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.

Some examples of Biomarker Trajectory Analysis along with further discussion. Renal State-Space (BUN/Creatinine) Metabolic Lipid Trajectory (TG/HDL) Metabolic 3D Landscape 2D Ca-P Mineralization Trajectory 3D Ca-P-ALP Trajectory

This dashboard aims to show key variables that can impact an individual's likelihood of receiving an ASD diagnosis, including the economic status of the country of residence, age, and sex, and how these factors have changed over time.