Race gaps and politics
Init
options(
digits = 3
)
library(pacman)
p_load(
kirkegaard,
readxl,
rms,
haven,
sf,
ggeffects,
choroplethr, choroplethrMaps
)
theme_set(theme_bw())Data
#politics
#https://electionlab.mit.edu/
pol = read_csv("data/countypres_2000-2020.csv") %>%
mutate(
county_fips = county_fips %>% as.numeric(),
vote_frac = candidatevotes / totalvotes
)## Rows: 72617 Columns: 12## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): state, state_po, county_name, county_fips, office, candidate, party...
## dbl (4): year, candidatevotes, totalvotes, version##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#test
#https://edopportunity.org/get-the-data/
test = read_dta("data/seda_county_long_cs_4.1.dta")
test_cov = read_dta("data/seda_cov_county_poolyr_4.1.dta")
#pop counts
#https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html#par_textimage_70769902
pop = read_csv("data/co-est2019-alldata.csv") %>%
mutate(
FIPS = (STATE + COUNTY) %>% as.numeric()
)## Rows: 3193 Columns: 164## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): SUMLEV, STATE, COUNTY, STNAME, CTYNAME
## dbl (159): REGION, DIVISION, CENSUS2010POP, ESTIMATESBASE2010, POPESTIMATE20...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#Race IAT data
#https://github.com/lizre/map-iat
race_IAT = read_csv("data/map-iat/raceiatdat.csv")## Rows: 1000000 Columns: 15## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): religion, MSANo, CountyNo, MSAName, STATE
## dbl (10): Implicit, Explicit, raceomb, sexnum, politics, age, year, datecode...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# this .csv contains all state abbrevs, state nos., and lowercase state names
state_info = read_csv("data/map-iat/state_info.csv")## Rows: 51 Columns: 3## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): state, state.name
## dbl (1): state.no##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#join
race_IAT = left_join(
race_IAT,
state_info,
by = c("STATE" = "state")
)
# however, county number needs to be 3 digits, so leading zeroes must be added
race_IAT$CountyNo <- str_pad(race_IAT$CountyNo, 3, pad = "0")
# now concatenation can happen
race_IAT <- race_IAT %>%
mutate(FIPS = paste(state.no, CountyNo, sep = ""))
#mean by county, remove NAs
race_IAT %>%
filter(
!is.na(CountyNo), !is.na(state.no)
) %>%
group_by(FIPS) %>%
summarise(mean_IAT = mean(Implicit, na.rm = T), n_IAT = n()) %>%
mutate(
FIPS = as.numeric(FIPS)
) ->
race_IAT_countyPrep test and pol data
Compute means across years.
#num vars
ach_vars = test %>% names() %>% str_subset("_mn_")
ach_weights = test %>% names() %>% str_subset("totgyb")
test_aggr = test %>% plyr::ddply("sedacounty", function(dd) {
# browser()
#average the numerical variables based on the sample size
map2_dbl(ach_vars, ach_weights, function(v, w) {
# browser()
wtd_mean(dd[[v]], dd[[w]])
}) -> means
#avg sample sizes
map_dbl(ach_weights, function(v) {
# browser()
wtd_mean(dd[[v]])
}) -> avg_ns
#make data frame from named lists
c(
means %>% set_names(ach_vars),
avg_ns %>% set_names(ach_weights)
) %>% as.list() %>% as.data.frame()
})
cov_vars = test_cov %>% select(urban:seswhtnam) %>% names()
#average the test covariates, no weights
test_cov_aggr = test_cov %>% plyr::ddply("sedacounty", function(dd) {
# browser()
#average the numerical variables
map_dbl(cov_vars, function(v) {
# browser()
wtd_mean(dd[[v]])
}) -> means
#make data frame from named lists
c(
means %>% set_names(cov_vars)
) %>% as.list() %>% as.data.frame()
})
#in fact, the data are broken! some totals are missing for 2020
#have to loop and do totals manually first
pol_aggr = pol %>%
filter(!is.na(county_fips)) %>%
plyr::ddply(c("year", "county_fips", "party"), function(dd) {
#if we have totals, use them
if (any(dd$mode == "TOTAL")) {
dd %>% filter(mode == "TOTAL") %>% select(year, party, vote_frac, totalvotes) -> y
return(y)
} else {
#if we dont have totals row, make one
# browser()
tibble(
dd %>% filter(mode != "TOTAL") %>% group_by(party) %>% summarise(vote_frac = sum(vote_frac)),
totalvotes = dd$totalvotes[1],
year = dd$year[1],
) -> y
return(y)
}
})
#change format to wide
pol_aggr %>%
pivot_wider(
names_from = c(party, year),
values_from = vote_frac,
id_cols = county_fips
) -> pol_aggr2
#missing cases?
pol_aggr2 %>% miss_by_var()## county_fips DEMOCRAT_2000 GREEN_2000 OTHER_2000
## 0 3 274 3
## REPUBLICAN_2000 DEMOCRAT_2004 OTHER_2004 REPUBLICAN_2004
## 3 1 78 1
## DEMOCRAT_2008 OTHER_2008 REPUBLICAN_2008 DEMOCRAT_2012
## 1 1 1 1
## OTHER_2012 REPUBLICAN_2012 DEMOCRAT_2016 OTHER_2016
## 1 1 1 1
## REPUBLICAN_2016 DEMOCRAT_2020 OTHER_2020 REPUBLICAN_2020
## 1 3 617 3
## GREEN_2020 LIBERTARIAN_2020
## 1345 165Merge
#no dups
test_aggr$sedacounty %>% anyDuplicated() %>% {assert_that(. == 0)}## [1] TRUEtest_cov_aggr$sedacounty %>% anyDuplicated() %>% {assert_that(. == 0)}## [1] TRUEpol_aggr2$county_fips %>% anyDuplicated() %>% {assert_that(. == 0)}## [1] TRUEpop$FIPS %>% anyDuplicated() %>% {assert_that(. == 0)}## [1] TRUErace_IAT_county$FIPS %>% anyDuplicated() %>% {assert_that(. == 0)}## [1] TRUE#no miss
test_aggr$sedacounty %>% anyNA() %>% {assert_that(!.)}## [1] TRUEtest_cov_aggr$sedacounty %>% anyNA() %>% {assert_that(!.)}## [1] TRUEpol_aggr2$county_fips %>% anyNA() %>% {assert_that(!.)}## [1] TRUEpop$FIPS %>% anyNA() %>% {assert_that(!.)}## [1] TRUErace_IAT_county$FIPS %>% anyNA() %>% {assert_that(!.)}## [1] TRUE#join
d = full_join(
test_aggr,
test_cov_aggr,
by = c("sedacounty" = "sedacounty")
) %>%
full_join(
pol_aggr2,
by = c("sedacounty" = "county_fips")
) %>% full_join(
pop %>% filter(COUNTY != "000"),
by = c("sedacounty" = "FIPS")
) %>% full_join(
race_IAT_county,
by = c("sedacounty" = "FIPS")
)
d$sedacounty %>% anyDuplicated() %>% {assert_that(. == 0)}## [1] TRUEd$sedacounty %>% anyNA() %>% {assert_that(!.)}## [1] TRUESpatial data
#load shapefile
#https://catalog.data.gov/dataset/tiger-line-shapefile-2017-nation-u-s-current-county-and-equivalent-national-shapefile
county_sf = st_read("data/spatial/tl_2017_us_county.shp")## Reading layer `tl_2017_us_county' from data source
## `/media/luks8tb1/projects/Systemic racism theory race gaps counties/data/spatial/tl_2017_us_county.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 3233 features and 17 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -179 ymin: -14.6 xmax: 180 ymax: 71.4
## CRS: 4269#make FIPS
county_sf$FIPS = (as.character(county_sf$STATEFP) + as.character(county_sf$COUNTYFP)) %>% as.numeric()
county_sf = county_sf %>% filter(FIPS %in% d$sedacounty)
#join in data
county_sf_data = full_join(
county_sf,
d,
by = c("FIPS" = "sedacounty")
)
#z score versions
county_sf_data %<>% mutate(
#rename
land_area = ALAND,
frac_white = perwht,
bw_gap = cs_mn_wbg,
hw_gap = cs_mn_whg,
bw_gap_test = cs_mn_wbg,
hw_gap_test = cs_mn_whg,
white_mean_score = cs_mn_wht,
black_mean_score = cs_mn_blk,
hispanic_mean_score = cs_mn_hsp,
white_mean_ses = seswht,
black_mean_ses = sesblk,
hispanic_mean_ses = seshsp,
bw_gap_ses = seswhtblk,
hw_gap_ses = seswhthsp,
state = STATEFP,
popsize2019 = POPESTIMATE2019,
#pop density
pop_density = POPESTIMATE2019 / land_area,
pop_density_log10 = pop_density %>% log10(),
pop_density_log10_z = pop_density_log10 %>% standardize()
)
#back to normal
d = county_sf_data %>% st_drop_geometry()Analysis
Basics
#primary variables
primary_vars = d %>%
select(bw_gap_test, hw_gap_test, bw_gap_ses, hw_gap_ses,
REPUBLICAN_2020, REPUBLICAN_2016, mean_IAT, white_mean_score, black_mean_score, hispanic_mean_score, frac_white, pop_density_log10_z, urban, suburb) %>%
names()
#descriptives
d %>%
select(bw_gap_test, hw_gap_test, bw_gap_ses, hw_gap_ses,
REPUBLICAN_2020, mean_IAT,
white_mean_score, black_mean_score, hispanic_mean_score,
frac_white,
pop_density_log10, urban, suburb) %>%
describe2()#bw gap, whole country
wtd_mean(d$white_mean_score, w = d$totgyb_wht %>% sqrt())## [1] 0.175wtd_mean(d$black_mean_score, w = d$totgyb_blk %>% sqrt())## [1] -0.474wtd_mean(d$black_mean_score, w = d$totgyb_blk %>% sqrt()) - wtd_mean(d$white_mean_score, w = d$totgyb_wht %>% sqrt())## [1] -0.649#hw
wtd_mean(d$hispanic_mean_score, w = d$totgyb_hsp %>% sqrt())## [1] -0.297wtd_mean(d$hispanic_mean_score, w = d$totgyb_hsp %>% sqrt()) - wtd_mean(d$white_mean_score, w = d$totgyb_wht %>% sqrt())## [1] -0.472#SES gaps
wtd_mean(d$black_mean_ses, w = d$totgyb_blk %>% sqrt()) - wtd_mean(d$white_mean_ses, w = d$totgyb_wht %>% sqrt())## [1] -2.45wtd_mean(d$hispanic_mean_ses, w = d$totgyb_hsp %>% sqrt()) - wtd_mean(d$white_mean_ses, w = d$totgyb_wht %>% sqrt())## [1] -1.47#correlations
combine_upperlower(
.upper.tri = wtd.cors(d %>% select(!!primary_vars), weight = sqrt(d$popsize2019)),
.lower.tri = wtd.cors(d %>% select(!!primary_vars))
)## bw_gap_test hw_gap_test bw_gap_ses hw_gap_ses
## bw_gap_test NA 0.7165 0.5622 0.3642
## hw_gap_test 0.6620 NA 0.2750 0.5870
## bw_gap_ses 0.4504 0.2365 NA 0.4006
## hw_gap_ses 0.3497 0.5389 0.3761 NA
## REPUBLICAN_2020 -0.4335 -0.4523 -0.1321 -0.2834
## REPUBLICAN_2016 -0.4288 -0.4635 -0.1586 -0.3011
## mean_IAT 0.0456 -0.0215 -0.2260 -0.0401
## white_mean_score 0.5865 0.5329 -0.1063 0.2509
## black_mean_score -0.3443 -0.0439 -0.5910 -0.1233
## hispanic_mean_score -0.1398 -0.4792 -0.3201 -0.3066
## frac_white -0.1808 -0.2553 -0.2753 -0.2652
## pop_density_log10_z 0.4498 0.2803 -0.1248 0.1821
## urban 0.3724 0.3110 0.0959 0.2237
## suburb 0.2425 0.2216 -0.2357 0.0638
## REPUBLICAN_2020 REPUBLICAN_2016 mean_IAT white_mean_score
## bw_gap_test -0.53498 -0.5312 -0.03563 0.6098
## hw_gap_test -0.59340 -0.5963 -0.09000 0.6172
## bw_gap_ses -0.12378 -0.1480 -0.15141 -0.0110
## hw_gap_ses -0.38197 -0.3954 -0.07554 0.2893
## REPUBLICAN_2020 NA 0.9860 0.27655 -0.5255
## REPUBLICAN_2016 0.98390 NA 0.27450 -0.4956
## mean_IAT 0.23878 0.2373 NA 0.0217
## white_mean_score -0.35882 -0.3317 0.07067 NA
## black_mean_score 0.00692 0.0474 0.23721 0.5415
## hispanic_mean_score 0.03239 0.0824 0.04549 0.4599
## frac_white 0.51616 0.5318 0.30532 -0.0452
## pop_density_log10_z -0.51943 -0.4688 -0.03716 0.3329
## urban -0.42904 -0.4113 -0.05268 0.2706
## suburb -0.39080 -0.3471 -0.00384 0.3874
## black_mean_score hispanic_mean_score frac_white
## bw_gap_test -0.39911 -0.2041 -0.297
## hw_gap_test -0.08965 -0.4730 -0.383
## bw_gap_ses -0.62099 -0.3039 -0.138
## hw_gap_ses -0.10841 -0.3585 -0.312
## REPUBLICAN_2020 0.00533 0.1216 0.596
## REPUBLICAN_2016 0.04322 0.1627 0.617
## mean_IAT 0.20603 0.1004 0.382
## white_mean_score 0.47554 0.3893 -0.218
## black_mean_score NA 0.6209 0.196
## hispanic_mean_score 0.59280 NA 0.280
## frac_white 0.29186 0.2821 NA
## pop_density_log10_z 0.29050 0.2017 -0.129
## urban -0.00641 -0.0601 -0.244
## suburb 0.31524 0.1883 -0.257
## pop_density_log10_z urban suburb
## bw_gap_test 0.5209 0.398 0.24126
## hw_gap_test 0.4715 0.390 0.27257
## bw_gap_ses 0.0329 0.208 -0.21004
## hw_gap_ses 0.3381 0.303 0.07109
## REPUBLICAN_2020 -0.6634 -0.537 -0.44553
## REPUBLICAN_2016 -0.6266 -0.535 -0.40189
## mean_IAT -0.0912 -0.132 -0.00418
## white_mean_score 0.5675 0.319 0.49196
## black_mean_score 0.1731 -0.133 0.28734
## hispanic_mean_score 0.0869 -0.182 0.18120
## frac_white -0.3812 -0.460 -0.19015
## pop_density_log10_z NA 0.572 0.62541
## urban 0.4976 NA 0.05799
## suburb 0.5868 0.160 NA#with p stars
cor_matrix(d %>% select(!!primary_vars), weights = sqrt(d$popsize2019), p_val = T)## bw_gap_test hw_gap_test bw_gap_ses hw_gap_ses
