Introdução ao uso de dados geoespaciais no R

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# Introdução ao uso de dados geoespaciais no R 
## 7 Estrutura e manipulação de dados vetoriais
### Maurício H. Vancine Milton C. Ribeiro
### UNESP - Rio Claro Laboratório de Ecologia Espacial e Conservação (LEEC)
### 25/10/2021-05/11/2021

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[@allison_horst](https://twitter.com/allison_horst)

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# 7 Estrutura e manipulação de vetores

## Tópicos

1. Principais pacotes
1. Geometrias sf
1. Classes sf
1. Importar dados vetoriais
1. Descrição de objetos sf
1. Converter objetos para sf
1. Converter CRS de objetos sf
1. Operações de objetos sf
1. Exportar objetos sf

[Lovelace et al. (2020)](https://geocompr.robinlovelace.net/)

---

# 7´ Estrutura e manipulação de vetores

## Script

## .center[`07_script_intro_geoespacial_r.R`]

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# 1. Principais pacotes

## Pacote sp

```r
# sp
install.packages("sp")
library(sp)
```

[sp](https://github.com/edzer/sp/)

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# 1. Principais pacotes

## Pacote sf

```r
# sf
install.packages("sf")
library(sf)
```

[sf](https://r-spatial.github.io/sf/)

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# 1. Principais pacotes

## Pacote sf

### Spatial manipulation with sf cheat sheets

[Spatial manipulation with sf](https://raw.githubusercontent.com/rstudio/cheatsheets/master/sf.pdf)

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# 1. Principais pacotes

## Pacote sf

### Artigo
> - Pebesma, Edzer. ["Simple Features for R: Standardized Support for Spatial Vector Data."](https://journal.r-project.org/archive/2018/RJ-2018-009/) The R Journal 10.01 (2018): 439-446.

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# 1. Principais pacotes

## Pacote sf

### Dependências de outros pacotes

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# 1. Principais pacotes

## Pacote sf

### Métodos

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# 1. Principais pacotes

## Pacote sf

### Funções

---

# 1. Principais pacotes

## Pacote sf - Descrição

### **Rápida importação e exportação** de dados

### **Desempenho aprimorado** de mapas e gráficos

### Objetos `sf` podem ser tratados como `data frames` (`tibbles`) na maioria das **operações de manipulação**

### As funções `sf` **podem ser combinadas** usando o operador `%>%` e funcionam bem com `tidyverse`

### Os nomes das funções sf são relativamente **consistentes e intuitivos** (todos começam com `sf::st_`)

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# 2. Tipos de geometrias sf

## Tipos de dados geoespaciais vetoriais

[Shin et al. (2017)](https://catalog.flatworldknowledge.com/catalog/editions/campbell_2_1-essentials-of-geographic-information-systems-2-1),
[Silva et al. (in prep.)](https://paternogbc.github.io/livro_aer/)

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# 2. Tipos de geometrias sf

## Tipos de geometrias

[Lovelace et al. (2020)](https://geocompr.robinlovelace.net/)

---

# 3. Classes sf

## O pacote sf define um sistema de três classes hierárquicas

|Classes                              |Hierarquia          |Informação                      |
|:------------------------------------|:-------------------|:-------------------------------|
|1. Simple feature geometries (`sfg`) |Geometria           |Tipo de geometria e coordenadas |
|2. Simple feature columns (`sfc`)    |Coluna de geometria |Conjunto de `sfg` + CRS         |
|3. Simple feature (`sf`)             |Camada              |Conjunto de `sfc` + atributos   |

-   1. Classe `sfg` - uma geometria única
-   2. Classe `sfc` - uma coluna de geometria, que é um conjunto de geometrias `sfg` e informações Sistema de Referência de Coordenadas (do inglês *Coordinate Reference Systems - CRS*)
-   3. Classe `sf` - uma camada, que é uma coluna de geometria `sfc` dentro de um data frame com atributos não espaciais (tabela de atributos)

---

# 3. Classes sf

## **1. Simple feature geometries (`sfg`)**

### A classe `sfg` representa os **diferentes tipos de geometrias** no R: ponto, linha, polígono (e seus equivalentes ‘multi’) ou coleção de geometrias

#### Funções para criar geometrias da classe `sfg`:

```r
# simple
sf::st_point()
sf::st_linestring()
sf::st_polygon()

# multi
sf::st_multipoint()
sf::st_multilinestring()
sf::st_multipolygon()

# collections
sf::st_geometrycollection()
```

---

# 3. Classes sf

## **1. Simple feature geometries (`sfg`)**

### Os objetos `sfg` podem ser **criados** a partir de **três tipos de dados R base**:

### 1. **vetor numérico**: um único ponto
### 2. **matriz**: um conjunto de pontos, onde cada linha representa um ponto, um multiponto ou uma linha
### 3. **lista**: uma coleção de objetos como matrizes, cadeias multilinha ou coleções de geometrias

---

# 3. Classes sf

## **1. Simple feature geometries (`sfg`)**

```r
# vector - point
vec <- c(5, 2)
po <- sf::st_point(vec)
po
```

```r
# plot
plot(po, pch = 20, cex = 4, axes = TRUE, graticule = TRUE)
```

---

# 3. Classes sf

## **1. Simple feature geometries (`sfg`)**

```r
# matrix - multipoint
multipoint_matrix <- rbind(c(5, 2), c(1, 3), c(3, 4), c(3, 2))
po_mul <- sf::st_multipoint(multipoint_matrix)
po_mul
```

```r
# plot
plot(po_mul, pch = 20, cex = 4, axes = TRUE, graticule = TRUE)
```

---

# 3. Classes sf

## **1. Simple feature geometries (`sfg`)**

```r
# matrix - linestring
multipoint_matrix <- rbind(c(5, 2), c(1, 3), c(3, 4), c(3, 2))
lin <- sf::st_linestring(multipoint_matrix)
lin
```

```r
# plot
plot(lin, lwd = 2, axes = TRUE, graticule = TRUE)
```

---

# 3. Classes sf

## **1. Simple feature geometries (`sfg`)**

```r
# list - polygon
polygon_list <- list(rbind(c(1, 5), c(2, 2), c(4, 1), c(4, 4), c(1, 5)))
pol <- sf::st_polygon(polygon_list)
pol
```

```r
# plot
plot(pol, col = "gray", axes = TRUE, graticule = TRUE)
```

---

# 3. Classes sf

## **2. Simple feature columns (`sfc`)**

### Uma **lista de objetos** `sfg` que possui o **mesmo CSR** (*Coordinate Reference System*)

### Pode conter objetos da **mesma geometria** ou de **geometrias diferentes**

### Funções para **criar** a classe `sfc` e verificar o **tipo da geometria e CRS**:

```r
sf::st_sfc()
sf::st_geometry_type()
sf::st_crs()
```

---

# 3. Classes sf

## **2. Simple feature columns (`sfc`)**

```r
# sfc point
point1 <- sf::st_point(c(5, 2))
point2 <- sf::st_point(c(1, 3))
points_sfc <- sf::st_sfc(point1, point2)
points_sfc
```

```
## Geometry set for 2 features 
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 3
## CRS:           NA
```

```r
sf::st_geometry_type(points_sfc)
```

```
## [1] POINT POINT
## 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE
```

---

# 3. Classes sf

## **2. Simple feature columns (`sfc`)**

```r
# plot
plot(points_sfc, pch = 20, cex = 4, axes = TRUE, graticule = TRUE)
```

---

# 3. Classes sf

## **2. Simple feature columns (`sfc`)**

```r
# sfc geometry
po_pol_sfc <- sf::st_sfc(po, pol)
po_pol_sfc
```

```
## Geometry set for 2 features 
## Geometry type: GEOMETRY
## Dimension:     XY
## Bounding box:  xmin: 1 ymin: 1 xmax: 5 ymax: 5
## CRS:           NA
```

```r
sf::st_geometry_type(po_pol_sfc)
```

```
## [1] POINT   POLYGON
## 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE
```

---

# 3. Classes sf

## **2. Simple feature columns (`sfc`)**

```r
# plot
plot(po_pol_sfc, pch = 20, cex = 4, lwd = 2, col = "gray", axes = TRUE, graticule = TRUE)
```

---

# 3. Classes sf

## **2. Simple feature columns (`sfc`)**

### Coordinate Reference Systems (CRS) - EPSG

```r
# epsg definition
points_sfc_wgs <- sf::st_sfc(point1, point2, crs = 4326)
sf::st_crs(points_sfc_wgs)
```

```
## Coordinate Reference System:
##   User input: EPSG:4326 
##   wkt:
## GEOGCRS["WGS 84",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["geodetic latitude (Lat)",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["geodetic longitude (Lon)",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     USAGE[
##         SCOPE["Horizontal component of 3D system."],
##         AREA["World."],
##         BBOX[-90,-180,90,180]],
##     ID["EPSG",4326]]
```

---

# 3. Classes sf

## **2. Simple feature columns (`sfc`)**

### Coordinate Reference Systems (CRS) - proj4string

```r
# proj4string definition
points_sfc_wgs <- sf::st_sfc(point1, point2, crs = "+proj=longlat +datum=WGS84 +no_defs")
sf::st_crs(points_sfc_wgs)
```

```
## Coordinate Reference System:
##   User input: +proj=longlat +datum=WGS84 +no_defs 
##   wkt:
## GEOGCRS["unknown",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ID["EPSG",6326]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433],
##         ID["EPSG",8901]],
##     CS[ellipsoidal,2],
##         AXIS["longitude",east,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]],
##         AXIS["latitude",north,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]]]
```

---

# 3. Classes sf

## **2. Simple feature columns (`sfc`)**

```r
# plot
plot(points_sfc_wgs, pch = 20, cex = 4, axes = TRUE, graticule = TRUE)
```

---

# 3. Classes sf

## **3. Simple feature (`sf`)**

### A classe sf (simple features) são *data frames* com **atributos espaciais** armazenados em uma coluna, geralmente chamada de **geometry**

### Essa **dualidade** é central para o conceito de **simple features**: na maioria das vezes, um sf pode **ser tratado e se comporta** como um *data frame *

### **Simple features** são, em essência, *data frames* com uma **extensão espacial**

---

# 3. Classes sf

## **3. Simple feature (`sf`)**

### Criar um objeto sf

```r
rc_point <- sf::st_point(c(-47.57, -22.39)) # `sfg` object
rc_geom <- sf::st_sfc(rc_point, crs = 4326) # `sfc` object
rc_attrib <- data.frame( # data frame object
 name = "Rio Claro",
 temperature = 19,
 date = as.Date("2020-10-13")
)
rc_sf <- sf::st_sf(rc_attrib, geometry = rc_geom) # sf object
```

---

# 3. Classes sf

## **3. Simple feature (`sf`)**

### A estrutura de um objeto sf

```r
rc_sf
```

```
## Simple feature collection with 1 feature and 3 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -47.57 ymin: -22.39 xmax: -47.57 ymax: -22.39
## Geodetic CRS:  WGS 84
##        name temperature       date              geometry
## 1 Rio Claro          19 2020-10-13 POINT (-47.57 -22.39)
```

### As duas classes do objeto sf

```r
class(rc_sf)
```

```
## [1] "sf"         "data.frame"
```

---

# 3. Classes sf

## **3. Simple feature (`sf`)**

```r
# plot
plot(rc_sf[1], pch = 20, cex = 4, axes = TRUE, graticule = TRUE) # rc_sf[1] - plotar a primeira coluna
```

---

# 3. Classes sf

## **3. Simple feature (`sf`)**

```r
# plot
plot(rc_sf[2], pch = 20, cex = 4, axes = TRUE, graticule = TRUE) # rc_sf[2] - plotar a segunda coluna
```

---

# 3. Classes sf

## **3. Simple feature (`sf`)**

```r
# plot
plot(rc_sf$geometry, pch = 20, cex = 4, axes = TRUE, graticule = TRUE) # rc_sf$geometry - plotar apenas a geometria
```

---

background-image: url(img/geo_data_fbds.png)
background-size: 500px
background-position: 50% 90%

# 4. Importar dados vetoriais

### Fundação Brasileira Desenvolvimento Sustentável (FBDS)

Em 2015, a FBDS deu início ao *Projeto de Mapeamento em Alta Resolução dos Biomas Brasileiros*: 
- Cobertura da terra
- Hidrografia (nascentes, rios e lagos)
- Áreas de Preservação Permanente (APP)

O mapeamento foi concluído para os municípios dos biomas *Mata Atlântica e Cerrado*

Site: https://www.fbds.org.br/ 
Repositório: http://geo.fbds.org.br/

---

# 4. Importar dados vetoriais

## Download

### Criar um diretório

```r
# create directory
dir.create(here::here("03_dados", "vetor"))
```

### Download de **pontos de nascentes** para Rio Claro/SP

```r
# increase time to download
options(timeout = 600)

# download points
for(i in c(".dbf", ".prj", ".shp", ".shx")){
  download.file(url = paste0("http://geo.fbds.org.br/SP/RIO_CLARO/HIDROGRAFIA/SP_3543907_NASCENTES", i),
                destfile = here::here("03_dados", "vetor", paste0("SP_3543907_NASCENTES", i)), mode = "wb")
}
```

---

# 4. Importar dados vetoriais

## Download

### Download de **linhas da hidrografia** para Rio Claro/SP

```r
# download lines
for(i in c(".dbf", ".prj", ".shp", ".shx")){
  download.file(url = paste0("http://geo.fbds.org.br/SP/RIO_CLARO/HIDROGRAFIA/SP_3543907_RIOS_SIMPLES", i),
                destfile = here::here("03_dados", "vetor", paste0("SP_3543907_RIOS_SIMPLES", i)), mode = "wb")
}
```

### Download de **polígonos de cobertura da terra** para Rio Claro/SP

```r
# download polygon
for(i in c(".dbf", ".prj", ".shp", ".shx")){
  download.file(url = paste0("http://geo.fbds.org.br/SP/RIO_CLARO/USO/SP_3543907_USO", i),
                destfile = here::here("03_dados", "vetor", paste0("SP_3543907_USO", i)), mode = "wb")
}
```

---

# 4. Importar dados vetoriais

## Dados preexistentes

```r
# importar pontos
rc_nas <- sf::st_read(here::here("03_dados", "vetor", "SP_3543907_NASCENTES.shp"), quiet = TRUE)
rc_nas
```

```
## Simple feature collection with 1220 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 217622.9 ymin: 7504132 xmax: 246367.4 ymax: 7537855
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 10 features:
##    GEOCODIGO MUNICIPIO UF CD_UF    HIDRO                 geometry
## 1    3543907 RIO CLARO SP    35 nascente POINT (217622.9 7528315)
## 2    3543907 RIO CLARO SP    35 nascente POINT (217836.5 7528103)
## 3    3543907 RIO CLARO SP    35 nascente POINT (217988.9 7528203)
## 4    3543907 RIO CLARO SP    35 nascente POINT (218288.9 7528237)
## 5    3543907 RIO CLARO SP    35 nascente POINT (218346.6 7530583)
## 6    3543907 RIO CLARO SP    35 nascente POINT (218393.1 7528031)
## 7    3543907 RIO CLARO SP    35 nascente POINT (218508.3 7528470)
## 8    3543907 RIO CLARO SP    35 nascente POINT (218535.4 7530642)
## 9    3543907 RIO CLARO SP    35 nascente POINT (218583.5 7528215)
## 10   3543907 RIO CLARO SP    35 nascente POINT (218760.1 7530258)
```

---

# 4. Importar dados vetoriais

## Dados preexistentes

```r
# plot
plot(rc_nas$geometry, pch = 20, col = "blue", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados preexistentes

```r
# importar linhas
rc_hid <- sf::st_read(here::here("03_dados", "vetor", "SP_3543907_RIOS_SIMPLES.shp"), quiet = TRUE)
rc_hid
```

```
## Simple feature collection with 1 feature and 6 fields
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: 215155.3 ymin: 7504132 xmax: 246367.4 ymax: 7537978
## Projected CRS: SIRGAS 2000 / UTM zone 23S
##   GEOCODIGO MUNICIPIO UF CD_UF                  HIDRO COMP_KM
## 1   3543907 RIO CLARO SP    35 curso d'água (0 - 10m) 1142.98
##                         geometry
## 1 MULTILINESTRING ((231815.7 ...
```

---

# 4. Importar dados vetoriais

## Dados preexistentes

```r
# plot
plot(rc_hid$geometry, col = "steelblue", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados preexistentes

```r
# importar poligonos
rc_cob <- sf::st_read(here::here("03_dados", "vetor", "SP_3543907_USO.shp"), quiet = TRUE)
rc_cob
```

```
## Simple feature collection with 5 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 215151.7 ymin: 7503723 xmax: 246582.4 ymax: 7537978
## Projected CRS: SIRGAS 2000 / UTM zone 23S
##   GEOCODIGO MUNICIPIO UF CD_UF         CLASSE_USO   AREA_HA
## 1   3543907 RIO CLARO SP    35               água   357.027
## 2   3543907 RIO CLARO SP    35   área antropizada 37297.800
## 3   3543907 RIO CLARO SP    35     área edificada  5078.330
## 4   3543907 RIO CLARO SP    35 formação florestal  7017.990
## 5   3543907 RIO CLARO SP    35       silvicultura   138.173
##                         geometry
## 1 MULTIPOLYGON (((235487.6 75...
## 2 MULTIPOLYGON (((232275 7504...
## 3 MULTIPOLYGON (((233123.6 75...
## 4 MULTIPOLYGON (((232355 7504...
## 5 MULTIPOLYGON (((243052.1 75...
```

---

# 4. Importar dados vetoriais

## Dados preexistentes

```r
# plot
plot(rc_cob[5], col = c("blue", "orange", "gray30", "forestgreen", "green"), main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados de GPS (.gpx)

```r
# importar dados de gps
gps_gpx <- sf::st_read(here::here("03_dados", "vetor", "waypoints.gpx"), layer = "waypoints", quiet = TRUE)
gps_gpx
```

```
## Simple feature collection with 11 features and 23 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -47.54245 ymin: -22.39524 xmax: -47.53969 ymax: -22.39209
## Geodetic CRS: WGS 84
## First 10 features:
## ele time magvar geoidheight name cmt desc src
## 1 603.0454 2020-08-20 10:13:35 NA NA 102 33 <NA> <NA>
## 2 595.2947 2020-08-20 10:22:52 NA NA 103 71 <NA> <NA>
## 3 581.9885 2020-08-20 10:32:30 NA NA 104 11 <NA> <NA>
## 4 587.4568 2020-08-20 10:42:05 NA NA 105 72 <NA> <NA>
## 5 581.2265 2020-08-20 10:49:32 NA NA 106 68 <NA> <NA>
## 6 593.6844 2020-08-20 11:06:53 NA NA 107 121 <NA> <NA>
## 7 591.4097 2020-08-20 11:16:36 NA NA 108 83 <NA> <NA>
## 8 594.1143 2020-08-20 11:27:59 NA NA 109 162 <NA> <NA>
## 9 594.4582 2020-08-20 11:43:04 NA NA 110 103 <NA> <NA>
## 10 602.0156 2020-08-20 11:58:32 NA NA 111 52 <NA> <NA>
## link1_href link1_text link1_type link2_href link2_text link2_type
## 1 <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA>
## 7 <NA> <NA> <NA> <NA> <NA> <NA>
## 8 <NA> <NA> <NA> <NA> <NA> <NA>
## 9 <NA> <NA> <NA> <NA> <NA> <NA>
## 10 <NA> <NA> <NA> <NA> <NA> <NA>
## sym type fix sat hdop vdop pdop ageofdgpsdata dgpsid
## 1 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 2 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 3 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 4 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 5 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 6 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 7 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 8 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 9 Scenic Area <NA> <NA> NA NA NA NA NA NA
## 10 Scenic Area <NA> <NA> NA NA NA NA NA NA
## geometry
## 1 POINT (-47.5405 -22.39524)
## 2 POINT (-47.5398 -22.39511)
## 3 POINT (-47.53969 -22.39424)
## 4 POINT (-47.54019 -22.39424)
## 5 POINT (-47.53973 -22.39353)
## 6 POINT (-47.54073 -22.39395)
## 7 POINT (-47.54059 -22.39284)
## 8 POINT (-47.54166 -22.39209)
## 9 POINT (-47.54161 -22.39258)
## 10 POINT (-47.54245 -22.39246)
```

---

# 4. Importar dados vetoriais

## Dados de GPS (.gpx)

```r
# plot
plot(gps_gpx$geometry, cex = 4, pch = 20, col = "red", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados de GPS (.kml)

```r
# importar dados de gps
gps_kml <- sf::st_read(here::here("03_dados", "vetor", "waypoints.kml"), quiet = TRUE)
gps_kml
```

```
## Simple feature collection with 11 features and 16 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -47.54245 ymin: -22.39524 xmax: -47.53969 ymax: -22.39209
## Geodetic CRS: WGS 84
## First 10 features:
## Name description timestamp begin end altitudeMode tessellate extrude
## 1 102 <NA> <NA> <NA> <NA> <NA> -1 0
## 2 103 <NA> <NA> <NA> <NA> <NA> -1 0
## 3 104 <NA> <NA> <NA> <NA> <NA> -1 0
## 4 105 <NA> <NA> <NA> <NA> <NA> -1 0
## 5 106 <NA> <NA> <NA> <NA> <NA> -1 0
## 6 107 <NA> <NA> <NA> <NA> <NA> -1 0
## 7 108 <NA> <NA> <NA> <NA> <NA> -1 0
## 8 109 <NA> <NA> <NA> <NA> <NA> -1 0
## 9 110 <NA> <NA> <NA> <NA> <NA> -1 0
## 10 111 <NA> <NA> <NA> <NA> <NA> -1 0
## visibility drawOrder icon elevation comment source url url.name
## 1 -1 NA <NA> 603.0454 33 <NA> <NA> <NA>
## 2 -1 NA <NA> 595.2947 71 <NA> <NA> <NA>
## 3 -1 NA <NA> 581.9885 11 <NA> <NA> <NA>
## 4 -1 NA <NA> 587.4568 72 <NA> <NA> <NA>
## 5 -1 NA <NA> 581.2265 68 <NA> <NA> <NA>
## 6 -1 NA <NA> 593.6844 121 <NA> <NA> <NA>
## 7 -1 NA <NA> 591.4097 83 <NA> <NA> <NA>
## 8 -1 NA <NA> 594.1143 162 <NA> <NA> <NA>
## 9 -1 NA <NA> 594.4582 103 <NA> <NA> <NA>
## 10 -1 NA <NA> 602.0156 52 <NA> <NA> <NA>
## geometry
## 1 POINT (-47.5405 -22.39524)
## 2 POINT (-47.5398 -22.39511)
## 3 POINT (-47.53969 -22.39424)
## 4 POINT (-47.54019 -22.39424)
## 5 POINT (-47.53973 -22.39353)
## 6 POINT (-47.54073 -22.39395)
## 7 POINT (-47.54059 -22.39284)
## 8 POINT (-47.54166 -22.39209)
## 9 POINT (-47.54161 -22.39258)
## 10 POINT (-47.54245 -22.39246)
```

---

# 4. Importar dados vetoriais

## Dados de GPS (.kml)

```r
# plot
plot(gps_kml$geometry, cex = 4, pch = 20, col = "blue", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados de uma tabela de coordenadas

```r
# importar tabela
si <- readr::read_csv(
 here::here("03_dados", "tabelas", "ATLANTIC_AMPHIBIANS_sites.csv"))
si
```

```
## # A tibble: 1,163 × 25
## id reference_number species_number record sampled_habitat active_methods
## <chr> <dbl> <dbl> <chr> <chr> <chr> 
## 1 amp1001 1001 19 ab fo,ll as 
## 2 amp1002 1002 16 co fo,la,ll as 
## 3 amp1003 1002 14 co fo,la,ll as 
## 4 amp1004 1002 13 co fo,la,ll as 
## 5 amp1005 1003 30 co fo,ll,br as 
## 6 amp1006 1004 42 co tp,pp,la,ll,is <NA> 
## 7 amp1007 1005 23 co sp as 
## 8 amp1008 1005 19 co sp,la,sw as,sb,tr 
## 9 amp1009 1005 13 ab fo <NA> 
## 10 amp1010 1006 1 ab fo <NA> 
## # … with 1,153 more rows, and 19 more variables: passive_methods <chr>,
## # complementary_methods <chr>, period <chr>, month_start <dbl>,
## # year_start <dbl>, month_finish <dbl>, year_finish <dbl>,
## # effort_months <dbl>, country <chr>, state <chr>, state_abbreviation <chr>,
## # municipality <chr>, site <chr>, latitude <dbl>, longitude <dbl>,
## # coordinate_precision <chr>, altitude <dbl>, temperature <dbl>,
## # precipitation <dbl>
```

---

# 4. Importar dados vetoriais

## Dados de uma tabela de coordenadas (.csv | .xlsx)

```r
# converter para sf
si_ve <- si %>% 
 sf::st_as_sf(coords = c("longitude", "latitude"), crs = 4326)
si_ve
```

```
## Simple feature collection with 1163 features and 23 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -56.74194 ymin: -33.51083 xmax: -34.79667 ymax: -3.51525
## Geodetic CRS: WGS 84
## # A tibble: 1,163 × 24
## id reference_number species_number record sampled_habitat active_methods
## * <chr> <dbl> <dbl> <chr> <chr> <chr> 
## 1 amp1001 1001 19 ab fo,ll as 
## 2 amp1002 1002 16 co fo,la,ll as 
## 3 amp1003 1002 14 co fo,la,ll as 
## 4 amp1004 1002 13 co fo,la,ll as 
## 5 amp1005 1003 30 co fo,ll,br as 
## 6 amp1006 1004 42 co tp,pp,la,ll,is <NA> 
## 7 amp1007 1005 23 co sp as 
## 8 amp1008 1005 19 co sp,la,sw as,sb,tr 
## 9 amp1009 1005 13 ab fo <NA> 
## 10 amp1010 1006 1 ab fo <NA> 
## # … with 1,153 more rows, and 18 more variables: passive_methods <chr>,
## # complementary_methods <chr>, period <chr>, month_start <dbl>,
## # year_start <dbl>, month_finish <dbl>, year_finish <dbl>,
## # effort_months <dbl>, country <chr>, state <chr>, state_abbreviation <chr>,
## # municipality <chr>, site <chr>, coordinate_precision <chr>, altitude <dbl>,
## # temperature <dbl>, precipitation <dbl>, geometry <POINT [°]>
```

---

# 4. Importar dados vetoriais

## Dados de uma tabela de coordenadas (.csv | .xlsx)

```r
# plot
plot(si_ve$geometry, pch = 20, main = NA, axes = TRUE, graticule = TRUE)
```

---

background-image: url(img/package_geobr.png)
background-size: 300px
background-position: 50% 70%

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
install.packages("geobr")
library(geobr)
```

[geobr](https://github.com/ipeaGIT/geobr)

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# brasil 2020
br_2020 <- geobr::read_country(year = 2020, showProgress = FALSE)
br_2020
```

