Compare subsets (types) of places that are all from one list
Source:R/ejamit_compare_types_of_places.R
ejamit_compare_types_of_places.Rd*** DRAFT - May change but works as currently drafted. e.g., change output formats of results_bytype vs results_overall
Usage
ejamit_compare_types_of_places(
sitepoints,
typeofsite = NULL,
shapefile = NULL,
fips = NULL,
silentinteractive = TRUE,
...
)Examples
out <- ejamit_compare_types_of_places(testpoints_10[1:4, ],
typeofsite = c("A", "B", "B", "C"))
#> Type 1 of 3 = A -- Analyzing 1 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 1 of 1 sites. Rate of 2,025 buffers per hour: 1 lat/long pairs took 2 seconds
#> Type 2 of 3 = B --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 2 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 3 of 3 sites. Rate of 1,827 buffers per hour: 3 lat/long pairs took 6 seconds
#> Type 3 of 3 = C --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 1 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 4 of 4 sites. Rate of 1,750 buffers per hour: 4 lat/long pairs took 8 seconds
#>
#>
#> type valid sitecount pctvalid pop pop_persite pctofallpop
#> 1 A 1 1 100 124566 124566 9
#> 2 B 2 2 100 1176931 588466 89
#> 3 C 1 1 100 19637 19637 1
#> pctofallsitecount
#> 1 25
#> 2 50
#> 3 25
#> ratio.to.state.avg.Demog.Index ratio.to.state.avg.Demog.Index.Supp
#> 1 0.9 0.8
#> 2 1.2 1.0
#> 3 0.7 0.9
#> ratio.to.state.avg.pctlowinc ratio.to.state.avg.pctlingiso
#> 1 0.5 1.2
#> 2 1.1 1.0
#> 3 0.8 0.7
#> ratio.to.state.avg.pctunemployed ratio.to.state.avg.pctlths
#> 1 0.6 0.7
#> 2 1.1 1.0
#> 3 1.0 1.0
#> ratio.to.state.avg.pctunder5 ratio.to.state.avg.pctover64
#> 1 0.9 1.1
#> 2 1.2 0.7
#> 3 1.2 0.8
#> ratio.to.state.avg.pctmin
#> 1 1.2
#> 2 1.2
#> 3 0.6
#> Use ejam2excel(out) to view results, and see the types of sites compared, one row each, in the Overall tab
#> Use ejam2barplot_sitegroups() to plot results.
#>
#>
#> 4 sites in 3 groups (types of sites).
#> Rate of 1,748 buffers per hour: 4 lat/long pairs took 8 seconds
cbind(Rows_or_length = sapply(out, NROW))
#> Rows_or_length
#> types 3
#> sitecount_bytype 3
#> results_bytype 3
#> results_overall 3
#> ejam_uniq_id 4
#> typeofsite 4
#> results_bysite 4
#> longnames 469
#> validstats 3
#> ratiostats 3
ejam2barplot_sitegroups(out, names_these_ratio_to_avg[1], topn = 3)
ejam2barplot_sitegroups(out, "sitecount_unique", topn=3, sortby = F)
ejam2barplot_sitegroups(out, "pop", topn = 3, sortby = F)
# use calculated variable not in original table
df <- out$results_bytype
df$share <- df$pop / sum(df$pop)
df$pop_per_site <- df$pop / df$sitecount_unique
plot_barplot_sites(df,
"share", ylab = "Share of Total Population",
topn = 3, names.arg = out$types , sortby = F)
plot_barplot_sites(df,
"pop_per_site", ylab = "Pop. at Avg. Site in Group",
topn = 3, main = "Nearby Residents per Site, by Site Type",
names.arg = out$types , sortby = F)
# \donttest{
# Analyze by EPA Region
pts <- data.frame(testpoints_1000)
# Get State and EPA Region of each point from lat/lon
x <- state_from_latlon(lat = pts$lat, lon = pts$lon)
pts <- data.frame(pts, x)
out_byregion <- ejamit_compare_types_of_places(
pts, typeofsite = pts$REGION)
#> Type 1 of 10 = 3 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 56 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 56 of 56 sites. Rate of 45,610 buffers per hour: 56 lat/long pairs took 4 seconds
#> Type 2 of 10 = 9 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 195 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 251 of 251 sites. Rate of 100,153 buffers per hour: 251 lat/long pairs took 9 seconds
#> Type 3 of 10 = 7 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 67 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 318 of 318 sites. Rate of 90,655 buffers per hour: 318 lat/long pairs took 13 seconds
#> Type 4 of 10 = 4 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 125 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 443 of 443 sites. Rate of 105,919 buffers per hour: 443 lat/long pairs took 15 seconds
#> Type 5 of 10 = 5 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 156 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 599 of 599 sites. Rate of 113,809 buffers per hour: 599 lat/long pairs took 19 seconds
#> Type 6 of 10 = 2 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 154 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> No percentile info is available in the percentile lookup table (all or at least some values here are NA, which is not allowed in lookup table), so percentile will be reported as NA, in zone = PR for drinking.