## bw_gap_test "1" "0.72***" "0.56***" "0.36***"
## hw_gap_test "0.72***" "1" "0.28***" "0.59***"
## bw_gap_ses "0.56***" "0.28***" "1" "0.40***"
## hw_gap_ses "0.36***" "0.59***" "0.40***" "1"
## REPUBLICAN_2020 "-0.53***" "-0.59***" "-0.12***" "-0.38***"
## REPUBLICAN_2016 "-0.53***" "-0.60***" "-0.15***" "-0.40***"
## mean_IAT "-0.04" "-0.09***" "-0.15***" "-0.08**"
## white_mean_score "0.61***" "0.62***" "-0.01" "0.29***"
## black_mean_score "-0.40***" "-0.09**" "-0.62***" "-0.11***"
## hispanic_mean_score "-0.20***" "-0.47***" "-0.30***" "-0.36***"
## frac_white "-0.30***" "-0.38***" "-0.14***" "-0.31***"
## pop_density_log10_z "0.52***" "0.47***" "0.03" "0.34***"
## urban "0.40***" "0.39***" "0.21***" "0.30***"
## suburb "0.24***" "0.27***" "-0.21***" "0.07*"
## REPUBLICAN_2020 REPUBLICAN_2016 mean_IAT white_mean_score
## bw_gap_test "-0.53***" "-0.53***" "-0.04" "0.61***"
## hw_gap_test "-0.59***" "-0.60***" "-0.09***" "0.62***"
## bw_gap_ses "-0.12***" "-0.15***" "-0.15***" "-0.01"
## hw_gap_ses "-0.38***" "-0.40***" "-0.08**" "0.29***"
## REPUBLICAN_2020 "1" "0.99***" "0.28***" "-0.53***"
## REPUBLICAN_2016 "0.99***" "1" "0.27***" "-0.50***"
## mean_IAT "0.28***" "0.27***" "1" "0.02"
## white_mean_score "-0.53***" "-0.50***" "0.02" "1"
## black_mean_score "0.01" "0.04" "0.21***" "0.48***"
## hispanic_mean_score "0.12***" "0.16***" "0.10***" "0.39***"
## frac_white "0.60***" "0.62***" "0.38***" "-0.22***"
## pop_density_log10_z "-0.66***" "-0.63***" "-0.09***" "0.57***"
## urban "-0.54***" "-0.53***" "-0.13***" "0.32***"
## suburb "-0.45***" "-0.40***" "0.00" "0.49***"
## black_mean_score hispanic_mean_score frac_white
## bw_gap_test "-0.40***" "-0.20***" "-0.30***"
## hw_gap_test "-0.09**" "-0.47***" "-0.38***"
## bw_gap_ses "-0.62***" "-0.30***" "-0.14***"
## hw_gap_ses "-0.11***" "-0.36***" "-0.31***"
## REPUBLICAN_2020 "0.01" "0.12***" "0.60***"
## REPUBLICAN_2016 "0.04" "0.16***" "0.62***"
## mean_IAT "0.21***" "0.10***" "0.38***"
## white_mean_score "0.48***" "0.39***" "-0.22***"
## black_mean_score "1" "0.62***" "0.20***"
## hispanic_mean_score "0.62***" "1" "0.28***"
## frac_white "0.20***" "0.28***" "1"
## pop_density_log10_z "0.17***" "0.09***" "-0.38***"
## urban "-0.13***" "-0.18***" "-0.46***"
## suburb "0.29***" "0.18***" "-0.19***"
## pop_density_log10_z urban suburb
## bw_gap_test "0.52***" "0.40***" "0.24***"
## hw_gap_test "0.47***" "0.39***" "0.27***"
## bw_gap_ses "0.03" "0.21***" "-0.21***"
## hw_gap_ses "0.34***" "0.30***" "0.07*"
## REPUBLICAN_2020 "-0.66***" "-0.54***" "-0.45***"
## REPUBLICAN_2016 "-0.63***" "-0.53***" "-0.40***"
## mean_IAT "-0.09***" "-0.13***" "0.00"
## white_mean_score "0.57***" "0.32***" "0.49***"
## black_mean_score "0.17***" "-0.13***" "0.29***"
## hispanic_mean_score "0.09***" "-0.18***" "0.18***"
## frac_white "-0.38***" "-0.46***" "-0.19***"
## pop_density_log10_z "1" "0.57***" "0.63***"
## urban "0.57***" "1" "0.06**"
## suburb "0.63***" "0.06**" "1"cor_matrix(d %>% select(!!primary_vars), p_val = T)## bw_gap_test hw_gap_test bw_gap_ses hw_gap_ses
## bw_gap_test "1" "0.66***" "0.45***" "0.35***"
## hw_gap_test "0.66***" "1" "0.24***" "0.54***"
## bw_gap_ses "0.45***" "0.24***" "1" "0.38***"
## hw_gap_ses "0.35***" "0.54***" "0.38***" "1"
## REPUBLICAN_2020 "-0.43***" "-0.45***" "-0.13***" "-0.28***"
## REPUBLICAN_2016 "-0.43***" "-0.46***" "-0.16***" "-0.30***"
## mean_IAT "0.05" "-0.02" "-0.23***" "-0.04"
## white_mean_score "0.59***" "0.53***" "-0.11***" "0.25***"
## black_mean_score "-0.34***" "-0.04" "-0.59***" "-0.12***"
## hispanic_mean_score "-0.14***" "-0.48***" "-0.32***" "-0.31***"
## frac_white "-0.18***" "-0.26***" "-0.28***" "-0.27***"
## pop_density_log10_z "0.45***" "0.28***" "-0.12***" "0.18***"
## urban "0.37***" "0.31***" "0.10***" "0.22***"
## suburb "0.24***" "0.22***" "-0.24***" "0.06"
## REPUBLICAN_2020 REPUBLICAN_2016 mean_IAT white_mean_score
## bw_gap_test "-0.43***" "-0.43***" "0.05" "0.59***"
## hw_gap_test "-0.45***" "-0.46***" "-0.02" "0.53***"
## bw_gap_ses "-0.13***" "-0.16***" "-0.23***" "-0.11***"
## hw_gap_ses "-0.28***" "-0.30***" "-0.04" "0.25***"
## REPUBLICAN_2020 "1" "0.98***" "0.24***" "-0.36***"
## REPUBLICAN_2016 "0.98***" "1" "0.24***" "-0.33***"
## mean_IAT "0.24***" "0.24***" "1" "0.07***"
## white_mean_score "-0.36***" "-0.33***" "0.07***" "1"
## black_mean_score "0.01" "0.05" "0.24***" "0.54***"
## hispanic_mean_score "0.03" "0.08***" "0.05" "0.46***"
## frac_white "0.52***" "0.53***" "0.31***" "-0.05"
## pop_density_log10_z "-0.52***" "-0.47***" "-0.04" "0.33***"
## urban "-0.43***" "-0.41***" "-0.05**" "0.27***"
## suburb "-0.39***" "-0.35***" "0.00" "0.39***"
## black_mean_score hispanic_mean_score frac_white
## bw_gap_test "-0.34***" "-0.14***" "-0.18***"
## hw_gap_test "-0.04" "-0.48***" "-0.26***"
## bw_gap_ses "-0.59***" "-0.32***" "-0.28***"
## hw_gap_ses "-0.12***" "-0.31***" "-0.27***"
## REPUBLICAN_2020 "0.01" "0.03" "0.52***"
## REPUBLICAN_2016 "0.05" "0.08***" "0.53***"
## mean_IAT "0.24***" "0.05" "0.31***"
## white_mean_score "0.54***" "0.46***" "-0.05"
## black_mean_score "1" "0.59***" "0.29***"
## hispanic_mean_score "0.59***" "1" "0.28***"
## frac_white "0.29***" "0.28***" "1"
## pop_density_log10_z "0.29***" "0.20***" "-0.13***"
## urban "-0.01" "-0.06" "-0.24***"
## suburb "0.32***" "0.19***" "-0.26***"
## pop_density_log10_z urban suburb
## bw_gap_test "0.45***" "0.37***" "0.24***"
## hw_gap_test "0.28***" "0.31***" "0.22***"
## bw_gap_ses "-0.12***" "0.10***" "-0.24***"
## hw_gap_ses "0.18***" "0.22***" "0.06"
## REPUBLICAN_2020 "-0.52***" "-0.43***" "-0.39***"
## REPUBLICAN_2016 "-0.47***" "-0.41***" "-0.35***"
## mean_IAT "-0.04" "-0.05**" "0.00"
## white_mean_score "0.33***" "0.27***" "0.39***"
## black_mean_score "0.29***" "-0.01" "0.32***"
## hispanic_mean_score "0.20***" "-0.06" "0.19***"
## frac_white "-0.13***" "-0.24***" "-0.26***"
## pop_density_log10_z "1" "0.50***" "0.59***"
## urban "0.50***" "1" "0.16***"
## suburb "0.59***" "0.16***" "1"#sample sizes
pairwiseCount(d %>% select(!!primary_vars))## bw_gap_test hw_gap_test bw_gap_ses hw_gap_ses
## bw_gap_test 1473 1171 1225 1000
## hw_gap_test 1171 1750 984 1401
## bw_gap_ses 1225 984 1274 859
## hw_gap_ses 1000 1401 859 1469
## REPUBLICAN_2020 1469 1743 1270 1456
## REPUBLICAN_2016 1471 1745 1272 1458
## mean_IAT 1470 1743 1272 1455
## white_mean_score 1473 1750 1242 1450
## black_mean_score 1473 1171 1258 1001
## hispanic_mean_score 1171 1750 987 1416
## frac_white 1473 1750 1274 1469
## pop_density_log10_z 1473 1750 1274 1463
## urban 1473 1750 1274 1469
## suburb 1473 1750 1274 1469
## REPUBLICAN_2020 REPUBLICAN_2016 mean_IAT white_mean_score
## bw_gap_test 1469 1471 1470 1473
## hw_gap_test 1743 1745 1743 1750
## bw_gap_ses 1270 1272 1272 1242
## hw_gap_ses 1456 1458 1455 1450
## REPUBLICAN_2020 3152 3152 3039 2999
## REPUBLICAN_2016 3152 3154 3041 3001
## mean_IAT 3039 3041 3064 2968
## white_mean_score 2999 3001 2968 3016
## black_mean_score 1507 1509 1508 1474
## hispanic_mean_score 1767 1769 1766 1753
## frac_white 3107 3109 3057 3016
## pop_density_log10_z 3112 3114 3059 3016
## urban 3107 3109 3057 3016
## suburb 3107 3109 3057 3016
## black_mean_score hispanic_mean_score frac_white
## bw_gap_test 1473 1171 1473
## hw_gap_test 1171 1750 1750
## bw_gap_ses 1258 987 1274
## hw_gap_ses 1001 1416 1469
## REPUBLICAN_2020 1507 1767 3107
## REPUBLICAN_2016 1509 1769 3109
## mean_IAT 1508 1766 3057
## white_mean_score 1474 1753 3016
## black_mean_score 1511 1174 1511
## hispanic_mean_score 1174 1774 1774
## frac_white 1511 1774 3214
## pop_density_log10_z 1511 1774 3136
## urban 1511 1774 3214
## suburb 1511 1774 3214
## pop_density_log10_z urban suburb
## bw_gap_test 1473 1473 1473
## hw_gap_test 1750 1750 1750
## bw_gap_ses 1274 1274 1274
## hw_gap_ses 1463 1469 1469
## REPUBLICAN_2020 3112 3107 3107
## REPUBLICAN_2016 3114 3109 3109
## mean_IAT 3059 3057 3057
## white_mean_score 3016 3016 3016
## black_mean_score 1511 1511 1511
## hispanic_mean_score 1774 1774 1774
## frac_white 3136 3214 3214
## pop_density_log10_z 3142 3136 3136
## urban 3136 3214 3214
## suburb 3136 3214 3214#vote share sums
d %>%
select(DEMOCRAT_2020, REPUBLICAN_2020) %>%
rowSums() %>%
describe2()wtd.cor(d$DEMOCRAT_2020, d$REPUBLICAN_2020)## correlation std.err t.value p.value
## Y -0.999 0.000952 -1049 0African americans
#plot reuseables
xaxis_rep = scale_x_continuous("Republican vote share 2020", labels = scales::percent, limits = c(0, 1))
yaxis_bw = scale_y_continuous("Black-White gap on test score")
xaxis_white = scale_x_continuous("White population %", labels = scales::percent, limits = c(0, 1))
xaxis_iat = scale_x_continuous("Race implicit association test mean score")
#basic results
#Republicans
GG_scatter(d, "REPUBLICAN_2020", "bw_gap", weights = sqrt(d$totgyb_wbg), alpha = .5) +
geom_smooth() +
xaxis_rep +
yaxis_bw## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/rep2020_bw.png")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#Race IAT
d %>% {
dd = d %>% filter(n_IAT >= 25)
GG_scatter(dd, "mean_IAT", "bw_gap", weights = sqrt(dd$n_IAT), alpha = .5) +
geom_smooth() +
xaxis_iat +
yaxis_bw ->
plot
print(plot)
wtd.cor(dd$mean_IAT, dd$bw_gap, weight = sqrt(dd$n_IAT)) %>% print()
GG_save("figs/raceIAT_bw.png")
}## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## correlation std.err t.value p.value
## Y -0.11 0.0295 -3.72 0.000207## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
#white pop
GG_scatter(d, "frac_white", "bw_gap", weights = sqrt(d$totgyb_wbg), alpha = .5) +
geom_smooth() +
xaxis_white +
yaxis_bw## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/white_bw.png")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#models
list(
basic = ols(bw_gap ~ REPUBLICAN_2020, data = d, weights = sqrt(d$totgyb_wbg)),
white_mean = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score, data = d, weights = sqrt(d$totgyb_wbg)),
urbanicity = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z, data = d, weights = sqrt(d$totgyb_wbg)),
percent_white = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white, data = d, weights = sqrt(d$totgyb_wbg)),
IAT = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT, data = d, weights = sqrt(d$totgyb_wbg)),
state = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + STNAME, data = d, weights = sqrt(d$totgyb_wbg)),
interaction = ols(bw_gap ~ REPUBLICAN_2020*frac_white + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + STNAME, data = d, weights = sqrt(d$totgyb_wbg)),
nonlinear = ols(bw_gap ~ rcs(REPUBLICAN_2020) + white_mean_score + urban + suburb + pop_density_log10_z + rcs(frac_white) + rcs(mean_IAT) + STNAME, data = d, weights = sqrt(d$totgyb_wbg))
) -> model_set_bw
model_set_bw %>%
summarize_models(asterisks_only = F)#models without weights
list(
basic = ols(bw_gap ~ REPUBLICAN_2020, data = d),
white_mean = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score, data = d),
urbanicity = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z, data = d),
percent_white = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white, data = d),
IAT = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT, data = d),
state = ols(bw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + state, data = d),
interaction = ols(bw_gap ~ REPUBLICAN_2020*frac_white + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + state, data = d),
nonlinear = ols(bw_gap ~ rcs(REPUBLICAN_2020) + white_mean_score + urban + suburb + pop_density_log10_z + rcs(frac_white) + rcs(mean_IAT) + STNAME, data = d)
) %>%
summarize_models()#interpret interaction
model_set_bw$interaction %>%
ggpredict(terms = c("REPUBLICAN_2020", "frac_white")) %>%
plot(show.title = F,
add.data = T,
use.theme = F
) +
xaxis_rep +
yaxis_bw +
labs(color = "White\npopulation %")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 10 rows containing missing values (geom_point).
