More graphics with `ggplot2`

Practical Computing and Data Science Tools

Agenda

Midterm reminder
Spatial data with sf
Discussion: any questions about spatial data and the upcoming midterm

Midterm II

The second midterm is this Thursday, Nov 21st, during lab time.
Closed book, closed notes.
Handwritten “cheat sheet” allowed, just like Midterm I
Same format as Midterm I: 5 questions, top 4 are graded.

`sf`

sf is an R package for handling spatial data. It includes functions to read, write, modify, and plot spatial data.

To install sf, you can run install.packages("sf"), but will likely need to install a few more things if you are running Linux.

After you’ve installed sf, try loading the package:

library(sf)

Reading spatial data

Consider the following dataset:

https://www.portlandmaps.com/metadata/index.cfm?action=DisplayLayer&LayerID=52451

Reading spatial data

Now that we’ve taken a look at the data, downloaded and unzipped the file, let’s load it with sf

park_shape <- st_read("Parks/Parks.shp", as_tibble = TRUE)

Reading layer `Parks' from data source 
  `/home/grayson/courses/FOR128/FOR128.github.io/slides/Parks/Parks.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 318 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -13673090 ymin: 5689736 xmax: -13622730 ymax: 5724555
Projected CRS: WGS 84 / Pseudo-Mercator

Examining spatial data

park_shape

Simple feature collection with 318 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -13673090 ymin: 5689736 xmax: -13622730 ymax: 5724555
Projected CRS: WGS 84 / Pseudo-Mercator
# A tibble: 318 × 7
   OBJECTID PROPERTYID NAME                       ACRES Shape_Leng Shape_Area
      <int>      <int> <chr>                      <dbl>      <dbl>      <dbl>
 1        1         48 Albert Kelly Park         12.1        1439.     99518.
 2        2         49 Alberta Park              16.4        1597.    135442.
 3        3         51 April Hill Park           10.1        1455.     83301.
 4        4         52 Arbor Lodge Park           8.69       1089.     71784.
 5        5         53 Argay Park                 8.93       1172.     73685.
 6        6       1221 Arnold Creek Natural Area  0.479       258.      3940.
 7        7       1218 Beach Community Garden     0.413       244.      3411.
 8        8         57 Berkeley Park              6.48       1150.     53338.
 9        9         59 Berrydale Park             6.31       1110.     52025.
10       10         63 Brentwood Park            14.1        1703.    115738.
# ℹ 308 more rows
# ℹ 1 more variable: geometry <MULTIPOLYGON [m]>

class(park_shape)

[1] "sf"         "tbl_df"     "tbl"        "data.frame"

Examining spatial data

str(park_shape)

sf [318 × 7] (S3: sf/tbl_df/tbl/data.frame)
 $ OBJECTID  : int [1:318] 1 2 3 4 5 6 7 8 9 10 ...
 $ PROPERTYID: int [1:318] 48 49 51 52 53 1221 1218 57 59 63 ...
 $ NAME      : chr [1:318] "Albert Kelly Park" "Alberta Park" "April Hill Park" "Arbor Lodge Park" ...
 $ ACRES     : num [1:318] 12.09 16.4 10.12 8.69 8.93 ...
 $ Shape_Leng: num [1:318] 1439 1597 1455 1089 1172 ...
 $ Shape_Area: num [1:318] 99518 135442 83301 71784 73685 ...
 $ geometry  :sfc_MULTIPOLYGON of length 318; first list element: List of 1
  ..$ :List of 1
  .. ..$ : num [1:38, 1:2] -13660625 -13660626 -13660545 -13660425 -13660339 ...
  ..- 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] "OBJECTID" "PROPERTYID" "NAME" "ACRES" ...

Spatial data structure

When you load in spatial data with sf, you get a modified version of a tibble to work with. Full functionality of dplyr, tidyr, ggplot2, etc. is thus available for sf objects.

Plotting spatial data with `ggplot2`

By default, geom_sf() takes a “geometry” aesthetic mapping. It will assign this mapping automatically.

library(tidyverse)
ggplot(park_shape) + 
  geom_sf()

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Equivalently, you could write

ggplot(park_shape) + 
  geom_sf(aes(geometry = geometry))

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

You can add layers just like any ggplot object

ggplot(park_shape) + 
  geom_sf() + 
  theme_bw()

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

You can specify aesthetic mappings just like any ggplot object

ggplot(park_shape) + 
  geom_sf(aes(fill = ACRES)) + 
  theme_bw()

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

You can set values just like an ggplot object

ggplot(park_shape) + 
  geom_sf(fill = "forestgreen") + 
  theme_bw()

Plotting spatial data with `ggplot2`

Projections

We live on a globe, but we like to represent where we live on flat maps.
Therefore, we need to choose how to project this curved surface onto a map.
Spatial data has a projection chosen when loaded. With sf, we can easily modify the projection.

