Spatial_Info_Analysis CH2

code
Spatial_Info_Analysis
rstudio
Author

Seongtaek

Published

April 22, 2023

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속성 데이터 - 벡터 & 래스터

1 공간 정보 분석 CH3 : Attribute data operations

2 패키지 불러오기

library(sf)
library(raster)
library(dplyr)
library(stringr) # for working with strings (pattern matching)
library(tidyr)   # for unite() and separate()
library(spData)  # 데이터 세트를 로드

3 3.1 속성데이터 (벡터 vs 래스터)

  • 지리공간 벡터 데이터
    • 점, 선, 면 등의 리스트로 이뤄진 지리공간 데이터
    • 지리공간 데이터 제외한 속성데이터
  • sf 객체: 벡터 데이터에서 속성 정보 가져옴, 클래스 지원
    • 속성 정보만 가져오기 : st_drop_geometry()
    • base R 구문, dplyr : 벡더 데이터 속성 정보의 dim
### sf 객체 methods
methods(class = 'sf') %>% head()
[1] "$<-.sf"       "[.sf"         "[[<-.sf"      "aggregate.sf" "anti_join.sf"
[6] "arrange.sf"  
methods(class = 'sf') %>% tail()
[1] "summarise.sf" "transform.sf" "transmute.sf" "ungroup.sf"   "unite.sf"    
[6] "unnest.sf"   
length(methods(class = 'sf'))
[1] 123

4 spData : 세계 나라 데이터(world)

### 10개의 속성 데이터, 1개의 지리기하 열

dim(world)
[1] 177  11
world
Simple feature collection with 177 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 11
   iso_a2 name_l…¹ conti…² regio…³ subre…⁴ type  area_…⁵     pop lifeExp gdpPe…⁶
 * <chr>  <chr>    <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji     Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0   8222.
 2 TZ     Tanzania Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2   2402.
 3 EH     Western… Africa  Africa  Northe… Inde…  9.63e4 NA         NA       NA 
 4 CA     Canada   North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0  43079.
 5 US     United … North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8  51922.
 6 KZ     Kazakhs… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6  23587.
 7 UZ     Uzbekis… Asia    Asia    Centra… Sove…  4.61e5  3.08e7    71.0   5371.
 8 PG     Papua N… Oceania Oceania Melane… Sove…  4.65e5  7.76e6    65.2   3709.
 9 ID     Indones… Asia    Asia    South-… Sove…  1.82e6  2.55e8    68.9  10003.
10 AR     Argenti… South … Americ… South … Sove…  2.78e6  4.30e7    76.3  18798.
# … with 167 more rows, 1 more variable: geom <MULTIPOLYGON [°]>, and
#   abbreviated variable names ¹​name_long, ²​continent, ³​region_un, ⁴​subregion,
#   ⁵​area_km2, ⁶​gdpPercap
names(world)
 [1] "iso_a2"    "name_long" "continent" "region_un" "subregion" "type"     
 [7] "area_km2"  "pop"       "lifeExp"   "gdpPercap" "geom"

5 sf 객체에서 속성 정보만 가져오기 : st_drop_geometry()

  • 점, 선, 면 등의 지리기하 데이터를 리스트로 갖는 geom 컬럼 항상 존재
  • geom 컬럼을 제거하고 나머지 속성 정보만으로 d.f 만들 때 사용
  • geom 컬럼 : 지리기하 리스트 정보를 가지기때문에 메모리 점유 큼
  • 사용할 필요 없으면 웬만하면 제거하기
### geom 컬럼 제거

world_df = st_drop_geometry(world)

world_df
# A tibble: 177 × 10
   iso_a2 name_l…¹ conti…² regio…³ subre…⁴ type  area_…⁵     pop lifeExp gdpPe…⁶
 * <chr>  <chr>    <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji     Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0   8222.
 2 TZ     Tanzania Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2   2402.
 3 EH     Western… Africa  Africa  Northe… Inde…  9.63e4 NA         NA       NA 
 4 CA     Canada   North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0  43079.
 5 US     United … North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8  51922.
 6 KZ     Kazakhs… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6  23587.
 7 UZ     Uzbekis… Asia    Asia    Centra… Sove…  4.61e5  3.08e7    71.0   5371.
 8 PG     Papua N… Oceania Oceania Melane… Sove…  4.65e5  7.76e6    65.2   3709.
 9 ID     Indones… Asia    Asia    South-… Sove…  1.82e6  2.55e8    68.9  10003.
10 AR     Argenti… South … Americ… South … Sove…  2.78e6  4.30e7    76.3  18798.
# … with 167 more rows, and abbreviated variable names ¹​name_long, ²​continent,
#   ³​region_un, ⁴​subregion, ⁵​area_km2, ⁶​gdpPercap
class(world_df)
[1] "tbl_df"     "tbl"        "data.frame"
names(world_df)
 [1] "iso_a2"    "name_long" "continent" "region_un" "subregion" "type"     
 [7] "area_km2"  "pop"       "lifeExp"   "gdpPercap"

6 Base R 구문으로 벡터 데이터 속성정보의 차원 가져오기

  • 행, 열 가져올때 df[i,j], subset() 등 활용
    • i행, j열에는 정수로 위치 파악
    • j열의 이름 사용
    • 논리 벡터 사용가능 subset 등
  • dplyr 패키지 : select(), filter(), pull() 등 _ 특정 행, 열의 부분집합 가져오면 끝에 geom 칼럼 따라옴

