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.
#> Finished 1 of 1 sites. Rate of 1,692 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.
#> Finished 3 of 3 sites. Rate of 2,416 buffers per hour: 3 lat/long pairs took 4 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.
#> Finished 4 of 4 sites. Rate of 2,477 buffers per hour: 4 lat/long pairs took 6 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 2,473 buffers per hour: 4 lat/long pairs took 6 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 417
#> 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$types , sortby = F)
if (FALSE) { # \dontrun{
# Analyze by EPA Region
pts <- data.frame(testpoints_1000)
# Get State and EPA Region of each point from lat/lon
x <- state_from_latlon(lat = pts$lat, lon = pts$lon)
pts <- data.frame(pts, x)
out_byregion <- ejamit_compare_types_of_places(
pts, typeofsite = pts$REGION)
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$ST)
ejam2barplot_sitegroups(out_bystate, "sitecount_unique",
names.arg = out_bystate$types, topn = 52, cex.names = 0.5,
main = "Sites by State")
} # }