Projections

# get current projection
st_crs(park_shape)

Coordinate Reference System:
  User input: WGS 84 / Pseudo-Mercator 
  wkt:
PROJCRS["WGS 84 / Pseudo-Mercator",
    BASEGEOGCRS["WGS 84",
        ENSEMBLE["World Geodetic System 1984 ensemble",
            MEMBER["World Geodetic System 1984 (Transit)"],
            MEMBER["World Geodetic System 1984 (G730)"],
            MEMBER["World Geodetic System 1984 (G873)"],
            MEMBER["World Geodetic System 1984 (G1150)"],
            MEMBER["World Geodetic System 1984 (G1674)"],
            MEMBER["World Geodetic System 1984 (G1762)"],
            MEMBER["World Geodetic System 1984 (G2139)"],
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]],
            ENSEMBLEACCURACY[2.0]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4326]],
    CONVERSION["Popular Visualisation Pseudo-Mercator",
        METHOD["Popular Visualisation Pseudo Mercator",
            ID["EPSG",1024]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",0,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["False easting",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["easting (X)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["northing (Y)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Web mapping and visualisation."],
        AREA["World between 85.06°S and 85.06°N."],
        BBOX[-85.06,-180,85.06,180]],
    ID["EPSG",3857]]

Projections

# modify projection
park_shape_3310 <- st_transform(park_shape, crs = "EPSG:3310")
park_shape_3875 <- st_transform(park_shape, crs = "EPSG:3875")

Projections

ggplot(park_shape) + 
  geom_sf()

Projections

ggplot(park_shape_3310) + 
  geom_sf()

Projections

ggplot(park_shape_3875) + 
  geom_sf()

Joining with other data

Let’s join this spatial data to the pdxTrees parks data

library(pdxTrees)
parks <- get_pdxTrees_parks()
parks

# A tibble: 25,534 × 34
   Longitude Latitude UserID Genus      Family   DBH Inventory_Date      Species
       <dbl>    <dbl> <chr>  <chr>      <chr>  <dbl> <dttm>              <chr>  
 1     -123.     45.6 1      Pseudotsu… Pinac…  37.4 2017-05-09 00:00:00 PSME   
 2     -123.     45.6 2      Pseudotsu… Pinac…  32.5 2017-05-09 00:00:00 PSME   
 3     -123.     45.6 3      Crataegus  Rosac…   9.7 2017-05-09 00:00:00 CRLA   
 4     -123.     45.6 4      Quercus    Fagac…  10.3 2017-05-09 00:00:00 QURU   
 5     -123.     45.6 5      Pseudotsu… Pinac…  33.2 2017-05-09 00:00:00 PSME   
 6     -123.     45.6 6      Pseudotsu… Pinac…  32.1 2017-05-09 00:00:00 PSME   
 7     -123.     45.6 7      Pseudotsu… Pinac…  28.4 2017-05-09 00:00:00 PSME   
 8     -123.     45.6 8      Pseudotsu… Pinac…  27.2 2017-05-09 00:00:00 PSME   
 9     -123.     45.6 9      Pseudotsu… Pinac…  35.2 2017-05-09 00:00:00 PSME   
10     -123.     45.6 10     Pseudotsu… Pinac…  32.4 2017-05-09 00:00:00 PSME   
# ℹ 25,524 more rows
# ℹ 26 more variables: Common_Name <chr>, Condition <chr>, Tree_Height <dbl>,
#   Crown_Width_NS <dbl>, Crown_Width_EW <dbl>, Crown_Base_Height <dbl>,
#   Collected_By <chr>, Park <chr>, Scientific_Name <chr>,
#   Functional_Type <chr>, Mature_Size <fct>, Native <chr>, Edible <chr>,
#   Nuisance <chr>, Structural_Value <dbl>, Carbon_Storage_lb <dbl>,
#   Carbon_Storage_value <dbl>, Carbon_Sequestration_lb <dbl>, …

Joining with other data

We need to find a “key” to join these data

Joining with other data

# first 6 values of Park column, sorted alphabetically
head(sort(unique(parks$Park)))

[1] "Albert Kelly Park" "Alberta Park"      "Ankeny Plaza"     
[4] "April Hill Park"   "Arbor Lodge Park"  "Argay Park"

# first 6 values of NAME column, sorted alphabetically
head(sort(unique(park_shape$NAME)))

[1] "A Park"                   "Albert Kelly Park"       
[3] "Alberta Park"             "Alder Ridge Natural Area"
[5] "Ankeny Plaza"             "April Hill Park"

It looks like this might do!