GG_save("figs/rep2020_bw_interact.png")## Warning: Removed 13 rows containing missing values (geom_point).#nonlinear terms
model_set_bw$nonlinear %>%
ggpredict(terms = c("REPUBLICAN_2020")) %>%
plot(show.title = F,
add.data = T,
use.theme = F
) +
xaxis_rep +
yaxis_bw +
labs(color = "White\npopulation %")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 13 rows containing missing values (geom_point).
GG_save("figs/rep2020_bw_nonlinear.png")## Warning: Removed 3 rows containing missing values (geom_point).model_set_bw$nonlinear %>%
ggpredict(terms = c("frac_white")) %>%
plot(show.title = F,
add.data = T,
use.theme = F
) +
xaxis_white +
yaxis_bw +
labs(color = "White\npopulation %")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 93 rows containing missing values (geom_point).
GG_save("figs/white_bw_nonlinear.png")## Warning: Removed 109 rows containing missing values (geom_point).#IAT
model_set_bw$nonlinear %>%
ggpredict(terms = c("mean_IAT")) %>%
plot(show.title = F,
add.data = T,
use.theme = F
) +
xaxis_iat +
yaxis_bw +
labs(color = "White\npopulation %")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
GG_save("figs/IAT_bw_nonlinear.png")Hispanics
#reuseables
yaxis_hw = scale_y_continuous("Hispanic-White gap on test score")
#basic results
#Republicans
GG_scatter(d, "REPUBLICAN_2020", "hw_gap", weights = sqrt(d$totgyb_wbg), alpha = .5) +
geom_smooth() +
xaxis_rep +
yaxis_hw## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/rep2020_hw.png")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#Race IAT
d %>% {
dd = d %>% filter(n_IAT >= 25)
GG_scatter(dd, "mean_IAT", "hw_gap", weights = sqrt(dd$n_IAT), alpha = .5) +
geom_smooth() +
scale_x_continuous("Race implicit association test") +
yaxis_hw ->
plot
print(plot)
wtd.cor(dd$mean_IAT, dd$hw_gap, weight = sqrt(dd$n_IAT)) %>% print()
GG_save("figs/raceIAT_wh.png")
}## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## correlation std.err t.value p.value
## Y -0.158 0.0271 -5.85 6.23e-09## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
#white pop
GG_scatter(d, "frac_white", "hw_gap", weights = sqrt(d$totgyb_wbg), alpha = .5) +
geom_smooth() +
xaxis_white +
yaxis_hw## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/white_hw.png")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#models
list(
basic = ols(hw_gap ~ REPUBLICAN_2020, data = d, weights = sqrt(d$totgyb_whg)),
white_mean = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score, data = d, weights = sqrt(d$totgyb_whg)),
urbanicity = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z, data = d, weights = sqrt(d$totgyb_whg)),
percent_white = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white, data = d, weights = sqrt(d$totgyb_whg)),
IAT = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT, data = d, weights = sqrt(d$totgyb_whg)),
state = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + STNAME, data = d, weights = sqrt(d$totgyb_whg)),
interact = ols(hw_gap ~ REPUBLICAN_2020*frac_white + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + STNAME, data = d, weights = sqrt(d$totgyb_whg)),
nonlinear = ols(hw_gap ~ rcs(REPUBLICAN_2020) + white_mean_score + urban + suburb + pop_density_log10_z + rcs(frac_white) + rcs(mean_IAT) + STNAME, data = d, weights = sqrt(d$totgyb_whg))
) ->
model_set_hw
model_set_hw %>%
summarize_models(asterisks_only = F)#no weights
list(
basic = ols(hw_gap ~ REPUBLICAN_2020, data = d),
white_mean = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score, data = d),
urbanicity = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z, data = d),
percent_white = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white, data = d),
IAT = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT, data = d),
state = ols(hw_gap ~ REPUBLICAN_2020 + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + state, data = d),
interact = ols(hw_gap ~ REPUBLICAN_2020*frac_white + white_mean_score + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + state, data = d),
nonlinear = ols(hw_gap ~ rcs(REPUBLICAN_2020) + white_mean_score + urban + suburb + pop_density_log10_z + rcs(frac_white) + rcs(mean_IAT) + state, data = d)
) %>%
summarize_models()#interpret interaction
model_set_hw$interact %>%
ggpredict(terms = c("REPUBLICAN_2020", "frac_white")) %>%
plot(show.title = F,
add.data = T,
use.theme = F
) +
xaxis_rep +
yaxis_hw +
labs(color = "White\npopulation %")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 9 rows containing missing values (geom_point).
GG_save("figs/rep2020_hw_interact.png")## Warning: Removed 25 rows containing missing values (geom_point).#nonlinear terms
model_set_hw$nonlinear %>%
ggpredict(terms = c("REPUBLICAN_2020")) %>%
plot(show.title = F,
add.data = T,
use.theme = F
) +
xaxis_rep +
yaxis_hw +
labs(color = "White\npopulation %")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 27 rows containing missing values (geom_point).
GG_save("figs/rep2020_hw_nonlinear.png")## Warning: Removed 25 rows containing missing values (geom_point).model_set_hw$nonlinear %>%
ggpredict(terms = c("frac_white")) %>%
plot(show.title = F,
add.data = T,
use.theme = F
) +
xaxis_white +
yaxis_hw +
labs(color = "White\npopulation %")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Warning: Removed 128 rows containing missing values (geom_point).
GG_save("figs/white_hw_nonlinear.png")## Warning: Removed 125 rows containing missing values (geom_point).#IAT
model_set_hw$nonlinear %>%
ggpredict(terms = c("mean_IAT")) %>%
plot(show.title = F,
add.data = T,
use.theme = F
) +
xaxis_iat +
yaxis_bw +
labs(color = "White\npopulation %")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
GG_save("figs/IAT_hw_nonlinear.png")SES gaps
#SES and IQ gaps
GG_scatter(d, "bw_gap", "bw_gap_ses", weights = sqrt(d$totgyb_wbg), alpha = .5) +
geom_smooth() +
scale_x_continuous(yaxis_bw$name) +
scale_y_continuous("Black-White gap on SES")## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/bw_test_ses.png")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#Hispanics
GG_scatter(d, "hw_gap", "hw_gap_ses", weights = sqrt(d$totgyb_whg), alpha = .5) +
geom_smooth() +
scale_x_continuous(yaxis_hw$name) +
scale_y_continuous("Hispanic-White gap on SES")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/hw_test_ses.png")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#gaps combined
d %>% select(
hw_gap, hw_gap_ses, bw_gap, bw_gap_ses,
FIPS
) %>% pivot_longer(
cols = -FIPS,
names_sep = "_gap",
names_to = c("race", "metric")
) %>% mutate(
metric = mapvalues(metric, c("", "_ses"), c("test", "ses"))
) %>%
pivot_wider(
id_cols = c(FIPS, race),
values_from = "value",
names_from = "metric"
) %>%
arrange(FIPS, race) %>%
#need the sample sizes
mutate(
sample_size = {
d %>%
select(
totgyb_wbg, totgyb_whg,
FIPS
) %>% pivot_longer(cols = -FIPS) %>% mutate(name = mapvalues(name, c("totgyb_wbg", "totgyb_whg"), c("black", "hispanic"))) %>%
arrange(FIPS, name) %>%
pull(value)
},
sample_size_sqrt = sqrt(sample_size)
) %>%
mutate(
race = mapvalues(race, c("bw", "hw"), c("black-white", "hispanic-white"))
) %>%
GG_scatter(
x_var = "test",
y_var = "ses",
color = "race",
weights = "sample_size_sqrt",
alpha = .2
) +
geom_smooth(method = "lm") +
geom_smooth(aes(color = NULL), color = "brown") +
labs(x = "Test score gap", y = "Socioeconomic gap", color = "Gap")## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/gap_combo.png")## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#mean test and SES combined
d %>%
select(
white_mean_score, black_mean_score, hispanic_mean_score,
white_mean_ses, black_mean_ses, hispanic_mean_ses,
FIPS
) %>%
pivot_longer(
cols = -FIPS,
names_sep = "_mean_",
names_to = c("race", "metric")
) %>%
pivot_wider(
id_cols = c(FIPS, race),
names_from = "metric",
values_from = "value"
) %>%
#add the sample sizes, which is tricky
mutate(
sample_size = {
d %>%
select(
totgyb_wht, totgyb_blk, totgyb_hsp,
FIPS
) %>% pivot_longer(cols = -FIPS) %>% mutate(name = mapvalues(name, c("totgyb_wht", "totgyb_blk", "totgyb_hsp"), c("white", "black", "hispanic"))) %>%
pull(value)
},
sample_size_sqrt = sqrt(sample_size)
) %>%
GG_scatter(x_var = "score", y_var = "ses", color = "race", alpha = .2, weights = "sample_size_sqrt") +
geom_smooth(method = "lm") +
geom_smooth(aes(color = NULL), color = "brown") +
labs(color = "race", x = "Test score", y = "Socioeconomic status")## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/combo_test_ses.png")## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#models
#blacks
list(
basic = ols(bw_gap_ses ~ REPUBLICAN_2020, data = d, weights = sqrt(d$totgyb_wbg)),
white_mean = ols(bw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses, data = d, weights = sqrt(d$totgyb_wbg)),
urbanicity = ols(bw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses + urban + suburb + pop_density_log10_z, data = d, weights = sqrt(d$totgyb_wbg)),
percent_white = ols(bw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses + urban + suburb + pop_density_log10_z + frac_white, data = d, weights = sqrt(d$totgyb_wbg)),
IAT = ols(bw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses + urban + suburb + pop_density_log10_z + frac_white + mean_IAT, data = d, weights = sqrt(d$totgyb_wbg)),
state = ols(bw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + STNAME, data = d, weights = sqrt(d$totgyb_wbg))
) %>%
summarize_models(asterisks_only = F)#hispanics
list(
basic = ols(hw_gap_ses ~ REPUBLICAN_2020, data = d, weights = sqrt(d$totgyb_whg)),
white_mean = ols(hw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses, data = d, weights = sqrt(d$totgyb_whg)),
urbanicity = ols(hw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses + urban + suburb + pop_density_log10_z, data = d, weights = sqrt(d$totgyb_whg)),
percent_white = ols(hw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses + urban + suburb + pop_density_log10_z + frac_white, data = d, weights = sqrt(d$totgyb_whg)),
IAT = ols(hw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses + urban + suburb + pop_density_log10_z + frac_white + mean_IAT, data = d, weights = sqrt(d$totgyb_whg)),
state = ols(hw_gap_ses ~ REPUBLICAN_2020 + white_mean_ses + urban + suburb + pop_density_log10_z + frac_white + mean_IAT + STNAME, data = d, weights = sqrt(d$totgyb_whg))
) %>%
summarize_models(asterisks_only = F)Maps
Maps of key variables.
#prep data
county_sf_data$region = county_sf_data$FIPS %>% as.numeric()
#loop and map
for (v in primary_vars) {
#rename variables to value and region
county_sf_data$value = county_sf_data[[v]]
#plot
county_choropleth(
county_sf_data,
legend = v %>% str_clean(),
)
#save
GG_save(str_glue("figs/maps/{v}.png"))
}## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 16067, 27091, 27095, 27097, 27115, 27117, 16081, 27129, 27133, 19059, 27149,
