library(sf)
## Linking to GEOS 3.8.1, GDAL 3.1.1, PROJ 6.3.1
library(sp)
library(spData)
library(raster)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:raster':
##
## intersect, select, union
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(stringr)
library(tidyr)
##
## Attaching package: 'tidyr'
## The following object is masked from 'package:raster':
##
## extract
library(ggplot2)
library(classInt)
library(RColorBrewer)
seminar <- st_read("SeminarR.gdb")
## Multiple layers are present in data source /Users/devinhainje/Desktop/R/SeminarR.gdb, reading layer `USA_counties'.
## Use `st_layers' to list all layer names and their type in a data source.
## Set the `layer' argument in `st_read' to read a particular layer.
## Warning in evalq((function (..., call. = TRUE, immediate. = FALSE, noBreaks. =
## FALSE, : automatically selected the first layer in a data source containing more
## than one.
## Reading layer `USA_counties' from data source `/Users/devinhainje/Desktop/R/SeminarR.gdb' using driver `OpenFileGDB'
## Simple feature collection with 3140 features and 53 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -6293474 ymin: 311822.4 xmax: 2256319 ymax: 6198811
## projected CRS: NAD83 / Conus Albers
layersR <- st_layers("SeminarR.gdb")
layersR
## Driver: OpenFileGDB
## Available layers:
## layer_name geometry_type features fields
## 1 USA_counties Multi Polygon 3140 53
## 2 USA_roads Multi Line String 679 8
Counties <- st_read("SeminarR.gdb", layer = "USA_counties")
## Reading layer `USA_counties' from data source `/Users/devinhainje/Desktop/R/SeminarR.gdb' using driver `OpenFileGDB'
## Simple feature collection with 3140 features and 53 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -6293474 ymin: 311822.4 xmax: 2256319 ymax: 6198811
## projected CRS: NAD83 / Conus Albers
head(Counties)
## Simple feature collection with 6 features and 53 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -1826165 ymin: 2819070 xmax: 117332.6 ymax: 3127678
