install.packages("sf")

install.packages("raster")

install.packages("spData")

install.packages("spDataLarge", repos =

"https://nowosad.github.io/drat/", type = "source")

library(sf) #Classes e funções para dados vetoriais

library(raster) #Classes e funções para dados raster

library(spData) #Acessar dados geográficos

library(spDataLarge) #Acessar dados geográficos grandes

names(world)

plot(world)

summary(world["pop"])

world_sub = world[1:3, 1:5]

world_sub

library(sp)

world_sp = as(world, Class = "Spatial")

world_sp

world_sf = st_as_sf(world_sp)

world_sf

help(“raster-package”)

vignette("functions", package = "raster")

raster_filepath = system.file("raster/srtm.tif", package =

"spDataLarge")

Zion = raster(raster_filepath)

Zion

plot(Zion)

dim(Zion)

ncell(Zion)

res(Zion)

extent(Zion)

crs(Zion)

inMemory(Zion)

plot(world[1:3])

plot(world[3:6])

plot(world[1:6])

plot(world["lifeExp"])

world_america = world[world$continent == "South America", ]

plot(world_america)

brazil = world[world$name_long == "Brazil", ]

plot(st_geometry(brazil), expandBB = c(0, 0.2, 0.1, 1), col =

"gray", lwd = 3)

plot(world_america[0], add = TRUE)

plot(world["continent"], reset = FALSE)

cex = sqrt(world$pop) / 10000

world_cents = st_centroid(world, of_largest = TRUE)

plot(st_geometry(world_cents), add = TRUE, cex = cex)

st_proj_info(type = "datum")

st_proj_info(type = "proj")

crs_data = rgdal::make_EPSG()

View(crs_data)

st_crs(world)

world = st_set_crs(world, 4326) # set CRS

projection(Zion)

brazil = world[world$name_long == "Brazil", ]

st_area(brazil)

st_area(brazil)/1000000

units::set_units(st_area(brazil), km^2)

library(sf)

library(raster)

library(dplyr)

library(spData)

download.file(url =

"http://www.sergeo.deg.ufla.br/geo/downloads/brasil.zip",

destfile = "brasil.zip")

unzip(zipfile = "brasil.zip")

mun = st_read(dsn = "mun_01_wgs84.shp")

plot(mun)

install.packages("rnaturalearth")

library(rnaturalearth)

brasil = ne_countries(country = "Brazil") # Fronteiras do Brasil

class(brasil)

brasil_sf = st_as_sf(brasil)

class(brasil_sf)

plot(brasil_sf)

library(raster)

worldclim_prec = getData(name = "worldclim", var = "prec", res =

10)

class(worldclim_prec)

install.packages("osmdata")

library(osmdata)

vignette('osmdata')

parks = opq(bbox = "leeds uk") %>%

add_osm_feature(key = "leisure", value = "park") %>%

osmdata_sf()

parks

world2 = spData::world

world3 = st_read(system.file("shapes/world.gpkg", package =

"spData"))

base_url = "http://www.fao.org"

endpoint = "/figis/geoserver/wfs"

q = list(request = "GetCapabilities")

res = httr::GET(url = httr::modify_url(base_url, path =

endpoint), query = q)

res$url

res

txt = httr::content(res, "text")

xml = xml2::read_xml(txt)

qf = list(request = "GetFeature", typeName = "area:FAO_AREAS")

file = tempfile(fileext = ".gml")

httr::GET(url = base_url, query = qf, httr::write_disk(file))

fao_areas = sf::read_sf(file)

install.packages("ows4R")

library(ows4R)

wfs = WFSClient$new("http://www.fao.org/figis/geoserver/wfs",

serviceVersion = "1.0.0", logger = "INFO")

fao_areas = wfs$getFeatures("area:FAO_AREAS")

sf_drivers = st_drivers()

head(sf_drivers, n = 5)

vector_filepath = system.file("shapes/world.gpkg", package =

"spData")

world = st_read(vector_filepath)

cycle_hire_txt = system.file("misc/cycle_hire_xy.csv", package =

"spData")

cycle_hire_xy = st_read(cycle_hire_txt, options =

c("X_POSSIBLE_NAMES=X", "Y_POSSIBLE_NAMES=Y"))

world_txt = system.file("misc/world_wkt.csv", package = "spData")