6.0.1 1. 위치 지정해서 지리공간 벡터 데이터 가져오기

### 1행 ~ 6행 정보
world[1:6,]
Simple feature collection with 6 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -18.28799 xmax: 180 ymax: 83.23324
Geodetic CRS:  WGS 84
# A tibble: 6 × 11
  iso_a2 name_long conti…¹ regio…² subre…³ type  area_…⁴     pop lifeExp gdpPe…⁵
  <chr>  <chr>     <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
1 FJ     Fiji      Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0   8222.
2 TZ     Tanzania  Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2   2402.
3 EH     Western … Africa  Africa  Northe… Inde…  9.63e4 NA         NA       NA 
4 CA     Canada    North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0  43079.
5 US     United S… North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8  51922.
6 KZ     Kazakhst… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6  23587.
# … with 1 more variable: geom <MULTIPOLYGON [°]>, and abbreviated variable
#   names ¹​continent, ²​region_un, ³​subregion, ⁴​area_km2, ⁵​gdpPercap
### 1열 ~ 3열 정보
world[,1:3]
Simple feature collection with 177 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 4
   iso_a2 name_long        continent                                        geom
   <chr>  <chr>            <chr>                              <MULTIPOLYGON [°]>
 1 FJ     Fiji             Oceania       (((-180 -16.55522, -179.9174 -16.50178…
 2 TZ     Tanzania         Africa        (((33.90371 -0.95, 31.86617 -1.02736, …
 3 EH     Western Sahara   Africa        (((-8.66559 27.65643, -8.817828 27.656…
 4 CA     Canada           North America (((-132.71 54.04001, -133.18 54.16998,…
 5 US     United States    North America (((-171.7317 63.78252, -171.7911 63.40…
 6 KZ     Kazakhstan       Asia          (((87.35997 49.21498, 86.82936 49.8266…
 7 UZ     Uzbekistan       Asia          (((55.96819 41.30864, 57.09639 41.3223…
 8 PG     Papua New Guinea Oceania       (((141.0002 -2.600151, 141.0171 -5.859…
 9 ID     Indonesia        Asia          (((104.37 -1.084843, 104.0108 -1.05921…
10 AR     Argentina        South America (((-68.63401 -52.63637, -68.63335 -54.…
# … with 167 more rows

6.0.2 2. 열이름 사용해서 지리공간 벡터 데이터 열 가져오기

world[, c('name_long', 'lifeExp')]
Simple feature collection with 177 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 3
   name_long        lifeExp                                                 geom
   <chr>              <dbl>                                   <MULTIPOLYGON [°]>
 1 Fiji                70.0 (((-180 -16.55522, -179.9174 -16.50178, -179.7933 -…
 2 Tanzania            64.2 (((33.90371 -0.95, 31.86617 -1.02736, 30.76986 -1.0…
 3 Western Sahara      NA   (((-8.66559 27.65643, -8.817828 27.65643, -8.794884…
 4 Canada              82.0 (((-132.71 54.04001, -133.18 54.16998, -133.2397 53…
 5 United States       78.8 (((-171.7317 63.78252, -171.7911 63.40585, -171.553…
 6 Kazakhstan          71.6 (((87.35997 49.21498, 86.82936 49.82667, 85.54127 4…
 7 Uzbekistan          71.0 (((55.96819 41.30864, 57.09639 41.32231, 56.93222 4…
 8 Papua New Guinea    65.2 (((141.0002 -2.600151, 141.0171 -5.859022, 141.0339…
 9 Indonesia           68.9 (((104.37 -1.084843, 104.0108 -1.059212, 103.4376 -…
10 Argentina           76.3 (((-68.63401 -52.63637, -68.63335 -54.8695, -67.562…
# … with 167 more rows

6.1 3. 논리 벡터 사용해서 데이터 가져오기

sel_area <-  world$area_km2 < 10000
summary(sel_area)
   Mode   FALSE    TRUE 
logical     170       7 
small_countries <-  world[sel_area, ]
small_countries
Simple feature collection with 7 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -67.24243 ymin: -16.59785 xmax: 167.8449 ymax: 50.12805
Geodetic CRS:  WGS 84
# A tibble: 7 × 11
  iso_a2 name_long conti…¹ regio…² subre…³ type  area_…⁴     pop lifeExp gdpPe…⁵
  <chr>  <chr>     <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
1 PR     Puerto R… North … Americ… Caribb… Depe…   9225. 3534874    79.4  35066.
2 PS     Palestine Asia    Asia    Wester… Disp…   5037. 4294682    73.1   4320.
3 VU     Vanuatu   Oceania Oceania Melane… Sove…   7490.  258850    71.7   2892.
4 LU     Luxembou… Europe  Europe  Wester… Sove…   2417.  556319    82.2  93655.
5 <NA>   Northern… Asia    Asia    Wester… Sove…   3786.      NA    NA       NA 
6 CY     Cyprus    Asia    Asia    Wester… Sove…   6207. 1152309    80.2  29786.
7 TT     Trinidad… North … Americ… Caribb… Sove…   7738. 1354493    70.4  31182.
# … with 1 more variable: geom <MULTIPOLYGON [°]>, and abbreviated variable
#   names ¹​continent, ²​region_un, ³​subregion, ⁴​area_km2, ⁵​gdpPercap
small_countries <- world[world$area_km2 < 10000, ]
small_countries
Simple feature collection with 7 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -67.24243 ymin: -16.59785 xmax: 167.8449 ymax: 50.12805
Geodetic CRS:  WGS 84
# A tibble: 7 × 11
  iso_a2 name_long conti…¹ regio…² subre…³ type  area_…⁴     pop lifeExp gdpPe…⁵
  <chr>  <chr>     <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
1 PR     Puerto R… North … Americ… Caribb… Depe…   9225. 3534874    79.4  35066.
2 PS     Palestine Asia    Asia    Wester… Disp…   5037. 4294682    73.1   4320.
3 VU     Vanuatu   Oceania Oceania Melane… Sove…   7490.  258850    71.7   2892.
4 LU     Luxembou… Europe  Europe  Wester… Sove…   2417.  556319    82.2  93655.
5 <NA>   Northern… Asia    Asia    Wester… Sove…   3786.      NA    NA       NA 
6 CY     Cyprus    Asia    Asia    Wester… Sove…   6207. 1152309    80.2  29786.
7 TT     Trinidad… North … Americ… Caribb… Sove…   7738. 1354493    70.4  31182.
# … with 1 more variable: geom <MULTIPOLYGON [°]>, and abbreviated variable
#   names ¹​continent, ²​region_un, ³​subregion, ⁴​area_km2, ⁵​gdpPercap
small_countries <- subset(world, area_km2 < 10000)
small_countries
Simple feature collection with 7 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -67.24243 ymin: -16.59785 xmax: 167.8449 ymax: 50.12805
Geodetic CRS:  WGS 84
# A tibble: 7 × 11
  iso_a2 name_long conti…¹ regio…² subre…³ type  area_…⁴     pop lifeExp gdpPe…⁵
  <chr>  <chr>     <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
1 PR     Puerto R… North … Americ… Caribb… Depe…   9225. 3534874    79.4  35066.
2 PS     Palestine Asia    Asia    Wester… Disp…   5037. 4294682    73.1   4320.
3 VU     Vanuatu   Oceania Oceania Melane… Sove…   7490.  258850    71.7   2892.
4 LU     Luxembou… Europe  Europe  Wester… Sove…   2417.  556319    82.2  93655.
5 <NA>   Northern… Asia    Asia    Wester… Sove…   3786.      NA    NA       NA 
6 CY     Cyprus    Asia    Asia    Wester… Sove…   6207. 1152309    80.2  29786.
7 TT     Trinidad… North … Americ… Caribb… Sove…   7738. 1354493    70.4  31182.
# … with 1 more variable: geom <MULTIPOLYGON [°]>, and abbreviated variable
#   names ¹​continent, ²​region_un, ³​subregion, ⁴​area_km2, ⁵​gdpPercap