## projected CRS: NAD83 / Conus Albers
## NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS POP1990 POP1999
## 1 Lake of the Woods Minnesota 27 077 27077 4076 4597
## 2 Ferry Washington 53 019 53019 6295 7150
## 3 Stevens Washington 53 065 53065 30948 39965
## 4 Okanogan Washington 53 047 53047 33350 38596
## 5 Pend Oreille Washington 53 051 53051 8915 11788
## 6 Boundary Idaho 16 021 16021 8332 9840
## POP90_SQMI HOUSEHOLDS MALES FEMALES WHITE BLACK AMERI_ES ASIAN_PI OTHER
## 1 2 1576 2037 2039 4042 1 19 10 4
## 2 3 2247 3280 3015 5084 20 1131 24 36
## 3 12 11241 15454 15494 28747 65 1807 179 150
## 4 6 12654 16828 16522 27615 52 3597 166 1920
## 5 6 3395 4426 4489 8640 12 206 25 32
## 6 7 2857 4252 4080 7950 3 150 26 203
## HISPANIC AGE_UNDER5 AGE_5_17 AGE_18_29 AGE_30_49 AGE_50_64 AGE_65_UP
## 1 25 337 791 549 1122 584 693
## 2 85 486 1499 887 1929 827 667
## 3 483 2271 7486 3586 9605 4145 3855
## 4 2779 2536 7051 4492 9749 4890 4632
## 5 120 660 1963 996 2670 1384 1242
## 6 310 635 2065 1138 2351 1121 1022
## NEVERMARRY MARRIED SEPARATED WIDOWED DIVORCED HSEHLD_1_M HSEHLD_1_F MARHH_CHD
## 1 538 2102 24 248 200 185 184 465
## 2 1118 2741 82 251 498 302 195 652
## 3 4037 14795 384 1486 2096 1146 1210 3618
## 4 5042 15320 561 1889 2406 1410 1633 3355
## 5 1124 4349 114 507 641 373 402 961
## 6 1247 3935 89 391 480 300 299 938
## MARHH_NO_C MHH_CHILD FHH_CHILD HSE_UNITS VACANT OWNER_OCC RENTER_OCC
## 1 565 26 57 3050 1474 1332 244
## 2 660 90 169 3239 992 1568 679
## 3 3591 292 731 14601 3360 8566 2675
## 4 3946 401 933 16629 3975 8439 4215
## 5 1157 75 229 5404 2009 2500 895
## 6 928 55 164 3242 385 2237 620
## MEDIAN_VAL MEDIANRENT UNITS_1DET UNITS_1ATT UNITS2 UNITS3_9 UNITS10_49
## 1 40900 185 1927 14 24 29 80
## 2 50100 197 2128 10 40 51 44
## 3 55900 231 10388 109 133 324 264
## 4 50300 222 11281 147 346 816 286
## 5 49500 237 3915 29 41 97 89
## 6 49500 217 2393 7 31 107 34
## UNITS50_UP MOBILEHOME NO_FARMS87 AVG_SIZE87 CROP_ACR87 AVG_SALE87
## 1 0 937 222 536 83787 27958
## 2 0 936 218 3489 29482 22155
## 3 0 3264 1073 490 131700 18138
## 4 0 3431 1476 907 144053 71970
## 5 0 1185 227 276 22923 10367
## 6 0 643 297 267 51806 29463
## Shape_Length Shape_Area Shape
## 1 370200.2 4620576797 MULTIPOLYGON (((49031.84 28...
## 2 361954.6 5905616645 MULTIPOLYGON (((-1704274 29...
## 3 453727.4 6552443738 MULTIPOLYGON (((-1598429 29...
## 4 582790.3 13742574263 MULTIPOLYGON (((-1713358 29...
## 5 298909.7 3742506837 MULTIPOLYGON (((-1574882 30...
## 6 254544.8 3313277935 MULTIPOLYGON (((-1549203 30...
roads <- st_read("SeminarR.gdb", layer = "USA_roads")
## Reading layer `USA_roads' from data source `/Users/devinhainje/Desktop/R/SeminarR.gdb' using driver `OpenFileGDB'
## Simple feature collection with 679 features and 8 fields
## geometry type: MULTILINESTRING
## dimension: XY
## bbox: xmin: -6146140 ymin: 316215.5 xmax: 2242259 ymax: 5729382
## projected CRS: NAD83 / Conus Albers
cities <- st_read("cities1.csv")
## Reading layer `cities1' from data source `/Users/devinhainje/Desktop/R/cities1.csv' using driver `CSV'
## Warning: no simple feature geometries present: returning a data.frame or tbl_df
nrow(cities)
## [1] 23435
crs(roads)
## CRS arguments:
## +proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0
## +datum=NAD83 +units=m +no_defs
crs(cities)
## [1] NA
class(cities)
## [1] "data.frame"
cities2 <- st_as_sf(cities, coords = c("long", "lat"), crs = 4269)
crs(cities2)
## CRS arguments: +proj=longlat +datum=NAD83 +no_defs
cities3 <- st_transform(cities2, st_crs(roads))
crs(cities3)
