US Census 데이터를 지도에 그리기
Tidycensus, Tigris 패키지 활용
MiyoungYoon
7/9/2021
setting
R 버젼 확인
mapview 패키지는 최신 R에서만 설치됨.
package_version(R.version) 시스템 설정
sf, rgdal, sp 설치시 시스템 설정해야 하는 것이 있음.
GDAL 설치:
GDAL설치시 맥킨토시에서 문제가 많았음. GDAL: 콘다로 제대로 설치 안되서 brew로 설치함.
brew install pkg-config
brew install gdalPROJ 설치:
conda install -c conda-forge proj-dataMAC OS의 R에서 명령어:
install.packages("rgdal", type = "source",
configure.args = c("--with-proj-include=/usr/local/include",
"--with-proj-lib=/usr/local/lib”))Setup packages
install.packages("sp")
install.packages("rgdal")
install.packages("raster)
install.packages("sf")
install.packages("tigris")
install.packages("tidycensus")
install.packages("tidyverse")
install.packages("mapview")
라이브러리 로딩
library(sp)
library(rgdal)
library(raster)
library(sf)
library(tigris)
library(mapview)
library(tidyverse)
library(tidycensus)tidycensus option: API key 입력
참고: API key는 개인정보이므로 공개하면 안됨
census_api_key("너의 API 코드 ")
# 설치된 api key 확인
Sys.getenv("CENSUS_API_KEY") # Check your API keytigris option
tigris_cache_dir("/Users/miyoungyoon/Google 드라이브/연습_RStudio/map") # Set the cache directory
options(tigris_use_cache=TRUE)# Set the tigris_use_cache option
getOption("tigris_use_cache") # Check to see that you've modified the option correctly
# Get data from tigris as simple features:
# shpae file 을 simple file 로 변환해야 ggplot으로 그릴 수 있음
options(tigris_class = "sf")#tidycensus 데이터 테이블 보기
# tidycensus variable import
v19 <- load_variables(2019, "acs5", cache = TRUE)
# 전체 변수를 필터링해서 검색 가능
head(v19) ## # A tibble: 6 x 3
## name label concept
## <chr> <chr> <chr>
## 1 B01001_001 Estimate!!Total: SEX BY AGE
## 2 B01001_002 Estimate!!Total:!!Male: SEX BY AGE
## 3 B01001_003 Estimate!!Total:!!Male:!!Under 5 years SEX BY AGE
## 4 B01001_004 Estimate!!Total:!!Male:!!5 to 9 years SEX BY AGE
## 5 B01001_005 Estimate!!Total:!!Male:!!10 to 14 years SEX BY AGE
## 6 B01001_006 Estimate!!Total:!!Male:!!15 to 17 years SEX BY AGEtidycensus 패키지로 US census 데이터 가져오기
# Get a dataset of median home values from the 1-year ACS
state_value <- get_acs(geography = "state",
variables = "B25077_001",
survey = "acs1",
geometry = TRUE,
shift_geo = TRUE)## Warning: The `shift_geo` argument is deprecated and will be removed in a future
## release. We recommend using `tigris::shift_geometry()` instead.## The 1-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2019 1-year ACS## Using feature geometry obtained from the albersusa package## Please note: Alaska and Hawaii are being shifted and are not to scale.# Plot the dataset to view the shifted geometry
plot(state_value["estimate"])
ggplot으로 tidycensus 데이터 그리기
demographic mapping: US - median housing values
centers <- st_centroid(state_value) # st_centroid() tool in the sf package ## Warning in st_centroid.sf(state_value): st_centroid assumes attributes are
## constant over geometries of x# US지도에 동그라미 크기로 표현됨
ggplot() +
geom_sf(data = state_value, fill = "white") +
geom_sf(data = centers, aes(size = estimate), shape = 21,
fill = "lightblue", alpha = 0.7, show.legend = "point") +
scale_size_continuous(range = c(1, 20))
tidycensus 패키지로 데이터 가져오기
variable에 필요한 데이터 값 번호 입력 variable = "B25077_001: Milwuakee county - median housing values
# median_value: B25077_001
# Milwuakee county - median housing values " Estimate!!Median value (dollars)"
mk_county_median_income <- get_acs(geography = "tract",
state = "WI",
county = "Milwaukee",
variable = "B25077_001",
geometry = TRUE)##
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|======================================================================| 100%head(mk_county_median_income)## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -88.02764 ymin: 43.03472 xmax: -87.90517 ymax: 43.105