7 dplyr 패키지로 벡터 데이터 속성정보 차원 가져오기

  • 가독성 좋고 속도 빠름
  • 체인 (%>%) 사용가능
  • select(), slice(), filter(), pull() 등의 함수 사용가능
### select로 강력하게 지정가능

# 선택지정
world1 <- select(world, name_long, pop)
names(world1)
[1] "name_long" "pop"       "geom"     
# 범위지정
world2 <- select(world, name_long:pop)
names(world2)
[1] "name_long" "continent" "region_un" "subregion" "type"      "area_km2" 
[7] "pop"       "geom"     
# 컬럼 번호로 선택지정
world3 <- select(world,2,7)
names(world3)
[1] "name_long" "area_km2"  "geom"     
# 특정 컬럼 제외
world4 <- select(world, -subregion, -area_km2)
names(world4)
[1] "iso_a2"    "name_long" "continent" "region_un" "type"      "pop"      
[7] "lifeExp"   "gdpPercap" "geom"     
# 컬럼 이름 재설정
world5 <- select(world, name_long, population = pop)
names(world5)
[1] "name_long"  "population" "geom"      
# 특정 단어가 포함된 열 추출
world6 <- select(world, contains('Ex')) 
names(world6)
[1] "lifeExp" "geom"   
# 특정 단어로 시작하는 열 추출
world7  <- select(world, starts_with('life')) 
names(world7)
[1] "lifeExp" "geom"   
# 특정 단어로 끝나는 열 추출
world8 <-  select(world, ends_with('Exp')) 
names(world8)
[1] "lifeExp" "geom"   
# 특정 단어의 소문자 or 대문자 포함된 열 추출
world9 <-  select(world, matches("[p]")) 
names(world9)
[1] "type"      "pop"       "lifeExp"   "gdpPercap" "geom"     
### num_range() 함수
x1 <- c(1:5) 
x2 <- c(6:10) 
x3 <- c(11:15) 
x4 <- c(16:20) 
x5 <- c(21:25)
df <- data.frame(x1, x2, x3, x4, x5) 
df
  x1 x2 x3 x4 x5
1  1  6 11 16 21
2  2  7 12 17 22
3  3  8 13 18 23
4  4  9 14 19 24
5  5 10 15 20 25
df2 <- select(df, num_range("x", 1:3))
df2
  x1 x2 x3
1  1  6 11
2  2  7 12
3  3  8 13
4  4  9 14
5  5 10 15
### filter() 함수 subset과 비슷

# 조건 걸기
world10  <-  filter(world, lifeExp > 82)
world10
Simple feature collection with 9 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -24.32618 ymin: -43.6346 xmax: 153.5695 ymax: 69.10625
Geodetic CRS:  WGS 84
# A tibble: 9 × 11
  iso_a2 name_long  conti…¹ regio…² subre…³ type  area_…⁴    pop lifeExp gdpPe…⁵
* <chr>  <chr>      <chr>   <chr>   <chr>   <chr>   <dbl>  <dbl>   <dbl>   <dbl>
1 IL     Israel     Asia    Asia    Wester… Coun…  2.30e4 8.22e6    82.2  31702.
2 SE     Sweden     Europe  Europe  Northe… Sove…  4.51e5 9.70e6    82.3  44168.
3 CH     Switzerla… Europe  Europe  Wester… Sove…  4.62e4 8.19e6    83.2  57218.
4 LU     Luxembourg Europe  Europe  Wester… Sove…  2.42e3 5.56e5    82.2  93655.
5 ES     Spain      Europe  Europe  Southe… Sove…  5.02e5 4.65e7    83.2  31195.
6 AU     Australia  Oceania Oceania Austra… Coun…  7.69e6 2.35e7    82.3  43547.
7 IT     Italy      Europe  Europe  Southe… Sove…  3.15e5 6.08e7    83.1  33946.
8 IS     Iceland    Europe  Europe  Northe… Sove…  1.08e5 3.27e5    82.9  41701.
9 JP     Japan      Asia    Asia    Easter… Sove…  4.05e5 1.27e8    83.6  37337.
# … with 1 more variable: geom <MULTIPOLYGON [°]>, and abbreviated variable
#   names ¹​continent, ²​region_un, ³​subregion, ⁴​area_km2, ⁵​gdpPercap
### 체인(%>%) 활용, 가독성 좋음

world11 <-  world %>%
  filter(continent == "Asia") %>%
  select(name_long, continent) %>%
  slice(1:5)
world11
Simple feature collection with 5 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 34.26543 ymin: -10.35999 xmax: 141.0339 ymax: 55.38525
Geodetic CRS:  WGS 84
# A tibble: 5 × 3
  name_long   continent                                                     geom
  <chr>       <chr>                                           <MULTIPOLYGON [°]>
1 Kazakhstan  Asia      (((87.35997 49.21498, 86.82936 49.82667, 85.54127 49.69…
2 Uzbekistan  Asia      (((55.96819 41.30864, 57.09639 41.32231, 56.93222 41.82…
3 Indonesia   Asia      (((104.37 -1.084843, 104.0108 -1.059212, 103.4376 -0.71…
4 Timor-Leste Asia      (((124.9687 -8.89279, 125.07 -9.089987, 125.0885 -9.393…
5 Israel      Asia      (((35.71992 32.70919, 35.7008 32.71601, 35.8364 32.8681…
### aggregate() 함수 사용해서 그룹별로 집계