Joining with other data

joined_parks <- parks %>%
  left_join(park_shape, by = c("Park" = "NAME"))

Joining with other data

joined_parks

# A tibble: 25,534 × 40
   Longitude Latitude UserID Genus      Family   DBH Inventory_Date      Species
       <dbl>    <dbl> <chr>  <chr>      <chr>  <dbl> <dttm>              <chr>  
 1     -123.     45.6 1      Pseudotsu… Pinac…  37.4 2017-05-09 00:00:00 PSME   
 2     -123.     45.6 2      Pseudotsu… Pinac…  32.5 2017-05-09 00:00:00 PSME   
 3     -123.     45.6 3      Crataegus  Rosac…   9.7 2017-05-09 00:00:00 CRLA   
 4     -123.     45.6 4      Quercus    Fagac…  10.3 2017-05-09 00:00:00 QURU   
 5     -123.     45.6 5      Pseudotsu… Pinac…  33.2 2017-05-09 00:00:00 PSME   
 6     -123.     45.6 6      Pseudotsu… Pinac…  32.1 2017-05-09 00:00:00 PSME   
 7     -123.     45.6 7      Pseudotsu… Pinac…  28.4 2017-05-09 00:00:00 PSME   
 8     -123.     45.6 8      Pseudotsu… Pinac…  27.2 2017-05-09 00:00:00 PSME   
 9     -123.     45.6 9      Pseudotsu… Pinac…  35.2 2017-05-09 00:00:00 PSME   
10     -123.     45.6 10     Pseudotsu… Pinac…  32.4 2017-05-09 00:00:00 PSME   
# ℹ 25,524 more rows
# ℹ 32 more variables: Common_Name <chr>, Condition <chr>, Tree_Height <dbl>,
#   Crown_Width_NS <dbl>, Crown_Width_EW <dbl>, Crown_Base_Height <dbl>,
#   Collected_By <chr>, Park <chr>, Scientific_Name <chr>,
#   Functional_Type <chr>, Mature_Size <fct>, Native <chr>, Edible <chr>,
#   Nuisance <chr>, Structural_Value <dbl>, Carbon_Storage_lb <dbl>,
#   Carbon_Storage_value <dbl>, Carbon_Sequestration_lb <dbl>, …

class(joined_parks)

[1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

Oh no, it is no longer an sf object!

Easily convert back to an `sf` object

joined_parks <- st_as_sf(joined_parks)
joined_parks

Simple feature collection with 25534 features and 39 fields (with 290 geometries empty)
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -13667490 ymin: 5692151 xmax: -13635000 ymax: 5724555
Projected CRS: WGS 84 / Pseudo-Mercator
# A tibble: 25,534 × 40
   Longitude Latitude UserID Genus      Family   DBH Inventory_Date      Species
       <dbl>    <dbl> <chr>  <chr>      <chr>  <dbl> <dttm>              <chr>  
 1     -123.     45.6 1      Pseudotsu… Pinac…  37.4 2017-05-09 00:00:00 PSME   
 2     -123.     45.6 2      Pseudotsu… Pinac…  32.5 2017-05-09 00:00:00 PSME   
 3     -123.     45.6 3      Crataegus  Rosac…   9.7 2017-05-09 00:00:00 CRLA   
 4     -123.     45.6 4      Quercus    Fagac…  10.3 2017-05-09 00:00:00 QURU   
 5     -123.     45.6 5      Pseudotsu… Pinac…  33.2 2017-05-09 00:00:00 PSME   
 6     -123.     45.6 6      Pseudotsu… Pinac…  32.1 2017-05-09 00:00:00 PSME   
 7     -123.     45.6 7      Pseudotsu… Pinac…  28.4 2017-05-09 00:00:00 PSME   
 8     -123.     45.6 8      Pseudotsu… Pinac…  27.2 2017-05-09 00:00:00 PSME   
 9     -123.     45.6 9      Pseudotsu… Pinac…  35.2 2017-05-09 00:00:00 PSME   
10     -123.     45.6 10     Pseudotsu… Pinac…  32.4 2017-05-09 00:00:00 PSME   
# ℹ 25,524 more rows
# ℹ 32 more variables: Common_Name <chr>, Condition <chr>, Tree_Height <dbl>,
#   Crown_Width_NS <dbl>, Crown_Width_EW <dbl>, Crown_Base_Height <dbl>,
#   Collected_By <chr>, Park <chr>, Scientific_Name <chr>,
#   Functional_Type <chr>, Mature_Size <fct>, Native <chr>, Edible <chr>,
#   Nuisance <chr>, Structural_Value <dbl>, Carbon_Storage_lb <dbl>,
#   Carbon_Storage_value <dbl>, Carbon_Sequestration_lb <dbl>, …

