Introduction to the sf Package

Background

From the sf vignette:

Simple features or simple feature access refers to a formal standard (ISO 19125-1:2004) that describes how objects in the real world can be represented in computers, with emphasis on the spatial geometry of these objects. It also describes how such objects can be stored in and retrieved from databases, and which geometrical operations should be defined for them.

The standard is widely implemented in spatial databases (such as PostGIS), commercial GIS (e.g., ESRI ArcGIS) and forms the vector data basis for libraries such as GDAL. A subset of simple features forms the GeoJSON standard.

R has well-supported classes for storing spatial data (sp) and interfacing to the above mentioned environments (rgdal, rgeos), but has so far lacked a complete implementation of simple features, making conversions at times convoluted, inefficient or incomplete. The package sf tries to fill this gap, and aims at succeeding sp in the long term.

The sf package is an R implementation of Simple Features. This package incorporates:

• a new spatial data class system in R
  
• functions for reading and writing data
  
• tools for spatial operations on vectors

Most of the functions in this package starts with prefix st_ which stands for spatial and temporal.

Reading a shapefile

If you’ve used readOGR() from the rgdal package, you’ll notice the similarities in arguments for the st_read() function. We’ll do a quick comparison of the two functions here.

Read in a shapefile of Alaska using readOGR() from the rgdal package, and st_read from the sf package.

• dsn is the path name
  
• layer is the name of the file

NOTE: you do not need to add an extension to the layer name

## Read in shapefile using rgdal

system.time(ak_shp_rgdal <- readOGR(dsn="shapefiles", layer="ak_regions"))

## OGR data source with driver: ESRI Shapefile 
## Source: "shapefiles", layer: "ak_regions"
## with 13 features
## It has 3 fields

##    user  system elapsed 
##   0.400   0.012   0.412

object.size(ak_shp_rgdal)

## 3046304 bytes

plot(ak_shp_rgdal)

## Read in shapefile using sf
system.time(ak_shp_sf <- read_sf("shapefiles/ak_regions.shp")) 

##    user  system elapsed 
##   0.088   0.012   0.101

object.size(ak_shp_sf)

## 2916296 bytes

head(ak_shp_sf)  

## Simple feature collection with 6 features and 3 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -179.2296 ymin: 51.15683 xmax: 179.8567 ymax: 71.43957
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 6 x 4
##   region_id region     mgmt_area                                  geometry
##       <int> <chr>          <dbl>                        <MULTIPOLYGON [°]>
## 1         1 Aleutian …         3 (((-171.1345 52.44974, -171.1399 52.4431…
## 2         2 Arctic             4 (((-139.9552 68.70597, -139.9588 68.7056…
## 3         3 Bristol B…         3 (((-159.8745 58.62778, -159.8708 58.6229…
## 4         4 Chignik            3 (((-155.8282 55.84638, -155.8226 55.8527…
## 5         5 Copper Ri…         2 (((-143.8874 59.93931, -143.9027 59.9393…
## 6         6 Kodiak             3 (((-151.9997 58.83077, -152.0093 58.8290…

You’ll notice right away that these two objects are being plotted differently. This is because these two objects are of different types.

sf objects usually have two classes - sf and data.frame. Two main differences comparing to a regular data.frame object are spatial metadata (geometry type, dimension, bbox, epsg (SRID), proj4string) and additional column - typically named geom or geometry.

class(ak_shp_sf)

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

Coordinate Reference System

Every sf object needs a coordinate reference system (or crs) defined in order to work with it correctly. A coordinate reference system contains both a datum and a projection. The datum is how you georeference your points (in 3 dimensions!) onto a spheroid. The projection is how these points are mathematically transformed to represent the georeferenced point on a flat piece of paper. All coordinate reference systems require a datum. However, some coordinate reference systems are “unprojected” (also called geographic coordinate systems). Coordinates in latitude/longitude use a geographic (unprojected) coordinate system. One of the most commonly used geographic coordinate systems is WGS 1984.