# x(속성정보:인구) ~ group(그룹:대륙), sum집계
world_agg1  <-  aggregate(pop ~ continent, FUN = sum, data = world, na.rm = TRUE)

world_agg1
      continent        pop
1        Africa 1154946633
2          Asia 4311408059
3        Europe  669036256
4 North America  565028684
5       Oceania   37757833
6 South America  412060811
str(world_agg1)
'data.frame':   6 obs. of  2 variables:
 $ continent: chr  "Africa" "Asia" "Europe" "North America" ...
 $ pop      : num  1.15e+09 4.31e+09 6.69e+08 5.65e+08 3.78e+07 ...
class(world_agg1)
[1] "data.frame"
### 2번째 방법
world_agg2  <-  aggregate(world["pop"], by = list(world$continent),
                       FUN = sum, na.rm = TRUE)
world_agg2
Simple feature collection with 8 features and 2 fields
Attribute-geometry relationship: 0 constant, 1 aggregate, 1 identity
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
                  Group.1        pop                       geometry
1                  Africa 1154946633 MULTIPOLYGON (((11.91496 -5...
2              Antarctica          0 MULTIPOLYGON (((-61.13898 -...
3                    Asia 4311408059 MULTIPOLYGON (((120.295 -10...
4                  Europe  669036256 MULTIPOLYGON (((-53.77852 2...
5           North America  565028684 MULTIPOLYGON (((-80.9473 8....
6                 Oceania   37757833 MULTIPOLYGON (((171.9487 -4...
7 Seven seas (open ocean)          0 POLYGON ((68.935 -48.625, 6...
8           South America  412060811 MULTIPOLYGON (((-57.75 -51....
class(world_agg2)
[1] "sf"         "data.frame"
# sf 객체는 집계 결과가 sf 객체로 반환
world['pop']
Simple feature collection with 177 features and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 2
         pop                                                                geom
       <dbl>                                                  <MULTIPOLYGON [°]>
 1    885806 (((-180 -16.55522, -179.9174 -16.50178, -179.7933 -16.02088, -180 …
 2  52234869 (((33.90371 -0.95, 31.86617 -1.02736, 30.76986 -1.01455, 30.4191 -…
 3        NA (((-8.66559 27.65643, -8.817828 27.65643, -8.794884 27.1207, -9.41…
 4  35535348 (((-132.71 54.04001, -133.18 54.16998, -133.2397 53.85108, -133.05…
 5 318622525 (((-171.7317 63.78252, -171.7911 63.40585, -171.5531 63.31779, -17…
 6  17288285 (((87.35997 49.21498, 86.82936 49.82667, 85.54127 49.69286, 85.115…
 7  30757700 (((55.96819 41.30864, 57.09639 41.32231, 56.93222 41.82603, 57.786…
 8   7755785 (((141.0002 -2.600151, 141.0171 -5.859022, 141.0339 -9.117893, 142…
 9 255131116 (((104.37 -1.084843, 104.0108 -1.059212, 103.4376 -0.7119459, 103.…
10  42981515 (((-68.63401 -52.63637, -68.63335 -54.8695, -67.56244 -54.87001, -…
# … with 167 more rows
class(world['pop'])
[1] "sf"         "tbl_df"     "tbl"        "data.frame"
# 숫자형 벡터는 aggregate 적용하면 d.f로 반환
world$pop %>% head()
[1]    885806  52234869        NA  35535348 318622525  17288285
class(world$pop)
[1] "numeric"
### group_by + summarize : aggregate보다 가독성 좋음

# 대륙별 인구의 합계
world_agg3  <-  world %>%
  group_by(continent) %>%
  summarize(pop = sum(pop, na.rm = TRUE))
world_agg3
Simple feature collection with 8 features and 2 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 8 × 3
  continent                      pop                                        geom
  <chr>                        <dbl>                              <GEOMETRY [°]>
1 Africa                  1154946633 MULTIPOLYGON (((36.86623 22, 36.69069 22.2…
2 Antarctica                       0 MULTIPOLYGON (((-180 -89.9, 180 -89.9, 180…
3 Asia                    4311408059 MULTIPOLYGON (((36.14976 35.82153, 35.9050…
4 Europe                   669036256 MULTIPOLYGON (((26.29 35.29999, 25.74502 3…
5 North America            565028684 MULTIPOLYGON (((-82.26815 23.18861, -82.51…
6 Oceania                   37757833 MULTIPOLYGON (((166.7932 -15.66881, 167.00…
7 Seven seas (open ocean)          0 POLYGON ((68.935 -48.625, 68.8675 -48.83, …
8 South America            412060811 MULTIPOLYGON (((-66.95992 -54.89681, -66.4…
# 대륙별 인구의 합계 + 대륙별 국가 수
world_agg4  <-  world %>%
  group_by(continent) %>%
  summarize(pop = sum(pop, na.rm = TRUE), n_countries = n())
world_agg4
Simple feature collection with 8 features and 3 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 8 × 4
  continent                      pop n_countries                            geom
  <chr>                        <dbl>       <int>                  <GEOMETRY [°]>
1 Africa                  1154946633          51 MULTIPOLYGON (((36.86623 22, 3…
2 Antarctica                       0           1 MULTIPOLYGON (((-180 -89.9, 18…
3 Asia                    4311408059          47 MULTIPOLYGON (((36.14976 35.82…
4 Europe                   669036256          39 MULTIPOLYGON (((26.29 35.29999…
5 North America            565028684          18 MULTIPOLYGON (((-82.26815 23.1…
6 Oceania                   37757833           7 MULTIPOLYGON (((166.7932 -15.6…
7 Seven seas (open ocean)          0           1 POLYGON ((68.935 -48.625, 68.8…
8 South America            412060811          13 MULTIPOLYGON (((-66.95992 -54.…
# 대륙별 인구의 합계 + 대륙별 국가 수 + 인구 탑3 대륙 + 인구 내림차순
world %>% 
  select(pop, continent) %>% 
  group_by(continent) %>% 
  summarize(pop = sum(pop, na.rm = TRUE), n_countries = n()) %>% 
  top_n(n = 3, wt = pop) %>%
  arrange(desc(pop)) %>%
  st_drop_geometry() # geom 컬럼 제거
# A tibble: 3 × 3
  continent        pop n_countries
* <chr>          <dbl>       <int>
1 Asia      4311408059          47
2 Africa    1154946633          51
3 Europe     669036256          39

