# 2. Reading, Writing and Converting Simple Features
Author: Edzer Pebesma

#### Edzer Pebesma

For a better version of the sf vignettes see https://r-spatial.github.io/sf/articles/

This vignette describes how simple features can be read in R from files or databases, and how they can be converted to other formats (text, sp)

## Reading and writing through GDAL

The Geospatial Data Abstraction Library (GDAL) is the Swiss Army Knife for spatial data: it reads and writes vector and raster data from and to practically every file format, or database, of significance. Package `sf` reads and writes using GDAL using `st_read()` and `st_write()`.

The data model GDAL uses needs

- a data source, which may be a file, directory, or database
- a layer, which is a single geospatial dataset inside a file or directory or e.g. a table in a database.
- the specification of a driver (i.e., which format)
- driver-specific reading or writing data sources, or layers

This may sound complex, but it is needed to map to over 200 data formats! Package `sf` tries hard to simplify this where possible (e.g. a file contains a single layer), but this vignette will try to point you to the options.

### Using st_read

As an example, we read the North Carolina counties SIDS dataset, which comes shipped with the `sf` package by:

    library(sf)
    fname <- system.file("shape/nc.shp", package="sf")
    fname
    ## [1] "/tmp/RtmpkFGuqe/Rinst7bc1e495b982c/sf/shape/nc.shp"
    nc <- st_read(fname)
    ## Reading layer `nc' from data source
    ##   `/tmp/RtmpkFGuqe/Rinst7bc1e495b982c/sf/shape/nc.shp' using driver `ESRI Shapefile'
    ## Simple feature collection with 100 features and 14 fields
    ## Geometry type: MULTIPOLYGON
    ## Dimension:     XY
    ## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
    ## Geodetic CRS:  NAD27

Typical users will use a file name with path for `fname`, or first set R’s working directory with `setwd()` and use file name without path.

We see here that a single argument is used to find both the datasource and the layer. This works when the datasource contains a single layer. In case the number of layers is zero (e.g. a database with no tables), an error message is given. In case there are more layers than one, the first layer is returned, but a message and a warning are given:

    > st_read("PG:dbname=postgis")
    Multiple layers are present in data source PG:dbname=postgis, reading layer `meuse'.
    Use `st_layers' to list all layer names and their type in a data source.
    Set the `layer' argument in `st_read' to read a particular layer.
    Reading layer `meuse' from data source `PG:dbname=postgis' using driver `PostgreSQL'
    Simple feature collection with 155 features and 12 fields
    geometry type:  POINT
    dimension:      XY
    bbox:           xmin: 178605 ymin: 329714 xmax: 181390 ymax: 333611
    epsg (SRID):    28992
    proj4string:    +proj=sterea +lat_0=52.15616055555555 ...
    Warning message:
    In eval(substitute(expr), envir, enclos) :
      automatically selected the first layer in a data source containing more than one.

The message points to the `st_layers()` command, which lists the driver and layers in a datasource, e.g.

    > st_layers("PG:dbname=postgis")
    Driver: PostgreSQL
    Available layers:
      layer_name geometry_type features fields
    1      meuse         Point      155     12
    2   meuse_sf         Point      155     12
    3       sids Multi Polygon      100     14
    4  meuse_tbl         Point      155     13
    5 meuse_tbl2         Point      155     13
    >

A particular layer can now be read by e.g.

    st_read("PG:dbname=postgis", "sids")

`st_layers()` has the option to count the number of features in case these are missing: some datasources (e.g. OSM xml files) do not report the number of features, but need to be completely read for this. GDAL allows for more than one geometry column for a feature layer; these are reported by `st_layers()`.

In case a layer contains only geometries but no attributes (fields), `st_read()` still returns an `sf` object, with a geometry column only.

We see that GDAL automatically detects the driver (file format) of the datasource, by trying them all in turn.

`st_read()` follows the conventions of base R, similar to how it reads tabular data into `data.frame`s. This means that character data are read, by default as `factor`s. For those who insist on retrieving character data as character vectors, the argument `stringsAsFactors` can be set to `FALSE`:

    st_read(fname, stringsAsFactors = FALSE)

Alternatively, a user can set the global option `stringsAsFactors`, and this will have the same effect:

    options(stringsAsFactors = FALSE)
    st_read(fname)
    ## Reading layer `nc' from data source
    ##   `/tmp/RtmpkFGuqe/Rinst7bc1e495b982c/sf/shape/nc.shp' using driver `ESRI Shapefile'
    ## Simple feature collection with 100 features and 14 fields
    ## Geometry type: MULTIPOLYGON
    ## Dimension:     XY
    ## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
    ## Geodetic CRS:  NAD27

### Using st_write

To write a simple features object to a file, we need at least two arguments, the object and a filename:

    st_write(nc, "nc1.shp")

The file name is taken as the data source name. The default for the layer name is the basename (filename without path) of the the data source name. For this, `st_write()` needs to guess the driver. The above command is, for instance, equivalent to:

    st_write(nc, dsn = "nc1.shp", layer = "nc.shp", driver = "ESRI Shapefile")
    ## Writing layer `nc' to data source `nc1.shp' using driver `ESRI Shapefile'
    ## Writing 100 features with 14 fields and geometry type Multi Polygon.

