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ikashnitsky/a-minimal-example.R

Last active January 23, 2018 08:11
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Reproducible example for my SO question on how to fit 2 sf seamlessly -- https://stackoverflow.com/questions/48360446
Raw
a-minimal-example.R
# install dev version of ggplot2
devtools::dev_mode()
devtools::install_github("tidyverse/ggplot2")
library(tidyverse)
library(sf)
library(rmapshaper)
library(ggthemes)
# load data
source(file = url("https://gist.githubusercontent.com/ikashnitsky/4b92f6b9f4bcbd8b2190fb0796fd1ec0/raw/1e281b7bb8ec74c9c9989fe50a87b6021ddbad03/minimal-data.R"))
# test how good they fit together
ggplot() +
geom_sf(data = REG, color = "black", size = .2, fill = NA) +
geom_sf(data = NEI, color = "red", size = .2, fill = NA)+
coord_sf(datum = NA)+
theme_map()
ggsave("test-1.pdf", width = 12, height = 10)
# simplify
REGs <- REG %>% ms_simplify(keep = .5, keep_shapes = TRUE)
NEIs <- NEI %>% ms_simplify(keep = .5, keep_shapes = TRUE)
ggplot() +
geom_sf(data = REGs, color = "black", size = .2, fill = NA) +
geom_sf(data = NEIs, color = "red", size = .2, fill = NA)+
coord_sf(datum = NA)+
theme_map()
ggsave("test-2.pdf", width = 12, height = 10)
Raw
fit-two-sf-seamlessly.R
################################################################################
#
# ikashnitsky.github.io 2018-01-20
# Reproducible example to the SO question
# https://stackoverflow.com/questions/48360446
# How to fit 2 sf seamlessly?
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com
#
################################################################################
# install dev version of ggplot2
devtools::dev_mode()
devtools::install_github("tidyverse/ggplot2")
library(tidyverse) # R 2.0
library(lubridate) # deal with times
library(janitor) # auto clean dataframes
library(sf) # GIS in R 2.0
library(rmapshaper) # to simplify polygons nicely
library(rgdal) # to read the shapefile not as sf
library(ggthemes) # theme_map()
# geodata will be stored in a directory "geodata"
ifelse(!dir.exists('geodata'),
dir.create('geodata'),
paste("Directory already exists"))
# Download geodata
# Eurostat official shapefiles for regions
# http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
# download zipped shapefile (1.9 MB)
f <- tempfile()
download.file("http://ec.europa.eu/eurostat/cache/GISCO/geodatafiles/NUTS_2013_20M_SH.zip", destfile = f)
unzip(f, exdir = "geodata/.")
# remote areas to remove (NUTS-2)
remote <- c(paste0('ES',c(63,64,70)),paste('FRA',1:5,sep=''),'PT20','PT30')
# read and clean the shapefile
gd_nuts <- st_read("geodata/NUTS_2013_20M_SH/data/.", "NUTS_RG_20M_2013") %>%
clean_names() %>%
# re-project using EPSG:3035, an appropreate projection for Europe
st_transform(3035) %>%
# filter out remote areas
filter(!str_sub(nuts_id, 1, 4) %in% remote,
!str_sub(nuts_id, 1, 2) == "AL") %>%
mutate(nuts_id = nuts_id %>% as.character())
# filter NUTS-3
gd_nuts3 <- gd_nuts %>% filter(stat_levl==3)
# aggregate NUTS-2 from NUTS-3
gd_nuts2 <- gd_nuts3 %>%
mutate(nuts_id = nuts_id %>% str_sub(1, 4)) %>%
ms_dissolve(field = "nuts_id")
# aggregate NUTS-0 from NUTS-2
gd_nuts0 <- gd_nuts2 %>%
mutate(nuts_id = nuts_id %>% str_sub(1, 2)) %>%
ms_dissolve(field = "nuts_id")
# test plot NUTS-2 with a random color
gd_nuts2 %>%
mutate(rcol = rgb(runif(310), runif(310), runif(310))) %>%
ggplot()+
geom_sf(aes(fill = rcol), color = NA)+
scale_fill_identity() +
theme_map()
# borders -----------------------------------------------------------------
gd_borders <- gd_nuts0 %>%
ms_innerlines() %>%
as_tibble() %>%
st_as_sf()
ggplot() +
geom_sf(data = gd_nuts0, color = "brown") +
geom_sf(data = gd_borders, color = "yellow", size = 1)
# get neighbouring countries ----------------------------------------------
# let's add neighbouring countries
f <- tempfile()
download.file("http://ec.europa.eu/eurostat/cache/GISCO/geodatafiles/CNTR_2010_20M_SH.zip", destfile = f)
unzip(f, exdir = "geodata/.")