8 data.table로 벡터 데이터의 속성정보 그룹별 집계

  • data.table 데이터 구조를 조작, 관리, 처리할 때 사용. 간결성
#install.packages("data.table")
library(data.table)

Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':

    between, first, last
The following object is masked from 'package:raster':

    shift
world_dt <-  data.table(world)
world_dt
     iso_a2           name_long     continent region_un        subregion
  1:     FJ                Fiji       Oceania   Oceania        Melanesia
  2:     TZ            Tanzania        Africa    Africa   Eastern Africa
  3:     EH      Western Sahara        Africa    Africa  Northern Africa
  4:     CA              Canada North America  Americas Northern America
  5:     US       United States North America  Americas Northern America
 ---                                                                    
173:     RS              Serbia        Europe    Europe  Southern Europe
174:     ME          Montenegro        Europe    Europe  Southern Europe
175:     XK              Kosovo        Europe    Europe  Southern Europe
176:     TT Trinidad and Tobago North America  Americas        Caribbean
177:     SS         South Sudan        Africa    Africa   Eastern Africa
                  type    area_km2       pop  lifeExp gdpPercap     geom
  1: Sovereign country    19289.97    885806 69.96000  8222.254  <XY[3]>
  2: Sovereign country   932745.79  52234869 64.16300  2402.099  <XY[1]>
  3:     Indeterminate    96270.60        NA       NA        NA  <XY[1]>
  4: Sovereign country 10036042.98  35535348 81.95305 43079.143 <XY[30]>
  5:           Country  9510743.74 318622525 78.84146 51921.985 <XY[10]>
 ---                                                                    
173: Sovereign country    76388.61   7130576 75.33659 13112.909  <XY[1]>
174: Sovereign country    13443.68    621810 76.71200 14796.635  <XY[1]>
175: Sovereign country    11230.26   1821800 71.09756  8698.292  <XY[1]>
176: Sovereign country     7737.81   1354493 70.42600 31181.821  <XY[1]>
177: Sovereign country   624909.10  11530971 55.81700  1935.879  <XY[1]>
# 간결하게 top3 대륙 추출
world_dt[, .(pop = sum(pop, na.rm = TRUE), n = .N), by = list(continent)][order(-pop)][1:3] 
   continent        pop  n
1:      Asia 4311408059 47
2:    Africa 1154946633 51
3:    Europe  669036256 39

9 두개의 지리 벡터 데이터 테이블 join 하기

### name_long 컬럼 기준으로 join
world_coffee <-  left_join(world, coffee_data) # by='name_long'
Joining with `by = join_by(name_long)`
world_coffee
Simple feature collection with 177 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 13
   iso_a2 name_l…¹ conti…² regio…³ subre…⁴ type  area_…⁵     pop lifeExp gdpPe…⁶
   <chr>  <chr>    <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji     Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0   8222.
 2 TZ     Tanzania Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2   2402.
 3 EH     Western… Africa  Africa  Northe… Inde…  9.63e4 NA         NA       NA 
 4 CA     Canada   North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0  43079.
 5 US     United … North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8  51922.
 6 KZ     Kazakhs… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6  23587.
 7 UZ     Uzbekis… Asia    Asia    Centra… Sove…  4.61e5  3.08e7    71.0   5371.
 8 PG     Papua N… Oceania Oceania Melane… Sove…  4.65e5  7.76e6    65.2   3709.
 9 ID     Indones… Asia    Asia    South-… Sove…  1.82e6  2.55e8    68.9  10003.
10 AR     Argenti… South … Americ… South … Sove…  2.78e6  4.30e7    76.3  18798.
# … with 167 more rows, 3 more variables: geom <MULTIPOLYGON [°]>,
#   coffee_production_2016 <int>, coffee_production_2017 <int>, and abbreviated
#   variable names ¹​name_long, ²​continent, ³​region_un, ⁴​subregion, ⁵​area_km2,
#   ⁶​gdpPercap
class(world_coffee)
[1] "sf"         "tbl_df"     "tbl"        "data.frame"
names(world_coffee)
 [1] "iso_a2"                 "name_long"              "continent"             
 [4] "region_un"              "subregion"              "type"                  
 [7] "area_km2"               "pop"                    "lifeExp"               
[10] "gdpPercap"              "geom"                   "coffee_production_2016"
[13] "coffee_production_2017"
plot(world_coffee["coffee_production_2017"])

### 두 데이터 셋에 같은 이름의 컬럼이 없는 경우
# coffee_data의 name_long변수 이름을 nm으로 변경
coffee_renamed  <-  rename(coffee_data, nm = name_long)

