>>> arcpy.RasterToPolygon_conversion(in_raster="D:/curso_de_backend_js_python_R_arcgis_qgis_-01-20-26^^/servers/DATOS/descargas_shp/descarga_img/clip-mapa_base-terreno22_dem^utm.tif", out_polygon_features="D:/curso_de_backend_r_y_python/python_rstudio_1242_pubs/shapes_export-arcpy/RasterT_clipma2.shp", simplify="SIMPLIFY", raster_field="Value", create_multipart_features="SINGLE_OUTER_PART", max_vertices_per_feature="") <Result 'D:\\curso_de_backend_r_y_python\\python_rstudio_1242_pubs\\shapes_export-arcpy\\RasterT_clipma2.shp'> >>> arcpy.Clip_analysis(in_features="D:/curso_de_backend_r_y_python/python_rstudio_1242_pubs/shapes_export-arcpy/rasterxpoligono_RasterT_tif2.shp", clip_features="D:/curso_de_backend_js_python_R_arcgis_qgis_-01-20-26^^/servers/DATOS/descargas_shp/descarga_img4/ant_shp.shp", out_feature_class="D:/curso_de_backend_r_y_python/python_rstudio_1242_pubs/shapes_export-arcpy/rasterxpoligono_RastercolT_tif2_Clip.shp", cluster_tolerance="") <Result 'D:\\curso_de_backend_r_y_python\\python_rstudio_1242_pubs\\shapes_export-arcpy\\rasterxpoligono_RastercolT_tif2_Clip.shp'> >>> raxpolcolT_clip2 <- st_read("rasterxpoligono_RastercolT_tif2_Clip.shp") plot(st_geometry(raxpolcolT_clip2), axes=TRUE) raxpolcolT_clip2 <- st_transform(raxpolcolT_clip2, crs = 4324) ggplot() + geom_sf(data = raxpolcolT_clip2 ) mapview(raxpolcolT_clip2)

>>> arcpy.RasterToPolygon_conversion(in_raster="D:/curso_de_backend_js_python_R_arcgis_qgis_-01-20-26^^/servers/DATOS/descargas_shp/descarga_img/clip-mapa_base-terreno22_dem^utm.tif", out_polygon_features="D:/curso_de_backend_r_y_python/python_rstudio_1242_pubs/shapes_export-arcpy/RasterT_clipma2.shp", simplify="SIMPLIFY", raster_field="Value", create_multipart_features="SINGLE_OUTER_PART", max_vertices_per_feature="") <Result 'D:\\curso_de_backend_r_y_python\\python_rstudio_1242_pubs\\shapes_export-arcpy\\RasterT_clipma2.shp'> >>> arcpy.Clip_analysis(in_features="D:/curso_de_backend_r_y_python/python_rstudio_1242_pubs/shapes_export-arcpy/rasterxpoligono_RasterT_tif2.shp", clip_features="D:/curso_de_backend_js_python_R_arcgis_qgis_-01-20-26^^/servers/DATOS/descargas_shp/descarga_img4/ant_shp.shp", out_feature_class="D:/curso_de_backend_r_y_python/python_rstudio_1242_pubs/shapes_export-arcpy/rasterxpoligono_RastercolT_tif2_Clip.shp", cluster_tolerance="") <Result 'D:\\curso_de_backend_r_y_python\\python_rstudio_1242_pubs\\shapes_export-arcpy\\rasterxpoligono_RastercolT_tif2_Clip.shp'> >>>raxpolcolT_tif2 <- st_read("rasterxpoligono_RasterT_tif2.shp") plot(st_geometry(raxpolcolT_tif2), axes=TRUE) raxpolcolT_tif2 <- st_transform(raxpolcolT_tif2, crs = 4324) ggplot() + geom_sf(data = raxpolcolT_tif2 ) mapview(raxpolcolT_tif2)

datos<-c(10,10,10,20,20,20,20,20,25,25,25,25,25,25,25,25,30,30,30,30,30,30,30,30) hist(datos, main="histograma de datos", xlab="valores", ylab="frecuencia", col="skyblue", border="pink" ) summary(datos) plot(datos) hist(datos) boxplot(datos)

require(stats) mosaicplot(Titanic, main = "Survival on the Titanic", color = TRUE) mosaicplot(HairEyeColor, shade = TRUE) ## Independence model of hair and eye color and sex. Indicates that ## there are more blue eyed blonde females than expected in the case ## of independence and too few brown eyed blonde females. ## The corresponding model is: fm <- loglin(HairEyeColor, list(1, 2, 3)) pchisq(fm$pearson, fm$df, lower.tail = FALSE) mosaicplot(HairEyeColor, shade = TRUE, margin = list(1:2, 3)) ## Model of joint independence of sex from hair and eye color. Males ## are underrepresented among people with brown hair and eyes, and are ## overrepresented among people with brown hair and blue eyes. ## The corresponding model is: fm <- loglin(HairEyeColor, list(1:2, 3)) pchisq(fm$pearson, fm$df, lower.tail = FALSE) ## Formula interface for raw data: visualize cross-tabulation of numbers ## of gears and carburettors in Motor Trend car data. mosaicplot(~ gear + carb, data = mtcars, color = TRUE, las = 1) # color recycling mosaicplot(~ gear + carb, data = mtcars, color = 2:3, las = 1)