## CRS arguments:
## +proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0
## +datum=NAD83 +units=m +no_defs
roads
## Simple feature collection with 679 features and 8 fields
## geometry type: MULTILINESTRING
## dimension: XY
## bbox: xmin: -6146140 ymin: 316215.5 xmax: 2242259 ymax: 5729382
## projected CRS: NAD83 / Conus Albers
## First 10 features:
## LENGTH TYPE ADMN_CLASS TOLL_RD RTE_NUM1 RTE_NUM2
## 1 140.931 Multi-Lane Divided Interstate N 82
## 2 186.060 Multi-Lane Divided Interstate N 84
## 3 8.642 Multi-Lane Divided Interstate N 94
## 4 9.439 Multi-Lane Divided Interstate N 94
## 5 8.208 Multi-Lane Divided Interstate N 35
## 6 22.106 Multi-Lane Divided Interstate N 494
## 7 18.154 Multi-Lane Divided Interstate N 35
## 8 8.297 Multi-Lane Divided Interstate N 35
## 9 156.827 Multi-Lane Divided Interstate N 90
## 10 74.106 Multi-Lane Divided Interstate N 35
## ROUTE Shape_Length Shape
## 1 Interstate 82 226804.48 MULTILINESTRING ((-1785974 ...
## 2 Interstate (OR/ID/UT) 84 299427.61 MULTILINESTRING ((-1785974 ...
## 3 Interstate 94 13908.32 MULTILINESTRING ((213049.5 ...
## 4 Interstate 94 15190.89 MULTILINESTRING ((215235.7 ...
## 5 Interstate 35 13208.87 MULTILINESTRING ((212781.6 ...
## 6 Interstate 494 35576.04 MULTILINESTRING ((201766 24...
## 7 Interstate 35 29216.80 MULTILINESTRING ((228943.8 ...
## 8 Interstate 35 13352.68 MULTILINESTRING ((212781.6 ...
## 9 Interstate 90 252386.65 MULTILINESTRING ((-845058.6...
## 10 Interstate 35 119263.95 MULTILINESTRING ((214968.3 ...
cities3
## Simple feature collection with 23435 features and 7 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -6238723 ymin: 279478.7 xmax: 2250123 ymax: 6181247
## projected CRS: NAD83 / Conus Albers
## First 10 features:
## FID AREANAME CLASS ST STFIPS HOUSEUNITS POPULATION
## 1 0 Abbeville city AL 1 1320 3173
## 2 1 Adamsville city AL 1 1554 4161
## 3 2 Addison town AL 1 286 626
## 4 3 Akron town AL 1 220 468
## 5 4 Alabaster city AL 1 5144 14732
## 6 5 Albertville city AL 1 6238 14507
## 7 6 Alexander City city AL 1 6170 14917
## 8 7 Aliceville city AL 1 1293 3009
## 9 8 Allgood town AL 1 188 464
## 10 9 Altoona town AL 1 405 960
## geometry
## 1 POINT (1013892 1001068)
## 2 POINT (832138.6 1209416)
## 3 POINT (805695.3 1274759)
## 4 POINT (767443.9 1122862)
## 5 POINT (848347.7 1169580)
## 6 POINT (893133.4 1290370)
## 7 POINT (933935 1144671)
## 8 POINT (726298.6 1147252)
## 9 POINT (869036.9 1247921)
## 10 POINT (886294.4 1263702)
ggplot() + geom_sf(data = roads) + geom_sf(data = cities3)
#lots of cites lol
road_buf <- st_buffer(roads, 20000)
plot(road_buf)
plot(roads)
road_buf
## Simple feature collection with 679 features and 8 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -6166138 ymin: 296217 xmax: 2262258 ymax: 5749381