#> Finished 753 of 753 sites. Rate of 112,057 buffers per hour: 753 lat/long pairs took 24 seconds
#> Type 7 of 10 = 1 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 53 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 806 of 806 sites. Rate of 108,278 buffers per hour: 806 lat/long pairs took 27 seconds
#> Type 8 of 10 = 8 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 48 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 854 of 854 sites. Rate of 104,677 buffers per hour: 854 lat/long pairs took 29 seconds
#> Type 9 of 10 = 6 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 100 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 954 of 954 sites. Rate of 101,472 buffers per hour: 954 lat/long pairs took 34 seconds
#> Type 10 of 10 = 10 --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 46 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 1000 of 1000 sites. Rate of 100,260 buffers per hour: 1,000 lat/long pairs took 36 seconds
#>
#>
#> type valid sitecount pctvalid pop pop_persite pctofallpop
#> 1 3 56 56 100 2913936 52035 6
#> 2 9 195 195 100 16960745 86978 34
#> 3 7 67 67 100 1101208 16436 2
#> 4 4 125 125 100 3344207 26754 7
#> 5 5 156 156 100 5248470 33644 10
#> 6 2 154 154 100 9852536 63978 20
#> 7 1 53 53 100 2747347 51837 5
#> 8 8 45 48 94 1776904 37019 4
#> 9 6 98 100 98 4065964 40660 8
#> 10 10 46 46 100 2173060 47240 4
#> pctofallsitecount
#> 1 6
#> 2 20
#> 3 7
#> 4 12
#> 5 16
#> 6 15
#> 7 5
#> 8 5
#> 9 10
#> 10 5
#> ratio.to.state.avg.Demog.Index ratio.to.state.avg.Demog.Index.Supp
#> 1 1.2 1.1
#> 2 1.1 1.0
#> 3 1.2 1.0
#> 4 1.1 1.0
#> 5 1.2 1.0
#> 6 1.2 1.1
#> 7 1.3 1.1
#> 8 1.1 1.1
#> 9 1.1 1.0
#> 10 1.1 0.9
#> ratio.to.state.avg.pctlowinc ratio.to.state.avg.pctlingiso
#> 1 1.1 1.2
#> 2 1.1 1.2
#> 3 1.0 1.3
#> 4 1.0 1.1
#> 5 1.1 1.4
#> 6 1.1 1.2
#> 7 1.2 1.3
#> 8 1.0 1.3
#> 9 1.0 1.3
#> 10 0.9 1.4
#> ratio.to.state.avg.pctunemployed ratio.to.state.avg.pctlths
#> 1 1.1 1.0
#> 2 1.0 1.1
#> 3 1.0 0.9
#> 4 1.1 0.9
#> 5 1.1 1.1
#> 6 1.1 1.2
#> 7 1.1 1.1
#> 8 1.0 1.1
#> 9 1.0 1.1
#> 10 1.0 0.9
#> ratio.to.state.avg.pctunder5 ratio.to.state.avg.pctover64
#> 1 1.1 0.9
#> 2 1.0 0.9
#> 3 1.1 0.9
#> 4 1.1 0.8
#> 5 1.0 0.9
#> 6 1.1 0.9
#> 7 1.1 0.8
#> 8 1.0 0.9
#> 9 1.1 0.8
#> 10 1.0 0.8
#> ratio.to.state.avg.pctmin
#> 1 1.3
#> 2 1.1
#> 3 1.6
#> 4 1.2
#> 5 1.4
#> 6 1.3
#> 7 1.3
#> 8 1.2
#> 9 1.2
#> 10 1.3
#> Use ejam2excel(out) to view results, and see the types of sites compared, one row each, in the Overall tab
#> Use ejam2barplot_sitegroups() to plot results.