world_wkt = read_sf(world_txt, options =

"GEOM_POSSIBLE_NAMES=WKT")

world_wkt = st_read(world_txt, options =

"GEOM_POSSIBLE_NAMES=WKT", quiet = TRUE, stringsAsFactors =

FALSE, as_tibble = TRUE)

u =

"https://developers.google.com/kml/documentation/KML_Samples.kml

download.file(u, "KML_Samples.kml")

st_layers("KML_Samples.kml")

raster_filepath = system.file("raster/srtm.tif", package =

"spDataLarge")

single_layer = raster(raster_filepath)

multilayer_filepath = system.file("raster/landsat.tif", package

= "spDataLarge")

band3 = raster(multilayer_filepath, band = 3)

multilayer_brick = brick(multilayer_filepath)

multilayer_stack = stack(multilayer_filepath)

st_write(obj = world, dsn = "world.gpkg")

st_write(obj = world, dsn = "world.gpkg", layer_options =

"OVERWRITE=YES")

st_write(obj = world, dsn = "world.gpkg", delete_layer = TRUE)

write_sf(obj = world, dsn = "world.gpkg")

st_write(cycle_hire_xy, "cycle_hire_xy.csv", layer_options =

"GEOMETRY=AS_XY")

st_write(world_wkt, "world_wkt.csv", layer_options =

"GEOMETRY=AS_WKT")

writeRaster(single_layer, filename = "my_raster.tif", datatype =

"INT2U")

writeRaster(x = single_layer,

filename = "my_raster.tif",

datatype = "INT2U",

options = c("COMPRESS=DEFLATE"),

overwrite = TRUE)

png(filename = "lifeExp.png", width = 500, height = 350)

plot(world["lifeExp"])

dev.off()

library(tmap)

tmap_obj = tm_shape(world) + tm_polygons(col = "lifeExp")

tmap_save(tm = tmap_obj, filename = "lifeExp_tmap.png")

library(mapview)

mapview_obj = mapview(world, zcol = "lifeExp", legend = TRUE)

mapshot(mapview_obj, file = "my_interactive_map.html")

library(sf)

library(raster)

library(dplyr)

library(spData)

library(spDataLarge)

library(rgdal)

library(maptools)

library(sf)

library(tmap) # para mapeamento estático e interativo

library(leaflet) # para mapeamento interativo

library(mapview) # para mapeamento interativo

library(ggplot2) # visualização de dados

library(shiny) # aplicações para web

brasil <-

readOGR(dsn="C:/livros/geo/4_tipos_mapas/brasil/mun_01_wgs84.shp

", "mun_01_wgs84")

names(brasil)

download.file(url =

"http://www.sergeo.deg.ufla.br/geo/downloads/brasil.zip",

destfile = "brasil.zip")

unzip(zipfile = "brasil.zip")

brasil = st_read(dsn = "mun_01_wgs84.shp")

plot(brasil, border = "transparent")

tm_shape(brasil) +

tm_fill()

tm_shape(brasil) +

tm_borders()

tm_shape(brasil) +

tm_fill() +

tm_borders()

map_brasil = tm_shape(brasil) + tm_polygons()

class(map_brasil)

map_brasil

soja=tm_shape(brasil) + tm_fill(col = "soja")

cafe=tm_shape(brasil) + tm_fill(col = "cafe")

tmap_arrange(soja, cafe)

ma1 = tm_shape(brasil) + tm_fill(col = "red")

ma2 = tm_shape(brasil) + tm_fill(col = "red", alpha = 0.3)

ma3 = tm_shape(brasil) + tm_borders(col = "blue")

ma4 = tm_shape(brasil) + tm_borders(lwd = 3)

ma5 = tm_shape(brasil) + tm_borders(lty = 2)

ma6 = tm_shape(brasil) + tm_fill(col = "red", alpha = 0.3) +

tm_borders(col = "blue", lwd = 3, lty = 2)

tmap_arrange(ma1, ma2, ma3, ma4, ma5, ma6)

legend_title = expression("Area (km"^2*")")

map_brasil1 = tm_shape(brasil) +

tm_fill(col = "AREA_TOT_G", title = legend_title) +

tm_borders()

map_brasil1

tm_shape(brasil) + tm_polygons(col = "cafe")

breaks = c(0, 3, 4, 5) * 10000

tm_shape(brasil) + tm_polygons(col = "cafe", breaks = breaks)

