From bb7925bff080dcd112cf9547cf13eed61436d5c4 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Mon, 11 Mar 2024 17:51:30 +0100 Subject: [PATCH 01/24] Fix phrasing and markdown formatting --- episodes/08-intro-to-geospatial-concepts.Rmd | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/episodes/08-intro-to-geospatial-concepts.Rmd b/episodes/08-intro-to-geospatial-concepts.Rmd index a5ded189..f15c368f 100644 --- a/episodes/08-intro-to-geospatial-concepts.Rmd +++ b/episodes/08-intro-to-geospatial-concepts.Rmd @@ -8,13 +8,15 @@ author: 'Ana Petrović' ::: questions - How do I describe the location of a geographic feature on the surface of the earth? -- What is coordinate reference system (CRS) and how do I describe different types of CRS? +- What is a coordinate reference system (CRS) and how do I describe different types of CRS? - How do I decide on what CRS to use? ::: ::: objectives +After completing this episode, participants should be able to… + - Identify the CRS that is best fit for a specific research question. ::: @@ -45,7 +47,7 @@ The black lines in the figure above show the equator and the prime meridian runn knitr::include_graphics("https://datacarpentry.org/organization-geospatial/fig/orange-peel-earth.jpg") ``` -Many different map projections are in use for different purposes. Generally, they can be categorised into the following groups: cylindrical, conic, and azimuthal (see @fig-projections). +Many different map projections are in use for different purposes. Generally, they can be categorised into the following groups: cylindrical, conic, and azimuthal. ```{r fig.cap="Cylindrical, conic, and azimuthal map projections. Source: Knippers (2009).", echo=FALSE} knitr::include_graphics("https://kartoweb.itc.nl/geometrics/Bitmaps/Intro%201.9a.gif") @@ -157,6 +159,7 @@ Correct answer: b. EPSG:28992 ::: callout + # Useful resources - Campbell, J., Shin, M. E. (2011). Essentials of Geographic Information Systems. Textbooks. 2. (Accessed 22-01-2024) From 887edeb32961e2d507002d727db651a6a545ce37 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Mon, 11 Mar 2024 17:56:44 +0100 Subject: [PATCH 02/24] Sharpen explanations in second vector episode --- episodes/09-open-and-plot-vector-layers.Rmd | 38 +++++++++++++-------- 1 file changed, 24 insertions(+), 14 deletions(-) diff --git a/episodes/09-open-and-plot-vector-layers.Rmd b/episodes/09-open-and-plot-vector-layers.Rmd index ed571e25..70b7701b 100644 --- a/episodes/09-open-and-plot-vector-layers.Rmd +++ b/episodes/09-open-and-plot-vector-layers.Rmd @@ -29,7 +29,6 @@ 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 last-minute installation issues. - :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: ::: prereq @@ -47,9 +46,9 @@ library(sf) # work with spatial vector data ::: callout -# The `sf` package +# 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. +`sf` stands for Simple Features which is a standard defined by the Open Geospatial Consortium for storing and accessing geospatial vector data. Read more about simple features and its implementation in R [here](https://r-spatial.github.io/sf/articles/sf1.html). ::: @@ -74,9 +73,13 @@ Note that all functions from the `sf` package start with the standard prefix `st ## Spatial Metadata -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. +By default (with `quiet = FALSE`), the `st_read()` function provides a message with a summary of metadata about the file that was read in. -The `st_geometry_type()` function, for instance, gives us information about the geometry type, which in this case is `POLYGON`. +```{r} +st_read("data/delft-boundary.shp") +``` + +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) @@ -92,27 +95,34 @@ st_crs(boundary_Delft) # Examining the output of `st_crs()` -As the output of `st_crs()` can be long, you can use `$Name` and `$epsg` after the `crs()` call to extract the projection name and EPSG code respectively. +As the output of `st_crs()` can be long, you can use `$Name` and `$epsg` after the `crs()` call to extract the projection name and EPSG code respectively. + +```{r} +st_crs(boundary_Delft)$Name +st_crs(boundary_Delft)$epsg +``` + +The `$` operator is used to extract a specific part of an object. We used it in a [previous episode]() to subset a data frame by column name. In this case, it is used to extract named elements stored in a `crs` object. For more information, see [the documentation of the `st_crs` function](https://r-spatial.github.io/sf/reference/st_crs.html#details). ::: -The `st_bbox()` function shows the extent of the layer. As `WGS 84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees. +The `st_bbox()` function shows the extent of the layer. ```{r} st_bbox(boundary_Delft) ``` -We need a **projected CRS**, which in the case of the Netherlands is typically the Amersfort / RD New projection. To reproject our shapefile, we will use the `st_transform()` function. For the `crs` argument we can use the EPSG code of the CRS we want to use, which is `28992` for the `Amersfort / RD New` projection. +As `WGS 84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees. We need a **projected CRS**, which in the case of the Netherlands is the `Amersfoort / RD New` projection. To reproject our shapefile, we will use the `st_transform()` function. For the `crs` argument we can use the EPSG code of the CRS we want to use, which is `28992` for the `Amersfort / RD New` projection. To check the EPSG code of any CRS, we can check this website: https://epsg.io/ ```{r} -boundary_Delft <- st_transform(boundary_Delft, 28992) +boundary_Delft <- st_transform(boundary_Delft, crs = 28992) st_crs(boundary_Delft)$Name st_crs(boundary_Delft)$epsg ``` -Notice that the bounding box is measured in meters after the transformation. +Notice that the bounding box is measured in meters after the transformation. The `$units_gdal` named element confirms that the new CRS uses metric units. ```{r} st_bbox(boundary_Delft) @@ -135,7 +145,7 @@ Read more about Coordinate Reference Systems [here](https://datacarpentry.org/or ## Plot a vector layer -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. +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. We use `coord_sf()` to ensure that the coordinates shown on the two axes are displayed in meters. ```{r} ggplot(data = boundary_Delft) + @@ -169,7 +179,7 @@ st_geometry_type(lines_Delft)[1] st_geometry_type(points_Delft)[2] ``` -`lines_Delft` and `points_Delft` are in the correct CRS. +Both `lines_Delft` and `points_Delft` are in `EPSG:28992`. ```{r} st_crs(lines_Delft)$epsg @@ -191,8 +201,8 @@ st_bbox(points_Delft) ::::::::::::::::::::::::::::::::::::: keypoints -- Metadata for vector layers include geometry type, CRS, and extent. -- Load spatial objects into R with the `st_read()` function. +- Metadata for vector layers include geometry type, CRS, and extent and can be examined with the `sf` functions `st_geometry_type()`, `st_crs()`, and `st_bbox()`, respectively. +- Load spatial objects into R with the `sf` function `st_read()`. - Spatial objects can be plotted directly with `ggplot2` using the `geom_sf()` function. No need to convert to a data frame. :::::::::::::::::::::::::::::::::::::::::::::::: From 72dc82694cc8a94b885e15a50b3ece9483a832de Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Mon, 11 Mar 2024 17:57:15 +0100 Subject: [PATCH 03/24] Fix challenge phrasing in second vector episode --- episodes/09-open-and-plot-vector-layers.Rmd | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/episodes/09-open-and-plot-vector-layers.Rmd b/episodes/09-open-and-plot-vector-layers.Rmd index 70b7701b..816a61e4 100644 --- a/episodes/09-open-and-plot-vector-layers.Rmd +++ b/episodes/09-open-and-plot-vector-layers.Rmd @@ -160,10 +160,9 @@ ggplot(data = boundary_Delft) + 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? -3. Do the files contain points, lines, or polygons? -4. How many features are in each file? +1. What is the CRS and extent for each object? +2. Do the files contain points, lines, or polygons? +3. How many features are in each file? :::::::::::::::::::::::: solution From eccb89e6e81b37c8152cc8870aaa2e655745361e Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Mon, 11 Mar 2024 17:59:41 +0100 Subject: [PATCH 04/24] Sharpen third vector lesson The changes include improved phrasing, challenge removal, transformation of one of the demos into a challenge. --- ...re-and-plot-by-vector-layer-attributes.Rmd | 206 +++++------------- 1 file changed, 56 insertions(+), 150 deletions(-) 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 bf947df7..25ea8e97 100644 --- a/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd +++ b/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd @@ -29,7 +29,7 @@ After completing this episode, participants should be able to… - Subset vector objects using specific attribute values. -- Plot a vector feature, colored by unique attribute values. +- Plot a vector feature, coloured by unique attribute values. :::::::::::::::::::::::::::::::::::::::::::::::: @@ -47,7 +47,7 @@ This means that we can examine and manipulate them as data frames. For instance, ncol(lines_Delft) ``` -In the case of `point_Delft` those columns are `"osm_id"`, `"highway"` and `"geometry"`. We can check the names of the columns with the base R function `names()`. +In the case of `point_Delft` those columns are `"osm_id"`, `"highway"` and `"geometry"`. We can check the names of the columns with the function `names()`. ```{r} names(lines_Delft) @@ -65,21 +65,19 @@ attribute table. We can also preview the content of the object by looking at the first 6 rows with the `head()` function, which in the case of an `sf` object is similar to examining the object directly. ```{r} -head (lines_Delft) +head(lines_Delft) ``` ## Explore values within one attribute -Using the `$` operator, we can examine the content of a single field of our lines feature. - -We can see the contents of the `highway` field of our lines feature: +Using the `$` operator, we can examine the content of a single field of our lines object. Let's have a look at the `highway` field, a categorical variable stored in the `lines_Delft` object as `character`. To avoid displaying all `r length(lines_Delft$highway)` values of `highway`, we will preview it with the `head()` function: ```{r} head(lines_Delft$highway, 10) ``` -To see only unique values within the `highway` field, we can use the `unique()` function. This function extracts all possible values of a character variable. +The first rows returned by the `head()` function do not necessarily contain all unique values within the `highway` field. To see all unique values, we can use the `unique()` function. This function extracts all possible values of a character variable. For the `highway` field, this returns all types of roads stored in `lines_Delft`. ```{r} unique(lines_Delft$highway) @@ -90,7 +88,7 @@ unique(lines_Delft$highway) R is also able to handle categorical variables called factors. With factors, we can use the `levels()` function to show unique values. To examine unique values of the `highway` variable this way, we have to first transform it into a factor with the `factor()` function: ```{r} -levels(factor(lines_Delft$highway)) +factor(lines_Delft$highway) %>% levels() ``` Note that this way the values are shown by default in alphabetical order and `NA`s are not displayed, whereas using `unique()` returns unique values in the order of their occurrence in the data frame and it also shows `NA` values. @@ -103,15 +101,15 @@ Note that this way the