## projected CRS: NAD83 / Conus Albers
## First 10 features:
## LENGTH TYPE ADMN_CLASS TOLL_RD RTE_NUM1 RTE_NUM2
## 1 140.931 Multi-Lane Divided Interstate N 82
## 2 186.060 Multi-Lane Divided Interstate N 84
## 3 8.642 Multi-Lane Divided Interstate N 94
## 4 9.439 Multi-Lane Divided Interstate N 94
## 5 8.208 Multi-Lane Divided Interstate N 35
## 6 22.106 Multi-Lane Divided Interstate N 494
## 7 18.154 Multi-Lane Divided Interstate N 35
## 8 8.297 Multi-Lane Divided Interstate N 35
## 9 156.827 Multi-Lane Divided Interstate N 90
## 10 74.106 Multi-Lane Divided Interstate N 35
## ROUTE Shape_Length Shape
## 1 Interstate 82 226804.48 POLYGON ((-1881179 2860185,...
## 2 Interstate (OR/ID/UT) 84 299427.61 POLYGON ((-2073004 2786723,...
## 3 Interstate 94 13908.32 POLYGON ((232986.1 2454433,...
## 4 Interstate 94 15190.89 POLYGON ((211826.5 2425524,...
## 5 Interstate 35 13208.87 POLYGON ((192631.4 2433567,...
## 6 Interstate 494 35576.04 POLYGON ((220474.6 2451199,...
## 7 Interstate 35 29216.80 POLYGON ((244613.6 2431884,...
## 8 Interstate 35 13352.68 POLYGON ((194361.3 2424967,...
## 9 Interstate 90 252386.65 POLYGON ((-931544.5 2571693...
## 10 Interstate 35 119263.95 POLYGON ((195456.3 2313764,...
roads
## Simple feature collection with 679 features and 8 fields
## geometry type: MULTILINESTRING
## dimension: XY
## bbox: xmin: -6146140 ymin: 316215.5 xmax: 2242259 ymax: 5729382
## projected CRS: NAD83 / Conus Albers
## First 10 features:
## LENGTH TYPE ADMN_CLASS TOLL_RD RTE_NUM1 RTE_NUM2
## 1 140.931 Multi-Lane Divided Interstate N 82
## 2 186.060 Multi-Lane Divided Interstate N 84
## 3 8.642 Multi-Lane Divided Interstate N 94
## 4 9.439 Multi-Lane Divided Interstate N 94
## 5 8.208 Multi-Lane Divided Interstate N 35
## 6 22.106 Multi-Lane Divided Interstate N 494
## 7 18.154 Multi-Lane Divided Interstate N 35
## 8 8.297 Multi-Lane Divided Interstate N 35
## 9 156.827 Multi-Lane Divided Interstate N 90
## 10 74.106 Multi-Lane Divided Interstate N 35
## ROUTE Shape_Length Shape
## 1 Interstate 82 226804.48 MULTILINESTRING ((-1785974 ...
## 2 Interstate (OR/ID/UT) 84 299427.61 MULTILINESTRING ((-1785974 ...
## 3 Interstate 94 13908.32 MULTILINESTRING ((213049.5 ...
## 4 Interstate 94 15190.89 MULTILINESTRING ((215235.7 ...
## 5 Interstate 35 13208.87 MULTILINESTRING ((212781.6 ...
## 6 Interstate 494 35576.04 MULTILINESTRING ((201766 24...
## 7 Interstate 35 29216.80 MULTILINESTRING ((228943.8 ...
## 8 Interstate 35 13352.68 MULTILINESTRING ((212781.6 ...
## 9 Interstate 90 252386.65 MULTILINESTRING ((-845058.6...
## 10 Interstate 35 119263.95 MULTILINESTRING ((214968.3 ...
cities_int <- st_intersection(road_buf, cities3)
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
cities_int
## Simple feature collection with 33574 features and 15 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -6147074 ymin: 320719.5 xmax: 2250123 ymax: 5728886
## projected CRS: NAD83 / Conus Albers
## First 10 features:
## LENGTH TYPE ADMN_CLASS TOLL_RD RTE_NUM1 RTE_NUM2
## 294 19.394 Multi-Lane Divided Interstate N 20 59
## 545 4.023 Multi-Lane Divided Interstate N 20 59
## 546 185.079 Multi-Lane Divided Interstate N 65
## 552 9.462 Multi-Lane Divided Interstate N 65
## 307 130.709 Multi-Lane Divided Interstate N 20 59
## 293 13.861 Multi-Lane Divided Interstate N 459
## 567 80.676 Multi-Lane Divided Interstate N 65
## 544 128.992 Multi-Lane Divided Interstate N 59
## 544.1 128.992 Multi-Lane Divided Interstate N 59
## 547 139.752 Multi-Lane Divided Interstate N 20
## ROUTE Shape_Length FID AREANAME CLASS ST STFIPS HOUSEUNITS
## 294 Interstate 20 31212.462 1 Adamsville city AL 1 1554
## 545 Interstate 20 6475.059 1 Adamsville city AL 1 1554
## 546 Interstate 65 297864.061 1 Adamsville city AL 1 1554
## 552 Interstate 65 15228.694 1 Adamsville city AL 1 1554
## 307 Interstate 20 210355.823 3 Akron town AL 1 220
## 293 Interstate 459 22306.822 4 Alabaster city AL 1 5144
## 567 Interstate 65 129838.057 4 Alabaster city AL 1 5144
## 544 Interstate 59 207595.534 8 Allgood town AL 1 188
## 544.1 Interstate 59 207595.534 9 Altoona town AL 1 405
## 547 Interstate 20 224905.166 12 Anniston city AL 1 12100
## POPULATION Shape
## 294 4161 POINT (832138.6 1209416)
## 545 4161 POINT (832138.6 1209416)
## 546 4161 POINT (832138.6 1209416)
## 552 4161 POINT (832138.6 1209416)
## 307 468 POINT (767443.9 1122862)
## 293 14732 POINT (848347.7 1169580)
## 567 14732 POINT (848347.7 1169580)
## 544 464 POINT (869036.9 1247921)
## 544.1 960 POINT (886294.4 1263702)
## 547 26623 POINT (936246.4 1227518)
city_2 <- st_intersects(road_buf, cities3)
nrow(city_2)