```
## Simple feature collection with 27 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.99045 ymin: -33.75118 xmax: -28.84784 ymax: 5.271841
## Geodetic CRS:  SIRGAS 2000
## First 10 features:
##    code_state abbrev_state name_state code_region name_region
## 1          11           RO   Rondônia           1       Norte
## 2          12           AC       Acre           1       Norte
## 3          13           AM   Amazônas           1       Norte
## 4          14           RR    Roraima           1       Norte
## 5          15           PA       Pará           1       Norte
## 6          16           AP      Amapá           1       Norte
## 7          17           TO  Tocantins           1       Norte
## 8          21           MA   Maranhão           2    Nordeste
## 9          22           PI      Piauí           2    Nordeste
## 10         23           CE      Ceará           2    Nordeste
##                              geom
## 1  MULTIPOLYGON (((-65.3815 -1...
## 2  MULTIPOLYGON (((-71.07772 -...
## 3  MULTIPOLYGON (((-69.83766 -...
## 4  MULTIPOLYGON (((-63.96008 2...
## 5  MULTIPOLYGON (((-51.43248 -...
## 6  MULTIPOLYGON (((-53.27918 2...
## 7  MULTIPOLYGON (((-48.23291 -...
## 8  MULTIPOLYGON (((-46.16128 -...
## 9  MULTIPOLYGON (((-42.91509 -...
## 10 MULTIPOLYGON (((-41.18292 -...
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# plot
plot(br_2020$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# brasil 1872
br_1872 <- geobr::read_country(year = 1872, showProgress = FALSE)
br_1872
```

```
## Simple feature collection with 1 feature and 0 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.80132 ymin: -33.74912 xmax: -34.79313 ymax: 5.271891
## Geodetic CRS:  SIRGAS 2000
##                             geom
## 1 MULTIPOLYGON (((-48.40975 -...
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# plot
plot(br_1872$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# sao paulo municipios
sp_mun_2020 <- geobr::read_municipality(code_muni = "SP", year = 2020, showProgress = FALSE)
sp_mun_2020
```

```
## Simple feature collection with 645 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -53.10986 ymin: -25.35794 xmax: -44.16137 ymax: -19.77966
## Geodetic CRS:  SIRGAS 2000
## First 10 features:
##    code_muni              name_muni code_state abbrev_state name_state
## 1    3500105             Adamantina         35           SP  São Paulo
## 2    3500204                 Adolfo         35           SP  São Paulo
## 3    3500303                  Aguaí         35           SP  São Paulo
## 4    3500402         Águas Da Prata         35           SP  São Paulo
## 5    3500501       Águas De Lindóia         35           SP  São Paulo
## 6    3500550 Águas De Santa Bárbara         35           SP  São Paulo
## 7    3500600     Águas De São Pedro         35           SP  São Paulo
## 8    3500709                 Agudos         35           SP  São Paulo
## 9    3500758               Alambari         35           SP  São Paulo
## 10   3500808      Alfredo Marcondes         35           SP  São Paulo
##    code_region name_region                           geom
## 1            3     Sudeste MULTIPOLYGON (((-51.05425 -...
## 2            3     Sudeste MULTIPOLYGON (((-49.65795 -...
## 3            3     Sudeste MULTIPOLYGON (((-46.9764 -2...
## 4            3     Sudeste MULTIPOLYGON (((-46.73501 -...
## 5            3     Sudeste MULTIPOLYGON (((-46.60614 -...
## 6            3     Sudeste MULTIPOLYGON (((-49.19076 -...
## 7            3     Sudeste MULTIPOLYGON (((-47.86297 -...
## 8            3     Sudeste MULTIPOLYGON (((-48.91186 -...
## 9            3     Sudeste MULTIPOLYGON (((-47.80901 -...
## 10           3     Sudeste MULTIPOLYGON (((-51.32552 -...
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# plot
plot(sp_mun_2020$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# rio claro
rc_2020 <- geobr::read_municipality(code_muni = 3543907, year = 2020, showProgress = FALSE)
rc_2020
```

```
## Simple feature collection with 1 feature and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -47.76521 ymin: -22.55203 xmax: -47.46188 ymax: -22.24368
## Geodetic CRS:  SIRGAS 2000
##     code_muni name_muni code_state abbrev_state name_state code_region
## 493   3543907 Rio Claro         35           SP  São Paulo           3
##     name_region                           geom
## 493     Sudeste MULTIPOLYGON (((-47.46875 -...
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# plot
plot(rc_2020$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# biomas
bi_2019 <- geobr::read_biomes(year = 2019, showProgress = FALSE)
bi_2019
```

```
## Simple feature collection with 7 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.98304 ymin: -34.95942 xmax: -28.84785 ymax: 7.053767
## Geodetic CRS:  SIRGAS 2000
##         name_biome code_biome year                           geom
## 1         Amazônia          1 2019 MULTIPOLYGON (((-44.08515 -...
## 2         Caatinga          2 2019 MULTIPOLYGON (((-41.7408 -2...
## 3          Cerrado          3 2019 MULTIPOLYGON (((-43.39009 -...
## 4   Mata Atlântica          4 2019 MULTIPOLYGON (((-48.70814 -...
## 5            Pampa          5 2019 MULTIPOLYGON (((-52.82472 -...
## 6         Pantanal          6 2019 MULTIPOLYGON (((-57.75946 -...
## 7 Sistema Costeiro         NA 2019 MULTIPOLYGON (((-44.64799 -...
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# plot
plot(bi_2019$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

|Function|Geographies available|Years available|Source|
|-----|-----|-----|-----|
|`read_country()`| Country | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019 | IBGE |
|`read_state()`| States | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019 | IBGE |
|`read_municipality()`| Municipality | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2005, 2007, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019 | IBGE | 
|`read_biomes()`| Biomes | 2004, 2019 | IBGE |

---

# 4. Importar dados vetoriais

## Dados de pacotes: *geobr*

```r
# list all datasets available in the geobr package
geobr::list_geobr()
```

```
## # A tibble: 27 × 4
## `function` geography years source
## <chr> <chr> <chr> <chr> 
## 1 `read_country` Country 1872, 1900, 1911, 1920,… IBGE 
## 2 `read_region` Region 2000, 2001, 2010, 2013,… IBGE 
## 3 `read_state` States 1872, 1900, 1911, 1920,… IBGE 
## 4 `read_meso_region` Meso region 2000, 2001, 2010, 2013,… IBGE 
## 5 `read_micro_region` Micro region 2000, 2001, 2010, 2013,… IBGE 
## 6 `read_intermediate_region` Intermediate regi… 2017, 2019, 2020 IBGE 
## 7 `read_immediate_region` Immediate region 2017, 2019, 2020 IBGE 
## 8 `read_municipality` Municipality 1872, 1900, 1911, 1920,… IBGE 
## 9 `read_municipal_seat` Municipality seat… 1872, 1900, 1911, 1920,… IBGE 
## 10 `read_weighting_area` Census weighting … 2010 IBGE 
## # … with 17 more rows
```

---

background-image: url(img/package_ropensci.png)
background-size: 500px
background-position: 50% 70%

# 4. Importar dados vetoriais

## Dados de pacotes: *rnaturalearth*

```r
# package
install.packages("rnaturalearth")
library(rnaturalearth)
```

[naturalearthdata](http://www.naturalearthdata.com/),
[rnaturalearth](https://docs.ropensci.org/rnaturalearth/)

---

# 4. Importar dados vetoriais

## Dados de pacotes: *rnaturalearth*

```r
# south america
sa <- rnaturalearth::ne_countries(scale = "small", continent = "South America", returnclass = "sf")
sa
```

```
## Simple feature collection with 13 features and 63 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -81.41094 ymin: -55.61183 xmax: -34.72999 ymax: 12.4373
## CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## First 10 features:
## scalerank featurecla labelrank sovereignt sov_a3 adm0_dif level
## 4 1 Admin-0 country 2 Argentina ARG 0 2
## 21 1 Admin-0 country 3 Bolivia BOL 0 2
## 22 1 Admin-0 country 2 Brazil BRA 0 2
## 29 1 Admin-0 country 2 Chile CHL 0 2
## 35 1 Admin-0 country 2 Colombia COL 0 2
## 46 1 Admin-0 country 3 Ecuador ECU 0 2
## 54 1 Admin-0 country 5 United Kingdom GB1 1 2
## 67 1 Admin-0 country 4 Guyana GUY 0 2
## 124 1 Admin-0 country 2 Peru PER 0 2
## 131 1 Admin-0 country 4 Paraguay PRY 0 2
## type admin adm0_a3 geou_dif geounit gu_a3
## 4 Sovereign country Argentina ARG 0 Argentina ARG
## 21 Sovereign country Bolivia BOL 0 Bolivia BOL
## 22 Sovereign country Brazil BRA 0 Brazil BRA
## 29 Sovereign country Chile CHL 0 Chile CHL
## 35 Sovereign country Colombia COL 0 Colombia COL
## 46 Sovereign country Ecuador ECU 0 Ecuador ECU
## 54 Dependency Falkland Islands FLK 0 Falkland Islands FLK
## 67 Sovereign country Guyana GUY 0 Guyana GUY
## 124 Sovereign country Peru PER 0 Peru PER
## 131 Sovereign country Paraguay PRY 0 Paraguay PRY
## su_dif subunit su_a3 brk_diff name name_long brk_a3
## 4 0 Argentina ARG 0 Argentina Argentina ARG
## 21 0 Bolivia BOL 0 Bolivia Bolivia BOL
## 22 0 Brazil BRA 0 Brazil Brazil BRA
## 29 0 Chile CHL 0 Chile Chile CHL
## 35 0 Colombia COL 0 Colombia Colombia COL
## 46 0 Ecuador ECU 0 Ecuador Ecuador ECU
## 54 0 Falkland Islands FLK 1 Falkland Is. Falkland Islands B12
## 67 0 Guyana GUY 0 Guyana Guyana GUY
## 124 0 Peru PER 0 Peru Peru PER
## 131 0 Paraguay PRY 0 Paraguay Paraguay PRY
## brk_name brk_group abbrev postal formal_en
## 4 Argentina <NA> Arg. AR Argentine Republic
## 21 Bolivia <NA> Bolivia BO Plurinational State of Bolivia
## 22 Brazil <NA> Brazil BR Federative Republic of Brazil
## 29 Chile <NA> Chile CL Republic of Chile
## 35 Colombia <NA> Col. CO Republic of Colombia
## 46 Ecuador <NA> Ecu. EC Republic of Ecuador
## 54 Falkland Is. <NA> Flk. Is. FK Falkland Islands
## 67 Guyana <NA> Guy. GY Co-operative Republic of Guyana
## 124 Peru <NA> Peru PE Republic of Peru
## 131 Paraguay <NA> Para. PY Republic of Paraguay
## formal_fr note_adm0 note_brk name_sort
## 4 <NA> <NA> <NA> Argentina
## 21 <NA> <NA> <NA> Bolivia
## 22 <NA> <NA> <NA> Brazil
## 29 <NA> <NA> <NA> Chile
## 35 <NA> <NA> <NA> Colombia
## 46 <NA> <NA> <NA> Ecuador
## 54 <NA> U.K. Admin. by U.K.; Claimed by Argentina Falkland Islands
## 67 <NA> <NA> <NA> Guyana
## 124 <NA> <NA> <NA> Peru
## 131 <NA> <NA> <NA> Paraguay
## name_alt mapcolor7 mapcolor8 mapcolor9 mapcolor13 pop_est
## 4 <NA> 3 1 3 13 40913584
## 21 <NA> 1 5 2 3 9775246
## 22 <NA> 5 6 5 7 198739269
## 29 <NA> 5 1 5 9 16601707
## 35 <NA> 2 1 3 1 45644023
## 46 <NA> 1 5 2 12 14573101
## 54 Islas Malvinas 6 6 6 3 3140
## 67 <NA> 3 1 4 8 772298
## 124 <NA> 4 4 4 11 29546963
## 131 <NA> 6 3 6 2 6995655
## gdp_md_est pop_year lastcensus gdp_year economy
## 4 573900.0 NA 2010 NA 5. Emerging region: G20
## 21 43270.0 NA 2001 NA 5. Emerging region: G20
## 22 1993000.0 NA 2010 NA 3. Emerging region: BRIC
## 29 244500.0 NA 2002 NA 5. Emerging region: G20
## 35 395400.0 NA 2006 NA 6. Developing region
## 46 107700.0 NA 2010 NA 6. Developing region
## 54 105.1 NA NA NA 2. Developed region: nonG7
## 67 2966.0 NA 2002 NA 6. Developing region
## 124 247300.0 NA 2007 NA 5. Emerging region: G20
## 131 28890.0 NA 2002 NA 5. Emerging region: G20
## income_grp wikipedia fips_10 iso_a2 iso_a3 iso_n3 un_a3 wb_a2
## 4 3. Upper middle income NA <NA> AR ARG 032 032 AR
## 21 4. Lower middle income NA <NA> BO BOL 068 068 BO
## 22 3. Upper middle income NA <NA> BR BRA 076 076 BR
## 29 3. Upper middle income NA <NA> CL CHL 152 152 CL
## 35 3. Upper middle income NA <NA> CO COL 170 170 CO
## 46 3. Upper middle income NA <NA> EC ECU 218 218 EC
## 54 1. High income: OECD NA <NA> FK FLK 238 238 <NA>
## 67 4. Lower middle income NA <NA> GY GUY 328 328 GY
## 124 3. Upper middle income NA <NA> PE PER 604 604 PE
## 131 4. Lower middle income NA <NA> PY PRY 600 600 PY
## wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un adm0_a3_wb continent
## 4 ARG NA ARG ARG NA NA South America
## 21 BOL NA BOL BOL NA NA South America
## 22 BRA NA BRA BRA NA NA South America
## 29 CHL NA CHL CHL NA NA South America
## 35 COL NA COL COL NA NA South America
## 46 ECU NA ECU ECU NA NA South America
## 54 <NA> NA FLK FLK NA NA South America
## 67 GUY NA GUY GUY NA NA South America
## 124 PER NA PER PER NA NA South America
## 131 PRY NA PRY PRY NA NA South America
## region_un subregion region_wb name_len long_len
## 4 Americas South America Latin America & Caribbean 9 9
## 21 Americas South America Latin America & Caribbean 7 7
## 22 Americas South America Latin America & Caribbean 6 6
## 29 Americas South America Latin America & Caribbean 5 5
## 35 Americas South America Latin America & Caribbean 8 8
## 46 Americas South America Latin America & Caribbean 7 7
## 54 Americas South America Latin America & Caribbean 12 16
## 67 Americas South America Latin America & Caribbean 6 6
## 124 Americas South America Latin America & Caribbean 4 4
## 131 Americas South America Latin America & Caribbean 8 8
## abbrev_len tiny homepart geometry
## 4 4 NA 1 MULTIPOLYGON (((-65.5 -55.2...
## 21 7 NA 1 MULTIPOLYGON (((-62.84647 -...
## 22 6 NA 1 MULTIPOLYGON (((-57.62513 -...
## 29 5 NA 1 MULTIPOLYGON (((-68.63401 -...
## 35 4 NA 1 MULTIPOLYGON (((-75.37322 -...
## 46 4 NA 1 MULTIPOLYGON (((-80.30256 -...
## 54 8 NA NA MULTIPOLYGON (((-61.2 -51.8...
## 67 4 NA 1 MULTIPOLYGON (((-59.75828 8...
## 124 4 NA 1 MULTIPOLYGON (((-69.59042 -...
## 131 5 NA 1 MULTIPOLYGON (((-62.68506 -...
```

---

# 4. Importar dados vetoriais

## Dados de pacotes: *rnaturalearth*

```r
# plot
plot(sa$geometry, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
```

---

# 5. Descrição de objetos sf

## Informações geográficas e tabela de atributos

```r
# rio claro
rc_2020
```

---

background-image: url(img/geo_sf_xfig.png)
background-size: 700px
background-position: 50% 90%

# 5. Descrição de objetos sf

## Informações

- *resumo do vetor*: indica o número de feições (linhas) e campos (colunas)
- *tipo da geometria*: umas das sete classes listadas anteriormente
- *dimensão*: número de dimensões, geralmente duas (XY)
- *bbox (bordas)*: coordenadas mínimas e máximas da longitude e latitude
- *informação do CRS*: `epsg` ou `proj4string` indicando o CRS
- *tibble*: tabela de atributos, com destaque para a coluna `geom` ou `geometry` que representa cada feição ou geometria

---

# 5. Descrição de objetos sf

## Informações geográficas

### Geometry type

```r
# tipo de geometria
sf::st_geometry_type(rc_2020)
```

```
## [1] MULTIPOLYGON
## 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE
```

### Bounding box

```r
# extensao
sf::st_bbox(rc_2020)
```

```
##      xmin      ymin      xmax      ymax 
## -47.76521 -22.55203 -47.46188 -22.24368
```

---

# 5. Descrição de objetos sf

## Informações geográficas

### Coordinate Reference System (CRS)

```r
# crs
sf::st_crs(rc_2020)
```

```
## Coordinate Reference System:
##   User input: SIRGAS 2000 
##   wkt:
## GEOGCRS["SIRGAS 2000",
##     DATUM["Sistema de Referencia Geocentrico para las AmericaS 2000",
##         ELLIPSOID["GRS 1980",6378137,298.257222101,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["geodetic latitude (Lat)",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["geodetic longitude (Lon)",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     USAGE[
##         SCOPE["Horizontal component of 3D system."],
##         AREA["Latin America - Central America and South America - onshore and offshore. Brazil - onshore and offshore."],
##         BBOX[-59.87,-122.19,32.72,-25.28]],
##     ID["EPSG",4674]]
```

---

background-image: url(img/geo_vector_attribute_table.png)
background-size: 450px
background-position: 45% 80%

# 5. Descrição de objetos sf

## Tabela de atributos

### Relação entre a geometria e suas características

---

# 5. Descrição de objetos sf

## Tabela de atributos

### Acessar a tabela de atributos de um `sf`

```r
# acessar a tabela de atributos
rc_2020_tab <- sf::st_drop_geometry(rc_2020)
rc_2020_tab
```

```
##     code_muni name_muni code_state abbrev_state name_state code_region
## 493   3543907 Rio Claro         35           SP  São Paulo           3
##     name_region
## 493     Sudeste
```

```r
# classe
class(rc_2020_tab)
```

```
## [1] "data.frame"
```

---

background-image: url(img/geo_sp.png),url(img/geo_sp_dep.png)
background-size: 250px, 700px
background-position: 20% 90%, 93% 50%

# 6. Converter objetos para sf

## Tipos de objetos `sp`

- SpatialPoints 
- SpatialLines
- SpatialPolygons
- SpatialPointsdata frame
- SpatialLinesdata frame
- SpatialPolygonsdata frame