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d_filled() + guides(fill = guide_legend(title = "Nivel"))

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_point() + geom_density_2d_filled(alpha = 0.4) + geom_density_2d(colour = "black")

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d_filled() + geom_density_2d(colour = "black")

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d_filled(alpha = 0.75)

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d_filled() + scale_fill_brewer()

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d_filled()

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y, fill = ..level..)) + stat_density_2d(geom = "polygon")

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d(aes(color = ..level..)) + scale_color_viridis_c()

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d(aes(color = ..level..))

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d(color = "red")

# install.packages("ggplot2") library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_point() + geom_density_2d()

library(ggplot2) # Datos set.seed(1) df <- data.frame(x = rnorm(200), y = rnorm(200)) ggplot(df, aes(x = x, y = y)) + geom_density_2d()

# install.packages("MASS") library(MASS) library(raster) library(sp) library(sf) library(gstat) library(rasterVis) puntos<- st_read("puntos.shp") # Datos z <- kde2d(x, y, n = 50) plot(x, y, pch = 19) contour(z, lwd = 2, add = TRUE, col = hcl.colors(10, "Spectral"))

library(sf) |> suppressPackageStartupMessages() par(mfrow = c(2,4)) par(mar = c(1,1,1.2,1)) # 1 p <- st_point(0:1) plot(p, pch = 16) title("point") box(col = 'grey') # 2 mp <- st_multipoint(rbind(c(1,1), c(2, 2), c(4, 1), c(2, 3), c(1,4))) plot(mp, pch = 16) title("multipoint") box(col = 'grey') # 3 ls <- st_linestring(rbind(c(1,1), c(5,5), c(5, 6), c(4, 6), c(3, 4), c(2, 3))) plot(ls, lwd = 2) title("linestring") box(col = 'grey') # 4 mls <- st_multilinestring(list( rbind(c(1,1), c(5,5), c(5, 6), c(4, 6), c(3, 4), c(2, 3)), rbind(c(3,0), c(4,1), c(2,1)))) plot(mls, lwd = 2) title("multilinestring") box(col = 'grey') # 5 polygon po <- st_polygon(list(rbind(c(2,1), c(3,1), c(5,2), c(6,3), c(5,3), c(4,4), c(3,4), c(1,3), c(2,1)), rbind(c(2,2), c(3,3), c(4,3), c(4,2), c(2,2)))) plot(po, border = 'black', col = '#ff8888', lwd = 2) title("polygon") box(col = 'grey') # 6 multipolygon mpo <- st_multipolygon(list( list(rbind(c(2,1), c(3,1), c(5,2), c(6,3), c(5,3), c(4,4), c(3,4), c(1,3), c(2,1)), rbind(c(2,2), c(3,3), c(4,3), c(4,2), c(2,2))), list(rbind(c(3,7), c(4,7), c(5,8), c(3,9), c(2,8), c(3,7))))) plot(mpo, border = 'black', col = '#ff8888', lwd = 2) title("multipolygon") box(col = 'grey') # 7 geometrycollection gc <- st_geometrycollection(list(po, ls + c(0,5), st_point(c(2,5)), st_point(c(5,4)))) plot(gc, border = 'black', col = '#ff6666', pch = 16, lwd = 2) title("geometrycollection") box(col = 'grey')

# install.packages("MASS") library(MASS) # Datos x <- rnorm(500) y <- rnorm(500) z <- kde2d(x, y, n = 50) plot(x, y, pch = 19) contour(z, lwd = 2, add = TRUE, col = hcl.colors(10, "Spectral")) filled.contour(volcano) filled.contour(volcano, nlevels = 10) filled.contour(volcano, color.palette = terrain.colors) filled.contour(volcano, plot.axes = { axis(1) axis(2) contour(volcano, add = TRUE, lwd = 2) } )

getwd() setwd("D:/curso_de_backend_r_y_python/python_rstudio_1242_pubs") library(raster) library(sp) library(sf) library(gstat) library(rastervis) Contour_tif1<- st_read("Contour_tif1") plot(st_geometry(Contour_tif1), graticule = TRUE, axes=TRUE) Contour_tif1 <- st_transform(Contour_tif1, crs = 4324)

