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Map projection represented as flattening an orange peel. Source: Data Carpentry (2023).

-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.
Cylindrical, conic, and azimuthal map projections. Source: Knippers (2009). @@ -162,6 +164,7 @@ 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) diff --git a/09-open-and-plot-vector-layers.md b/09-open-and-plot-vector-layers.md index acb6850e..247081a1 100644 --- a/09-open-and-plot-vector-layers.md +++ b/09-open-and-plot-vector-layers.md @@ -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 @@ -48,9 +47,9 @@ 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). ::: @@ -76,9 +75,25 @@ 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. + + +```r +st_read("data/delft-boundary.shp") +``` + +```{.output} +Reading layer `delft-boundary' from data source + `/home/runner/work/r-geospatial-urban/r-geospatial-urban/site/built/data/delft-boundary.shp' + using driver `ESRI Shapefile' +Simple feature collection with 1 feature and 1 field +Geometry type: POLYGON +Dimension: XY +Bounding box: xmin: 4.320218 ymin: 51.96632 xmax: 4.407911 ymax: 52.0326 +Geodetic CRS: WGS 84 +``` -The `st_geometry_type()` function, for instance, gives us information about the geometry type, which in this case is `POLYGON`. +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 @@ -121,11 +136,30 @@ GEOGCRS["WGS 84", # 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)$Name +``` + +```{.output} +[1] "WGS 84" +``` + +```r +st_crs(boundary_Delft)$epsg +``` + +```{.output} +[1] 4326 +``` + +The `$` operator is used to extract a specific part of an object. We used it in a [previous episode]() to subset a data frame by column name. In this case, it is used to extract named elements stored in a `crs` object. For more information, see [the documentation of the `st_crs` function](https://r-spatial.github.io/sf/reference/st_crs.html#details). ::: -The `st_bbox()` function shows the extent of the layer. As `WGS 84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees. +The `st_bbox()` function shows the extent of the layer. @@ -138,12 +172,12 @@ st_bbox(boundary_Delft) 4.320218 51.966316 4.407911 52.032599 ``` -We need a **projected CRS**, which in the case of the Netherlands is typically the Amersfort / RD New projection. To reproject our shapefile, we will use the `st_transform()` function. For the `crs` argument we can use the EPSG code of the CRS we want to use, which is `28992` for the `Amersfort / RD New` projection. +As `WGS 84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees. We need a **projected CRS**, which in the case of the Netherlands is the `Amersfoort / RD New` projection. To reproject our shapefile, we will use the `st_transform()` function. For the `crs` argument we can use the EPSG code of the CRS we want to use, which is `28992` for the `Amersfort / RD New` projection. To check the EPSG code of any CRS, we can check this website: https://epsg.io/ ```r -boundary_Delft <- st_transform(boundary_Delft, 28992) +boundary_Delft <- st_transform(boundary_Delft, crs = 28992) st_crs(boundary_Delft)$Name ``` @@ -159,7 +193,7 @@ st_crs(boundary_Delft)$epsg [1] 28992 ``` -Notice that the bounding box is measured in meters after the transformation. +Notice that the bounding box is measured in meters after the transformation. The `$units_gdal` named element confirms that the new CRS uses metric units. ```r @@ -206,7 +240,7 @@ 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 @@ -216,7 +250,7 @@ ggplot(data = boundary_Delft) + coord_sf(datum = st_crs(28992)) # displays the axes in meters ``` - + ::::::::::::::::::::::::::::::::::::: challenge @@ -224,10 +258,9 @@ 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 @@ -258,7 +291,7 @@ st_geometry_type(points_Delft)[2] 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE ``` -`lines_Delft` and `points_Delft` are in the correct CRS. +Both `lines_Delft` and `points_Delft` are in `EPSG:28992`. ```r @@ -306,8 +339,8 @@ 