---

# 6. Converter objetos para sf

## Dados `sp`

```r
# paises sp
co110_sp <- rnaturalearth::countries110
co110_sp
```

```
## An object of class "SpatialPolygonsDataFrame"
## Slot "data":
## scalerank featurecla labelrank sovereignt sov_a3
## 0 1 Admin-0 country 3 Afghanistan AFG
## 1 1 Admin-0 country 3 Angola AGO
## 2 1 Admin-0 country 6 Albania ALB
## 3 1 Admin-0 country 4 United Arab Emirates ARE
## 4 1 Admin-0 country 2 Argentina ARG
## 5 1 Admin-0 country 6 Armenia ARM
## 6 1 Admin-0 country 4 Antarctica ATA
## 7 3 Admin-0 country 6 France FR1
## 8 1 Admin-0 country 2 Australia AU1
## 9 1 Admin-0 country 4 Austria AUT
## 10 1 Admin-0 country 5 Azerbaijan AZE
## 11 1 Admin-0 country 6 Burundi BDI
## 12 1 Admin-0 country 2 Belgium BEL
## 13 1 Admin-0 country 5 Benin BEN
## 14 1 Admin-0 country 3 Burkina Faso BFA
## 15 1 Admin-0 country 3 Bangladesh BGD
## 16 1 Admin-0 country 4 Bulgaria BGR
## 17 1 Admin-0 country 4 The Bahamas BHS
## 18 1 Admin-0 country 5 Bosnia and Herzegovina BIH
## 19 1 Admin-0 country 4 Belarus BLR
## 20 1 Admin-0 country 6 Belize BLZ
## 21 1 Admin-0 country 3 Bolivia BOL
## 22 1 Admin-0 country 2 Brazil BRA
## 23 1 Admin-0 country 6 Brunei BRN
## 24 1 Admin-0 country 5 Bhutan BTN
## 25 1 Admin-0 country 4 Botswana BWA
## 26 1 Admin-0 country 4 Central African Republic CAF
## 27 1 Admin-0 country 2 Canada CAN
## 28 1 Admin-0 country 4 Switzerland CHE
## 29 1 Admin-0 country 2 Chile CHL
## 30 1 Admin-0 country 2 China CH1
## 31 1 Admin-0 country 3 Ivory Coast CIV
## 32 1 Admin-0 country 3 Cameroon CMR
## 33 1 Admin-0 country 2 Democratic Republic of the Congo COD
## 34 1 Admin-0 country 4 Republic of Congo COG
## 35 1 Admin-0 country 2 Colombia COL
## 36 1 Admin-0 country 5 Costa Rica CRI
## 37 1 Admin-0 country 3 Cuba CUB
## 38 1 Admin-0 country 6 Northern Cyprus CYN
## 39 1 Admin-0 country 5 Cyprus CYP
## 40 1 Admin-0 country 5 Czech Republic CZE
## 41 1 Admin-0 country 2 Germany DEU
## 42 1 Admin-0 country 5 Djibouti DJI
## 43 1 Admin-0 country 4 Denmark DN1
## 44 1 Admin-0 country 5 Dominican Republic DOM
## 45 1 Admin-0 country 3 Algeria DZA
## 46 1 Admin-0 country 3 Ecuador ECU
## 47 1 Admin-0 country 2 Egypt EGY
## 48 1 Admin-0 country 4 Eritrea ERI
## 49 1 Admin-0 country 2 Spain ESP
## 50 1 Admin-0 country 6 Estonia EST
## 51 1 Admin-0 country 2 Ethiopia ETH
## 52 1 Admin-0 country 3 Finland FI1
## 53 1 Admin-0 country 6 Fiji FJI
## 54 1 Admin-0 country 5 United Kingdom GB1
## 55 1 Admin-0 country 2 France FR1
## 56 1 Admin-0 country 4 Gabon GAB
## 57 1 Admin-0 country 2 United Kingdom GB1
## 58 1 Admin-0 country 5 Georgia GEO
## 59 1 Admin-0 country 3 Ghana GHA
## 60 1 Admin-0 country 3 Guinea GIN
## 61 1 Admin-0 country 6 Gambia GMB
## 62 1 Admin-0 country 6 Guinea Bissau GNB
## 63 1 Admin-0 country 4 Equatorial Guinea GNQ
## 64 1 Admin-0 country 3 Greece GRC
## 65 1 Admin-0 country 3 Denmark DN1
## 66 1 Admin-0 country 3 Guatemala GTM
## 67 1 Admin-0 country 4 Guyana GUY
## 68 1 Admin-0 country 5 Honduras HND
## 69 1 Admin-0 country 6 Croatia HRV
## 70 1 Admin-0 country 5 Haiti HTI
## 71 1 Admin-0 country 5 Hungary HUN
## 72 1 Admin-0 country 2 Indonesia IDN
## 73 1 Admin-0 country 2 India IND
## 74 1 Admin-0 country 3 Ireland IRL
## 75 1 Admin-0 country 2 Iran IRN
## 76 1 Admin-0 country 3 Iraq IRQ
## 77 1 Admin-0 country 3 Iceland ISL
## 78 1 Admin-0 country 4 Israel ISR
## 79 1 Admin-0 country 2 Italy ITA
## 80 1 Admin-0 country 4 Jamaica JAM
## 81 1 Admin-0 country 4 Jordan JOR
## 82 1 Admin-0 country 2 Japan JPN
## 83 1 Admin-0 country 3 Kazakhstan KAZ
## 84 1 Admin-0 country 2 Kenya KEN
## 85 1 Admin-0 country 4 Kyrgyzstan KGZ
## 86 1 Admin-0 country 3 Cambodia KHM
## 87 1 Admin-0 country 2 South Korea KOR
## 88 1 Admin-0 country 6 Kosovo KOS
## 89 1 Admin-0 country 6 Kuwait KWT
## 90 1 Admin-0 country 4 Laos LAO
## 91 1 Admin-0 country 5 Lebanon LBN
## 92 1 Admin-0 country 4 Liberia LBR
## 93 1 Admin-0 country 3 Libya LBY
## 94 1 Admin-0 country 3 Sri Lanka LKA
## 95 1 Admin-0 country 6 Lesotho LSO
## 96 1 Admin-0 country 5 Lithuania LTU
## 97 1 Admin-0 country 6 Luxembourg LUX
## 98 1 Admin-0 country 5 Latvia LVA
## 99 1 Admin-0 country 3 Morocco MAR
## 100 1 Admin-0 country 6 Moldova MDA
## 101 1 Admin-0 country 3 Madagascar MDG
## 102 1 Admin-0 country 2 Mexico MEX
## 103 1 Admin-0 country 6 Macedonia MKD
## 104 1 Admin-0 country 3 Mali MLI
## 105 1 Admin-0 country 3 Myanmar MMR
## 106 1 Admin-0 country 6 Montenegro MNE
## 107 1 Admin-0 country 3 Mongolia MNG
## 108 1 Admin-0 country 3 Mozambique MOZ
## 109 1 Admin-0 country 3 Mauritania MRT
## 110 1 Admin-0 country 6 Malawi MWI
## 111 1 Admin-0 country 3 Malaysia MYS
## 112 1 Admin-0 country 3 Namibia NAM
## 113 1 Admin-0 country 3 France FR1
## 114 1 Admin-0 country 3 Niger NER
## 115 1 Admin-0 country 2 Nigeria NGA
## 116 1 Admin-0 country 5 Nicaragua NIC
## 117 1 Admin-0 country 5 Netherlands NL1
## 118 1 Admin-0 country 3 Norway NOR
## 119 1 Admin-0 country 3 Nepal NPL
## 120 1 Admin-0 country 2 New Zealand NZ1
## 121 1 Admin-0 country 4 Oman OMN
## 122 1 Admin-0 country 2 Pakistan PAK
## 123 1 Admin-0 country 4 Panama PAN
## 124 1 Admin-0 country 2 Peru PER
## 125 1 Admin-0 country 2 Philippines PHL
## 126 1 Admin-0 country 2 Papua New Guinea PNG
## 127 1 Admin-0 country 3 Poland POL
## 128 1 Admin-0 country 5 United States of America US1
## 129 1 Admin-0 country 3 North Korea PRK
## 130 1 Admin-0 country 2 Portugal PRT
## 131 1 Admin-0 country 4 Paraguay PRY
## 132 1 Admin-0 country 5 Israel ISR
## 133 1 Admin-0 country 5 Qatar QAT
## 134 1 Admin-0 country 3 Romania ROU
## 135 1 Admin-0 country 2 Russia RUS
## 136 1 Admin-0 country 3 Rwanda RWA
## 137 1 Admin-0 country 7 Western Sahara SAH
## 138 1 Admin-0 country 2 Saudi Arabia SAU
## 139 1 Admin-0 country 3 Sudan SDN
## 140 1 Admin-0 country 3 South Sudan SDS
## 141 1 Admin-0 country 3 Senegal SEN
## 142 1 Admin-0 country 3 Solomon Islands SLB
## 143 1 Admin-0 country 4 Sierra Leone SLE
## 144 1 Admin-0 country 6 El Salvador SLV
## 145 1 Admin-0 country 5 Somaliland SOL
## 146 1 Admin-0 country 6 Somalia SOM
## 147 1 Admin-0 country 5 Republic of Serbia SRB
## 148 1 Admin-0 country 4 Suriname SUR
## 149 1 Admin-0 country 6 Slovakia SVK
## 150 1 Admin-0 country 6 Slovenia SVN
## 151 1 Admin-0 country 3 Sweden SWE
## 152 1 Admin-0 country 4 Swaziland SWZ
## 153 1 Admin-0 country 3 Syria SYR
## 154 1 Admin-0 country 3 Chad TCD
## 155 1 Admin-0 country 6 Togo TGO
## 156 1 Admin-0 country 3 Thailand THA
## 157 1 Admin-0 country 4 Tajikistan TJK
## 158 1 Admin-0 country 4 Turkmenistan TKM
## 159 1 Admin-0 country 5 East Timor TLS
## 160 1 Admin-0 country 5 Trinidad and Tobago TTO
## 161 1 Admin-0 country 3 Tunisia TUN
## 162 1 Admin-0 country 2 Turkey TUR
## 163 1 Admin-0 country 3 Taiwan TWN
## 164 1 Admin-0 country 3 United Republic of Tanzania TZA
## 165 1 Admin-0 country 3 Uganda UGA
## 166 1 Admin-0 country 3 Ukraine UKR
## 167 1 Admin-0 country 4 Uruguay URY
## 168 1 Admin-0 country 2 United States of America US1
## 169 1 Admin-0 country 3 Uzbekistan UZB
## 170 1 Admin-0 country 3 Venezuela VEN
## 171 1 Admin-0 country 2 Vietnam VNM
## 172 1 Admin-0 country 4 Vanuatu VUT
## 173 1 Admin-0 country 3 Yemen YEM
## 174 1 Admin-0 country 2 South Africa ZAF
## 175 1 Admin-0 country 3 Zambia ZMB
## 176 1 Admin-0 country 3 Zimbabwe ZWE
## adm0_dif level type admin
## 0 0 2 Sovereign country Afghanistan
## 1 0 2 Sovereign country Angola
## 2 0 2 Sovereign country Albania
## 3 0 2 Sovereign country United Arab Emirates
## 4 0 2 Sovereign country Argentina
## 5 0 2 Sovereign country Armenia
## 6 0 2 Indeterminate Antarctica
## 7 1 2 Dependency French Southern and Antarctic Lands
## 8 1 2 Country Australia
## 9 0 2 Sovereign country Austria
## 10 0 2 Sovereign country Azerbaijan
## 11 0 2 Sovereign country Burundi
## 12 0 2 Sovereign country Belgium
## 13 0 2 Sovereign country Benin
## 14 0 2 Sovereign country Burkina Faso
## 15 0 2 Sovereign country Bangladesh
## 16 0 2 Sovereign country Bulgaria
## 17 0 2 Sovereign country The Bahamas
## 18 0 2 Sovereign country Bosnia and Herzegovina
## 19 0 2 Sovereign country Belarus
## 20 0 2 Sovereign country Belize
## 21 0 2 Sovereign country Bolivia
## 22 0 2 Sovereign country Brazil
## 23 0 2 Sovereign country Brunei
## 24 0 2 Sovereign country Bhutan
## 25 0 2 Sovereign country Botswana
## 26 0 2 Sovereign country Central African Republic
## 27 0 2 Sovereign country Canada
## 28 0 2 Sovereign country Switzerland
## 29 0 2 Sovereign country Chile
## 30 1 2 Country China
## 31 0 2 Sovereign country Ivory Coast
## 32 0 2 Sovereign country Cameroon
## 33 0 2 Sovereign country Democratic Republic of the Congo
## 34 0 2 Sovereign country Republic of Congo
## 35 0 2 Sovereign country Colombia
## 36 0 2 Sovereign country Costa Rica
## 37 0 2 Sovereign country Cuba
## 38 0 2 Sovereign country Northern Cyprus
## 39 0 2 Sovereign country Cyprus
## 40 0 2 Sovereign country Czech Republic
## 41 0 2 Sovereign country Germany
## 42 0 2 Sovereign country Djibouti
## 43 1 2 Country Denmark
## 44 0 2 Sovereign country Dominican Republic
## 45 0 2 Sovereign country Algeria
## 46 0 2 Sovereign country Ecuador
## 47 0 2 Sovereign country Egypt
## 48 0 2 Sovereign country Eritrea
## 49 0 2 Sovereign country Spain
## 50 0 2 Sovereign country Estonia
## 51 0 2 Sovereign country Ethiopia
## 52 1 2 Country Finland
## 53 0 2 Sovereign country Fiji
## 54 1 2 Dependency Falkland Islands
## 55 1 2 Country France
## 56 0 2 Sovereign country Gabon
## 57 1 2 Country United Kingdom
## 58 0 2 Sovereign country Georgia
## 59 0 2 Sovereign country Ghana
## 60 0 2 Sovereign country Guinea
## 61 0 2 Sovereign country Gambia
## 62 0 2 Sovereign country Guinea Bissau
## 63 0 2 Sovereign country Equatorial Guinea
## 64 0 2 Sovereign country Greece
## 65 1 2 Country Greenland
## 66 0 2 Sovereign country Guatemala
## 67 0 2 Sovereign country Guyana
## 68 0 2 Sovereign country Honduras
## 69 0 2 Sovereign country Croatia
## 70 0 2 Sovereign country Haiti
## 71 0 2 Sovereign country Hungary
## 72 0 2 Sovereign country Indonesia
## 73 0 2 Sovereign country India
## 74 0 2 Sovereign country Ireland
## 75 0 2 Sovereign country Iran
## 76 0 2 Sovereign country Iraq
## 77 0 2 Sovereign country Iceland
## 78 0 2 Sovereign country Israel
## 79 0 2 Sovereign country Italy
## 80 0 2 Sovereign country Jamaica
## 81 0 2 Sovereign country Jordan
## 82 0 2 Sovereign country Japan
## 83 0 2 Sovereign country Kazakhstan
## 84 0 2 Sovereign country Kenya
## 85 0 2 Sovereign country Kyrgyzstan
## 86 0 2 Sovereign country Cambodia
## 87 0 2 Sovereign country South Korea
## 88 0 2 Sovereign country Kosovo
## 89 0 2 Sovereign country Kuwait
## 90 0 2 Sovereign country Laos
## 91 0 2 Sovereign country Lebanon
## 92 0 2 Sovereign country Liberia
## 93 0 2 Sovereign country Libya
## 94 0 2 Sovereign country Sri Lanka
## 95 0 2 Sovereign country Lesotho
## 96 0 2 Sovereign country Lithuania
## 97 0 2 Sovereign country Luxembourg
## 98 0 2 Sovereign country Latvia
## 99 0 2 Sovereign country Morocco
## 100 0 2 Sovereign country Moldova
## 101 0 2 Sovereign country Madagascar
## 102 0 2 Sovereign country Mexico
## 103 0 2 Sovereign country Macedonia
## 104 0 2 Sovereign country Mali
## 105 0 2 Sovereign country Myanmar
## 106 0 2 Sovereign country Montenegro
## 107 0 2 Sovereign country Mongolia
## 108 0 2 Sovereign country Mozambique
## 109 0 2 Sovereign country Mauritania
## 110 0 2 Sovereign country Malawi
## 111 0 2 Sovereign country Malaysia
## 112 0 2 Sovereign country Namibia
## 113 1 2 Dependency New Caledonia
## 114 0 2 Sovereign country Niger
## 115 0 2 Sovereign country Nigeria
## 116 0 2 Sovereign country Nicaragua
## 117 1 2 Country Netherlands
## 118 0 2 Sovereign country Norway
## 119 0 2 Sovereign country Nepal
## 120 1 2 Country New Zealand
## 121 0 2 Sovereign country Oman
## 122 0 2 Sovereign country Pakistan
## 123 0 2 Sovereign country Panama
## 124 0 2 Sovereign country Peru
## 125 0 2 Sovereign country Philippines
## 126 0 2 Sovereign country Papua New Guinea
## 127 0 2 Sovereign country Poland
## 128 1 2 Dependency Puerto Rico
## 129 0 2 Sovereign country North Korea
## 130 0 2 Sovereign country Portugal
## 131 0 2 Sovereign country Paraguay
## 132 1 2 Disputed Palestine
## 133 0 2 Sovereign country Qatar
## 134 0 2 Sovereign country Romania
## 135 0 2 Sovereign country Russia
## 136 0 2 Sovereign country Rwanda
## 137 0 2 Indeterminate Western Sahara
## 138 0 2 Sovereign country Saudi Arabia
## 139 0 2 Sovereign country Sudan
## 140 0 2 Sovereign country South Sudan
## 141 0 2 Sovereign country Senegal
## 142 0 2 Sovereign country Solomon Islands
## 143 0 2 Sovereign country Sierra Leone
## 144 0 2 Sovereign country El Salvador
## 145 0 2 Indeterminate Somaliland
## 146 0 2 Sovereign country Somalia
## 147 0 2 Sovereign country Republic of Serbia
## 148 0 2 Sovereign country Suriname
## 149 0 2 Sovereign country Slovakia
## 150 0 2 Sovereign country Slovenia
## 151 0 2 Sovereign country Sweden
## 152 0 2 Sovereign country Swaziland
## 153 0 2 Sovereign country Syria
## 154 0 2 Sovereign country Chad
## 155 0 2 Sovereign country Togo
## 156 0 2 Sovereign country Thailand
## 157 0 2 Sovereign country Tajikistan
## 158 0 2 Sovereign country Turkmenistan
## 159 0 2 Sovereign country East Timor
## 160 0 2 Sovereign country Trinidad and Tobago
## 161 0 2 Sovereign country Tunisia
## 162 0 2 Sovereign country Turkey
## 163 0 2 Sovereign country Taiwan
## 164 0 2 Sovereign country United Republic of Tanzania
## 165 0 2 Sovereign country Uganda
## 166 0 2 Sovereign country Ukraine
## 167 0 2 Sovereign country Uruguay
## 168 1 2 Country United States of America
## 169 0 2 Sovereign country Uzbekistan
## 170 0 2 Sovereign country Venezuela
## 171 0 2 Sovereign country Vietnam
## 172 0 2 Sovereign country Vanuatu
## 173 0 2 Sovereign country Yemen
## 174 0 2 Sovereign country South Africa
## 175 0 2 Sovereign country Zambia
## 176 0 2 Sovereign country Zimbabwe
## adm0_a3 geou_dif geounit gu_a3 su_dif
## 0 AFG 0 Afghanistan AFG 0
## 1 AGO 0 Angola AGO 0
## 2 ALB 0 Albania ALB 0
## 3 ARE 0 United Arab Emirates ARE 0
## 4 ARG 0 Argentina ARG 0
## 5 ARM 0 Armenia ARM 0
## 6 ATA 0 Antarctica ATA 0
## 7 ATF 0 French Southern and Antarctic Lands ATF 0
## 8 AUS 0 Australia AUS 0
## 9 AUT 0 Austria AUT 0
## 10 AZE 0 Azerbaijan AZE 0
## 11 BDI 0 Burundi BDI 0
## 12 BEL 0 Belgium BEL 0
## 13 BEN 0 Benin BEN 0
## 14 BFA 0 Burkina Faso BFA 0
## 15 BGD 0 Bangladesh BGD 0
## 16 BGR 0 Bulgaria BGR 0
## 17 BHS 0 The Bahamas BHS 0
## 18 BIH 0 Bosnia and Herzegovina BIH 0
## 19 BLR 0 Belarus BLR 0
## 20 BLZ 0 Belize BLZ 0
## 21 BOL 0 Bolivia BOL 0
## 22 BRA 0 Brazil BRA 0
## 23 BRN 0 Brunei BRN 0
## 24 BTN 0 Bhutan BTN 0
## 25 BWA 0 Botswana BWA 0
## 26 CAF 0 Central African Republic CAF 0
## 27 CAN 0 Canada CAN 0
## 28 CHE 0 Switzerland CHE 0
## 29 CHL 0 Chile CHL 0
## 30 CHN 0 China CHN 0
## 31 CIV 0 Ivory Coast CIV 0
## 32 CMR 0 Cameroon CMR 0
## 33 COD 0 Democratic Republic of the Congo COD 0
## 34 COG 0 Republic of Congo COG 0
## 35 COL 0 Colombia COL 0
## 36 CRI 0 Costa Rica CRI 0
## 37 CUB 0 Cuba CUB 0
## 38 CYN 0 Northern Cyprus CYN 0
## 39 CYP 0 Cyprus CYP 0
## 40 CZE 0 Czech Republic CZE 0
## 41 DEU 0 Germany DEU 0
## 42 DJI 0 Djibouti DJI 0
## 43 DNK 0 Denmark DNK 0
## 44 DOM 0 Dominican Republic DOM 0
## 45 DZA 0 Algeria DZA 0
## 46 ECU 0 Ecuador ECU 0
## 47 EGY 0 Egypt EGY 0
## 48 ERI 0 Eritrea ERI 0
## 49 ESP 0 Spain ESP 0
## 50 EST 0 Estonia EST 0
## 51 ETH 0 Ethiopia ETH 0
## 52 FIN 0 Finland FIN 0
## 53 FJI 0 Fiji FJI 0
## 54 FLK 0 Falkland Islands FLK 0
## 55 FRA 0 France FRA 0
## 56 GAB 0 Gabon GAB 0
## 57 GBR 0 United Kingdom GBR 0
## 58 GEO 0 Georgia GEO 0
## 59 GHA 0 Ghana GHA 0
## 60 GIN 0 Guinea GIN 0
## 61 GMB 0 Gambia GMB 0
## 62 GNB 0 Guinea Bissau GNB 0
## 63 GNQ 0 Equatorial Guinea GNQ 0
## 64 GRC 0 Greece GRC 0
## 65 GRL 0 Greenland GRL 0
## 66 GTM 0 Guatemala GTM 0
## 67 GUY 0 Guyana GUY 0
## 68 HND 0 Honduras HND 0
## 69 HRV 0 Croatia HRV 0
## 70 HTI 0 Haiti HTI 0
## 71 HUN 0 Hungary HUN 0
## 72 IDN 0 Indonesia IDN 0
## 73 IND 0 India IND 0
## 74 IRL 0 Ireland IRL 0
## 75 IRN 0 Iran IRN 0
## 76 IRQ 0 Iraq IRQ 0
## 77 ISL 0 Iceland ISL 0
## 78 ISR 0 Israel ISR 0
## 79 ITA 0 Italy ITA 0
## 80 JAM 0 Jamaica JAM 0
## 81 JOR 0 Jordan JOR 0
## 82 JPN 0 Japan JPN 0
## 83 KAZ 0 Kazakhstan KAZ 0
## 84 KEN 0 Kenya KEN 0
## 85 KGZ 0 Kyrgyzstan KGZ 0
## 86 KHM 0 Cambodia KHM 0
## 87 KOR 0 South Korea KOR 0
## 88 KOS 0 Kosovo KOS 0
## 89 KWT 0 Kuwait KWT 0
## 90 LAO 0 Laos LAO 0
## 91 LBN 0 Lebanon LBN 0
## 92 LBR 0 Liberia LBR 0
## 93 LBY 0 Libya LBY 0
## 94 LKA 0 Sri Lanka LKA 0
## 95 LSO 0 Lesotho LSO 0
## 96 LTU 0 Lithuania LTU 0
## 97 LUX 0 Luxembourg LUX 0
## 98 LVA 0 Latvia LVA 0
## 99 MAR 0 Morocco MAR 0
## 100 MDA 0 Moldova MDA 0
## 101 MDG 0 Madagascar MDG 0
## 102 MEX 0 Mexico MEX 0
## 103 MKD 0 Macedonia MKD 0
## 104 MLI 0 Mali MLI 0
## 105 MMR 0 Myanmar MMR 0
## 106 MNE 0 Montenegro MNE 0
## 107 MNG 0 Mongolia MNG 0
## 108 MOZ 0 Mozambique MOZ 0
## 109 MRT 0 Mauritania MRT 0
## 110 MWI 0 Malawi MWI 0
## 111 MYS 0 Malaysia MYS 0
## 112 NAM 0 Namibia NAM 0
## 113 NCL 0 New Caledonia NCL 0
## 114 NER 0 Niger NER 0
## 115 NGA 0 Nigeria NGA 0
## 116 NIC 0 Nicaragua NIC 0
## 117 NLD 0 Netherlands NLD 0
## 118 NOR 0 Norway NOR 0
## 119 NPL 0 Nepal NPL 0
## 120 NZL 0 New Zealand NZL 0
## 121 OMN 0 Oman OMN 0
## 122 PAK 0 Pakistan PAK 0
## 123 PAN 0 Panama PAN 0
## 124 PER 0 Peru PER 0
## 125 PHL 0 Philippines PHL 0
## 126 PNG 0 Papua New Guinea PNG 1
## 127 POL 0 Poland POL 0
## 128 PRI 0 Puerto Rico PRI 0
## 129 PRK 0 North Korea PRK 0
## 130 PRT 0 Portugal PRT 1
## 131 PRY 0 Paraguay PRY 0
## 132 PSX 0 Palestine PSX 0
## 133 QAT 0 Qatar QAT 0
## 134 ROU 0 Romania ROU 0
## 135 RUS 0 Russia RUS 0
## 136 RWA 0 Rwanda RWA 0
## 137 SAH 0 Western Sahara SAH 0
## 138 SAU 0 Saudi Arabia SAU 0
## 139 SDN 0 Sudan SDN 0
## 140 SDS 0 South Sudan SDS 0
## 141 SEN 0 Senegal SEN 0
## 142 SLB 0 Solomon Islands SLB 0
## 143 SLE 0 Sierra Leone SLE 0
## 144 SLV 0 El Salvador SLV 0
## 145 SOL 0 Somaliland SOL 0
## 146 SOM 0 Somalia SOM 0
## 147 SRB 0 Republic of Serbia SRB 0
## 148 SUR 0 Suriname SUR 0
## 149 SVK 0 Slovakia SVK 0
## 150 SVN 0 Slovenia SVN 0
## 151 SWE 0 Sweden SWE 0
## 152 SWZ 0 Swaziland SWZ 0
## 153 SYR 0 Syria SYR 0
## 154 TCD 0 Chad TCD 0
## 155 TGO 0 Togo TGO 0
## 156 THA 0 Thailand THA 0
## 157 TJK 0 Tajikistan TJK 0
## 158 TKM 0 Turkmenistan TKM 0
## 159 TLS 0 East Timor TLS 0
## 160 TTO 0 Trinidad and Tobago TTO 0
## 161 TUN 0 Tunisia TUN 0
## 162 TUR 0 Turkey TUR 0
## 163 TWN 0 Taiwan TWN 0
## 164 TZA 0 Tanzania TZA 0
## 165 UGA 0 Uganda UGA 0
## 166 UKR 0 Ukraine UKR 0
## 167 URY 0 Uruguay URY 0
## 168 USA 0 United States of America USA 0
## 169 UZB 0 Uzbekistan UZB 0
## 170 VEN 0 Venezuela VEN 0
## 171 VNM 0 Vietnam VNM 0
## 172 VUT 0 Vanuatu VUT 0
## 173 YEM 0 Yemen YEM 0
## 174 ZAF 0 South Africa ZAF 0
## 175 ZMB 0 Zambia ZMB 0
## 176 ZWE 0 Zimbabwe ZWE 0
## subunit su_a3 brk_diff name
## 0 Afghanistan AFG 0 Afghanistan
## 1 Angola AGO 0 Angola
## 2 Albania ALB 0 Albania
## 3 United Arab Emirates ARE 0 United Arab Emirates
## 4 Argentina ARG 0 Argentina
## 5 Armenia ARM 0 Armenia
## 6 Antarctica ATA 0 Antarctica
## 7 French Southern and Antarctic Lands ATF 0 Fr. S. Antarctic Lands
## 8 Australia AUS 0 Australia
## 9 Austria AUT 0 Austria
## 10 Azerbaijan AZE 0 Azerbaijan
## 11 Burundi BDI 0 Burundi
## 12 Belgium BEL 0 Belgium
## 13 Benin BEN 0 Benin
## 14 Burkina Faso BFA 0 Burkina Faso
## 15 Bangladesh BGD 0 Bangladesh
## 16 Bulgaria BGR 0 Bulgaria
## 17 The Bahamas BHS 0 Bahamas
## 18 Bosnia and Herzegovina BIH 0 Bosnia and Herz.
## 19 Belarus BLR 0 Belarus
## 20 Belize BLZ 0 Belize
## 21 Bolivia BOL 0 Bolivia
## 22 Brazil BRA 0 Brazil
## 23 Brunei BRN 0 Brunei
## 24 Bhutan BTN 0 Bhutan
## 25 Botswana BWA 0 Botswana
## 26 Central African Republic CAF 0 Central African Rep.
## 27 Canada CAN 0 Canada
## 28 Switzerland CHE 0 Switzerland
## 29 Chile CHL 0 Chile
## 30 China CHN 0 China
## 31 Ivory Coast CIV 0 Côte d'Ivoire
## 32 Cameroon CMR 0 Cameroon
## 33 Democratic Republic of the Congo COD 0 Dem. Rep. Congo
## 34 Republic of Congo COG 0 Congo
## 35 Colombia COL 0 Colombia
## 36 Costa Rica CRI 0 Costa Rica
## 37 Cuba CUB 0 Cuba
## 38 Northern Cyprus CYN 1 N. Cyprus
## 39 Cyprus CYP 0 Cyprus
## 40 Czech Republic CZE 0 Czech Rep.
## 41 Germany DEU 0 Germany
## 42 Djibouti DJI 0 Djibouti
## 43 Denmark DNK 0 Denmark
## 44 Dominican Republic DOM 0 Dominican Rep.
## 45 Algeria DZA 0 Algeria
## 46 Ecuador ECU 0 Ecuador
## 47 Egypt EGY 0 Egypt
## 48 Eritrea ERI 0 Eritrea
## 49 Spain ESP 0 Spain
## 50 Estonia EST 0 Estonia
## 51 Ethiopia ETH 0 Ethiopia
## 52 Finland FIN 0 Finland
## 53 Fiji FJI 0 Fiji
## 54 Falkland Islands FLK 1 Falkland Is.
