 1) +
+  geom_sf(data = lines_Delft_bicycle, 
+          aes(color = highway), 
+          linewidth = 1
+          ) +
   scale_color_manual(values = "magenta") +
-  labs(title = "Mobility network in Delft", subtitle = "Roads dedicated to Bikes") +
+  labs(title = "Mobility network in Delft", 
+       subtitle = "Roads dedicated to Bikes"
+       ) +
   coord_sf(datum = st_crs(28992))
 ```
 
diff --git a/episodes/11-plot-multiple-shape-files.Rmd b/episodes/11-plot-multiple-shape-files.Rmd
index 06496fa0..7acb16bf 100644
--- a/episodes/11-plot-multiple-shape-files.Rmd
+++ b/episodes/11-plot-multiple-shape-files.Rmd
@@ -57,8 +57,14 @@ To begin, we will create a plot with the site boundary as the first layer. Then
 
 ```{r}
 ggplot() + 
-  geom_sf(data = boundary_Delft, fill = "lightgrey", color = "lightgrey") +
-  geom_sf(data = lines_Delft_selection, aes(color = highway), size = 1) +
+  geom_sf(data = boundary_Delft, 
+          fill = "lightgrey", 
+          color = "lightgrey"
+          ) +
+  geom_sf(data = lines_Delft_selection, 
+          aes(color = highway), 
+          size = 1
+          ) +
   geom_sf(data = point_Delft) +
   labs(title = "Mobility network of Delft") + 
   coord_sf(datum = st_crs(28992))
@@ -69,23 +75,49 @@ Next, let’s build a custom legend using the functions `scale_color_manual()` a
 ```{r}
 leisure_colors <- rainbow(15)
 point_Delft$leisure <- factor(point_Delft$leisure)
+
 ggplot() + 
-  geom_sf(data = boundary_Delft, fill = "lightgrey", color = "lightgrey") +
-  geom_sf(data = lines_Delft_selection, aes(color = highway), size = 1) + 
-  geom_sf(data = point_Delft, aes(fill = leisure), shape = 21) +
-  scale_color_manual(values = road_colors, name = "Road Type") + 
-  scale_fill_manual(values = leisure_colors, name = "Lesiure Location") + 
+  geom_sf(data = boundary_Delft, 
+          fill = "lightgrey", 
+          color = "lightgrey"
+          ) +
+  geom_sf(data = lines_Delft_selection, 
+          aes(color = highway), 
+          size = 1
+          ) + 
+  geom_sf(data = point_Delft, 
+          aes(fill = leisure), 
+          shape = 21) +
+  scale_color_manual(values = road_colors, 
+                     name = "Road Type"
+                     ) + 
+  scale_fill_manual(values = leisure_colors, 
+                    name = "Lesiure Location"
+                    ) + 
   labs(title = "Mobility network and leisure in Delft") + 
   coord_sf(datum = st_crs(28992))
 ```
 
 ```{r}
 ggplot() +
-  geom_sf(data = boundary_Delft, fill = "lightgrey", color = "lightgrey") +
-  geom_sf(data = lines_Delft_selection, aes(color = highway), size = 1) +
-  geom_sf(data = point_Delft, aes(fill = leisure), shape = 22) +
-  scale_color_manual(values = road_colors, name = "Line Type") +
-  scale_fill_manual(values = leisure_colors, name = "Leisure Location") +
+  geom_sf(data = boundary_Delft, 
+          fill = "lightgrey", 
+          color = "lightgrey"
+          ) +
+  geom_sf(data = lines_Delft_selection, 
+          aes(color = highway), 
+          size = 1
+          ) +
+  geom_sf(data = point_Delft, 
+          aes(fill = leisure), 
+          shape = 22
+          ) +
+  scale_color_manual(values = road_colors, 
+                     name = "Line Type"
+                     ) +
+  scale_fill_manual(values = leisure_colors, 
+                    name = "Leisure Location"
+                    ) +
   labs(title = "Mobility network and leisure in Delft") + 
   coord_sf(datum = st_crs(28992))
 ```
@@ -118,26 +150,56 @@ blue_orange <- c("cornflowerblue", "darkorange")
 
 ```{r}
 ggplot() + 
-  geom_sf(data = lines_Delft_selection, aes(color = highway)) + 
-  geom_sf(data = leisure_locations_selection, aes(fill = leisure), 
-          shape = 21) + 
-  scale_color_manual(name = "Line Type", values = road_colors,
-     guide = guide_legend(override.aes = list(linetype = "solid", shape = NA))) + 
-  scale_fill_manual(name = "Soil Type", values = blue_orange,
-     guide = guide_legend(override.aes = list(linetype = "blank", shape = 21, colour = NA))) + 
+  geom_sf(data = lines_Delft_selection, 
+          aes(color = highway)
+          ) + 
+  geom_sf(data = leisure_locations_selection, 
+          aes(fill = leisure), 
+          shape = 21
+          ) + 
+  scale_color_manual(name = "Line Type", 
+                     values = road_colors,
+                     guide = guide_legend(override.aes = list(
+                                         linetype = "solid", 
+                                         shape = NA
+                                         ))
+                     ) + 
+  scale_fill_manual(name = "Soil Type", 
+                    values = blue_orange,
+                    guide = guide_legend(override.aes = list(
+                                         linetype = "blank", 