class(joined_parks)

[1] "sf"          "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

Let’s check how many rows did not get joined

anti_join(parks, park_shape, by = c("Park" = "NAME"))

# A tibble: 290 × 34
   Longitude Latitude UserID Genus Family        DBH Inventory_Date      Species
       <dbl>    <dbl> <chr>  <chr> <chr>       <dbl> <dttm>              <chr>  
 1     -123.     45.6 3157   Acer  Sapindaceae  17.1 2017-09-13 00:00:00 ACPL   
 2     -123.     45.6 3158   Acer  Sapindaceae  17.7 2017-09-13 00:00:00 ACPL   
 3     -123.     45.6 3159   Acer  Sapindaceae  11.1 2017-09-13 00:00:00 ACPL   
 4     -123.     45.6 3160   Acer  Sapindaceae  13   2017-09-13 00:00:00 ACPL   
 5     -123.     45.6 3161   Acer  Sapindaceae   9.5 2017-09-13 00:00:00 ACPL   
 6     -123.     45.6 3162   Acer  Sapindaceae  19.6 2017-09-13 00:00:00 ACPL   
 7     -123.     45.6 3163   Acer  Sapindaceae  13.2 2017-09-13 00:00:00 ACPL   
 8     -123.     45.6 3164   Acer  Sapindaceae  14.4 2017-09-13 00:00:00 ACPL   
 9     -123.     45.6 3507   Acer  Sapindaceae  19.5 2017-09-13 00:00:00 ACPL   
10     -123.     45.6 3508   Acer  Sapindaceae  15.4 2017-09-13 00:00:00 ACPL   
# ℹ 280 more rows
# ℹ 26 more variables: Common_Name <chr>, Condition <chr>, Tree_Height <dbl>,
#   Crown_Width_NS <dbl>, Crown_Width_EW <dbl>, Crown_Base_Height <dbl>,
#   Collected_By <chr>, Park <chr>, Scientific_Name <chr>,
#   Functional_Type <chr>, Mature_Size <fct>, Native <chr>, Edible <chr>,
#   Nuisance <chr>, Structural_Value <dbl>, Carbon_Storage_lb <dbl>,
#   Carbon_Storage_value <dbl>, Carbon_Sequestration_lb <dbl>, …

Just 290 (out of ~25,000) rows, that is okay for our purposes.

Summarizing with our joined data

joined_parks %>%
  group_by(Park) %>%
  summarize(mean_DBH = mean(DBH),
            n_trees = n())

Simple feature collection with 174 features and 3 fields (with 9 geometries empty)
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -13667490 ymin: 5692151 xmax: -13635000 ymax: 5724555
Projected CRS: WGS 84 / Pseudo-Mercator
# A tibble: 174 × 4
   Park              mean_DBH n_trees                                   geometry
   <chr>                <dbl>   <int>                         <MULTIPOLYGON [m]>
 1 Albert Kelly Park     20.5     260 (((-13660625 5698511, -13660626 5698522, …
 2 Alberta Park          28.4     338 (((-13652823 5710560, -13652823 5710622, …
 3 Ankeny Plaza          30.8       6 (((-13655615 5704121, -13655615 5704121, …
 4 April Hill Park       18.9      34 (((-13662756 5695900, -13662757 5695832, …
 5 Arbor Lodge Park      19.2     127 (((-13658347 5712261, -13658303 5712261, …
 6 Argay Park            19.6     113 (((-13638648 5708598, -13638649 5708599, …
 7 Berkeley Park         26.7     116 (((-13650497 5696405, -13650475 5696405, …
 8 Berrydale Park        23.6     131 (((-13644658 5702841, -13644658 5702897, …
 9 Bloomington Park      24.1     153 (((-13643128 5698057, -13643128 5698055, …
10 Brentwood Park        17.4     159 (((-13647741 5696172, -13647884 5696172, …
# ℹ 164 more rows