## 27157, 17005, 28021, 19149, 28051, 28053, 17009, 23007, 13239, 27077, 47025,
## 47035, 47041, 47049, 47061, 23015, 23021, 23023, 31149, 31151, 31155, 17035,
## 31161, 31169, 31173, 31181, 32005, 32007, 32013, 32017, 17049, 39105, 39107,
## 39117, 39125, 39135, 17055, 39149, 39157, 39161, 47067, 47083, 47087, 17065,
## 47101, 47111, 47135, 47137, 18051, 24023, 32510, 33003, 33009, 33019, 39167,
## 39171, 39175, 40003, 40011, 18069, 40013, 40025, 40029, 40035, 40039, 40045,
## 40049, 40055, 18079, 48045, 48065, 48077, 48081, 48087, 25007, 35007, 30055,
## 30057, 37199, 38005, 38009, 38027, 38029, 38037, 38039, 38043, 38047, 38053,
## 18115, 38059, 38063, 40085, 40091, 40099, 18125, 40107, 40117, 26003, 18131,
## 26007, 30065, 30071, 30077, 30083, 30087, 30091, 30097, 30101, 18139, 38065,
## 38067, 38071, 38075, 38081, 38093, 38097, 39001, 18153, 39005, 39011, 39019,
## 48369, 48383, 18173, 48385, 48399, 48415, 48417, 48427, 18179, 48431, 26019,
## 26031, 26033, 30109, 31007, 31011, 18183, 31013, 31019, 31025, 31029, 31033,
## 31039, 31045, 31051, 31057, 31071, 19009, 31075, 39033, 39037, 35019, 19017,
## 35021, 35033, 35039, 47143, 47161, 19021, 47175, 45061, 26041, 26043, 26047,
## 19031, 26051, 26069, 17131, 31081, 31083, 31087, 31099, 19039, 31101, 31105,
## 31115, 28119, 2050, 35041, 35057, 36033, 17079, 40149, 41011, 41013, 41021,
## 41027, 26079, 26087, 26095, 26101, 28141, 28143, 17103, 28157, 29017, 29029,
## 29035, 29041, 29045, 17107, 36041, 36051, 48435, 48437, 48447, 48483, 17129,
## 48489, 49001, 49007, 26071, 26131, 26135, 17139, 26157, 29055, 29057, 29061,
## 29067, 29073, 17145, 29079, 29087, 37011, 2105, 17155, 36099, 42023, 17169,
## 46005, 46011, 46019, 46023, 46033, 46039, 46041, 46045, 46051, 17175, 46059,
## 27001, 27011, 27017, 27025, 27035, 27041, 27043, 27045, 27059, 37039, 17189,
## 37075, 37087, 17203, 42033, 42061, 42067, 18009, 41033, 41049, 41059, 41065,
## 27063, 27065, 27069, 29105, 29111, 29119, 29125, 29129, 29139, 29141, 18021,
## 29147, 29153, 29157, 29167, 37099, 37115, 18033, 37121, 2122, 13125, 42109,
## 42111, 42115, 49009, 49013, 49019, 49023, 48013, 48017, 48023, 48025, 48033,
## 48035, 29171, 29177, 29181, 29186, 29197, 29203, 29207, 29213, 29225, 30003,
## 39071, 39077, 44001, 46063, 46065, 46069, 46075, 46079, 46085, 46093, 46097,
## 46101, 46113, 46115, 46117, 46121, 13187, 46127, 30005, 30009, 30015, 30019,
## 30023, 30029, 30035, 30041, 31129, 31133, 31141, 19007, 19011, 19015, 19019,
## 19023, 19027, 17101, 17105, 17117, 17125, 2150, 13213, 13241, 20183, 20187,
## 20189, 20197, 20207, 21191, 21193, 21201, 21205, 21207, 21231, 21233, 21237,
## 22091, 21011, 21013, 21027, 17133, 17141, 13265, 17147, 17159, 17165, 17191,
## 17195, 18001, 13281, 13301, 20035, 20041, 20047, 20051, 20053, 20057, 20063,
## 20073, 20077, 20079, 20087, 22023, 22035, 21039, 21041, 21045, 21057, 21065,
## 21071, 21079, 21085, 2164, 20027, 21091, 21097, 48267, 48271, 48281, 20029,
## 48295, 48301, 48305, 48307, 48317, 48323, 48327, 20039, 48333, 53001, 53003,
## 53017, 53019, 20049, 53023, 55107, 55113, 55119, 55123, 55125, 20065, 56003,
## 56009, 56017, 56019, 54023, 54027, 20075, 54051, 54053, 54059, 54065, 54075,
## 48341, 54083, 20085, 54087, 54091, 54093, 54097, 54099, 54103, 54105, 55011,
## 55013, 20095, 55019, 55023, 49039, 49043, 50003, 20105, 50011, 50015, 50021,
## 56023, 20115, 51045, 51051, 2180, 20127, 51091, 49037, 50025, 20135, 53037,
## 53039, 53059, 53075, 54017, 54029, 54043, 54073, 54085, 20145, 54095, 51115,
## 21001, 21005, 21007, 21061, 21069, 21081, 21109, 21125, 21131, 20163, 38003,
## 38013, 38025, 38041, 38055, 38069, 38087, 38095, 39009, 39021, 54109, 49027,
## 49033, 49041, 49055, 50013, 20185, 51035, 20195, 21147, 21165, 21181, 19041,
## 19043, 20203, 19073, 19081, 19091, 19099, 19115, 19131, 19147, 19157, 20205,
## 19165, 19197, 39031, 35017, 35037, 35059, 2188, 21183, 36017, 36031, 36049,
## 13011, 21187, 13061, 2240, 5149, 6011, 6027, 6063, 21189, 6091, 8011, 8019,
## 8027, 20013, 20023, 27071, 27079, 27087, 27101, 27107, 27125, 27143, 27159,
## 42037, 42053, 42057, 42099, 42117, 42131, 8049, 13007, 28069, 23017, 26039,
## 47085, 2198, 47127, 6035, 13085, 8051, 8055, 8065, 8079, 8089, 8117, 26059,
## 26063, 26113, 26119, 26129, 26137, 27005, 27023, 27033, 27047, 27061, 47133,
## 47159, 46015, 46025, 46037, 46049, 46057, 46067, 46077, 1063, 21003, 12067,
## 12077, 4001, 4007, 5023, 5065, 29115, 29121, 29135, 29163, 29173, 29185, 29215,
## 29223, 30007, 30021, 30031, 46091, 21025, 46107, 46119, 46129, 40057, 40061,
## 40069, 40075, 40093, 40103, 40115, 5141, 21031, 40141, 41009, 5109, 5135, 21043,
## 13307, 13311, 15005, 16039, 16063, 16075, 21053, 17017, 30047, 31123, 31127,
## 31143, 31167, 31179, 31185, 32011, 21063, 32027, 33005, 41023, 41061, 42009,
## 21077, 48363, 48389, 48413, 48421, 13083, 21087, 13111, 8067, 8071, 17025,
## 17039, 21099, 17045, 17061, 17069, 18049, 18075, 18093, 18099, 18113, 21103,
## 18119, 18133, 18145, 35011, 30049, 30053, 30075, 30085, 31001, 21115, 31005,
## 31017, 31027, 31041, 31059, 31069, 31077, 31089, 48493, 21121, 48503, 49017,
## 48003, 48019, 48059, 21137, 48075, 48083, 2100, 1105, 21143, 18165, 19003,
## 19025, 19035, 17071, 17075, 17087, 17123, 17135, 17151, 17187, 31107, 21157,
## 28133, 29003, 29015, 29039, 21169, 29049, 29059, 48107, 48119, 48131, 48143,
## 48153, 48163, 48171, 21175, 48173, 48175, 48205, 48219, 48255, 48269, 48279,
## 8033, 8039, 8045, 6093, 6105, 19047, 18017, 18027, 18041, 19055, 13227, 29083,
## 29093, 19063, 37009, 6005, 19067, 48299, 48335, 19079, 51167, 51515, 51720,
## 19087, 13259, 20025, 20031, 20033, 19095, 20055, 20069, 20081, 20093, 20101,
## 20111, 20123, 20133, 20139, 20151, 19105, 20159, 37173, 39059, 39069, 39083,
## 39091, 39115, 39127, 39137, 40005, 40015, 19121, 55053, 55067, 55078, 55099,
## 55121, 55135, 56005, 56015, 19125, 56041, 1133, 2016, 2060, 20179, 20191, 21197,
## 19137, 21203, 21215, 21235, 19141, 48101, 48105, 48109, 48111, 48125, 48133,
## 6021, 19151, 48151, 48193, 48345, 19161, 51139, 51163, 51169, 55037, 55041,
## 55043, 55047, 55049, 55057, 19175, 56027, 56029, 56035, 56037, 56043, 53043,
## 53051, 48197, 19181, 48211, 48235, 48237, 48243, 19195, 48249, 48253, 48259,
## 51191, 51195, 51610, 51640, 51678, 51730, 55065, 55075, 55077, 55083, 55085,
## 55091, 55093, 53069, 27073, 54005, 54009, 54015, 8093, 8099, 8107, 8113, 8115,
## 2290, 18171, 27085, 19005, 17059, 17067, 18061, 18073, 18083, 18107, 20121,
## 27093, 20129, 20137, 20147, 20153, 20175, 22065, 5021, 21119, 21127, 21133,
## 21149, 21163, 19051, 19065, 27113, 19071, 19083, 19085, 19097, 19109, 18111,
## 19119, 19133, 19143, 19167, 27121, 19173, 19185, 19191, 20001, 20007, 20011,
## 27075, 27127, 27135, 27151, 27155, 27165, 27167, 27173, 19117, 42035, 20021,
## 28055, 28063, 27153, 23003, 38021, 38023, 38033, 38049, 27161, 28125, 35047,
## 35051, 35055, 42059, 42065, 42083, 41035, 6043, 38051, 38057, 40067, 40077,
## 40095, 40105, 36025, 40139, 40151, 41001, 41007, 41041, 31139, 31145, 19029,
## 19037, 17083, 17085, 47039, 23013, 23025, 40129, 26001, 41015, 41025, 26089,
## 26105, 42127, 49015, 49021, 49025, 23027, 31157, 31165, 31175, 31177, 32001,
## 26011, 30067, 30073, 30079, 30081, 30089, 30095, 26109, 29005, 29011, 29025,
## 32015, 32029, 39111, 39123, 39145, 30103, 30105, 38077, 38085, 38099, 36053,
## 41045, 41055, 23009, 41057, 41063, 29123, 39163, 23029, 47091, 47121, 48371,
## 48377, 48391, 48443, 48461, 48465, 48479, 48495, 48501, 48505, 49003, 48007,
## 48043, 29199, 26013, 26029, 31015, 31021, 26127, 26141, 26151, 26153, 46071,
## 46081, 46083, 46089, 46105, 46109, 33001, 33007, 31031, 31049, 31061, 31067,
## 39051, 39053, 29063, 29081, 29089, 29091, 29149, 37113, 40001, 40007, 40021,
## 40033, 40041, 37189, 35023, 35029, 35031, 47153, 47171, 47181, 25011, 36097,
## 36115, 46009, 46013, 46021, 29209, 26009, 29217, 29227, 30001, 39067, 39073,
## 39079, 48057, 26015, 48093, 26053, 46027, 46031, 46047, 26023, 46053, 27007,
## 27015, 27021, 46123, 46135, 30011, 30017, 30025, 26035, 30027, 30033, 30039,
## 30045, 31131, 25019, 26055, 31091, 31095, 31103, 31111, 27031, 27055, 37149,
## 26057, 35003, 30061, 38001, 26061, 5075, 5077, 5005, 16073, 16085, 16087, 17013,
## 17021, 16031, 16035, 16037, 16049, 16059, 18023, 2282, 16007, 16011, 16013,
## 26083, 5105, 18123, 18129, 18143, 18147, 18155,## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 1099, 27095, 27097, 27115, 27133, 19059, 27149, 28005, 17005, 28009, 28011,
## 28015, 28021, 28031, 28039, 28045, 28051, 28053, 17009, 23007, 13239, 27077,
## 47017, 47025, 47049, 47061, 23015, 23021, 23023, 28041, 31149, 17035, 31161,
## 31169, 31173, 31181, 32017, 17049, 39105, 39117, 39125, 39129, 39135, 17055,
## 39149, 47067, 47071, 47083, 47087, 17065, 47095, 47101, 47109, 47135, 47137,
## 1107, 18051, 24023, 33003, 33019, 39167, 39175, 40003, 40029, 40035, 40045,
## 48045, 48065, 48077, 30055, 30057, 38005, 38009, 38027, 38029, 38037, 38039,
## 38043, 38047, 18115, 38063, 40091, 18125, 40107, 40117, 26003, 18131, 26007,
## 30065, 30071, 30077, 30083, 30087, 30091, 30097, 30101, 18139, 38065, 38067,
## 38071, 38075, 38081, 38093, 38097, 39001, 18153, 39005, 39011, 39013, 39019,
## 48383, 48403, 48417, 48427, 48431, 26019, 26031, 26033, 30109, 31007, 31011,
## 31025, 31045, 31051, 31057, 31071, 19009, 31075, 39033, 39037, 35019, 19017,
## 35021, 35033, 47161, 47175, 45061, 45065, 26041, 26043, 26047, 19031, 26051,
## 26069, 17131, 13191, 31081, 31083, 31087, 31099, 31101, 31105, 31115, 28091,
## 28097, 28101, 28119, 2050, 28129, 36033, 17079, 41021, 26079, 26095, 26101,
## 28141, 28143, 28153, 28155, 28157, 28163, 29017, 29023, 29035, 29041, 29045,
## 17107, 36041, 17121, 48447, 17129, 48489, 26071, 26131, 26135, 17139, 29055,
## 29057, 29061, 29067, 29073, 17145, 29079, 29087, 2105, 17155, 36099, 36107,
## 42023, 17169, 45089, 46019, 46023, 46033, 46039, 46041, 46045, 46051, 17175,
## 46059, 27001, 27011, 27017, 27035, 27041, 27045, 37041, 17189, 37075, 42033,
## 42061, 18009, 27063, 27065, 27069, 29111, 29125, 29129, 29139, 29141, 18021,
## 29147, 29153, 29157, 29167, 37095, 37115, 37143, 13125, 42111, 42115, 42121,
## 49009, 49023, 48023, 48033, 29171, 29175, 29177, 29181, 29186, 29187, 29197,
## 29203, 29207, 30003, 39071, 39081, 39087, 13169, 45001, 45017, 45023, 46063,
## 46065, 46069, 46075, 46079, 46085, 46097, 46101, 46113, 46115, 46117, 46121,
## 46127, 30005, 30009, 30015, 30019, 30023, 30035, 30041, 31129, 13197, 31133,
## 19007, 19011, 19015, 19019, 19027, 17081, 13201, 17101, 17109, 17117, 17125,
## 17127, 13199, 13209, 13231, 13235, 13257, 13269, 20183, 20197, 20207, 21191,
## 21193, 21201, 21205, 21217, 21221, 13243, 21237, 22091, 22093, 22099, 13253,
## 21011, 21013, 21027, 17133, 17137, 13265, 17147, 17153, 17159, 17165, 17191,
## 13281, 13289, 13301, 20047, 20053, 20063, 20073, 20077, 20087, 22023, 22031,
## 22035, 22041, 22043, 22049, 13303, 21039, 21045, 21057, 21065, 21071, 21085,
## 2164, 20027, 21091, 21097, 20029, 48301, 48323, 48327, 20039, 53019, 20049,
## 53023, 55107, 55113, 55123, 55125, 20065, 56017, 56019, 54023, 54025, 54027,
## 54033, 54045, 54051, 54053, 54059, 54065, 54069, 54075, 54083, 20085, 54087,
## 54091, 54093, 54097, 54099, 54103, 54105, 54107, 55011, 55013, 20095, 55023,
## 50003, 20105, 50011, 50015, 50021, 51005, 51011, 20115, 51025, 51036, 51045,
## 51049, 51051, 54081, 2180, 20127, 51081, 51091, 51103, 49037, 51119, 50025,
## 20135, 54017, 54029, 54043, 54055, 54073, 54085, 20145, 54095, 51057, 51115,
## 21001, 21005, 21007, 21019, 21061, 21069, 21109, 21131, 20163, 38003, 38013,
## 38025, 38041, 38055, 38069, 38087, 38095, 39009, 39021, 54109, 49033, 49055,
## 50013, 51009, 1027, 1041, 1057, 20195, 1091, 1129, 21147, 21165, 21181, 19043,
## 19073, 19081, 19091, 19131, 19147, 19157, 20205, 19165, 39031, 2188, 36017,
## 36031, 36049, 12129, 21187, 13033, 13061, 2240, 21189, 6091, 8019, 8027, 20013,
## 20023, 27071, 27087, 27101, 27107, 27125, 27159, 28007, 28023, 28043, 42053,
## 42057, 42099, 42117, 42131, 1013, 1019, 21229, 12133, 13007, 13035, 13065,
## 28057, 28069, 