You can view what crs is set by using the function st_crs

st_crs(ak_shp_sf)

## Coordinate Reference System:
##   EPSG: 4326 
##   proj4string: "+proj=longlat +datum=WGS84 +no_defs"

This is pretty confusing looking. Without getting into the details, that long string says that this data has a greographic coordinate system (WGS84) with no projection. A convenient way to reference crs quickly is by using the EPSG code, a number that represents a standard projection and datum. You can check out a list of (lots!) of EPSG codes here.

You will often need to transform your geospatial data from one coordinate system to another. The st_transform function does this quickly for us. You may have noticed the maps above looked wonky because of the dateline. We might want to set a different projection for this data so it plots nicer. A good one for Alaska is called the Alaska Albers projection, with an EPSG code of 3338.

ak_shp_sf <- ak_shp_sf %>%
  st_transform(crs = 3338)

st_crs(ak_shp_sf)

## Coordinate Reference System:
##   EPSG: 3338 
##   proj4string: "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"

plot(ak_shp_sf)

Much better!

Attributes

sf objects can be used as a regular data.frame object in many operations

ak_shp_sf

## Simple feature collection with 13 features and 3 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -2175343 ymin: 405519.1 xmax: 1579226 ymax: 2383770
## epsg (SRID):    3338
## proj4string:    +proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## # A tibble: 13 x 4
##    region_id region      mgmt_area                                geometry
##        <int> <chr>           <dbl>                      <MULTIPOLYGON [m]>
##  1         1 Aleutian I…         3 (((-1156666 420855.1, -1157211 420247.…
##  2         2 Arctic              4 (((571289.9 2143072, 571154 2143006, 5…
##  3         3 Bristol Bay         3 (((-339688.6 973904.9, -339520.8 97334…
##  4         4 Chignik             3 (((-114381.9 649966.8, -114015.9 65066…
##  5         5 Copper Riv…         2 (((561012 1148301, 560165.9 1148178, 5…
##  6         6 Kodiak              3 (((115112.5 983293, 114570.4 983086.9,…
##  7         7 Kotzebue            4 (((-678815.3 1819519, -678441.9 181994…
##  8         8 Kuskokwim           4 (((-1030125 1281198, -1030021 1281773,…
##  9         9 Cook Inlet          2 (((35214.98 1002457, 35568.81 1002488,…
## 10        10 Norton Sou…         4 (((-848357 1636692, -847300.5 1635907,…
## 11        11 Prince Wil…         2 (((426007.1 1087250, 426240.8 1087939,…
## 12        12 Southeast           1 (((1287777 744574.1, 1288136 744899.2,…
## 13        13 Yukon               4 (((-375318 1473998, -374946.7 1474058,…

nrow(ak_shp_sf)

## [1] 13

ncol(ak_shp_sf)

## [1] 4

Art by Allison Horst

sf & the Tidyverse

Since sf objects are dataframes, they play nicely with packages in the tidyverse. Here are a couple of simple examples:

select()

ak_shp_sf %>%
  select(region)

## Simple feature collection with 13 features and 1 field
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -2175343 ymin: 405519.1 xmax: 1579226 ymax: 2383770
## epsg (SRID):    3338
## proj4string:    +proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## # A tibble: 13 x 2
##    region                                                         geometry
##    <chr>                                                <MULTIPOLYGON [m]>
##  1 Aleutian Islan… (((-1156666 420855.1, -1157211 420247.7, -1157636 4198…
##  2 Arctic          (((571289.9 2143072, 571154 2143006, 571005.2 2143017,…
##  3 Bristol Bay     (((-339688.6 973904.9, -339520.8 973346.6, -339412.7 9…
##  4 Chignik         (((-114381.9 649966.8, -114015.9 650665.6, -113611.9 6…
##  5 Copper River    (((561012 1148301, 560165.9 1148178, 559780 1148160, 5…
##  6 Kodiak          (((115112.5 983293, 114570.4 983086.9, 113915 982920.1…
##  7 Kotzebue        (((-678815.3 1819519, -678441.9 1819948, -678018.3 182…
##  8 Kuskokwim       (((-1030125 1281198, -1030021 1281773, -1029858 128233…
##  9 Cook Inlet      (((35214.98 1002457, 35568.81 1002488, 35869.5 1002469…
## 10 Norton Sound    (((-848357 1636692, -847300.5 1635907, -846510 1635203…
## 11 Prince William… (((426007.1 1087250, 426240.8 1087939, 426562.5 108859…
## 12 Southeast       (((1287777 744574.1, 1288136 744899.2, 1288443 745136,…
## 13 Yukon           (((-375318 1473998, -374946.7 1474058, -374450.7 14739…