tm_shape(brasil) + tm_polygons(col = "cafe", n = 10)

tm_shape(brasil) + tm_polygons(col = "cafe", palette = "BuGn")

tm_shape(brasil) + tm_polygons(col = "cafe", palette = "Blues")

tm_shape(brasil) + tm_polygons(col = "cafe", palette = "YlOrBr")

tm_shape(brasil) + tm_polygons(col = "cafe", style = "pretty")

tm_shape(brasil) + tm_polygons(col = "cafe", style = "equal")

tm_shape(brasil) + tm_polygons(col = "cafe", style = "quantile")

tm_shape(brasil) + tm_polygons(col = "cafe", style = "jenks")

tm_shape(brasil) + tm_polygons(col = "cafe", style = "cont")

tm_shape(brasil) + tm_polygons(col = "cafe", style = "cat")

map_brasil +

tm_compass(type = "8star", position = c("right", "top")) +

tm_scale_bar(breaks = c(0, 200, 400), text.size = 1)

map_brasil + tm_layout(title = "Brasil")

map_brasil + tm_layout(scale = 5)

map_brasil + tm_layout(bg.color = "lightblue")

map_brasil + tm_layout(frame = FALSE)

map_brasil + tm_style("bw")

map_brasil + tm_style("classic")

map_brasil + tm_style("cobalt")

map_brasil + tm_style("col_blind")

urb_1970_2030 = urban_agglomerations %>%

filter(year %in% c(1970, 1990, 2010, 2030))

tm_shape(world) +

tm_polygons() +

tm_shape(urb_1970_2030) +

tm_symbols(col = "black", border.col = "white", size =

"population_millions") +

tm_facets(by = "year", nrow = 2, free.coords = FALSE)

install.packages("installr")

install.packages("magick")

install.packages("ImageMagick")

require(installr)

require(magick)

install.ImageMagick()

require(ImageMagick)

urb_anim = tm_shape(world) + tm_polygons() +

tm_shape(urban_agglomerations) + tm_dots(size =

"population_millions") +

tm_facets(along = "year", free.coords = FALSE)

urb_anim

tmap_animation(urb_anim, filename = "urb_anim.gif", delay = 25)

tmap_mode("view")

map_brasil

map_brasil + tm_basemap(server = "OpenTopoMap")

world_coffee = left_join(world, coffee_data, by = "name_long")

facets = c("coffee_production_2016", "coffee_production_2017")

tm_shape(world_coffee) + tm_polygons(facets) +

tm_facets(nrow = 1, sync = TRUE)

mapview::mapview(brasil)

install.packages("mapdeck")

library(mapdeck)

set_token(Sys.getenv("MAPBOX"))

crash_data = read.csv("https://git.io/geocompr-mapdeck")

crash_data = na.omit(crash_data)

ms = mapdeck_style("dark")

mapdeck(style = ms, pitch = 45, location = c(0, 52), zoom = 4)

%>%

add_grid(data = crash_data, lat = "lat", lon = "lng", cell_size

= 1000,

elevation_scale = 50, layer_id = "grid_layer",

colour_range = viridisLite::plasma(6))

pal = colorNumeric("RdYlBu", domain = cycle_hire$nbikes)

leaflet(data = cycle_hire) %>%

addProviderTiles(providers$OpenStreetMap.BlackAndWhite) %>%

addCircles(col = ~pal(nbikes), opacity = 0.9) %>%

addPolygons(data = lnd, fill = FALSE) %>%

addLegend(pal = pal, values = ~nbikes) %>%

setView(lng = -0.1, 51.5, zoom = 12) %>%

addMiniMap()

library(shiny) # para aplicativos shiny

library(leaflet) # funções leaflet

library(spData) # base de dados global

ui = fluidPage(

sliderInput(inputId = "life", "Life expectancy", 49, 84, value

= 80),

leafletOutput(outputId = "map")

)

server = function(input, output) {

output$map = renderLeaflet({

leaflet() %>% addProviderTiles("OpenStreetMap.BlackAndWhite")

%>%

addPolygons(data = world[world$lifeExp < input$life, ])})

}

shinyApp(ui, server)

install.packages("cartogram")

library(cartogram)

brasil_carto = cartogram_cont(brasil, "cafe", itermax = 5)

tm_shape(brasil_carto) + tm_polygons("cafe")