values are shown by default in alphabetical order and `NA Explore the attributes associated with the `point_Delft` spatial object. -1. How many attributes does it have? +1. How many fields does it have? 2. What types of leisure points do the points represent? Give three examples. -3. Which of the following is NOT an attribute of the `point_Delft` data object? +3. Which of the following is NOT a field of the `point_Delft` object? - A) location B) leisure C) osm_id + A) `location` B) `leisure` C) `osm_id` :::::::::::::::::::::::: solution -1. To find the number of attributes, we use the `ncol()` function: +1. To find the number of fields, we use the `ncol()` function: ```{r} ncol(point_Delft) @@ -125,32 +123,37 @@ Using the `head()` function which displays 6 rows by default, we only see two va 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. +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 +head(point_Delft, 10) ``` 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. ```{r} -head(na.omit(point_Delft$leisure)) # this is better +# this is better +na.omit(point_Delft$leisure) %>% head() ``` 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$leisure)), n = 3) # this is even better +factor(point_Delft$leisure) %>% + levels() %>% + head(n = 3) ``` -3. To see a list of all attribute names, we can use the `names()` function. +3. To see a list of all attribute names and answer the last wuestion, we can use the `names()` function. ```{r} names(point_Delft) ``` +A) `location` is not a field of the `point_Delft` object. + ::::::::::::::::::::::::::::::::: :::::::::::::::::::::::::::::::::::::::::::::::: @@ -158,10 +161,10 @@ names(point_Delft) ## Subset features -We can use the `filter()` function to select a subset of features from a spatial object, just like with data frames. Let's select only cycleways from our street data. +We can use the `filter()` function to select a subset of features from a spatial object, just like with data frames. Let's select only `cycleways` from our street data. ```{r} -cycleway_Delft <- lines_Delft %>% +cycleway_Delft <- lines_Delft %>% filter(highway == "cycleway") ``` @@ -172,7 +175,7 @@ nrow(lines_Delft) nrow(cycleway_Delft) ``` -This can be useful, for instance, to calculate the total length of cycleways. +This can be useful, for instance, to calculate the total length of cycleways. For that, we first need to calculate the length of each segment with `st_length()` ```{r} cycleway_Delft <- cycleway_Delft %>% @@ -188,21 +191,19 @@ Now we can plot only the cycleways. ggplot(data = cycleway_Delft) + geom_sf() + labs(title = "Slow mobility network in Delft", - subtitle = "Cycleways" - ) + + subtitle = "Cycleways") + coord_sf(datum = st_crs(28992)) ``` ::: challenge -Challenge: Now with motorways (and pedestrian streets) +Challenge: Now with motorways 1. Create a new object that only contains the motorways in Delft. 2. How many features does the new object have? 3. What is the total length of motorways? 4. Plot the motorways. -5. Extra: follow the same steps with pedestrian streets. ::: solution @@ -223,6 +224,10 @@ motorway_Delft 2. There are `r nrow(motorway_Delft)` features with the value `motorway`. ```{r} +nrow(motorway_Delft) +``` + +```{r include=FALSE} motorway_Delft_length <- motorway_Delft %>% mutate(length = st_length(.)) %>% select(everything(), geometry) %>% @@ -232,7 +237,10 @@ motorway_Delft_length <- motorway_Delft %>% 3. The total length of motorways is `r motorway_Delft_length$total_length`. ```{r} -nrow(motorway_Delft) +motorway_Delft_length <- motorway_Delft %>% + mutate(length = st_length(.)) %>% + select(everything(), geometry) %>% + summarise(total_length = sum(length)) ``` 4. Plot the motorways. @@ -241,31 +249,7 @@ nrow(motorway_Delft) ggplot(data = motorway_Delft) + geom_sf(linewidth = 1.5) + labs(title = "Fast mobility network", - subtitle = "Motorways" - ) + - coord_sf(datum = st_crs(28992)) -``` - -5. Follow the same steps with pedestrian streets. - -```{r} -pedestrian_Delft <- lines_Delft %>% - filter(highway == "pedestrian") - -pedestrian_Delft %>% - mutate(length = st_length(.)) %>% - select(everything(), geometry) %>% - summarise(total_length = sum(length)) - -nrow(pedestrian_Delft) -``` - -```{r} -ggplot() + - geom_sf(data = pedestrian_Delft) + - labs(title = "Slow mobility network", - subtitle = "Pedestrian" - ) + + subtitle = "Motorways") + coord_sf(datum = st_crs(28992)) ``` @@ -281,7 +265,7 @@ Let's say that we want to color different road types with different colors and t unique(lines_Delft$highway) ``` -If we look at all the unique values of the highway field of our street network we see more than 20 values. Let's focus on a subset of four values to illustrate the use of distinct colors. We use a piped expression in which we only filter the rows of our data frame that have one of the four given values `"motorway"`, `"primary"`, `"secondary"`, and `"cycleway"`. Note that we do this with the `%in` operator which is a more compact equivalent of a series of conditions joined by the `|` (or) operator. We also make sure that the highway column is a factor column. +If we look at all the unique values of the highway field of our street network we see more than 20 values. Let's focus on a subset of four values to illustrate the use of distinct colours. We filter the roads that have one of the four given values `"motorway"`, `"primary"`, `"secondary"`, and `"cycleway"`. Note that we do this with the `%in%` operator which is a more compact equivalent of a series of `==` equality conditions joined by the `|` (or) operator. We also make sure that the highway column is a factor column. ```{r} road_types <- c("motorway", "primary", "secondary", "cycleway") @@ -291,108 +275,57 @@ lines_Delft_selection <- lines_Delft %>% mutate(highway = factor(highway, levels = road_types)) ``` -Next we define the four colors we want to use, one for each type of road in our vector object. Note that in R you can use named colors like `"blue"`, `"green"`, `"navy"`, and `"purple"`. If you are using RStudio, you will see the named colors previewed in line. A full list of named colors can be listed with the `colors()` function. +Next we define the four colours we want to use, one for each type of road in our vector object. Note that in R you can use named colours like `"blue"`, `"green"`, `"navy"`, and `"purple"`. If you are using RStudio, you will see the named colours previewed in line. A full list of named colours can be listed with the `colors()` function. ```{r} road_colors <- c("blue", "green", "navy", "purple") ``` -We can use the defined color palette in ggplot. +We can use the defined colour palette in a ggplot. ```{r} ggplot(data = lines_Delft_selection) + geom_sf(aes(color = highway)) + scale_color_manual(values = road_colors) + - labs(color = 'Road Type', - title = "Road network of Delft", - subtitle = "Roads & Cycleways") + - coord_sf(datum = st_crs(28992)) -``` - -## Adjust line width - -Earlier we adjusted the line width universally. We can also adjust line widths for every factor level. Note that in this case the `size` argument, like the `color` argument, are within the `aes()` mapping function. This means that the values of that visual property will be mapped from a variable of the object that is being plotted. - -```{r} -line_widths <- c(1, 0.75, 0.5, 0.25) -``` - -```{r} -ggplot(data = lines_Delft_selection) + - geom_sf(aes(color = highway, linewidth = highway)) + - scale_color_manual(values = road_colors) + - labs(color = 'Road Type', - linewidth = 'Road Type', - title = "Mobility network of Delft", - subtitle = "Roads & Cycleways") + - scale_linewidth_manual(values = line_widths) + + labs(color = "Road Type", + title = "Mobility Network of Delft", + subtitle = "Main Roads & Cycleways") + coord_sf(datum = st_crs(28992)) ``` ::: challenge -# Challenge: Plot line width by attribute +## Adjust line width + +Follow the same steps to add custom line widths for every road type. -In the example above, we set the line widths to be 1, 0.75, 0.5, and 0.25. In our case line thicknesses are consistent with the hierarchy of the selected road types, but in some cases we might want to show a different hierarchy. +1. Assign the custom values `1`, `0.75`, `0.5`, `0.25` in this order to an object called `line_widths`. These values will represent line thicknesses that are consistent with the hierarchy of the selected road types. -Let’s create another plot where we show the different line types with the following thicknesses: +2. In this case the `linewidth` argument, like the `color` argument above, should be within the `aes()` mapping function and should take the values of the custom line widths. -- motorways linewidth = 0.25 -- primary linewidth = 0.75 -- secondary linewidth = 0.5 -- cycleway linewidth = 1 +3. Plot the result, making sure that `linewidth` is named the same way as `color` in the legend. ::: solution ```{r} -levels(factor(lines_Delft_selection$highway)) -``` - -```{r} -line_width <- c(0.25, 0.75, 0.5, 1) +line_widths <- c(1, 0.75, 0.5, 0.25) ``` ```{r} 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" - ) + + geom_sf(aes(color = highway, linewidth = highway)) + + scale_color_manual(values = road_colors) + + scale_linewidth_manual(values = line_widths) + + labs(color = "Road Type", + linewidth = "Road Type", + title = "Mobility Network of Delft", + subtitle = "Main Roads & Cycleways") + coord_sf(datum = st_crs(28992)) ``` - ::: ::: - -## Add plot legend - -Let’s add a legend to our plot. We will use the `road_colors` object that we created above to color the legend. We can customize the appearance of our legend by manually setting different parameters. - -```{r fig.cap="Mobility network in Delft using thicker lines than the previous example."} -p1 <- ggplot(data = lines_Delft_selection) + - geom_sf(aes(color = highway), linewidth = 1.5) + - scale_color_manual(values = road_colors) + - labs(color = 'Road Type') + - 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(linewidth = 1)) - -# show plot -p2 -``` - ::: challenge # Challenge: Plot lines by attributes @@ -400,6 +333,8 @@ p2 Create a plot that emphasizes only roads where bicycles are allowed. To emphasize this, make the lines where bicycles are not allowed THINNER than the roads where bicycles are allowed. Be sure to add a title and legend to your map. You might consider a color palette that has all bike-friendly roads displayed in a bright color. All other lines can be black. +Tip: `geom_sf()` can be called multiple times for multi-layer maps. + ::: solution ```{r} @@ -434,35 +369,6 @@ ggplot(data = lines_Delft) + ::: -::: challenge - -# Challenge: Plot polygon by attribute - - -Create a map of the municipal boundaries in the Netherlands using the data located in your data folder: `nl-gemeenten.shp`. Apply a line color to each state using its region value. Add a legend. - -::: solution - -```{r} -municipal_boundaries_NL <- st_read("data/nl-gemeenten.shp") -``` - -```{r} -str(municipal_boundaries_NL) -levels(factor(municipal_boundaries_NL$ligtInPr_1)) -``` - -```{r} -ggplot(data = municipal_boundaries_NL) + - geom_sf(aes(color = ligtInPr_1), linewidth = 1) + - labs(title = "Contiguous NL Municipal Boundaries") + - coord_sf(datum = st_crs(28992)) -``` - -::: - -::: - ::::::::::::::::::::::::::::::::::::: keypoints From f4951940ddc91be0fddf5113562a75c796a0feab Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 12 Mar 2024 13:14:45 +0100 Subject: [PATCH 05/24] Update episode on plotting multiple vector layers --- episodes/11-plot-multiple-shape-files.Rmd | 91 ++++++++--------------- 1 file changed, 33 insertions(+), 58 deletions(-) diff --git a/episodes/11-plot-multiple-shape-files.Rmd b/episodes/11-plot-multiple-shape-files.Rmd index 7acb16bf..65996816 100644 --- a/episodes/11-plot-multiple-shape-files.Rmd +++ b/episodes/11-plot-multiple-shape-files.Rmd @@ -41,19 +41,18 @@ This episode builds upon the [previous episode](../episodes/10-explore-and-plot- ## Load the data -To work with vector data in R, we use the `sf` library. Make sure that it is loaded. - -We will continue to work with the three ESRI shapefiles that we loaded in the [Open and Plot Vector Layers](../episodes/09-open-and-plot-vector-layers.Rmd) episode. +To work with vector data in R, we use the `sf` package. Make sure that it is loaded. +We will continue to work with the three shapefiles that we loaded in the [Open and Plot Vector Layers](../episodes/09-open-and-plot-vector-layers.Rmd) episode. ## Plotting Multiple Vector Layers So far we learned how to plot information from a single shapefile and do some plot customization. What if we want to create a more complex plot with many shapefiles and unique symbols that need to be represented clearly in a legend? -We will create a plot that combines our leisure locations (`point_Delft`), municipal boundary (`boundary_Delft`) and streets (`lines_Delft`) spatial objects. We will need to build a custom legend as well. +We will create a plot that combines our leisure locations (`point_Delft`), municipal boundary (`boundary_Delft`) and street (`lines_Delft`) objects. We will also build a custom legend. -To begin, we will create a plot with the site boundary as the first layer. Then layer the leisure locations and street data on top using `+`. +To begin, we create a plot with the site boundary as the first layer. Then layer the leisure locations and street data on top in consecutive calls to `geom_sf()`. ```{r} ggplot() + @@ -70,11 +69,14 @@ ggplot() + coord_sf(datum = st_crs(28992)) ``` -Next, let’s build a custom legend using the functions `scale_color_manual()` and `scale_fill_manual()`. +Next, let’s build a custom legend using the functions `scale_color_manual()` and `scale_fill_manual()`. We will use the custom `road_colors` object created in the previous episode and we will create a new object called `leisure_colors` to store values of all 15 types of leisure with the `rainbow()` function. + +We also need to customise the shape of the points with the `shape` aesthetic if we want to determine the colours inside the points. `shape = 21` will show the points as circles with a custom fill. ```{r} -leisure_colors <- rainbow(15) point_Delft$leisure <- factor(point_Delft$leisure) +levels(point_Delft$leisure) %>% length() +leisure_colors <- rainbow(15) ggplot() + geom_sf(data = boundary_Delft, @@ -98,6 +100,16 @@ ggplot() + coord_sf(datum = st_crs(28992)) ``` +::: challenge + +# Customizing point shapes + +What value of `shape` will display points as squares with custom fills? + +::: solution + +`shape = 22` will display points as squares with custom fills. Our previous plot would look like this: + ```{r} ggplot() + geom_sf(data = boundary_Delft, @@ -122,6 +134,10 @@ ggplot() + coord_sf(datum = st_crs(28992)) ``` +::: + +::: + We notice that there are quite some playgrounds in the residential parts of Delft, whereas on campus there is a concentration of picnic tables. So that is what our next challenge is about. @@ -129,9 +145,9 @@ We notice that there are quite some playgrounds in the residential parts of Delf ## Challenge: Visualising multiple layers with a custom legend -Create a map of leisure locations only including `playground` and `picnic_table`, with each point colored by the leisure type. Overlay this layer on top of the `lines_Delft` layer (the streets). Create a custom legend that applies line symbols to lines and point symbols to the points. +Create a map of leisure locations only including `playground` and `picnic_table`, with each point coloured by the leisure type. Overlay this layer on top of the `lines_Delft` layer (the streets). Tell R to plot playgrounds and picnic tables with different `shape` values. Make sure your plot has a custom legend. -Modify the plot above. Tell R to plot each point, using a different symbol of shape value. +Tip: You can call `scale_` functions multiple times for the same layer, for any of the aesthetics used in `aes()`. :::::::::::::::::::::::: solution @@ -141,7 +157,7 @@ leisure_locations_selection <- st_read("data/delft-leisure.shp") %>% ``` ```{r} -levels(factor(leisure_locations_selection$leisure)) +factor(leisure_locations_selection$leisure) %>% levels() ``` ```{r} @@ -151,55 +167,15 @@ blue_orange <- c("cornflowerblue", "darkorange") ```{r} ggplot() + geom_sf(data = lines_Delft_selection, - aes(color = highway) - ) + + 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 - ) + + aes(fill = leisure, shape = leisure)) + scale_shape_manual(name = "Leisure Type", - values = c(21, 22) - ) + + values = c(21, 22)) + scale_color_manual(name = "Line Type", - values = road_colors - ) + + 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" - )) - ) + + values = blue_orange) + labs(title = "Road network and leisure") + coord_sf(datum = st_crs(28992)) ``` @@ -211,9 +187,8 @@ ggplot() + ::::::::::::::::::::::::::::::::::::: keypoints -- Use the `+` operator to add multiple layers to a ggplot. -- A plot can be a combination of multiple vector layers. -- Use the `scale_color_manual()` and `scale_fill_manual()` functions to set legend colors. +- A plot can be a combination of multiple vector layers, each added with a separate call to `geom_sf()`. +- Use the `scale__manual()` functions to customise aesthetics of vector layers such as `color`, `fill`, and `shape`. :::::::::::::::::::::::::::::::::::::::::::::::: From da1e88011e1ec5eb0cd7dfb0ea159a3550c7137e Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 12 Mar 2024 13:15:01 +0100 Subject: [PATCH 06/24] Update episode on handling projections --- ...12-handling-spatial-projection-and-crs.Rmd | 33 +++++-------------- 1 file changed, 9 insertions(+), 24 deletions(-) diff --git a/episodes/12-handling-spatial-projection-and-crs.Rmd b/episodes/12-handling-spatial-projection-and-crs.Rmd index ff0d77b0..c0a48edf 100644 --- a/episodes/12-handling-spatial-projection-and-crs.Rmd +++ b/episodes/12-handling-spatial-projection-and-crs.Rmd @@ -8,7 +8,6 @@ exercises: 5 library(tidyverse) # tools for wrangling, reshaping and visualizing data library(here) # managing paths library(sf) # work with spatial vector data -library(terra) # work with spatial vector and raster metadata leisure_locations_selection <- st_read("data/delft-leisure.shp") %>% filter(leisure %in% c("playground", "picnic_table")) @@ -52,19 +51,13 @@ country_boundary_NL <- st_read("data/nl-boundary.shp") ggplot() + geom_sf(data = country_boundary_NL, color = "gray18", - linewidth = 2 - ) + + linewidth = 2) + geom_sf(data = municipal_boundary_NL, - color = "gray40" - ) + + color = "gray40") + labs(title = "Map of Contiguous NL Municipal Boundaries") + coord_sf(datum = st_crs(28992)) ``` -```{r} -# st_crs(point_Delft) -``` - ```{r} st_crs(municipal_boundary_NL)$epsg ``` @@ -83,15 +76,12 @@ boundary_Delft <- st_transform(boundary_Delft, 28992) ggplot() + geom_sf(data = country_boundary_NL, linewidth = 2, - color = "gray18" - ) + + color = "gray18") + geom_sf(data = municipal_boundary_NL, - color = "gray40" - ) + + color = "gray40") + geom_sf(data = boundary_Delft, color = "purple", - fill = "purple" - ) + + fill = "purple") + labs(title = "Map of Contiguous NL Municipal Boundaries") + coord_sf(datum = st_crs(28992)) ``` @@ -107,7 +97,7 @@ Create a map of South Holland as follows: 2. Plot it and adjust line width as necessary. 3. Layer the boundary of Delft onto the plot. 4. Add a title. -5. Add a legend that shows both the province boundaries (as a line) and the boundary of Delft (as a filled polygon). +5. Add a legend that shows both the municipal boundaries (as a line) and the boundary of Delft (as a filled polygon). ::: solution @@ -120,17 +110,14 @@ boundary_ZH <- municipal_boundary_NL %>% ggplot() + geom_sf(data = boundary_ZH, aes(color ="color"), - show.legend = "line" - ) + + show.legend = "line") + scale_color_manual(name = "", labels = "Municipal Boundaries in South Holland", - values = c("color" = "gray18") - ) + + values = c("color" = "gray18")) + geom_sf(data = boundary_Delft, aes(shape = "shape"), color = "purple", - fill = "purple" - ) + + fill = "purple") + scale_shape_manual(name = "", labels = "Municipality of Delft", values = c("shape" = 19) @@ -157,8 +144,6 @@ st_write(leisure_locations_selection, ) ``` - - ::::::::::::::::::::::::::::::::::::: keypoints - `ggplot2` automatically converts all objects in a plot to the same CRS. From d36a940f13475d4e89e944f5e867aca3c0e58089 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Thu, 11 Apr 2024 08:09:35 +0200 Subject: [PATCH 07/24] Update slides --- instructors/2-vector-slides.html | 18 +++++----- .../libs/clipboard/clipboard.min.js | 4 +-- .../libs/quarto-html/popper.min.js | 4 +-- .../quarto-line-highlight/line-highlight.js | 8 ++--- .../revealjs/plugin/quarto-support/footer.css | 1 + .../revealjs/plugin/quarto-support/support.js | 34 +++++++++++++++++-- .../plugin/reveal-chalkboard/plugin.js | 9 +++++ .../plugin/reveal-menu/quarto-menu.js | 3 ++ 8 files changed, 62 insertions(+), 19 deletions(-) diff --git a/instructors/2-vector-slides.html b/instructors/2-vector-slides.html index 7d6dd9fd..e2b96ccc 100644 --- a/instructors/2-vector-slides.html +++ b/instructors/2-vector-slides.html @@ -8,7 +8,7 @@ - + @@ -647,7 +647,7 @@

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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); diff --git a/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js b/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js index 5bffdc77..a69ca1df 100644 --- a/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js +++ b/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js @@ -38,7 +38,7 @@ window.QuartoLineHighlight = function () { divSourceCode.forEach((el) => { if (el.hasAttribute(kCodeLineNumbersAttr)) { const codeLineAttr = el.getAttribute(kCodeLineNumbersAttr); - el.removeAttribute("data-code-line-numbers"); + el.removeAttribute(kCodeLineNumbersAttr); if (handleLinesSelector(deck, codeLineAttr)) { // Only process if attr is a string to select lines to highlights // e.g "1|3,6|8-11" @@ -165,9 +165,9 @@ window.QuartoLineHighlight = function () { if (typeof highlight.last === "number") { spanToHighlight = [].slice.call( codeBlock.querySelectorAll( - ":scope > span:nth-child(n+" + + ":scope > span:nth-of-type(n+" + highlight.first + - "):nth-child(-n+" + + "):nth-of-type(-n+" + highlight.last + ")" ) @@ -175,7 +175,7 @@ window.QuartoLineHighlight = function () { } else if (typeof highlight.first === "number") { spanToHighlight = [].slice.call( codeBlock.querySelectorAll( - ":scope > span:nth-child(" + highlight.first + ")" + ":scope > span:nth-of-type(" + highlight.first + ")" ) ); } diff --git a/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-support/footer.css b/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-support/footer.css index 385473d8..390d5b38 100644 --- a/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-support/footer.css +++ b/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-support/footer.css @@ -6,6 +6,7 @@ max-height: 2.2rem; height: 100%; width: auto; + z-index: 2; } .reveal .footer { diff --git