## 55 France FRA 0 France
## 56 Gabon GAB 0 Gabon
## 57 United Kingdom GBR 0 United Kingdom
## 58 Georgia GEO 0 Georgia
## 59 Ghana GHA 0 Ghana
## 60 Guinea GIN 0 Guinea
## 61 Gambia GMB 0 Gambia
## 62 Guinea Bissau GNB 0 Guinea-Bissau
## 63 Equatorial Guinea GNQ 0 Eq. Guinea
## 64 Greece GRC 0 Greece
## 65 Greenland GRL 0 Greenland
## 66 Guatemala GTM 0 Guatemala
## 67 Guyana GUY 0 Guyana
## 68 Honduras HND 0 Honduras
## 69 Croatia HRV 0 Croatia
## 70 Haiti HTI 0 Haiti
## 71 Hungary HUN 0 Hungary
## 72 Indonesia IDN 0 Indonesia
## 73 India IND 0 India
## 74 Ireland IRL 0 Ireland
## 75 Iran IRN 0 Iran
## 76 Iraq IRQ 0 Iraq
## 77 Iceland ISL 0 Iceland
## 78 Israel ISR 0 Israel
## 79 Italy ITA 0 Italy
## 80 Jamaica JAM 0 Jamaica
## 81 Jordan JOR 0 Jordan
## 82 Japan JPN 0 Japan
## 83 Kazakhstan KAZ 0 Kazakhstan
## 84 Kenya KEN 0 Kenya
## 85 Kyrgyzstan KGZ 0 Kyrgyzstan
## 86 Cambodia KHM 0 Cambodia
## 87 South Korea KOR 0 Korea
## 88 Kosovo KOS 1 Kosovo
## 89 Kuwait KWT 0 Kuwait
## 90 Laos LAO 0 Lao PDR
## 91 Lebanon LBN 0 Lebanon
## 92 Liberia LBR 0 Liberia
## 93 Libya LBY 0 Libya
## 94 Sri Lanka LKA 0 Sri Lanka
## 95 Lesotho LSO 0 Lesotho
## 96 Lithuania LTU 0 Lithuania
## 97 Luxembourg LUX 0 Luxembourg
## 98 Latvia LVA 0 Latvia
## 99 Morocco MAR 0 Morocco
## 100 Moldova MDA 0 Moldova
## 101 Madagascar MDG 0 Madagascar
## 102 Mexico MEX 0 Mexico
## 103 Macedonia MKD 0 Macedonia
## 104 Mali MLI 0 Mali
## 105 Myanmar MMR 0 Myanmar
## 106 Montenegro MNE 0 Montenegro
## 107 Mongolia MNG 0 Mongolia
## 108 Mozambique MOZ 0 Mozambique
## 109 Mauritania MRT 0 Mauritania
## 110 Malawi MWI 0 Malawi
## 111 Malaysia MYS 0 Malaysia
## 112 Namibia NAM 0 Namibia
## 113 New Caledonia NCL 0 New Caledonia
## 114 Niger NER 0 Niger
## 115 Nigeria NGA 0 Nigeria
## 116 Nicaragua NIC 0 Nicaragua
## 117 Netherlands NLD 0 Netherlands
## 118 Norway NOR 0 Norway
## 119 Nepal NPL 0 Nepal
## 120 New Zealand NZL 0 New Zealand
## 121 Oman OMN 0 Oman
## 122 Pakistan PAK 0 Pakistan
## 123 Panama PAN 0 Panama
## 124 Peru PER 0 Peru
## 125 Philippines PHL 0 Philippines
## 126 Papua New Guinea PN1 0 Papua New Guinea
## 127 Poland POL 0 Poland
## 128 Puerto Rico PRI 0 Puerto Rico
## 129 North Korea PRK 0 Dem. Rep. Korea
## 130 Portugal PR1 0 Portugal
## 131 Paraguay PRY 0 Paraguay
## 132 Palestine PSX 0 Palestine
## 133 Qatar QAT 0 Qatar
## 134 Romania ROU 0 Romania
## 135 Russia RUS 0 Russia
## 136 Rwanda RWA 0 Rwanda
## 137 Western Sahara SAH 1 W. Sahara
## 138 Saudi Arabia SAU 0 Saudi Arabia
## 139 Sudan SDN 0 Sudan
## 140 South Sudan SDS 0 S. Sudan
## 141 Senegal SEN 0 Senegal
## 142 Solomon Islands SLB 0 Solomon Is.
## 143 Sierra Leone SLE 0 Sierra Leone
## 144 El Salvador SLV 0 El Salvador
## 145 Somaliland SOL 1 Somaliland
## 146 Somalia SOM 0 Somalia
## 147 Republic of Serbia SRB 0 Serbia
## 148 Suriname SUR 0 Suriname
## 149 Slovakia SVK 0 Slovakia
## 150 Slovenia SVN 0 Slovenia
## 151 Sweden SWE 0 Sweden
## 152 Swaziland SWZ 0 Swaziland
## 153 Syria SYR 0 Syria
## 154 Chad TCD 0 Chad
## 155 Togo TGO 0 Togo
## 156 Thailand THA 0 Thailand
## 157 Tajikistan TJK 0 Tajikistan
## 158 Turkmenistan TKM 0 Turkmenistan
## 159 East Timor TLS 0 Timor-Leste
## 160 Trinidad and Tobago TTO 0 Trinidad and Tobago
## 161 Tunisia TUN 0 Tunisia
## 162 Turkey TUR 0 Turkey
## 163 Taiwan TWN 1 Taiwan
## 164 Tanzania TZA 0 Tanzania
## 165 Uganda UGA 0 Uganda
## 166 Ukraine UKR 0 Ukraine
## 167 Uruguay URY 0 Uruguay
## 168 United States of America USA 0 United States
## 169 Uzbekistan UZB 0 Uzbekistan
## 170 Venezuela VEN 0 Venezuela
## 171 Vietnam VNM 0 Vietnam
## 172 Vanuatu VUT 0 Vanuatu
## 173 Yemen YEM 0 Yemen
## 174 South Africa ZAF 0 South Africa
## 175 Zambia ZMB 0 Zambia
## 176 Zimbabwe ZWE 0 Zimbabwe
## name_long brk_a3 brk_name
## 0 Afghanistan AFG Afghanistan
## 1 Angola AGO Angola
## 2 Albania ALB Albania
## 3 United Arab Emirates ARE United Arab Emirates
## 4 Argentina ARG Argentina
## 5 Armenia ARM Armenia
## 6 Antarctica ATA Antarctica
## 7 French Southern and Antarctic Lands ATF Fr. S. and Antarctic Lands
## 8 Australia AUS Australia
## 9 Austria AUT Austria
## 10 Azerbaijan AZE Azerbaijan
## 11 Burundi BDI Burundi
## 12 Belgium BEL Belgium
## 13 Benin BEN Benin
## 14 Burkina Faso BFA Burkina Faso
## 15 Bangladesh BGD Bangladesh
## 16 Bulgaria BGR Bulgaria
## 17 Bahamas BHS Bahamas
## 18 Bosnia and Herzegovina BIH Bosnia and Herz.
## 19 Belarus BLR Belarus
## 20 Belize BLZ Belize
## 21 Bolivia BOL Bolivia
## 22 Brazil BRA Brazil
## 23 Brunei Darussalam BRN Brunei
## 24 Bhutan BTN Bhutan
## 25 Botswana BWA Botswana
## 26 Central African Republic CAF Central African Rep.
## 27 Canada CAN Canada
## 28 Switzerland CHE Switzerland
## 29 Chile CHL Chile
## 30 China CHN China
## 31 Côte d'Ivoire CIV Côte d'Ivoire
## 32 Cameroon CMR Cameroon
## 33 Democratic Republic of the Congo COD Democratic Republic of the Congo
## 34 Republic of Congo COG Republic of Congo
## 35 Colombia COL Colombia
## 36 Costa Rica CRI Costa Rica
## 37 Cuba CUB Cuba
## 38 Northern Cyprus B20 N. Cyprus
## 39 Cyprus CYP Cyprus
## 40 Czech Republic CZE Czech Rep.
## 41 Germany DEU Germany
## 42 Djibouti DJI Djibouti
## 43 Denmark DNK Denmark
## 44 Dominican Republic DOM Dominican Rep.
## 45 Algeria DZA Algeria
## 46 Ecuador ECU Ecuador
## 47 Egypt EGY Egypt
## 48 Eritrea ERI Eritrea
## 49 Spain ESP Spain
## 50 Estonia EST Estonia
## 51 Ethiopia ETH Ethiopia
## 52 Finland FIN Finland
## 53 Fiji FJI Fiji
## 54 Falkland Islands B12 Falkland Is.
## 55 France FRA France
## 56 Gabon GAB Gabon
## 57 United Kingdom GBR United Kingdom
## 58 Georgia GEO Georgia
## 59 Ghana GHA Ghana
## 60 Guinea GIN Guinea
## 61 The Gambia GMB Gambia
## 62 Guinea-Bissau GNB Guinea-Bissau
## 63 Equatorial Guinea GNQ Eq. Guinea
## 64 Greece GRC Greece
## 65 Greenland GRL Greenland
## 66 Guatemala GTM Guatemala
## 67 Guyana GUY Guyana
## 68 Honduras HND Honduras
## 69 Croatia HRV Croatia
## 70 Haiti HTI Haiti
## 71 Hungary HUN Hungary
## 72 Indonesia IDN Indonesia
## 73 India IND India
## 74 Ireland IRL Ireland
## 75 Iran IRN Iran
## 76 Iraq IRQ Iraq
## 77 Iceland ISL Iceland
## 78 Israel ISR Israel
## 79 Italy ITA Italy
## 80 Jamaica JAM Jamaica
## 81 Jordan JOR Jordan
## 82 Japan JPN Japan
## 83 Kazakhstan KAZ Kazakhstan
## 84 Kenya KEN Kenya
## 85 Kyrgyzstan KGZ Kyrgyzstan
## 86 Cambodia KHM Cambodia
## 87 Republic of Korea KOR Republic of Korea
## 88 Kosovo B57 Kosovo
## 89 Kuwait KWT Kuwait
## 90 Lao PDR LAO Laos
## 91 Lebanon LBN Lebanon
## 92 Liberia LBR Liberia
## 93 Libya LBY Libya
## 94 Sri Lanka LKA Sri Lanka
## 95 Lesotho LSO Lesotho
## 96 Lithuania LTU Lithuania
## 97 Luxembourg LUX Luxembourg
## 98 Latvia LVA Latvia
## 99 Morocco MAR Morocco
## 100 Moldova MDA Moldova
## 101 Madagascar MDG Madagascar
## 102 Mexico MEX Mexico
## 103 Macedonia MKD Macedonia
## 104 Mali MLI Mali
## 105 Myanmar MMR Myanmar
## 106 Montenegro MNE Montenegro
## 107 Mongolia MNG Mongolia
## 108 Mozambique MOZ Mozambique
## 109 Mauritania MRT Mauritania
## 110 Malawi MWI Malawi
## 111 Malaysia MYS Malaysia
## 112 Namibia NAM Namibia
## 113 New Caledonia NCL New Caledonia
## 114 Niger NER Niger
## 115 Nigeria NGA Nigeria
## 116 Nicaragua NIC Nicaragua
## 117 Netherlands NLD Netherlands
## 118 Norway NOR Norway
## 119 Nepal NPL Nepal
## 120 New Zealand NZL New Zealand
## 121 Oman OMN Oman
## 122 Pakistan PAK Pakistan
## 123 Panama PAN Panama
## 124 Peru PER Peru
## 125 Philippines PHL Philippines
## 126 Papua New Guinea PN1 Papua New Guinea
## 127 Poland POL Poland
## 128 Puerto Rico PRI Puerto Rico
## 129 Dem. Rep. Korea PRK Dem. Rep. Korea
## 130 Portugal PR1 Portugal
## 131 Paraguay PRY Paraguay
## 132 Palestine PSX Palestine
## 133 Qatar QAT Qatar
## 134 Romania ROU Romania
## 135 Russian Federation RUS Russia
## 136 Rwanda RWA Rwanda
## 137 Western Sahara B28 W. Sahara
## 138 Saudi Arabia SAU Saudi Arabia
## 139 Sudan SDN Sudan
## 140 South Sudan SDS S. Sudan
## 141 Senegal SEN Senegal
## 142 Solomon Islands SLB Solomon Is.
## 143 Sierra Leone SLE Sierra Leone
## 144 El Salvador SLV El Salvador
## 145 Somaliland B30 Somaliland
## 146 Somalia SOM Somalia
## 147 Serbia SRB Serbia
## 148 Suriname SUR Suriname
## 149 Slovakia SVK Slovakia
## 150 Slovenia SVN Slovenia
## 151 Sweden SWE Sweden
## 152 Swaziland SWZ Swaziland
## 153 Syria SYR Syria
## 154 Chad TCD Chad
## 155 Togo TGO Togo
## 156 Thailand THA Thailand
## 157 Tajikistan TJK Tajikistan
## 158 Turkmenistan TKM Turkmenistan
## 159 Timor-Leste TLS Timor-Leste
## 160 Trinidad and Tobago TTO Trinidad and Tobago
## 161 Tunisia TUN Tunisia
## 162 Turkey TUR Turkey
## 163 Taiwan B77 Taiwan
## 164 Tanzania TZA Tanzania
## 165 Uganda UGA Uganda
## 166 Ukraine UKR Ukraine
## 167 Uruguay URY Uruguay
## 168 United States USA United States
## 169 Uzbekistan UZB Uzbekistan
## 170 Venezuela VEN Venezuela
## 171 Vietnam VNM Vietnam
## 172 Vanuatu VUT Vanuatu
## 173 Yemen YEM Yemen
## 174 South Africa ZAF South Africa
## 175 Zambia ZMB Zambia
## 176 Zimbabwe ZWE Zimbabwe
## brk_group abbrev postal
## 0 <NA> Afg. AF
## 1 <NA> Ang. AO
## 2 <NA> Alb. AL
## 3 <NA> U.A.E. AE
## 4 <NA> Arg. AR
## 5 <NA> Arm. ARM
## 6 <NA> Ant. AQ
## 7 <NA> Fr. S.A.L. TF
## 8 <NA> Auz. AU
## 9 <NA> Aust. A
## 10 <NA> Aze. AZ
## 11 <NA> Bur. BI
## 12 <NA> Belg. B
## 13 <NA> Benin BJ
## 14 <NA> B.F. BF
## 15 <NA> Bang. BD
## 16 <NA> Bulg. BG
## 17 <NA> Bhs. BS
## 18 <NA> B.H. BiH
## 19 <NA> Bela. BY
## 20 <NA> Belize BZ
## 21 <NA> Bolivia BO
## 22 <NA> Brazil BR
## 23 <NA> Brunei BN
## 24 <NA> Bhutan BT
## 25 <NA> Bwa. BW
## 26 <NA> C.A.R. CF
## 27 <NA> Can. CA
## 28 <NA> Switz. CH
## 29 <NA> Chile CL
## 30 <NA> China CN
## 31 <NA> I.C. CI
## 32 <NA> Cam. CM
## 33 <NA> D.R.C. DRC
## 34 <NA> Rep. Congo CG
## 35 <NA> Col. CO
## 36 <NA> C.R. CR
## 37 <NA> Cuba CU
## 38 <NA> N. Cy. CN
## 39 <NA> Cyp. CY
## 40 <NA> Cz. Rep. CZ
## 41 <NA> Ger. D
## 42 <NA> Dji. DJ
## 43 <NA> Den. DK
## 44 <NA> Dom. Rep. DO
## 45 <NA> Alg. DZ
## 46 <NA> Ecu. EC
## 47 <NA> Egypt EG
## 48 <NA> Erit. ER
## 49 <NA> Sp. E
## 50 <NA> Est. EST
## 51 <NA> Eth. ET
## 52 <NA> Fin. FIN
## 53 <NA> Fiji FJ
## 54 <NA> Flk. Is. FK
## 55 <NA> Fr. F
## 56 <NA> Gabon GA
## 57 <NA> U.K. GB
## 58 <NA> Geo. GE
## 59 <NA> Ghana GH
## 60 <NA> Gin. GN
## 61 <NA> Gambia GM
## 62 <NA> GnB. GW
## 63 <NA> Eq. G. GQ
## 64 <NA> Greece GR
## 65 <NA> Grlnd. GL
## 66 <NA> Guat. GT
## 67 <NA> Guy. GY
## 68 <NA> Hond. HN
## 69 <NA> Cro. HR
## 70 <NA> Haiti HT
## 71 <NA> Hun. HU
## 72 <NA> Indo. INDO
## 73 <NA> India IND
## 74 <NA> Ire. IRL
## 75 <NA> Iran IRN
## 76 <NA> Iraq IRQ
## 77 <NA> Iceland IS
## 78 <NA> Isr. IS
## 79 <NA> Italy I
## 80 <NA> Jam. J
## 81 <NA> Jord. J
## 82 <NA> Japan J
## 83 <NA> Kaz. KZ
## 84 <NA> Ken. KE
## 85 <NA> Kgz. KG
## 86 <NA> Camb. KH
## 87 <NA> S.K. KR
## 88 <NA> Kos. KO
## 89 <NA> Kwt. KW
## 90 <NA> Laos LA
## 91 <NA> Leb. LB
## 92 <NA> Liberia LR
## 93 <NA> Libya LY
## 94 <NA> Sri L. LK
## 95 <NA> Les. LS
## 96 <NA> Lith. LT
## 97 <NA> Lux. L
## 98 <NA> Lat. LV
## 99 <NA> Mor. MA
## 100 <NA> Mda. MD
## 101 <NA> Mad. MG
## 102 <NA> Mex. MX
## 103 <NA> Mkd. MK
## 104 <NA> Mali ML
## 105 <NA> Myan. MM
## 106 <NA> Mont. ME
## 107 <NA> Mong. MN
## 108 <NA> Moz. MZ
## 109 <NA> Mrt. MR
## 110 <NA> Mal. MW
## 111 <NA> Malay. MY
## 112 <NA> Nam. NA
## 113 <NA> New C. NC
## 114 <NA> Niger NE
## 115 <NA> Nigeria NG
## 116 <NA> Nic. NI
## 117 <NA> Neth. NL
## 118 <NA> Nor. N
## 119 <NA> Nepal NP
## 120 <NA> N.Z. NZ
## 121 <NA> Oman OM
## 122 <NA> Pak. PK
## 123 <NA> Pan. PA
## 124 <NA> Peru PE
## 125 <NA> Phil. PH
## 126 <NA> P.N.G. PG
## 127 <NA> Pol. PL
## 128 <NA> P.R. PR
## 129 <NA> N.K. KP
## 130 <NA> Port. P
## 131 <NA> Para. PY
## 132 <NA> Pal. PAL
## 133 <NA> Qatar QA
## 134 <NA> Rom. RO
## 135 <NA> Rus. RUS
## 136 <NA> Rwa. RW
## 137 <NA> W. Sah. WS
## 138 <NA> Saud. SA
## 139 <NA> Sudan SD
## 140 <NA> S. Sud. SS
## 141 <NA> Sen. SN
## 142 <NA> S. Is. SB
## 143 <NA> S.L. SL
## 144 <NA> El. S. SV
## 145 <NA> Solnd. SL
## 146 <NA> Som. SO
## 147 <NA> Serb. RS
## 148 <NA> Sur. SR
## 149 <NA> Svk. SK
## 150 <NA> Slo. SLO
## 151 <NA> Swe. S
## 152 <NA> Swz. SW
## 153 <NA> Syria SYR
## 154 <NA> Chad TD
## 155 <NA> Togo TG
## 156 <NA> Thai. TH
## 157 <NA> Tjk. TJ
## 158 <NA> Turkm. TM
## 159 <NA> T.L. TL
## 160 <NA> Tr.T. TT
## 161 <NA> Tun. TN
## 162 <NA> Tur. TR
## 163 <NA> Taiwan TW
## 164 <NA> Tanz. TZ
## 165 <NA> Uga. UG
## 166 <NA> Ukr. UA
## 167 <NA> Ury. UY
## 168 <NA> U.S.A. US
## 169 <NA> Uzb. UZ
## 170 <NA> Ven. VE
## 171 <NA> Viet. VN
## 172 <NA> Van. VU
## 173 <NA> Yem. YE
## 174 <NA> S.Af. ZA
## 175 <NA> Zambia ZM
## 176 <NA> Zimb. ZW
## formal_en
## 0 Islamic State of Afghanistan
## 1 People's Republic of Angola
## 2 Republic of Albania
## 3 United Arab Emirates
## 4 Argentine Republic
## 5 Republic of Armenia
## 6 <NA>
## 7 Territory of the French Southern and Antarctic Lands
## 8 Commonwealth of Australia
## 9 Republic of Austria
## 10 Republic of Azerbaijan
## 11 Republic of Burundi
## 12 Kingdom of Belgium
## 13 Republic of Benin
## 14 Burkina Faso
## 15 People's Republic of Bangladesh
## 16 Republic of Bulgaria
## 17 Commonwealth of the Bahamas
## 18 Bosnia and Herzegovina
## 19 Republic of Belarus
## 20 Belize
## 21 Plurinational State of Bolivia
## 22 Federative Republic of Brazil
## 23 Negara Brunei Darussalam
## 24 Kingdom of Bhutan
## 25 Republic of Botswana
## 26 Central African Republic
## 27 Canada
## 28 Swiss Confederation
## 29 Republic of Chile
## 30 People's Republic of China
## 31 Republic of Ivory Coast
## 32 Republic of Cameroon
## 33 Democratic Republic of the Congo
## 34 Republic of Congo
## 35 Republic of Colombia
## 36 Republic of Costa Rica
## 37 Republic of Cuba
## 38 Turkish Republic of Northern Cyprus
## 39 Republic of Cyprus
## 40 Czech Republic
## 41 Federal Republic of Germany
## 42 Republic of Djibouti
## 43 Kingdom of Denmark
## 44 Dominican Republic
## 45 People's Democratic Republic of Algeria
## 46 Republic of Ecuador
## 47 Arab Republic of Egypt
## 48 State of Eritrea
## 49 Kingdom of Spain
## 50 Republic of Estonia
## 51 Federal Democratic Republic of Ethiopia
## 52 Republic of Finland
## 53 Republic of Fiji
## 54 Falkland Islands
## 55 French Republic
## 56 Gabonese Republic
## 57 United Kingdom of Great Britain and Northern Ireland
## 58 Georgia
## 59 Republic of Ghana
## 60 Republic of Guinea
## 61 Republic of the Gambia
## 62 Republic of Guinea-Bissau
## 63 Republic of Equatorial Guinea
## 64 Hellenic Republic
## 65 Greenland
## 66 Republic of Guatemala
## 67 Co-operative Republic of Guyana
## 68 Republic of Honduras
## 69 Republic of Croatia
## 70 Republic of Haiti
## 71 Republic of Hungary
## 72 Republic of Indonesia
## 73 Republic of India
## 74 Ireland
## 75 Islamic Republic of Iran
## 76 Republic of Iraq
## 77 Republic of Iceland
## 78 State of Israel
## 79 Italian Republic
## 80 Jamaica
## 81 Hashemite Kingdom of Jordan
## 82 Japan
## 83 Republic of Kazakhstan
## 84 Republic of Kenya
## 85 Kyrgyz Republic
## 86 Kingdom of Cambodia
## 87 Republic of Korea
## 88 Republic of Kosovo
## 89 State of Kuwait
## 90 Lao People's Democratic Republic
## 91 Lebanese Republic
## 92 Republic of Liberia
## 93 Libya
## 94 Democratic Socialist Republic of Sri Lanka
## 95 Kingdom of Lesotho
## 96 Republic of Lithuania
## 97 Grand Duchy of Luxembourg
## 98 Republic of Latvia
## 99 Kingdom of Morocco
## 100 Republic of Moldova
## 101 Republic of Madagascar
## 102 United Mexican States
## 103 Former Yugoslav Republic of Macedonia
## 104 Republic of Mali
## 105 Republic of the Union of Myanmar
## 106 Montenegro
## 107 Mongolia
## 108 Republic of Mozambique
## 109 Islamic Republic of Mauritania
## 110 Republic of Malawi
## 111 Malaysia
## 112 Republic of Namibia
## 113 New Caledonia
## 114 Republic of Niger
## 115 Federal Republic of Nigeria
## 116 Republic of Nicaragua
## 117 Kingdom of the Netherlands
## 118 Kingdom of Norway
## 119 Nepal
## 120 New Zealand
## 121 Sultanate of Oman
## 122 Islamic Republic of Pakistan
## 123 Republic of Panama
## 124 Republic of Peru
## 125 Republic of the Philippines
## 126 Independent State of Papua New Guinea
## 127 Republic of Poland
## 128 Commonwealth of Puerto Rico
## 129 Democratic People's Republic of Korea
## 130 Portuguese Republic
## 131 Republic of Paraguay
## 132 West Bank and Gaza
## 133 State of Qatar
## 134 Romania
## 135 Russian Federation
## 136 Republic of Rwanda
## 137 Sahrawi Arab Democratic Republic
## 138 Kingdom of Saudi Arabia
## 139 Republic of the Sudan
## 140 Republic of South Sudan
## 141 Republic of Senegal
## 142 <NA>
## 143 Republic of Sierra Leone
## 144 Republic of El Salvador
## 145 Republic of Somaliland
## 146 Federal Republic of Somalia
## 147 Republic of Serbia
## 148 Republic of Suriname
## 149 Slovak Republic
## 150 Republic of Slovenia
## 151 Kingdom of Sweden
## 152 Kingdom of Swaziland
## 153 Syrian Arab Republic
## 154 Republic of Chad
## 155 Togolese Republic
## 156 Kingdom of Thailand
## 157 Republic of Tajikistan
## 158 Turkmenistan
## 159 Democratic Republic of Timor-Leste
## 160 Republic of Trinidad and Tobago
## 161 Republic of Tunisia
## 162 Republic of Turkey
## 163 <NA>
## 164 United Republic of Tanzania
## 165 Republic of Uganda
## 166 Ukraine
## 167 Oriental Republic of Uruguay
## 168 United States of America
## 169 Republic of Uzbekistan
## 170 Bolivarian Republic of Venezuela
## 171 Socialist Republic of Vietnam
## 172 Republic of Vanuatu
## 173 Republic of Yemen
## 174 Republic of South Africa
## 175 Republic of Zambia
## 176 Republic of Zimbabwe
## formal_fr note_adm0
## 0 <NA> <NA>
## 1 <NA> <NA>
## 2 <NA> <NA>
## 3 <NA> <NA>
## 4 <NA> <NA>
## 5 <NA> <NA>
## 6 <NA> <NA>
## 7 <NA> Fr.
## 8 <NA> <NA>
## 9 <NA> <NA>
## 10 <NA> <NA>
## 11 <NA> <NA>
## 12 <NA> <NA>
## 13 <NA> <NA>
## 14 <NA> <NA>
## 15 <NA> <NA>
## 16 <NA> <NA>
## 17 <NA> <NA>
## 18 <NA> <NA>
## 19 <NA> <NA>
## 20 <NA> <NA>
## 21 <NA> <NA>
## 22 <NA> <NA>
## 23 <NA> <NA>
## 24 <NA> <NA>
## 25 <NA> <NA>
## 26 <NA> <NA>
## 27 <NA> <NA>
## 28 <NA> <NA>
## 29 <NA> <NA>
## 30 <NA> <NA>
## 31 Republic of Cote D'Ivoire <NA>
## 32 <NA> <NA>
## 33 <NA> <NA>
## 34 <NA> <NA>
## 35 <NA> <NA>
## 36 <NA> <NA>
## 37 <NA> <NA>
## 38 <NA> Self admin.
## 39 <NA> <NA>
## 40 <NA> <NA>
## 41 <NA> <NA>
## 42 <NA> <NA>
## 43 <NA> <NA>
## 44 <NA> <NA>
## 45 <NA> <NA>
## 46 <NA> <NA>
## 47 <NA> <NA>
## 48 <NA> <NA>
## 49 <NA> <NA>
## 50 <NA> <NA>
## 51 <NA> <NA>
## 52 <NA> <NA>
## 53 <NA> <NA>
## 54 <NA> U.K.
## 55 <NA> <NA>
## 56 <NA> <NA>
## 57 <NA> <NA>
## 58 <NA> <NA>
## 59 <NA> <NA>
## 60 <NA> <NA>
## 61 <NA> <NA>
## 62 <NA> <NA>
## 63 <NA> <NA>
## 64 <NA> <NA>
## 65 <NA> Den.
## 66 <NA> <NA>
## 67 <NA> <NA>
## 68 <NA> <NA>
## 69 <NA> <NA>
## 70 <NA> <NA>
## 71 <NA> <NA>
## 72 <NA> <NA>
## 73 <NA> <NA>
## 74 <NA> <NA>
## 75 <NA> <NA>
## 76 <NA> <NA>
## 77 <NA> <NA>
## 78 <NA> <NA>
## 79 <NA> <NA>
## 80 <NA> <NA>
## 81 <NA> <NA>
## 82 <NA> <NA>
## 83 <NA> <NA>
## 84 <NA> <NA>
## 85 <NA> <NA>
## 86 <NA> <NA>
## 87 <NA> <NA>
## 88 <NA> <NA>
## 89 <NA> <NA>
## 90 <NA> <NA>
## 91 <NA> <NA>
## 92 <NA> <NA>
## 93 <NA> <NA>
## 94 <NA> <NA>
## 95 <NA> <NA>
## 96 <NA> <NA>
## 97 <NA> <NA>
## 98 <NA> <NA>
## 99 <NA> <NA>
## 100 <NA> <NA>
## 101 <NA> <NA>
## 102 <NA> <NA>
## 103 <NA> <NA>
## 104 <NA> <NA>
## 105 <NA> <NA>
## 106 <NA> <NA>
## 107 <NA> <NA>
## 108 <NA> <NA>
## 109 <NA> <NA>
## 110 <NA> <NA>
## 111 <NA> <NA>
## 112 <NA> <NA>
## 113 Nouvelle-Calédonie Fr.
## 114 <NA> <NA>
## 115 <NA> <NA>
## 116 <NA> <NA>
## 117 <NA> <NA>
## 118 <NA> <NA>
## 119 <NA> <NA>
## 120 <NA> <NA>
## 121 <NA> <NA>
## 122 <NA> <NA>
## 123 <NA> <NA>
## 124 <NA> <NA>
## 125 <NA> <NA>
## 126 <NA> <NA>
## 127 <NA> <NA>
## 128 <NA> Commonwealth of U.S.A.
## 129 <NA> <NA>
## 130 <NA> <NA>
## 131 <NA> <NA>
## 132 <NA> Partial self-admin.
## 133 <NA> <NA>
## 134 <NA> <NA>
## 135 <NA> <NA>
## 136 <NA> <NA>
## 137 <NA> Self admin.
## 138 <NA> <NA>
## 139 <NA> <NA>
## 140 <NA> <NA>
## 141 <NA> <NA>
## 142 <NA> <NA>
## 143 <NA> <NA>
## 144 <NA> <NA>
## 145 <NA> Self admin.
## 146 <NA> <NA>
## 147 <NA> <NA>
## 148 <NA> <NA>
## 149 <NA> <NA>
## 150 <NA> <NA>
## 151 <NA> <NA>
## 152 <NA> <NA>
## 153 <NA> <NA>
## 154 <NA> <NA>
## 155 République Togolaise <NA>
## 156 <NA> <NA>
## 157 <NA> <NA>
## 158 <NA> <NA>
## 159 <NA> <NA>
## 160 <NA> <NA>
## 161 <NA> <NA>
## 162 <NA> <NA>
## 163 <NA> <NA>
## 164 <NA> <NA>
## 165 <NA> <NA>
## 166 <NA> <NA>
## 167 <NA> <NA>
## 168 <NA> <NA>
## 169 <NA> <NA>
## 170 República Bolivariana de Venezuela <NA>
## 171 <NA> <NA>
## 172 <NA> <NA>
## 173 <NA> <NA>
## 174 <NA> <NA>
## 175 <NA> <NA>
## 176 <NA> <NA>
## note_brk name_sort
## 0 <NA> Afghanistan
## 1 <NA> Angola
## 2 <NA> Albania
## 3 <NA> United Arab Emirates
## 4 <NA> Argentina
## 5 <NA> Armenia
## 6 Multiple claims held in abeyance Antarctica
## 7 <NA> French Southern and Antarctic Lands
## 8 <NA> Australia
## 9 <NA> Austria
## 10 <NA> Azerbaijan
## 11 <NA> Burundi
## 12 <NA> Belgium
## 13 <NA> Benin
## 14 <NA> Burkina Faso
## 15 <NA> Bangladesh
## 16 <NA> Bulgaria
## 17 <NA> Bahamas, The
## 18 <NA> Bosnia and Herzegovina
## 19 <NA> Belarus
## 20 <NA> Belize
## 21 <NA> Bolivia
## 22 <NA> Brazil
## 23 <NA> Brunei
## 24 <NA> Bhutan
## 25 <NA> Botswana
## 26 <NA> Central African Republic
## 27 <NA> Canada
## 28 <NA> Switzerland
## 29 <NA> Chile
## 30 <NA> China
## 31 <NA> Côte d'Ivoire
## 32 <NA> Cameroon
## 33 <NA> Congo, Dem. Rep.
## 34 <NA> Congo, Rep.
## 35 <NA> Colombia
## 36 <NA> Costa Rica
## 37 <NA> Cuba
## 38 Self admin.; Claimed by Cyprus Cyprus, Northern
## 39 <NA> Cyprus
## 40 <NA> Czech Republic
## 41 <NA> Germany
## 42 <NA> Djibouti
## 43 <NA> Denmark
## 44 <NA> Dominican Republic
## 45 <NA> Algeria
## 46 <NA> Ecuador
## 47 <NA> Egypt, Arab Rep.
## 48 <NA> Eritrea
## 49 <NA> Spain
## 50 <NA> Estonia
## 51 <NA> Ethiopia
## 52 <NA> Finland
## 53 <NA> Fiji
## 54 Admin. by U.K.; Claimed by Argentina Falkland Islands
## 55 <NA> France
## 56 <NA> Gabon
## 57 <NA> United Kingdom
## 58 <NA> Georgia
## 59 <NA> Ghana
## 60 <NA> Guinea
## 61 <NA> Gambia, The
## 62 <NA> Guinea-Bissau
## 63 <NA> Equatorial Guinea
## 64 <NA> Greece
## 65 <NA> Greenland
## 66 <NA> Guatemala
## 67 <NA> Guyana
## 68 <NA> Honduras
## 69 <NA> Croatia
## 70 <NA> Haiti
## 71 <NA> Hungary
## 72 <NA> Indonesia
## 73 <NA> India
## 74 <NA> Ireland
## 75 <NA> Iran, Islamic Rep.
## 76 <NA> Iraq
## 77 <NA> Iceland
## 78 <NA> Israel
## 79 <NA> Italy
## 80 <NA> Jamaica
## 81 <NA> Jordan
## 82 <NA> Japan
## 83 <NA> Kazakhstan
## 84 <NA> Kenya
## 85 <NA> Kyrgyz Republic
## 86 <NA> Cambodia
## 87 <NA> Korea, Rep.
## 88 Self admin.; Claimed by Serbia Kosovo
## 89 <NA> Kuwait
## 90 <NA> Lao PDR
## 91 <NA> Lebanon
## 92 <NA> Liberia
## 93 <NA> Libya
## 94 <NA> Sri Lanka
## 95 <NA> Lesotho
## 96 <NA> Lithuania
## 97 <NA> Luxembourg
## 98 <NA> Latvia
## 99 <NA> Morocco
## 100 <NA> Moldova
## 101 <NA> Madagascar
## 102 <NA> Mexico
## 103 <NA> Macedonia, FYR
## 104 <NA> Mali
## 105 <NA> Myanmar
## 106 <NA> Montenegro
## 107 <NA> Mongolia
## 108 <NA> Mozambique
## 109 <NA> Mauritania
## 110 <NA> Malawi
## 111 <NA> Malaysia
## 112 <NA> Namibia
## 113 <NA> New Caledonia
## 114 <NA> Niger
## 115 <NA> Nigeria
## 116 <NA> Nicaragua
## 117 <NA> Netherlands
## 118 <NA> Norway
## 119 <NA> Nepal
## 120 <NA> New Zealand
## 121 <NA> Oman
## 122 <NA> Pakistan
## 123 <NA> Panama
## 124 <NA> Peru
## 125 <NA> Philippines
## 126 <NA> Papua New Guinea
## 127 <NA> Poland
## 128 <NA> Puerto Rico
## 129 <NA> Korea, Dem. Rep.