## Geodetic CRS: NAD83
## GEOID NAME variable
## 1 55079007000 Census Tract 70, Milwaukee County, Wisconsin B25077_001
## 2 55079003300 Census Tract 33, Milwaukee County, Wisconsin B25077_001
## 3 55079006100 Census Tract 61, Milwaukee County, Wisconsin B25077_001
## 4 55079006400 Census Tract 64, Milwaukee County, Wisconsin B25077_001
## 5 55079014800 Census Tract 148, Milwaukee County, Wisconsin B25077_001
## 6 55079186400 Census Tract 1864, Milwaukee County, Wisconsin B25077_001
## estimate moe geometry
## 1 59500 10741 MULTIPOLYGON (((-87.91693 4...
## 2 115800 6142 MULTIPOLYGON (((-88.02752 4...
## 3 113000 23050 MULTIPOLYGON (((-87.97244 4...
## 4 60200 23838 MULTIPOLYGON (((-87.94732 4...
## 5 NA NA MULTIPOLYGON (((-87.94288 4...
## 6 NA NA MULTIPOLYGON (((-87.94499 4...plot(mk_county_median_income["estimate"])
Milwuakee county - median housing values
# Milwuakee county - median housing values " Estimate!!Median value (dollars)"
centers_mk <- st_centroid(mk_county_median_income)## Warning in st_centroid.sf(mk_county_median_income): st_centroid assumes
## attributes are constant over geometries of xggplot() +
geom_sf(data = mk_county_median_income, fill="white") +
geom_sf(data=centers_mk, aes(size=estimate), shape=21,
fill="red", alpha=0.3, show.legend="point")+
scale_size_continuous(range=c(0.1,20))## Warning: Removed 9 rows containing missing values (geom_sf).
head(centers_mk )## Simple feature collection with 6 features and 5 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -88.0173 ymin: 43.03688 xmax: -87.91123 ymax: 43.10105
## Geodetic CRS: NAD83
## GEOID NAME variable
## 1 55079007000 Census Tract 70, Milwaukee County, Wisconsin B25077_001
## 2 55079003300 Census Tract 33, Milwaukee County, Wisconsin B25077_001
## 3 55079006100 Census Tract 61, Milwaukee County, Wisconsin B25077_001
## 4 55079006400 Census Tract 64, Milwaukee County, Wisconsin B25077_001
## 5 55079014800 Census Tract 148, Milwaukee County, Wisconsin B25077_001
## 6 55079186400 Census Tract 1864, Milwaukee County, Wisconsin B25077_001
## estimate moe geometry
## 1 59500 10741 POINT (-87.91123 43.07425)
## 2 115800 6142 POINT (-88.0173 43.10105)
## 3 113000 23050 POINT (-87.96848 43.06791)
## 4 60200 23838 POINT (-87.94235 43.07422)
## 5 NA NA POINT (-87.94021 43.04213)
## 6 NA NA POINT (-87.93049 43.03688)Joining data from tigris and tidycensus: Milwaukee school district
# Joining data from tigris and tidycensus: Milwaukee school district
# B19013_001: Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
wi_income <- get_acs(geography="school district (unified)",
variables = "B19013_001",
state = "WI")
wi_school <- school_districts(state = "WI", type="unified", class="sf")##
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|======================================================================| 100%str(wi_income)## tibble [369 × 5] (S3: tbl_df/tbl/data.frame)
## $ GEOID : chr [1:369] "5500016" "5500017" "5500030" "5500056" ...
## $ NAME : chr [1:369] "Blair-Taylor School District, Wisconsin" "River Ridge School District, Wisconsin" "Abbotsford School District, Wisconsin" "Gresham School District, Wisconsin" ...
## $ variable: chr [1:369] "B19013_001" "B19013_001" "B19013_001" "B19013_001" ...
## $ estimate: num [1:369] 54889 52533 50601 58036 45549 ...
## $ moe : num [1:369] 4495 2507 6030 5830 3515 ...str(wi_school)## Classes 'sf' and 'data.frame': 369 obs. of 15 variables:
## $ STATEFP : chr "55" "55" "55" "55" ...
## $ UNSDLEA : chr "09660" "03030" "13830" "00210" ...
## $ GEOID : chr "5509660" "5503030" "5513830" "5500210" ...
## $ NAME : chr "Mineral Point School District" "Cuba City School District" "Shullsburg School District" "Alma Center School District" ...