## [1] 679
city_2
## Sparse geometry binary predicate list of length 679, where the predicate was `intersects'
## first 10 elements:
## 1: 17030, 17070, 17080, 17199, 17219, 21582, 21597, 21658, 21674, 21682, ...
## 2: 16966, 16970, 16982, 16984, 16996, 16998, 16999, 17001, 17003, 17004, ...
## 3: 9737, 9741, 9792, 9795, 9812, 9813, 9848, 9857, 9879, 9884, ...
## 4: 9741, 9787, 9795, 9812, 9813, 9857, 9879, 9895, 9916, 9934, ...
## 5: 9739, 9741, 9795, 9812, 9813, 9828, 9879, 9895, 9908, 9934, ...
## 6: 9737, 9739, 9792, 9795, 9812, 9813, 9828, 9842, 9848, 9850, ...
## 7: 9739, 9741, 9787, 9795, 9828, 9873, 9879, 9888, 9916, 9934, ...
## 8: 9739, 9795, 9828, 9873, 9908, 9934, 9942, 9945, 9952, 9973, ...
## 9: 11878, 11900, 11933, 11964, 11965, 22895, 22906, 22966, 22975
## 10: 9726, 9739, 9794, 9795, 9828, 9862, 9873, 9882, 9931, 9934, ...
class(city_2)
## [1] "sgbp" "list"
nrow(cities_int)
## [1] 33574
cities_int
## Simple feature collection with 33574 features and 15 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -6147074 ymin: 320719.5 xmax: 2250123 ymax: 5728886
## projected CRS: NAD83 / Conus Albers
## First 10 features:
## LENGTH TYPE ADMN_CLASS TOLL_RD RTE_NUM1 RTE_NUM2
## 294 19.394 Multi-Lane Divided Interstate N 20 59
## 545 4.023 Multi-Lane Divided Interstate N 20 59
## 546 185.079 Multi-Lane Divided Interstate N 65
## 552 9.462 Multi-Lane Divided Interstate N 65
## 307 130.709 Multi-Lane Divided Interstate N 20 59
## 293 13.861 Multi-Lane Divided Interstate N 459
## 567 80.676 Multi-Lane Divided Interstate N 65
## 544 128.992 Multi-Lane Divided Interstate N 59
## 544.1 128.992 Multi-Lane Divided Interstate N 59
## 547 139.752 Multi-Lane Divided Interstate N 20
## ROUTE Shape_Length FID AREANAME CLASS ST STFIPS HOUSEUNITS
## 294 Interstate 20 31212.462 1 Adamsville city AL 1 1554
## 545 Interstate 20 6475.059 1 Adamsville city AL 1 1554
## 546 Interstate 65 297864.061 1 Adamsville city AL 1 1554
## 552 Interstate 65 15228.694 1 Adamsville city AL 1 1554
## 307 Interstate 20 210355.823 3 Akron town AL 1 220
## 293 Interstate 459 22306.822 4 Alabaster city AL 1 5144
## 567 Interstate 65 129838.057 4 Alabaster city AL 1 5144
## 544 Interstate 59 207595.534 8 Allgood town AL 1 188
## 544.1 Interstate 59 207595.534 9 Altoona town AL 1 405
## 547 Interstate 20 224905.166 12 Anniston city AL 1 12100
## POPULATION Shape
## 294 4161 POINT (832138.6 1209416)
## 545 4161 POINT (832138.6 1209416)
## 546 4161 POINT (832138.6 1209416)
## 552 4161 POINT (832138.6 1209416)
## 307 468 POINT (767443.9 1122862)
## 293 14732 POINT (848347.7 1169580)
## 567 14732 POINT (848347.7 1169580)
## 544 464 POINT (869036.9 1247921)
## 544.1 960 POINT (886294.4 1263702)
## 547 26623 POINT (936246.4 1227518)
#33574
nrow(cities3)
## [1] 23435
#23435
class(cities_int)
## [1] "sf" "data.frame"
new_cities <- na.omit(cities_int)
new_cities