+                                         shape = 21, 
+                                         colour = NA
+                                         ))
+                    ) + 
   labs(title = "Traffic and leisure") + 
   coord_sf(datum = st_crs(28992))
 ```
 
 ```{r}
 ggplot() + 
-  geom_sf(data = lines_Delft_selection, aes(color = highway), size = 1) + 
-  geom_sf(data = leisure_locations_selection, aes(fill = leisure, shape = leisure), size = 2) + 
-  scale_shape_manual(name = "Leisure Type", values = c(21, 22)) +
-  scale_color_manual(name = "Line Type", values = road_colors) + 
-  scale_fill_manual(name = "Leisure Type", values = rainbow(15),
-     guide = guide_legend(override.aes = list(linetype = "blank", shape = c(21, 22),
-     color = "black"))) + 
+  geom_sf(data = lines_Delft_selection, 
+          aes(color = highway), 
+          size = 1
+          ) + 
+  geom_sf(data = leisure_locations_selection, 
+          aes(fill = leisure, shape = leisure), 
+          size = 2
+          ) + 
+  scale_shape_manual(name = "Leisure Type", 
+                     values = c(21, 22)
+                     ) +
+  scale_color_manual(name = "Line Type", 
+                     values = road_colors
+                     ) + 
+  scale_fill_manual(name = "Leisure Type", 
+                    values = rainbow(15),
+                    guide = guide_legend(override.aes = list(
+                                         linetype = "blank", 
+                                         shape = c(21, 22),
+                                         color = "black"
+                                         ))
+                    ) + 
   labs(title = "Road network and leisure") + 
   coord_sf(datum = st_crs(28992))
 ```
diff --git a/episodes/12-handling-spatial-projection-and-crs.Rmd b/episodes/12-handling-spatial-projection-and-crs.Rmd
index 24811951..ff0d77b0 100644
--- a/episodes/12-handling-spatial-projection-and-crs.Rmd
+++ b/episodes/12-handling-spatial-projection-and-crs.Rmd
@@ -50,8 +50,13 @@ country_boundary_NL <- st_read("data/nl-boundary.shp")
 
 ```{r}
 ggplot() +
-  geom_sf(data = country_boundary_NL, color = "gray18", linewidth = 2) +
-  geom_sf(data = municipal_boundary_NL, color = "gray40") +
+  geom_sf(data = country_boundary_NL, 
+          color = "gray18", 
+          linewidth = 2
+          ) +
+  geom_sf(data = municipal_boundary_NL, 
+          color = "gray40"
+          ) +
   labs(title = "Map of Contiguous NL Municipal Boundaries") +
   coord_sf(datum = st_crs(28992))
 ```
@@ -76,9 +81,17 @@ boundary_Delft <- st_transform(boundary_Delft, 28992)
 
 ```{r}
 ggplot() +
-  geom_sf(data = country_boundary_NL, linewidth = 2, color = "gray18") +
-  geom_sf(data = municipal_boundary_NL, color = "gray40") +
-  geom_sf(data = boundary_Delft, color = "purple", fill = "purple") +
+  geom_sf(data = country_boundary_NL, 
+          linewidth = 2, 
+          color = "gray18"
+          ) +
+  geom_sf(data = municipal_boundary_NL, 
+          color = "gray40"
+          ) +
+  geom_sf(data = boundary_Delft, 
+          color = "purple", 
+          fill = "purple"
+          ) +
   labs(title = "Map of Contiguous NL Municipal Boundaries") +
   coord_sf(datum = st_crs(28992))
 ```
@@ -105,10 +118,23 @@ boundary_ZH <- municipal_boundary_NL %>%
 
 ```{r}
 ggplot() +
-    geom_sf(data = boundary_ZH, aes(color ="color"), show.legend = "line") +
-    scale_color_manual(name = "", labels = "Municipal Boundaries in South Holland", values = c("color" = "gray18")) +
-    geom_sf(data = boundary_Delft, aes(shape = "shape"), color = "purple", fill = "purple") +
-    scale_shape_manual(name = "", labels = "Municipality of Delft", values = c("shape" = 19)) +
+    geom_sf(data = boundary_ZH, 
+            aes(color ="color"), 
+            show.legend = "line"
+            ) +
+    scale_color_manual(name = "", 
+                       labels = "Municipal Boundaries in South Holland", 
+                       values = c("color" = "gray18")
+                       ) +
+    geom_sf(data = boundary_Delft, 
+            aes(shape = "shape"), 
+            color = "purple", 
+            fill = "purple"
+            ) +
+    scale_shape_manual(name = "", 
+                       labels = "Municipality of Delft", 
+                       values = c("shape" = 19)
+                       ) +
     labs(title = "Delft location") +
     theme(legend.background = element_rect(color = NA)) +
     coord_sf(datum = st_crs(28992))
@@ -126,7 +152,9 @@ To save a file, use the `st_write()` function from the `sf` package. Although `s
 
 ```{r, eval=FALSE}
 st_write(leisure_locations_selection,
-         "data/leisure_locations_selection.shp", driver = "ESRI Shapefile")
+         "data/leisure_locations_selection.shp", 
+         driver = "ESRI Shapefile"
+         )
 ```