## 130 <NA> Portugal
## 131 <NA> Paraguay
## 132 Partial self-admin. Palestine (West Bank and Gaza)
## 133 <NA> Qatar
## 134 <NA> Romania
## 135 <NA> Russian Federation
## 136 <NA> Rwanda
## 137 Self admin.; Claimed by Morocco Western Sahara
## 138 <NA> Saudi Arabia
## 139 <NA> Sudan
## 140 <NA> South Sudan
## 141 <NA> Senegal
## 142 <NA> Solomon Islands
## 143 <NA> Sierra Leone
## 144 <NA> El Salvador
## 145 Self admin.; Claimed by Somalia Somaliland
## 146 <NA> Somalia
## 147 <NA> Serbia
## 148 <NA> Suriname
## 149 <NA> Slovak Republic
## 150 <NA> Slovenia
## 151 <NA> Sweden
## 152 <NA> Swaziland
## 153 <NA> Syrian Arab Republic
## 154 <NA> Chad
## 155 <NA> Togo
## 156 <NA> Thailand
## 157 <NA> Tajikistan
## 158 <NA> Turkmenistan
## 159 <NA> Timor-Leste
## 160 <NA> Trinidad and Tobago
## 161 <NA> Tunisia
## 162 <NA> Turkey
## 163 Self admin.; Claimed by China Taiwan
## 164 <NA> Tanzania
## 165 <NA> Uganda
## 166 <NA> Ukraine
## 167 <NA> Uruguay
## 168 <NA> United States of America
## 169 <NA> Uzbekistan
## 170 <NA> Venezuela, RB
## 171 <NA> Vietnam
## 172 <NA> Vanuatu
## 173 <NA> Yemen, Rep.
## 174 <NA> South Africa
## 175 <NA> Zambia
## 176 <NA> Zimbabwe
## name_alt mapcolor7 mapcolor8 mapcolor9 mapcolor13 pop_est
## 0 <NA> 5 6 8 7 28400000
## 1 <NA> 3 2 6 1 12799293
## 2 <NA> 1 4 1 6 3639453
## 3 <NA> 2 1 3 3 4798491
## 4 <NA> 3 1 3 13 40913584
## 5 <NA> 3 1 2 10 2967004
## 6 <NA> 4 5 1 NA 3802
## 7 <NA> 7 5 9 11 140
## 8 <NA> 1 2 2 7 21262641
## 9 <NA> 3 1 3 4 8210281
## 10 <NA> 1 6 5 8 8238672
## 11 <NA> 2 2 5 8 8988091
## 12 <NA> 3 2 1 8 10414336
## 13 <NA> 1 2 2 12 8791832
## 14 <NA> 2 1 5 11 15746232
## 15 <NA> 3 4 7 7 156050883
## 16 <NA> 4 5 1 8 7204687
## 17 <NA> 1 1 2 5 309156
## 18 <NA> 1 1 1 2 4613414
## 19 <NA> 1 1 5 11 9648533
## 20 <NA> 1 4 5 7 307899
## 21 <NA> 1 5 2 3 9775246
## 22 <NA> 5 6 5 7 198739269
## 23 <NA> 4 6 6 12 388190
## 24 <NA> 5 6 1 8 691141
## 25 <NA> 6 5 7 3 1990876
## 26 <NA> 5 6 6 9 4511488
## 27 <NA> 6 6 2 2 33487208
## 28 <NA> 5 2 7 3 7604467
## 29 <NA> 5 1 5 9 16601707
## 30 <NA> 4 4 4 3 1338612970
## 31 <NA> 4 6 3 3 20617068
## 32 <NA> 1 4 1 3 18879301
## 33 <NA> 4 4 4 7 68692542
## 34 <NA> 2 1 3 10 4012809
## 35 <NA> 2 1 3 1 45644023
## 36 <NA> 3 2 4 2 4253877
## 37 <NA> 3 5 3 4 11451652
## 38 <NA> 3 1 4 8 265100
## 39 <NA> 1 2 3 7 531640
## 40 <NA> 1 1 2 6 10211904
## 41 <NA> 2 5 5 1 82329758
## 42 <NA> 1 2 4 8 516055
## 43 <NA> 4 1 3 12 5500510
## 44 <NA> 5 2 5 7 9650054
## 45 <NA> 5 1 6 3 34178188
## 46 <NA> 1 5 2 12 14573101
## 47 <NA> 4 6 7 2 83082869
## 48 <NA> 3 1 2 12 5647168
## 49 <NA> 4 5 5 5 40525002
## 50 <NA> 3 2 1 10 1299371
## 51 <NA> 4 4 1 13 85237338
## 52 <NA> 4 1 4 6 5250275
## 53 <NA> 5 1 2 2 944720
## 54 Islas Malvinas 6 6 6 3 3140
## 55 <NA> 7 5 9 11 64057792
## 56 <NA> 6 2 5 5 1514993
## 57 <NA> 6 6 6 3 62262000
## 58 <NA> 5 1 3 2 4615807
## 59 <NA> 5 3 1 4 23832495
## 60 <NA> 6 3 7 2 10057975
## 61 <NA> 1 4 1 8 1782893
## 62 <NA> 3 5 3 4 1533964
## 63 <NA> 4 1 4 8 650702
## 64 <NA> 2 2 2 9 10737428
## 65 <NA> 4 1 3 12 57600
## 66 <NA> 3 3 3 6 13276517
## 67 <NA> 3 1 4 8 772298
## 68 <NA> 2 5 2 5 7792854
## 69 <NA> 5 4 5 1 4489409
## 70 <NA> 2 1 7 2 9035536
## 71 <NA> 4 6 1 5 9905596
## 72 <NA> 6 6 6 11 240271522
## 73 <NA> 1 3 2 2 1166079220
## 74 <NA> 2 3 2 2 4203200
## 75 <NA> 4 3 4 13 66429284
## 76 <NA> 1 4 3 1 31129225
## 77 <NA> 1 4 4 9 306694
## 78 <NA> 3 2 5 9 7233701
## 79 <NA> 6 7 8 7 58126212
## 80 <NA> 1 2 4 10 2825928
## 81 <NA> 5 3 4 4 6342948
## 82 <NA> 5 3 5 4 127078679
## 83 <NA> 6 1 6 1 15399437
## 84 <NA> 5 2 7 3 39002772
## 85 <NA> 5 7 7 6 5431747
## 86 <NA> 6 3 6 5 14494293
## 87 <NA> 4 1 1 5 48508972
## 88 <NA> 2 2 3 11 1804838
## 89 <NA> 2 2 2 2 2691158
## 90 <NA> 1 1 1 9 6834942
## 91 <NA> 4 4 4 12 4017095
## 92 <NA> 2 3 4 9 3441790
## 93 <NA> 1 2 2 11 6310434
## 94 <NA> 3 5 4 9 21324791
## 95 <NA> 1 5 2 8 2130819
## 96 <NA> 6 3 3 9 3555179
## 97 <NA> 1 7 3 7 491775
## 98 <NA> 4 7 6 13 2231503
## 99 <NA> 2 2 3 9 34859364
## 100 <NA> 3 5 4 12 4320748
## 101 <NA> 6 5 2 3 20653556
## 102 <NA> 6 1 7 3 111211789
## 103 <NA> 5 3 7 3 2066718
## 104 <NA> 1 4 1 7 12666987
## 105 <NA> 2 2 5 13 48137741
## 106 <NA> 4 1 4 5 672180
## 107 <NA> 3 5 5 6 3041142
## 108 <NA> 4 2 1 4 21669278
## 109 <NA> 3 3 2 1 3129486
## 110 <NA> 1 3 4 5 14268711
## 111 <NA> 2 4 3 6 25715819
## 112 <NA> 4 1 1 7 2108665
## 113 <NA> 7 5 9 11 227436
## 114 <NA> 4 5 3 13 15306252
## 115 <NA> 3 2 5 2 149229090
## 116 <NA> 1 4 1 9 5891199
## 117 <NA> 4 2 2 9 16715999
## 118 <NA> 5 3 8 12 4676305
## 119 <NA> 2 2 3 12 28563377
## 120 <NA> 3 3 4 4 4213418
## 121 <NA> 1 4 1 6 3418085
## 122 <NA> 2 2 3 11 176242949
## 123 <NA> 4 4 6 3 3360474
## 124 <NA> 4 4 4 11 29546963
## 125 <NA> 3 2 2 8 97976603
## 126 <NA> 4 2 3 1 6057263
## 127 <NA> 3 7 1 2 38482919
## 128 <NA> 4 5 1 1 3971020
## 129 <NA> 3 5 3 9 22665345
## 130 <NA> 1 7 1 4 10707924
## 131 <NA> 6 3 6 2 6995655
## 132 <NA> 3 2 5 8 4119083
## 133 <NA> 3 6 2 4 833285
## 134 <NA> 1 4 3 13 22215421
## 135 <NA> 2 5 7 7 140041247
## 136 <NA> 5 2 3 10 10473282
## 137 <NA> 4 7 4 4 NA
## 138 <NA> 6 1 6 7 28686633
## 139 <NA> 2 6 4 1 25946220
## 140 <NA> 1 3 3 5 10625176
## 141 <NA> 2 6 5 5 13711597
## 142 <NA> 1 4 1 6 595613
## 143 <NA> 1 4 1 7 6440053
## 144 <NA> 1 4 6 8 7185218
## 145 <NA> 3 6 5 2 3500000
## 146 <NA> 2 8 6 7 9832017
## 147 <NA> 3 3 2 10 7379339
## 148 <NA> 1 4 7 6 481267
## 149 <NA> 2 4 4 9 5463046
## 150 <NA> 2 3 2 12 2005692
## 151 <NA> 1 4 2 4 9059651
## 152 <NA> 3 6 2 5 1123913
## 153 <NA> 2 6 2 6 20178485
## 154 <NA> 6 1 8 6 10329208
## 155 <NA> 3 1 3 5 6019877
## 156 <NA> 3 6 8 1 65905410
## 157 <NA> 3 6 2 5 7349145
## 158 <NA> 3 2 1 9 4884887
## 159 East Timor 2 2 4 3 1131612
## 160 <NA> 5 6 2 5 1310000
## 161 <NA> 4 3 3 2 10486339
## 162 <NA> 6 3 8 4 76805524
## 163 <NA> 1 5 7 2 22974347
## 164 <NA> 3 6 2 2 41048532
## 165 <NA> 6 3 6 4 32369558
## 166 <NA> 5 1 6 3 45700395
## 167 <NA> 1 2 2 10 3494382
## 168 <NA> 4 5 1 1 313973000
## 169 <NA> 2 3 5 4 27606007
## 170 <NA> 1 3 1 4 26814843
## 171 <NA> 5 6 5 4 86967524
## 172 <NA> 6 3 7 3 218519
## 173 <NA> 5 3 3 11 23822783
## 174 <NA> 2 3 4 2 49052489
## 175 <NA> 5 8 5 13 11862740
## 176 <NA> 1 5 3 9 12619600
## gdp_md_est pop_year lastcensus gdp_year economy
## 0 22270.00 NA 1979 NA 7. Least developed region
## 1 110300.00 NA 1970 NA 7. Least developed region
## 2 21810.00 NA 2001 NA 6. Developing region
## 3 184300.00 NA 2010 NA 6. Developing region
## 4 573900.00 NA 2010 NA 5. Emerging region: G20
## 5 18770.00 NA 2001 NA 6. Developing region
## 6 760.40 NA NA NA 6. Developing region
## 7 16.00 NA NA NA 6. Developing region
## 8 800200.00 NA 2006 NA 2. Developed region: nonG7
## 9 329500.00 NA 2011 NA 2. Developed region: nonG7
## 10 77610.00 NA 2009 NA 6. Developing region
## 11 3102.00 NA 2008 NA 7. Least developed region
## 12 389300.00 NA 2011 NA 2. Developed region: nonG7
## 13 12830.00 NA 2002 NA 7. Least developed region
## 14 17820.00 NA 2006 NA 7. Least developed region
## 15 224000.00 NA 2011 NA 7. Least developed region
## 16 93750.00 NA 2011 NA 2. Developed region: nonG7
## 17 9093.00 NA 2010 NA 6. Developing region
## 18 29700.00 NA 1991 NA 6. Developing region
## 19 114100.00 NA 2009 NA 6. Developing region
## 20 2536.00 NA 2010 NA 6. Developing region
## 21 43270.00 NA 2001 NA 5. Emerging region: G20
## 22 1993000.00 NA 2010 NA 3. Emerging region: BRIC
## 23 20250.00 NA 2001 NA 6. Developing region
## 24 3524.00 NA 2005 NA 7. Least developed region
## 25 27060.00 NA 2011 NA 6. Developing region
## 26 3198.00 NA 2003 NA 7. Least developed region
## 27 1300000.00 NA 2011 NA 1. Developed region: G7
## 28 316700.00 NA 2010 NA 2. Developed region: nonG7
## 29 244500.00 NA 2002 NA 5. Emerging region: G20
## 30 7973000.00 NA 2010 NA 3. Emerging region: BRIC
## 31 33850.00 NA 1998 NA 6. Developing region
## 32 42750.00 NA 2005 NA 6. Developing region
## 33 20640.00 NA 1984 NA 7. Least developed region
## 34 15350.00 NA 2007 NA 6. Developing region
## 35 395400.00 NA 2006 NA 6. Developing region
## 36 48320.00 NA 2011 NA 5. Emerging region: G20
## 37 108200.00 NA 2002 NA 5. Emerging region: G20
## 38 3600.00 NA NA NA 6. Developing region
## 39 22700.00 NA 2001 NA 6. Developing region
## 40 265200.00 NA 2011 NA 2. Developed region: nonG7
## 41 2918000.00 NA 2011 NA 1. Developed region: G7
## 42 1885.00 NA 2009 NA 7. Least developed region
## 43 203600.00 NA 2011 NA 2. Developed region: nonG7
## 44 78000.00 NA 2010 NA 6. Developing region
## 45 232900.00 NA 2008 NA 6. Developing region
## 46 107700.00 NA 2010 NA 6. Developing region
## 47 443700.00 NA 2006 NA 5. Emerging region: G20
## 48 3945.00 NA 1984 NA 7. Least developed region
## 49 1403000.00 NA 2001 NA 2. Developed region: nonG7
## 50 27410.00 NA 2000 NA 2. Developed region: nonG7
## 51 68770.00 NA 2007 NA 7. Least developed region
## 52 193500.00 NA 2010 NA 2. Developed region: nonG7
## 53 3579.00 NA 2007 NA 6. Developing region
## 54 105.10 NA NA NA 2. Developed region: nonG7
## 55 2128000.00 NA NA NA 1. Developed region: G7
## 56 21110.00 NA 2003 NA 6. Developing region
## 57 1977704.00 0 2011 2009 1. Developed region: G7
## 58 21510.00 NA 2002 NA 6. Developing region
## 59 34200.00 NA 2010 NA 6. Developing region
## 60 10600.00 NA 1996 NA 7. Least developed region
## 61 2272.00 NA 2003 NA 7. Least developed region
## 62 904.20 NA 2009 NA 7. Least developed region
## 63 14060.00 0 2002 0 7. Least developed region
## 64 343000.00 NA 2011 NA 2. Developed region: nonG7
## 65 1100.00 NA 2010 NA 2. Developed region: nonG7
## 66 68580.00 NA 2002 NA 6. Developing region
## 67 2966.00 NA 2002 NA 6. Developing region
## 68 33720.00 NA 2001 NA 6. Developing region
## 69 82390.00 NA 2011 NA 2. Developed region: nonG7
## 70 11500.00 NA 2003 NA 7. Least developed region
## 71 196600.00 NA 2001 NA 2. Developed region: nonG7
## 72 914600.00 NA 2010 NA 4. Emerging region: MIKT
## 73 3297000.00 NA 2011 NA 3. Emerging region: BRIC
## 74 188400.00 NA 2011 NA 2. Developed region: nonG7
## 75 841700.00 NA 2006 NA 5. Emerging region: G20
## 76 103900.00 NA 1997 NA 6. Developing region
## 77 12710.00 NA NA NA 2. Developed region: nonG7
## 78 201400.00 NA 2009 NA 2. Developed region: nonG7
## 79 1823000.00 NA 2012 NA 1. Developed region: G7
## 80 20910.00 NA 2011 NA 6. Developing region
## 81 31610.00 NA 2004 NA 6. Developing region
## 82 4329000.00 NA 2010 NA 1. Developed region: G7
## 83 175800.00 NA 2009 NA 6. Developing region
## 84 61510.00 NA 2009 NA 5. Emerging region: G20
## 85 11610.00 NA 2009 NA 6. Developing region
## 86 27940.00 NA 2008 NA 7. Least developed region
## 87 1335000.00 NA 2010 NA 4. Emerging region: MIKT
## 88 5352.00 NA 1981 NA 6. Developing region
## 89 149100.00 NA 2010 NA 6. Developing region
## 90 13980.00 NA 2005 NA 7. Least developed region
## 91 44060.00 NA 1970 NA 6. Developing region
## 92 1526.00 NA 2008 NA 7. Least developed region
## 93 88830.00 NA 2006 NA 6. Developing region
## 94 91870.00 NA 2001 NA 6. Developing region
## 95 3293.00 NA 2006 NA 7. Least developed region
## 96 63330.00 NA 2011 NA 2. Developed region: nonG7
## 97 39370.00 NA 2011 NA 2. Developed region: nonG7
## 98 38860.00 NA 2011 NA 2. Developed region: nonG7
## 99 136600.00 NA 2004 NA 6. Developing region
## 100 10670.00 NA 2004 NA 6. Developing region
## 101 20130.00 NA 1993 NA 7. Least developed region
## 102 1563000.00 NA 2010 NA 4. Emerging region: MIKT
## 103 18780.00 NA 2010 NA 6. Developing region
## 104 14590.00 NA 2009 NA 7. Least developed region
## 105 55130.00 NA 1983 NA 7. Least developed region
## 106 6816.00 NA 2011 NA 6. Developing region
## 107 9476.00 NA 2010 NA 6. Developing region
## 108 18940.00 NA 2007 NA 7. Least developed region
## 109 6308.00 NA 2000 NA 7. Least developed region
## 110 11810.00 NA 2008 NA 7. Least developed region
## 111 384300.00 NA 2010 NA 6. Developing region
## 112 13250.00 NA 2001 NA 6. Developing region
## 113 3158.00 NA 2009 NA 6. Developing region
## 114 10040.00 NA 2001 NA 7. Least developed region
## 115 335400.00 NA 2006 NA 5. Emerging region: G20
## 116 16790.00 NA 2005 NA 6. Developing region
## 117 672000.00 NA 2011 NA 2. Developed region: nonG7
## 118 276400.00 NA 2001 NA 2. Developed region: nonG7
## 119 31080.00 NA 2001 NA 7. Least developed region
## 120 116700.00 NA 2006 NA 2. Developed region: nonG7
## 121 66980.00 NA 2010 NA 6. Developing region
## 122 427300.00 NA 1998 NA 5. Emerging region: G20
## 123 38830.00 NA 2010 NA 6. Developing region
## 124 247300.00 NA 2007 NA 5. Emerging region: G20
## 125 317500.00 NA 2010 NA 5. Emerging region: G20
## 126 13210.00 NA 2000 NA 6. Developing region
## 127 667900.00 NA 2011 NA 2. Developed region: nonG7
## 128 70230.00 NA 2010 NA 6. Developing region
## 129 40000.00 NA 2009 NA 7. Least developed region
## 130 208627.00 NA 2011 0 2. Developed region: nonG7
## 131 28890.00 NA 2002 NA 5. Emerging region: G20
## 132 11950.77 NA 2007 NA 6. Developing region
## 133 91330.00 NA 2010 NA 6. Developing region
## 134 271400.00 NA 2011 NA 2. Developed region: nonG7
## 135 2266000.00 NA 2010 NA 3. Emerging region: BRIC
## 136 9706.00 NA 2002 NA 7. Least developed region
## 137 NA NA NA NA 7. Least developed region
## 138 576500.00 NA 2010 NA 2. Developed region: nonG7
## 139 88080.00 NA 2008 NA 6. Developing region
## 140 13227.00 NA 2008 NA 7. Least developed region
## 141 21980.00 NA 2002 NA 7. Least developed region
## 142 1078.00 NA 2009 NA 7. Least developed region
## 143 4285.00 NA 2004 NA 7. Least developed region
## 144 43630.00 NA 2007 NA 6. Developing region
## 145 12250.00 NA NA NA 6. Developing region
## 146 5524.00 NA 1987 NA 7. Least developed region
## 147 80340.00 NA 2011 NA 6. Developing region
## 148 4254.00 NA 2004 NA 6. Developing region
## 149 119500.00 NA 2011 NA 2. Developed region: nonG7
## 150 59340.00 NA 2011 NA 2. Developed region: nonG7
## 151 344300.00 NA NA NA 2. Developed region: nonG7
## 152 5702.00 NA 2007 NA 6. Developing region
## 153 98830.00 NA 2004 NA 6. Developing region
## 154 15860.00 NA 2009 NA 7. Least developed region
## 155 5118.00 NA 2010 NA 7. Least developed region
## 156 547400.00 NA 2010 NA 5. Emerging region: G20
## 157 13160.00 NA 2010 NA 6. Developing region
## 158 29780.00 NA 1995 NA 6. Developing region
## 159 2520.00 NA 2010 NA 7. Least developed region
## 160 29010.00 NA 2011 NA 6. Developing region
## 161 81710.00 NA 2004 NA 6. Developing region
## 162 902700.00 NA 2000 NA 4. Emerging region: MIKT
## 163 712000.00 NA NA NA 2. Developed region: nonG7
## 164 54250.00 NA 2002 NA 7. Least developed region
## 165 39380.00 NA 2002 NA 7. Least developed region
## 166 339800.00 NA 2001 NA 6. Developing region
## 167 43160.00 NA 2004 NA 5. Emerging region: G20
## 168 15094000.00 0 2010 0 1. Developed region: G7
## 169 71670.00 NA 1989 NA 6. Developing region
## 170 357400.00 NA 2001 NA 5. Emerging region: G20
## 171 241700.00 NA 2009 NA 5. Emerging region: G20
## 172 988.50 NA 2009 NA 7. Least developed region
## 173 55280.00 NA 2004 NA 7. Least developed region
## 174 491000.00 NA 2001 NA 5. Emerging region: G20
## 175 17500.00 NA 2010 NA 7. Least developed region
## 176 9323.00 0 2002 0 5. Emerging region: G20
## income_grp wikipedia fips_10 iso_a2 iso_a3 iso_n3 un_a3 wb_a2
## 0 5. Low income NA <NA> AF AFG 004 004 AF
## 1 3. Upper middle income NA <NA> AO AGO 024 024 AO
## 2 4. Lower middle income NA <NA> AL ALB 008 008 AL
## 3 2. High income: nonOECD NA <NA> AE ARE 784 784 AE
## 4 3. Upper middle income NA <NA> AR ARG 032 032 AR
## 5 4. Lower middle income NA <NA> AM ARM 051 051 AM
## 6 2. High income: nonOECD NA <NA> AQ ATA 010 <NA> <NA>
## 7 2. High income: nonOECD NA <NA> TF ATF 260 <NA> <NA>
## 8 1. High income: OECD NA <NA> AU AUS 036 036 AU
## 9 1. High income: OECD NA <NA> AT AUT 040 040 AT
## 10 3. Upper middle income NA <NA> AZ AZE 031 031 AZ
## 11 5. Low income NA <NA> BI BDI 108 108 BI
## 12 1. High income: OECD NA <NA> BE BEL 056 056 BE
## 13 5. Low income NA <NA> BJ BEN 204 204 BJ
## 14 5. Low income NA <NA> BF BFA 854 854 BF
## 15 5. Low income NA <NA> BD BGD 050 050 BD
## 16 3. Upper middle income NA <NA> BG BGR 100 100 BG
## 17 2. High income: nonOECD NA <NA> BS BHS 044 044 BS
## 18 3. Upper middle income NA <NA> BA BIH 070 070 BA
## 19 3. Upper middle income NA <NA> BY BLR 112 112 BY
## 20 4. Lower middle income NA <NA> BZ BLZ 084 084 BZ
## 21 4. Lower middle income NA <NA> BO BOL 068 068 BO
## 22 3. Upper middle income NA <NA> BR BRA 076 076 BR
## 23 2. High income: nonOECD NA <NA> BN BRN 096 096 BN
## 24 4. Lower middle income NA <NA> BT BTN 064 064 BT
## 25 3. Upper middle income NA <NA> BW BWA 072 072 BW
## 26 5. Low income NA <NA> CF CAF 140 140 CF
## 27 1. High income: OECD NA <NA> CA CAN 124 124 CA
## 28 1. High income: OECD NA <NA> CH CHE 756 756 CH
## 29 3. Upper middle income NA <NA> CL CHL 152 152 CL
## 30 3. Upper middle income NA <NA> CN CHN 156 156 CN
## 31 4. Lower middle income NA <NA> CI CIV 384 384 CI
## 32 4. Lower middle income NA <NA> CM CMR 120 120 CM
## 33 5. Low income NA <NA> CD COD 180 180 ZR
## 34 4. Lower middle income NA <NA> CG COG 178 178 CG
## 35 3. Upper middle income NA <NA> CO COL 170 170 CO
## 36 3. Upper middle income NA <NA> CR CRI 188 188 CR
## 37 3. Upper middle income NA <NA> CU CUB 192 192 CU
## 38 3. Upper middle income NA <NA> <NA> <NA> <NA> <NA> <NA>
## 39 2. High income: nonOECD NA <NA> CY CYP 196 196 CY
## 40 1. High income: OECD NA <NA> CZ CZE 203 203 CZ
## 41 1. High income: OECD NA <NA> DE DEU 276 276 DE
## 42 4. Lower middle income NA <NA> DJ DJI 262 262 DJ
## 43 1. High income: OECD NA <NA> DK DNK 208 208 DK
## 44 3. Upper middle income NA <NA> DO DOM 214 214 DO
## 45 3. Upper middle income NA <NA> DZ DZA 012 012 DZ
## 46 3. Upper middle income NA <NA> EC ECU 218 218 EC
## 47 4. Lower middle income NA <NA> EG EGY 818 818 EG
## 48 5. Low income NA <NA> ER ERI 232 232 ER
## 49 1. High income: OECD NA <NA> ES ESP 724 724 ES
## 50 1. High income: OECD NA <NA> EE EST 233 233 EE
## 51 5. Low income NA <NA> ET ETH 231 231 ET
## 52 1. High income: OECD NA <NA> FI FIN 246 246 FI
## 53 4. Lower middle income NA <NA> FJ FJI 242 242 FJ
## 54 1. High income: OECD NA <NA> FK FLK 238 238 <NA>
## 55 1. High income: OECD NA <NA> FR FRA 250 250 FR
## 56 3. Upper middle income NA <NA> GA GAB 266 266 GA
## 57 1. High income: OECD NA <NA> GB GBR 826 826 GB
## 58 4. Lower middle income NA <NA> GE GEO 268 268 GE
## 59 4. Lower middle income NA <NA> GH GHA 288 288 GH
## 60 5. Low income NA <NA> GN GIN 324 324 GN
## 61 5. Low income NA <NA> GM GMB 270 270 GM
## 62 5. Low income NA <NA> GW GNB 624 624 GW
## 63 2. High income: nonOECD NA <NA> GQ GNQ 226 226 GQ
## 64 1. High income: OECD NA <NA> GR GRC 300 300 GR
## 65 2. High income: nonOECD NA <NA> GL GRL 304 304 GL
## 66 4. Lower middle income NA <NA> GT GTM 320 320 GT
## 67 4. Lower middle income NA <NA> GY GUY 328 328 GY
## 68 4. Lower middle income NA <NA> HN HND 340 340 HN
## 69 2. High income: nonOECD NA <NA> HR HRV 191 191 HR
## 70 5. Low income NA <NA> HT HTI 332 332 HT
## 71 1. High income: OECD NA <NA> HU HUN 348 348 HU
## 72 4. Lower middle income NA <NA> ID IDN 360 360 ID
## 73 4. Lower middle income NA <NA> IN IND 356 356 IN
## 74 1. High income: OECD NA <NA> IE IRL 372 372 IE
## 75 3. Upper middle income NA <NA> IR IRN 364 364 IR
## 76 4. Lower middle income NA <NA> IQ IRQ 368 368 IQ
## 77 1. High income: OECD NA <NA> IS ISL 352 352 IS
## 78 1. High income: OECD NA <NA> IL ISR 376 376 IL
## 79 1. High income: OECD NA <NA> IT ITA 380 380 IT
## 80 3. Upper middle income NA <NA> JM JAM 388 388 JM
## 81 3. Upper middle income NA <NA> JO JOR 400 400 JO
## 82 1. High income: OECD NA <NA> JP JPN 392 392 JP
## 83 3. Upper middle income NA <NA> KZ KAZ 398 398 KZ
## 84 5. Low income NA <NA> KE KEN 404 404 KE
## 85 5. Low income NA <NA> KG KGZ 417 417 KG
## 86 5. Low income NA <NA> KH KHM 116 116 KH
## 87 1. High income: OECD NA <NA> KR KOR 410 410 KR
## 88 4. Lower middle income NA <NA> <NA> <NA> <NA> <NA> KV
## 89 2. High income: nonOECD NA <NA> KW KWT 414 414 KW
## 90 4. Lower middle income NA <NA> LA LAO 418 418 LA
## 91 3. Upper middle income NA <NA> LB LBN 422 422 LB
## 92 5. Low income NA <NA> LR LBR 430 430 LR
## 93 3. Upper middle income NA <NA> LY LBY 434 434 LY
## 94 4. Lower middle income NA <NA> LK LKA 144 144 LK
## 95 4. Lower middle income NA <NA> LS LSO 426 426 LS
## 96 3. Upper middle income NA <NA> LT LTU 440 440 LT
## 97 1. High income: OECD NA <NA> LU LUX 442 442 LU
## 98 3. Upper middle income NA <NA> LV LVA 428 428 LV
## 99 4. Lower middle income NA <NA> MA MAR 504 504 MA
## 100 4. Lower middle income NA <NA> MD MDA 498 498 MD
## 101 5. Low income NA <NA> MG MDG 450 450 MG
## 102 3. Upper middle income NA <NA> MX MEX 484 484 MX
## 103 3. Upper middle income NA <NA> MK MKD 807 807 MK
## 104 5. Low income NA <NA> ML MLI 466 466 ML
## 105 5. Low income NA <NA> MM MMR 104 104 MM
## 106 3. Upper middle income NA <NA> ME MNE 499 499 ME
## 107 4. Lower middle income NA <NA> MN MNG 496 496 MN
## 108 5. Low income NA <NA> MZ MOZ 508 508 MZ
## 109 5. Low income NA <NA> MR MRT 478 478 MR
## 110 5. Low income NA <NA> MW MWI 454 454 MW
## 111 3. Upper middle income NA <NA> MY MYS 458 458 MY
## 112 3. Upper middle income NA <NA> NA NAM 516 516 NA
## 113 2. High income: nonOECD NA <NA> NC NCL 540 540 NC
## 114 5. Low income NA <NA> NE NER 562 562 NE
## 115 4. Lower middle income NA <NA> NG NGA 566 566 NG
## 116 4. Lower middle income NA <NA> NI NIC 558 558 NI
## 117 1. High income: OECD NA <NA> NL NLD 528 528 NL
## 118 1. High income: OECD NA <NA> NO NOR 578 578 NO
## 119 5. Low income NA <NA> NP NPL 524 524 NP
## 120 1. High income: OECD NA <NA> NZ NZL 554 554 NZ
## 121 2. High income: nonOECD