# uncomment to run, then recomment it out so you don't run it every time
census_api_key("2a6f8c21a30d3024e038d67d7d4eba647dc79cd4", overwrite=TRUE)To install your API key for use in future sessions, run this function with `install = TRUE`.Interactive Maps
Interactive Maps with MapView Package
A h/t to Sharon Machlis for turning people on to the Mapview package, and her excellent walkthroughs of it.
For the data tonight we’ll be using the tidycensus package to pull both census data, as well as geospatial boundaries. Let’s quickly review how we use it.
#chose variables we want
myvars <- c(totalpop = "B01003_001",
medincome = "B19013_001",
medage = "B01002_001"
)#pull for NC counties
nc_counties_withgeo <- get_acs(geography = "county",
variables = c(myvars),
state = "NC",
output = "wide",
geometry = TRUE)Getting data from the 2017-2021 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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|======================================================================| 100%nc_counties_withgeoSimple feature collection with 100 features and 8 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -84.32187 ymin: 33.84232 xmax: -75.46062 ymax: 36.58812
Geodetic CRS: NAD83
First 10 features:
GEOID NAME totalpopE totalpopM medincomeE
1 37127 Nash County, North Carolina 94628 NA 52837
2 37043 Clay County, North Carolina 10997 NA 47155
3 37119 Mecklenburg County, North Carolina 1100984 NA 73124
4 37147 Pitt County, North Carolina 170600 NA 50422
5 37007 Anson County, North Carolina 22388 NA 39862
6 37031 Carteret County, North Carolina 67944 NA 61805
7 37163 Sampson County, North Carolina 59468 NA 45127
8 37131 Northampton County, North Carolina 17917 NA 39764
9 37053 Currituck County, North Carolina 27575 NA 79416
10 37103 Jones County, North Carolina 9326 NA 42401
medincomeM medageE medageM geometry
1 1980 41.5 0.5 MULTIPOLYGON (((-78.2556 35...
2 8679 53.5 0.3 MULTIPOLYGON (((-84.00582 3...
3 840 35.4 0.1 MULTIPOLYGON (((-81.05803 3...
4 2325 32.9 0.3 MULTIPOLYGON (((-77.70069 3...
5 3340 41.6 1.1 MULTIPOLYGON (((-80.3102 34...
6 2094 49.4 0.2 MULTIPOLYGON (((-76.33014 3...
7 3225 40.2 0.4 MULTIPOLYGON (((-78.67179 3...
8 3566 50.3 0.3 MULTIPOLYGON (((-77.89977 3...
9 7406 43.2 0.6 MULTIPOLYGON (((-76.3133 36...
10 7091 48.5 1.0 MULTIPOLYGON (((-77.73078 3...#all counties in the US
all_counties_withgeo <- get_acs(geography = "county",
variables = c(myvars),
output = "wide",
geometry = TRUE)Getting data from the 2017-2021 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.all_counties_withgeoSimple feature collection with 3221 features and 8 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -179.1489 ymin: 17.88328 xmax: 179.7785 ymax: 71.36516
Geodetic CRS: NAD83
First 10 features:
GEOID NAME totalpopE totalpopM medincomeE
1 20161 Riley County, Kansas 72602 NA 53296
2 19159 Ringgold County, Iowa 4739 NA 57700
3 30009 Carbon County, Montana 10488 NA 63178
4 16007 Bear Lake County, Idaho 6327 NA 60337
5 55011 Buffalo County, Wisconsin 13314 NA 61167
6 31185 York County, Nebraska 14164 NA 66337
7 08037 Eagle County, Colorado 55693 NA 91338
8 42129 Westmoreland County, Pennsylvania 355107 NA 64708
9 40079 Le Flore County, Oklahoma 48436 NA 43049
10 48053 Burnet County, Texas 48424 NA 65363
medincomeM medageE medageM geometry
1 2489 25.5 0.1 MULTIPOLYGON (((-96.96095 3...
2 5058 44.3 1.0 MULTIPOLYGON (((-94.47167 4...
3 4261 50.7 0.9 MULTIPOLYGON (((-109.7987 4...
4 7039 38.9 1.1 MULTIPOLYGON (((-111.6345 4...
5 2352 46.5 0.5 MULTIPOLYGON (((-92.08384 4...
6 4128 39.5 1.2 MULTIPOLYGON (((-97.82629 4...
7 4058 37.8 0.8 MULTIPOLYGON (((-107.1137 3...
8 1350 47.1 0.2 MULTIPOLYGON (((-79.90487 4...
9 1869 38.8 0.3 MULTIPOLYGON (((-95.05996 3...
10 4694 44.7 0.3 MULTIPOLYGON (((-98.45924 3...#remove MOE columns - they all end with "M"
nc_counties_withgeo <- nc_counties_withgeo %>%
select(-ends_with("M"))
nc_counties_withgeoSimple feature collection with 100 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -84.32187 ymin: 33.84232 xmax: -75.46062 ymax: 36.58812
Geodetic CRS: NAD83
First 10 features:
GEOID NAME totalpopE medincomeE medageE
1 37127 Nash County, North Carolina 94628 52837 41.5
2 37043 Clay County, North Carolina 10997 47155 53.5
3 37119 Mecklenburg County, North Carolina 1100984 73124 35.4
4 37147 Pitt County, North Carolina 170600 50422 32.9
5 37007 Anson County, North Carolina 22388 39862 41.6
6 37031 Carteret County, North Carolina 67944 61805 49.4
7 37163 Sampson County, North Carolina 59468 45127 40.2
8 37131 Northampton County, North Carolina 17917 39764 50.3
9 37053 Currituck County, North Carolina 27575 79416 43.2
10 37103 Jones County, North Carolina 9326 42401 48.5
geometry
1 MULTIPOLYGON (((-78.2556 35...
2 MULTIPOLYGON (((-84.00582 3...
3 MULTIPOLYGON (((-81.05803 3...
4 MULTIPOLYGON (((-77.70069 3...
5 MULTIPOLYGON (((-80.3102 34...
6 MULTIPOLYGON (((-76.33014 3...
7 MULTIPOLYGON (((-78.67179 3...
8 MULTIPOLYGON (((-77.89977 3...
9 MULTIPOLYGON (((-76.3133 36...
10 MULTIPOLYGON (((-77.73078 3...#remove that trailing "E"
colnames(nc_counties_withgeo) <- sub("E$", "", colnames(nc_counties_withgeo)) # $ means end of string only
nc_counties_withgeoSimple feature collection with 100 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -84.32187 ymin: 33.84232 xmax: -75.46062 ymax: 36.58812
Geodetic CRS: NAD83
First 10 features:
GEOID NAM totalpop medincome medage
1 37127 Nash County, North Carolina 94628 52837 41.5
2 37043 Clay County, North Carolina 10997 47155 53.5
3 37119 Mecklenburg County, North Carolina 1100984 73124 35.4
4 37147 Pitt County, North Carolina 170600 50422 32.9
5 37007 Anson County, North Carolina 22388 39862 41.6
6 37031 Carteret County, North Carolina 67944 61805 49.4
7 37163 Sampson County, North Carolina 59468 45127 40.2
8 37131 Northampton County, North Carolina 17917 39764 50.3
9 37053 Currituck County, North Carolina 27575 79416 43.2
10 37103 Jones County, North Carolina 9326 42401 48.5
geometry
1 MULTIPOLYGON (((-78.2556 35...
2 MULTIPOLYGON (((-84.00582 3...
3 MULTIPOLYGON (((-81.05803 3...
4 MULTIPOLYGON (((-77.70069 3...
5 MULTIPOLYGON (((-80.3102 34...
6 MULTIPOLYGON (((-76.33014 3...
7 MULTIPOLYGON (((-78.67179 3...
8 MULTIPOLYGON (((-77.89977 3...
9 MULTIPOLYGON (((-76.3133 36...
10 MULTIPOLYGON (((-77.73078 3...Mapping Virginia counties with mapview
mapview(nc_counties_withgeo, zcol = "medincome")Customize colors
mapview(nc_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1)Warning: Found less unique colors (9) than unique zcol values (100)!
Interpolating color vector to match number of zcol values.This map’s dark background appeared automatically, because mapview determined the map included a lot of light colors. You can turn off that feature.
mapviewOptions("basemaps.color.shuffle" = FALSE)mapview(nc_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1)Warning: Found less unique colors (9) than unique zcol values (100)!
Interpolating color vector to match number of zcol values.Two maps together
map_income <- mapview(nc_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1)Warning: Found less unique colors (9) than unique zcol values (100)!
Interpolating color vector to match number of zcol values.map_age <- mapview(nc_counties_withgeo, zcol = "medage",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1)Warning: Found less unique colors (9) than unique zcol values (76)!
Interpolating color vector to match number of zcol values.sync(map_income, map_age)Side-by-side slider to compare the two, from the leaflet.extras2 package
map_income | map_ageTo turn off legends, hover text, popups
mapview(nc_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1,
legend = FALSE,
label = FALSE,
popup = FALSE)Warning: Found less unique colors (9) than unique zcol values (100)!
Interpolating color vector to match number of zcol values.Customize labels
mylabel <- glue::glue("{nc_counties_withgeo$NAM} {nc_counties_withgeo$medincome}")
mapview(nc_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1,
label = mylabel)Warning: Found less unique colors (9) than unique zcol values (100)!
Interpolating color vector to match number of zcol values.Customize popups
mypopup <- glue::glue("<strong>{nc_counties_withgeo$NAM}</strong><br />
Total Population: {nc_counties_withgeo$totalpop}<br />
Median Income: {nc_counties_withgeo$medincome}") %>%
lapply(htmltools::HTML)
# mylabel <- glue::glue("{all_data$State} {all_data$PctChange10_20}%") %>%
# lapply(htmltools::HTML)head(mypopup)[[1]]
<strong>Nash County, North Carolina</strong><br />
Total Population: 94628<br />
Median Income: 52837
[[2]]
<strong>Clay County, North Carolina</strong><br />
Total Population: 10997<br />
Median Income: 47155
[[3]]
<strong>Mecklenburg County, North Carolina</strong><br />
Total Population: 1100984<br />
Median Income: 73124
[[4]]
<strong>Pitt County, North Carolina</strong><br />
Total Population: 170600<br />
Median Income: 50422
[[5]]
<strong>Anson County, North Carolina</strong><br />
Total Population: 22388<br />
Median Income: 39862
[[6]]
<strong>Carteret County, North Carolina</strong><br />
Total Population: 67944<br />
Median Income: 61805mapview(nc_counties_withgeo, zcol = "medincome",
col.regions = RColorBrewer::brewer.pal(9, "Greens"),
alpha.regions = 1,
popup = mypopup)Warning: Found less unique colors (9) than unique zcol values (100)!
Interpolating color vector to match number of zcol values.