plot(st_rasterize(cl204_4234["gridcode"], dx = 0.1 ), col = sf.colors(), breaks = "equal" )

plot(st_rasterize(cl20_4234["gridcode"], dx = 0.1 ), col = sf.colors(), breaks = "equal")

cl204 <- st_read("RasterT_cover_l4.shp") plot(st_geometry(cl204), graticule = TRUE, axes=TRUE) cl204 <- st_transform(cl204, crs = 4324) library(tidyverse) library(sf) cl204_4234 <- st_transform(cl204, 'EPSG:4234') cl204_4234 |> select(gridcode) |> plot(graticule = TRUE, axes = TRUE) cl204 |> select(id, gridcode, geometry) |> print(n = 3) print(cl204) install.packages("mapview") library(mapview) |> suppressPackageStartupMessages() mapviewOptions(fgb = FALSE) cl204_4234 |> mapview(zcol = "gridcode", legend = TRUE, col.regions = sf.colors)

library(tidyverse) library(sf) cl20_4234 <- st_transform(cl20, 'EPSG:4234') cl20_4234 |> select(gridcode) |> plot(graticule = TRUE, axes = TRUE) cl20 |> select(id, gridcode, geometry) |> print(n = 3) print(cl20) install.packages("mapview") library(mapview) |> suppressPackageStartupMessages() mapviewOptions(fgb = FALSE) cl20_4234 |> mapview(zcol = "gridcode", legend = TRUE, col.regions = sf.colors)

library(tidyverse) library(sf) cl20_4234 <- st_transform(cl20, 'EPSG:4234') cl20_4234 |> select(gridcode) |> plot(graticule = TRUE, axes = TRUE) cl20 |> select(id, gridcode, geometry) |> print(n = 3) print(cl20)

x <- 1:5 y <- 1:4 d <- st_dimensions(x = x, y = y, .raster = c("x", "y")) m <- matrix(runif(20),5,4) r1 <- st_as_stars(r = m, dimensions = d) r <- attr(d, "raster") r$affine <- c(0.2, -0.2) attr(d, "raster") = r r2 <- st_as_stars(r = m, dimensions = d) r <- attr(d, "raster") r$affine <- c(0.1, -0.3) attr(d, "raster") = r r3 = st_as_stars(r = m, dimensions = d) x <- c(1, 2, 3.5, 5, 6) y <- c(1, 1.5, 3, 3.5) d <- st_dimensions(x = x, y = y, .raster = c("x", "y")) r4 <- st_as_stars(r = m, dimensions = d) grd <- st_make_grid(cellsize = c(10,10), offset = c(-130,10), n = c(8,5), crs = st_crs('OGC:CRS84')) r5 <- st_transform(grd, "+proj=laea +lon_0=-70 +lat_0=35") par(mfrow = c(2,3), mar = c(0.1, 1, 1.1, 1)) r1 <- st_make_grid(cellsize = c(1,1), n = c(5,4), offset = c(0,0)) plot(r1, main = "regular") plot(st_geometry(st_as_sf(r2)), main = "rotated") plot(st_geometry(st_as_sf(r3)), main = "sheared") plot(st_geometry(st_as_sf(r4, as_points = FALSE)), main = "rectilinear") plot(st_geometry((r5)), main = "curvilinear")

plot(st_rasterize(nc["BIR74"], dx = 0.1), col = sf.colors(), breaks = "equal")

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)

install.packages("mapview") library(mapview) |> suppressPackageStartupMessages() mapviewOptions(fgb = FALSE) nc.32119 |> mapview(zcol = "BIR74", legend = TRUE, col.regions = sf.colors)

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"))

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)

attributes(nc) package (sf)

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)

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)

tmap_mode("plot")

tm_shape(r) + tm_raster()

library(tmap) system.file("gpkg/nc.gpkg", package = "sf") |> read_sf() |> st_transform('EPSG:32119') -> nc.32119 tm_shape(nc.32119) + tm_polygons(c("SID74", "SID79"), title = "SIDS") + tm_layout(legend.outside = TRUE, panel.labels = c("1974-78", "1979-84")) + tm_facets(free.scales=FALSE)

library(ggplot2) library(stars) r <- read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) ggplot() + geom_stars(data = r) + facet_wrap(~band) + coord_equal() + theme_void() + scale_x_discrete(expand = c(0,0)) + scale_y_discrete(expand = c(0,0)) + scale_fill_viridis_c()

library(tidyverse) |> suppressPackageStartupMessages() nc.32119 <- st_transform(nc, 32119) 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(colours = sf.colors(20)) + theme(panel.grid.major = element_line(colour = "white"))

library(stars) # Loading required package: abind system.file("tif/L7_ETMs.tif", package = "stars") |> read_stars() -> r st_bbox(r) |> st_as_sfc() |> st_sample(5) |> st_buffer(300) -> circ hook <- function() { plot(circ, col = NA, border = 'yellow', add = TRUE) } plot(r, hook = hook, key.pos = 4)