NA <NA> OM OMN 512 512 OM
## 122 4. Lower middle income NA <NA> PK PAK 586 586 PK
## 123 3. Upper middle income NA <NA> PA PAN 591 591 PA
## 124 3. Upper middle income NA <NA> PE PER 604 604 PE
## 125 4. Lower middle income NA <NA> PH PHL 608 608 PH
## 126 4. Lower middle income NA <NA> PG PNG 598 598 PG
## 127 1. High income: OECD NA <NA> PL POL 616 616 PL
## 128 2. High income: nonOECD NA <NA> PR PRI 630 630 PR
## 129 5. Low income NA <NA> KP PRK 408 408 KP
## 130 1. High income: OECD NA <NA> PT PRT 620 620 PT
## 131 4. Lower middle income NA <NA> PY PRY 600 600 PY
## 132 4. Lower middle income NA <NA> PS PSE 275 275 GZ
## 133 2. High income: nonOECD NA <NA> QA QAT 634 634 QA
## 134 3. Upper middle income NA <NA> RO ROU 642 642 RO
## 135 3. Upper middle income NA <NA> RU RUS 643 643 RU
## 136 5. Low income NA <NA> RW RWA 646 646 RW
## 137 5. Low income NA <NA> EH ESH 732 732 <NA>
## 138 2. High income: nonOECD NA <NA> SA SAU 682 682 SA
## 139 4. Lower middle income NA <NA> SD SDN 729 729 SD
## 140 5. Low income NA <NA> SS SSD 728 728 SS
## 141 4. Lower middle income NA <NA> SN SEN 686 686 SN
## 142 4. Lower middle income NA <NA> SB SLB 090 090 SB
## 143 5. Low income NA <NA> SL SLE 694 694 SL
## 144 4. Lower middle income NA <NA> SV SLV 222 222 SV
## 145 4. Lower middle income NA <NA> <NA> <NA> <NA> <NA> <NA>
## 146 5. Low income NA <NA> SO SOM 706 706 SO
## 147 3. Upper middle income NA <NA> RS SRB 688 688 YF
## 148 3. Upper middle income NA <NA> SR SUR 740 740 SR
## 149 1. High income: OECD NA <NA> SK SVK 703 703 SK
## 150 1. High income: OECD NA <NA> SI SVN 705 705 SI
## 151 1. High income: OECD NA <NA> SE SWE 752 752 SE
## 152 4. Lower middle income NA <NA> SZ SWZ 748 748 SZ
## 153 4. Lower middle income NA <NA> SY SYR 760 760 SY
## 154 5. Low income NA <NA> TD TCD 148 148 TD
## 155 5. Low income NA <NA> TG TGO 768 768 TG
## 156 3. Upper middle income NA <NA> TH THA 764 764 TH
## 157 5. Low income NA <NA> TJ TJK 762 762 TJ
## 158 3. Upper middle income NA <NA> TM TKM 795 795 TM
## 159 4. Lower middle income NA <NA> TL TLS 626 626 TP
## 160 2. High income: nonOECD NA <NA> TT TTO 780 780 TT
## 161 3. Upper middle income NA <NA> TN TUN 788 788 TN
## 162 3. Upper middle income NA <NA> TR TUR 792 792 TR
## 163 2. High income: nonOECD NA <NA> TW TWN 158 <NA> <NA>
## 164 5. Low income NA <NA> TZ TZA 834 834 TZ
## 165 5. Low income NA <NA> UG UGA 800 800 UG
## 166 4. Lower middle income NA <NA> UA UKR 804 804 UA
## 167 3. Upper middle income NA <NA> UY URY 858 858 UY
## 168 1. High income: OECD 0 <NA> US USA 840 840 US
## 169 4. Lower middle income NA <NA> UZ UZB 860 860 UZ
## 170 3. Upper middle income NA <NA> VE VEN 862 862 VE
## 171 4. Lower middle income NA <NA> VN VNM 704 704 VN
## 172 4. Lower middle income NA <NA> VU VUT 548 548 VU
## 173 4. Lower middle income NA <NA> YE YEM 887 887 RY
## 174 3. Upper middle income NA <NA> ZA ZAF 710 710 ZA
## 175 4. Lower middle income NA <NA> ZM ZMB 894 894 ZM
## 176 5. Low income NA <NA> ZW ZWE 716 716 ZW
## wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un adm0_a3_wb
## 0 AFG NA AFG AFG NA NA
## 1 AGO NA AGO AGO NA NA
## 2 ALB NA ALB ALB NA NA
## 3 ARE NA ARE ARE NA NA
## 4 ARG NA ARG ARG NA NA
## 5 ARM NA ARM ARM NA NA
## 6 <NA> NA ATA ATA NA NA
## 7 <NA> NA ATF ATF NA NA
## 8 AUS NA AUS AUS NA NA
## 9 AUT NA AUT AUT NA NA
## 10 AZE NA AZE AZE NA NA
## 11 BDI NA BDI BDI NA NA
## 12 BEL NA BEL BEL NA NA
## 13 BEN NA BEN BEN NA NA
## 14 BFA NA BFA BFA NA NA
## 15 BGD NA BGD BGD NA NA
## 16 BGR NA BGR BGR NA NA
## 17 BHS NA BHS BHS NA NA
## 18 BIH NA BIH BIH NA NA
## 19 BLR NA BLR BLR NA NA
## 20 BLZ NA BLZ BLZ NA NA
## 21 BOL NA BOL BOL NA NA
## 22 BRA NA BRA BRA NA NA
## 23 BRN NA BRN BRN NA NA
## 24 BTN NA BTN BTN NA NA
## 25 BWA NA BWA BWA NA NA
## 26 CAF NA CAF CAF NA NA
## 27 CAN NA CAN CAN NA NA
## 28 CHE NA CHE CHE NA NA
## 29 CHL NA CHL CHL NA NA
## 30 CHN NA CHN CHN NA NA
## 31 CIV NA CIV CIV NA NA
## 32 CMR NA CMR CMR NA NA
## 33 ZAR NA COD COD NA NA
## 34 COG NA COG COG NA NA
## 35 COL NA COL COL NA NA
## 36 CRI NA CRI CRI NA NA
## 37 CUB NA CUB CUB NA NA
## 38 <NA> NA CYP CYP NA NA
## 39 CYP NA CYP CYP NA NA
## 40 CZE NA CZE CZE NA NA
## 41 DEU NA DEU DEU NA NA
## 42 DJI NA DJI DJI NA NA
## 43 DNK NA DNK DNK NA NA
## 44 DOM NA DOM DOM NA NA
## 45 DZA NA DZA DZA NA NA
## 46 ECU NA ECU ECU NA NA
## 47 EGY NA EGY EGY NA NA
## 48 ERI NA ERI ERI NA NA
## 49 ESP NA ESP ESP NA NA
## 50 EST NA EST EST NA NA
## 51 ETH NA ETH ETH NA NA
## 52 FIN NA FIN FIN NA NA
## 53 FJI NA FJI FJI NA NA
## 54 <NA> NA FLK FLK NA NA
## 55 FRA NA FRA FRA NA NA
## 56 GAB NA GAB GAB NA NA
## 57 GBR NA GBR GBR NA NA
## 58 GEO NA GEO GEO NA NA
## 59 GHA NA GHA GHA NA NA
## 60 GIN NA GIN GIN NA NA
## 61 GMB NA GMB GMB NA NA
## 62 GNB NA GNB GNB NA NA
## 63 GNQ NA GNQ GNQ NA NA
## 64 GRC NA GRC GRC NA NA
## 65 GRL NA GRL GRL NA NA
## 66 GTM NA GTM GTM NA NA
## 67 GUY NA GUY GUY NA NA
## 68 HND NA HND HND NA NA
## 69 HRV NA HRV HRV NA NA
## 70 HTI NA HTI HTI NA NA
## 71 HUN NA HUN HUN NA NA
## 72 IDN NA IDN IDN NA NA
## 73 IND NA IND IND NA NA
## 74 IRL NA IRL IRL NA NA
## 75 IRN NA IRN IRN NA NA
## 76 IRQ NA IRQ IRQ NA NA
## 77 ISL NA ISL ISL NA NA
## 78 ISR NA ISR ISR NA NA
## 79 ITA NA ITA ITA NA NA
## 80 JAM NA JAM JAM NA NA
## 81 JOR NA JOR JOR NA NA
## 82 JPN NA JPN JPN NA NA
## 83 KAZ NA KAZ KAZ NA NA
## 84 KEN NA KEN KEN NA NA
## 85 KGZ NA KGZ KGZ NA NA
## 86 KHM NA KHM KHM NA NA
## 87 KOR NA KOR KOR NA NA
## 88 KSV NA SRB KOS NA NA
## 89 KWT NA KWT KWT NA NA
## 90 LAO NA LAO LAO NA NA
## 91 LBN NA LBN LBN NA NA
## 92 LBR NA LBR LBR NA NA
## 93 LBY NA LBY LBY NA NA
## 94 LKA NA LKA LKA NA NA
## 95 LSO NA LSO LSO NA NA
## 96 LTU NA LTU LTU NA NA
## 97 LUX NA LUX LUX NA NA
## 98 LVA NA LVA LVA NA NA
## 99 MAR NA MAR MAR NA NA
## 100 MDA NA MDA MDA NA NA
## 101 MDG NA MDG MDG NA NA
## 102 MEX NA MEX MEX NA NA
## 103 MKD NA MKD MKD NA NA
## 104 MLI NA MLI MLI NA NA
## 105 MMR NA MMR MMR NA NA
## 106 MNE NA MNE MNE NA NA
## 107 MNG NA MNG MNG NA NA
## 108 MOZ NA MOZ MOZ NA NA
## 109 MRT NA MRT MRT NA NA
## 110 MWI NA MWI MWI NA NA
## 111 MYS NA MYS MYS NA NA
## 112 NAM NA NAM NAM NA NA
## 113 NCL NA NCL NCL NA NA
## 114 NER NA NER NER NA NA
## 115 NGA NA NGA NGA NA NA
## 116 NIC NA NIC NIC NA NA
## 117 NLD NA NLD NLD NA NA
## 118 NOR NA NOR NOR NA NA
## 119 NPL NA NPL NPL NA NA
## 120 NZL NA NZL NZL NA NA
## 121 OMN NA OMN OMN NA NA
## 122 PAK NA PAK PAK NA NA
## 123 PAN NA PAN PAN NA NA
## 124 PER NA PER PER NA NA
## 125 PHL NA PHL PHL NA NA
## 126 PNG NA PNG PNG NA NA
## 127 POL NA POL POL NA NA
## 128 PRI NA PRI PRI NA NA
## 129 PRK NA PRK PRK NA NA
## 130 PRT NA PRT PRT NA NA
## 131 PRY NA PRY PRY NA NA
## 132 WBG NA PSE PSX NA NA
## 133 QAT NA QAT QAT NA NA
## 134 ROM NA ROU ROU NA NA
## 135 RUS NA RUS RUS NA NA
## 136 RWA NA RWA RWA NA NA
## 137 <NA> NA MAR SAH NA NA
## 138 SAU NA SAU SAU NA NA
## 139 SDN NA SDN SDN NA NA
## 140 SSD NA SSD SDS NA NA
## 141 SEN NA SEN SEN NA NA
## 142 SLB NA SLB SLB NA NA
## 143 SLE NA SLE SLE NA NA
## 144 SLV NA SLV SLV NA NA
## 145 <NA> NA SOM SOM NA NA
## 146 SOM NA SOM SOM NA NA
## 147 SRB NA SRB SRB NA NA
## 148 SUR NA SUR SUR NA NA
## 149 SVK NA SVK SVK NA NA
## 150 SVN NA SVN SVN NA NA
## 151 SWE NA SWE SWE NA NA
## 152 SWZ NA SWZ SWZ NA NA
## 153 SYR NA SYR SYR NA NA
## 154 TCD NA TCD TCD NA NA
## 155 TGO NA TGO TGO NA NA
## 156 THA NA THA THA NA NA
## 157 TJK NA TJK TJK NA NA
## 158 TKM NA TKM TKM NA NA
## 159 TMP NA TLS TLS NA NA
## 160 TTO NA TTO TTO NA NA
## 161 TUN NA TUN TUN NA NA
## 162 TUR NA TUR TUR NA NA
## 163 <NA> NA TWN TWN NA NA
## 164 TZA NA TZA TZA NA NA
## 165 UGA NA UGA UGA NA NA
## 166 UKR NA UKR UKR NA NA
## 167 URY NA URY URY NA NA
## 168 USA NA USA USA NA NA
## 169 UZB NA UZB UZB NA NA
## 170 VEN NA VEN VEN NA NA
## 171 VNM NA VNM VNM NA NA
## 172 VUT NA VUT VUT NA NA
## 173 YEM NA YEM YEM NA NA
## 174 ZAF NA ZAF ZAF NA NA
## 175 ZMB NA ZMB ZMB NA NA
## 176 ZWE NA ZWE ZWE NA NA
## continent region_un subregion
## 0 Asia Asia Southern Asia
## 1 Africa Africa Middle Africa
## 2 Europe Europe Southern Europe
## 3 Asia Asia Western Asia
## 4 South America Americas South America
## 5 Asia Asia Western Asia
## 6 Antarctica Antarctica Antarctica
## 7 Seven seas (open ocean) Seven seas (open ocean) Seven seas (open ocean)
## 8 Oceania Oceania Australia and New Zealand
## 9 Europe Europe Western Europe
## 10 Asia Asia Western Asia
## 11 Africa Africa Eastern Africa
## 12 Europe Europe Western Europe
## 13 Africa Africa Western Africa
## 14 Africa Africa Western Africa
## 15 Asia Asia Southern Asia
## 16 Europe Europe Eastern Europe
## 17 North America Americas Caribbean
## 18 Europe Europe Southern Europe
## 19 Europe Europe Eastern Europe
## 20 North America Americas Central America
## 21 South America Americas South America
## 22 South America Americas South America
## 23 Asia Asia South-Eastern Asia
## 24 Asia Asia Southern Asia
## 25 Africa Africa Southern Africa
## 26 Africa Africa Middle Africa
## 27 North America Americas Northern America
## 28 Europe Europe Western Europe
## 29 South America Americas South America
## 30 Asia Asia Eastern Asia
## 31 Africa Africa Western Africa
## 32 Africa Africa Middle Africa
## 33 Africa Africa Middle Africa
## 34 Africa Africa Middle Africa
## 35 South America Americas South America
## 36 North America Americas Central America
## 37 North America Americas Caribbean
## 38 Asia Asia Western Asia
## 39 Asia Asia Western Asia
## 40 Europe Europe Eastern Europe
## 41 Europe Europe Western Europe
## 42 Africa Africa Eastern Africa
## 43 Europe Europe Northern Europe
## 44 North America Americas Caribbean
## 45 Africa Africa Northern Africa
## 46 South America Americas South America
## 47 Africa Africa Northern Africa
## 48 Africa Africa Eastern Africa
## 49 Europe Europe Southern Europe
## 50 Europe Europe Northern Europe
## 51 Africa Africa Eastern Africa
## 52 Europe Europe Northern Europe
## 53 Oceania Oceania Melanesia
## 54 South America Americas South America
## 55 Europe Europe Western Europe
## 56 Africa Africa Middle Africa
## 57 Europe Europe Northern Europe
## 58 Asia Asia Western Asia
## 59 Africa Africa Western Africa
## 60 Africa Africa Western Africa
## 61 Africa Africa Western Africa
## 62 Africa Africa Western Africa
## 63 Africa Africa Middle Africa
## 64 Europe Europe Southern Europe
## 65 North America Americas Northern America
## 66 North America Americas Central America
## 67 South America Americas South America
## 68 North America Americas Central America
## 69 Europe Europe Southern Europe
## 70 North America Americas Caribbean
## 71 Europe Europe Eastern Europe
## 72 Asia Asia South-Eastern Asia
## 73 Asia Asia Southern Asia
## 74 Europe Europe Northern Europe
## 75 Asia Asia Southern Asia
## 76 Asia Asia Western Asia
## 77 Europe Europe Northern Europe
## 78 Asia Asia Western Asia
## 79 Europe Europe Southern Europe
## 80 North America Americas Caribbean
## 81 Asia Asia Western Asia
## 82 Asia Asia Eastern Asia
## 83 Asia Asia Central Asia
## 84 Africa Africa Eastern Africa
## 85 Asia Asia Central Asia
## 86 Asia Asia South-Eastern Asia
## 87 Asia Asia Eastern Asia
## 88 Europe Europe Southern Europe
## 89 Asia Asia Western Asia
## 90 Asia Asia South-Eastern Asia
## 91 Asia Asia Western Asia
## 92 Africa Africa Western Africa
## 93 Africa Africa Northern Africa
## 94 Asia Asia Southern Asia
## 95 Africa Africa Southern Africa
## 96 Europe Europe Northern Europe
## 97 Europe Europe Western Europe
## 98 Europe Europe Northern Europe
## 99 Africa Africa Northern Africa
## 100 Europe Europe Eastern Europe
## 101 Africa Africa Eastern Africa
## 102 North America Americas Central America
## 103 Europe Europe Southern Europe
## 104 Africa Africa Western Africa
## 105 Asia Asia South-Eastern Asia
## 106 Europe Europe Southern Europe
## 107 Asia Asia Eastern Asia
## 108 Africa Africa Eastern Africa
## 109 Africa Africa Western Africa
## 110 Africa Africa Eastern Africa
## 111 Asia Asia South-Eastern Asia
## 112 Africa Africa Southern Africa
## 113 Oceania Oceania Melanesia
## 114 Africa Africa Western Africa
## 115 Africa Africa Western Africa
## 116 North America Americas Central America
## 117 Europe Europe Western Europe
## 118 Europe Europe Northern Europe
## 119 Asia Asia Southern Asia
## 120 Oceania Oceania Australia and New Zealand
## 121 Asia Asia Western Asia
## 122 Asia Asia Southern Asia
## 123 North America Americas Central America
## 124 South America Americas South America
## 125 Asia Asia South-Eastern Asia
## 126 Oceania Oceania Melanesia
## 127 Europe Europe Eastern Europe
## 128 North America Americas Caribbean
## 129 Asia Asia Eastern Asia
## 130 Europe Europe Southern Europe
## 131 South America Americas South America
## 132 Asia Asia Western Asia
## 133 Asia Asia Western Asia
## 134 Europe Europe Eastern Europe
## 135 Europe Europe Eastern Europe
## 136 Africa Africa Eastern Africa
## 137 Africa Africa Northern Africa
## 138 Asia Asia Western Asia
## 139 Africa Africa Northern Africa
## 140 Africa Africa Eastern Africa
## 141 Africa Africa Western Africa
## 142 Oceania Oceania Melanesia
## 143 Africa Africa Western Africa
## 144 North America Americas Central America
## 145 Africa Africa Eastern Africa
## 146 Africa Africa Eastern Africa
## 147 Europe Europe Southern Europe
## 148 South America Americas South America
## 149 Europe Europe Eastern Europe
## 150 Europe Europe Southern Europe
## 151 Europe Europe Northern Europe
## 152 Africa Africa Southern Africa
## 153 Asia Asia Western Asia
## 154 Africa Africa Middle Africa
## 155 Africa Africa Western Africa
## 156 Asia Asia South-Eastern Asia
## 157 Asia Asia Central Asia
## 158 Asia Asia Central Asia
## 159 Asia Asia South-Eastern Asia
## 160 North America Americas Caribbean
## 161 Africa Africa Northern Africa
## 162 Asia Asia Western Asia
## 163 Asia Asia Eastern Asia
## 164 Africa Africa Eastern Africa
## 165 Africa Africa Eastern Africa
## 166 Europe Europe Eastern Europe
## 167 South America Americas South America
## 168 North America Americas Northern America
## 169 Asia Asia Central Asia
## 170 South America Americas South America
## 171 Asia Asia South-Eastern Asia
## 172 Oceania Oceania Melanesia
## 173 Asia Asia Western Asia
## 174 Africa Africa Southern Africa
## 175 Africa Africa Eastern Africa
## 176 Africa Africa Eastern Africa
## region_wb name_len long_len abbrev_len tiny homepart
## 0 South Asia 11 11 4 NA 1
## 1 Sub-Saharan Africa 6 6 4 NA 1
## 2 Europe & Central Asia 7 7 4 NA 1
## 3 Middle East & North Africa 20 20 6 NA 1
## 4 Latin America & Caribbean 9 9 4 NA 1
## 5 Europe & Central Asia 7 7 4 NA 1
## 6 Antarctica 10 10 4 NA 1
## 7 Sub-Saharan Africa 22 35 10 2 NA
## 8 East Asia & Pacific 9 9 4 NA 1
## 9 Europe & Central Asia 7 7 5 NA 1
## 10 Europe & Central Asia 10 10 4 NA 1
## 11 Sub-Saharan Africa 7 7 4 NA 1
## 12 Europe & Central Asia 7 7 5 NA 1
## 13 Sub-Saharan Africa 5 5 5 NA 1
## 14 Sub-Saharan Africa 12 12 4 NA 1
## 15 South Asia 10 10 5 NA 1
## 16 Europe & Central Asia 8 8 5 NA 1
## 17 Latin America & Caribbean 7 7 4 NA 1
## 18 Europe & Central Asia 16 22 4 NA 1
## 19 Europe & Central Asia 7 7 5 NA 1
## 20 Latin America & Caribbean 6 6 6 NA 1
## 21 Latin America & Caribbean 7 7 7 NA 1
## 22 Latin America & Caribbean 6 6 6 NA 1
## 23 East Asia & Pacific 6 17 6 2 1
## 24 South Asia 6 6 6 NA 1
## 25 Sub-Saharan Africa 8 8 4 NA 1
## 26 Sub-Saharan Africa 20 24 6 NA 1
## 27 North America 6 6 4 NA 1
## 28 Europe & Central Asia 11 11 6 NA 1
## 29 Latin America & Caribbean 5 5 5 NA 1
## 30 East Asia & Pacific 5 5 5 NA 1
## 31 Sub-Saharan Africa 13 13 4 NA 1
## 32 Sub-Saharan Africa 8 8 4 NA 1
## 33 Sub-Saharan Africa 15 32 6 NA 1
## 34 Sub-Saharan Africa 5 17 10 NA 1
## 35 Latin America & Caribbean 8 8 4 NA 1
## 36 Latin America & Caribbean 10 10 4 NA 1
## 37 Latin America & Caribbean 4 4 4 NA 1
## 38 Europe & Central Asia 9 15 6 NA 1
## 39 Europe & Central Asia 6 6 4 NA 1
## 40 Europe & Central Asia 10 14 8 NA 1
## 41 Europe & Central Asia 7 7 4 NA 1
## 42 Middle East & North Africa 8 8 4 NA 1
## 43 Europe & Central Asia 7 7 4 NA 1
## 44 Latin America & Caribbean 14 18 9 NA 1
## 45 Middle East & North Africa 7 7 4 NA 1
## 46 Latin America & Caribbean 7 7 4 NA 1
## 47 Middle East & North Africa 5 5 5 NA 1
## 48 Sub-Saharan Africa 7 7 5 NA 1
## 49 Europe & Central Asia 5 5 3 NA 1
## 50 Europe & Central Asia 7 7 4 NA 1
## 51 Sub-Saharan Africa 8 8 4 NA 1
## 52 Europe & Central Asia 7 7 4 NA 1
## 53 East Asia & Pacific 4 4 4 NA 1
## 54 Latin America & Caribbean 12 16 8 NA NA
## 55 Europe & Central Asia 6 6 3 NA 1
## 56 Sub-Saharan Africa 5 5 5 3 1
## 57 Europe & Central Asia 14 14 4 NA 1
## 58 Europe & Central Asia 7 7 4 NA 1
## 59 Sub-Saharan Africa 5 5 5 NA 1
## 60 Sub-Saharan Africa 6 6 4 NA 1
## 61 Sub-Saharan Africa 6 10 6 NA 1
## 62 Sub-Saharan Africa 13 13 4 NA 1
## 63 Sub-Saharan Africa 10 17 6 NA 1
## 64 Europe & Central Asia 6 6 6 NA 1
## 65 Europe & Central Asia 9 9 6 NA NA
## 66 Latin America & Caribbean 9 9 5 4 1
## 67 Latin America & Caribbean 6 6 4 NA 1
## 68 Latin America & Caribbean 8 8 5 NA 1
## 69 Europe & Central Asia 7 7 4 NA 1
## 70 Latin America & Caribbean 5 5 5 NA 1
## 71 Europe & Central Asia 7 7 4 NA 1
## 72 East Asia & Pacific 9 9 5 NA 1
## 73 South Asia 5 5 5 NA 1
## 74 Europe & Central Asia 7 7 4 NA 1
## 75 Middle East & North Africa 4 4 4 NA 1
## 76 Middle East & North Africa 4 4 4 NA 1
## 77 Europe & Central Asia 7 7 7 NA 1
## 78 Middle East & North Africa 6 6 4 NA 1
## 79 Europe & Central Asia 5 5 5 NA 1
## 80 Latin America & Caribbean 7 7 4 NA 1
## 81 Middle East & North Africa 6 6 5 NA 1
## 82 East Asia & Pacific 5 5 5 NA 1
## 83 Europe & Central Asia 10 10 4 NA 1
## 84 Sub-Saharan Africa 5 5 4 NA 1
## 85 Europe & Central Asia 10 10 4 NA 1
## 86 East Asia & Pacific 8 8 5 NA 1
## 87 East Asia & Pacific 5 17 4 NA 1
## 88 Europe & Central Asia 6 6 4 NA 1
## 89 Middle East & North Africa 6 6 4 NA 1
## 90 East Asia & Pacific 7 7 4 NA 1
## 91 Middle East & North Africa 7 7 4 4 1
## 92 Sub-Saharan Africa 7 7 7 NA 1
## 93 Middle East & North Africa 5 5 5 NA 1
## 94 South Asia 9 9 6 NA 1
## 95 Sub-Saharan Africa 7 7 4 NA 1
## 96 Europe & Central Asia 9 9 5 NA 1
## 97 Europe & Central Asia 10 10 4 5 1
## 98 Europe & Central Asia 6 6 4 NA 1
## 99 Middle East & North Africa 7 7 4 NA 1
## 100 Europe & Central Asia 7 7 4 NA 1
## 101 Sub-Saharan Africa 10 10 4 NA 1
## 102 Latin America & Caribbean 6 6 4 NA 1
## 103 Europe & Central Asia 9 9 4 NA 1
## 104 Sub-Saharan Africa 4 4 4 NA 1
## 105 East Asia & Pacific 7 7 5 NA 1
## 106 Europe & Central Asia 10 10 5 NA 1
## 107 East Asia & Pacific 8 8 5 NA 1
## 108 Sub-Saharan Africa 10 10 4 NA 1
## 109 Sub-Saharan Africa 10 10 4 NA 1
## 110 Sub-Saharan Africa 6 6 4 NA 1
## 111 East Asia & Pacific 8 8 6 NA 1
## 112 Sub-Saharan Africa 7 7 4 NA 1
## 113 East Asia & Pacific 13 13 6 NA NA
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## [41,] 27.16442 -11.608748
## [42,] 27.38880 -12.132747
## [43,] 28.15511 -12.272481
## [44,] 28.52356 -12.698604
## [45,] 28.93429 -13.248958
## [46,] 29.69961 -13.257227
## [47,] 29.61600 -12.178895
## [48,] 29.34155 -12.360744
## [49,] 28.64242 -11.971569
## [50,] 28.37225 -11.793647
## [51,] 28.49607 -10.789884
## [52,] 28.67368 -9.605925
## [53,] 28.44987 -9.164918
## [54,] 28.73487 -8.526559
## [55,] 29.00291 -8.407032
## [56,] 30.34609 -8.238257
## [57,] 30.74002 -8.340007
## [58,] 31.15775 -8.594579
## [59,] 31.55635 -8.762049
## [60,] 32.19186 -8.930359
## [61,] 32.75938 -9.230599
## 
## 
## 
## Slot "plotOrder":
## [1] 1
## 
## Slot "labpt":
## [1] 27.72759 -13.39507
## 
## Slot "ID":
## [1] "175"
## 
## Slot "area":
## [1] 62.7895
## 
## 
## [[177]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] 29.78855 -18.90699
## 
## Slot "area":
## [1] 32.28037
## 
## Slot "hole":
## [1] FALSE
## 
## Slot "ringDir":
## [1] 1
## 
## Slot "coords":
## [,1] [,2]
## [1,] 31.19141 -22.25151
## [2,] 30.65987 -22.15157
## [3,] 30.32288 -22.27161
## [4,] 29.83904 -22.10222
## [5,] 29.43219 -22.09131
## [6,] 28.79466 -21.63945
## [7,] 28.02137 -21.48598
## [8,] 27.72723 -20.85180
## [9,] 27.72475 -20.49906
## [10,] 27.29650 -20.39152
## [11,] 26.16479 -19.29309
## [12,] 25.85039 -18.71441
## [13,] 25.64916 -18.53603
## [14,] 25.26423 -17.73654
## [15,] 26.38194 -17.84604
## [16,] 26.70677 -17.96123
## [17,] 27.04443 -17.93803
## [18,] 27.59824 -17.29083
## [19,] 28.46791 -16.46840
## [20,] 28.82587 -16.38975
## [21,] 28.94746 -16.04305
## [22,] 29.51683 -15.64468
## [23,] 30.27426 -15.50779
## [24,] 30.33895 -15.88084
## [25,] 31.17306 -15.86094
## [26,] 31.63650 -16.07199
## [27,] 31.85204 -16.31942
## [28,] 32.32824 -16.39207
## [29,] 32.84764 -16.71340
## [30,] 32.84986 -17.97906
## [31,] 32.65489 -18.67209
## [32,] 32.61199 -19.41938
## [33,] 32.77271 -19.71559
## [34,] 32.65974 -20.30429
## [35,] 32.50869 -20.39529
## [36,] 32.24499 -21.11649
## [37,] 31.19141 -22.25151
## 
## 
## 
## Slot "plotOrder":
## [1] 1
## 
## Slot "labpt":
## [1] 29.78855 -18.90699
## 
## Slot "ID":
## [1] "176"
## 
## Slot "area":
## [1] 32.28037
## 
## 
## 
## Slot "plotOrder":
## [1] 7 136 28 169 31 23 9 66 84 5 74 46 34 108 103 139 140 76
## [19] 94 73 175 125 155 105 2 115 36 52 22 110 119 48 30 163 123 152
## [37] 165 116 171 167 56 113 109 53 1 176 106 100 50 26 141 27 102 159
## [55] 170 85 42 157 77 83 128 147 174 127 33 132 80 58 177 121 172 20
## [73] 135 35 112 32 122 126 15 57 86 78 47 166 60 61 91 154 68 168
## [91] 142 162 87 158 120 146 65 130 17 16 149 72 117 10 41 49 38 88
## [109] 131 11 14 69 99 111 67 97 148 138 82 93 75 59 4 51 70 144
## [127] 124 44 150 19 29 95 118 156 37 45 101 13 25 3 164 6 63 104
## [145] 96 71 151 64 79 12 55 143 114 137 21 43 145 153 54 90 107 8
## [163] 18 89 160 62 81 134 92 24 129 161 173 40 133 39 98
## 
## Slot "bbox":
## min max
## x -180 180.00000
## y -90 83.64513
## 
## Slot "proj4string":
## CRS arguments:
## +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
```