# by 사용하여 결합 변수를 지정하여 다른이름변수를 기준으로 조인하기
world_coffee2 <- left_join(world, coffee_renamed, by = c(name_long = "nm"))

world_coffee2
Simple feature collection with 177 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 13
   iso_a2 name_l…¹ conti…² regio…³ subre…⁴ type  area_…⁵     pop lifeExp gdpPe…⁶
   <chr>  <chr>    <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji     Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0   8222.
 2 TZ     Tanzania Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2   2402.
 3 EH     Western… Africa  Africa  Northe… Inde…  9.63e4 NA         NA       NA 
 4 CA     Canada   North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0  43079.
 5 US     United … North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8  51922.
 6 KZ     Kazakhs… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6  23587.
 7 UZ     Uzbekis… Asia    Asia    Centra… Sove…  4.61e5  3.08e7    71.0   5371.
 8 PG     Papua N… Oceania Oceania Melane… Sove…  4.65e5  7.76e6    65.2   3709.
 9 ID     Indones… Asia    Asia    South-… Sove…  1.82e6  2.55e8    68.9  10003.
10 AR     Argenti… South … Americ… South … Sove…  2.78e6  4.30e7    76.3  18798.
# … with 167 more rows, 3 more variables: geom <MULTIPOLYGON [°]>,
#   coffee_production_2016 <int>, coffee_production_2017 <int>, and abbreviated
#   variable names ¹​name_long, ²​continent, ³​region_un, ⁴​subregion, ⁵​area_km2,
#   ⁶​gdpPercap
### inner_join (left_join과 다른점 : 공통된 값이 있는 행만 살림)
world_coffee_inner <- inner_join(world, coffee_data)
Joining with `by = join_by(name_long)`
world_coffee_inner
Simple feature collection with 45 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -117.1278 ymin: -33.76838 xmax: 156.02 ymax: 35.49401
Geodetic CRS:  WGS 84
# A tibble: 45 × 13
   iso_a2 name_long conti…¹ regio…² subre…³ type  area_…⁴    pop lifeExp gdpPe…⁵
   <chr>  <chr>     <chr>   <chr>   <chr>   <chr>   <dbl>  <dbl>   <dbl>   <dbl>
 1 TZ     Tanzania  Africa  Africa  Easter… Sove…  9.33e5 5.22e7    64.2   2402.
 2 PG     Papua Ne… Oceania Oceania Melane… Sove…  4.65e5 7.76e6    65.2   3709.
 3 ID     Indonesia Asia    Asia    South-… Sove…  1.82e6 2.55e8    68.9  10003.
 4 KE     Kenya     Africa  Africa  Easter… Sove…  5.91e5 4.60e7    66.2   2753.
 5 DO     Dominica… North … Americ… Caribb… Sove…  4.82e4 1.04e7    73.5  12663.
 6 TL     Timor-Le… Asia    Asia    South-… Sove…  1.47e4 1.21e6    68.3   6263.
 7 MX     Mexico    North … Americ… Centra… Sove…  1.97e6 1.24e8    76.8  16623.
 8 BR     Brazil    South … Americ… South … Sove…  8.51e6 2.04e8    75.0  15374.
 9 BO     Bolivia   South … Americ… South … Sove…  1.09e6 1.06e7    68.4   6325.
10 PE     Peru      South … Americ… South … Sove…  1.31e6 3.10e7    74.5  11548.
# … with 35 more rows, 3 more variables: geom <MULTIPOLYGON [°]>,
#   coffee_production_2016 <int>, coffee_production_2017 <int>, and abbreviated
#   variable names ¹​continent, ²​region_un, ³​subregion, ⁴​area_km2, ⁵​gdpPercap
### 비교
dim(world_coffee_inner)
[1] 45 13
dim(world_coffee)
[1] 177  13
nrow(coffee_data)
[1] 47
### setdiff(x,y) : x에서 y에는 없는 데이터 추출
setdiff(coffee_data$name_long, world$name_long)
[1] "Congo, Dem. Rep. of" "Others"             
### 문자열 추출 (Dem으로 시작, Congo로 끝나는 단어 추출)
str_subset(world$name_long, 'Dem*.+Congo')
[1] "Democratic Republic of the Congo"
### grepl : 문자열에서 특정 문자열 지정
# 이름 동일하게 변경
coffee_data$name_long[grepl("Congo,", coffee_data$name_long)]  <-  
  str_subset(world$name_long, "Dem*.+Congo")

str_subset(coffee_data$name_long, 'Dem*.+Congo')
[1] "Democratic Republic of the Congo"
# 46개의 국가 모두 반환
world_coffee_match <- inner_join(world, coffee_data)
Joining with `by = join_by(name_long)`
dim(world_coffee_match)
[1] 46 13
### left_join(x,y) : x기준으로 y 병합
coffee_world = left_join(world, coffee_data)
Joining with `by = join_by(name_long)`
dim(coffee_world)
[1] 177  13
coffee_world2 = left_join(coffee_data, world)
Joining with `by = join_by(name_long)`
dim(coffee_world2)
[1] 47 13

10 지리 벡터 데이터의 새로운 속성 만들기/지리정보 제거하기

### 원본 데이터 덮어쓰기 방지
world_new <- world

### 새로운 속성 pop_dens 생성 (Km2 면적당 인구밀도)
world_new$pop_dens <- world_new$pop / world_new$area_km2

### 속성값으로 보기 vs 지도상으로 보기(geom 정보가 있기때문)
plot(world_new$pop_dens)

plot(world_new['pop_dens'])

### dplyr로 새로운 속성 만들기
# mutate
world_new <- world_new %>% mutate(pop_dens2 = pop / area_km2)

# transmute : 기존 열 모두 제거, 새로 만든 열 + geom 열만 반환
world_new <- world %>% transmute(pop_dens3 = pop/area_km2)

world_new %>% head()
Simple feature collection with 6 features and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -18.28799 xmax: 180 ymax: 83.23324
Geodetic CRS:  WGS 84
# A tibble: 6 × 2
  pop_dens3                                                                 geom
      <dbl>                                                   <MULTIPOLYGON [°]>
1     45.9  (((-180 -16.55522, -179.9174 -16.50178, -179.7933 -16.02088, -180 -…
2     56.0  (((33.90371 -0.95, 31.86617 -1.02736, 30.76986 -1.01455, 30.4191 -1…
3     NA    (((-8.66559 27.65643, -8.817828 27.65643, -8.794884 27.1207, -9.413…
4      3.54 (((-132.71 54.04001, -133.18 54.16998, -133.2397 53.85108, -133.054…
5     33.5  (((-171.7317 63.78252, -171.7911 63.40585, -171.5531 63.31779, -170…
6      6.33 (((87.35997 49.21498, 86.82936 49.82667, 85.54127 49.69286, 85.1155…
### tidyr로 기존 속성 합치거나 분리하기