# filter without European countries - to reduce the final size of geodata
neigh_subset <- c("IM", "FO", "GI", "AD", "MC", "VA", "SM", "BA", "AL", "RS",
"MD", "UA", "BY", "RU", "EG", "LY", "TN", "DZ", "MA", "GG",
"JE", "KZ", "AM", "GE", "AZ", "SY", "IQ", "IR", "IL", "JO",
"PS", "SA", "LB", "MN", "LY", "EG")
# cut off everything behind the map borders
rect <- rgeos::readWKT(
"POLYGON((25e5 13.5e5,
75e5 13.5e5,
75e5 55.5e5,
25e5 55.5e5,
25e5 13.5e5))"
) %>%
st_as_sf() %>%
`st_crs<-`(3035)
# read in the world shapefile as sf object and do all the cleaning
gd_neigh <- st_read("geodata/CNTR_2010_20M_SH/CNTR_2010_20M_SH/Data/.",
"CNTR_RG_20M_2010") %>%
clean_names() %>%
filter(cntr_id %in% neigh_subset) %>%
st_transform(3035) %>%
ms_dissolve() %>%
st_intersection(rect) # cut behind the map
# test how god they fit together
ggplot() +
geom_sf(data = gd_nuts2, color = "black", size = 1, fill = NA) +
geom_sf(data = gd_neigh, color = "red", size = .2, fill = NA)+
theme_map()
ggsave("test-1.pdf", width = 25, height = 20)
# simplify polygons -------------------------------------------------------
# merge neighbours trick --------------------------------------------------
# treat the dissolved polygon for neighbouring countries as a single NUTS-3
gd_trick <- gd_neigh %>%
transmute(nuts_id = "NEIGH",
stat_levl = 3L,
geometry) %>%
rbind(gd_nuts3 %>% select(nuts_id, stat_levl, geometry))
# simp NUTS-3
gd_trick_s <- gd_trick %>% ms_simplify(keep = .5, keep_shapes = TRUE)
gd_n3s_nei <- gd_trick_s %>% filter(nuts_id == "NEIGH")
gd_n3s <- gd_trick_s %>% filter(!nuts_id == "NEIGH")
ggplot() +
geom_sf(data = gd_n3s, size = .1)+
geom_sf(data = gd_n3s_nei, size = .1, color = "red")+
theme_map()
ggsave("test-2.pdf", width = 25, height = 20)
Raw
minimal-data.R
REG = structure(list(id = c("HR03", "HR04"), geometry = structure(list(
structure(list(list(structure(c(4681404.9717497, 4660789.5055228,
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4807989.5701462, 4809414.3668482, 4813081.7322406, 4810031.5689926,
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2491745.7156516, 2487224.7718621, 2490971.3001578, 2496418.20287,
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2392439.5205032, 2411114.9411216, 2404000.3011629), .Dim = c(12L,
2L))), list(structure(c(4726698.5350467, 4741471.6282309,
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structure(c(4881347.5854145, 4878121.2600126, 4841498.1939288,
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structure(c(5014573.7599384, 5015050.807588, 5023279.372219,
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5014573.7599384, 2200024.63284, 2192083.0295848, 2185927.9970114,
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2232980.6019638, 2223168.6861861, 2203437.3540959, 2206028.3808448,
2200024.63284), .Dim = c(16L, 2L))), list(structure(c(4896592.0908832,
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2232557.0292488, 2233444.5764789, 2235129.6621158), .Dim = c(6L,
2L)))), class = c("XY", "MULTIPOLYGON", "sfg")), structure(list(
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2531543.4869999), .Dim = c(94L, 2L)))), class = c("XY",
"MULTIPOLYGON", "sfg"))), class = c("sfc_MULTIPOLYGON", "sfc"
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5060947.4131334, 2625262.2117889), .Names = c("xmin", "ymin",
"xmax", "ymax"), class = "bbox"), crs = structure(list(epsg = 3035L,
proj4string = "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"), .Names = c("epsg",
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"geometry"), row.names = c(NA, -2L), class = c("sf", "data.frame"
), sf_column = "geometry", agr = structure(NA_integer_, class = "factor", .Label = c("constant",
"aggregate", "identity"), .Names = "id"))
NEI = structure(list(id = "BA", geometry = structure(list(structure(list(
list(structure(c(19.0219725000001, 19.171969, 19.3676279990001,
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