## Simple feature collection with 33574 features and 15 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -6147074 ymin: 320719.5 xmax: 2250123 ymax: 5728886
## projected CRS: NAD83 / Conus Albers
## First 10 features:
## LENGTH TYPE ADMN_CLASS TOLL_RD RTE_NUM1 RTE_NUM2
## 294 19.394 Multi-Lane Divided Interstate N 20 59
## 545 4.023 Multi-Lane Divided Interstate N 20 59
## 546 185.079 Multi-Lane Divided Interstate N 65
## 552 9.462 Multi-Lane Divided Interstate N 65
## 307 130.709 Multi-Lane Divided Interstate N 20 59
## 293 13.861 Multi-Lane Divided Interstate N 459
## 567 80.676 Multi-Lane Divided Interstate N 65
## 544 128.992 Multi-Lane Divided Interstate N 59
## 544.1 128.992 Multi-Lane Divided Interstate N 59
## 547 139.752 Multi-Lane Divided Interstate N 20
## ROUTE Shape_Length FID AREANAME CLASS ST STFIPS HOUSEUNITS
## 294 Interstate 20 31212.462 1 Adamsville city AL 1 1554
## 545 Interstate 20 6475.059 1 Adamsville city AL 1 1554
## 546 Interstate 65 297864.061 1 Adamsville city AL 1 1554
## 552 Interstate 65 15228.694 1 Adamsville city AL 1 1554
## 307 Interstate 20 210355.823 3 Akron town AL 1 220
## 293 Interstate 459 22306.822 4 Alabaster city AL 1 5144
## 567 Interstate 65 129838.057 4 Alabaster city AL 1 5144
## 544 Interstate 59 207595.534 8 Allgood town AL 1 188
## 544.1 Interstate 59 207595.534 9 Altoona town AL 1 405
## 547 Interstate 20 224905.166 12 Anniston city AL 1 12100
## POPULATION Shape
## 294 4161 POINT (832138.6 1209416)
## 545 4161 POINT (832138.6 1209416)
## 546 4161 POINT (832138.6 1209416)
## 552 4161 POINT (832138.6 1209416)
## 307 468 POINT (767443.9 1122862)
## 293 14732 POINT (848347.7 1169580)
## 567 14732 POINT (848347.7 1169580)
## 544 464 POINT (869036.9 1247921)
## 544.1 960 POINT (886294.4 1263702)
## 547 26623 POINT (936246.4 1227518)
library_join <- cities_int %>% select(ST, POPULATION) %>%
st_join(road_buf) %>% group_by(ST) %>% summarise(mean = mean(as.numeric(POPULATION)))
## `summarise()` ungrouping output (override with `.groups` argument)
library_join
## Simple feature collection with 51 features and 2 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: -6147074 ymin: 320719.5 xmax: 2250123 ymax: 5728886
## projected CRS: NAD83 / Conus Albers
## # A tibble: 51 x 3
## ST mean Shape
## <chr> <dbl> <GEOMETRY [m]>
## 1 AK 5049. MULTIPOINT ((-3243604 4998339), (-3222090 5043018), (-3219357 …
## 2 AL 20281. MULTIPOINT ((712433.5 1069506), (715340.7 1101958), (719217.4 …
## 3 AR 12615. MULTIPOINT ((145482.9 1369212), (148202.4 1378079), (153194.7 …
## 4 AZ 94708. MULTIPOINT ((-1735191 1229715), (-1725270 1236880), (-1706563 …
## 5 CA 71213. MULTIPOINT ((-2290566 1930753), (-2289130 1933093), (-2287655 …
## 6 CO 38879. MULTIPOINT ((-1086478 1865316), (-1081047 1856503), (-1072702 …
## 7 CT 24749. MULTIPOINT ((1851766 2264549), (1852069 2286089), (1852849 225…
## 8 DC 606900 POINT (1620172 1926646)
## 9 DE 9636. MULTIPOINT ((1707720 2033320), (1710851 2040225), (1711399 203…
## 10 FL 20922. MULTIPOINT ((827611.7 887086.3), (831276.2 858756), (832453.9 …
## # … with 41 more rows
ggplot() + geom_sf(data = road_buf)
ggplot() + geom_sf(data = roads) + geom_sf(data = cities_int)
##############################################
elevation <- raster("Terrain1.tif")
plot(elevation)
cities3$elev = raster::extract(elevation, cities3)
head(cities3$elev)
## [1] 123.1404 175.6370 235.7433 41.9953 157.3699 320.6448
head(cities3)