22119, 22125, 22013, 26039, 47085, 2198, 22053, 47127, 13099,
## 8079, 12007, 22077, 26059, 26063, 26113, 26119, 26129, 26137, 27005, 27061,
## 47133, 47159, 45069, 45087, 46015, 46025, 46037, 46049, 46057, 46067, 46077,
## 51097, 1035, 1039, 1053, 1063, 21003, 1075, 12077, 5023, 5037, 5039, 5065, 5079,
## 29115, 29121, 29163, 29173, 29185, 29215, 29223, 30007, 30021, 46091, 21025,
## 46107, 46119, 46129, 40057, 40061, 40103, 5141, 5117, 5135, 21043, 13307, 13315,
## 15005, 21053, 30047, 31127, 31143, 31167, 32011, 21063, 41023, 42009, 21077,
## 13083, 21087, 13111, 5095, 17025, 17039, 21099, 17045, 17061, 17069, 18075,
## 18119, 18133, 35011, 30053, 30075, 30085, 21115, 31005, 31017, 31027, 31041,
## 31059, 31069, 31077, 31089, 21121, 49017, 47183, 21137, 2100, 1105, 21143,
## 18165, 19003, 19025, 17071, 17087, 17123, 17135, 17151, 31107, 28077, 28085,
## 21157, 28087, 28107, 28133, 28159, 29003, 29015, 29027, 29039, 21169, 29049,
## 29059, 48119, 48131, 21175, 48173, 48269, 8033, 6105, 18041, 13143, 19055,
## 13163, 13193, 13207, 13249, 29083, 29093, 37007, 37073, 19067, 51133, 51167,
## 51515, 51620, 51720, 12123, 13259, 13271, 13283, 20025, 20031, 20033, 19095,
## 20101, 20123, 20139, 19105, 37173, 39059, 39115, 39127, 40005, 19121, 55067,
## 55078, 55099, 19125, 2016, 2060, 20179, 21197, 19137, 21203, 21215, 21235,
## 22007, 22021, 22025, 22047, 22059, 22067, 22083, 48101, 48109, 48125, 19151,
## 48345, 48351, 19161, 51135, 51139, 51163, 51169, 51181, 51183, 55037, 55041,
## 55049, 55057, 19175, 56027, 53043, 53051, 48235, 48243, 19195, 51185, 51195,
## 51520, 20005, 51678, 51730, 51750, 51790, 55075, 55077, 55085, 55091, 53069,
## 27073, 54005, 54009, 54011, 54015, 54019, 12037, 12065, 8113, 8115, 2290, 18171,
## 17059, 17067, 18083, 20129, 20137, 20147, 20153, 22065, 5021, 21119, 21127,
## 21133, 21149, 21163, 21177, 19051, 19065, 27113, 19071, 19085, 19097, 19109,
## 19119, 19133, 19143, 27121, 19173, 19185, 19191, 20001, 20007, 20011, 27075,
## 21161, 27127, 27135, 27151, 27155, 27167, 28001, 28019, 28025, 28027, 19117,
## 42035, 20021, 28055, 28063, 23003, 38021, 38023, 38033, 38049, 28105, 28111,
## 28113, 28125, 28131, 42059, 42065, 42083, 47023, 38051, 38057, 40077, 40105,
## 36023, 40151, 31139, 31145, 19029, 19037, 17083, 17085, 47039, 28037, 47055,
## 23013, 23025, 40129, 26001, 41025, 26089, 28147, 28061, 49025, 23027, 31165,
## 31175, 31177, 26011, 28065, 30067, 30073, 30079, 30089, 30095, 26109, 28149,
## 28161, 29005, 29011, 29025, 45011, 32029, 39111, 30103, 30105, 38077, 38085,
## 22127, 36053, 41055, 23009, 41063, 29123, 29127, 39163, 23029, 47079, 47091,
## 47097, 47115, 47121, 24001, 48377, 48443, 48505, 29199, 26013, 26029, 31015,
## 31021, 26141, 26153, 46071, 46081, 46089, 46105, 46109, 33007, 24029, 31031,
## 31049, 31061, 31067, 39053, 29063, 29081, 29089, 29091, 37015, 37029, 29133,
## 29143, 29149, 29155, 40023, 40033, 47153, 47181, 36097, 36115, 46009, 46021,
## 29209, 26009, 29217, 29227, 30001, 37177, 39067, 39073, 39079, 26053, 46027,
## 46031, 46047, 46053, 27007, 27021, 46123, 30011, 30017, 30025, 26035, 30027,
## 30033, 30039, 30045, 31091, 31095, 31103, 27031, 27055, 37131, 37137, 35003,
## 30061, 38001, 26061, 5075, 5077, 16085, 17013, 17021, 16035, 16037, 16049,
## 16059, 2282, 16007, 26083, 5105, 5107, 18123, 18129, 18143, 18155, 26085, 18029,
## 13167, 20107, 20117, 17149, 17157, 17171, 17173, 17185, 17193, 26097, 13287,
## 13291, 21105, 48311, 48315, 26103, 54047, 54057, 54063, 54071, 54077, 51071,
## 51077, 51083, 26107, 51105, 21033, 48261, 48155, 51147, 51159, 51175, 48129,
## 48229, 55031, 55051, 26133, 51735, 53055, 26143, 22039, 31113, 37043, 47169,
## 46073, 54067, 1109, 5049, 19123, 47015, 38011, 38083, 31065, 28135, 26165,
## 29013, 55001, 18047, 22123, 37185, 21051, 13251, 13263, 13273, 20193, 20199,
## 20201, 21195, 21213, 21223, 21225, 27029, 20043, 20071, 20083, 20089, 22027,
## 22029, 22037, 50001, 27051, 50009, 50017, 50023, 22003, 22009, 22107, 21023,
## 27057, 21055, 21075, 21089, 21095, 56011, 54031, 54035, 54041, 50027, 51021,
## 51027, 51037, 29103, 29117, 29131, 29137, 29151, 29179, 29205, 29221, 29229,
## 30037, 30043, 31121, 31125, 31135, 31137, 31147, 31159, 31163, 31171, 8025,
## 31183, 32009, 32021, 30051, 30059, 30069, 30099, 30107, 31003, 31009, 31023,
## 31035, 31063, 31073, 31085, 31093, 31097, 31117, 28083, 8047, 28095, 28099,
## 28103, 28117, 28127, 28151, 29001, 29033, 29053, 29065, 29075, 29085, 37091,
## 37103, 37165, 39065, 39075, 39121, 39131, 40043, 40053, 38007, 38019, 38031,
## 12125, 38045, 38061, 38073, 38079, 38091, 38103, 39015, 36003, 36043, 36095,
## 36121, 36123, 42015, 42031, 42047, 42063, 42093, 42105, 42113, 42123, 45005,
## 45009, 45039, 47007, 47013, 47027, 47069, 47081,## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set
## to NA: 1009, 16067, 27091, 27095, 27097, 27105, 27111, 27115, 27117, 27119,
## 16081, 27129, 27133, 19059, 27141, 27149, 27157, 27171, 17005, 19149, 17009,
## 23007, 13239, 27077, 47021, 47025, 47035, 47041, 47049, 17029, 47061, 23015,
## 23021, 23023, 31149, 31151, 31155, 17035, 31161, 31169, 31173, 31181, 32005,
## 32007, 32013, 32017, 32023, 23011, 17049, 39105, 39107, 39117, 39119, 39125,
## 39129, 39135, 17055, 39149, 39157, 39161, 47067, 47071, 47077, 47083, 47087,
## 17065, 47095, 47101, 47109, 47111, 47135, 47137, 18051, 24023, 32510, 33003,
## 33009, 33019, 39167, 39171, 39175, 40003, 40011, 18069, 40013, 40025, 40029,
## 40035, 40045, 40055, 18079, 47141, 48045, 48065, 48077, 48081, 18085, 48087,
## 25007, 35007, 30055, 30057, 37199, 38005, 38009, 38027, 38029, 38037, 38039,
## 38043, 38047, 38053, 18115, 38059, 38063, 40085, 40091, 40099, 18125, 40107,
## 40117, 26003, 26005, 18131, 26007, 30063, 30065, 30071, 30077, 30083, 30087,
## 30091, 30097, 30101, 18139, 38065, 38067, 38071, 38075, 38081, 38089, 38093,
## 38097, 39001, 18153, 39005, 39011, 39013, 39019, 48369, 48383, 18173, 48385,
## 48399, 48403, 48415, 48417, 48427, 18179, 48431, 26019, 26031, 26033, 30109,
## 31007, 31011, 18183, 31013, 31019, 31025, 31029, 31033, 31039, 31045, 31051,
## 31057, 31071, 19009, 31075, 39029, 39033, 39037, 35019, 19017, 35021, 35025,
## 35033, 35039, 47143, 47155, 47161, 19021, 47175, 26041, 26043, 26047, 19031,
## 26051, 26069, 17131, 31081, 31083, 31087, 31099, 19039, 31101, 31105, 31115,
## 31119, 2050, 17073, 35041, 35049, 35057, 35061, 36011, 36019, 36033, 36035,
## 40135, 40137, 17079, 40149, 41003, 41011, 41013, 41021, 41027, 26079, 26087,
## 26093, 26095, 26101, 28141, 17103, 28157, 29007, 29017, 29029, 29035, 29041,
## 29045, 17107, 36041, 36051, 36073, 36075, 48435, 48437, 48447, 48483, 17129,
## 48489, 48499, 49001, 49007, 26071, 26131, 26135, 17139, 26157, 29055, 29057,
## 29061, 29067, 29073, 17145, 29079, 29087, 37011, 36089, 2105, 17155, 36099,
## 36107, 42023, 17169, 46005, 46011, 46019, 46023, 46033, 46039, 46041, 46045,
## 46051, 17175, 46059, 27001, 27011, 27017, 27025, 27035, 17179, 27041, 27043,
## 27045, 27059, 37039, 17189, 37075, 37087, 17203, 42033, 42039, 42061, 42067,
## 18009, 41029, 41033, 41043, 41049, 41059, 41065, 41071, 18011, 27063, 27065,
## 27069, 29105, 29111, 29119, 29125, 29129, 29139, 29141, 18021, 29147, 29153,
## 29157, 29161, 29167, 37095, 37099, 37115, 18033, 37121, 2122, 13125, 42109,
## 42111, 42115, 42119, 42121, 49009, 49013, 49019, 49023, 48013, 48017, 48023,
## 48025, 48033, 48035, 29171, 29175, 29177, 29181, 29186, 29187, 29197, 29203,
## 29207, 29213, 29219, 29225, 30003, 39071, 39077, 39101, 44001, 46063, 46065,
## 46069, 46075, 46079, 46085, 46093, 46097, 46101, 46113, 46115, 46117, 46121,
## 13187, 46127, 30005, 30009, 30015, 30019, 30023, 30029, 30035, 30041, 31129,
## 31133, 31141, 19007, 19011, 19015, 19019, 19023, 19027, 19033, 17101, 17105,
## 17109, 17117, 17125, 2150, 13213, 13241, 20183, 20187, 20189, 20197, 20207,
## 21191, 21193, 21201, 21205, 21207, 21217, 21221, 21231, 21233, 21237, 21011,
## 21013, 21027, 21029, 17133, 17137, 17141, 13265, 17147, 17159, 17165, 17191,
## 17195, 18001, 13281, 20035, 20041, 20047, 20051, 20053, 20057, 20063, 20073,
## 20077, 20079, 20087, 22023, 21035, 21039, 21041, 21045, 21057, 21065, 21071,
## 21079, 21083, 21085, 2164, 20027, 21091, 21097, 48267, 48271, 20029, 48295,
## 48301, 48305, 48307, 48317, 48323, 48327, 20039, 48333, 53001, 53003, 53015,
## 53017, 53019, 20049, 53023, 53025, 55107, 55111, 55113, 55119, 55123, 55125,
## 20065, 55139, 56003, 56009, 56017, 56019, 54023, 54027, 54033, 20075, 54045,
## 54051, 54053, 54059, 54065, 54075, 48341, 54083, 20085, 54087, 54091, 54093,
## 54097, 54099, 54103, 54105, 54107, 55011, 55013, 20095, 55019, 55021, 55023,
## 49039, 49043, 49045, 50003, 20105, 50011, 50015, 50021, 56023, 51005, 20115,
## 51045, 51051, 2180, 20127, 51091, 49037, 50025, 20135, 53037, 53039, 53059,
## 53075, 54017, 54029, 54043, 54073, 54085, 20145, 54095, 21001, 21005, 21007,
## 21019, 21037, 20155, 21061, 21069, 21081, 21109, 21125, 21131, 20163, 38003,
## 38013, 38025, 38041, 38055, 38069, 38087, 38095, 39021, 54109, 55015, 55027,
## 49027, 49033, 49041, 49055, 50013, 20185, 51035, 20195, 21147, 21165, 21181,
## 19041, 19043, 20203, 19049, 19057, 19073, 19081, 19091, 19099, 19115, 19131,
## 19147, 19157, 20205, 19165, 19179, 19187, 19197, 39031, 35017, 35037, 35059,
## 2188, 21183, 36017, 36031, 36049, 36077, 13011, 21187, 13061, 2240, 5149, 6011,
## 6027, 6063, 21189, 6091, 8011, 8019, 8027, 20013, 20023, 27071, 27079, 21199,
## 27087, 27101, 27107, 27125, 27143, 27159, 27169, 36113, 42019, 42037, 42053,
## 21219, 42057, 42099, 42117, 42131, 1019, 21229, 8049, 13065, 23017, 25003,
## 26039, 47019, 47085, 2198, 47099, 47127, 6023, 6035, 13085, 8051, 8055, 8065,
## 8079, 8089, 8117, 26059, 26063, 26113, 26119, 26129, 26137, 27005, 27023, 27033,
## 27047, 27061, 47133, 46015, 46025, 46037, 46049, 46057, 46067, 46077, 1049,
## 1063, 21003, 12067, 12077, 4001, 4007, 5023, 5055, 5065, 29099, 29115, 29121,
## 29135, 29145, 29163, 29173, 29185, 29215, 29223, 30007, 30021, 30031, 46091,
## 21025, 46107, 46119, 46129, 40057, 40061, 40069, 40093, 40103, 40115, 5141,
## 21031, 40133, 40141, 41009, 5109, 5135, 21043, 13311, 15005, 16005, 16027,
## 16039, 16063, 16075, 21053, 17017, 30047, 31123, 31127, 31143, 31167, 31179,
## 31185, 32011, 21063, 32027, 33005, 33017, 41023, 41061, 42009, 21077, 48363,
## 48389, 48413, 48421, 13083, 21087, 13111, 8067, 8071, 17025, 17039, 17045,
## 17061, 17069, 18049, 18075, 18093, 18099, 18113, 21103, 18119, 18133, 18145,
## 35011, 30049, 30053, 30075, 30085, 31001, 21115, 31005, 31017, 31027, 31041,
## 31059, 31069, 31077, 31089, 48493, 21121, 48503, 49005, 49017, 48003, 48019,
## 48059, 21137, 48075, 48083, 2100, 21143, 18165, 19003, 19025, 19035, 17071,
## 17075, 17087, 17123, 17135, 17151, 17187, 17199, 31107, 21157, 29003, 29015,
## 29039, 21169, 29049, 29059, 29071, 48107, 48119, 48131, 48143, 48153, 48163,
## 48171, 21175, 48173, 48175, 48205, 48219, 48255, 48269, 48279, 8033, 8039, 8045,
## 6093, 6105, 19047, 6017, 18017, 18027, 18041, 13143, 19055, 13227, 29083, 29093,
## 19063, 37009, 6005, 19067, 48299, 48313, 48325, 48335, 19079, 51167, 51515,
## 51720, 19087, 20025, 20031, 20033, 19095, 20055, 20069, 20081, 20093, 20101,
## 20111, 20123, 20133, 20139, 20151, 19105, 20159, 37173, 39059, 39069, 39083,
## 39091, 19113, 39115, 39127, 39137, 40005, 40015, 19121, 55053, 55067, 55078,
## 55087, 55099, 55109, 55121, 55135, 56005, 56015, 19125, 56041, 1133, 