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. :::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/10-explore-and-plot-by-vector-layer-attributes.md b/10-explore-and-plot-by-vector-layer-attributes.md index d8162dce..6afaefea 100644 --- a/10-explore-and-plot-by-vector-layer-attributes.md +++ b/10-explore-and-plot-by-vector-layer-attributes.md @@ -21,7 +21,7 @@ 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. :::::::::::::::::::::::::::::::::::::::::::::::: @@ -65,7 +65,7 @@ ncol(lines_Delft) [1] 3 ``` -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 @@ -89,7 +89,7 @@ We can also preview the content of the object by looking at the first 6 rows wit ```r -head (lines_Delft) +head(lines_Delft) ``` ```{.output} @@ -110,9 +110,7 @@ Projected CRS: Amersfoort / RD New ## 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 11244 values of `highway`, we will preview it with the `head()` function: ```r @@ -124,7 +122,7 @@ head(lines_Delft$highway, 10) [7] "service" "steps" "footway" "footway" ``` -To see only unique values within the `highway` field, we can use the `unique()` function. This function extracts all possible values of a character variable. +The first rows returned by the `head()` function do not necessarily contain all unique values within the `highway` field. To see all unique values, we can use the `unique()` function. This function extracts all possible values of a character variable. For the `highway` field, this returns all types of roads stored in `lines_Delft`. ```r @@ -147,7 +145,7 @@ R is also able to handle categorical variables called factors. With factors, we ```r -levels(factor(lines_Delft$highway)) +factor(lines_Delft$highway) %>% levels() ``` ```{.output} @@ -170,15 +168,15 @@ Note that this way the values are shown by default in alphabetical order and `NA Explore the attributes associated with the `point_Delft` spatial object. -1. How many attributes does it have? +1. How many fields does it have? 2. What types of leisure points do the points represent? Give three examples. -3. Which of the following is NOT an attribute of the `point_Delft` data object? +3. Which of the following is NOT a field of the `point_Delft` object? - A) location B) leisure C) osm_id + A) `location` B) `leisure` C) `osm_id` :::::::::::::::::::::::: solution -1. To find the number of attributes, we use the `ncol()` function: +1. To find the number of fields, we use the `ncol()` function: ```r @@ -213,10 +211,11 @@ Projected CRS: Amersfoort / RD New 6 484697814 POINT (83892.53 447475.5) ``` -We can increase the number of rows with the n argument (e.g., `head(n = 10)` to show 10 rows) until we see at least three distinct values in the leisure column. Note that printing an `sf` object will also display the first 10 rows. +We can increase the number of rows with the `n` argument (e.g., `head(n = 10)` to show 10 rows) until we see at least three distinct values in the leisure column. Note that printing an `sf` object will also display the first 10 rows. ```r +# you might be lucky to see three distinct values head(point_Delft, 10) ``` @@ -239,15 +238,12 @@ Projected CRS: Amersfoort / RD New 10 1148515039 playground POINT (84661.3 446818) ``` -```r -# you might be lucky to see three distinct values -``` - We have our answer (`sports_centre` is the third value), but in general this is not a good approach as the first rows might still have many `NA`s and three distinct values might still not be present in the first `n` rows of the data frame. To remove `NA`s, we can use the function `na.omit()` on the leisure column to remove `NA`s completely. Note that we use the `$` operator to examine the content of a single variable. ```r -head(na.omit(point_Delft$leisure)) # this is better +# this is better +na.omit(point_Delft$leisure) %>% head() ``` ```{.output} @@ -259,18 +255,17 @@ To show only unique values, we can use the `levels()` function on a factor to on ```r -head(levels(factor(point_Delft$leisure)), n = 3) +# this is even better +factor(point_Delft$leisure) %>% + levels() %>% + head(n = 3) ``` ```{.output} [1] "dance" "dog_park" "escape_game" ``` -```r -# this