Filling polygons of a sp map object by another variable in R

Question

My goal is generating a Los Angles zipcode map filled by another variable, say number of crime events using ggplot. As the filling variable that is originally not stored in

Answer 1

You may need to install ggplot2 from GitHub for this. Probably not, but I'm always on the dev version of it and can't really downgrade for this example.

Switch to sf:

library(sf)
library(tidyverse)

sf::st_read(
  path.expand("~/Data/cb_2017_us_zcta510_500k/cb_2017_us_zcta510_500k.shp"),
  stringsAsFactors = FALSE
) -> ztca
## Reading layer `cb_2017_us_zcta510_500k' from data source `/Users/hrbrmstr/Data/cb_2017_us_zcta510_500k/cb_2017_us_zcta510_500k.shp' using driver `ESRI Shapefile'
## Simple feature collection with 33144 features and 5 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -176.6847 ymin: -14.37374 xmax: 145.8304 ymax: 71.34122
## epsg (SRID):    4269
## proj4string:    +proj=longlat +datum=NAD83 +no_defs

ztca
## Simple feature collection with 33144 features and 5 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -176.6847 ymin: -14.37374 xmax: 145.8304 ymax: 71.34122
## epsg (SRID):    4269
## proj4string:    +proj=longlat +datum=NAD83 +no_defs
## First 10 features:
##    ZCTA5CE10     AFFGEOID10 GEOID10    ALAND10  AWATER10                       geometry
## 1      35442 8600000US35442   35442  610213891  10838694 MULTIPOLYGON (((-88.25262 3...
## 2      85365 8600000US85365   85365 3545016067   9766486 MULTIPOLYGON (((-114.6847 3...
## 3      71973 8600000US71973   71973  204670474   1264709 MULTIPOLYGON (((-94.46643 3...
## 4      95445 8600000US95445   95445  221559097   7363179 MULTIPOLYGON (((-123.6431 3...
## 5      06870 8600000US06870   06870    5945321   3841130 MULTIPOLYGON (((-73.58766 4...
## 6      19964 8600000US19964   19964   24601672    124382 MULTIPOLYGON (((-75.74767 3...
## 7      32233 8600000US32233   32233   26427514   8163517 MULTIPOLYGON (((-81.45931 3...
## 8      90401 8600000US90401   90401    2199165    439378 MULTIPOLYGON (((-118.5028 3...
## 9      30817 8600000US30817   30817  498860155 105853904 MULTIPOLYGON (((-82.5951 33...
## 10     30458 8600000US30458   30458  378422575  12351325 MULTIPOLYGON (((-81.73032 3...

It should have read that in much faster.

Now we need your data. First the zips:

c(
  91306, 90008, 90014, 90094, 91326, 90212, 90001, 90006, 90301, 91401, 90095,
  90021, 90089, 90057, 90029, 90502, 90036, 90745, 91330, 91411, 91601, 91607,
  90302, 90073, 91340, 91602, 90071, 90039, 90011, 90248, 91303, 90020, 90732,
  90405, 91342, 90077, 90061, 90015, 90002, 90041, 90211, 90059, 90066, 90210,
  91343, 90062, 91606, 91403, 90063, 90012, 91307, 90293, 91402, 91364, 90007,
  91331, 90005, 91040, 90031, 91324, 90043, 91316, 91352, 90069, 91325, 91344,
  90019, 90056, 90032, 90291, 90027, 90247, 90232, 90013, 90042, 90035, 90024,
  90026, 90034, 90016, 90018, 90004, 90067, 90010, 90025, 90047, 90037, 91605,
  90049, 90744, 90028, 90272, 90275, 90731, 90710, 91042, 91345, 90003, 91335,
  91304, 91311, 91214, 91604, 91608, 91405, 91367, 91406, 91436, 91504, 91423,
  91505, 91356, 90292, 90402, 90065, 90501, 90230, 90048, 90046, 90038, 90044,
  90810, 90023, 90068, 90045, 90717, 90064, 90058, 90033, 90017, 90090
) %>% as.character() -> zip_interest

The ZCTA5CE10 field is the zip (IIRC) so we filter those out:

la_df <- filter(ztca, ZCTA5CE10 %in% zip_interest)

And, then we make your zip-fill data:

data_frame(
  zipcode = c(
    90001, 90002, 90003, 90004, 90005, 90006,
    90007, 90008, 90010, 90011, 90012, 90013, 90014, 90015, 90016,
    90017, 90018, 90019, 90020, 90021, 90023, 90024, 90025, 90026,
    90027, 90028, 90029, 90031, 90032, 90033, 90034, 90035, 90036,
    90037, 90038, 90039, 90041, 90042, 90043, 90044, 90045, 90046,
    90047, 90048, 90049, 90056, 90057, 90058, 90059, 90061, 90062,
    90063, 90064, 90065, 90066, 90067, 90068, 90069, 90071, 90073,
    90077, 90089, 90090, 90094, 90095, 90210, 90211, 90212, 90230,
    90232, 90247, 90248, 90272, 90275, 90291, 90292, 90293, 90301,
    90302, 90402, 90405, 90501, 90502, 90710, 90717, 90731, 90732,
    90744, 90745, 90810, 91040, 91042, 91214, 91303, 91304, 91306,
    91307, 91311, 91316, 91324, 91325, 91326, 91330, 91331, 91335,
    91340, 91342, 91343, 91344, 91345, 91352, 91356, 91364, 91367,
    91401, 91402, 91403, 91405, 91406, 91411, 91423, 91436, 91504,
    91505, 91601, 91602, 91604, 91605, 91606, 91607, 91608) %>%
    as.character(),
  fillvar = c(
    7086,
    20731, 45814, 24883, 15295, 23099, 22163, 24461, 6351, 40047,
    16028, 22822, 9500, 22859, 23201, 19959, 21000, 25806, 12917,
    11767, 12791, 9221, 14967, 26850, 21314, 36956, 14556, 12233,
    12986, 19864, 17239, 10843, 17399, 35476, 13786, 10083, 9214,
    18613, 22267, 46012, 26956, 14627, 24949, 10958, 10238, 294,
    21961, 3037, 15441, 12432, 20470, 3673, 12165, 14869, 14516,
    2299, 10013, 2458, 1862, 473, 1755, 3772, 691, 1418, 200, 1689,
    813, 181, 3553, 912, 3807, 2693, 5017, 558, 18841, 5872, 3890,
    573, 322, 592, 331, 5795, 1119, 7323, 667, 29879, 5318, 22950,
    110, 144, 5919, 7465, 135, 17871, 14904, 16814, 6811, 14521,
    9806, 17144, 9857, 7175, 647, 32723, 25985, 3182, 29040, 23947,
    18500, 7194, 19031, 12226, 11075, 16189, 17455, 26382, 9818,
    22774, 22124, 12000, 14122, 4965, 566, 522, 20601, 7204, 14051,
    24116, 18162, 11174, 478)
) -> zip_df

Now, it's just a matter of joining that data to the sf object and plotting it:

left_join(zip_df, by=c("ZCTA5CE10"="zipcode")) %>%
  ggplot() +
  geom_sf(aes(fill=fillvar), size=0.125, color="#b2b2b2") + # thinner lines
  coord_sf(datum = NA) + # this gets rid of the graticule
  viridis::scale_fill_viridis(name="Better legend name:", labels=scales::comma) + # color-blind friendly color map
  ggthemes::theme_map() + # clean map
  theme(legend.position="top") + # legend on top
  theme(legend.key.width = unit(3, "lines")) # wider legend