a/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-support/support.js b/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-support/support.js index 9adc9213..b0262ee6 100644 --- a/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-support/support.js +++ b/instructors/2-vector-slides_files/libs/revealjs/plugin/quarto-support/support.js @@ -4,6 +4,20 @@ window.QuartoSupport = function () { return /print-pdf/gi.test(window.location.search); } + // helper for theme toggling + function toggleBackgroundTheme(el, onDarkBackground, onLightBackground) { + if (onDarkBackground) { + el.classList.add('has-dark-background') + } else { + el.classList.remove('has-dark-background') + } + if (onLightBackground) { + el.classList.add('has-light-background') + } else { + el.classList.remove('has-light-background') + } + } + // implement controlsAudo function controlsAuto(deck) { const config = deck.getConfig(); @@ -111,8 +125,19 @@ window.QuartoSupport = function () { } } - // add footer text - function addFooter(deck) { + // tweak slide-number element + function tweakSlideNumber(deck) { + deck.on("slidechanged", function (ev) { + const revealParent = deck.getRevealElement(); + const slideNumberEl = revealParent.querySelector(".slide-number"); + const onDarkBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-dark-background'); + const onLightBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-light-background'); + toggleBackgroundTheme(slideNumberEl, onDarkBackground, onLightBackground); + }) + } + + // add footer text + function addFooter(deck) { const revealParent = deck.getRevealElement(); const defaultFooterDiv = document.querySelector(".footer-default"); if (defaultFooterDiv) { @@ -127,13 +152,17 @@ window.QuartoSupport = function () { prevSlideFooter.remove(); } const currentSlideFooter = ev.currentSlide.querySelector(".footer"); + const onDarkBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-dark-background') + const onLightBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-light-background') if (currentSlideFooter) { defaultFooterDiv.style.display = "none"; const slideFooter = currentSlideFooter.cloneNode(true); handleLinkClickEvents(deck, slideFooter); deck.getRevealElement().appendChild(slideFooter); + toggleBackgroundTheme(slideFooter, onDarkBackground, onLightBackground) } else { defaultFooterDiv.style.display = "block"; + toggleBackgroundTheme(defaultFooterDiv, onDarkBackground, onLightBackground) } }); } @@ -280,6 +309,7 @@ window.QuartoSupport = function () { fixupForPrint(deck); applyGlobalStyles(deck); addLogoImage(deck); + tweakSlideNumber(deck); addFooter(deck); addChalkboardButtons(deck); handleTabbyClicks(); diff --git a/instructors/2-vector-slides_files/libs/revealjs/plugin/reveal-chalkboard/plugin.js b/instructors/2-vector-slides_files/libs/revealjs/plugin/reveal-chalkboard/plugin.js index 76b736e2..23edd1e5 100644 --- a/instructors/2-vector-slides_files/libs/revealjs/plugin/reveal-chalkboard/plugin.js +++ b/instructors/2-vector-slides_files/libs/revealjs/plugin/reveal-chalkboard/plugin.js @@ -671,6 +671,15 @@ console.warn( "toggleNotesButton is deprecated, use customcontrols plugin instea } ); a.href = window.URL.createObjectURL( blob ); } catch ( error ) { + // https://stackoverflow.com/a/6234804 + // escape data for proper handling of quotes and line breaks + // in case malicious gets a chance to craft the exception message + error = String(error).replace(/&/g, "&") + .replace(//g, ">") + .replace(/"/g, """) + .replace(/'/g, "'"); + a.innerHTML += ' (' + error + ')'; } a.click(); diff --git a/instructors/2-vector-slides_files/libs/revealjs/plugin/reveal-menu/quarto-menu.js b/instructors/2-vector-slides_files/libs/revealjs/plugin/reveal-menu/quarto-menu.js index 1245b0d6..96740536 100644 --- a/instructors/2-vector-slides_files/libs/revealjs/plugin/reveal-menu/quarto-menu.js +++ b/instructors/2-vector-slides_files/libs/revealjs/plugin/reveal-menu/quarto-menu.js @@ -37,4 +37,7 @@ window.RevealMenuToolHandlers = { downloadDrawings: revealMenuToolHandler(function () { RevealChalkboard.download(); }), + togglePdfExport: revealMenuToolHandler(function () { + PdfExport.togglePdfExport(); + }), }; From 81e082d685fa14d4bcaa4475076b7d4d7d954cc8 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci <33600128+cforgaci@users.noreply.github.com> Date: Wed, 17 Apr 2024 14:51:49 +0200 Subject: [PATCH 08/24] Update episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd --- episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 25ea8e97..e84c47e9 100644 --- a/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd +++ b/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd @@ -146,7 +146,7 @@ factor(point_Delft$leisure) %>% head(n = 3) ``` -3. To see a list of all attribute names and answer the last wuestion, we can use the `names()` function. +3. To see a list of all attribute names and answer the last question, we can use the `names()` function. ```{r} names(point_Delft) From cd37ad369eb80a851825421339376628c30189a3 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Wed, 17 Apr 2024 15:22:53 +0200 Subject: [PATCH 09/24] Update keypoint in geospatial concepts episode --- episodes/08-intro-to-geospatial-concepts.Rmd | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/episodes/08-intro-to-geospatial-concepts.Rmd b/episodes/08-intro-to-geospatial-concepts.Rmd index f15c368f..211e5cc0 100644 --- a/episodes/08-intro-to-geospatial-concepts.Rmd +++ b/episodes/08-intro-to-geospatial-concepts.Rmd @@ -177,7 +177,6 @@ Correct answer: b. EPSG:28992 - Each location on the Earth has its geographical latitude and longitude, which can be transformed on a plane using a map projection. -- Depending on the research question, we need a global, regional, or local CRS suitable properties. - +- Depending on the research question, we need a global, regional, or local CRS with suitable properties such as the least possible distortion and an appropriate measurement unit. ::: From 91d839966ccde17f67cbda085fc73420d90e7f9a Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Wed, 17 Apr 2024 15:23:21 +0200 Subject: [PATCH 10/24] Update keypoint phrasing in vetor crs episode --- episodes/12-handling-spatial-projection-and-crs.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/12-handling-spatial-projection-and-crs.Rmd b/episodes/12-handling-spatial-projection-and-crs.Rmd index c0a48edf..0bf574b8 100644 --- a/episodes/12-handling-spatial-projection-and-crs.Rmd +++ b/episodes/12-handling-spatial-projection-and-crs.Rmd @@ -147,7 +147,7 @@ st_write(leisure_locations_selection, ::::::::::::::::::::::::::::::::::::: keypoints - `ggplot2` automatically converts all objects in a plot to the same CRS. -- Still be aware of the CRS and extent for each object. +- For geoprocessing purposes, you still need to reproject the layers you use to the same CRS. - You can export an `sf` object to a shapefile with `st_write()`. :::::::::::::::::::::::::::::::::::::::::::::::: From 162798f0b18f9b6cb68b7c141585f5a4f21037f6 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Wed, 1 May 2024 15:40:10 +0200 Subject: [PATCH 11/24] Fix wording of question in vector crs episode --- episodes/12-handling-spatial-projection-and-crs.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/12-handling-spatial-projection-and-crs.Rmd b/episodes/12-handling-spatial-projection-and-crs.Rmd index 0bf574b8..32eca61c 100644 --- a/episodes/12-handling-spatial-projection-and-crs.Rmd +++ b/episodes/12-handling-spatial-projection-and-crs.Rmd @@ -15,7 +15,7 @@ leisure_locations_selection <- st_read("data/delft-leisure.shp") %>% :::::::::::::::::::::::::::::::::::::: questions -- What do I do when vector data don’t line up? +- What do I do when vector data do not line up? :::::::::::::::::::::::::::::::::::::::::::::::: From a62b97ff16ba61641cfce5b1fd78697a0d802fef Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Wed, 1 May 2024 16:07:20 +0200 Subject: [PATCH 12/24] Update code chunk labels and challenge titles in vector episodes --- episodes/08-intro-to-geospatial-concepts.Rmd | 10 +-- episodes/09-open-and-plot-vector-layers.Rmd | 28 ++++---- ...re-and-plot-by-vector-layer-attributes.Rmd | 66 +++++++++---------- episodes/11-plot-multiple-shape-files.Rmd | 16 ++--- ...12-handling-spatial-projection-and-crs.Rmd | 22 +++---- 5 files changed, 71 insertions(+), 71 deletions(-) diff --git a/episodes/08-intro-to-geospatial-concepts.Rmd b/episodes/08-intro-to-geospatial-concepts.Rmd index 211e5cc0..7028af47 100644 --- a/episodes/08-intro-to-geospatial-concepts.Rmd +++ b/episodes/08-intro-to-geospatial-concepts.Rmd @@ -25,13 +25,13 @@ After completing this episode, participants should be able to… The shape of the Earth is approximately a sphere which is slightly wider than it is tall, and which is called **ellipsoid**. The true shape of the Earth is an irregular ellipsoid, the so-called **geoid**, as illustrated in the image below. -```{r fig.cap="The shape of the Earth. Source: United Nations Statistics Division and International Cartographic Association (2012a).", echo=FALSE} +```{r fig-earth, fig.cap="The shape of the Earth. Source: United Nations Statistics Division and International Cartographic Association (2012a).", echo=FALSE} knitr::include_graphics("https://unstats.un.org/unsd/geoinfo/ungegn/docs/_data_icacourses/_ImagesModules/_selfstudy/S06_images/S06_03_a00.jpg") ``` The most common and basic representation of the position of points on the Earth is the combination of the **geographical latitude and longitude**, as shown below. -```{r fig.cap="Geographical latitude and longitude. Source: van der Marel (2014).", echo=FALSE} +```{r fig-latlon, fig.cap="Geographical latitude and longitude. Source: van der Marel (2014).", echo=FALSE} knitr::include_graphics("fig/latlon.png") ``` @@ -43,13 +43,13 @@ The black lines in the figure above show the equator and the prime meridian runn **Map projection** is a systematic transformation of the latitudes and longitudes of locations on the surface of an ellipsoid into locations on a plane. It is a transformation of the three-dimensional Earth’s surface into its two-dimensional representation on a sheet of paper or computer screen (see the image below for a comparison with flattening an orange peel). -```{r fig.cap="Map projection represented as flattening an orange peel. Source: Data Carpentry (2023).", echo=FALSE} +```{r fig-orange, fig.cap="Map projection represented as flattening an orange peel. Source: Data Carpentry (2023).", echo=FALSE} knitr::include_graphics("https://datacarpentry.org/organization-geospatial/fig/orange-peel-earth.jpg") ``` Many different map projections are in use for different purposes. Generally, they can be categorised into the following groups: cylindrical, conic, and azimuthal. -```{r fig.cap="Cylindrical, conic, and azimuthal map projections. Source: Knippers (2009).", echo=FALSE} +```{r fig-projections, fig.cap="Cylindrical, conic, and azimuthal map projections. Source: Knippers (2009).", echo=FALSE} knitr::include_graphics("https://kartoweb.itc.nl/geometrics/Bitmaps/Intro%201.9a.gif") ``` @@ -91,7 +91,7 @@ In this workshop, we use the following two CRS: The figure below shows the same city (Rotterdam) in these two CRS. -```{r fig.cap="Rotterdam in two different CRS", echo=FALSE} +```{r fig-crs, fig.cap="Rotterdam in two different CRS", echo=FALSE} knitr::include_graphics("fig/rotterdam-crs.png") ``` diff --git a/episodes/09-open-and-plot-vector-layers.Rmd b/episodes/09-open-and-plot-vector-layers.Rmd index 816a61e4..29e223fa 100644 --- a/episodes/09-open-and-plot-vector-layers.Rmd +++ b/episodes/09-open-and-plot-vector-layers.Rmd @@ -56,7 +56,7 @@ 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. Note that a shapefile consists of multiple files and it is important to keep them all together in the same location. We will read a shapefile with the administrative boundary of Delft with the function `st_read()` from the `sf` package. -```{r} +```{r read-boundary} boundary_Delft <- st_read("data/delft-boundary.shp", quiet = TRUE) ``` @@ -75,19 +75,19 @@ Note that all functions from the `sf` package start with the standard prefix `st By default (with `quiet = FALSE`), the `st_read()` function provides a message with a summary of metadata about the file that was read in. -```{r} +```{r read-boundary-loud} st_read("data/delft-boundary.shp") ``` 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} +```{r geom-type} st_geometry_type(boundary_Delft) ``` 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)$epsg)``. -```{r} +```{r crs} st_crs(boundary_Delft) ``` @@ -97,7 +97,7 @@ st_crs(boundary_Delft) As the output of `st_crs()` can be long, you can use `$Name` and `$epsg` after the `crs()` call to extract the projection name and EPSG code respectively. -```{r} +```{r crs2} st_crs(boundary_Delft)$Name st_crs(boundary_Delft)$epsg ``` @@ -109,14 +109,14 @@ The `$` operator is used to extract a specific part of an object. We used it in The `st_bbox()` function shows the extent of the layer. -```{r} +```{r bbox} st_bbox(boundary_Delft) ``` As `WGS 84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees. We need a **projected CRS**, which in the case of the Netherlands is the `Amersfoort / RD New` projection. To reproject our shapefile, we will use the `st_transform()` function. For the `crs` argument we can use the EPSG code of the CRS we want to use, which is `28992` for the `Amersfort / RD New` projection. To check the EPSG code of any CRS, we can check this website: https://epsg.io/ -```{r} +```{r projected-crs} boundary_Delft <- st_transform(boundary_Delft, crs = 28992) st_crs(boundary_Delft)$Name st_crs(boundary_Delft)$epsg @@ -124,14 +124,14 @@ st_crs(boundary_Delft)$epsg Notice that the bounding box is measured in meters after the transformation. The `$units_gdal` named element confirms that the new CRS uses metric units. -```{r} +```{r units} st_bbox(boundary_Delft) st_crs(boundary_Delft)$units_gdal ``` We confirm the transformation by examining the reprojected shapefile. -```{r} +```{r check-boundary} boundary_Delft ``` @@ -147,7 +147,7 @@ Read more about Coordinate Reference Systems [here](https://datacarpentry.org/or Now, let's plot this shapefile. You are already familiar with the `ggplot2` package from [Introduction to Visualisation](../episodes/04-intro-to-visualisation.Rmd). `ggplot2` has special `geom_` functions for spatial data. We will use the `geom_sf()` function for `sf` data. We use `coord_sf()` to ensure that the coordinates shown on the two axes are displayed in meters. -```{r} +```{r plot-boundary} ggplot(data = boundary_Delft) + geom_sf(size = 3, color = "black", fill = "cyan1") + labs(title = "Delft Administrative Boundary") + @@ -166,28 +166,28 @@ Read in `delft-streets.shp` and `delft-leisure.shp` and assign them to `lines_De :::::::::::::::::::::::: solution -```{r results='hide'} +```{r lp-read, results='hide'} lines_Delft <- st_read("data/delft-streets.shp") points_Delft <- st_read("data/delft-leisure.shp") ``` We can check the type of type of geometry with the `st_geometry_type()` function. `lines_Delft` contains `"LINESTRING"` geometry and `points_Delft` is made of `"POINT"` geometries. -```{r} +```{r lp-geom-type} st_geometry_type(lines_Delft)[1] st_geometry_type(points_Delft)[2] ``` Both `lines_Delft` and `points_Delft` are in `EPSG:28992`. -```{r} +```{r lp-crs} st_crs(lines_Delft)$epsg st_crs(points_Delft)$epsg ``` When looking at the bounding boxes with the `st_bbox()` function, we see the spatial extent of the two objects in a projected CRS using meters as units. `lines_Delft()` and `points_Delft` have similar extents. -```{r} +```{r lp-bbox} st_bbox(lines_Delft) st_bbox(points_Delft) ``` diff --git a/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd b/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd index e84c47e9..1751278e 100644 --- a/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd +++ b/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd @@ -37,19 +37,19 @@ After completing this episode, participants should be able to… Let's have a look at the content of the loaded data, starting with `lines_Delft`. In essence, an `"sf"` object is a data.frame with a "sticky" geometry column and some extra metadata, like the CRS, extent and geometry type we examined earlier. -```{r} +```{r lines} lines_Delft ``` This means that we can examine and manipulate them as data frames. For instance, we can look at the number of variables (columns in a data frame) with `ncol()`. -```{r} +```{r lines-ncol} ncol(lines_Delft) ``` In the case of `point_Delft` those columns are `"osm_id"`, `"highway"` and `"geometry"`. We can check the names of the columns with the function `names()`. -```{r} +```{r lines-names} names(lines_Delft) ``` @@ -64,7 +64,7 @@ attribute table. We can also preview the content of the object by looking at the first 6 rows with the `head()` function, which in the case of an `sf` object is similar to examining the object directly. -```{r} +```{r lines-head} head(lines_Delft) ``` @@ -73,13 +73,13 @@ head(lines_Delft) Using the `$` operator, we can examine the content of a single field of our lines object. Let's have a look at the `highway` field, a categorical variable stored in the `lines_Delft` object as `character`. To avoid displaying all `r length(lines_Delft$highway)` values of `highway`, we will preview it with the `head()` function: -```{r} +```{r lines-head-10} head(lines_Delft$highway, 10) ``` The first rows returned by the `head()` function do not necessarily contain all unique values within the `highway` field. To see all unique values, we can use the `unique()` function. This function extracts all possible values of a character variable. For the `highway` field, this returns all types of roads stored in `lines_Delft`. -```{r} +```{r lines-unique} unique(lines_Delft$highway) ``` @@ -87,7 +87,7 @@ unique(lines_Delft$highway) R is also able to handle categorical variables called factors. With factors, we can use the `levels()` function to show unique values. To examine unique values of the `highway` variable this way, we have to first transform it into a factor with the `factor()` function: -```{r} +```{r lines-factor-levels} factor(lines_Delft$highway) %>% levels() ``` Note that this way the values are shown by default in alphabetical order and `NA`s are not displayed, whereas using `unique()` returns unique values in the order of their occurrence in the data frame and it also shows `NA` values. @@ -111,7 +111,7 @@ Explore the attributes associated with the `point_Delft` spatial object. 1. To find the number of fields, we use the `ncol()` function: -```{r} +```{r points-ncol} ncol(point_Delft) ``` @@ -119,27 +119,27 @@ ncol(point_Delft) Using the `head()` function which displays 6 rows by default, we only see two values and `NA`s. -```{r} +```{r points-head} head(point_Delft) ``` We can increase the number of rows with the `n` argument (e.g., `head(n = 10)` to show 10 rows) until we see at least three distinct values in the leisure column. Note that printing an `sf` object will also display the first 10 rows. -```{r} +```{r points-head-10} # you might be lucky to see three distinct values head(point_Delft, 10) ``` We have our answer (`sports_centre` is the third value), but in general this is not a good approach as the first rows might still have many `NA`s and three distinct values might still not be present in the first `n` rows of the data frame. To remove `NA`s, we can use the function `na.omit()` on the leisure column to remove `NA`s completely. Note that we use the `$` operator to examine the content of a single variable. -```{r} +```{r points-leisure} # this is better na.omit(point_Delft$leisure) %>% head() ``` To show only unique values, we can use the `levels()` function on a factor to only see the first occurrence of each distinct value. Note `NA`s are dropped in this case and that we get the first three of the unique alphabetically ordered values. -```{r} +```{r points-leisure-head-3} # this is even better factor(point_Delft$leisure) %>% levels() %>% @@ -148,7 +148,7 @@ factor(point_Delft$leisure) %>% 3. To see a list of all attribute names and answer the last question, we can use the `names()` function. -```{r} +```{r points-names} names(point_Delft) ``` @@ -163,21 +163,21 @@ A) `location` is not a field of the `point_Delft` object. We can use the `filter()` function to select a subset of features from a spatial object, just like with data frames. Let's select only `cycleways` from our street data. -```{r} +```{r cycleway} cycleway_Delft <- lines_Delft %>% filter(highway == "cycleway") ``` Our subsetting operation reduces the number of features from `r nrow(lines_Delft)` to `r nrow(cycleway_Delft)`. -```{r} +```{r cycleway-nrow} nrow(lines_Delft) nrow(cycleway_Delft) ``` This can be useful, for instance, to calculate the total length of cycleways. For that, we first need to calculate the length of each segment with `st_length()` -```{r} +```{r cycleway-length} cycleway_Delft <- cycleway_Delft %>% mutate(length = st_length(.)) @@ -187,7 +187,7 @@ cycleway_Delft %>% Now we can plot only the cycleways. -```{r fig.cap="Map of cycleways in Delft."} +```{r plot-cycleway, fig.cap="Map of cycleways in Delft."} ggplot(data = cycleway_Delft) + geom_sf() + labs(title = "Slow mobility network in Delft", @@ -209,12 +209,12 @@ Challenge: Now with motorways 1. To create the new object, we first need to see which value of the `highway` column holds motorways. There is a value called `motorway`. -```{r} +```{r highway-unique} unique(lines_Delft$highway) ``` We extract only the features with the value `motorway`. -```{r} +```{r motorway} motorway_Delft <- lines_Delft %>% filter(highway == "motorway") @@ -223,11 +223,11 @@ motorway_Delft 2. There are `r nrow(motorway_Delft)` features with the value `motorway`. -```{r} +```{r motorway-nrow} nrow(motorway_Delft) ``` -```{r include=FALSE} +```{r motorway-length-hidden, include=FALSE} motorway_Delft_length <- motorway_Delft %>% mutate(length = st_length(.)) %>% select(everything(), geometry) %>% @@ -236,7 +236,7 @@ motorway_Delft_length <- motorway_Delft %>% 3. The total length of motorways is `r motorway_Delft_length$total_length`. -```{r} +```{r motorway-length} motorway_Delft_length <- motorway_Delft %>% mutate(length = st_length(.)) %>% select(everything(), geometry) %>% @@ -245,7 +245,7 @@ motorway_Delft_length <- motorway_Delft %>% 4. Plot the motorways. -```{r} +```{r plot-motorway} ggplot(data = motorway_Delft) + geom_sf(linewidth = 1.5) + labs(title = "Fast mobility network", @@ -261,13 +261,13 @@ ggplot(data = motorway_Delft) + Let's say that we want to color different road types with different colors and that we want to determine those colors. -```{r} +```{r highway-unique2} unique(lines_Delft$highway) ``` If we look at all the unique values of the highway field of our street network we see more than 20 values. Let's focus on a subset of four values to illustrate the use of distinct colours. We filter the roads that have one of the four given values `"motorway"`, `"primary"`, `"secondary"`, and `"cycleway"`. Note that we do this with the `%in%` operator which is a more compact equivalent of a series of `==` equality conditions joined by the `|` (or) operator. We also make sure that the highway column is a factor column. -```{r} +```{r highway-types} road_types <- c("motorway", "primary", "secondary", "cycleway") lines_Delft_selection <- lines_Delft %>% @@ -277,13 +277,13 @@ lines_Delft_selection <- lines_Delft %>% Next we define the four colours we want to use, one for each type of road in our vector object. Note that in R you can use named colours like `"blue"`, `"green"`, `"navy"`, and `"purple"`. If you are using RStudio, you will see the named colours previewed in line. A full list of named colours can be listed with the `colors()` function. -```{r} +```{r highway-colors} road_colors <- c("blue", "green", "navy", "purple") ``` We can use the defined colour palette in a ggplot. -```{r} +```{r plot-highway-types} ggplot(data = lines_Delft_selection) + geom_sf(aes(color = highway)) + scale_color_manual(values = road_colors) + @@ -295,7 +295,7 @@ ggplot(data = lines_Delft_selection) + ::: challenge -## Adjust line width +## Challenge: Adjust line width Follow the same steps to add custom line widths for every road type. @@ -307,11 +307,11 @@ Follow the same steps to add custom line widths for every road type. ::: solution -```{r} +```{r highway-linewidths} line_widths <- c(1, 0.75, 0.5, 0.25) ``` -```{r} +```{r plot-highway-types2} ggplot(data = lines_Delft_selection) + geom_sf(aes(color = highway, linewidth = highway)) + scale_color_manual(values = road_colors) + @@ -337,15 +337,15 @@ Tip: `geom_sf()` can be called multiple times for multi-layer maps. ::: solution -```{r} +```{r highway-types-class} class(lines_Delft_selection$highway) ``` -```{r} +```{r highway-types-levels} levels(factor(lines_Delft$highway)) ``` -```{r} +```{r plot-cycleways2} # First, create a data frame with only roads where bicycles # are allowed lines_Delft_bicycle <- lines_Delft %>% diff --git a/episodes/11-plot-multiple-shape-files.Rmd b/episodes/11-plot-multiple-shape-files.Rmd index 65996816..60dfb446 100644 --- a/episodes/11-plot-multiple-shape-files.Rmd +++ b/episodes/11-plot-multiple-shape-files.Rmd @@ -54,7 +54,7 @@ We will create a plot that combines our leisure locations (`point_Delft`), munic To begin, we create a plot with the site boundary as the first layer. Then layer the leisure locations and street data on top in consecutive calls to `geom_sf()`. -```{r} +```{r plot-multiple} ggplot() + geom_sf(data = boundary_Delft, fill = "lightgrey", @@ -73,7 +73,7 @@ Next, let’s build a custom legend using the functions `scale_color_manual()` a We also need to customise the shape of the points with the `shape` aesthetic if we want to determine the colours inside the points. `shape = 21` will show the points as circles with a custom fill. -```{r} +```{r plot-multiple2} point_Delft$leisure <- factor(point_Delft$leisure) levels(point_Delft$leisure) %>% length() leisure_colors <- rainbow(15) @@ -102,7 +102,7 @@ ggplot() + ::: challenge -# Customizing point shapes +# Challenge: Customizing point shapes What value of `shape` will display points as squares with custom fills? @@ -110,7 +110,7 @@ What value of `shape` will display points as squares with custom fills? `shape = 22` will display points as squares with custom fills. Our previous plot would look like this: -```{r} +```{r plot-multiple3} ggplot() + geom_sf(data = boundary_Delft, fill = "lightgrey", @@ -151,20 +151,20 @@ Tip: You can call `scale_` functions multiple times for the same layer, for any :::::::::::::::::::::::: solution -```{r} +```{r leisure-locations} leisure_locations_selection <- st_read("data/delft-leisure.shp") %>% filter(leisure %in% c("playground", "picnic_table")) ``` -```{r} +```{r leisure-locations2} factor(leisure_locations_selection$leisure) %>% levels() ``` -```{r} +```{r leisure-locations3} blue_orange <- c("cornflowerblue", "darkorange") ``` -```{r} +```{r plot-leisure-locations} ggplot() + geom_sf(data = lines_Delft_selection, aes(color = highway)) + diff --git a/episodes/12-handling-spatial-projection-and-crs.Rmd b/episodes/12-handling-spatial-projection-and-crs.Rmd index 32eca61c..9aebfa67 100644 --- a/episodes/12-handling-spatial-projection-and-crs.Rmd +++ b/episodes/12-handling-spatial-projection-and-crs.Rmd @@ -30,11 +30,11 @@ After completing this episode, participants should be able to… ## Working with spatial data from different sources -```{r} +```{r minicipal-boundary} municipal_boundary_NL <- st_read("data/nl-gemeenten.shp") ``` -```{r} +```{r plot-minicipal-boundary} ggplot() + geom_sf(data = municipal_boundary_NL) + labs(title = "Map of Contiguous NL Municipal Boundaries") + @@ -43,11 +43,11 @@ ggplot() + We can add a country boundary layer to make it look nicer. If we specify a thicker line width using size = 2 for the country boundary layer, it will make our map pop! -```{r} +```{r country-boundary} country_boundary_NL <- st_read("data/nl-boundary.shp") ``` -```{r} +```{r plot-boundaries} ggplot() + geom_sf(data = country_boundary_NL, color = "gray18", @@ -58,21 +58,21 @@ ggplot() + coord_sf(datum = st_crs(28992)) ``` -```{r} +```{r minicipal-boundary-crs} st_crs(municipal_boundary_NL)$epsg ``` -```{r} +```{r country-boundary-crs} st_crs(country_boundary_NL)$epsg ``` -```{r} +```{r municipal-boundary-crs-2} boundary_Delft <- st_read("data/delft-boundary.shp") st_crs(boundary_Delft)$epsg boundary_Delft <- st_transform(boundary_Delft, 28992) ``` -```{r} +```{r plot-boundaries2} ggplot() + geom_sf(data = country_boundary_NL, linewidth = 2, @@ -101,12 +101,12 @@ Create a map of South Holland as follows: ::: solution -```{r} +```{r boundary-zh} boundary_ZH <- municipal_boundary_NL %>% filter(ligtInPr_1 == "Zuid-Holland") ``` -```{r} +```{r plot-boundary-zh} ggplot() + geom_sf(data = boundary_ZH, aes(color ="color"), @@ -137,7 +137,7 @@ ggplot() + To save a file, use the `st_write()` function from the `sf` package. Although `sf` guesses the driver needed for a specified output file name from its extension, this can be made explicitly via the `driver` argument. In our case `driver = "ESRI Shapefile"` ensures that the output is correctly saved as a `.shp` file. -```{r, eval=FALSE} +```{r write-leisure-locations, eval=FALSE} st_write(leisure_locations_selection, "data/leisure_locations_selection.shp", driver = "ESRI Shapefile" From 1539951ae52bf8f2290cf9a37fa14071b574db23 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Wed, 1 May 2024 16:33:39 +0200 Subject: [PATCH 13/24] Do not use code type in callout headings --- episodes/09-open-and-plot-vector-layers.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/episodes/09-open-and-plot-vector-layers.Rmd b/episodes/09-open-and-plot-vector-layers.Rmd index 29e223fa..3003763c 100644 --- a/episodes/09-open-and-plot-vector-layers.Rmd +++ b/episodes/09-open-and-plot-vector-layers.Rmd @@ -46,7 +46,7 @@ library(sf) # work with spatial vector data ::: callout -# The `sf` package +# 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. Read more about simple features and its implementation in R [here](https://r-spatial.github.io/sf/articles/sf1.html). @@ -62,7 +62,7 @@ boundary_Delft <- st_read("data/delft-boundary.shp", quiet = TRUE) ::: callout -# All `sf` functions start with `st_` +# All 'sf' functions start with 'st_' Note that all functions from the `sf` package start with the standard prefix `st_` which stands for Spatial Type. This is helpful in at least two ways: @@ -93,7 +93,7 @@ st_crs(boundary_Delft) ::: callout -# Examining the output of `st_crs()` +# Examining the output of 'st_crs()' As the output of `st_crs()` can be long, you can use `$Name` and `$epsg` after the `crs()` call to extract the projection name and EPSG code respectively. From 5cc2e17fc9592b81980d749a42337b33d94eb6db Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Thu, 2 May 2024 15:57:19 +0200 Subject: [PATCH 14/24] Update geospatial concepts episode --- episodes/08-intro-to-geospatial-concepts.Rmd | 99 +++++++++---------- episodes/fig/vector-raster-1.png | Bin 0 -> 54860 bytes episodes/fig/vector-raster-2.png | Bin 0 -> 589366 bytes 3 files changed, 48 insertions(+), 51 deletions(-) create mode 100644 episodes/fig/vector-raster-1.png create mode 100644 episodes/fig/vector-raster-2.png diff --git a/episodes/08-intro-to-geospatial-concepts.Rmd b/episodes/08-intro-to-geospatial-concepts.Rmd index 7028af47..9f41de1d 100644 --- a/episodes/08-intro-to-geospatial-concepts.Rmd +++ b/episodes/08-intro-to-geospatial-concepts.Rmd @@ -6,19 +6,15 @@ author: 'Ana Petrović' --- ::: questions - - How do I describe the location of a geographic feature on the surface of the earth? - What is a coordinate reference system (CRS) and how do I describe different types of CRS? -- How do I decide on what CRS to use? - +- How do I decide on what CRS to use? ::: ::: objectives - After completing this episode, participants should be able to… - Identify the CRS that is best fit for a specific research question. - ::: ## The shape of the Earth @@ -35,11 +31,11 @@ The most common and basic representation of the position of points on the Earth knitr::include_graphics("fig/latlon.png") ``` -**Meridians** are **vertical** circles with constant longitude, called **__great circles__**, which run from the North Pole to the South Pole. **Parallels** are **horizontal** circles with constant latitude, which are called **__small circles__**. Only the equator (the largest parallel) is also a great circle. +**Meridians** are **vertical** circles with constant longitude, called **great circles**, which run from the North Pole to the South Pole. **Parallels** are **horizontal** circles with constant latitude, which are called **small circles**. Only the equator (the largest parallel) is also a great circle. The black lines in the figure above show the equator and the prime meridian running through Greenwich, with latitude and longitude labels. The red dotted lines show the meridian and parallel running through Karachi, Pakistan (25°45’N, 67°01’E). -## Map projection +## Map projection: From the 3D Earth to a 2D map **Map projection** is a systematic transformation of the latitudes and longitudes of locations on the surface of an ellipsoid into locations on a plane. It is a transformation of the three-dimensional Earth’s surface into its two-dimensional representation on a sheet of paper or computer screen (see the image below for a comparison with flattening an orange peel). @@ -53,19 +49,19 @@ Many different map projections are in use for different purposes. Generally, the knitr::include_graphics("https://kartoweb.itc.nl/geometrics/Bitmaps/Intro%201.9a.gif") ``` -Each map projection introduces a **distortion** in geometrical elements – **distance**, **angle**, and **area**. Depending on which of these geometrical elements are more relevant for a specific map, we can choose an appropriate map projection. **Conformal projections** are the best for preserving angles between any two curves; **equal area (equivalent) projections** preserve the area or scale; **equal distance (conventional) projections** are the best for preserving distances. +Each map projection introduces a **distortion** in geometrical elements – **distance, angle, and area**. Depending on which of these geometrical elements are more relevant for a specific map, we can choose an appropriate map projection. **Conformal projections** are the best for preserving angles between any two curves, which means preserving the correct shapes of small