is even better -``` - -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 @@ -281,6 +276,8 @@ names(point_Delft) [1] "osm_id" "leisure" "geometry" ``` +A) `location` is not a field of the `point_Delft` object. + ::::::::::::::::::::::::::::::::: :::::::::::::::::::::::::::::::::::::::::::::::: @@ -288,11 +285,11 @@ 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") ``` @@ -315,7 +312,7 @@ nrow(cycleway_Delft) [1] 1397 ``` -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 @@ -343,8 +340,7 @@ 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)) ``` @@ -356,13 +352,12 @@ ggplot(data = cycleway_Delft) + ::: 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 @@ -415,16 +410,6 @@ First 10 features: 2. There are 48 features with the value `motorway`. -```r -motorway_Delft_length <- motorway_Delft %>% - mutate(length = st_length(.)) %>% - select(everything(), geometry) %>% - summarise(total_length = sum(length)) -``` - -3. The total length of motorways is 14877.4361477941. - - ```r nrow(motorway_Delft) ``` @@ -433,62 +418,30 @@ nrow(motorway_Delft) [1] 48 ``` -4. Plot the motorways. - - -```r -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. +3. The total length of motorways is 14877.4361477941. ```r -pedestrian_Delft <- lines_Delft %>% - filter(highway == "pedestrian") - -pedestrian_Delft %>% +motorway_Delft_length <- motorway_Delft %>% mutate(length = st_length(.)) %>% select(everything(), geometry) %>% summarise(total_length = sum(length)) ``` -```{.output} -Simple feature collection with 1 feature and 1 field -Geometry type: MULTILINESTRING -Dimension: XY -Bounding box: xmin: 82388.15 ymin: 444400.2 xmax: 85875.95 ymax: 447987.8 -Projected CRS: Amersfoort / RD New - total_length geometry -1 12778.84 [m] MULTILINESTRING ((85538.03 ... -``` - -```r -nrow(pedestrian_Delft) -``` - -```{.output} -[1] 234 -``` +4. Plot the motorways. ```r -ggplot() + - geom_sf(data = pedestrian_Delft) + - labs(title = "Slow mobility network", - subtitle = "Pedestrian" - ) + +ggplot(data = motorway_Delft) + + geom_sf(linewidth = 1.5) + + labs(title = "Fast mobility network", + subtitle = "Motorways") + coord_sf(datum = st_crs(28992)) ``` - + ::: @@ -513,7 +466,7 @@ unique(lines_Delft$highway) [25] "platform" "proposed" NA ``` -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 @@ -524,137 +477,65 @@ 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 - -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. +## Adjust line width -Let’s create another plot where we show the different line types with the following thicknesses: +Follow the same steps to add custom line widths for every road type. -- motorways linewidth = 0.25 -- primary linewidth = 0.75 -- secondary linewidth = 0.5 -- cycleway linewidth = 1 +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. -::: solution +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. +3. Plot the result, making sure that `linewidth` is named the same way as `color` in the legend. -```r -levels(factor(lines_Delft_selection$highway)) -``` - -```{.output} -[1] "motorway" "primary" "secondary" "cycleway" -``` +::: solution ```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 -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 -``` - -
-Mobility network in Delft using thicker lines than the previous example. -

Mobility network in Delft using thicker lines than the previous example.

-
- - -```r -p2 <- p1 + - theme(legend.text = element_text(size = 20), - legend.box.background = element_rect(linewidth = 1)) - -# show plot -p2 -``` - -
-Map of the mobility network in Delft with large-font and border around the legend. -

Map of the mobility network in Delft with large-font and border around the legend.

-