Note the sticky geometry column! The geometry column will stay with your sf object even if it is not called explicitly.

filter()

ak_shp_sf %>%
  filter(region == "Southeast")

## Simple feature collection with 1 feature and 3 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 559475.7 ymin: 722450 xmax: 1579226 ymax: 1410576
## epsg (SRID):    3338
## proj4string:    +proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## # A tibble: 1 x 4
##   region_id region   mgmt_area                                    geometry
##       <int> <chr>        <dbl>                          <MULTIPOLYGON [m]>
## 1        12 Southea…         1 (((1287777 744574.1, 1288136 744899.2, 128…

Joins

You can also use the sf package to create spatial joins, useful for when you want to utilize two datasets together. As an example, let’s ask a question: how many people live in each of these Alaska regions?

We have some population data, but it gives the number of people by city, not by region. To determine the number of people per region we will need to:

• read in the city data from a csv and turn it into an sf object
• use a spatial join (st_join) to assign each city to a region
• use group_by and summarize to calculate the total population by region

First, read in the population data as a regular data.frame.

pop <- read.csv("shapefiles/alaska_population.csv")

The st_join function is a spatial left join. The arguments for both the left and right tables are objects of class sf which means we will first need to turn our population data.frame with latitude and longitude coordinates into an sf object.

We can do this easily using the st_as_sf function, which takes as arguments the coordinates and the crs. The remove = F specification here ensures that when we create our geometry column, we retain our original lat lng columns, which we will need later for plotting. Although it isn’t said anywhere explicitly in the file, let’s assume that the coordinate system used to reference the latitude longitude coordinates is WGS84, which has a crs number of 4236.

pop_sf <- st_as_sf(pop, 
                  coords = c('lng', 'lat'),
                  crs = 4326,
                  remove = F)

head(pop_sf)

## Simple feature collection with 6 features and 5 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -176.6581 ymin: 51.88 xmax: -154.1703 ymax: 62.68889
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##   year     city      lat       lng population                   geometry
## 1 2015     Adak 51.88000 -176.6581        122    POINT (-176.6581 51.88)
## 2 2015   Akhiok 56.94556 -154.1703         84 POINT (-154.1703 56.94556)
## 3 2015 Akiachak 60.90944 -161.4314        562 POINT (-161.4314 60.90944)
## 4 2015    Akiak 60.91222 -161.2139        399 POINT (-161.2139 60.91222)
## 5 2015   Akutan 54.13556 -165.7731        899 POINT (-165.7731 54.13556)
## 6 2015 Alakanuk 62.68889 -164.6153        777 POINT (-164.6153 62.68889)

Now we can do our spatial join! You can specify what geometry function the join uses (st_intersects, st_within, st_crosses, st_is_within_distance, …) in the join argument. The geometry function you use will depend on what kind of operation you want to do, and the geometries of your shapefiles.

In this case, we want to find what region each city falls within, so we will use st_within.

pop_joined_sf <- st_join(pop_sf, ak_shp_sf, join = st_within)

This gives an error!

Error: st_crs(x) == st_crs(y) is not TRUE

Turns out, this won’t work right now because our coordinate reference systems are not the same. Luckily, this is easily resolved using st_transform, and projecting our population object into Alaska Albers.

pop_sf <- st_transform(pop_sf, crs = 3338)

pop_joined_sf <- st_join(pop_sf, ak_shp_sf, join = st_within)

head(pop_joined_sf)

