Código para implementação de funções de representação e manipulação de dados espaciais
vignette(package = "sf")
library(sf)
## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
library(terra)
## terra version 1.5.2
library(spData)
## Warning: múltiplas tabelas de métodos encontradas para 'approxNA'
library(tmap)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::extract() masks terra::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::simplify() masks terra::simplify()
## x dplyr::src() masks terra::src()
class(world)
## [1] "sf" "tbl_df" "tbl" "data.frame"
names(world)
## [1] "iso_a2" "name_long" "continent" "region_un" "subregion" "type"
## [7] "area_km2" "pop" "lifeExp" "gdpPercap" "geom"
plot(world)
## Warning: plotting the first 9 out of 10 attributes; use max.plot = 10 to plot
## all
plot(world["continent"], reset = FALSE)
cex = sqrt(world$pop) / 10000
world_cents = st_centroid(world, of_largest = TRUE)
## Warning in st_centroid.sf(world, of_largest = TRUE): st_centroid assumes
## attributes are constant over geometries of x
plot(st_geometry(world_cents), add = TRUE, cex = cex)
autonomos <- st_read("shapefile/autonomos.shp")
autonomos <- st_transform(autonomos, "EPSG:4326")
st_crs(autonomos)
st_bbox(autonomos)
economica <- st_read("shapefile/economica.shp")
economica_auto <- st_transform(autonomos)
st_crs(economica_auto)
st_crs(autonomos)
new_shape <- st_transform(autonomos, "EPSG:4326") # set CRS
st_crs(new_shape)
st_bbox(autonomos)
st_bbox(new_shape)
Representação
autonomos <- st_read("shapefile/autonomos.shp")
economica <- st_read("shapefile/economica.shp")
regional <- st_read("shapefile/regional.shp")
tm_shape(regional) +
tm_polygons("NOME",palette = "RdYlBu")
tm_shape(regional) +
tm_borders() +
tm_shape(autonomos) +
tm_dots(col = "red") +
tm_scale_bar()
Manipulação de dados espaciais
farmacias <- economica %>%
filter(CNAE_PRINC == '4771701' | CNAE_PRINC == '4771702')
tm_shape(regional) +
tm_borders() +
tm_shape(farmacias) +
tm_dots()
world
## Simple feature collection with 177 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84
## # A tibble: 177 x 11
## iso_a2 name_long continent region_un subregion type area_km2 pop lifeExp
## * <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 FJ Fiji Oceania Oceania Melanesia Sove~ 1.93e4 8.86e5 70.0
## 2 TZ Tanzania Africa Africa Eastern ~ Sove~ 9.33e5 5.22e7 64.2
## 3 EH Western ~ Africa Africa Northern~ Inde~ 9.63e4 NA NA
## 4 CA Canada North Am~ Americas Northern~ Sove~ 1.00e7 3.55e7 82.0
## 5 US United S~ North Am~ Americas Northern~ Coun~ 9.51e6 3.19e8 78.8
## 6 KZ Kazakhst~ Asia Asia Central ~ Sove~ 2.73e6 1.73e7 71.6
## 7 UZ Uzbekist~ Asia Asia Central ~ Sove~ 4.61e5 3.08e7 71.0
## 8 PG Papua Ne~ Oceania Oceania Melanesia Sove~ 4.65e5 7.76e6 65.2
## 9 ID Indonesia Asia Asia South-Ea~ Sove~ 1.82e6 2.55e8 68.9
## 10 AR Argentina South Am~ Americas South Am~ Sove~ 2.78e6 4.30e7 76.3
## # ... with 167 more rows, and 2 more variables: gdpPercap <dbl>,
## # geom <MULTIPOLYGON [°]>
world_agg1 <- world %>%
group_by(continent) %>%
summarize(pop = sum(pop, na.rm = TRUE))
tm_shape(world) +
tm_polygons("pop", style = "jenks" )
world_agg2 <- world %>%
group_by(continent) %>%
summarize(pop = sum(pop, na.rm = TRUE), `area (sqkm)` = sum(area_km2), n = n())
Junção não espacial - relaciona dados por meio de uma chave
coffee_data
## # A tibble: 47 x 3
## name_long coffee_production_2016 coffee_production_2017
## <chr> <int> <int>
## 1 Angola NA NA
## 2 Bolivia 3 4
## 3 Brazil 3277 2786
## 4 Burundi 37 38
## 5 Cameroon 8 6
## 6 Central African Republic NA NA
## 7 Congo, Dem. Rep. of 4 12
## 8 Colombia 1330 1169
## 9 Costa Rica 28 32
## 10 Côte d'Ivoire 114 130
## # ... with 37 more rows
juntos <- world %>%
left_join(coffee_data)
## Joining, by = "name_long"
tm_shape(juntos) +
tm_polygons('coffee_production_2017', style = "jenks")
