Converting “sp” objects to “sf” objects

The following makes use of the sf, cshapes, and sp packages. You may need to install them from CRAN using the code install.packages(c("sf","cshapes","sp")) if you want to run this on your computer. (The package is already installed in the notebook container, however.)

library(sf)

Linking to GEOS 3.7.1, GDAL 2.4.0, PROJ 5.2.0

library(cshapes)

Loading required package: sp

Loading required package: maptools

Checking rgeos availability: TRUE

Loading required package: plyr

cshapes.1990 <- cshp(as.Date("1990-01-01"))
cshapes.1990 <- as(cshapes.1990,"sf")

Warning message:
“readShapePoly is deprecated; use rgdal::readOGR or sf::st_read”

options(width=200)
print(cshapes.1990[c(1:3,10)])

Simple feature collection with 171 features and 4 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -55.90223 xmax: 180 ymax: 83.11387
CRS:            +proj=longlat +ellps=WGS84
First 10 features:
           CNTRY_NAME         AREA       CAPNAME COWSDAY                       geometry
0              Guyana  211982.0050    Georgetown      26 MULTIPOLYGON (((-58.17262 6...
1            Suriname  145952.2740    Paramaribo      25 MULTIPOLYGON (((-55.12796 5...
2 Trinidad and Tobago    5041.7290 Port-of-Spain      31 MULTIPOLYGON (((-61.07945 1...
3           Venezuela  916782.2172       Caracas       1 MULTIPOLYGON (((-66.31029 1...
4               Samoa    2955.2124          Apia      15 MULTIPOLYGON (((-172.5965 -...
5               Tonga     464.7473    Nuku'alofa      14 MULTIPOLYGON (((-175.1453 -...
6           Argentina 2787442.0977  Buenos Aires       1 MULTIPOLYGON (((-71.85916 -...
7             Bolivia 1092697.4356        La Paz       1 MULTIPOLYGON (((-62.19884 -...
8              Brazil 8523619.5715      Brasilia      21 MULTIPOLYGON (((-44.69501 -...
9               Chile  745808.4936      Santiago       1 MULTIPOLYGON (((-73.0421 -4...

SthAmCntry.names <- c(
    "Argentina",
    "Bolivia",
    "Brazil",
    "Chile",
    "Colombia",
    "Ecuador",
    "Guyana",
    "Paraguay",
    "Peru",
    "Suriname",
    "Uruguay",
    "Venezuela")

SthAmCountries <-
    subset(cshapes.1990,
           CNTRY_NAME %in% SthAmCntry.names)

Brazil <- subset(cshapes.1990,CNTRY_NAME=="Brazil")
Chile <-  subset(cshapes.1990,CNTRY_NAME=="Chile")
Colombia <-  subset(cshapes.1990,CNTRY_NAME=="Colombia")

cap.latlong <- with(cshapes.1990,cbind(CAPLONG,CAPLAT))

cap.latlong <- lapply(1:nrow(cap.latlong),
                      function(i)cap.latlong[i,])

cap.latlong <- lapply(cap.latlong,st_point)
cap.latlong <- st_sfc(cap.latlong)

cshapes.capitals.1990 <- cshapes.1990
st_geometry(cshapes.capitals.1990) <- cap.latlong

st_crs(cshapes.capitals.1990) <- st_crs(cshapes.1990)

print(cshapes.capitals.1990[c(1:3,10)])

Simple feature collection with 171 features and 4 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: -175 ymin: -41.3 xmax: 179 ymax: 64.15
CRS:            +proj=longlat +ellps=WGS84
First 10 features:
           CNTRY_NAME         AREA       CAPNAME COWSDAY               geometry
0              Guyana  211982.0050    Georgetown      26      POINT (-58.2 6.8)
1            Suriname  145952.2740    Paramaribo      25 POINT (-55.2 5.833333)
2 Trinidad and Tobago    5041.7290 Port-of-Spain      31    POINT (-61.5 10.65)
3           Venezuela  916782.2172       Caracas       1     POINT (-66.9 10.5)
4               Samoa    2955.2124          Apia      15     POINT (-172 -13.8)
5               Tonga     464.7473    Nuku'alofa      14     POINT (-175 -21.1)
6           Argentina 2787442.0977  Buenos Aires       1    POINT (-58.7 -34.6)
7             Bolivia 1092697.4356        La Paz       1    POINT (-68.2 -16.5)
8              Brazil 8523619.5715      Brasilia      21    POINT (-47.9 -15.8)
9               Chile  745808.4936      Santiago       1    POINT (-70.7 -33.5)

Brasilia <- subset(cshapes.capitals.1990,CNTRY_NAME=="Brazil")
Santiago <-  subset(cshapes.capitals.1990,CNTRY_NAME=="Chile")
Bogota <-  subset(cshapes.capitals.1990,CNTRY_NAME=="Colombia")

graypal <- function(n)gray.colors(n,start=.2,end=.9,alpha=.5)
plot(SthAmCountries,pal=graypal)

Warning message:
“plotting the first 9 out of 24 attributes; use max.plot = 24 to plot all”

plot(st_geometry(SthAmCountries))

plot(st_geometry(Brazil))

save(cshapes.1990,cshapes.capitals.1990,file="cshapes-1990.RData")
save(Brazil,Chile,Colombia,
     Brasilia,Santiago,Bogota,
     SthAmCountries,
     file="south-america-1990.RData")

Downloadable R script and interactive version

• R Script: converting-sp-to-sf.R
• Interactive version (shuts down after 60s):
• Interactive version (sign in required):

Explanation

The link with the “jupyterhub” icon directs you to an interactive Jupyter¹ notebook, which runs inside a Docker container². There are two variants of the interative notebook. One shuts down after 60 seconds and does not require a sign it. The other requires sign in using your ORCID³ credentials, yet shuts down only after 24 hours. (There is no guarantee that such a container persists that long, it may be shut down earlier for maintenance purposes.) After shutdown all data within the container will be reset, i.e. all files created by the user will be deleted.⁴

Above you see a rendered version of the Jupyter notebook.⁵

1

For more information about Jupyter see http://jupyter.org. The Jupyter notebooks make use of the IRKernel package.

2

For more information about Docker see https://docs.docker.com/. The container images were created with repo2docker, while containers are run with docker spawner.

3

ORCID is a free service for the authentication of researchers. It also allows to showcase publications and contributions to the academic community such as peer review.. See https://info.orcid.org/what-is-orcid/ for more information.

4

The Jupyter notebooks come with NO WARRANTY whatsoever. They are provided for educational and illustrative purposes only. Do not use them for production work.

5

The notebook is rendered with the help of the nbsphinx extension.