#>
#>
#> 1000 sites in 10 groups (types of sites).
#> Rate of 100,226 buffers per hour: 1,000 lat/long pairs took 36 seconds
dvarname <- names_d[3]
ejam2barplot_sitegroups(out_byregion, dvarname)
abline(h = usastats_means(dvarname))
ejam2barplot_sitegroups(out_byregion, "ratio.to.avg.pctmin",
main = "By EPA Region", ylim = c(0, 2))
abline(h = 1)
# Analyze by State (slow)
out_bystate <- ejamit_compare_types_of_places(pts, typeofsite = pts$ST)
#> Type 1 of 51 = PA --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 21 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 21 of 21 sites. Rate of 29,720 buffers per hour: 21 lat/long pairs took 3 seconds
#> Type 2 of 51 = CA --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 170 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 191 of 191 sites. Rate of 87,252 buffers per hour: 191 lat/long pairs took 8 seconds
#> Type 3 of 51 = IA --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 21 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 212 of 212 sites. Rate of 73,740 buffers per hour: 212 lat/long pairs took 10 seconds
#> Type 4 of 51 = NC --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 18 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 230 of 230 sites. Rate of 62,459 buffers per hour: 230 lat/long pairs took 13 seconds
#> Type 5 of 51 = IL --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 30 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 260 of 260 sites. Rate of 57,877 buffers per hour: 260 lat/long pairs took 16 seconds
#> Type 6 of 51 = AZ --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 14 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 274 of 274 sites. Rate of 53,185 buffers per hour: 274 lat/long pairs took 19 seconds
#> Type 7 of 51 = MS --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 11 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 285 of 285 sites. Rate of 48,816 buffers per hour: 285 lat/long pairs took 21 seconds
#> Type 8 of 51 = NY --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 57 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 342 of 342 sites. Rate of 52,038 buffers per hour: 342 lat/long pairs took 24 seconds
#> Type 9 of 51 = NJ --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 93 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 435 of 435 sites. Rate of 58,629 buffers per hour: 435 lat/long pairs took 27 seconds
#> Type 10 of 51 = VA --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 11 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 446 of 446 sites. Rate of 53,243 buffers per hour: 446 lat/long pairs took 30 seconds
#> Type 11 of 51 = CT --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 15 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 461 of 461 sites. Rate of 52,008 buffers per hour: 461 lat/long pairs took 32 seconds
#> Type 12 of 51 = MN --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 44 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 505 of 505 sites. Rate of 52,749 buffers per hour: 505 lat/long pairs took 34 seconds
#> Type 13 of 51 = NE --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 14 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 519 of 519 sites. Rate of 50,792 buffers per hour: 519 lat/long pairs took 37 seconds
#> Type 14 of 51 = IN --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 26 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 545 of 545 sites. Rate of 48,159 buffers per hour: 545 lat/long pairs took 41 seconds
#> Type 15 of 51 = CO --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 9 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 554 of 554 sites. Rate of 46,395 buffers per hour: 554 lat/long pairs took 43 seconds
#> Type 16 of 51 = MA --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 26 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 580 of 580 sites. Rate of 46,521 buffers per hour: 580 lat/long pairs took 45 seconds
#> Type 17 of 51 = OH --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 26 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 606 of 606 sites. Rate of 45,639 buffers per hour: 606 lat/long pairs took 48 seconds
#> Type 18 of 51 = TX --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 71 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 677 of 677 sites. Rate of 47,789 buffers per hour: 677 lat/long pairs took 51 seconds
#> Type 19 of 51 = KS --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 16 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 693 of 693 sites. Rate of 46,809 buffers per hour: 693 lat/long pairs took 53 seconds
#> Type 20 of 51 = MT --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 10 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 703 of 703 sites. Rate of 45,377 buffers per hour: 703 lat/long pairs took 56 seconds
#> Type 21 of 51 = UT --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 18 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 721 of 721 sites. Rate of 44,641 buffers per hour: 721 lat/long pairs took 58 seconds
#> Type 22 of 51 = MI --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 16 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 737 of 737 sites. Rate of 44,171 buffers per hour: 737 lat/long pairs took 60 seconds
#> Type 23 of 51 = DE --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 5 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 742 of 742 sites. Rate of 42,518 buffers per hour: 742 lat/long pairs took 63 seconds
#> Type 24 of 51 = LA --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 9 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 751 of 751 sites. Rate of 40,868 buffers per hour: 751 lat/long pairs took 66 seconds
#> Type 25 of 51 = AL --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 13 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 764 of 764 sites. Rate of 39,572 buffers per hour: 764 lat/long pairs took 70 seconds