How the guessing of drivers works is explained in the next section.

### Guessing a driver for output

The output driver is guessed from the datasource name, either from its extension (`.shp`: `ESRI Shapefile`), or its prefix (`PG:`: `PostgreSQL`). The list of extensions with corresponding driver (short driver name) is:

extension | driver short name
--- | ---
`bna` | `BNA`
`csv` | `CSV`
`e00` | `AVCE00`
`gdb` | `FileGDB`
`geojson` | `GeoJSON`
`gml` | `GML`
`gmt` | `GMT`
`gpkg` | `GPKG`
`gps` | `GPSBabel`
`gtm` | `GPSTrackMaker`
`gxt` | `Geoconcept`
`jml` | `JML`
`map` | `WAsP`
`mdb` | `Geomedia`
`nc` | `netCDF`
`ods` | `ODS`
`osm` | `OSM`
`pbf` | `OSM`
`shp` | `ESRI Shapefile`
`sqlite` | `SQLite`
`vdv` | `VDV`
`xls` | `xls`
`xlsx` | `XLSX`

The list with prefixes is:

prefix | driver short name
--- | ---
`couchdb:` | `CouchDB`
`DB2ODBC:` | `DB2ODBC`
`DODS:` | `DODS`
`GFT:` | `GFT`
`MSSQL:` | `MSSQLSpatial`
`MySQL:` | `MySQL`
`OCI:` | `OCI`
`ODBC:` | `ODBC`
`PG:` | `PostgreSQL`
`SDE:` | `SDE`

### Dataset and layer reading or creation options

Various GDAL drivers have options that influences the reading or writing process, for example what the driver should do when a table already exists in a database: append records to the table or overwrite it:

    st_write(st_as_sf(meuse), "PG:dbname=postgis", "meuse",
        layer_options = "OVERWRITE=true")

In case the table exists and the option is not specified, the driver will give an error. Driver-specific options are documented in the driver manual of gdal. Multiple options can be given by multiple strings in `options`.

For `st_read()`, there is only `options`; for `st_write()`, one needs to distinguish between `dataset_options` and `layer_options`, the first related to opening a dataset, the second to creating layers in the dataset.

## Reading and writing directly to and from spatial databases

Package `sf` supports reading and writing from and to spatial databases using the `DBI` interface. So far, testing has mainly be done with `PostGIS`, other databases might work but may also need more work. An example of reading is:

    library(RPostgreSQL)
    conn = dbConnect(PostgreSQL(), dbname = "postgis")
    meuse = st_read(conn, "meuse")
    meuse_1_3 = st_read(conn, query = "select * from meuse limit 3;")
    dbDisconnect(conn)

We see here that in the second example a query is given. This query may contain spatial predicates, which could be a way to work through massive spatial datasets in R without having to read them completely in memory.

Similarly, tables can be written:

    conn = dbConnect(PostgreSQL(), dbname = "postgis")
    st_write(conn, meuse, drop = TRUE)
    dbDisconnect(conn)

Here, the default table (layer) name is taken from the object name (`meuse`). Argument `drop` informs to drop (remove) the table before writing; logical argument `binary` determines whether to use well-known binary or well-known text when writing the geometry (where well-known binary is faster and lossless).

## Conversion to other formats: WKT, WKB, sp

### Conversion to and from well-known text

The usual form in which we see simple features printed is well-known text:

    st_point(c(0,1))
    ## POINT (0 1)
    st_linestring(matrix(0:9,ncol=2,byrow=TRUE))
    ## LINESTRING (0 1, 2 3, 4 5, 6 7, 8 9)

We can create these well-known text strings explicitly using `st_as_text`:

    x = st_linestring(matrix(0:9,ncol=2,byrow=TRUE))
    str = st_as_text(x)
    x
    ## LINESTRING (0 1, 2 3, 4 5, 6 7, 8 9)

We can convert back from WKT by using `st_as_sfc()`:

    st_as_sfc(str)
    ## Geometry set for 1 feature
    ## Geometry type: LINESTRING
    ## Dimension:     XY
    ## Bounding box:  xmin: 0 ymin: 1 xmax: 8 ymax: 9
    ## CRS:           NA
    ## LINESTRING (0 1, 2 3, 4 5, 6 7, 8 9)

