R version 4.2.0 (2022-04-22) -- "Vigorous Calisthenics"
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> suppressPackageStartupMessages(library(sf))
>
> if (require(dplyr, quietly = TRUE)) {
+ options(dplyr.summarise.inform=FALSE)
+ read_sf(system.file("shape/nc.shp", package="sf"), quiet = TRUE) %>%
+ st_transform(3857) -> nc
+ nc %>% filter(AREA > .1) %>% plot()
+
+ # plot 10 smallest counties in grey:
+ nc %>%
+ select(BIR74, geometry) %>%
+ plot()
+
+ nc %>%
+ select(AREA, geometry) %>%
+ arrange(AREA) %>%
+ slice(1:10) %>%
+ plot(add = TRUE, col = 'grey', main ="")
+
+ # select: check both when geometry is part of the selection, and when not:
+ nc %>% select(SID74, SID79) %>% names()
+ nc %>% select(SID74, SID79, geometry) %>% names()
+ nc %>% select(SID74, SID79) %>% class()
+ nc %>% select(SID74, SID79, geometry) %>% class()
+
+ # group_by:
+ nc$area_cl = cut(nc$AREA, c(0, .1, .12, .15, .25))
+ nc %>% group_by(area_cl) %>% class()
+
+ # mutate:
+ nc2 <- nc %>% mutate(area10 = AREA/10)
+
+ # transmute:
+ nc %>% transmute(AREA = AREA/10, geometry = geometry) %>% class()
+ nc %>% transmute(AREA = AREA/10) %>% class()
+
+ # rename:
+ nc2 <- nc %>% rename(area = AREA)
+
+ # distinct:
+ nc[c(1:100,1:10),] %>% distinct() %>% nrow()
+
+ # summarize:
+ nc$area_cl = cut(nc$AREA, c(0, .1, .12, .15, .25))
+ nc.g <- nc %>% group_by(area_cl)
+ nc.g %>% summarise(mean(AREA))
+ nc.g %>% summarize(mean(AREA)) %>% plot(col = 3:6/7)
+
+ library(tidyr)
+
+ # time-wide to long table, using tidyr::gather
+ # stack the two SID columns for the July 1, 1974 - June 30, 1978 and July 1, 1979 - June 30, 1984 periods
+ # (see https://cran.r-project.org/web/packages/spdep/vignettes/sids.pdf)
+ nc %>% select(SID74, SID79, geometry) %>% gather("VAR", "SID", -geometry) %>% summary()
+
+ # spread:
+ nc$row = 1:100
+ nc.g <- nc %>% select(SID74, SID79, row) %>% gather("VAR", "SID", -row, -geometry)
+ nc.g %>% tail()
+ nc.g %>% spread(VAR, SID) %>% head()
+ nc %>% select(SID74, SID79, geometry, row) %>% gather("VAR", "SID", -geometry, -row) %>% spread(VAR, SID) %>% head()
+
+ # test st_set_crs in pipe:
+ sfc = st_sfc(st_point(c(0,0)), st_point(c(1,1)))
+ x <- sfc %>% st_set_crs(4326) %>% st_transform(3857)
+ x
+
+ read_sf(system.file("shape/nc.shp", package="sf"), quiet = TRUE) %>%
+ st_transform(3857) -> nc
+ nc.merc <- st_transform(nc, 32119) # NC State Plane
+ suppressPackageStartupMessages(library(units))
+ install_unit("person")
+ person = as_units("person")
+ nc.merc <- nc.merc %>% mutate(area = st_area(nc.merc), dens = BIR74 * person / area)
+
+ # summary(nc.merc$dens) # requires units 0.4-2
+ nc.merc$area_cl <- cut(nc$AREA, c(0, .1, .12, .15, .25))
+ nc.grp <- nc.merc %>% group_by(area_cl)
+
+ out <- nc.grp %>% summarise(A = sum(area), pop = sum(dens * area),
+ new_dens = sum(dens * area)/sum(area))
+
+ # mean densities depend on grouping:
+ nc.merc %>% summarize(mean(dens))
+ out %>% summarise(mean(new_dens))
+
+ # total densities don't:
+ nc.merc %>% summarise(sum(area * dens))
+ out %>% summarise(sum(A * new_dens))
+
+ conn = system.file("gpkg/nc.gpkg", package = "sf")
+
+ if (require(DBI, quietly = TRUE) && require(RSQLite, quietly = TRUE)) {
+ con = dbConnect(SQLite(), dbname = system.file("gpkg/nc.gpkg", package = "sf"))
+ dbReadTable(con, "nc.gpkg") %>% filter(AREA > 0.2) %>% collect %>% st_sf
+
+ # nest:
+ storms.sf = st_as_sf(storms, coords = c("long", "lat"), crs = 4326)
+ x <- storms.sf %>% group_by(name, year) %>% nest
+
+ nrow(distinct(nc[c(1,1,1,2,2,3:100),]))
+
+ # set.seed(1331)
+ nc$gp <- sample(10, 100, replace=TRUE)
+ # Get centroid of each group of polygons; https://github.com/r-spatial/sf/issues/969
+ nc_gp_cent <- nc %>%
+ group_by(gp) %>%
+ group_map(st_area)
+
+ nc %>% st_filter(nc[1,]) %>% nrow
+ } # DBI & SQLITE
+ } # dplyr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
[1] 4
Warning message:
plotting the first 10 out of 14 attributes; use max.plot = 14 to plot all
>
> proc.time()
user system elapsed
3.146 0.104 3.289
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