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)

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)

cl20;antioquia land cover 20

cl20_1; antioquia cover land 2020-1

antRasterT_coverlan20polyshp

antRasterT_coverlan201polyshp

antrasterT_coverl20shp

ant-rasterT-cover201-Export_Output_3

clipterrain2utmContif3shp

Sentinel-2_Land_Cover_exportmap24.tif

Sentinel-2-Land-Cover-exportmap(20).tif

Sentinel-2_LandCover_exportmap2024med.tif

raster_T_sentine32024shp

RasterT_S2shptemp2020

my_map2-googleearthengine^classification^

Plottempcol2

rastercontourntif_3rPlot

rasterpolygonotif4_shp <- st_read("rasterxpolygonoRasterT_tif4.shp") plot(st_geometry(rasterpolygonotif4_shp), axes=TRUE) rasterpolygonotif4_shp <- st_transform(rasterpolygonotif4_shp, crs = 4324)

rasterpolygonotif4_shp-rplot

my_map-geodata^elevation-global^

my_map-geodata^elevation-col^

qcol <- worldclim_global(res=0.5, var="tmin", path=tempdir()) plot(qcol)

my_map-geodata_temperatura^global^PlotPlot

my_map-geodata_temperatura^prmefio.anual^Plot

my_map_previpitacion^anual^Plot

rcol <- worldclim_country("Colombia", var="tmax", path=tempdir()) plot(rcol)

geodata rcol <- worldclim_country("Colombia", var="tmin", path=tempdir()) plot(rcol)

sf rPlot_departamentos de colombia antioquia

may_map-stack^b1-b2-b3-b4-b5^Plot_lansat_tif en rstudio

stack b5 y b7 raster

reclassificación aspect-terrain rPlot_lansat_tif en rstudio

aspect-terrain-rPlot_lansat_tif en rstudio

angle-terrain-rPlot_lansat_tif en rstudio

rPlot slope-terrain_lansat_tif en rstudio

rPlot_lansat_tif en rstudio

first map1240r

trees-tmap^raster-...

r^^tmap-raster_cover-^^VA

HTML tmap-raster_poptotwor-population^total-worldly_^^vA

Rplot06cover-qtm_tmap-raster^land V ^^

two_wiews-tmap^qtm

tmap_raster-elevation

qtm()world

tmap qtm

arrange tm1 tm2 tmap

tmap tm_shape(world) red , blue

m_shape(world) tmap plot world shape

vcol <- worldclim_country("Colombia", var="tavg", path=tempdir()) plot(vcol)

tcol <- worldclim_country("Colombia", var="prec", path=tempdir()) plot(tcol)

scol <- worldclim_country("Colombia", var="tmax", path=tempdir()) plot(scol)

bant <- bdptos[bdptos@data$dpto_cnmbr == "ANTIOQUIA",] plot(bant, add = TRUE, col = "yellow")

Rplot06osm(country)_geodata_railway paqute y libreria con atributo

Rplot06osm(country)_geodata_highways paquete y libreria

Rplot06osm(country)_geodata paquete y libreria

Rplot06soil_worl(-)_geodata paquete y libreria

Rplot06geodata_worldclim_country(colombia)

paquete y libreria geodata Colombia

rPlot demar_all b1b2b3b4b5 con paquete y libreria raster

Rplot rastermetod_aspectterrain-b1-ant con paquete y libreria raster

Rplot-rastermetod_slopeterrain_b1-ant con paquete y libreria raster

Rplot_rastermetod_sñopeterrain-b1-med con paqute y libreria raster

Rplot_rastermetod_slopeterrain_b7 con pquete y libreria raster

Plot_R-raster-slope_terrain raster metod con paquete y libreria raster

Plot_R-raster-slope_terrain con la libreria raster y matodo raster

Plot b6.vcid en rstudio

Plot_lansat_tif en rstudio con la libreria raster

-rosm_descarga_ruta_betulia

clip geojson alerta hidrologica antioquia publicado en la web con R y leaflet

shape medellin

shape de atlantico

shape archipielago de san andres

shape arauca

shape antioquia

shape amazonas

medellin

mapa leaflet

HTML_shape de Colombia

PloT_ColombiA en RStudIO

Plot_municipios de colombia en rstudio

plot en rstudio de los departamentos de colombia

publicar mapa con rstudio leaflet y html