2016, 2060,
## 20179, 20191, 21197, 19137, 21203, 21215, 21235, 22021, 22059, 19141, 48101,
## 48105, 48109, 48111, 48125, 48133, 6021, 19151, 48151, 48189, 48193, 48345,
## 19161, 51139, 51163, 51169, 51171, 55035, 55037, 55041, 55043, 55047, 55049,
## 55055, 55057, 19175, 56027, 56029, 56035, 56037, 56043, 53043, 53051, 48197,
## 19181, 48211, 48235, 48237, 48243, 19195, 48249, 48253, 48259, 51191, 51195,
## 51520, 51580, 51610, 20005, 51640, 51678, 51750, 55065, 55075, 20015, 55077,
## 55083, 55085, 55091, 55093, 55097, 53069, 27073, 54005, 54009, 54015, 12037,
## 8093, 27083, 8099, 8107, 8113, 8115, 2290, 18171, 6033, 27085, 19005, 17059,
## 17067, 18061, 18073, 18081, 18083, 18107, 27093, 20129, 20137, 20147, 20153,
## 5021, 21119, 21127, 21133, 21141, 21149, 21155, 21177, 19051, 19065, 27113,
## 19071, 19083, 19085, 19097, 19109, 18111, 19119, 19133, 19143, 19167, 27121,
## 19173, 19185, 19191, 20001, 20007, 20011, 27075, 27127, 27135, 27151, 27155,
## 27165, 27167, 27173, 19117, 42035, 20021, 28055, 27153, 23003, 38015, 38021,
## 38023, 38033, 38049, 27161, 35047, 35051, 35055, 42059, 42065, 42083, 41035,
## 6043, 38051, 38057, 40067, 40077, 40079, 40095, 40105, 36023, 36025, 40139,
## 40151, 41001, 41007, 41041, 31139, 31145, 19029, 19037, 17083, 17085, 47039,
## 47043, 23013, 23019, 23025, 40121, 40129, 26001, 41015, 41025, 26089, 26105,
## 42107, 42127, 49015, 49021, 49025, 47029, 23027, 31157, 31165, 31175, 31177,
## 32001, 26011, 30067, 30073, 30079, 30081, 30089, 30095, 26109, 29005, 29011,
## 29025, 32015, 32019, 32029, 39111, 39123, 39145, 30103, 30105, 38077, 38085,
## 38099, 38105, 36053, 41045, 41055, 23009, 41057, 41063, 29113, 29123, 29127,
## 39163, 47073, 23029, 47089, 47091, 47115, 47121, 47123, 48371, 48377, 48391,
## 48409, 48443, 48461, 48465, 48479, 48487, 48495, 4## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 1099, 27095, 27115, 27117, 27119, 27133, 19059, 27149, 27157, 28005, 17005,
## 28009, 28011, 28015, 28021, 28031, 28039, 28045, 28051, 28053, 17009, 28073,
## 23007, 13239, 27077, 45031, 45037, 47017, 47021, 47025, 47035, 47047, 47049,
## 47059, 17029, 47061, 23015, 23021, 23023, 28041, 31149, 31155, 17035, 31161,
## 31169, 31173, 31181, 32017, 23011, 17049, 39105, 39107, 39117, 39119, 39129,
## 39135, 39141, 17055, 39149, 39151, 39157, 47067, 47071, 47077, 47083, 47087,
## 17065, 47095, 47101, 47107, 47109, 47111, 47135, 47137, 1107, 18051, 24023,
## 33003, 33009, 33019, 39167, 39171, 39175, 40003, 40011, 18069, 40029, 40035,
## 40055, 18079, 48045, 48065, 48077, 48087, 24047, 25007, 30055, 30057, 38005,
## 38009, 38027, 38029, 38037, 38039, 38043, 38047, 38053, 18115, 38059, 38063,
## 40091, 40099, 18125, 40107, 40117, 26003, 18131, 26007, 30065, 30071, 30077,
## 30083, 30087, 30091, 30097, 30101, 1121, 18139, 38065, 38067, 38071, 38075,
## 38081, 38089, 38093, 38097, 39001, 18153, 39005, 39011, 39013, 39019, 48385,
## 48387, 48403, 48417, 48427, 48431, 26019, 26027, 26031, 26033, 30109, 31007,
## 31011, 18183, 31025, 31029, 31039, 31045, 31051, 31057, 31071, 19009, 31075,
## 39029, 39033, 39037, 35019, 19017, 35021, 35033, 47143, 47161, 47167, 47175,
## 45061, 45065, 26041, 26043, 26047, 19031, 26051, 26069, 17131, 13191, 31081,
## 31083, 31087, 31099, 31101, 31105, 31115, 28091, 28097, 28101, 28109, 28119,
## 2050, 28129, 36011, 36033, 36035, 40135, 17079, 41021, 26079, 26095, 28137,
## 28141, 28143, 28153, 17103, 28155, 28157, 28163, 29007, 29017, 29023, 29029,
## 29035, 29041, 29045, 17107, 36041, 17121, 48447, 48457, 17129, 49001, 26071,
## 26131, 26135, 17139, 29055, 29057, 29061, 29067, 29073, 17145, 29079, 29087,
## 37011, 36089, 2105, 17155, 36099, 36107, 42023, 17169, 45089, 46011, 46019,
## 46023, 46033, 46039, 46041, 46045, 46051, 17175, 46059, 27001, 27011, 27017,
## 27041, 27045, 37039, 37041, 17189, 37075, 37083, 37087, 17203, 42033, 42051,
## 42061, 18009, 27063, 27065, 27069, 29105, 29111, 29125, 29129, 29139, 29141,
## 18021, 29147, 29153, 29157, 29161, 29167, 37095, 37099, 37115, 37121, 37143,
## 13125, 42109, 42115, 42119, 42121, 49009, 49019, 49023, 48023, 48033, 13131,
## 29171, 29175, 29177, 29181, 29186, 29187, 29197, 29203, 29207, 29225, 37169,
## 37175, 39071, 39081, 39101, 44001, 13169, 45001, 45017, 45023, 45025, 46063,
## 46065, 46069, 13175, 46075, 46079, 46085, 46097, 46101, 46113, 46115, 46117,
## 46121, 46127, 30005, 30009, 30015, 30019, 30023, 30035, 30041, 31129, 13197,
## 31133, 19007, 19011, 19015, 19019, 19023, 19027, 17081, 13201, 17101, 17105,
## 17109, 17117, 17125, 17127, 13189, 13195, 13199, 13205, 13209, 13221, 13229,
## 13231, 13235, 13257, 13269, 20183, 20197, 20207, 21191, 21193, 21201, 21205,
## 21207, 21217, 21221, 21231, 13243, 21233, 21237, 22091, 22093, 22099, 13253,
## 22117, 21011, 21013, 21027, 21029, 17133, 17137, 17143, 13265, 17147, 17153,
## 17159, 17165, 17177, 17191, 13275, 13281, 13289, 13295, 13301, 20051, 20053,
## 20063, 20073, 20077, 20087, 13293, 22023, 22031, 22035, 22041, 22043, 22049,
## 21035, 13303, 21039, 21041, 21045, 21057, 21065, 21071, 21079, 21085, 2164,
## 20027, 21091, 21097, 21101, 21107, 20029, 48301, 20039, 53003, 53019, 20049,
## 53023, 55107, 55113, 55119, 55125, 20065, 56017, 56019, 54023, 54025, 54027,
## 54033, 54045, 54051, 54053, 54059, 54065, 54069, 54075, 54083, 20085, 54087,
## 54091, 54093, 54097, 54099, 54103, 54105, 54107, 55013, 20095, 55023, 50003,
## 20105, 50015, 50021, 56023, 51005, 51011, 51019, 20115, 51025, 51031, 51033,
## 51036, 51045, 51049, 51051, 54081, 51067, 2180, 20127, 51073, 51081, 51091,
## 51103, 49037, 51119, 20135, 53059, 54017, 54029, 54043, 54055, 54073, 54085,
## 20145, 54095, 51057, 51115, 22111, 21001, 21005, 21007, 21019, 21049, 21061,
## 21069, 21081, 21109, 21125, 21131, 20163, 37187, 38003, 38013, 38025, 38041,
## 38055, 38069, 38087, 38095, 39009, 39021, 54109, 49033, 49041, 49055, 50013,
## 51009, 51023, 51035, 51053, 51065, 1027, 1041, 1057, 20195, 1091, 1129, 21147,
## 21165, 21181, 19041, 19043, 19057, 19073, 19081, 19091, 19099, 19131, 19147,
## 19157, 20205, 19165, 19187, 39031, 2188, 21183, 36031, 36049, 51079, 12129,
## 13019, 13027, 21187, 13033, 13061, 2240, 21189, 6091, 8019, 8027, 20013, 20023,
## 27071, 21199, 27087, 27107, 27125, 27159, 27169, 28007, 28023, 28043, 42037,
## 42053, 21219, 42057, 42081, 42099, 42117, 42131, 1013, 1019, 21229, 12133,
## 22001, 13007, 13035, 13047, 13065, 28057, 28069, 22119, 22125, 22013, 23017,
## 26039, 45033, 47019, 47051, 47085, 1017, 2198, 22053, 47099, 47127, 13085,
## 13099, 8079, 12007, 22077, 26113, 26119, 26129, 26137, 27005, 27023, 27033,
## 47133, 47145, 47159, 45069, 45087, 46015, 46025, 46037, 46049, 46057, 46067,
## 46077, 51097, 51109, 1035, 1039, 1053, 1063, 21003, 1075, 12047, 12067, 12077,
## 5023, 21009, 5037, 5039, 5055, 5065, 5079, 29115, 29121, 29135, 29163, 29173,
## 29185, 29201, 29215, 29223, 30007, 30021, 46091, 21025, 46107, 46119, 46129,
## 40061, 40069, 40093, 40103, 5141, 21031, 5109, 5117, 5135, 21043, 13307, 13311,
## 13315, 15005, 21053, 30047, 31127, 31143, 31167, 31179, 31185, 32011, 21063,
## 32027, 33005, 41023, 42009, 21077, 48405, 48455, 13083, 21087, 13105, 5095,
## 17025, 17039, 21099, 17045, 17061, 17069, 18049, 18075, 18093, 21103, 18119,
## 18133, 30053, 30075, 30085, 21115, 31005, 31017, 31027, 31041, 31059, 31069,
## 31077, 31089, 21121, 49017, 47183, 21137, 48075, 2100, 1105, 1111, 1123, 21143,
## 18165, 19003, 19025, 19035, 17071, 17087, 17095, 17123, 17135, 17151, 17187,
## 17199, 31107, 28077, 28085, 21157, 28107, 28133, 28159, 29003, 29015, 29027,
## 29039, 21169, 29059, 29071, 48119, 21175, 48205, 19045, 48269, 8033, 6105,
## 18027, 18041, 13133, 13143, 19055, 13163, 13183, 13193, 13207, 13219, 13227,
## 13249, 29083, 29093, 19063, 37003, 37007, 37033, 37073, 19067, 37145, 51127,
## 51133, 51167, 51515, 51620, 51680, 51720, 51830, 19087, 12123, 13259, 13271,
## 13283, 20025, 20031, 20033, 19095, 20101, 20123, 20133, 20139, 20151, 19105,
## 37173, 39059, 39083, 39091, 39115, 39127, 40005, 19121, 55053, 55067, 55078,
## 55099, 55135, 19125, 1133, 2016, 2060, 20179, 21197, 19137, 21203, 21215, 21235,
## 22007, 22021, 22025, 22047, 22059, 22067, 19141, 22083, 48101, 48125, 19151,
## 48345, 48351, 19161, 51135, 51137, 51139, 51163, 51169, 51181, 51183, 55037,
## 55041, 55047, 55049, 19175, 56027, 56035, 53043, 53051, 48197, 19181, 48235,
## 48237, 19195, 51185, 51195, 51520, 51580, 51610, 20005, 51640, 51678, 51750,
## 51790, 55065, 55075, 55077, 55083, 55085, 55091, 55093, 53069, 27073, 54005,
## 54009, 54015, 54019, 12037, 12065, 8093, 8113, 8115, 5139, 2290, 18171, 17059,
## 17067, 18061, 18073, 18083, 20121, 20129, 20137, 20147, 20153, 22065, 5021,
## 21119, 27103, 21127, 21133, 21141, 21149, 21155, 21163, 21177, 19051, 19065,
## 27113, 19071, 19083, 19085, 19097, 19109, 18111, 19119, 19133, 19143, 27121,
## 19185, 19191, 20001, 20007, 20011, 27075, 21161, 27127, 27135, 27151, 27155,
## 27167, 27173, 28001, 28019, 28025, 28027, 19117, 37053, 42035, 20021, 28055,
## 28063, 28071, 27153, 38015, 38021, 38023, 38033, 38049, 27161, 28111, 28113,
## 28125, 28131, 28003, 42059, 42065, 42083, 45029, 28013, 47023, 38051, 38057,
## 40077, 40105, 28017, 36023, 40151, 41001, 31139, 28029, 31145, 19029, 19037,
## 17083, 17085, 47039, 47043, 28037, 47055, 23013, 23025, 40129, 26001, 41025,
## 28147, 42127, 49015, 28061, 49025, 47029, 23027, 31165, 31175, 31177, 26011,
## 28065, 30067, 30073, 30079, 30081, 30089, 30095, 26109, 28149, 28161, 29005,
## 28075, 29011, 29025, 45011, 32015, 22121, 32029, 39111, 39145, 30103, 30105,
## 38077, 38085, 22127, 38105, 36053, 41055, 23009, 41063, 29107, 29113, 29123,
## 29127, 39163, 23029, 47079, 47091, 47097, 47103, 47115, 47121, 47123, 24001,
## 48443, 48505, 29199, 26013, 26029, 31015, 31021, 26141, 26153, 46071, 46081,
## 24019, 46083, 46089, 46105, 46109, 33007, 31031, 31049, 31061, 31067, 39053,
## 29063, 29081, 29089, 37015, 37029, 29133, 29143, 29149, 29155, 24039, 40001,
## 40023, 40033, 47153, 47171, 47181, 36097, 46009, 46013, 46021, 29209, 26009,
## 29217, 29227, 30001, 37177, 39067, 39073, 39079, 26053, 46027, 46031, 46047,
## 46053, 27015, 27021, 46123, 46135, 30011, 30017, 30025, 26035, 30027, 30033,
## 30039, 30045, 25019, 31091, 31095, 31103, 28093, 27031, 27055, 37131, 37137,
## 37149, 30061, 38001, 26061, 5035, 5041, 50## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set
## to NA: 2050, 2105, 2122, 2150, 2164, 2180, 2188, 2240, 6077, 2090, 2198, 15005,
## 2100, 2170, 51515, 2060, 2290, 2282, 2070, 2110, 2130, 2185, 2195, 2220, 2230,
## 2068, 2261, 2270, 11001, 2275## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set
## to NA: 2050, 2105, 2122, 2150, 2164, 2180, 2188, 2240, 2090, 2198, 15005, 2100,
## 2170, 51515, 2060, 2290, 2282, 2070, 2110, 2130, 2185, 2195, 2220, 2230, 2068,
## 2261, 2270, 2275## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 31149, 48065, 25015, 38027, 38053, 30109, 31007, 31057, 31075, 35021, 31115,
## 2105, 49009, 46097, 20187, 21201, 48271, 48301, 20039, 53023, 51081, 38013,
## 38025, 38087, 20023, 25003, 2198, 46107, 15005, 35011, 31077, 48119, 48173,
## 51515, 20033, 20101, 20179, 48345, 48235, 20153, 27155, 28055, 38057, 31165,
## 30079, 30103, 48443, 46071, 46105, 25011, 46021, 29227, 46123, 30011, 38001,
## 2282, 21105, 48311, 48261, 31113, 38083, 20071, 30037, 31125, 31171, 30069,
## 31009, 31085, 31117, 38007, 46003, 46017, 46043, 40127, 48393, 48095, 8053,
## 49031, 