#> Type 26 of 51 = WA --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 20 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 784 of 784 sites. Rate of 39,529 buffers per hour: 784 lat/long pairs took 71 seconds
#> Type 27 of 51 = HI --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 5 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 789 of 789 sites. Rate of 38,492 buffers per hour: 789 lat/long pairs took 74 seconds
#> Type 28 of 51 = KY --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 9 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 798 of 798 sites. Rate of 38,007 buffers per hour: 798 lat/long pairs took 76 seconds
#> Type 29 of 51 = FL --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 28 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 826 of 826 sites. Rate of 37,814 buffers per hour: 826 lat/long pairs took 79 seconds
#> Type 30 of 51 = OR --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 21 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 847 of 847 sites. Rate of 37,146 buffers per hour: 847 lat/long pairs took 82 seconds
#> Type 31 of 51 = WI --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 14 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 861 of 861 sites. Rate of 36,749 buffers per hour: 861 lat/long pairs took 84 seconds
#> Type 32 of 51 = ID --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 5 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 866 of 866 sites. Rate of 36,224 buffers per hour: 866 lat/long pairs took 86 seconds
#> Type 33 of 51 = SC --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 15 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 881 of 881 sites. Rate of 35,670 buffers per hour: 881 lat/long pairs took 89 seconds
#> Type 34 of 51 = GA --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 24 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 905 of 905 sites. Rate of 35,496 buffers per hour: 905 lat/long pairs took 92 seconds
#> Type 35 of 51 = OK --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 12 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 917 of 917 sites. Rate of 35,112 buffers per hour: 917 lat/long pairs took 94 seconds
#> Type 36 of 51 = TN --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 7 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 924 of 924 sites. Rate of 34,501 buffers per hour: 924 lat/long pairs took 96 seconds
#> Type 37 of 51 = ND --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 6 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 930 of 930 sites. Rate of 33,938 buffers per hour: 930 lat/long pairs took 99 seconds
#> Type 38 of 51 = MO --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 16 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 946 of 946 sites. Rate of 33,890 buffers per hour: 946 lat/long pairs took 100 seconds
#> Type 39 of 51 = AR --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 2 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 948 of 948 sites. Rate of 33,040 buffers per hour: 948 lat/long pairs took 103 seconds
#> Type 40 of 51 = NH --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 4 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 952 of 952 sites. Rate of 32,159 buffers per hour: 952 lat/long pairs took 107 seconds
#> Type 41 of 51 = WV --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 7 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 959 of 959 sites. Rate of 31,734 buffers per hour: 959 lat/long pairs took 109 seconds
#> Type 42 of 51 = WY --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 4 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 963 of 963 sites. Rate of 31,052 buffers per hour: 963 lat/long pairs took 112 seconds
#> Type 43 of 51 = PR --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 4 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> No percentile info is available in the percentile lookup table (all or at least some values here are NA, which is not allowed in lookup table), so percentile will be reported as NA, in zone = PR for drinking.
#> Finished 967 of 967 sites. Rate of 30,549 buffers per hour: 967 lat/long pairs took 114 seconds
#> Type 44 of 51 = NV --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 6 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 973 of 973 sites. Rate of 30,276 buffers per hour: 973 lat/long pairs took 116 seconds
#> Type 45 of 51 = ME --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 3 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 976 of 976 sites. Rate of 29,766 buffers per hour: 976 lat/long pairs took 118 seconds
#> Type 46 of 51 = MD --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 11 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 987 of 987 sites. Rate of 29,655 buffers per hour: 987 lat/long pairs took 120 seconds
#> Type 47 of 51 = NM --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 6 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 993 of 993 sites. Rate of 29,168 buffers per hour: 993 lat/long pairs took 123 seconds
#> Type 48 of 51 = RI --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 3 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 996 of 996 sites. Rate of 28,491 buffers per hour: 996 lat/long pairs took 126 seconds
#> Type 49 of 51 = DC --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 1 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 997 of 997 sites. Rate of 28,006 buffers per hour: 997 lat/long pairs took 128 seconds
#> Type 50 of 51 = VT --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 2 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 999 of 999 sites. Rate of 27,693 buffers per hour: 999 lat/long pairs took 130 seconds
#> Type 51 of 51 = SD --
#> Note that ejam_uniq_id was already in sitepoints, and might not be 1:NROW(sitepoints), which might cause issues
#> Analyzing 1 points, radius of 3 miles around each.