Plotting with our joined data

joined_parks %>%
  group_by(Park) %>%
  summarize(mean_DBH = mean(DBH),
            n_trees = n()) %>%
  ggplot() + 
  geom_sf(aes(fill = mean_DBH))

Plotting with our joined data

Plotting with our joined data

joined_parks %>%
  group_by(Park) %>%
  summarize(mean_DBH = mean(DBH),
            n_trees = n()) %>%
  ggplot() + 
  geom_sf(aes(fill = n_trees))

Plotting with our joined data

Look at the Sellwood Riverfront Park

joined_parks %>%
  filter(Park == "Sellwood Riverfront Park") %>%
  summarize(mean_DBH = mean(DBH),
            n_trees = n()) %>%
  ggplot() + 
  geom_sf(aes(fill = n_trees))

Look at the Sellwood Riverfront Park

Look at the Sellwood Riverfront Park

sellwood <- get_pdxTrees_parks(park = "Sellwood Riverfront Park")
sellwood

# A tibble: 78 × 34
   Longitude Latitude UserID Genus      Family   DBH Inventory_Date      Species
       <dbl>    <dbl> <chr>  <chr>      <chr>  <dbl> <dttm>              <chr>  
 1     -123.     45.5 20625  Acer       Sapin…  12.6 2019-08-14 00:00:00 ACFR   
 2     -123.     45.5 20627  Fraxinus   Oleac…   6.2 2019-08-14 00:00:00 FRLA   
 3     -123.     45.5 20628  Acer       Sapin…   6.5 2019-08-14 00:00:00 ACPL   
 4     -123.     45.5 20631  Thuja      Cupre…  23.8 2019-08-14 00:00:00 THPL   
 5     -123.     45.5 20415  Populus    Salic…  30.3 2019-08-14 00:00:00 POTR   
 6     -123.     45.5 20418  Pseudotsu… Pinac…   6.2 2019-08-14 00:00:00 PSME   
 7     -123.     45.5 20420  Acer       Sapin…  23   2019-08-14 00:00:00 ACRU   
 8     -123.     45.5 20421  Acer       Sapin…  10.1 2019-08-14 00:00:00 ACPL   
 9     -123.     45.5 14891  Pinus      Pinac…  15.7 2019-08-14 00:00:00 PISY   
10     -123.     45.5 14892  Quercus    Fagac…  12.7 2019-08-14 00:00:00 QUGA   
# ℹ 68 more rows
# ℹ 26 more variables: Common_Name <chr>, Condition <chr>, Tree_Height <dbl>,
#   Crown_Width_NS <dbl>, Crown_Width_EW <dbl>, Crown_Base_Height <dbl>,
#   Collected_By <chr>, Park <chr>, Scientific_Name <chr>,
#   Functional_Type <chr>, Mature_Size <fct>, Native <chr>, Edible <chr>,
#   Nuisance <chr>, Structural_Value <dbl>, Carbon_Storage_lb <dbl>,
#   Carbon_Storage_value <dbl>, Carbon_Sequestration_lb <dbl>, …

Let’s convert `sellwood` into an `sf` point object

sellwood_sf <- st_as_sf(
  # a tibble 
  x = sellwood,
  # the column names for lon/lat
  coords = c("Longitude", "Latitude"),
  # the projection those columns are in
  crs = "EPSG:4269"
)
sellwood_sf