---

# 6. Converter objetos para sf

## Converter `sp` para `sf`

```r
# paises sf
co110_sf <- sf::st_as_sf(co110_sp)
co110_sf
```

```
## Simple feature collection with 177 features and 63 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## First 10 features:
## scalerank featurecla labelrank sovereignt sov_a3 adm0_dif
## 0 1 Admin-0 country 3 Afghanistan AFG 0
## 1 1 Admin-0 country 3 Angola AGO 0
## 2 1 Admin-0 country 6 Albania ALB 0
## 3 1 Admin-0 country 4 United Arab Emirates ARE 0
## 4 1 Admin-0 country 2 Argentina ARG 0
## 5 1 Admin-0 country 6 Armenia ARM 0
## 6 1 Admin-0 country 4 Antarctica ATA 0
## 7 3 Admin-0 country 6 France FR1 1
## 8 1 Admin-0 country 2 Australia AU1 1
## 9 1 Admin-0 country 4 Austria AUT 0
## level type admin adm0_a3 geou_dif
## 0 2 Sovereign country Afghanistan AFG 0
## 1 2 Sovereign country Angola AGO 0
## 2 2 Sovereign country Albania ALB 0
## 3 2 Sovereign country United Arab Emirates ARE 0
## 4 2 Sovereign country Argentina ARG 0
## 5 2 Sovereign country Armenia ARM 0
## 6 2 Indeterminate Antarctica ATA 0
## 7 2 Dependency French Southern and Antarctic Lands ATF 0
## 8 2 Country Australia AUS 0
## 9 2 Sovereign country Austria AUT 0
## geounit gu_a3 su_dif
## 0 Afghanistan AFG 0
## 1 Angola AGO 0
## 2 Albania ALB 0
## 3 United Arab Emirates ARE 0
## 4 Argentina ARG 0
## 5 Armenia ARM 0
## 6 Antarctica ATA 0
## 7 French Southern and Antarctic Lands ATF 0
## 8 Australia AUS 0
## 9 Austria AUT 0
## subunit su_a3 brk_diff name
## 0 Afghanistan AFG 0 Afghanistan
## 1 Angola AGO 0 Angola
## 2 Albania ALB 0 Albania
## 3 United Arab Emirates ARE 0 United Arab Emirates
## 4 Argentina ARG 0 Argentina
## 5 Armenia ARM 0 Armenia
## 6 Antarctica ATA 0 Antarctica
## 7 French Southern and Antarctic Lands ATF 0 Fr. S. Antarctic Lands
## 8 Australia AUS 0 Australia
## 9 Austria AUT 0 Austria
## name_long brk_a3 brk_name
## 0 Afghanistan AFG Afghanistan
## 1 Angola AGO Angola
## 2 Albania ALB Albania
## 3 United Arab Emirates ARE United Arab Emirates
## 4 Argentina ARG Argentina
## 5 Armenia ARM Armenia
## 6 Antarctica ATA Antarctica
## 7 French Southern and Antarctic Lands ATF Fr. S. and Antarctic Lands
## 8 Australia AUS Australia
## 9 Austria AUT Austria
## brk_group abbrev postal
## 0 <NA> Afg. AF
## 1 <NA> Ang. AO
## 2 <NA> Alb. AL
## 3 <NA> U.A.E. AE
## 4 <NA> Arg. AR
## 5 <NA> Arm. ARM
## 6 <NA> Ant. AQ
## 7 <NA> Fr. S.A.L. TF
## 8 <NA> Auz. AU
## 9 <NA> Aust. A
## formal_en formal_fr note_adm0
## 0 Islamic State of Afghanistan <NA> <NA>
## 1 People's Republic of Angola <NA> <NA>
## 2 Republic of Albania <NA> <NA>
## 3 United Arab Emirates <NA> <NA>
## 4 Argentine Republic <NA> <NA>
## 5 Republic of Armenia <NA> <NA>
## 6 <NA> <NA> <NA>
## 7 Territory of the French Southern and Antarctic Lands <NA> Fr.
## 8 Commonwealth of Australia <NA> <NA>
## 9 Republic of Austria <NA> <NA>
## note_brk name_sort name_alt
## 0 <NA> Afghanistan <NA>
## 1 <NA> Angola <NA>
## 2 <NA> Albania <NA>
## 3 <NA> United Arab Emirates <NA>
## 4 <NA> Argentina <NA>
## 5 <NA> Armenia <NA>
## 6 Multiple claims held in abeyance Antarctica <NA>
## 7 <NA> French Southern and Antarctic Lands <NA>
## 8 <NA> Australia <NA>
## 9 <NA> Austria <NA>
## mapcolor7 mapcolor8 mapcolor9 mapcolor13 pop_est gdp_md_est pop_year
## 0 5 6 8 7 28400000 22270.0 NA
## 1 3 2 6 1 12799293 110300.0 NA
## 2 1 4 1 6 3639453 21810.0 NA
## 3 2 1 3 3 4798491 184300.0 NA
## 4 3 1 3 13 40913584 573900.0 NA
## 5 3 1 2 10 2967004 18770.0 NA
## 6 4 5 1 NA 3802 760.4 NA
## 7 7 5 9 11 140 16.0 NA
## 8 1 2 2 7 21262641 800200.0 NA
## 9 3 1 3 4 8210281 329500.0 NA
## lastcensus gdp_year economy income_grp
## 0 1979 NA 7. Least developed region 5. Low income
## 1 1970 NA 7. Least developed region 3. Upper middle income
## 2 2001 NA 6. Developing region 4. Lower middle income
## 3 2010 NA 6. Developing region 2. High income: nonOECD
## 4 2010 NA 5. Emerging region: G20 3. Upper middle income
## 5 2001 NA 6. Developing region 4. Lower middle income
## 6 NA NA 6. Developing region 2. High income: nonOECD
## 7 NA NA 6. Developing region 2. High income: nonOECD
## 8 2006 NA 2. Developed region: nonG7 1. High income: OECD
## 9 2011 NA 2. Developed region: nonG7 1. High income: OECD
## wikipedia fips_10 iso_a2 iso_a3 iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is
## 0 NA <NA> AF AFG 004 004 AF AFG NA AFG
## 1 NA <NA> AO AGO 024 024 AO AGO NA AGO
## 2 NA <NA> AL ALB 008 008 AL ALB NA ALB
## 3 NA <NA> AE ARE 784 784 AE ARE NA ARE
## 4 NA <NA> AR ARG 032 032 AR ARG NA ARG
## 5 NA <NA> AM ARM 051 051 AM ARM NA ARM
## 6 NA <NA> AQ ATA 010 <NA> <NA> <NA> NA ATA
## 7 NA <NA> TF ATF 260 <NA> <NA> <NA> NA ATF
## 8 NA <NA> AU AUS 036 036 AU AUS NA AUS
## 9 NA <NA> AT AUT 040 040 AT AUT NA AUT
## adm0_a3_us adm0_a3_un adm0_a3_wb continent
## 0 AFG NA NA Asia
## 1 AGO NA NA Africa
## 2 ALB NA NA Europe
## 3 ARE NA NA Asia
## 4 ARG NA NA South America
## 5 ARM NA NA Asia
## 6 ATA NA NA Antarctica
## 7 ATF NA NA Seven seas (open ocean)
## 8 AUS NA NA Oceania
## 9 AUT NA NA Europe
## region_un subregion region_wb
## 0 Asia Southern Asia South Asia
## 1 Africa Middle Africa Sub-Saharan Africa
## 2 Europe Southern Europe Europe & Central Asia
## 3 Asia Western Asia Middle East & North Africa
## 4 Americas South America Latin America & Caribbean
## 5 Asia Western Asia Europe & Central Asia
## 6 Antarctica Antarctica Antarctica
## 7 Seven seas (open ocean) Seven seas (open ocean) Sub-Saharan Africa
## 8 Oceania Australia and New Zealand East Asia & Pacific
## 9 Europe Western Europe Europe & Central Asia
## name_len long_len abbrev_len tiny homepart geometry
## 0 11 11 4 NA 1 MULTIPOLYGON (((61.21082 35...
## 1 6 6 4 NA 1 MULTIPOLYGON (((16.32653 -5...
## 2 7 7 4 NA 1 MULTIPOLYGON (((20.59025 41...
## 3 20 20 6 NA 1 MULTIPOLYGON (((51.57952 24...
## 4 9 9 4 NA 1 MULTIPOLYGON (((-65.5 -55.2...
## 5 7 7 4 NA 1 MULTIPOLYGON (((43.58275 41...
## 6 10 10 4 NA 1 MULTIPOLYGON (((-59.57209 -...
## 7 22 35 10 2 NA MULTIPOLYGON (((68.935 -48....
## 8 9 9 4 NA 1 MULTIPOLYGON (((145.398 -40...
## 9 7 7 5 NA 1 MULTIPOLYGON (((16.97967 48...
```

---

# 6. Converter objetos para sf

## Converter `sf` para `sp`

```r
# plot
plot(co110_sf$geometry, col = "gray", main = NA, graticule = TRUE)
```

---

# 6. Converter objetos para sf

## Converter `sf` para `sp`

```r
# paises sp
co110_sp <- sf::as_Spatial(co110_sf)
co110_sp
```

---

# 7. Converter CRS de objetos sf

## Os principais CRSs

|CRS                       |Tipo de CRS |Descrição                                                                                             |epsg.io                             |spatialreference.org                                         |
|:-------------------------|:-----------|:-----------------------------------------------------------------------------------------------------|:-----------------------------------|:------------------------------------------------------------|
|WGS84                     |Geográfico  |CRS geográfico mais comum para o mundo                                                                |[EPSG:4326](http://epsg.io/4326)    |[EPSG:4326](https://spatialreference.org/ref/epsg/4326/)     |
|SIRGAS 2000               |Geográfico  |CRS geográfico oficial para o Brasil                                                                  |[EPSG:4674](http://epsg.io/4674)    |[EPSG:4674](https://spatialreference.org/ref/epsg/4674/)     |
|Projeção de Mollweide     |Projetado   |CRS projetado que  preserva as relações de área                                                       |[ESRI:54009](https://epsg.io/54009) |[SR-ORG:7099](https://spatialreference.org/ref/sr-org/7099/) |
|Projeção de Winkel Tripel |Projetado   |CRS projetado que preserva a área e com meridianos elípticos                                          |[EPSG:54012](https://epsg.io/54012) |[ESRI:54012](https://spatialreference.org/ref/esri/54012/)   |
|Projeção Cônica de Albers |Projetado   |CRS projetado para escala regional, mantendo a área constante em toda sua superfície                  |NA                                  |[SR-ORG:7823](https://spatialreference.org/ref/sr-org/7823/) |
|UTM                       |Projetado   |CRS projetado para escala local, distorcendo áreas e distâncias com a distância do centro da zona UTM |[EPSG:31983](https://epsg.io/31983) |[EPSG:31983](https://spatialreference.org/ref/epsg/31983/)   |

---

background-image: url(img/geo_proj_sirgas2000.png)
background-size: 200px
background-position: 50% 75%

# 7. Converter CRS de objetos sf

## Converter CRS local

### SIRGAS2000/GCS -> SIRGAS2000/UTM23S

```r
# converter sistema de coordenadas
rc_2020_sirgas2000_utm23s <- sf::st_transform(rc_2020, crs = 31983)
```

[EPSG: 31983](https://epsg.io/31983)
[SIRGAS2000](http://www.sirgas.org/pt/sirgas-realizations/sirgas2000/)

---

# 7. Converter CRS de objetos sf

## Converter CRS local

### SIRGAS2000/GCS -> SIRGAS2000/UTM23S

---

background-image: url(img/geo_utm_zones.png)
background-size: 500px
background-position: 50% 75%

# 7. Converter CRS de objetos sf

## Converter CRS local

### SIRGAS2000/GCS -> WGS84/UTM23S

```r
# converter sistema de coordenadas e datum
rc_2020_wgs84_utm23s <- sf::st_transform(rc_2020, crs = 32723)
```

[EPSG: 32723](https://epsg.io/32723)
[ESRI (2021)](https://pro.arcgis.com/en/pro-app/help/mapping/properties/transverse-mercator.htm)

---

background-image: url(img/geo_geographic.png)
background-size: 500px
background-position: 50% 80%

# 7. Converter CRS de objetos sf

## Converter CRS local

### SIRGAS2000/GCS -> WGS84/GCS

```r
# converter datum
rc_2020_wgs84_gcs <- sf::st_transform(rc_2020, crs = 4326)
```

[EPSG: 4326](https://epsg.io/4326)

---

# 7. Converter CRS de objetos sf

## Converter CRS global

```r
# paises - WGS84/GCS
co110_sf
```

---

# 7. Converter CRS de objetos sf

## Converter CRS global

```r
# plot
plot(co110_sf$geometry, col = "gray", graticule = TRUE)
```

---

background-image: url(img/geo_proj_moll.png)
background-size: 450px
background-position: 50% 70%

# 7. Converter CRS de objetos sf

## Converter CRS global

### Projeção de Mollweide: preserva as relações de área

```r
# projecao mollweide 
co110_sf_moll <- sf::st_transform(co110_sf, crs = "+proj=moll")
```

[EPSG: 54009](https://epsg.io/54009)
[ESRI (2021)](https://pro.arcgis.com/en/pro-app/help/mapping/properties/mollweide.html)

---

# 7. Converter CRS de objetos sf

## Converter CRS global

### Projeção de Mollweide

```r
# plot
plot(co110_sf_moll$geometry, col = "gray", graticule = TRUE)
```

---

background-image: url(img/geo_proj_laea.png)
background-size: 200px
background-position: 50% 80%

# 7. Converter CRS de objetos sf

## Converter CRS global

### Projeção azimutal de Lambert: preserva os tamanhos relativos e senso de direção a partir do centro

```r
# projecao lambert
co110_sf_laea <- sf::st_transform(co110_sf, crs = "+proj=laea +x_0=0 +y_0=0 +lon_0=0 +lat_0=0")
```

[ESRI(2021)](https://pro.arcgis.com/en/pro-app/help/mapping/properties/lambert-azimuthal-equal-area.html)

---

# 7. Converter CRS de objetos sf

## Converter CRS global

### Projeção azimutal de Lambert

```r
# plot
plot(co110_sf_laea$geometry, col = "gray", graticule = TRUE)
```

---

background-image: url(img/geo_sf_operations.png)
background-size: 400px
background-position: 50% 90%

# 8. Operações com objetos sf

## As operações podem ser separadas em três tipos

### 1. **Operações de atributos** (informações não espaciais, como tabela de atributos)
### 2. **Operações espaciais** (informações espaciais, como localização e formato)
### 3. **Operações geométricas** (informações geométricas, como mudanças geométricas unárias e binárias)

---

background-image: url(img/geo_attribute_table.jpg)
background-size: 350px
background-position: 75% 70%

# 8. Operações com objetos sf

## 1. Operações de atributos

### Modificação de objetos espaciais baseado em **informações não espaciais** associadas a objetos sf

### **Operações**

### 1. Filtro
### 2. Junção
### 3. Agregação
### 4. Manipulação da tabela de atributos

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.1 Filtro

```r
# filtro
rc_cob_floresta <- rc_cob %>% 
 dplyr::filter(CLASSE_USO == "formação florestal")
rc_cob_floresta
```

```
## Simple feature collection with 1 feature and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 215442.4 ymin: 7504235 xmax: 246282.3 ymax: 7537969
## Projected CRS: SIRGAS 2000 / UTM zone 23S
##   GEOCODIGO MUNICIPIO UF CD_UF         CLASSE_USO AREA_HA
## 1   3543907 RIO CLARO SP    35 formação florestal 7017.99
##                         geometry
## 1 MULTIPOLYGON (((232355 7504...
```

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.1 Filtro

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_cob_floresta$geometry, col = "forestgreen", add = TRUE)
```

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.2 Junção

```r
# dados
da_classes <- tibble::tibble(CLASSE_USO = rc_cob$CLASSE_USO, 
 classe = c("agua", "antropico", "edificado", "floresta", "silvicultura"))
da_classes
```

```
## # A tibble: 5 × 2
## CLASSE_USO classe 
## <chr> <chr> 
## 1 água agua 
## 2 área antropizada antropico 
## 3 área edificada edificado 
## 4 formação florestal floresta 
## 5 silvicultura silvicultura
```

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.2 Junção