#> doaggregate is predicted to take 24 seconds
#> Finished 1000 of 1000 sites. Rate of 27,037 buffers per hour: 1,000 lat/long pairs took 133 seconds
#>
#>
#> type valid sitecount pctvalid pop pop_persite pctofallpop
#> 1 PA 21 21 100 1159954 55236 2
#> 2 CA 170 170 100 15264350 89790 30
#> 3 IA 21 21 100 194052 9241 0
#> 4 NC 18 18 100 401732 22318 1
#> 5 IL 30 30 100 1589128 52971 3
#> 6 AZ 14 14 100 871648 62261 2
#> 7 MS 11 11 100 108080 9825 0
#> 8 NY 57 57 100 4839114 84897 10
#> 9 NJ 93 93 100 5340268 57422 11
#> 10 VA 11 11 100 491362 44669 1
#> 11 CT 15 15 100 454150 30277 1
#> 12 MN 44 44 100 1023437 23260 2
#> 13 NE 14 14 100 75159 5368 0
#> 14 IN 26 26 100 754959 29037 1
#> 15 CO 8 9 89 590346 65594 1
#> 16 MA 26 26 100 1831173 70430 4
#> 17 OH 26 26 100 780793 30030 2
#> 18 TX 69 71 97 3232024 45521 6
#> 19 KS 16 16 100 154902 9681 0
#> 20 MT 10 10 100 132544 13254 0
#> 21 UT 16 18 89 950899 52828 2
#> 22 MI 16 16 100 782767 48923 2
#> 23 DE 5 5 100 102823 20565 0
#> 24 LA 9 9 100 540233 60026 1
#> 25 AL 13 13 100 254235 19557 1
#> 26 WA 20 20 100 1054156 52708 2
#> 27 HI 5 5 100 161256 32251 0
#> 28 KY 9 9 100 61386 6821 0
#> 29 FL 28 28 100 1457251 52045 3
#> 30 OR 21 21 100 1107128 52720 2
#> 31 WI 14 14 100 317386 22670 1
#> 32 ID 5 5 100 11776 2355 0
#> 33 SC 15 15 100 210285 14019 0
#> 34 GA 24 24 100 703126 29297 1
#> 35 OK 12 12 100 205049 17087 0
#> 36 TN 7 7 100 148113 21159 0
#> 37 ND 6 6 100 43233 7206 0
#> 38 MO 16 16 100 677095 42318 1
#> 39 AR 2 2 100 7114 3557 0
#> 40 NH 4 4 100 47660 11915 0
#> 41 WV 7 7 100 31962 4566 0
#> 42 WY 4 4 100 59303 14826 0
#> 43 PR 4 4 100 225174 56294 0
#> 44 NV 6 6 100 663492 110582 1
#> 45 ME 3 3 100 62719 20906 0
#> 46 MD 11 11 100 821114 74647 2
#> 47 NM 6 6 100 81544 13591 0
#> 48 RI 3 3 100 362909 120970 1
#> 49 DC 1 1 100 353862 353862 1
#> 50 VT 2 2 100 5038 2519 0
#> 51 SD 1 1 100 579 579 0
#> pctofallsitecount
#> 1 2
#> 2 17
#> 3 2
#> 4 2
#> 5 3
#> 6 1
#> 7 1
#> 8 6
#> 9 9
#> 10 1
#> 11 2
#> 12 4
#> 13 1
#> 14 3
#> 15 1
#> 16 3
#> 17 3
#> 18 7
#> 19 2
#> 20 1
#> 21 2
#> 22 2
#> 23 0
#> 24 1
#> 25 1
#> 26 2