## $ LSAD : chr "00" "00" "00" "00" ...
## $ LOGRADE : chr "PK" "PK" "PK" "PK" ...
## $ HIGRADE : chr "12" "12" "12" "12" ...
## $ MTFCC : chr "G5420" "G5420" "G5420" "G5420" ...
## $ SDTYP : chr NA NA NA NA ...
## $ FUNCSTAT: chr "E" "E" "E" "E" ...
## $ ALAND : num 3.46e+08 2.05e+08 2.50e+08 3.59e+08 9.27e+08 ...
## $ AWATER : num 250893 258432 0 847352 16033326 ...
## $ INTPTLAT: chr "+42.8510666" "+42.6209709" "+42.5611933" "+44.4860642" ...
## $ INTPTLON: chr "-090.1497347" "-090.4944397" "-090.2049416" "-090.9093804" ...
## $ geometry:sfc_MULTIPOLYGON of length 369; first list element: List of 5
## ..$ :List of 1
## .. ..$ : num [1:7, 1:2] -90 -90 -90 -90 -90 ...
## ..$ :List of 1
## .. ..$ : num [1:19, 1:2] -90 -90 -90 -90 -90 ...
## ..$ :List of 1
## .. ..$ : num [1:24, 1:2] -90 -90 -90 -90 -90 ...
## ..$ :List of 1
## .. ..$ : num [1:9, 1:2] -90.2 -90.2 -90.2 -90.2 -90.2 ...
## ..$ :List of 7
## .. ..$ : num [1:825, 1:2] -90.3 -90.3 -90.3 -90.3 -90.3 ...
## .. ..$ : num [1:46, 1:2] -90.3 -90.3 -90.3 -90.3 -90.3 ...
## .. ..$ : num [1:13, 1:2] -90.3 -90.3 -90.3 -90.3 -90.3 ...
## .. ..$ : num [1:20, 1:2] -90.2 -90.2 -90.2 -90.2 -90.2 ...
## .. ..$ : num [1:81, 1:2] -90.2 -90.2 -90.2 -90.2 -90.2 ...
## .. ..$ : num [1:41, 1:2] -90.2 -90.2 -90.2 -90.2 -90.2 ...
## .. ..$ : num [1:24, 1:2] -90.3 -90.3 -90.3 -90.3 -90.3 ...
## ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "names")= chr [1:14] "STATEFP" "UNSDLEA" "GEOID" "NAME" ...
## - attr(*, "tigris")= chr "unsd"# Join the income dataset to the boundaries dataset
wi_school_joined <- left_join(wi_school, wi_income, by = "GEOID")
plot(wi_school_joined["estimate"])
head(wi_school_joined )## Simple feature collection with 6 features and 18 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -91.0652 ymin: 42.50642 xmax: -87.88419 ymax: 44.59626
## Geodetic CRS: NAD83
## STATEFP UNSDLEA GEOID NAME.x LSAD LOGRADE HIGRADE
## 1 55 09660 5509660 Mineral Point School District 00 PK 12
## 2 55 03030 5503030 Cuba City School District 00 PK 12
## 3 55 13830 5513830 Shullsburg School District 00 PK 12
## 4 55 00210 5500210 Alma Center School District 00 PK 12
## 5 55 11790 5511790 Pittsville School District 00 PK 12
## 6 55 05610 5505610 Grafton School District 00 PK 12
## MTFCC SDTYP FUNCSTAT ALAND AWATER INTPTLAT INTPTLON
## 1 G5420 <NA> E 345854244 250893 +42.8510666 -090.1497347
## 2 G5420 <NA> E 204731863 258432 +42.6209709 -090.4944397
## 3 G5420 <NA> E 249775168 0 +42.5611933 -090.2049416
## 4 G5420 <NA> E 359206625 847352 +44.4860642 -090.9093804
## 5 G5420 <NA> E 927303552 16033326 +44.3935064 -090.2768746
## 6 G5420 <NA> E 54819159 853927 +43.3223155 -087.9277005
## NAME.y variable estimate moe
## 1 Mineral Point School District, Wisconsin B19013_001 60463 5579
## 2 Cuba City School District, Wisconsin B19013_001 61131 5319
## 3 Shullsburg School District, Wisconsin B19013_001 55625 12133
## 4 Alma Center School District, Wisconsin B19013_001 49907 4810
## 5 Pittsville School District, Wisconsin B19013_001 58523 3982
## 6 Grafton School District, Wisconsin B19013_001 80185 4944
## geometry
## 1 MULTIPOLYGON (((-89.96697 4...
## 2 MULTIPOLYGON (((-90.28938 4...
## 3 MULTIPOLYGON (((-90.21101 4...
## 4 MULTIPOLYGON (((-91.0652 44...
## 5 MULTIPOLYGON (((-90.55875 4...
## 6 MULTIPOLYGON (((-87.96731 4...#
wi_school_joined_interactive <- mapview(wi_school_joined, zcol="estimate", legend=TRUE,
layer.name="chool district shpae에 2019년 median household income 맵핑")
# mapview로 인터랙티브하게 보기:
# 위스콘신 주를 school district shpae 위에 2019년 median household income
wi_school_joined_interactive@map