areas; **equal area (equivalent) projections** preserve the area or scale; **equal distance (conventional) projections** are the best for preserving distances. ## Coordinate reference systems (CRS) -A **coordinate reference system (CRS)** is a coordinate-based local, regional or global system for locating geographical entities, which uses a specific map projection. It defines how the two-dimensional, projected map relates to real places on the Earth. +**A** **coordinate reference system (CRS)** is a coordinate-based local, regional or global system for locating geographical entities, which uses a specific map projection. It defines how the two-dimensional, projected map relates to real places on the Earth. All coordinate reference systems are included in a public registry called the **EPSG Geodetic Parameter Dataset (EPSG registry)**, initiated in 1985 by a member of the European Petroleum Survey Group (EPSG). Each CRS has a unique **EPSG code**, which makes it possible to easily identify them among the large number of CRS. This is particularly important for transforming spatial data from one CRS to another. Some of the most commonly used CRS in the Netherlands are the following: -- **World Geodetic System 1984 (WGS84)** is the best known global reference system (EPSG:4326). -- **European Terrestrial Reference System 1989 (ETRS89)** is the standard coordinate system for Europe (EPSG:4258). -- The most popular projected CRS in the Netherlands is ‘Stelsel van de Rijksdriehoeksmeting (RD)’ registered in EPSG as **Amersfoort / RD New (EPSG:28992)**. +- **World Geodetic System 1984 (WGS84)** is the best known global reference system **(EPSG:4326)**. +- **European Terrestrial Reference System 1989 (ETRS89)** is the standard coordinate system for Europe **(EPSG:4258)**. +- The most popular projected CRS in the Netherlands is ‘Stelsel van de Rijksdriehoeksmeting (RD)’ registered in EPSG as **‘Amersfoort / RD New’ (EPSG:28992)**. The main parameters of each CRS are the following: @@ -73,93 +69,96 @@ The main parameters of each CRS are the following: - **Projection** is mathematical transformation of the angular measurements on the Earth to linear units (e.g. meters) on a flat surface (paper or a computer screen). - **Additional parameters**, such as a definition of the centre of the map, are often necessary to create the full CRS. -In this workshop, we use the following two CRS: +If you work with data for the Netherlands, you will most likely encounter the two CRS shown in the table below, namely the WGS 84 and Amersfoort / RD New. While WGS 84 is used for data for countries worldwide (for example, for OpenStreetMap data), Amersfoort / RD New is a Dutch local CRS. For other countries, other local CRS are available. | | WGS 84 (EPSG:4326) | Amersfoort / RD New (EPSG:28992) | -|-----------------|----------------------------|----------------------------| -| Definition | Dynamic (relies on a datum which is not plate-fixed) | Static (relies on a datum which is plate-fixed) | -| Celestial body | Earth | Earth | -| Ellipsoid | WGS-84 | Bessel 1841 | -| Prime meridian | International Reference Meridian | Greenwich | +|------------------|---------------------------|---------------------------| +| Definition | Dynamic (relies on a datum which is not plate-fixed) | Static (relies on a datum which is plate-fixed) | +| Celestial body | Earth | Earth | +| Ellipsoid | WGS-84 | Bessel 1841 | +| Prime meridian | International Reference Meridian | Greenwich | | Datum | World Geodetic System 1984 ensemble | Amersfoort | | Projection | Geographic (uses latitude and longitude for coordinates) | Projected (uses meters for coordinates) | | Method | Lat/long (Geodetic alias) | Oblique Stereographic Alternative | | Units | Degrees | Meters | - : Main properties of WGS 84 and Amersfoort / RD New coordinate reference systems The figure below shows the same city (Rotterdam) in these two CRS. - + ```{r fig-crs, fig.cap="Rotterdam in two different CRS", echo=FALSE} knitr::include_graphics("fig/rotterdam-crs.png") ``` -In addition to using different CRS, these two maps of Rotterdam also have different scales. - +In addition to using different CRS, these two maps of Rotterdam also have different scales. ## Map scale **Map scale** measures the ratio between distance on a map and the corresponding distance on the ground. For example, on a 1:100 000 scale map, 1cm on the map equals 1km (100 000 cm) on the ground. Map scale can be expressed in the following three ways: -||| -|---|----------| -|Verbal: | 1 centimetre represents 250 meters | -|Fraction: | 1:25000 | -|Graphic: | ![](fig/scalebar.png){width=75%} | +| | | +|-----------|------------------------------------| +| Verbal: | 1 centimetre represents 250 meters | +| Fraction: | 1:25000 | +| Graphic: | ![](fig/scalebar.png){width="75%"} | -::: challenge +## Map scale + +The map of Rotterdam in the figure above shows the area of the city as a discrete feature with precise boundaries. This type of data is called vector. Vector data can have the form of points, lines and polygons (areas). + +```{r fig-vector, fig.cap="Same geographical area as raster and vector data (Source: ???)", echo=FALSE} +knitr::include_graphics("fig/vector-raster-1.png") +``` + +We can also represent geographical features on the Earth as continuous phenomena and images of the Earth. This type of data is called raster. The figure below shows the two types of geospatial data which can be used to represent the real world, namely vector and raster. +```{r fig-vector-raster, fig.cap="Same geographical area as raster and vector data (Source: Saab, 2003)", echo=FALSE} +knitr::include_graphics("fig/vector-raster-2.png") +``` + +::: challenge # Challenge: CRS for calculating areas You want to investigate which European country has the largest urban area. Which CRS will you use? -a. EPSG:4326 -b. EPSG:28992 -c. EPSG:3035 +a. EPSG:4326 +b. EPSG:28992 +c. EPSG:3035 -Hint: Go to https://epsg.io/ or https://epsg.org/search/by-name and check properties of the given CRS, such as datum, type of map projection, units of measure etc. +Hint: Go to or and check properties of the given CRS, such as datum, type of map projection, units of measure etc. ::: solution - Correct answer: c. EPSG:3035 - ::: - ::: - ::: challenge - # Challenge: CRS for calculating shortest paths -You want to calculate the shortest path between two buildings in Delft. Which CRS will you use? +You want to calculate the shortest path between two buildings in Delft. Which CRS will you use? -a. EPSG:4326 -b. EPSG:28992 -c. EPSG:3035 +a. EPSG:4326 +b. EPSG:28992 +c. EPSG:3035 -Hint: Go to https://epsg.io/ or https://epsg.org/search/by-name and check properties of the given CRS, such as datum, type of map projection, units of measure etc. +Hint: Go to or and check properties of the given CRS, such as datum, type of map projection, units of measure etc. ::: solution - Correct answer: b. EPSG:28992 - ::: - ::: ## References -- Knippers, R. (2009): Geometric aspects of mapping. International Institute for Geo-Information Science and Earth Observation (ITC), Enschede. https://kartoweb.itc.nl/geometrics/ (Accessed 22-01-2024) +- Knippers, R. (2009): Geometric aspects of mapping. International Institute for Geo-Information Science and Earth Observation (ITC), Enschede. (Accessed 22-01-2024) -- United Nations Statistics Division and International Cartographic Association (2012): 3. Plane rectangular coordinate systems – A) The ellipsoid / geoid. https://unstats.un.org/unsd/geoinfo/ungegn/docs/_data_icacourses/_HtmlModules/_Selfstudy/S06/S06_03a.html (Accessed 22-01-2024) +- Saab, D. J. (2003). Conceptualizing space: Mapping schemas as meaningful representations. Unpublished Master's Thesis, Lesley University, Cambridge, MA, http://www.djsaab.info/thesis/djsaab_thesis.pdf. -- Van der Marel, H. (2014). Reference systems for surveying and mapping. Lecture notes. Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands. https://gnss1.tudelft.nl/pub/vdmarel/reader/CTB3310_RefSystems_1-2a_print.pdf (Accessed 22-01-2024) +- United Nations Statistics Division and International Cartographic Association (2012): 3. Plane rectangular coordinate systems – A) The ellipsoid / geoid. (Accessed 22-01-2024) +- Van der Marel, H. (2014). Reference systems for surveying and mapping. Lecture notes. Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands. (Accessed 22-01-2024) ::: callout - # Useful resources - Campbell, J., Shin, M. E. (2011). Essentials of Geographic Information Systems. Textbooks. 2. (Accessed 22-01-2024) @@ -174,9 +173,7 @@ Correct answer: b. 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a/renv.lock +++ b/renv.lock @@ -1,6 +1,6 @@ { "R": { - "Version": "4.3.0", + "Version": "4.3.3", "Repositories": [ { "Name": "CRAN", @@ -22,18 +22,18 @@ }, "KernSmooth": { "Package": "KernSmooth", - "Version": "2.23-20", + "Version": "2.23-22", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "stats" ], - "Hash": "8dcfa99b14c296bc9f1fd64d52fd3ce7" + "Hash": "2fecebc3047322fa5930f74fae5de70f" }, "MASS": { "Package": "MASS", - "Version": "7.3-58.4", + "Version": "7.3-60.0.1", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -44,15 +44,16 @@ "stats", "utils" ], - "Hash": "a3142b2a022b8174ca675bc8b80cdc4e" + "Hash": "b765b28387acc8ec9e9c1530713cb19c" }, "Matrix": { "Package": "Matrix", - "Version": "1.5-4", + "Version": "1.6-5", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", + "grDevices", "graphics", "grid", "lattice", @@ -60,7 +61,7 @@ "stats", "utils" ], - "Hash": "e779c7d9f35cc364438578f334cffee2" + "Hash": "8c7115cd3a0e048bda2a7cd110549f7a" }, "PROJ": { "Package": "PROJ", @@ -249,7 +250,7 @@ }, "class": { "Package": "class", - "Version": "7.3-21", + "Version": "7.3-22", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -258,7 +259,7 @@ "stats", "utils" ], - "Hash": "8ae0d4328e2eb3a582dfd5391a3663b7" + "Hash": "f91f6b29f38b8c280f2b9477787d4bb2" }, "classInt": { "Package": "classInt", @@ -934,7 +935,7 @@ }, "lattice": { "Package": "lattice", - "Version": "0.21-8", + "Version": "0.22-5", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -945,7 +946,7 @@ "stats", "utils" ], - "Hash": "0b8a6d63c8770f02a8b5635f3c431e6b" + "Hash": "7c5e89f04e72d6611c77451f6331a091" }, "lazyeval": { "Package": "lazyeval", @@ -1042,7 +1043,7 @@ }, "mgcv": { "Package": "mgcv", - "Version": "1.8-42", + "Version": "1.9-1", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -1055,7 +1056,7 @@ "stats", "utils" ], - "Hash": "3460beba7ccc8946249ba35327ba902a" + "Hash": "110ee9d83b496279960e162ac97764ce" }, "mime": { "Package": "mime", @@ -1098,7 +1099,7 @@ }, "nlme": { "Package": "nlme", - "Version": "3.1-162", + "Version": "3.1-164", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -1108,7 +1109,7 @@ "stats", "utils" ], - "Hash": "0984ce8da8da9ead8643c5cbbb60f83e" + "Hash": "a623a2239e642806158bc4dc3f51565d" }, "nominatimlite": { "Package": "nominatimlite", @@ -1382,13 +1383,13 @@ }, "renv": { "Package": "renv", - "Version": "1.0.3", + "Version": "1.0.7", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "utils" ], - "Hash": "41b847654f567341725473431dd0d5ab" + "Hash": "397b7b2a265bc5a7a06852524dabae20" }, "reprex": { "Package": "reprex", diff --git a/renv/activate.R b/renv/activate.R index 9b2e7f18..d13f9932 100644 --- a/renv/activate.R +++ b/renv/activate.R @@ -2,11 +2,13 @@ local({ # the requested version of renv - version <- "1.0.5" + version <- "1.0.7" attr(version, "sha") <- NULL # the project directory - project <- getwd() + project <- Sys.getenv("RENV_PROJECT") + if (!nzchar(project)) + project <- getwd() # use start-up diagnostics if enabled diagnostics <- Sys.getenv("RENV_STARTUP_DIAGNOSTICS", unset = "FALSE") @@ -129,6 +131,21 @@ local({ } + heredoc <- function(text, leave = 0) { + + # remove leading, trailing whitespace + trimmed <- gsub("^\\s*\\n|\\n\\s*$", "", text) + + # split into lines + lines <- strsplit(trimmed, "\n", fixed = TRUE)[[1L]] + + # compute common indent + indent <- regexpr("[^[:space:]]", lines) + common <- min(setdiff(indent, -1L)) - leave + paste(substring(lines, common), collapse = "\n") + + } + startswith <- function(string, prefix) { substring(string, 1, nchar(prefix)) == prefix } @@ -631,6 +648,9 @@ local({ # if the user has requested an automatic prefix, generate it auto <- Sys.getenv("RENV_PATHS_PREFIX_AUTO", unset = NA) + if (is.na(auto) && getRversion() >= "4.4.0") + auto <- "TRUE" + if (auto %in% c("TRUE", "True", "true", "1")) return(renv_bootstrap_platform_prefix_auto()) @@ -822,24 +842,23 @@ local({ # the loaded version of renv doesn't match the requested version; # give the user instructions on how to proceed - remote <- if (!is.null(description[["RemoteSha"]])) { + dev <- identical(description[["RemoteType"]], "github") + remote <- if (dev) paste("rstudio/renv", description[["RemoteSha"]], sep = "@") - } else { + else paste("renv", description[["Version"]], sep = "@") - } # display both loaded version + sha if available friendly <- renv_bootstrap_version_friendly( version = description[["Version"]], - sha = description[["RemoteSha"]] + sha = if (dev) description[["RemoteSha"]] ) - fmt <- paste( - "renv %1$s was loaded from project library, but this project is configured to use renv %2$s.", - "- Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile.", - "- Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library.", - sep = "\n" - ) + fmt <- heredoc(" + renv %1$s was loaded from project library, but this project is configured to use renv %2$s. + - Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile. + - Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library. + ") catf(fmt, friendly, renv_bootstrap_version_friendly(version), remote) FALSE From 04f5f258e6b8765da21b2f7ddbd6351e94979aff Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 21 May 2024 12:30:14 +0200 Subject: [PATCH 16/24] Fix reference year --- episodes/08-intro-to-geospatial-concepts.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/08-intro-to-geospatial-concepts.Rmd b/episodes/08-intro-to-geospatial-concepts.Rmd index 9f41de1d..0c048105 100644 --- a/episodes/08-intro-to-geospatial-concepts.Rmd +++ b/episodes/08-intro-to-geospatial-concepts.Rmd @@ -21,7 +21,7 @@ After completing this episode, participants should be able to… The shape of the Earth is approximately a sphere which is slightly wider than it is tall, and which is called **ellipsoid**. The true shape of the Earth is an irregular ellipsoid, the so-called **geoid**, as illustrated in the image below. -```{r fig-earth, fig.cap="The shape of the Earth. Source: United Nations Statistics Division and International Cartographic Association (2012a).", echo=FALSE} +```{r fig-earth, fig.cap="The shape of the Earth. Source: United Nations Statistics Division and International Cartographic Association (2012).", echo=FALSE} knitr::include_graphics("https://unstats.un.org/unsd/geoinfo/ungegn/docs/_data_icacourses/_ImagesModules/_selfstudy/S06_images/S06_03_a00.jpg") ``` From 992cb5264fbb1e3bd598e73e8dd9822af538ccaa Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 21 May 2024 12:30:27 +0200 Subject: [PATCH 17/24] Fix heading in geospatial concepts episode --- episodes/08-intro-to-geospatial-concepts.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/08-intro-to-geospatial-concepts.Rmd b/episodes/08-intro-to-geospatial-concepts.Rmd index 0c048105..a84a839e 100644 --- a/episodes/08-intro-to-geospatial-concepts.Rmd +++ b/episodes/08-intro-to-geospatial-concepts.Rmd @@ -102,7 +102,7 @@ In addition to using different CRS, these two maps of Rotterdam also have differ | Fraction: | 1:25000 | | Graphic: | ![](fig/scalebar.png){width="75%"} | -## Map scale +## Types of geospatial data The map of Rotterdam in the figure above shows the area of the city as a discrete feature with precise boundaries. This type of data is called vector. Vector data can have the form of points, lines and polygons (areas). From e2133ff7106ae2165fb1ee3cdfea74db0c0d5d36 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 21 May 2024 12:30:43 +0200 Subject: [PATCH 18/24] Add link to previous episode --- episodes/09-open-and-plot-vector-layers.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/09-open-and-plot-vector-layers.Rmd b/episodes/09-open-and-plot-vector-layers.Rmd index c461cef5..b43c3ee3 100644 --- a/episodes/09-open-and-plot-vector-layers.Rmd +++ b/episodes/09-open-and-plot-vector-layers.Rmd @@ -102,7 +102,7 @@ st_crs(boundary_Delft)$Name st_crs(boundary_Delft)$epsg ``` -The `$` operator is used to extract a specific part of an object. We used it in a [previous episode]() to subset a data frame by column name. In this case, it is used to extract named elements stored in a `crs` object. For more information, see [the documentation of the `st_crs` function](https://r-spatial.github.io/sf/reference/st_crs.html#details). +The `$` operator is used to extract a specific part of an object. We used it in a [previous episode](../episodes/03-explore-data.Rmd) to subset a data frame by column name. In this case, it is used to extract named elements stored in a `crs` object. For more information, see [the documentation of the `st_crs` function](https://r-spatial.github.io/sf/reference/st_crs.html#details). ::: From dea34adf9cb56a6c2e8226834fb7e075dacd7e52 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 21 May 2024 12:31:50 +0200 Subject: [PATCH 19/24] Update renv.lock --- renv.lock | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/renv.lock b/renv.lock index 94fa87bd..669c79eb 100644 --- a/renv.lock +++ b/renv.lock @@ -1384,12 +1384,9 @@ "renv": { "Package": "renv", "Version": "1.0.7", - "Source": "Repository", + "OS_type": null, "Repository": "CRAN", - "Requirements": [ - "utils" - ], - "Hash": "397b7b2a265bc5a7a06852524dabae20" + "Source": "Repository" }, "reprex": { "Package": "reprex", From dbc57069e3f267e59bd7197db017953b3e21d1e3 Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 21 May 2024 13:29:51 +0200 Subject: [PATCH 20/24] Link to our episode for more info on CRS --- episodes/09-open-and-plot-vector-layers.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/09-open-and-plot-vector-layers.Rmd b/episodes/09-open-and-plot-vector-layers.Rmd index b43c3ee3..da6c35a3 100644 --- a/episodes/09-open-and-plot-vector-layers.Rmd +++ b/episodes/09-open-and-plot-vector-layers.Rmd @@ -137,7 +137,7 @@ boundary_Delft ::: callout -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). +Read more about Coordinate Reference Systems [in the previous episode](../episodes/08-intro-to-geospatial-concepts.Rmd). We will also practice transformation between CRS in [Handling Spatial Projection & CRS](../episodes/12-handling-spatial-projection-and-crs.Rmd). ::: From be59db68e21de29a8dc8c58a4e0da5b6346e714c Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 21 May 2024 13:30:12 +0200 Subject: [PATCH 21/24] Add intro text to last vector episode --- episodes/12-handling-spatial-projection-and-crs.Rmd | 3 +++ 1 file changed, 3 insertions(+) diff --git a/episodes/12-handling-spatial-projection-and-crs.Rmd b/episodes/12-handling-spatial-projection-and-crs.Rmd index c79f8f8d..f6fba043 100644 --- a/episodes/12-handling-spatial-projection-and-crs.Rmd +++ b/episodes/12-handling-spatial-projection-and-crs.Rmd @@ -30,6 +30,9 @@ After completing this episode, participants should be able to… ## Working with spatial data from different sources +In this episode, we will work with a different dataset containing Dutch municipal +boundaries. We start by reading the data and plotting it. + ```{r minicipal-boundary} municipal_boundary_NL <- st_read("data/nl-gemeenten.shp") ``` From 2ade9c9d177061befbf4e80e4bfe59749100a45a Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 21 May 2024 13:30:40 +0200 Subject: [PATCH 22/24] Add more text to last vector episode --- episodes/12-handling-spatial-projection-and-crs.Rmd | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/episodes/12-handling-spatial-projection-and-crs.Rmd b/episodes/12-handling-spatial-projection-and-crs.Rmd index f6fba043..ab4b0d2c 100644 --- a/episodes/12-handling-spatial-projection-and-crs.Rmd +++ b/episodes/12-handling-spatial-projection-and-crs.Rmd @@ -44,7 +44,7 @@ ggplot() + coord_sf(datum = st_crs(28992)) ``` -We can add a country boundary layer to make it look nicer. If we specify a thicker line width using size = 2 for the country boundary layer, it will make our map pop! +We can add a country boundary layer to make it look nicer. If we specify a thicker line width using size = 2 for the country boundary layer, it will make our map pop! We read the country boundary from a different file. ```{r country-boundary} country_boundary_NL <- st_read("data/nl-boundary.shp") @@ -61,6 +61,8 @@ ggplot() + coord_sf(datum = st_crs(28992)) ``` +We confirm that the CRS of both boundaries is 28992. + ```{r minicipal-boundary-crs} st_crs(municipal_boundary_NL)$epsg ``` @@ -69,6 +71,9 @@ st_crs(municipal_boundary_NL)$epsg st_crs(country_boundary_NL)$epsg ``` +We read the municipal boundary of Delft and make sure that it is in the same CRS +as the country-level municipal boundaries and country boundary layers. + ```{r municipal-boundary-crs-2} boundary_Delft <- st_read("data/delft-boundary.shp") st_crs(boundary_Delft)$epsg From c4cee6f6723737201947fc33423c39d11d74195e Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci Date: Tue, 21 May 2024 13:31:04 +0200 Subject: [PATCH 23/24] Add callout on projecting for viz vs. GIS --- episodes/12-handling-spatial-projection-and-crs.Rmd | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/episodes/12-handling-spatial-projection-and-crs.Rmd b/episodes/12-handling-spatial-projection-and-crs.Rmd index ab4b0d2c..5e1e25d8 100644 --- a/episodes/12-handling-spatial-projection-and-crs.Rmd +++ b/episodes/12-handling-spatial-projection-and-crs.Rmd @@ -139,7 +139,13 @@ ggplot() + ::: +::: callout +# Projecting layers + +Note that `ggplot2` may reproject the layers on the fly for visualisation purposes, but for geoprocessing purposes, you still need to reproject the layers explicitly with `st_transform()`. This will become clear in a [later episode](../episodes/19-basic-gis-with-r-sf.Rmd) when we perform GIS operations. + +::: ## Export a shapefile From 101fb45611206f0e900dbda64fab644404767a0b Mon Sep 17 00:00:00 2001 From: Claudiu Forgaci <33600128+cforgaci@users.noreply.github.com> Date: Tue, 8 Oct 2024 21:58:42 +0200 Subject: [PATCH 24/24] Update episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd Co-authored-by: iurriayanez <152204909+iurriayanez@users.noreply.github.com> --- episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 78a9c82b..70fd766f 100644 --- a/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd +++ b/episodes/10-explore-and-plot-by-vector-layer-attributes.Rmd @@ -146,7 +146,7 @@ factor(point_Delft$leisure) %>% head(n = 3) ``` -3. To see a list of all attribute names and answer the last question, we can use the `names()` function. +3. To see a list of all fields names and answer the last question, we can use the `names()` function. ```{r points-names} names(point_Delft)