```r
# juncao
rc_cob_classes <- dplyr::left_join(rc_cob, da_classes, by = "CLASSE_USO") %>% 
 sf::st_drop_geometry()
rc_cob_classes
```

```
##   GEOCODIGO MUNICIPIO UF CD_UF         CLASSE_USO   AREA_HA       classe
## 1   3543907 RIO CLARO SP    35               água   357.027         agua
## 2   3543907 RIO CLARO SP    35   área antropizada 37297.800    antropico
## 3   3543907 RIO CLARO SP    35     área edificada  5078.330    edificado
## 4   3543907 RIO CLARO SP    35 formação florestal  7017.990     floresta
## 5   3543907 RIO CLARO SP    35       silvicultura   138.173 silvicultura
```

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.3 Agregação

```r
# agregar
rc_nas_n <- rc_nas %>% 
 dplyr::group_by(MUNICIPIO, HIDRO) %>% 
 dplyr::summarise(n = n())
rc_nas_n
```

```
## Simple feature collection with 1 feature and 3 fields
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: 217622.9 ymin: 7504132 xmax: 246367.4 ymax: 7537855
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## # A tibble: 1 × 4
## # Groups: MUNICIPIO [1]
## MUNICIPIO HIDRO n geometry
## <chr> <chr> <int> <MULTIPOINT [m]>
## 1 RIO CLARO nascente 1220 ((217622.9 7528315), (217836.5 7528103), (217988.9 7…
```

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.3 Agregação

```r
# plot 
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_nas_n$geometry, pch = 20, col = "blue", add = TRUE)
```

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.4 Manipulação da tabela de atributos

```r
# criar coluna
rc_cob_cob_col_area <- rc_cob %>% 
 dplyr::mutate(classe_area = paste0(CLASSE_USO, " (", AREA_HA, " ha)")) %>% 
 sf::st_drop_geometry()
rc_cob_cob_col_area
```

```
##   GEOCODIGO MUNICIPIO UF CD_UF         CLASSE_USO   AREA_HA
## 1   3543907 RIO CLARO SP    35               água   357.027
## 2   3543907 RIO CLARO SP    35   área antropizada 37297.800
## 3   3543907 RIO CLARO SP    35     área edificada  5078.330
## 4   3543907 RIO CLARO SP    35 formação florestal  7017.990
## 5   3543907 RIO CLARO SP    35       silvicultura   138.173
##                       classe_area
## 1               água (357.027 ha)
## 2   área antropizada (37297.8 ha)
## 3     área edificada (5078.33 ha)
## 4 formação florestal (7017.99 ha)
## 5       silvicultura (138.173 ha)
```

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.4 Manipulação da tabela de atributos

```r
# comprimento das linhas
rc_hid_comp <- rc_hid %>% 
 dplyr::mutate(comprimento = sf::st_length(.))
rc_hid_comp
```

```
## Simple feature collection with 1 feature and 7 fields
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: 215155.3 ymin: 7504132 xmax: 246367.4 ymax: 7537978
## Projected CRS: SIRGAS 2000 / UTM zone 23S
##   GEOCODIGO MUNICIPIO UF CD_UF                  HIDRO COMP_KM
## 1   3543907 RIO CLARO SP    35 curso d'água (0 - 10m) 1142.98
##                         geometry comprimento
## 1 MULTILINESTRING ((231815.7 ... 1142981 [m]
```

---

# 8. Operações com objetos sf

## 1. Operações de atributos

### 1.4 Manipulação da tabela de atributos

```r
# area das poligonos
rc_cob_area <- rc_cob %>% 
 dplyr::mutate(area_m2 = sf::st_area(.))
rc_cob_area
```

```
## Simple feature collection with 5 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 215151.7 ymin: 7503723 xmax: 246582.4 ymax: 7537978
## Projected CRS: SIRGAS 2000 / UTM zone 23S
##   GEOCODIGO MUNICIPIO UF CD_UF         CLASSE_USO   AREA_HA
## 1   3543907 RIO CLARO SP    35               água   357.027
## 2   3543907 RIO CLARO SP    35   área antropizada 37297.800
## 3   3543907 RIO CLARO SP    35     área edificada  5078.330
## 4   3543907 RIO CLARO SP    35 formação florestal  7017.990
## 5   3543907 RIO CLARO SP    35       silvicultura   138.173
##                         geometry         area_m2
## 1 MULTIPOLYGON (((235487.6 75...   3570267 [m^2]
## 2 MULTIPOLYGON (((232275 7504... 372978415 [m^2]
## 3 MULTIPOLYGON (((233123.6 75...  50783283 [m^2]
## 4 MULTIPOLYGON (((232355 7504...  70179895 [m^2]
## 5 MULTIPOLYGON (((243052.1 75...   1381726 [m^2]
```

---

background-image: url(img/geo_sf_spatial_join.webp)
background-size: 600px
background-position: 80% 75%

# 8. Operações com objetos sf

## 2 Operações espaciais

### Modificação de objetos espaciais baseado na sua **localização e formato**

### **Operações**

### 1. Filtro espacial
### 2. Junção espacial
### 3. Agregação espacial
### 4. Distância espacial

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.1 Filtro espacial

```r
# filtro espacial
sf::st_intersects(x = rc_nas, y = rc_cob_floresta)
```

```
## Sparse geometry binary predicate list of length 1220, where the
## predicate was `intersects'
## first 10 elements:
##  1: 1
##  2: 1
##  3: (empty)
##  4: 1
##  5: (empty)
##  6: (empty)
##  7: (empty)
##  8: (empty)
##  9: 1
##  10: (empty)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.1 Filtro espacial - interno

```r
# filtro espacial - interno
rc_nas_floresta_int <- rc_nas %>% 
 dplyr::filter(sf::st_intersects(x = ., y = rc_cob_floresta, sparse = FALSE))
rc_nas_floresta_int
```

```
## Simple feature collection with 169 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 217622.9 ymin: 7505568 xmax: 246181.4 ymax: 7537185
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 10 features:
##    GEOCODIGO MUNICIPIO UF CD_UF    HIDRO                 geometry
## 1    3543907 RIO CLARO SP    35 nascente POINT (217622.9 7528315)
## 2    3543907 RIO CLARO SP    35 nascente POINT (217836.5 7528103)
## 3    3543907 RIO CLARO SP    35 nascente POINT (218288.9 7528237)
## 4    3543907 RIO CLARO SP    35 nascente POINT (218583.5 7528215)
## 5    3543907 RIO CLARO SP    35 nascente POINT (219062.2 7528499)
## 6    3543907 RIO CLARO SP    35 nascente POINT (219246.2 7529011)
## 7    3543907 RIO CLARO SP    35 nascente POINT (219554.5 7528573)
## 8    3543907 RIO CLARO SP    35 nascente POINT (220534.8 7528014)
## 9    3543907 RIO CLARO SP    35 nascente POINT (220754.5 7530181)
## 10   3543907 RIO CLARO SP    35 nascente POINT (220958.3 7526278)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.1 Filtro espacial [] - interno

```r
# filtro espacial com [] - interno
rc_nas_floresta_int <- rc_nas[rc_cob_floresta, ]
rc_nas_floresta_int
```

```
## Simple feature collection with 169 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 217622.9 ymin: 7505568 xmax: 246181.4 ymax: 7537185
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 10 features:
##    GEOCODIGO MUNICIPIO UF CD_UF    HIDRO                 geometry
## 1    3543907 RIO CLARO SP    35 nascente POINT (217622.9 7528315)
## 2    3543907 RIO CLARO SP    35 nascente POINT (217836.5 7528103)
## 4    3543907 RIO CLARO SP    35 nascente POINT (218288.9 7528237)
## 9    3543907 RIO CLARO SP    35 nascente POINT (218583.5 7528215)
## 15   3543907 RIO CLARO SP    35 nascente POINT (219062.2 7528499)
## 19   3543907 RIO CLARO SP    35 nascente POINT (219246.2 7529011)
## 23   3543907 RIO CLARO SP    35 nascente POINT (219554.5 7528573)
## 46   3543907 RIO CLARO SP    35 nascente POINT (220534.8 7528014)
## 50   3543907 RIO CLARO SP    35 nascente POINT (220754.5 7530181)
## 54   3543907 RIO CLARO SP    35 nascente POINT (220958.3 7526278)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.1 Filtro espacial [] - interno

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_cob_floresta$geometry, col = "forestgreen", add = TRUE)
plot(rc_nas_floresta_int$geometry, col = "blue", pch = 20, cex = 1, add = TRUE)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.1 Filtro espacial - externo

```r
# filtro espacial - externo
rc_nas_floresta_ext <- rc_nas %>% 
 dplyr::filter(sf::st_disjoint(x = ., y = rc_cob_floresta, sparse = FALSE))
rc_nas_floresta_ext
```

```
## Simple feature collection with 1051 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 217988.9 ymin: 7504132 xmax: 246367.4 ymax: 7537855
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 10 features:
##    GEOCODIGO MUNICIPIO UF CD_UF    HIDRO                 geometry
## 1    3543907 RIO CLARO SP    35 nascente POINT (217988.9 7528203)
## 2    3543907 RIO CLARO SP    35 nascente POINT (218346.6 7530583)
## 3    3543907 RIO CLARO SP    35 nascente POINT (218393.1 7528031)
## 4    3543907 RIO CLARO SP    35 nascente POINT (218508.3 7528470)
## 5    3543907 RIO CLARO SP    35 nascente POINT (218535.4 7530642)
## 6    3543907 RIO CLARO SP    35 nascente POINT (218760.1 7530258)
## 7    3543907 RIO CLARO SP    35 nascente POINT (218821.1 7529750)
## 8    3543907 RIO CLARO SP    35 nascente POINT (218868.5 7529257)
## 9    3543907 RIO CLARO SP    35 nascente POINT (218919.2 7528697)
## 10   3543907 RIO CLARO SP    35 nascente POINT (218958.8 7527735)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.1 Filtro espacial [] - externo

```r
# filtro espacial com [] - externo
rc_nas_floresta_ext <- rc_nas[rc_cob_floresta, , op = st_disjoint]
rc_nas_floresta_ext
```

```
## Simple feature collection with 1051 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 217988.9 ymin: 7504132 xmax: 246367.4 ymax: 7537855
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 10 features:
##    GEOCODIGO MUNICIPIO UF CD_UF    HIDRO                 geometry
## 3    3543907 RIO CLARO SP    35 nascente POINT (217988.9 7528203)
## 5    3543907 RIO CLARO SP    35 nascente POINT (218346.6 7530583)
## 6    3543907 RIO CLARO SP    35 nascente POINT (218393.1 7528031)
## 7    3543907 RIO CLARO SP    35 nascente POINT (218508.3 7528470)
## 8    3543907 RIO CLARO SP    35 nascente POINT (218535.4 7530642)
## 10   3543907 RIO CLARO SP    35 nascente POINT (218760.1 7530258)
## 11   3543907 RIO CLARO SP    35 nascente POINT (218821.1 7529750)
## 12   3543907 RIO CLARO SP    35 nascente POINT (218868.5 7529257)
## 13   3543907 RIO CLARO SP    35 nascente POINT (218919.2 7528697)
## 14   3543907 RIO CLARO SP    35 nascente POINT (218958.8 7527735)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.1 Filtro espacial

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_cob_floresta$geometry, col = "forestgreen", add = TRUE)
plot(rc_nas_floresta_ext$geometry, col = "steelblue", pch = 20, cex = 1, add = TRUE)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.2 Junção espacial

```r
# juncao espacial
rc_nas_cob_jun <- rc_nas %>% 
 sf::st_join(x = ., y = rc_cob) %>% 
 dplyr::group_by(CLASSE_USO) %>% 
 dplyr::summarise(n = n())
rc_nas_cob_jun
```

```
## Simple feature collection with 5 features and 2 fields
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: 217622.9 ymin: 7504132 xmax: 246367.4 ymax: 7537855
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## # A tibble: 5 × 3
## CLASSE_USO n geometry
## <chr> <int> <MULTIPOINT [m]>
## 1 água 7 ((224989.9 7527331), (226855.6 7518588), (226859.8 7…
## 2 área antropizada 1027 ((217988.9 7528203), (218346.6 7530583), (218393.1 7…
## 3 área edificada 15 ((230717 7522791), (231255.1 7520519), (231420.5 752…
## 4 formação florestal 169 ((217622.9 7528315), (217836.5 7528103), (218288.9 7…
## 5 silvicultura 2 ((242677.3 7535898), (242885.6 7535759))
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.2 Junção espacial

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_nas_cob_jun[1], col = c("blue", "orange", "gray30", "forestgreen", "green"), pch = 20, add = TRUE)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.3 Agregação espacial

```r
# agregacao espacial
rc_cob_nas_agre <- rc_nas %>% 
 aggregate(x = ., by = rc_cob, FUN = length)
rc_cob_nas_agre
```

```
## Simple feature collection with 5 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 215151.7 ymin: 7503723 xmax: 246582.4 ymax: 7537978
## Projected CRS: SIRGAS 2000 / UTM zone 23S
##   GEOCODIGO MUNICIPIO   UF CD_UF HIDRO                       geometry
## 1         7         7    7     7     7 MULTIPOLYGON (((235487.6 75...
## 2      1027      1027 1027  1027  1027 MULTIPOLYGON (((232275 7504...
## 3        15        15   15    15    15 MULTIPOLYGON (((233123.6 75...
## 4       169       169  169   169   169 MULTIPOLYGON (((232355 7504...
## 5         2         2    2     2     2 MULTIPOLYGON (((243052.1 75...
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.3 Agregação espacial

```r
# plot
plot(rc_cob_nas_agre[1], axes = TRUE, graticule = TRUE, main = NA)
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.4 Distância espacial

```r
# distancia
rc_nas_dist_flo <- rc_nas %>% 
 dplyr::mutate(dist_flo = sf::st_distance(rc_nas, rc_cob_floresta))
rc_nas_dist_flo
```

```
## Simple feature collection with 1220 features and 6 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 217622.9 ymin: 7504132 xmax: 246367.4 ymax: 7537855
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 10 features:
##    GEOCODIGO MUNICIPIO UF CD_UF    HIDRO                 geometry      dist_flo
## 1    3543907 RIO CLARO SP    35 nascente POINT (217622.9 7528315)   0.00000 [m]
## 2    3543907 RIO CLARO SP    35 nascente POINT (217836.5 7528103)   0.00000 [m]
## 3    3543907 RIO CLARO SP    35 nascente POINT (217988.9 7528203)  37.41369 [m]
## 4    3543907 RIO CLARO SP    35 nascente POINT (218288.9 7528237)   0.00000 [m]
## 5    3543907 RIO CLARO SP    35 nascente POINT (218346.6 7530583) 714.91131 [m]
## 6    3543907 RIO CLARO SP    35 nascente POINT (218393.1 7528031)  21.87610 [m]
## 7    3543907 RIO CLARO SP    35 nascente POINT (218508.3 7528470)   0.44820 [m]
## 8    3543907 RIO CLARO SP    35 nascente POINT (218535.4 7530642) 541.45390 [m]
## 9    3543907 RIO CLARO SP    35 nascente POINT (218583.5 7528215)   0.00000 [m]
## 10   3543907 RIO CLARO SP    35 nascente POINT (218760.1 7530258) 297.01502 [m]
```

---

# 8. Operações com objetos sf

## 2 Operações espaciais

### 2.4 Distância espacial

---

background-image: url(img/geo_sf_simplify.png), url(img/geo_sf_venn.png)
background-size: 400px, 500px
background-position: 90% 85%, 50% 55%

# 8. Operações com objetos sf

## 3 Operações geométricas

### Modificação de objetos espaciais baseado em operações que mudam a **geometria do vetor**

### **Operações**

1. Simplificação
1. Centroides
1. Pontos aleatórios
1. Buffers
1. Polígono convexo
1. Polígonos de Voronoi
1. Quadrículas e hexágonos
1. União ("dissolver")
1. Recorte ("clipar")
1. Transformações de tipo

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.1 Simplificação

```r
# simplificacao
rc_hid_simplificado <- sf::st_simplify(x = rc_hid, dTolerance = 1000)
rc_hid_simplificado
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.1 Simplificação

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_hid$geometry, col = "steelblue", lwd = 2, add = TRUE)
plot(rc_hid_simplificado$geometry, col = adjustcolor("black", .7), add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.2 Centroides

```r
# centroides
rc_2020_sirgas2000_utm23s_cent <- sf::st_centroid(rc_2020_sirgas2000_utm23s)
rc_2020_sirgas2000_utm23s_cent
```

```
## Simple feature collection with 1 feature and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 234338.3 ymin: 7523516 xmax: 234338.3 ymax: 7523516
## Projected CRS: SIRGAS 2000 / UTM zone 23S
##     code_muni name_muni code_state abbrev_state name_state code_region
## 493   3543907 Rio Claro         35           SP  São Paulo           3
##     name_region                     geom
## 493     Sudeste POINT (234338.3 7523516)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.2 Centroides

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_2020_sirgas2000_utm23s_cent$geom, cex = 3, pch = 20, add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.3 Pontos aleatórios

```r
# fixar amostragem
set.seed(42)

# pontos aleatorios
rc_2020_sirgas2000_utm23s_pontos_aleatorios <- sf::st_sample(rc_2020_sirgas2000_utm23s, size = 30)
rc_2020_sirgas2000_utm23s_pontos_aleatorios
```

```
## Geometry set for 30 features 
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 223360.4 ymin: 7505201 xmax: 243540.8 ymax: 7536696
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 5 geometries:
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.3 Pontos aleatórios

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_2020_sirgas2000_utm23s_pontos_aleatorios, pch = 20, add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.4 Buffer

```r
# buffer
rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer <- sf::st_buffer(x = rc_2020_sirgas2000_utm23s_pontos_aleatorios, dist = 1000)
rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer
```

```
## Geometry set for 30 features 
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 222360.4 ymin: 7504201 xmax: 244540.8 ymax: 7537696
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 5 geometries:
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.4 Buffer

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer, col = NA, lwd = 2, border = "red", add = TRUE)
plot(rc_2020_sirgas2000_utm23s_pontos_aleatorios, pch = 20, cex = 1, add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.5 Polígono convexo

```r
# poligono convexo
rc_2020_sirgas2000_utm23s_convexo <- rc_2020_sirgas2000_utm23s_pontos_aleatorios %>% 
 sf::st_union() %>% 
 sf::st_convex_hull()
rc_2020_sirgas2000_utm23s_convexo 
```

```
## Geometry set for 1 feature 
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 223360.4 ymin: 7505201 xmax: 243540.8 ymax: 7536696
## Projected CRS: SIRGAS 2000 / UTM zone 23S
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.5 Polígono convexo

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_2020_sirgas2000_utm23s_convexo, col = NA, lwd = 2, border = "red", add = TRUE)
plot(rc_2020_sirgas2000_utm23s_pontos_aleatorios, pch = 20, cex = 1, add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.6 Polígonos de Voronoi

```r
# poligono de voronoi
rc_2020_sirgas2000_utm23s_voronoi <- rc_2020_sirgas2000_utm23s_pontos_aleatorios %>% 
 sf::st_union() %>% 
 sf::st_voronoi()
rc_2020_sirgas2000_utm23s_voronoi
```

```
## Geometry set for 1 feature 
## Geometry type: GEOMETRYCOLLECTION
## Dimension:     XY
## Bounding box:  xmin: 191865.2 ymin: 7473706 xmax: 275036.1 ymax: 7568192
## Projected CRS: SIRGAS 2000 / UTM zone 23S
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.6 Polígonos de Voronoi

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_2020_sirgas2000_utm23s_voronoi, col = NA, lwd = 2, border = "red", add = TRUE)
plot(rc_2020_sirgas2000_utm23s_pontos_aleatorios, pch = 20, cex = 1, add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.7 Quadrículas e hexágonos

```r
# quadriculas
rc_2020_sirgas2000_utm23s_grid <- sf::st_make_grid(x = rc_2020_sirgas2000_utm23s, cellsize = 2000, what = "polygons") %>%
 sf::st_as_sf() %>%
 dplyr::filter(sf::st_intersects(x = ., y = rc_2020_sirgas2000_utm23s, sparse = FALSE))

# centroides das quadriculas
rc_2020_sirgas2000_utm23s_grid_cent <- rc_2020_sirgas2000_utm23s %>% 
 sf::st_make_grid(cellsize = 2000, what = "centers") %>%
 sf::st_as_sf() %>%
 dplyr::filter(sf::st_intersects(x = ., y = sf::st_union(rc_2020_sirgas2000_utm23s_grid), sparse = FALSE))
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.7 Quadrículas e hexágonos

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_2020_sirgas2000_utm23s_grid, col = NA, border = "red", lwd = 2, add = TRUE)
plot(rc_2020_sirgas2000_utm23s_grid_cent, pch = 20, add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.7 Quadrículas e hexágonos

```r
# hexagonos
rc_2020_sirgas2000_utm23s_hex <- rc_2020_sirgas2000_utm23s %>% 
 sf::st_make_grid(cellsize = 2000, square = FALSE) %>% 
 sf::st_as_sf() %>%
 dplyr::filter(sf::st_intersects(x = ., y = rc_2020_sirgas2000_utm23s, sparse = FALSE))

# centroides de hexagonos
rc_2020_sirgas2000_utm23s_hex_cent <- rc_2020_sirgas2000_utm23s %>% 
 sf::st_make_grid(cellsize = 2000, square = FALSE, what = "centers") %>% 
 sf::st_as_sf() %>% 
 dplyr::filter(sf::st_intersects(x = ., y = sf::st_union(rc_2020_sirgas2000_utm23s_hex), sparse = FALSE))
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.7 Quadrículas e hexágonos

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_2020_sirgas2000_utm23s_hex, col = NA, border = "red", lwd = 2, add = TRUE)
plot(rc_2020_sirgas2000_utm23s_hex_cent, pch = 20, add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.8 União ("dissolver")

```r
# uniao
rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer_uniao <- sf::st_union(rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer)
rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer_uniao
```

```
## Geometry set for 1 feature 
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 222360.4 ymin: 7504201 xmax: 244540.8 ymax: 7537696
## Projected CRS: SIRGAS 2000 / UTM zone 23S
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.8 União ("dissolver")

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer_uniao, col = adjustcolor("blue", .1), add = TRUE)
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.9 Recorte ("clipar")

```r
# recorte - interseccao
rc_hid_interseccao <- sf::st_intersection(x = rc_hid, y = rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer_uniao)
rc_hid_interseccao
```

```
## Simple feature collection with 1 feature and 6 fields
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: 222421.9 ymin: 7504269 xmax: 244527.4 ymax: 7537655
## Projected CRS: SIRGAS 2000 / UTM zone 23S
##   GEOCODIGO MUNICIPIO UF CD_UF                  HIDRO COMP_KM
## 1   3543907 RIO CLARO SP    35 curso d'água (0 - 10m) 1142.98
##                         geometry
## 1 MULTILINESTRING ((231820 75...
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.9 Recorte ("clipar")

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.9 Recorte ("clipar") - diferença

```r
# recorte - diferenca
rc_hid_diferenca <- sf::st_difference(x = rc_hid, y = rc_2020_sirgas2000_utm23s_pontos_aleatorios_buffer_uniao)
rc_hid_diferenca
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.9 Recorte ("clipar") - diferença

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.10 Transformações de tipo

```r
# transformacao de tipo
rc_cob_floresta_polygon <- rc_cob_floresta %>% 
 sf::st_cast("POLYGON") %>% 
 dplyr::mutate(area_ha = sf::st_area(.)/1e4 %>% round(2))
rc_cob_floresta_polygon
```

```
## Simple feature collection with 1732 features and 7 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 215442.4 ymin: 7504235 xmax: 246282.3 ymax: 7537969
## Projected CRS: SIRGAS 2000 / UTM zone 23S
## First 10 features:
##     GEOCODIGO MUNICIPIO UF CD_UF         CLASSE_USO AREA_HA
## 1     3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.1   3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.2   3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.3   3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.4   3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.5   3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.6   3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.7   3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.8   3543907 RIO CLARO SP    35 formação florestal 7017.99
## 1.9   3543907 RIO CLARO SP    35 formação florestal 7017.99
##                           geometry            area_ha
## 1   POLYGON ((232355 7504440, 2... 3.232500e+00 [m^2]
## 1.1 POLYGON ((229470 7505200, 2... 7.015000e+00 [m^2]
## 1.2 POLYGON ((231185 7505305, 2... 2.992500e+00 [m^2]
## 1.3 POLYGON ((232175 7505460, 2... 3.750002e-01 [m^2]
## 1.4 POLYGON ((231720 7505435, 2... 5.017500e+00 [m^2]
## 1.5 POLYGON ((228495 7505505, 2... 5.553675e-02 [m^2]
## 1.6 POLYGON ((232120 7505525, 2... 3.275000e-01 [m^2]
## 1.7 POLYGON ((228225 7505650, 2... 1.523472e-04 [m^2]
## 1.8 POLYGON ((228215 7505655, 2... 3.237210e-04 [m^2]
## 1.9 POLYGON ((229945 7504655, 2... 3.801250e+01 [m^2]
```

---

# 8. Operações com objetos sf

## 3 Operações geométricas

### 3.10 Transformações de tipo

```r
# plot
plot(rc_2020_sirgas2000_utm23s$geom, col = "gray", main = NA, axes = TRUE, graticule = TRUE)
plot(rc_cob_floresta_polygon["area_ha"], col = viridis::viridis(100),  add = TRUE)
```

---

background-image: url(img/geo_shp_logo.png), url(img/geo_shp_formats.png)
background-size: 150px, 450px
background-position: 50% 50%, 50% 85%

# 9. Exportar objetos sf

## Exportar como shapefile

```r
# exportar o vetor de floresta na extensão esri shapefile
sf::st_write(obj = rc_cob_floresta_polygon, dsn = here::here("03_dados", "vetor", "rc_cob_floresta_polygon.shp"))
```

---

background-image: url(img/geo_ban_shp.png), url(img/geo_geopackage.png)
background-size: 250px, 600px
background-position: 15% 65%, 85% 70%

# 9. Exportar objetos sf

## Exportar como geopackage

```r
# exportar o vetor de floresta na extensão geopackage
sf::st_write(obj = rc_cob_floresta_polygon, dsn = here::here("03_dados", "vetor", "vetores.gpkg"), layer = "rc_cob_floresta_polygon")
```

[Switch from Shapefile](http://switchfromshapefile.org/),
[Using the Geopackage Format with R](https://jsta.rbind.io/blog/2016-07-14-geopackage-r/)

---

background-image: url(img/geo_ban_shp.png), url(img/geo_geopackage.png)
background-size: 250px, 600px
background-position: 15% 65%, 85% 70%

# 9. Exportar objetos sf

## Exportar como geopackage

```r
# exportar o vetor da america do sul na extensão geopackage
sf::st_write(obj = sa, dsn = here::here("03_dados", "vetor", "vetores.gpkg"), layer = "south_america")
```

[Switch from Shapefile](http://switchfromshapefile.org/),
[Using the Geopackage Format with R](https://jsta.rbind.io/blog/2016-07-14-geopackage-r/)

---

class: inverse, middle, center

# Dúvidas?

---

class: clear, middle
background-image: url(img/gif_frog.gif),url(img/gif_frogs.gif), url(img/package_xaringan.png)
background-size: 250px, 500px, 130px
background-position: 35% 50%, 90% 55%, 5% 86%

## Maurício Vancine

Contatos:

Slides criados via pacote [xaringan](https://github.com/yihui/xaringan) e tema [Metropolis](https://github.com/pat-s/xaringan-metropolis). Animação dos sapos por [@probzz](https://twitter.com/probzz/status/1367613720294170627).