### unite()
# con_reg : continent + region_un 합치고 ':' 로 분리, 합쳐진 열들 remove
world_unite <-  world %>%
  unite("con_reg", continent:region_un, sep = ":", remove = TRUE)

world_unite
Simple feature collection with 177 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 10
   iso_a2 name_long        con_reg subre…¹ type  area_…²     pop lifeExp gdpPe…³
   <chr>  <chr>            <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji             Oceani… Melane… Sove…  1.93e4  8.86e5    70.0   8222.
 2 TZ     Tanzania         Africa… Easter… Sove…  9.33e5  5.22e7    64.2   2402.
 3 EH     Western Sahara   Africa… Northe… Inde…  9.63e4 NA         NA       NA 
 4 CA     Canada           North … Northe… Sove…  1.00e7  3.55e7    82.0  43079.
 5 US     United States    North … Northe… Coun…  9.51e6  3.19e8    78.8  51922.
 6 KZ     Kazakhstan       Asia:A… Centra… Sove…  2.73e6  1.73e7    71.6  23587.
 7 UZ     Uzbekistan       Asia:A… Centra… Sove…  4.61e5  3.08e7    71.0   5371.
 8 PG     Papua New Guinea Oceani… Melane… Sove…  4.65e5  7.76e6    65.2   3709.
 9 ID     Indonesia        Asia:A… South-… Sove…  1.82e6  2.55e8    68.9  10003.
10 AR     Argentina        South … South … Sove…  2.78e6  4.30e7    76.3  18798.
# … with 167 more rows, 1 more variable: geom <MULTIPOLYGON [°]>, and
#   abbreviated variable names ¹​subregion, ²​area_km2, ³​gdpPercap
# 합쳐진 열들 제거X
world_unite2 <-  world %>%
  unite("con_reg", continent:region_un, sep = ":", remove = FALSE)

world_unite2
Simple feature collection with 177 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 12
   iso_a2 name_l…¹ con_reg conti…² regio…³ subre…⁴ type  area_…⁵     pop lifeExp
   <chr>  <chr>    <chr>   <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji     Oceani… Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0
 2 TZ     Tanzania Africa… Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2
 3 EH     Western… Africa… Africa  Africa  Northe… Inde…  9.63e4 NA         NA  
 4 CA     Canada   North … North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0
 5 US     United … North … North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8
 6 KZ     Kazakhs… Asia:A… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6
 7 UZ     Uzbekis… Asia:A… Asia    Asia    Centra… Sove…  4.61e5  3.08e7    71.0
 8 PG     Papua N… Oceani… Oceania Oceania Melane… Sove…  4.65e5  7.76e6    65.2
 9 ID     Indones… Asia:A… Asia    Asia    South-… Sove…  1.82e6  2.55e8    68.9
10 AR     Argenti… South … South … Americ… South … Sove…  2.78e6  4.30e7    76.3
# … with 167 more rows, 2 more variables: gdpPercap <dbl>,
#   geom <MULTIPOLYGON [°]>, and abbreviated variable names ¹​name_long,
#   ²​continent, ³​region_un, ⁴​subregion, ⁵​area_km2
### separate()
# con_reg를 continent, region_un로 분리
world_separate <-  world_unite %>% 
  separate(con_reg, c("continent", "region_un"), sep = ':')

