A presentation on simple linear regression using robot sensor data as an example. Covers the basics of how regression works, how to fit a model in R, and how it can be used to calibrate sensor readings to real-world distances. Made with ggplot2 and plotly.

med_shp <- st_read("med_shp") plot(st_geometry(med_shp), axes=TRUE) ggplot() + geom_sf(data = med_shp ) med_shape <- st_transform(med_shp, crs = 4324)

ANALISIS ADIDAS Zuliany Serrano - Jorge Hernandez

tnstall.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) DE <- st_geometry(ne_countries(country = "colombia", returnclass = "sf")) DE |> st_transform("+proj=eqc +lat_ts=51.14 +lon_0=90e") -> DE.eqc print(mean(st_bbox(DE)[c("ymin", "ymax")]), digits = 4) par(mfrow = c(1, 2), mar = c(2.2, 2.2, 0.3, 0.5)) plot(DE, axes = TRUE) plot(DE.eqc, axes = TRUE)