## Simple feature collection with 6 features and 8 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 767443.9 ymin: 1001068 xmax: 1013892 ymax: 1290370
## projected CRS: NAD83 / Conus Albers
## FID AREANAME CLASS ST STFIPS HOUSEUNITS POPULATION
## 1 0 Abbeville city AL 1 1320 3173
## 2 1 Adamsville city AL 1 1554 4161
## 3 2 Addison town AL 1 286 626
## 4 3 Akron town AL 1 220 468
## 5 4 Alabaster city AL 1 5144 14732
## 6 5 Albertville city AL 1 6238 14507
## geometry elev
## 1 POINT (1013892 1001068) 123.1404
## 2 POINT (832138.6 1209416) 175.6370
## 3 POINT (805695.3 1274759) 235.7433
## 4 POINT (767443.9 1122862) 41.9953
## 5 POINT (848347.7 1169580) 157.3699
## 6 POINT (893133.4 1290370) 320.6448
cities5 <- na.omit(cities3)
cities4 <- cities5 %>% group_by(ST) %>% summarise(mean= mean(elev), stdev = sd(elev))
## `summarise()` ungrouping output (override with `.groups` argument)
cities4
## Simple feature collection with 49 features and 3 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: -2345385 ymin: 279478.7 xmax: 2250123 ymax: 3164337
## projected CRS: NAD83 / Conus Albers
## # A tibble: 49 x 4
## ST mean stdev geometry
## <chr> <dbl> <dbl> <GEOMETRY [m]>
## 1 AL 156. 92.4 MULTIPOINT ((712433.5 1069506), (712934.2 1147641), (7142…
## 2 AR 137. 104. MULTIPOINT ((130060.1 1459923), (135039.4 1469705), (1368…
## 3 AZ 1025. 616. MULTIPOINT ((-1743743 1219525), (-1735191 1229715), (-172…
## 4 CA 248. 370. MULTIPOINT ((-2345385 2109126), (-2337473 2302033), (-233…
## 5 CO 1852. 506. MULTIPOINT ((-1122675 1712945), (-1115604 1649092), (-110…
## 6 CT 80.9 75.6 MULTIPOINT ((1846420 2335730), (1851766 2264549), (185206…
## 7 DC 17.3 NA POINT (1620172 1926646)
## 8 DE 15.1 19.6 MULTIPOINT ((1707720 2033320), (1710851 2040225), (171139…
## 9 FL 13.7 16.3 MULTIPOINT ((827611.7 887086.3), (831081.8 916217.6), (83…
## 10 GA 185. 127. MULTIPOINT ((949183.4 1365255), (956970 1322049), (961544…
## # … with 39 more rows
###############################################
usa <- Counties %>% group_by(STATE_NAME, AGE_5_17, AGE_65_UP) %>% summarise(rate = n(), ages = AGE_5_17 - AGE_65_UP)
## `summarise()` regrouping output by 'STATE_NAME', 'AGE_5_17' (override with `.groups` argument)
head(usa)
## Simple feature collection with 6 features and 5 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 723703.4 ymin: 977214.5 xmax: 992039.7 ymax: 1264708
## projected CRS: NAD83 / Conus Albers
## # A tibble: 6 x 6
## # Groups: STATE_NAME, AGE_5_17 [6]
## STATE_NAME AGE_5_17 AGE_65_UP rate ages Shape
## <chr> <int> <int> <int> <int> <MULTIPOLYGON [m]>
## 1 Alabama 2092 1602 1 490 (((878438.1 1150754, 878833.2 11583…
## 2 Alabama 2433 1773 1 660 (((991792.4 1064421, 991919.7 10633…
## 3 Alabama 2458 1705 1 753 (((980155.2 1231475, 984990.1 12135…
## 4 Alabama 2530 2232 1 298 (((925603.9 1162438, 925326.3 11642…
## 5 Alabama 2555 1590 1 965 (((768565.7 1138696, 768945.6 11361…
## 6 Alabama 2748 2496 1 252 (((896998.5 1033689, 901207 1034690…
class(usa)
## [1] "sf" "grouped_df" "tbl_df" "tbl" "data.frame"
plot(usa$ages)
plot(usa["ages"], axes=TRUE, main= "Difference in 5-17 and 65-up ages in the United States",
nbreaks=5,breaks="quantile")