### Conversion to and from well-known binary

Well-known binary is created from simple features by `st_as_binary()`:

    x = st_linestring(matrix(0:9,ncol=2,byrow=TRUE))
    (x = st_as_binary(x))
    ##  [1] 01 02 00 00 00 05 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 f0 3f
    ## [26] 00 00 00 00 00 00 00 40 00 00 00 00 00 00 08 40 00 00 00 00 00 00 10 40 00
    ## [51] 00 00 00 00 00 14 40 00 00 00 00 00 00 18 40 00 00 00 00 00 00 1c 40 00 00
    ## [76] 00 00 00 00 20 40 00 00 00 00 00 00 22 40
    class(x)
    ## [1] "raw"

The object returned by `st_as_binary()` is of class `WKB` and is either a list with raw vectors, or a single raw vector. These can be converted into a hexadecimal character vector using `rawToHex()`:

    rawToHex(x)
    ## [1] "0102000000050000000000000000000000000000000000f03f000000000000004000000000000008400000000000001040000000000000144000000000000018400000000000001c4000000000000020400000000000002240"

Converting back to `sf` uses `st_as_sfc()`:

    x = st_as_binary(st_sfc(st_point(0:1), st_point(5:6)))
    st_as_sfc(x)
    ## Geometry set for 2 features
    ## Geometry type: POINT
    ## Dimension:     XY
    ## Bounding box:  xmin: 0 ymin: 1 xmax: 5 ymax: 6
    ## CRS:           NA
    ## POINT (0 1)
    ## POINT (5 6)

### Conversion to and from sp

Spatial objects as maintained by package `sp` can be converted into simple feature objects or geometries by `st_as_sf()` and `st_as_sfc()`, respectively:

    methods(st_as_sf)
    ##  [1] st_as_sf.SpatVector*   st_as_sf.Spatial*      st_as_sf.data.frame*
    ##  [4] st_as_sf.lpp*          st_as_sf.map*          st_as_sf.owin*
    ##  [7] st_as_sf.ppp*          st_as_sf.ppplist*      st_as_sf.psp*
    ## [10] st_as_sf.s2_geography* st_as_sf.sf*           st_as_sf.sfc*
    ## [13] st_as_sf.wk_crc*       st_as_sf.wk_grd*       st_as_sf.wk_rct*
    ## [16] st_as_sf.wk_wkb*       st_as_sf.wk_wkt*       st_as_sf.wk_xy*
    ## see '?methods' for accessing help and source code
    methods(st_as_sfc)
    ##  [1] st_as_sfc.SpatialLines*       st_as_sfc.SpatialMultiPoints*
    ##  [3] st_as_sfc.SpatialPixels*      st_as_sfc.SpatialPoints*
    ##  [5] st_as_sfc.SpatialPolygons*    st_as_sfc.WKB*
    ##  [7] st_as_sfc.bbox*               st_as_sfc.blob*
    ##  [9] st_as_sfc.character*          st_as_sfc.dimensions*
    ## [11] st_as_sfc.factor*             st_as_sfc.list*
    ## [13] st_as_sfc.map*                st_as_sfc.owin*
    ## [15] st_as_sfc.pq_geometry*        st_as_sfc.psp*
    ## [17] st_as_sfc.raw*                st_as_sfc.s2_geography*
    ## [19] st_as_sfc.sf*                 st_as_sfc.tess*
    ## [21] st_as_sfc.wk_crc*             st_as_sfc.wk_grd*
    ## [23] st_as_sfc.wk_rct*             st_as_sfc.wk_wkb*
    ## [25] st_as_sfc.wk_wkt*             st_as_sfc.wk_xy*
    ## see '?methods' for accessing help and source code

An example would be:

    library(sp)
    data(meuse)
    coordinates(meuse) = ~x+y
    m.sf = st_as_sf(meuse)
    opar = par(mar=rep(0,4))
    plot(m.sf)
    ## Warning: plotting the first 10 out of 12 attributes; use max.plot = 12 to plot
    ## all

Conversion of simple feature objects of class `sf` or `sfc` into corresponding `Spatial*` objects is done using the `as` method, coercing to `Spatial`:

    x = st_sfc(st_point(c(5,5)), st_point(c(6,9)), crs = 4326)
    as(x, "Spatial")
    ## SpatialPoints:
    ##      coords.x1 coords.x2
    ## [1,]         5         5
    ## [2,]         6         9
    ## Coordinate Reference System (CRS) arguments: +proj=longlat +datum=WGS84
    ## +no_defs