#> 27 0
#> 28 1
#> 29 3
#> 30 2
#> 31 1
#> 32 0
#> 33 2
#> 34 2
#> 35 1
#> 36 1
#> 37 1
#> 38 2
#> 39 0
#> 40 0
#> 41 1
#> 42 0
#> 43 0
#> 44 1
#> 45 0
#> 46 1
#> 47 1
#> 48 0
#> 49 0
#> 50 0
#> 51 0
#> ratio.to.state.avg.Demog.Index ratio.to.state.avg.Demog.Index.Supp
#> 1 1.5 1.1
#> 2 1.1 1.0
#> 3 1.3 1.0
#> 4 1.2 1.0
#> 5 1.0 0.9
#> 6 1.2 1.1
#> 7 1.0 0.9
#> 8 1.2 1.0
#> 9 1.2 1.1
#> 10 1.1 1.0
#> 11 1.2 1.3
#> 12 1.1 1.0
#> 13 0.8 0.9
#> 14 1.6 1.2
#> 15 1.0 1.0
#> 16 1.2 1.1
#> 17 1.1 1.0
#> 18 1.1 1.0
#> 19 1.1 1.0
#> 20 1.2 1.1
#> 21 1.1 1.1
#> 22 1.4 1.1
#> 23 1.0 0.9
#> 24 1.0 0.9
#> 25 1.0 0.9
#> 26 1.2 1.0
#> 27 1.1 1.0
#> 28 0.7 0.7
#> 29 1.2 1.0
#> 30 1.0 0.9
#> 31 1.4 1.1
#> 32 1.2 1.4
#> 33 1.1 1.0
#> 34 1.1 0.9
#> 35 1.3 1.3
#> 36 1.5 1.1
#> 37 1.0 1.0
#> 38 1.3 1.0
#> 39 1.5 1.2
#> 40 0.9 0.9
#> 41 0.9 0.8
#> 42 1.2 1.1
#> 43 1.0 0.9
#> 44 1.1 1.0
#> 45 1.3 1.2
#> 46 1.3 1.2
#> 47 1.0 1.1
#> 48 1.4 1.2
#> 49 0.8 0.9
#> 50 1.2 1.2
#> 51 1.7 0.6
#> ratio.to.state.avg.pctlowinc ratio.to.state.avg.pctlingiso
#> 1 1.2 1.1
#> 2 1.1 1.3
#> 3 1.1 1.3
#> 4 1.1 1.7
#> 5 0.9 1.2
#> 6 1.2 1.0
#> 7 1.0 1.2
#> 8 1.0 1.2
#> 9 1.2 1.3
#> 10 1.0 1.6
#> 11 1.2 1.4
#> 12 0.9 1.6
#> 13 0.8 0.7
#> 14 1.3 1.5
#> 15 0.9 1.0
#> 16 1.1 1.3
#> 17 1.0 0.6
#> 18 1.0 1.3
#> 19 1.0 1.2
#> 20 1.2 1.4
#> 21 1.1 1.6
#> 22 1.2 2.4
#> 23 1.0 0.9
#> 24 0.9 1.6
#> 25 0.9 1.7
#> 26 1.0 1.3
#> 27 1.0 1.2
#> 28 0.7 0.7
#> 29 1.1 1.1
#> 30 0.9 1.3
#> 31 1.2 1.0
#> 32 1.1 2.8
#> 33 1.0 1.8
#> 34 0.9 0.8
#> 35 1.2 2.6
#> 36 1.2 1.3
#> 37 0.8 1.3
#> 38 1.1 1.5
#> 39 1.1 0.0
#> 40 0.9 1.2
#> 41 0.8 0.4
#> 42 1.1 0.6
#> 43 0.8 0.9
#> 44 1.1 1.1
#> 45 1.1 2.2
#> 46 1.3 1.3
#> 47 0.9 1.8
#> 48 1.3 1.5
#> 49 0.8 1.3
#> 50 1.3 0.8
#> 51 1.1 0.2
#> ratio.to.state.avg.pctunemployed ratio.to.state.avg.pctlths
#> 1 1.3 0.9
#> 2 1.0 1.1
#> 3 1.1 0.9
#> 4 1.1 1.0
#> 5 0.9 1.0
#> 6 1.1 1.1
#> 7 1.1 0.8
#> 8 1.2 1.1
#> 9 1.1 1.2
#> 10 1.1 1.0
#> 11 1.1 1.3
#> 12 1.1 0.9
#> 13 0.8 0.9
#> 14 1.5 1.3
#> 15 1.0 1.1
#> 16 1.1 1.1
#> 17 1.0 0.9
#> 18 1.0 1.1
#> 19 0.9 0.8
#> 20 0.9 0.9
#> 21 1.1 1.1
#> 22 1.2 1.3
#> 23 1.1 0.7