2195, 2230, 6003, 2275## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 28021, 28051, 28053, 13239, 48383, 48427, 48431, 30109, 31007, 35019, 35021,
## 35033, 45061, 31115, 28119, 2050, 28143, 28157, 48489, 2105, 46041, 49009,
## 46075, 46113, 46121, 22091, 13265, 13301, 22035, 2164, 48301, 48323, 2180,
## 38087, 2188, 13061, 13007, 28069, 8079, 1063, 15005, 35011, 31005, 1105, 28133,
## 48131, 48269, 51515, 13259, 55078, 2016, 2060, 48101, 48109, 48345, 48243,
## 51730, 22065, 28055, 28063, 28125, 31165, 30079, 30103, 38085, 48377, 48443,
## 48505, 46021, 46031, 30011, 31103, 5077, 2282, 26083, 48311, 48261, 48155,
## 48229, 38083, 13263, 13273, 22107, 30037, 31171, 31183, 32009, 30069, 31009,
## 31085, 31117, 28103, 38007, 45005, 46007, 46017, 46095, 46137, 41069, 48433,
## 48507, 48047, 8053, 48127, 8057, 48247, 48263, 48283, 51595, 8111, 1085, 1087,
## 2070, 2185, 2230, 6003, 1119, 1131, 1011, 8023, 2013, 2270, 13093, 13037, 16025,
## 16033, 13141## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set
## to NA: 16067, 27091, 27095, 27097, 27115, 27117, 16081, 27129, 27133, 19059,
## 27149, 27157, 17005, 19149, 17009, 23007, 27077, 47025, 47035, 47041, 47049,
## 47061, 23015, 23021, 23023, 31149, 31151, 31155, 17035, 31161, 31169, 31173,
## 31181, 32005, 32007, 32013, 32017, 17049, 39105, 39107, 39117, 39125, 39135,
## 17055, 39149, 39157, 39161, 47067, 47083, 47087, 17065, 47101, 47111, 47135,
## 47137, 18051, 24023, 32510, 33003, 33009, 33019, 39167, 39171, 39175, 40003,
## 40011, 18069, 40013, 40025, 40029, 40035, 40039, 40045, 40049, 40055, 18079,
## 48045, 48065, 48077, 48081, 48087, 25007, 35007, 30055, 30057, 37199, 38005,
## 38009, 38027, 38029, 38037, 38039, 38043, 38047, 38053, 18115, 38059, 38063,
## 40085, 40091, 40099, 18125, 40107, 40117, 26003, 18131, 26007, 30065, 30071,
## 30077, 30083, 30087, 30091, 30097, 30101, 18139, 38065, 38067, 38071, 38075,
## 38081, 38093, 38097, 39001, 18153, 39005, 39011, 39019, 48369, 48383, 18173,
## 48385, 48399, 48415, 48417, 48427, 18179, 48431, 26019, 26031, 26033, 30109,
## 31007, 31011, 18183, 31013, 31019, 31025, 31029, 31033, 31039, 31045, 31051,
## 31057, 31071, 19009, 31075, 39033, 39037, 35019, 19017, 35021, 35033, 35039,
## 47143, 47161, 19021, 47175, 26041, 26043, 26047, 19031, 26051, 26069, 17131,
## 31081, 31083, 31087, 31099, 19039, 31101, 31105, 31115, 2050, 35041, 35057,
## 36033, 17079, 40149, 41011, 41013, 41021, 41027, 26079, 26087, 26095, 26101,
## 28141, 17103, 29017, 29029, 29035, 29041, 29045, 17107, 36041, 36051, 48435,
## 48437, 48447, 48483, 17129, 48489, 49001, 49007, 26071, 26131, 26135, 17139,
## 26157, 29055, 29057, 29061, 29067, 29073, 17145, 29079, 29087, 37011, 2105,
## 17155, 36099, 42023, 17169, 46005, 46011, 46019, 46023, 46033, 46039, 46041,
## 46045, 46051, 17175, 46059, 27001, 27011, 27017, 27025, 27035, 27041, 27043,
## 27045, 27059, 37039, 17189, 37075, 37087, 17203, 42033, 42061, 42067, 18009,
## 41033, 41049, 41059, 41065, 27063, 27065, 27069, 29105, 29111, 29119, 29125,
## 29129, 29139, 29141, 18021, 29147, 29153, 29157, 29167, 37099, 37115, 18033,
## 37121, 2122, 13125, 42109, 42111, 42115, 49009, 49013, 49019, 49023, 48013,
## 48017, 48023, 48025, 48033, 48035, 29171, 29177, 29181, 29186, 29197, 29203,
## 29207, 29213, 29225, 30003, 39071, 39077, 44001, 46063, 46065, 46069, 46075,
## 46079, 46085, 46093, 46097, 46101, 46113, 46115, 46117, 46121, 13187, 46127,
## 30005, 30009, 30015, 30019, 30023, 30029, 30035, 30041, 31129, 31133, 31141,
## 19007, 19011, 19015, 19019, 19023, 19027, 17101, 17105, 17117, 17125, 2150,
## 13213, 13241, 20183, 20187, 20189, 20197, 20207, 21191, 21193, 21201, 21205,
## 21207, 21231, 21233, 21237, 21011, 21013, 21027, 17133, 17141, 17147, 17159,
## 17165, 17191, 17195, 18001, 13281, 20035, 20041, 20047, 20051, 20053, 20057,
## 20063, 20073, 20077, 20079, 20087, 22023, 21039, 21041, 21045, 21057, 21065,
## 21071, 21079, 21085, 2164, 20027, 21091, 21097, 48267, 48271, 48281, 20029,
## 48295, 48301, 48305, 48307, 48317, 48323, 48327, 20039, 48333, 53001, 53003,
## 53017, 53019, 20049, 53023, 55107, 55113, 55119, 55123, 55125, 20065, 56003,
## 56009, 56017, 56019, 54023, 54027, 20075, 54051, 54053, 54059, 54065, 54075,
## 48341, 54083, 20085, 54087, 54091, 54093, 54097, 54099, 54103, 54105, 55011,
## 55013, 20095, 55019, 55023, 49039, 49043, 50003, 20105, 50011, 50015, 50021,
## 56023, 20115, 51045, 51051, 2180, 20127, 51091, 49037, 50025, 20135, 53037,
## 53039, 53059, 53075, 54017, 54029, 54043, 54073, 54085, 20145, 54095, 51115,
## 21001, 21005, 21007, 21061, 21069, 21081, 21109, 21125, 21131, 20163, 38003,
## 38013, 38025, 38041, 38055, 38069, 38087, 38095, 39009, 39021, 54109, 49027,
## 49033, 49041, 49055, 50013, 20185, 51035, 20195, 21147, 21165, 21181, 19041,
## 19043, 20203, 19073, 19081, 19091, 19099, 19115, 19131, 19147, 19157, 20205,
## 19165, 19197, 39031, 35017, 35037, 35059, 2188, 21183, 36017, 36031, 36049,
## 13011, 21187, 2240, 5149, 6011, 6027, 6063, 21189, 6091, 8011, 8019, 8027,
## 20013, 20023, 27071, 27079, 27087, 27101, 27107, 27125, 27143, 27159, 42037,
## 42053, 42057, 42099, 42117, 42131, 8049, 23017, 26039, 47085, 2198, 47127, 6035,
## 13085, 8051, 8055, 8065, 8079, 8089, 8117, 26059, 26063, 26113, 26119, 26129,
## 26137, 27005, 27023, 27033, 27047, 27061, 47133, 47159, 46015, 46025, 46037,
## 46049, 46057, 46067, 46077, 21003, 12067, 12077, 4001, 4007, 5023, 5065, 29115,
## 29121, 29135, 29163, 29173, 29185, 29215, 29223, 30007, 30021, 30031, 46091,
## 21025, 46107, 46119, 46129, 40057, 40061, 40069, 40075, 40093, 40103, 40115,
## 5141, 21031, 40141, 41009, 5109, 5135, 21043, 13311, 15005, 16039, 16063, 16075,
## 21053, 17017, 30047, 31123, 31127, 31143, 31167, 31179, 31185, 32011, 21063,
## 32027, 33005, 41023, 41061, 42009, 21077, 48363, 48389, 48413, 48421, 13083,
## 21087, 13111, 8067, 8071, 17025, 17039, 21099, 17045, 17061, 17069, 18049,
## 18075, 18093, 18099, 18113, 21103, 18119, 18133, 18145, 35011, 30049, 30053,
## 30075, 30085, 31001, 21115, 31005, 31017, 31027, 31041, 31059, 31069, 31077,
## 31089, 48493, 21121, 48503, 49017, 48003, 48019, 48059, 21137, 48075, 48083,
## 2100, 21143, 18165, 19003, 19025, 19035, 17071, 17075, 17087, 17123, 17135,
## 17151, 17187, 31107, 21157, 29003, 29015, 29039, 21169, 29049, 29059, 48107,
## 48119, 48131, 48143, 48153, 48163, 48171, 21175, 48173, 48175, 48205, 48219,
## 48255, 48269, 48279, 8033, 8039, 8045, 6093, 6105, 19047, 18017, 18027, 18041,
## 19055, 13227, 29083, 29093, 19063, 37009, 6005, 19067, 48299, 48335, 19079,
## 51167, 51515, 51720, 19087, 20025, 20031, 20033, 19095, 20055, 20069, 20081,
## 20093, 20101, 20111, 20123, 20133, 20139, 20151, 19105, 20159, 37173, 39059,
## 39069, 39083, 39091, 39115, 39127, 39137, 40005, 40015, 19121, 55053, 55067,
## 55078, 55099, 55121, 55135, 56005, 56015, 19125, 56041, 1133, 2016, 2060, 20179,
## 20191, 21197, 19137, 21203, 21215, 21235, 19141, 48101, 48105, 48109, 48111,
## 48125, 48133, 6021, 19151, 48151, 48193, 48345, 19161, 51139, 51163, 51169,
## 55037, 55041, 55043, 55047, 55049, 55057, 19175, 56027, 56029, 56035, 56037,
## 56043, 53043, 53051, 48197, 19181, 48211, 48235, 48237, 48243, 19195, 48249,
## 48253, 48259, 51191, 51195, 51610, 51640, 51678, 55065, 55075, 55077, 55083,
## 55085, 55091, 55093, 53069, 27073, 54005, 54009, 54015, 8093, 8099, 8107, 8113,
## 8115, 2290, 18171, 27085, 19005, 17059, 17067, 18061, 18073, 18083, 18107,
## 20121, 27093, 20129, 20137, 20147, 20153, 20175, 5021, 21119, 21127, 21133,
## 21149, 21163, 19051, 19065, 27113, 19071, 19083, 19085, 19097, 19109, 18111,
## 19119, 19133, 19143, 19167, 27121, 19173, 19185, 19191, 20001, 20007, 20011,
## 27075, 27127, 27135, 27151, 27155, 27165, 27167, 27173, 19117, 42035, 20021,
## 28055, 27153, 23003, 38021, 38023, 38033, 38049, 27161, 35047, 35051, 35055,
## 42059, 42065, 42083, 41035, 6043, 38051, 38057, 40067, 40077, 40095, 40105,
## 36025, 40139, 40151, 41001, 41007, 41041, 31139, 31145, 19029, 19037, 17083,
## 17085, 47039, 23013, 23025, 40129, 26001, 41015, 41025, 26089, 26105, 42127,
## 49015, 49021, 49025, 23027, 31157, 31165, 31175, 31177, 32001, 26011, 30067,
## 30073, 30079, 30081, 30089, 30095, 26109, 29005, 29011, 29025, 32015, 32029,
## 39111, 39123, 39145, 30103, 30105, 38077, 38085, 38099, 36053, 41045, 41055,
## 23009, 41057, 41063, 29123, 39163, 23029, 47091, 47121, 48371, 48377, 48391,
## 48443, 48461, 48465, 48479, 48495, 48501, 48505, 49003, 48007, 48043, 29199,
## 26013, 26029, 31015, 31021, 26127, 26141, 26151, 26153, 46071, 46081, 46083,
## 46089, 46105, 46109, 33001, 33007, 31031, 31049, 31061, 31067, 39051, 39053,
## 29063, 29081, 29089, 29091, 29149, 37113, 40001, 40007, 40021, 40033, 40041,
## 37189, 35023, 35029, 35031, 47153, 47171, 47181, 25011, 36097, 36115, 46009,
## 46013, 46021, 29209, 26009, 29217, 29227, 30001, 39067, 39073, 39079, 48057,
## 26015, 48093, 26053, 46027, 46031, 46047, 26023, 46053, 27007, 27015, 27021,
## 46123, 46135, 30011, 30017, 30025, 26035, 30027, 30033, 30039, 30045, 31131,
## 25019, 26055, 31091, 31095, 31103, 31111, 27031, 27055, 37149, 26057, 35003,
## 30061, 38001, 26061, 5075, 5005, 16073, 16085, 16087, 17013, 17021, 16031,
## 16035, 16037, 16049, 16059, 18023, 2282, 16007, 16011, 16013, 26083, 5105,
## 18123, 18129, 18143, 18147, 18155, 18029, 18037, 20107, 20117, 17149, 17171,
## 17173, 17181, 17185, 17193, 26097, 13291, 21105, 48273, 48311, 48337, 26103,
## 54063, 54071, 54077, 51071, 51077, 51105, 21033, 5300## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 1099, 27095, 27097, 27115, 27133, 19059, 27149, 28005, 17005, 28009, 28011,
## 28015, 28021, 28031, 28039, 28045, 28051, 28053, 17009, 23007, 13239, 27077,
## 47017, 47025, 47049, 47061, 23015, 23021, 23023, 28041, 31149, 17035, 31161,
## 31169, 31173, 31181, 32017, 17049, 39105, 39117, 39125, 39129, 39135, 17055,
## 39149, 47067, 47071, 47083, 47087, 17065, 47095, 47101, 47109, 47135, 47137,
## 1107, 18051, 24023, 33003, 33019, 39167, 39175, 40003, 40029, 40035, 40045,
## 48045, 48065, 48077, 30055, 30057, 38005, 38009, 38027, 38029, 38037, 38039,
## 38043, 38047, 18115, 38063, 40091, 18125, 40107, 40117, 26003, 18131, 26007,
## 30065, 30071, 30077, 30083, 30087, 30091, 30097, 30101, 18139, 38065, 38067,
## 38071, 38075, 38081, 38093, 38097, 39001, 18153, 39005, 39011, 39013, 39019,
## 48403, 48417, 48431, 26019, 26031, 26033, 30109, 31007, 31011, 31025, 31045,
## 31051, 31057, 31071, 19009, 31075, 39033, 39037, 19017, 35021, 47161, 47175,
## 45061, 45065, 26041, 26043, 26047, 19031, 26051, 26069, 17131, 13191, 31081,
## 31083, 31087, 31099, 31101, 31105, 31115, 28091, 28097, 28101, 28119, 2050,
## 28129, 36033, 17079, 41021, 26079, 26095, 26101, 28141, 28143, 28153, 28155,
## 28157, 28163, 29017, 29023, 29035, 29041, 29045, 17107, 36041, 17121, 48447,
## 17129, 26071, 26131, 26135, 17139, 29055, 29057, 29061, 29067, 29073, 17145,
## 29079, 29087, 2105, 17155, 36099, 36107, 42023, 17169, 45089, 46019, 46023,
## 46033, 46039, 46041, 46045, 46051, 17175, 46059, 27001, 27011, 27017, 27035,
## 27041, 27045, 37041, 17189, 37075, 42033, 42061, 18009, 27063, 27065, 27069,
## 29111, 29125, 29129, 29139, 29141, 18021, 29147, 29153, 29157, 29167, 37095,
## 37115, 37143, 13125, 42111, 42115, 42121, 49009, 49023, 48023, 48033, 29171,
## 29175, 29177, 29181, 29186, 29187, 29197, 29203, 29207, 30003, 39071, 39081,
## 39087, 13169, 45001, 45017, 45023, 46063, 46065, 46069, 46075, 46079, 