## Simple feature collection with 6 features and 8 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -1537925 ymin: 472627.8 xmax: -10340.71 ymax: 1456223
## epsg (SRID):    3338
## proj4string:    +proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
##   year     city      lat       lng population region_id           region
## 1 2015     Adak 51.88000 -176.6581        122         1 Aleutian Islands
## 2 2015   Akhiok 56.94556 -154.1703         84         6           Kodiak
## 3 2015 Akiachak 60.90944 -161.4314        562         8        Kuskokwim
## 4 2015    Akiak 60.91222 -161.2139        399         8        Kuskokwim
## 5 2015   Akutan 54.13556 -165.7731        899         1 Aleutian Islands
## 6 2015 Alakanuk 62.68889 -164.6153        777        13            Yukon
##   mgmt_area                   geometry
## 1         3  POINT (-1537925 472627.8)
## 2         3 POINT (-10340.71 770998.4)
## 3         4  POINT (-400885.5 1236460)
## 4         4  POINT (-389165.7 1235475)
## 5         3 POINT (-766425.7 526057.8)
## 6         4  POINT (-539724.9 1456223)

plot(pop_joined_sf["region"])

Next we compute the total population for each region. In this case, we want to do a group_by and summarise as this were a regular data.frame - otherwise all of our point geometries would be aggregated by region which is not what we want. We remove the sticky geometry using as.data.frame, on the advice of the sf::tidyverse help page.

pop_region <- pop_joined_sf %>% 
  as.data.frame() %>% 
  group_by(region) %>% 
  summarise(total_pop = sum(population))

head(pop_region)

## # A tibble: 6 x 2
##   region           total_pop
##   <chr>                <int>
## 1 Aleutian Islands      8840
## 2 Arctic                8419
## 3 Bristol Bay           6947
## 4 Chignik                311
## 5 Cook Inlet          408254
## 6 Copper River          2294

And use a regular left_join to get the information back to the Alaska region shapefile. Note that we need this step in order to retain our region geometries so that we can make some maps.

ak_pop_sf <- left_join(ak_shp_sf, pop_region)

head(ak_pop_sf)

## Simple feature collection with 6 features and 4 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -2175343 ymin: 405519.1 xmax: 773854 ymax: 2383770
## epsg (SRID):    3338
## proj4string:    +proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## # A tibble: 6 x 5
##   region_id region   mgmt_area total_pop                          geometry
##       <int> <chr>        <dbl>     <int>                <MULTIPOLYGON [m]>
## 1         1 Aleutia…         3      8840 (((-1156666 420855.1, -1157211 4…
## 2         2 Arctic           4      8419 (((571289.9 2143072, 571154 2143…
## 3         3 Bristol…         3      6947 (((-339688.6 973904.9, -339520.8…
## 4         4 Chignik          3       311 (((-114381.9 649966.8, -114015.9…
## 5         5 Copper …         2      2294 (((561012 1148301, 560165.9 1148…
## 6         6 Kodiak           3      8126 (((115112.5 983293, 114570.4 983…

#plot to check
plot(ak_pop_sf["total_pop"])

group_by and summarize spatial objects

So far, we have learned how to use sf and dplyr to use a spatial join on two datasets and calculate a summary metric from the result of that join.

The group_by and summarize functions can also be used on sf objects to summarize within a dataset and combine geometries. Many of the tidyverse functions have methods specific for sf objects, some of which have additional arguments that wouldn’t be relevant to the data.frame methods. You can run ?sf::tidyverse to get documentation on the tidyverse sf methods.

Let’s try some out. Say we want to calculate the population by Alaska management area, as opposed to region.

ak_mgmt <- ak_pop_sf %>% 
  group_by(mgmt_area) %>% 
  summarize(total_pop = sum(total_pop))

plot(ak_mgmt["total_pop"])

Notice that the region geometries were combined into a single polygon for each management area.

If we don’t want to combine geometries, we can specifcy do_union = F as an argument.

ak_mgmt <- ak_pop_sf %>% 
  group_by(mgmt_area) %>% 
  summarize(total_pop = sum(total_pop), do_union = F)

plot(ak_mgmt["total_pop"])

Save

Save the spatial object to disk using write_sf() and specifying the filename. Writing your file with the extension .shp will assume an ESRI driver driver, but there are many other format options available.

write_sf(ak_pop_sf, "shapefiles/ak_regions_population.shp", delete_layer = TRUE)

Visualize with ggplot

ggplot2 now has integrated functionality to plot sf objects using geom_sf().

