library(sf) nc <- read_sf(system.file("gpkg/nc.gpkg", package = "sf")) plot(nc["BIR74"], reset = FALSE, key.pos = 4) plot(st_buffer(nc[1,1], units::set_units(10, km)), col = 'NA', border = 'red', lwd = 2, add = TRUE)

homework

install.packages("rnaturalearthdata") library(rnaturalearth) w <- ne_countries(scale = "medium", returnclass = "sf") suppressWarnings(st_crs(w) <- st_crs('OGC:CRS84')) layout(matrix(1:2, 1, 2), c(2,1)) par(mar = rep(0, 4)) plot(st_geometry(w)) # sphere: old <- options(s2_oriented = TRUE) # don't change orientation from here on countries <- s2::s2_data_countries() |> st_as_sfc() globe <- st_as_sfc("POLYGON FULL", crs = st_crs(countries)) oceans <- st_difference(globe, st_union(countries)) visible <- st_buffer(st_as_sfc("POINT(-30 -10)", crs = st_crs(countries)), 9800000) # visible half visible_ocean <- st_intersection(visible, oceans) visible_countries <- st_intersection(visible, countries) st_transform(visible_ocean, "+proj=ortho +lat_0=-10 +lon_0=-30") |> plot(col = 'lightblue') st_transform(visible_countries, "+proj=ortho +lat_0=-10 +lon_0=-30") |> plot(col = NA, add = TRUE)

Investigasi ini menganalisa hasil tes CPNS Kementerian Pendidikan 2024 untuk menguak tren nilai yang muncul guna memberikan gambaran karakteristik umum peserta CPNS sehingga peserta CPNS periode selanjutnya dapat mempersiapkan diri dengan lebih baik ketika menghadapi tes.