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_freeze/notebook/example-6/execute-results/html.json

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+    "markdown": "---\ntitle: \"6: duckdb\"\nauthor: Carl Boettiger\ndate: \"2024-03-06\"\n---\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(duckdbfs)\nlibrary(dplyr)\nlibrary(sf)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# SQL\n\npad <- open_dataset(\"https://data.source.coop/cboettig/pad-us-3/pad-us3-combined.parquet\")\n\npad_meta <- duckdbfs::st_read_meta(\"https://data.source.coop/cboettig/pad-us-3/pad-us3-combined.fgb\", tblname = \"pad_meta\")\npad_meta\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 7\n  feature_count geom_column_name geom_type     name  code   wkt            proj4\n          <dbl> <chr>            <chr>         <chr> <chr>  <chr>          <chr>\n1        440107 geom             Unknown (any) ESRI  102039 \"PROJCS[\\\"USA… +pro…\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\npad |> \n  filter(State_Nm == \"CA\") |> \n  group_by(FeatClass) |> \n  summarise(total_area = sum(SHAPE_Area),\n            n = n()) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning: Missing values are always removed in SQL aggregation functions.\nUse `na.rm = TRUE` to silence this warning\nThis warning is displayed once every 8 hours.\n```\n\n\n:::\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 5 × 3\n  FeatClass       total_area     n\n  <chr>                <dbl> <dbl>\n1 Proclamation 173843820097.   324\n2 Easement       7275244842. 11337\n3 Marine        36793465608.   214\n4 Designation  129887995064.  1293\n5 Fee          194547665576. 17873\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nduckdbfs::load_spatial()\n```\n:::\n\n\n\nReading in as a normal tibble, and then converting to a spatial object:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nca_fee <- pad |> \n  filter(State_Nm == \"CA\", FeatClass == \"Fee\") |> \n  collect()\n\nca_fee |> st_as_sf(sf_column_name = \"geometry\", crs = pad_meta$wkt)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSimple feature collection with 17873 features and 34 fields\nGeometry type: GEOMETRY\nDimension:     XY\nBounding box:  xmin: -2349024 ymin: 1242361 xmax: -1646723 ymax: 2452203\nProjected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version\n# A tibble: 17,873 × 35\n   FeatClass Category Own_Type Own_Name Loc_Own     Mang_Type Mang_Name Loc_Mang\n * <chr>     <chr>    <chr>    <chr>    <chr>       <chr>     <chr>     <chr>   \n 1 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 2 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 3 Fee       Fee      STAT     OTHS     University… STAT      OTHS      Univers…\n 4 Fee       Fee      STAT     OTHS     University… STAT      OTHS      Univers…\n 5 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 6 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 7 Fee       Fee      LOC      CITY     City of Sa… LOC       CITY      City of…\n 8 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 9 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n10 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n# ℹ 17,863 more rows\n# ℹ 27 more variables: Des_Tp <chr>, Loc_Ds <chr>, Unit_Nm <chr>, Loc_Nm <chr>,\n#   State_Nm <chr>, Agg_Src <chr>, GIS_Src <chr>, Src_Date <chr>,\n#   GIS_Acres <int>, Source_PAID <chr>, WDPA_Cd <int>, Pub_Access <chr>,\n#   Access_Src <chr>, Access_Dt <chr>, GAP_Sts <chr>, GAPCdSrc <chr>,\n#   GAPCdDt <chr>, IUCN_Cat <chr>, IUCNCtSrc <chr>, IUCNCtDt <chr>,\n#   Date_Est <chr>, Comments <chr>, EsmtHldr <chr>, EHoldTyp <chr>, …\n```\n\n\n:::\n:::\n\n\nSimilarly, any normal data frame can be coerced to a spatial `sf` object, e.g.:\n\n::: {.cell}\n\n```{.r .cell-code}\ndata.frame(lon = c(1,2), lat=c(0,0)) |> st_as_sf(coords = c(\"lon\", \"lat\"), crs=4326)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSimple feature collection with 2 features and 0 fields\nGeometry type: POINT\nDimension:     XY\nBounding box:  xmin: 1 ymin: 0 xmax: 2 ymax: 0\nGeodetic CRS:  WGS 84\n     geometry\n1 POINT (1 0)\n2 POINT (2 0)\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nspatial_ex <- paste0(\"https://raw.githubusercontent.com/cboettig/duckdbfs/\",\n                     \"main/inst/extdata/spatial-test.csv\") |>\n  open_dataset(format = \"csv\") \n\nspatial_ex |>\n  mutate(geometry = st_point(longitude, latitude)) |>\n  to_sf(crs = 4326)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSimple feature collection with 10 features and 3 fields\nGeometry type: POINT\nDimension:     XY\nBounding box:  xmin: 1 ymin: 1 xmax: 10 ymax: 10\nGeodetic CRS:  WGS 84\n   site latitude longitude          geom\n1     a        1         1   POINT (1 1)\n2     b        2         2   POINT (2 2)\n3     c        3         3   POINT (3 3)\n4     d        4         4   POINT (4 4)\n5     e        5         5   POINT (5 5)\n6     f        6         6   POINT (6 6)\n7     g        7         7   POINT (7 7)\n8     h        8         8   POINT (8 8)\n9     i        9         9   POINT (9 9)\n10    j       10        10 POINT (10 10)\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nca_fee <- pad |> \n  filter(State_Nm == \"CA\", FeatClass == \"Fee\") |> \n  group_by(Own_Type) |>\n  summarise(total = sum(SHAPE_Area)) |> head(1000) |> collect()\n```\n:::",
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_freeze/tutorials/example-1/execute-results/html.json

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+    "engine": "knitr",
+    "markdown": "---\ntitle: \"1: Satellite data\"\nauthor: Carl Boettiger\ndate: \"2024-01-31\"\n---\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rstac)\nlibrary(gdalcubes)\nlibrary(stars)\nlibrary(tmap)\nlibrary(dplyr)\nearthdatalogin::gdal_cloud_config()\ngdalcubes::gdalcubes_options(parallel = TRUE)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nbox <- c(xmin=-123, ymin=37, xmax=-121, ymax=39) \nstart_date <- \"2022-06-01\"\nend_date <- \"2022-08-01\"\nitems <-\n  stac(\"https://earth-search.aws.element84.com/v0/\") |>\n  stac_search(collections = \"sentinel-s2-l2a-cogs\",\n              bbox = box,\n              datetime = paste(start_date, end_date, sep=\"/\"),\n              limit = 100) |>\n  ext_query(\"eo:cloud_cover\" < 20) |>\n  post_request()\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ncol <- stac_image_collection(items$features, asset_names = c(\"B08\", \"B04\", \"SCL\"))\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning in stac_image_collection(items$features, asset_names = c(\"B08\", : STAC\nasset with name 'SCL' does not include eo:bands metadata and will be considered\nas a single band source\n```\n\n\n:::\n\n```{.r .cell-code}\ncube <- cube_view(srs =\"EPSG:4326\",\n                  extent = list(t0 = start_date, t1 = end_date,\n                                left = box[1], right = box[3],\n                                top = box[4], bottom = box[2]),\n                  dx = 0.001, dy = 0.001, dt = \"P1M\",\n                  aggregation = \"median\", resampling = \"average\")\n```\n:::\n\n\n\nThe  SCL data layer in Sentinel is one of three 'quality assurance' layers provided in this data catalog. Table 3 in this description of the [Sentinel-2 Level2A Specifications](https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_specs.html) summarizes the classification codes (Cloud shadows, medium probability cloud, high probability cloud).  An image mask basically drops these bad pixels.  \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmask <- image_mask(\"SCL\", values=c(3, 8, 9)) # mask clouds and cloud shadows\n\ndata <-  raster_cube(col, cube, mask = mask)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nndvi <- data |>\n  select_bands(c(\"B04\", \"B08\")) |>\n  apply_pixel(\"(B08-B04)/(B08+B04)\", \"NDVI\") |>\n  reduce_time(c(\"mean(NDVI)\")) \n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nndvi_stars <- st_as_stars(ndvi)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nmako <- tm_scale_continuous(values = viridisLite::mako(30))\nfill <- tm_scale_continuous(values = \"Greens\")\n\ntm_shape(ndvi_stars) + tm_raster(col.scale = mako)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nstars object downsampled to 1000 by 1000 cells.\n```\n\n\n:::\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning in value[[3L]](cond): could not rename the data.table\n```\n\n\n:::\n\n::: {.cell-output-display}\n![](example-1_files/figure-html/unnamed-chunk-7-1.png){width=672}\n:::\n:::",
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_freeze/tutorials/example-3/execute-results/html.json

@@ -0,0 +1,15 @@
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+    "markdown": "---\ntitle: \"3: animations\"\nauthor: Carl Boettiger\ndate: \"2024-02-05\"\n---\n\n\nFollowing the same template, but we compute over a larger bounding box and generate an animation \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rstac)\nlibrary(gdalcubes)\nlibrary(stars)\nlibrary(tmap)\nlibrary(dplyr)\nearthdatalogin::gdal_cloud_config()\nearthdatalogin::with_gdalcubes()\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nbox <- c(xmin=-123, ymin=37, xmax=-122, ymax=38) \nstart_date <- \"2022-01-01\"\nend_date <- \"2022-06-30\"\nitems <-\n  stac(\"https://planetarycomputer.microsoft.com/api/stac/v1\") |>\n  stac_search(collections = \"sentinel-2-l2a\",\n              bbox = box,\n              datetime = paste(start_date, end_date, sep=\"/\"),\n              limit = 1000) |>\n  ext_query(\"eo:cloud_cover\" < 20) |>\n  post_request() |>\n  items_sign(sign_planetary_computer())\n```\n:::\n\n\nLet's do a true-color RGB image this time by combining data from Blue, Green, and Red bands:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncol <- stac_image_collection(items$features, asset_names = c(\"B02\", \"B03\", \"B04\", \"SCL\"))\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning in stac_image_collection(items$features, asset_names = c(\"B02\", : STAC\nasset with name 'SCL' does not include eo:bands metadata and will be considered\nas a single band source\n```\n\n\n:::\n\n```{.r .cell-code}\ncube <- cube_view(srs =\"EPSG:4326\",\n                  extent = list(t0 = start_date, t1 = end_date,\n                                left = box[1], right = box[3],\n                                top = box[4], bottom = box[2]),\n                  dx = 0.001, dy = 0.001, dt = \"P1M\")\n\ndata <-  raster_cube(col, cube)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nndvi <- data |>\n  select_bands(c(\"B02\",\"B03\", \"B04\")) |>\n  write_ncdf(\"visual.nc\", overwrite=TRUE)\n```\n:::\n\n\nWhile we could go directly from `apply_pixel` to `animate`, here we show how to stash a copy of the computed, rescaled and reprojected data as a local netcdf file that can be used in any further analysis without going back to the original data. To continue our `gdalcubes` pipeline, we can easily load this space-time ncdf cube and continue as before:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nncdf_cube(\"visual.nc\") |>\n animate(rgb=3:1, \n         col = viridisLite::mako, fps=2, \n         save_as=\"visual.gif\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"visual.gif\"\n```\n\n\n:::\n:::\n\n\n\n![](visual.gif)",
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_freeze/tutorials/example-4/execute-results/html.json

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+    "engine": "knitr",