world_separate
Simple feature collection with 177 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 11
   iso_a2 name_l…¹ conti…² regio…³ subre…⁴ type  area_…⁵     pop lifeExp gdpPe…⁶
   <chr>  <chr>    <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji     Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0   8222.
 2 TZ     Tanzania Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2   2402.
 3 EH     Western… Africa  Africa  Northe… Inde…  9.63e4 NA         NA       NA 
 4 CA     Canada   North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0  43079.
 5 US     United … North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8  51922.
 6 KZ     Kazakhs… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6  23587.
 7 UZ     Uzbekis… Asia    Asia    Centra… Sove…  4.61e5  3.08e7    71.0   5371.
 8 PG     Papua N… Oceania Oceania Melane… Sove…  4.65e5  7.76e6    65.2   3709.
 9 ID     Indones… Asia    Asia    South-… Sove…  1.82e6  2.55e8    68.9  10003.
10 AR     Argenti… South … Americ… South … Sove…  2.78e6  4.30e7    76.3  18798.
# … with 167 more rows, 1 more variable: geom <MULTIPOLYGON [°]>, and
#   abbreviated variable names ¹​name_long, ²​continent, ³​region_un, ⁴​subregion,
#   ⁵​area_km2, ⁶​gdpPercap
### 데이터 속성 이름 바꾸기
# rename(x:new = y:old)
world %>% rename(name = name_long)
Simple feature collection with 177 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 11
   iso_a2 name     conti…¹ regio…² subre…³ type  area_…⁴     pop lifeExp gdpPe…⁵
   <chr>  <chr>    <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji     Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0   8222.
 2 TZ     Tanzania Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2   2402.
 3 EH     Western… Africa  Africa  Northe… Inde…  9.63e4 NA         NA       NA 
 4 CA     Canada   North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0  43079.
 5 US     United … North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8  51922.
 6 KZ     Kazakhs… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6  23587.
 7 UZ     Uzbekis… Asia    Asia    Centra… Sove…  4.61e5  3.08e7    71.0   5371.
 8 PG     Papua N… Oceania Oceania Melane… Sove…  4.65e5  7.76e6    65.2   3709.
 9 ID     Indones… Asia    Asia    South-… Sove…  1.82e6  2.55e8    68.9  10003.
10 AR     Argenti… South … Americ… South … Sove…  2.78e6  4.30e7    76.3  18798.
# … with 167 more rows, 1 more variable: geom <MULTIPOLYGON [°]>, and
#   abbreviated variable names ¹​continent, ²​region_un, ³​subregion, ⁴​area_km2,
#   ⁵​gdpPercap
world
Simple feature collection with 177 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 11
   iso_a2 name_l…¹ conti…² regio…³ subre…⁴ type  area_…⁵     pop lifeExp gdpPe…⁶
 * <chr>  <chr>    <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 FJ     Fiji     Oceania Oceania Melane… Sove…  1.93e4  8.86e5    70.0   8222.
 2 TZ     Tanzania Africa  Africa  Easter… Sove…  9.33e5  5.22e7    64.2   2402.
 3 EH     Western… Africa  Africa  Northe… Inde…  9.63e4 NA         NA       NA 
 4 CA     Canada   North … Americ… Northe… Sove…  1.00e7  3.55e7    82.0  43079.
 5 US     United … North … Americ… Northe… Coun…  9.51e6  3.19e8    78.8  51922.
 6 KZ     Kazakhs… Asia    Asia    Centra… Sove…  2.73e6  1.73e7    71.6  23587.
 7 UZ     Uzbekis… Asia    Asia    Centra… Sove…  4.61e5  3.08e7    71.0   5371.
 8 PG     Papua N… Oceania Oceania Melane… Sove…  4.65e5  7.76e6    65.2   3709.
 9 ID     Indones… Asia    Asia    South-… Sove…  1.82e6  2.55e8    68.9  10003.
10 AR     Argenti… South … Americ… South … Sove…  2.78e6  4.30e7    76.3  18798.
# … with 167 more rows, 1 more variable: geom <MULTIPOLYGON [°]>, and
#   abbreviated variable names ¹​name_long, ²​continent, ³​region_un, ⁴​subregion,
#   ⁵​area_km2, ⁶​gdpPercap
# 여러개의 이름 변경
new_names <- c("i", "n", "c", "r", "s", "t", "a", "p", "l", "gP", "geom")
world %>% setNames(new_names)
Simple feature collection with 177 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 11
   i     n                c        r     s     t          a       p     l     gP
 * <chr> <chr>            <chr>    <chr> <chr> <chr>  <dbl>   <dbl> <dbl>  <dbl>
 1 FJ    Fiji             Oceania  Ocea… Mela… Sove… 1.93e4  8.86e5  70.0  8222.
 2 TZ    Tanzania         Africa   Afri… East… Sove… 9.33e5  5.22e7  64.2  2402.
 3 EH    Western Sahara   Africa   Afri… Nort… Inde… 9.63e4 NA       NA      NA 
 4 CA    Canada           North A… Amer… Nort… Sove… 1.00e7  3.55e7  82.0 43079.
 5 US    United States    North A… Amer… Nort… Coun… 9.51e6  3.19e8  78.8 51922.
 6 KZ    Kazakhstan       Asia     Asia  Cent… Sove… 2.73e6  1.73e7  71.6 23587.
 7 UZ    Uzbekistan       Asia     Asia  Cent… Sove… 4.61e5  3.08e7  71.0  5371.
 8 PG    Papua New Guinea Oceania  Ocea… Mela… Sove… 4.65e5  7.76e6  65.2  3709.
 9 ID    Indonesia        Asia     Asia  Sout… Sove… 1.82e6  2.55e8  68.9 10003.
10 AR    Argentina        South A… Amer… Sout… Sove… 2.78e6  4.30e7  76.3 18798.
# … with 167 more rows, and 1 more variable: geom <MULTIPOLYGON [°]>

11 래스터 객체 조작

  • numeric, integer, logical, factor 데이처 유형 지원 (character지원X)
  • 문자형으로 래스터 객체 만들기 : 요인형 or 논리형으로 변환 필요
### raster 패키지를 이용한 integer 속성의 래스터객체
elev <- raster(nrows = 6,  
               ncols = 6,  
               xmn = -1.5, 
               xmx = 1.5, 
               ymn = -1.5,  
               ymx = 1.5, 
               vals = 1:36) 

elev
class      : RasterLayer 
dimensions : 6, 6, 36  (nrow, ncol, ncell)
resolution : 0.5, 0.5  (x, y)
extent     : -1.5, 1.5, -1.5, 1.5  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +datum=WGS84 +no_defs 
source     : memory
names      : layer 
values     : 1, 36  (min, max)
plot(elev, main = 'raster datasets with numeric valeus')

### 모래 굵기 수준 래스터객체 만들기
grain_order <- c("clay", "silt", "sand")                      # 모래 이름
grain_char <- sample(grain_order, 36, replace = TRUE)         # 개수지정(중복허용)
grain_fact <- factor(grain_char, levels = grain_order)        # 팩터 변환
grain <- raster(nrows = 6, ncols = 6, res = 0.5, 
               xmn = -1.5, xmx = 1.5, ymn = -1.5, ymx = 1.5,
               vals = grain_fact)

plot(grain)

###새로운 facter levels 추가
levels(grain)[[1]] <- cbind(levels(grain)[[1]], wetness = c("wet", "moist", "dry"))
levels(grain)
[[1]]
  ID VALUE wetness
1  1  clay     wet
2  2  silt   moist
3  3  sand     dry
grain[c(1, 11, 35)]
[1] 1 1 1
factorValues(grain, grain[c(1, 11, 35)])
  VALUE wetness
1  clay     wet
2  clay     wet
3  clay     wet

12 래스터 부분 설정

  • R에서 래스터 부분 설정 4가지
    • 행,열 인덱싱
    • 셀 ID
    • 좌표
    • 다른 공간 객체
  • 래스터 객체 elev의 왼쪽상단( the top left pixel) 의 값을 출력
  • 모든 값을 추출하거나 전체 행을 추출하려면 values, getValues를 사용
### row 1, column 1
elev[1, 1]
[1] 1
### cell ID 1
elev[1]
[1] 1