#simplest plot
ggplot(ak_pop_sf) +
  geom_sf()

This is useful to make sure your file looks correct but doesn’t display any information about the data. We can plot these regions and fill each polygon based on the population

ggplot(ak_pop_sf) +
  geom_sf(aes(fill = total_pop))

We can clean it up a bit, applying a cleaner theme and assigning a continuous color palette.

ggplot(ak_pop_sf) +
  geom_sf(aes(fill = total_pop)) +
  theme_bw() +
  labs(fill = "Total Population") +
  scale_fill_continuous(low = "khaki", high =  "firebrick", labels = comma)

We can also plot multiple shapefiles in the same plot. Say if we want to visualize rivers in Alaska, in addition to the location of communities, since many communities in Alaska are on rivers. We can read in a rivers shapefile, doublecheck the crs to make sure it is what we need, and then plot all three shapefiles.

rivers <- read_sf("shapefiles/ak_rivers.shp")
st_crs(rivers)

## Coordinate Reference System:
##   No EPSG code
##   proj4string: "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs"

ggplot() +
  geom_sf(data = ak_pop_sf, aes(fill = total_pop)) +
  geom_sf(data = rivers, aes(size = StrOrder), color = "black") +
  geom_sf(data = pop_sf, aes(), size = .5) +
  scale_size(range = c(0.01, 0.2), guide = F) +
  theme_bw() +
  labs(fill = "Total Population") +
  scale_fill_continuous(low = "khaki", high =  "firebrick", labels = comma)

Visualize with leaflet

We can also make an interactive map using leaflet.

Leaflet (unlike ggplot) will project data for you. The catch is that you have to give it both a projection (like Alaska Albers), and that your shapefile must use a geographic coordinate system. This means that we need to use our shapefile with the 4326 EPSG code. Remember you can always check what crs you have set using st_crs.

Here we define a leaflet projection for Alaska Albers, and save it as a variable to use later.

epsg3338 <- leaflet::leafletCRS(
  crsClass = "L.Proj.CRS",
  code = "EPSG:3338",
  proj4def =  "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs",
  resolutions = 2^(16:7))

You might notice that this looks familiar! The syntax is a bit different, but most of this information is also contained within the crs of our shapefile:

st_crs(ak_pop_sf)

## Coordinate Reference System:
##   EPSG: 3338 
##   proj4string: "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"

Since leaflet requires that we use an unprojected coordinate system, let’s use st_transform yet again to get back to WGS84.

ak_pop_crs <- ak_pop_sf %>% st_transform(crs = 4326)

m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
        addPolygons(data = ak_pop_crs, 
                    fillColor = "gray",
                    weight = 1)

m

We can add labels, legends, and a color scale.

pal <- colorNumeric(palette = "Reds", domain = ak_pop_crs$total_pop)

m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
        addPolygons(data = ak_pop_crs, 
                    fillColor = ~pal(total_pop),
                    weight = 1,
                    color = "black",
                    fillOpacity = 1,
                    label = ~region) %>% 
        addLegend(position = "bottomleft",
                  pal = pal,
                  values = range(ak_pop_crs$total_pop),
                  title = "Total Population")

m

We can also add the individual communities, with popup labels showing their population, on top of that!

pal <- colorNumeric(palette = "Reds", domain = ak_pop_crs$total_pop)

m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
        addPolygons(data = ak_pop_crs, 
                    fillColor = ~pal(total_pop),
                    weight = 1,
                    color = "black",
                    fillOpacity = 1) %>% 
        addCircleMarkers(data = pop_sf,
                         lat = ~lat,
                         lng = ~lng,
                         radius = ~log(population/500), # arbitrary scaling
                         fillColor = "gray",
                         fillOpacity = 1,
                         weight = 0.25,
                         color = "black",
                         label = ~paste0(pop_sf$city, ", population ", comma(pop_sf$population))) %>%
        
        addLegend(position = "bottomleft",
                  pal = pal,
                  values = range(ak_pop_crs$total_pop),
                  title = "Total Population")

m

There is a lot more functionality to sf including the ability to intersect polygons, calculate distance, create a buffer, and more. Here are some more great resources and tutorials for a deeper dive into this great package:

Spatial analysis in R with the sf package
Intro to Spatial Analysis
sf github repo
Tidy spatial data in R: using dplyr, tidyr, and ggplot2 with sf
mapping-fall-foliage-with-sf