+    "markdown": "---\ntitle: \"4: Biodiversity Intactness Index\"\nauthor: Carl Boettiger\ndate: \"2024-02-07\"\n---\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rstac)\nlibrary(gdalcubes)\nlibrary(stars)\nlibrary(tmap)\nlibrary(dplyr)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(spData)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nTo access larger datasets in this package, install the spDataLarge\npackage with: `install.packages('spDataLarge',\nrepos='https://nowosad.github.io/drat/', type='source')`\n```\n\n\n:::\n\n```{.r .cell-code}\nbox_ca <- spData::us_states |> filter(NAME==\"California\") |> st_bbox()\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nbox <- c(xmin=-123, ymin=37, xmax=-121, ymax=39) \nbox <- c(box_ca)\nitems <-  \n  stac(\"https://planetarycomputer.microsoft.com/api/stac/v1\")  |>\n  stac_search(collections = \"io-biodiversity\",\n              bbox = box,\n              limit = 100)  |>\n  post_request() |>\n  items_sign(sign_planetary_computer())\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ncol <- stac_image_collection(items$features, asset_names = c(\"data\"))\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning in stac_image_collection(items$features, asset_names = c(\"data\")): STAC\nasset with name 'data' does not include eo:bands metadata and will be\nconsidered as a single band source\n```\n\n\n:::\n\n```{.r .cell-code}\ncube <- cube_view(srs =\"EPSG:4326\",\n                  extent = list(t0 = \"2017-01-01\", t1 = \"2017-12-31\",\n                                left = box[1], right = box[3],\n                                top = box[4], bottom = box[2]),\n                  dx = 0.005, dy = 0.005, dt = \"P1Y\")\n\ndata <-  raster_cube(col, cube)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nbii <- data |> slice_time(\"2017-01-01\") |>  st_as_stars()\ntm_shape(bii) + tm_raster()\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nstars object downsampled to 1028 by 948 cells.\n```\n\n\n:::\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning in value[[3L]](cond): could not rename the data.table\n```\n\n\n:::\n\n::: {.cell-output-display}\n![](example-4_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n:::\n",
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_freeze/tutorials/example-6/execute-results/html.json

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+    "engine": "knitr",
+    "markdown": "---\ntitle: \"6: duckdb\"\nauthor: Carl Boettiger\ndate: \"2024-03-06\"\n---\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(duckdbfs)\nlibrary(dplyr)\nlibrary(sf)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# SQL\n\npad <- open_dataset(\"https://data.source.coop/cboettig/pad-us-3/pad-us3-combined.parquet\")\n\npad_meta <- duckdbfs::st_read_meta(\"https://data.source.coop/cboettig/pad-us-3/pad-us3-combined.fgb\", tblname = \"pad_meta\")\npad_meta\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 7\n  feature_count geom_column_name geom_type     name  code   wkt            proj4\n          <dbl> <chr>            <chr>         <chr> <chr>  <chr>          <chr>\n1        440107 geom             Unknown (any) ESRI  102039 \"PROJCS[\\\"USA… +pro…\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\npad |> \n  filter(State_Nm == \"CA\") |> \n  group_by(FeatClass) |> \n  summarise(total_area = sum(SHAPE_Area),\n            n = n()) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning: Missing values are always removed in SQL aggregation functions.\nUse `na.rm = TRUE` to silence this warning\nThis warning is displayed once every 8 hours.\n```\n\n\n:::\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 5 × 3\n  FeatClass       total_area     n\n  <chr>                <dbl> <dbl>\n1 Proclamation 173843820097.   324\n2 Easement       7275244842. 11337\n3 Marine        36793465608.   