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Plot packages(stars)
install.packages("stars")
library(stars)
par(mfrow = c(2, 2))
par(mar = rep(1, 4))
tif <- system.file("tif/L7_ETMs.tif", package = "stars")
x <- read_stars(tif)[,,,1]
image(x, main = "(a)")
image(x[,1:10,1:10], text_values = TRUE, border = 'grey', main = "(b)")
image(x, main = "(c)")
set.seed(131)
pts <- st_sample(st_as_sfc(st_bbox(x)), 3)
plot(pts, add = TRUE, pch = 3, col = 'blue')
image(x, main = "(d)")
plot(st_buffer(pts, 500), add = TRUE, pch = 3, border = 'blue', col = NA, lwd = 2)
HTML packages(sf, tydiverse, mapview)
install.packages("mapview")
library(mapview) |> suppressPackageStartupMessages()
mapviewOptions(fgb = FALSE)
nc.32119 |> mapview(zcol = "BIR74", legend = TRUE, col.regions = sf.colors)
Plot package (sf , tydiverse)
year_labels <- c("SID74" = "1974 - 1978", "SID79" = "1979 - 1984")
nc.32119 |> select(SID74, SID79) |>
pivot_longer(starts_with("SID")) -> nc_longer
ggplot() + geom_sf(data = nc_longer, aes(fill = value), linewidth = 0.4) +
facet_wrap(~ name, ncol = 1, labeller = labeller(name = year_labels)) +
scale_y_continuous(breaks = 34:36) +
scale_fill_gradientn(colors = sf.colors(20)) +
theme(panel.grid.major = element_line(color = "white"))
Plot package (sf,tysiverse)
library(tidyverse)
library(sf)
system.file("gpkg/nc.gpkg", package="sf") |>
read_sf() -> nc
nc.32119 <- st_transform(nc, 'EPSG:32119')
nc.32119 |>
select(BIR74) |>
plot(graticule = TRUE, axes = TRUE)
Plot package (sf) attributes(nc)
attributes(nc) package (sf)
literacy rate
according to states
Implementasi Clustering pada Prediksi Serangan Jantung
Melakukan perhitungan dalam penelitian analisis data mengenai prediksi serangan jantung menggunakan 5 metode
male female literacy rate
Male and female literacy rate according to states.