#> 24 0.9 0.8
#> 25 1.0 0.7
#> 26 1.0 1.0
#> 27 1.0 1.2
#> 28 0.8 0.6
#> 29 1.1 1.0
#> 30 1.0 0.8
#> 31 1.4 1.1
#> 32 0.5 2.2
#> 33 0.9 0.9
#> 34 1.0 0.7
#> 35 1.2 1.6
#> 36 1.5 1.1
#> 37 1.3 1.0
#> 38 1.1 0.9
#> 39 2.2 1.4
#> 40 0.9 0.6
#> 41 0.9 0.8
#> 42 1.1 1.2
#> 43 0.8 0.7
#> 44 1.0 1.0
#> 45 0.8 1.1
#> 46 1.2 1.3
#> 47 1.0 1.3
#> 48 1.1 1.4
#> 49 0.7 0.7
#> 50 0.6 1.0
#> 51 2.4 1.0
#> ratio.to.state.avg.pctunder5 ratio.to.state.avg.pctover64
#> 1 1.1 0.9
#> 2 1.0 0.9
#> 3 1.2 0.8
#> 4 1.1 0.8
#> 5 1.0 0.9
#> 6 1.0 0.7
#> 7 0.9 0.8
#> 8 1.1 0.9
#> 9 1.1 0.9
#> 10 1.1 0.8
#> 11 1.1 0.9
#> 12 1.0 0.9
#> 13 1.0 1.1
#> 14 1.1 0.8
#> 15 1.0 0.8
#> 16 1.1 0.8
#> 17 0.9 0.9
#> 18 1.1 0.8
#> 19 1.1 0.9
#> 20 0.9 0.8
#> 21 1.0 1.0
#> 22 1.2 0.8
#> 23 0.9 0.9
#> 24 1.0 1.0
#> 25 1.0 0.8
#> 26 1.0 0.8
#> 27 1.3 0.9
#> 28 1.1 0.8
#> 29 1.2 0.8
#> 30 1.0 0.8
#> 31 1.2 0.9
#> 32 1.0 1.0
#> 33 1.1 0.8
#> 34 1.0 0.9
#> 35 1.1 0.7
#> 36 1.2 0.9
#> 37 1.0 0.8
#> 38 1.0 0.9
#> 39 1.2 0.8
#> 40 1.2 0.8
#> 41 1.1 1.0
#> 42 1.0 0.9
#> 43 1.1 1.0
#> 44 1.1 0.9
#> 45 1.2 0.7
#> 46 1.2 0.9
#> 47 1.4 0.8
#> 48 1.3 0.8
#> 49 0.9 0.7
#> 50 0.8 1.1
#> 51 1.5 0.7
#> ratio.to.state.avg.pctmin
#> 1 1.8
#> 2 1.1
#> 3 1.5
#> 4 1.2
#> 5 1.2
#> 6 1.2
#> 7 1.0
#> 8 1.4
#> 9 1.2
#> 10 1.1
#> 11 1.2
#> 12 1.3
#> 13 0.8
#> 14 2.1
#> 15 1.1
#> 16 1.3
#> 17 1.1
#> 18 1.2
#> 19 1.1
#> 20 1.2
#> 21 1.2
#> 22 1.7
#> 23 1.1
#> 24 1.1
#> 25 1.0
#> 26 1.3
#> 27 1.1
#> 28 0.8
#> 29 1.2
#> 30 1.2
#> 31 1.7
#> 32 1.2
#> 33 1.1
#> 34 1.2
#> 35 1.4
#> 36 1.9
#> 37 1.2
#> 38 1.8
#> 39 2.2
#> 40 1.0
#> 41 1.3
#> 42 1.4
#> 43 1.0
#> 44 1.1
#> 45 1.7
#> 46 1.4
#> 47 1.0
#> 48 1.5
#> 49 0.8
#> 50 1.1
#> 51 2.7
#> Use ejam2excel(out) to view results, and see the types of sites compared, one row each, in the Overall tab
#> Use ejam2barplot_sitegroups() to plot results.
#>
#>
#> 1000 sites in 51 groups (types of sites).
#> Rate of 27,032 buffers per hour: 1,000 lat/long pairs took 133 seconds
ejam2barplot_sitegroups(out_bystate, "sitecount_unique",
names.arg = out_bystate$types, topn = 52, cex.names = 0.5,
main = "Sites by State")
# }