Simple feature collection with 78 features and 32 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -122.6644 ymin: 45.46566 xmax: -122.6624 ymax: 45.46707
Geodetic CRS:  NAD83
# A tibble: 78 × 33
   UserID Genus   Family   DBH Inventory_Date      Species Common_Name Condition
 * <chr>  <chr>   <chr>  <dbl> <dttm>              <chr>   <chr>       <chr>    
 1 20625  Acer    Sapin…  12.6 2019-08-14 00:00:00 ACFR    Red-Silver… Fair     
 2 20627  Fraxin… Oleac…   6.2 2019-08-14 00:00:00 FRLA    Oregon Ash  Fair     
 3 20628  Acer    Sapin…   6.5 2019-08-14 00:00:00 ACPL    Norway Map… Poor     
 4 20631  Thuja   Cupre…  23.8 2019-08-14 00:00:00 THPL    Western Re… Fair     
 5 20415  Populus Salic…  30.3 2019-08-14 00:00:00 POTR    Black Cott… Fair     
 6 20418  Pseudo… Pinac…   6.2 2019-08-14 00:00:00 PSME    Douglas-Fir Fair     
 7 20420  Acer    Sapin…  23   2019-08-14 00:00:00 ACRU    Red Maple   Fair     
 8 20421  Acer    Sapin…  10.1 2019-08-14 00:00:00 ACPL    Norway Map… Fair     
 9 14891  Pinus   Pinac…  15.7 2019-08-14 00:00:00 PISY    Scots Pine  Fair     
10 14892  Quercus Fagac…  12.7 2019-08-14 00:00:00 QUGA    Oregon Whi… Fair     
# ℹ 68 more rows
# ℹ 25 more variables: Tree_Height <dbl>, Crown_Width_NS <dbl>,
#   Crown_Width_EW <dbl>, Crown_Base_Height <dbl>, Collected_By <chr>,
#   Park <chr>, Scientific_Name <chr>, Functional_Type <chr>,
#   Mature_Size <fct>, Native <chr>, Edible <chr>, Nuisance <chr>,
#   Structural_Value <dbl>, Carbon_Storage_lb <dbl>,
#   Carbon_Storage_value <dbl>, Carbon_Sequestration_lb <dbl>, …

plotting `sellwood_sf`

ggplot(sellwood_sf) + 
  geom_sf()

plotting `sellwood_sf`

Plotting two `sf` objects

sellwood_shape <- park_shape %>% filter(NAME == "Sellwood Riverfront Park")
ggplot() + 
  geom_sf(data = sellwood_shape) +
  geom_sf(data = sellwood_sf)

Plotting two `sf` objects

Plotting two `sf` objects

ggplot() + 
  geom_sf(data = sellwood_shape) +
  geom_sf(data = sellwood_sf, aes(size = DBH))

Plotting two `sf` objects

Plotting two `sf` objects

ggplot() + 
  geom_sf(data = sellwood_shape) +
  geom_sf(data = sellwood_sf, aes(size = DBH), alpha = 0.7)

Plotting two `sf` objects

Plotting two `sf` objects

ggplot() + 
  geom_sf(data = sellwood_shape, fill = "forestgreen") +
  geom_sf(data = sellwood_sf, aes(size = DBH), alpha = 0.7)

Plotting two `sf` objects

Plotting two `sf` objects

ggplot() + 
  geom_sf(data = sellwood_shape, fill = "forestgreen") +
  geom_sf(data = sellwood_sf, aes(size = DBH,
                                  color = Common_Name),
          alpha = 0.7)

Plotting two `sf` objects

Questions

Regarding sf or spatial data?
Regarding the upcoming midterm?
Regarding Lab 11?

Next time

Review of material during lecture time
Midterm during lab time

More graphics with ggplot2

Agenda

Midterm II

sf

sf

sf

Reading spatial data

Reading spatial data

Examining spatial data

Examining spatial data

Spatial data structure

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Plotting spatial data with ggplot2

Projections

Projections

Projections

Projections

Projections

Projections

Joining with other data

Joining with other data

Joining with other data

Joining with other data

Joining with other data

Easily convert back to an sf object

Let’s check how many rows did not get joined

Summarizing with our joined data

Plotting with our joined data

Plotting with our joined data

Plotting with our joined data

Plotting with our joined data

Look at the Sellwood Riverfront Park

Look at the Sellwood Riverfront Park

Look at the Sellwood Riverfront Park

Let’s convert sellwood into an sf point object

plotting sellwood_sf

plotting sellwood_sf

Plotting two sf objects

Plotting two sf objects

Plotting two sf objects

Plotting two sf objects

Plotting two sf objects

Plotting two sf objects

Plotting two sf objects

Plotting two sf objects

Plotting two sf objects

Plotting two sf objects

Questions

Next time

More graphics with `ggplot2`

`sf`

`sf`

`sf`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Plotting spatial data with `ggplot2`

Easily convert back to an `sf` object

Let’s convert `sellwood` into an `sf` point object

plotting `sellwood_sf`

plotting `sellwood_sf`

Plotting two `sf` objects

Plotting two `sf` objects

Plotting two `sf` objects

Plotting two `sf` objects

Plotting two `sf` objects

Plotting two `sf` objects

Plotting two `sf` objects

Plotting two `sf` objects

Plotting two `sf` objects

Plotting two `sf` objects