214\n4 Designation  129887995064.  1293\n5 Fee          194547665576. 17873\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nduckdbfs::load_spatial()\n```\n:::\n\n\n\nReading in as a normal tibble, and then converting to a spatial object:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nca_fee <- pad |> \n  filter(State_Nm == \"CA\", FeatClass == \"Fee\") |> \n  collect()\n\nca_fee |> st_as_sf(sf_column_name = \"geometry\", crs = pad_meta$wkt)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSimple feature collection with 17873 features and 34 fields\nGeometry type: GEOMETRY\nDimension:     XY\nBounding box:  xmin: -2349024 ymin: 1242361 xmax: -1646723 ymax: 2452203\nProjected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version\n# A tibble: 17,873 × 35\n   FeatClass Category Own_Type Own_Name Loc_Own     Mang_Type Mang_Name Loc_Mang\n * <chr>     <chr>    <chr>    <chr>    <chr>       <chr>     <chr>     <chr>   \n 1 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 2 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 3 Fee       Fee      STAT     OTHS     University… STAT      OTHS      Univers…\n 4 Fee       Fee      STAT     OTHS     University… STAT      OTHS      Univers…\n 5 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 6 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 7 Fee       Fee      LOC      CITY     City of Sa… LOC       CITY      City of…\n 8 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n 9 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n10 Fee       Fee      LOC      CITY     San Diego,… LOC       CITY      San Die…\n# ℹ 17,863 more rows\n# ℹ 27 more variables: Des_Tp <chr>, Loc_Ds <chr>, Unit_Nm <chr>, Loc_Nm <chr>,\n#   State_Nm <chr>, Agg_Src <chr>, GIS_Src <chr>, Src_Date <chr>,\n#   GIS_Acres <int>, Source_PAID <chr>, WDPA_Cd <int>, Pub_Access <chr>,\n#   Access_Src <chr>, Access_Dt <chr>, GAP_Sts <chr>, GAPCdSrc <chr>,\n#   GAPCdDt <chr>, IUCN_Cat <chr>, IUCNCtSrc <chr>, IUCNCtDt <chr>,\n#   Date_Est <chr>, Comments <chr>, EsmtHldr <chr>, EHoldTyp <chr>, …\n```\n\n\n:::\n:::\n\n\nSimilarly, any normal data frame can be coerced to a spatial `sf` object, e.g.:\n\n::: {.cell}\n\n```{.r .cell-code}\ndata.frame(lon = c(1,2), lat=c(0,0)) |> st_as_sf(coords = c(\"lon\", \"lat\"), crs=4326)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSimple feature collection with 2 features and 0 fields\nGeometry type: POINT\nDimension:     XY\nBounding box:  xmin: 1 ymin: 0 xmax: 2 ymax: 0\nGeodetic CRS:  WGS 84\n     geometry\n1 POINT (1 0)\n2 POINT (2 0)\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nspatial_ex <- paste0(\"https://raw.githubusercontent.com/cboettig/duckdbfs/\",\n                     \"main/inst/extdata/spatial-test.csv\") |>\n  open_dataset(format = \"csv\") \n\nspatial_ex |>\n  mutate(geometry = st_point(longitude, latitude)) |>\n  to_sf(crs = 4326)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSimple feature collection with 10 features and 3 fields\nGeometry type: POINT\nDimension:     XY\nBounding box:  xmin: 1 ymin: 1 xmax: 10 ymax: 10\nGeodetic CRS:  WGS 84\n   site latitude longitude          geom\n1     a        1         1   POINT (1 1)\n2     b        2         2   POINT (2 2)\n3     c        3         3   POINT (3 3)\n4     d        4         4   POINT (4 4)\n5     e        5         5   POINT (5 5)\n6     f        6         6   POINT (6 6)\n7     g        7         7   POINT (7 7)\n8     h        8         8   POINT (8 8)\n9     i        9         9   POINT (9 9)\n10    j       10        10 POINT (10 10)\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nca_fee <- pad |> \n  filter(State_Nm == \"CA\", FeatClass == \"Fee\") |> \n  group_by(Own_Type) |>\n  summarise(total = sum(SHAPE_Area)) |> head(1000) |> collect()\n```\n:::",
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