- ::: challenge # Challenge: Plot lines by attributes @@ -662,6 +543,8 @@ 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 @@ -709,78 +592,7 @@ ggplot(data = lines_Delft) + coord_sf(datum = st_crs(28992)) ``` - - -::: - -::: - -::: 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") -``` - -```{.output} -Reading layer `nl-gemeenten' from data source - `/home/runner/work/r-geospatial-urban/r-geospatial-urban/site/built/data/nl-gemeenten.shp' - using driver `ESRI Shapefile' -Simple feature collection with 344 features and 6 fields -Geometry type: MULTIPOLYGON -Dimension: XY -Bounding box: xmin: 10425.16 ymin: 306846.2 xmax: 278026.1 ymax: 621876.3 -Projected CRS: Amersfoort / RD New -``` - - -```r -str(municipal_boundaries_NL) -``` - -```{.output} -Classes 'sf' and 'data.frame': 344 obs. of 7 variables: - $ identifica: chr "GM0014" "GM0034" "GM0037" "GM0047" ... - $ naam : chr "Groningen" "Almere" "Stadskanaal" "Veendam" ... - $ code : chr "0014" "0034" "0037" "0047" ... - $ ligtInProv: chr "20" "24" "20" "20" ... - $ ligtInPr_1: chr "Groningen" "Flevoland" "Groningen" "Groningen" ... - $ fuuid : chr "gemeentegebied.ee21436e-5a2d-4a8f-b2bf-113bddd028fc" "gemeentegebied.6e4378d7-0905-4dff-b351-57c1940c9c90" "gemeentegebied.515fbfe4-614e-463d-8b8c-91d35ca93b3b" "gemeentegebied.a3e71341-218c-44bf-ba12-01e2251ea2f6" ... - $ geometry :sfc_MULTIPOLYGON of length 344; first list element: List of 1 - ..$ :List of 1 - .. ..$ : num [1:4749, 1:2] 238742 238741 238740 238738 238735 ... - ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg" - - attr(*, "sf_column")= chr "geometry" - - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA - ..- attr(*, "names")= chr [1:6] "identifica" "naam" "code" "ligtInProv" ... -``` - -```r -levels(factor(municipal_boundaries_NL$ligtInPr_1)) -``` - -```{.output} - [1] "Drenthe" "Flevoland" "Fryslân" "Gelderland" - [5] "Groningen" "Limburg" "Noord-Brabant" "Noord-Holland" - [9] "Overijssel" "Utrecht" "Zeeland" "Zuid-Holland" -``` - - -```r -ggplot(data = municipal_boundaries_NL) + - geom_sf(aes(color = ligtInPr_1), linewidth = 1) + - labs(title = "Contiguous NL Municipal Boundaries") + - coord_sf(datum = st_crs(28992)) -``` - - + ::: diff --git a/18-import-and-visualise-osm-data.md b/18-import-and-visualise-osm-data.md index c65283f5..c8dddf2e 100644 --- a/18-import-and-visualise-osm-data.md +++ b/18-import-and-visualise-osm-data.md @@ -318,8 +318,8 @@ leaflet(buildings2) %>% bringToFront = TRUE)) ``` -
- +
+ ::::::::::::::::::::::::::::::::: diff --git a/19-basic-gis-with-r-sf.md b/19-basic-gis-with-r-sf.md index a0a46c0e..aeaaf48f 100644 --- a/19-basic-gis-with-r-sf.md +++ b/19-basic-gis-with-r-sf.md @@ -63,7 +63,7 @@ summary(buildings$start_date) ```{.output} Length Class Mode - 10601 character character + 10625 character character ``` ```r @@ -325,7 +325,7 @@ single_buffer$old_buildings_per_km2 <- as.numeric(single_buffer$n_buildings / si ``` ```{.error} -Error in `[[<-.data.frame`(`*tmp*`, i, value = numeric(0)): replacement has 0 rows, data has 158 +Error in `[[<-.data.frame`(`*tmp*`, i, value = numeric(0)): replacement has 0 rows, data has 159 ``` ```r diff --git a/fig/09-open-and-plot-vector-layers-rendered-unnamed-chunk-8-1.png b/fig/09-open-and-plot-vector-layers-rendered-unnamed-chunk-10-1.png similarity index 100% rename from fig/09-open-and-plot-vector-layers-rendered-unnamed-chunk-8-1.png rename to fig/09-open-and-plot-vector-layers-rendered-unnamed-chunk-10-1.png diff --git a/fig/10-explore-and-plot-by-vector-layer-attributes-rendered-unnamed-chunk-22-1.png b/fig/10-explore-and-plot-by-vector-layer-attributes-rendered-unnamed-chunk-23-1.png similarity index 100% rename from fig/10-explore-and-plot-by-vector-layer-attributes-rendered-unnamed-chunk-22-1.png rename to fig/10-explore-and-plot-by-vector-layer-attributes-rendered-unnamed-chunk-23-1.png diff --git a/fig/10-explore-and-plot-by-vector-layer-attributes-rendered-unnamed-chunk-24-1.png b/fig/10-explore-and-plot-by-vector-layer-attributes-rendered-unnamed-chunk-24-1.png deleted file mode 100644 index 66626ae73f043a4d31951a38e0abe733f07027c8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10838 zcmb_?bx@p7w7aBy;RK7IO>i;Ih! zo12G+hnJW4*|TSSe03Q<>loS6ciK{6_u2fl$DiLR8(HQdZntWs-~u z%*@=}+`_`b($dn(%F5c>+Q!Dl*4Ea}&d%Q6-oe4a(b3V#$;sK-*~P`h)z#I_&CT82 z-NVDf)6>(-%gfu_+sDTT3^2 zE-oo4DJ?Di{P}ZPSy_2`c|}DBeM3V-V`F1eQ&V$u 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