46085,
## 46097, 46101, 46113, 46115, 46117, 46121, 46127, 30005, 30009, 30015, 30019,
## 30023, 30035, 30041, 31129, 13197, 31133, 19007, 19011, 19015, 19019, 19027,
## 17081, 13201, 17101, 17109, 17117, 17125, 17127, 13199, 13209, 13231, 13235,
## 13257, 13269, 20183, 20197, 20207, 21191, 21193, 21201, 21205, 21217, 21221,
## 13243, 21237, 22091, 22093, 22099, 13253, 21011, 21013, 21027, 17133, 17137,
## 13265, 17147, 17153, 17159, 17165, 17191, 13281, 13289, 13301, 20053, 20063,
## 20073, 20077, 20087, 22023, 22031, 22035, 22041, 22043, 22049, 13303, 21039,
## 21045, 21057, 21065, 21071, 21085, 2164, 20027, 21091, 21097, 20029, 48301,
## 48327, 20039, 53019, 20049, 53023, 55107, 55113, 55123, 55125, 20065, 56017,
## 56019, 54023, 54025, 54027, 54033, 54045, 54051, 54053, 54059, 54065, 54069,
## 54075, 54083, 20085, 54087, 54091, 54093, 54097, 54099, 54103, 54105, 54107,
## 55011, 55013, 20095, 55023, 50003, 20105, 50011, 50015, 50021, 51005, 51011,
## 20115, 51025, 51036, 51045, 51049, 51051, 54081, 2180, 20127, 51081, 51091,
## 51103, 49037, 51119, 50025, 20135, 54017, 54029, 54043, 54055, 54073, 54085,
## 20145, 54095, 51057, 51115, 21001, 21005, 21007, 21019, 21061, 21069, 21109,
## 21131, 20163, 38003, 38013, 38025, 38041, 38055, 38069, 38087, 38095, 39009,
## 39021, 54109, 49033, 49055, 50013, 51009, 1027, 1041, 1057, 20195, 1091, 1129,
## 21147, 21165, 21181, 19043, 19073, 19081, 19091, 19131, 19147, 19157, 20205,
## 19165, 39031, 2188, 36017, 36031, 36049, 12129, 21187, 13033, 13061, 2240,
## 21189, 6091, 8019, 8027, 20013, 20023, 27071, 27087, 27101, 27107, 27125, 27159,
## 28007, 28023, 28043, 42053, 42057, 42099, 42117, 42131, 1013, 1019, 21229,
## 12133, 13007, 13035, 13065, 28057, 28069, 22119, 22125, 22013, 26039, 47085,
## 2198, 22053, 47127, 13099, 8079, 12007, 22077, 26059, 26063, 26113, 26119,
## 26129, 26137, 27005, 27061, 47133, 47159, 45069, 45087, 46015, 46025, 46037,
## 46049, 46057, 46067, 46077, 51097, 1035, 1039, 1053, 1063, 21003, 1075, 12077,
## 5023, 5037, 5039, 5065, 5079, 29115, 29121, 29163, 29173, 29185, 29215, 29223,
## 30007, 30021, 46091, 21025, 46107, 46119, 46129, 40061, 40103, 5141, 5117, 5135,
## 21043, 13307, 13315, 15005, 21053, 30047, 31127, 31143, 31167, 32011, 21063,
## 41023, 42009, 21077, 13083, 21087, 13111, 5095, 17025, 17039, 21099, 17045,
## 17061, 17069, 18075, 18119, 18133, 30053, 30075, 30085, 21115, 31005, 31017,
## 31027, 31041, 31059, 31069, 31077, 31089, 21121, 49017, 47183, 21137, 2100,
## 1105, 21143, 18165, 19003, 19025, 17071, 17087, 17123, 17135, 17151, 31107,
## 28077, 28085, 21157, 28087, 28107, 28133, 28159, 29003, 29015, 29027, 29039,
## 21169, 29049, 29059, 48119, 21175, 48173, 48269, 8033, 6105, 18041, 13143,
## 19055, 13163, 13193, 13207, 13249, 29083, 29093, 37007, 37073, 19067, 51133,
## 51167, 51515, 51620, 51720, 12123, 13259, 13271, 13283, 20025, 20031, 20033,
## 19095, 20101, 20123, 20139, 19105, 37173, 39059, 39115, 39127, 40005, 19121,
## 55067, 55078, 55099, 19125, 2016, 2060, 20179, 21197, 19137, 21203, 21215,
## 21235, 22007, 22021, 22025, 22047, 22059, 22067, 22083, 48101, 48125, 19151,
## 48345, 48351, 19161, 51135, 51139, 51163, 51169, 51181, 51183, 55037, 55041,
## 55049, 55057, 19175, 56027, 53043, 53051, 48235, 48243, 19195, 51185, 51195,
## 51520, 20005, 51678, 51750, 51790, 55075, 55077, 55085, 55091, 53069, 27073,
## 54005, 54009, 54011, 54015, 54019, 12037, 12065, 8113, 8115, 2290, 18171, 17059,
## 17067, 18083, 20129, 20137, 20147, 20153, 22065, 5021, 21119, 21127, 21133,
## 21149, 21163, 21177, 19051, 19065, 27113, 19071, 19085, 19097, 19109, 19119,
## 19133, 19143, 27121, 19173, 19185, 19191, 20001, 20007, 20011, 27075, 21161,
## 27127, 27135, 27151, 27155, 27167, 28001, 28019, 28025, 28027, 19117, 42035,
## 20021, 28055, 28063, 23003, 38021, 38023, 38033, 38049, 28105, 28111, 28113,
## 28125, 28131, 42059, 42065, 42083, 47023, 38051, 38057, 40077, 40105, 36023,
## 40151, 31139, 31145, 19029, 19037, 17083, 17085, 47039, 28037, 47055, 23013,
## 23025, 40129, 26001, 41025, 26089, 28147, 28061, 49025, 23027, 31165, 31175,
## 31177, 26011, 28065, 30067, 30073, 30079, 30089, 30095, 26109, 28149, 28161,
## 29005, 29011, 29025, 45011, 32029, 39111, 30103, 30105, 38077, 38085, 22127,
## 36053, 41055, 23009, 41063, 29123, 29127, 39163, 23029, 47079, 47091, 47097,
## 47115, 47121, 24001, 48443, 29199, 26013, 26029, 31015, 31021, 26141, 26153,
## 46071, 46081, 46089, 46105, 46109, 33007, 24029, 31031, 31049, 31061, 31067,
## 39053, 29063, 29081, 29089, 29091, 37015, 37029, 29133, 29143, 29149, 29155,
## 40023, 40033, 47153, 47181, 36097, 36115, 46009, 46021, 29209, 26009, 29217,
## 29227, 30001, 37177, 39067, 39073, 39079, 26053, 46027, 46031, 46047, 46053,
## 27007, 27021, 46123, 30011, 30017, 30025, 26035, 30027, 30033, 30039, 30045,
## 31091, 31095, 31103, 27031, 27055, 37131, 37137, 35003, 30061, 38001, 26061,
## 5075, 5077, 16085, 17013, 17021, 16035, 16037, 16049, 16059, 2282, 16007, 26083,
## 5105, 5107, 18123, 18129, 18143, 18155, 26085, 18029, 13167, 20107, 20117,
## 17149, 17157, 17171, 17173, 17185, 17193, 26097, 13287, 13291, 21105, 48311,
## 48315, 26103, 54047, 54057, 54063, 54071, 54077, 51071, 51077, 51083, 26107,
## 51105, 21033, 48261, 48155, 51147, 51159, 51175, 48129, 55031, 55051, 26133,
## 51735, 53055, 26143, 22039, 31113, 37043, 47169, 46073, 54067, 1109, 5049,
## 19123, 47015, 38011, 38083, 31065, 28135, 26165, 29013, 55001, 18047, 22123,
## 37185, 21051, 13251, 13263, 13273, 20193, 20199, 20201, 21195, 21213, 21223,
## 21225, 27029, 20043, 20071, 20083, 20089, 22027, 22029, 22037, 50001, 27051,
## 50009, 50017, 50023, 22003, 22009, 22107, 21023, 27057, 21055, 21075, 21089,
## 21095, 56011, 54031, 54035, 54041, 50027, 51021, 51027, 51037, 29103, 29117,
## 29131, 29137, 29151, 29179, 29205, 29221, 29229, 30037, 30043, 31121, 31125,
## 31135, 31137, 31147, 31159, 31163, 31171, 31183, 32009, 32021, 30051, 30059,
## 30069, 30099, 30107, 31003, 31009, 31023, 31035, 31063, 31073, 31085, 31093,
## 31097, 31117, 28083, 8047, 28095, 28099, 28103, 28117, 28127, 28151, 29001,
## 29033, 29053, 29065, 29075, 29085, 37091, 37103, 37165, 39065, 39075, 39121,
## 39131, 40043, 40053, 38007, 38019, 38031, 12125, 38045, 38061, 38073, 38079,
## 38091, 38103, 39015, 36003, 36043, 36095, 36121, 36123, 42015, 42031, 42047,
## 42063, 42093, 42105, 42113, 42123, 45005, 45009, 45039, 47007, 47013, 47027,
## 47069, 47081, 47129, 47139, 47151, 47173, 45049, 46003, 46007, 46017, 46029,
## 46035, 46043, 46055, 46061, 46087, 46095, 46111,## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 46113, 48301, 15005, 51515, 28055, 26083, 46017, 51595, 2270## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 46113, 51515, 2270## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 46113, 48301, 15005, 51515, 28055, 26083, 46017, 51595, 2270## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 72085, 72115, 72105, 72053, 72151,
## 72067, 72127, 72049, 72051, 72121, 72039, 72133, 72139, 72073, 72097, 72029,
## 72123, 72103, 72003, 72043, 72055, 72019, 72081, 72153, 72001, 72093, 72005,
## 72009, 72111, 72113, 72021, 72117, 72145, 72023, 72069, 72077, 72037, 72047,
## 72099, 72107, 72129, 72135, 72095, 72109, 72091, 72147, 72087, 72011, 72041,
## 72063, 72065, 72057, 72143, 72137, 2158, 72141, 72013, 72149, 72061, 72017,
## 72035, 72015, 72125, 72031, 72071, 72054, 72079, 72025, 72027, 72119, 72075,
## 72045, 72007, 72033, 72101, 72083, 72059, 72131, 46102, 72089, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2017, 2018,
## 2019, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032,
## 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2099, 36000, 2201, 2232, 2280## Warning in self$bind(): The following regions were missing and are being set to
## NA: 46113, 48301, 15005, 51515, 28055, 26083, 46017, 51595, 2270Meta
#versions
write_sessioninfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 19.3
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_DK.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_DK.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] choroplethrMaps_1.0.1 choroplethr_3.7.0 acs_2.1.4
## [4] XML_3.99-0.8 ggeffects_1.1.1 sf_1.0-5
## [7] haven_2.4.3 rms_6.2-0 SparseM_1.81
## [10] readxl_1.3.1 kirkegaard_2021-12-14 rlang_0.4.12
## [13] metafor_3.0-2 Matrix_1.4-0 psych_2.1.9
## [16] magrittr_2.0.1 assertthat_0.2.1 weights_1.0.4
## [19] Hmisc_4.6-0 Formula_1.2-4 survival_3.2-13
## [22] lattice_0.20-45 forcats_0.5.1 stringr_1.4.0
## [25] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
## [28] tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
## [31] tidyverse_1.3.1 pacman_0.5.1
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.1 lme4_1.1-27.1
## [4] htmlwidgets_1.5.4 grid_4.1.2 maptools_1.1-2
## [7] pROC_1.18.0 munsell_0.5.0 codetools_0.2-18
## [10] units_0.7-2 future_1.23.0 withr_2.4.3
## [13] colorspace_2.0-2 highr_0.9 knitr_1.37
## [16] uuid_1.0-3 rstudioapi_0.13 stats4_4.1.2
## [19] listenv_0.8.0 labeling_0.4.2 RgoogleMaps_1.4.5.3
## [22] WDI_2.7.4 mnormt_2.0.2 bit64_4.0.5
## [25] farver_2.1.0 parallelly_1.30.0 vctrs_0.3.8
## [28] generics_0.1.1 TH.data_1.1-0 ipred_0.9-12
## [31] xfun_0.29 R6_2.5.1 RJSONIO_1.3-1.6
## [34] bitops_1.0-7 scales_1.1.1 vroom_1.5.7
## [37] multcomp_1.4-18 nnet_7.3-16 gtable_0.3.0
## [40] globals_0.14.0 conquer_1.2.1 sandwich_3.0-1
## [43] timeDate_3043.102 MatrixModels_0.5-0 splines_4.1.2
## [46] rgdal_1.5-28 ModelMetrics_1.2.2.2 broom_0.7.11
## [49] checkmate_2.0.0 yaml_2.2.1 reshape2_1.4.4
## [52] modelr_0.1.8 backports_1.4.1 caret_6.0-90
## [55] tools_4.1.2 lava_1.6.10 ellipsis_0.3.2
## [58] jquerylib_0.1.4 multilevel_2.6 RColorBrewer_1.1-2
## [61] proxy_0.4-26 tigris_1.5 Rcpp_1.0.7
## [64] plyr_1.8.6 base64enc_0.1-3 classInt_0.4-3
## [67] rpart_4.1-15 zoo_1.8-9 ggmap_3.0.0
## [70] cluster_2.1.2 fs_1.5.2 data.table_1.14.2
## [73] reprex_2.0.1 tmvnsim_1.0-2 mvtnorm_1.1-3
## [76] matrixStats_0.61.0 hms_1.1.1 evaluate_0.14
## [79] jpeg_0.1-9 gridExtra_2.3 compiler_4.1.2
## [82] psychometric_2.2 mice_3.14.0 KernSmooth_2.23-20
## [85] crayon_1.4.2 minqa_1.2.4 htmltools_0.5.2
## [88] mgcv_1.8-38 tzdb_0.2.0 lubridate_1.8.0
## [91] DBI_1.1.2 sjlabelled_1.1.8 dbplyr_2.1.1
## [94] MASS_7.3-54 rappdirs_0.3.3 boot_1.3-28
## [97] cli_3.1.0 gdata_2.18.0 parallel_4.1.2
## [100] insight_0.14.5 gower_0.2.2 pkgconfig_2.0.3
## [103] foreign_0.8-81 sp_1.4-6 recipes_0.1.17
## [106] xml2_1.3.3 foreach_1.5.1 bslib_0.3.1
## [109] prodlim_2019.11.13 rvest_1.0.2 digest_0.6.29
## [112] rmarkdown_2.11 cellranger_1.1.0 htmlTable_2.4.0
## [115] tidycensus_1.1 gtools_3.9.2 quantreg_5.86
## [118] rjson_0.2.20 nloptr_1.2.2.3 lifecycle_1.0.1
## [121] nlme_3.1-152 jsonlite_1.7.2 fansi_0.5.0
## [124] pillar_1.6.4 fastmap_1.1.0 httr_1.4.2
## [127] glue_1.6.0 png_0.1-7 iterators_1.0.13
## [130] bit_4.0.4 class_7.3-19 stringi_1.7.6
## [133] sass_0.4.0 polspline_1.1.19 latticeExtra_0.6-29
## [136] mathjaxr_1.4-0 e1071_1.7-9 future.apply_1.8.1#write the data to a file
d %>% write_rds("data/data_out.rds", compress = "gz")
d %>% write_tsv("data/data_out.tsv.gz")
#upload to OSF
if (F) {
library(osfr)
#auth
osf_auth(read_lines("~/.config/osf_token"))
#the project we will use
osf_proj = osf_retrieve_node("https://osf.io/m8gs5/")
#upload all files in project
#overwrite existing (versioning)
osf_upload(osf_proj, conflicts = "overwrite", path = c(
"figs", "data",
"notebook.Rmd", "notebook.html", "session_info.txt"
))
}