update reprojection sec

ch2_a.qmd

@@ -53,7 +53,7 @@ list.files("./SampleData-master")   # List folder contents. Do you see sample da
 
 ```
 
-### Using R ad GIS
+### Using R as GIS
 
 A `geographic information system`, or `GIS` refers to a platform which can map, analyzes and manipulate **geographically referenced** dataset. A geographically referenced data (or geo-referenced data) is a spatial dataset which can be related to a point on Earth with the help of geographic coordinates. Types of geo-referenced spatial data include: rasters (grids of regularly sized pixels) and vectors (polygons, lines, points).
 
@@ -218,7 +218,7 @@ mapview(sm2,                  # RasterLayer
 )
 ```
 
-## Plotting raster data using `tidyterra`
+#### Plotting raster data using `tidyterra`
 
 `tidyterra` is a package that add common methods from the tidyverse for SpatRaster and SpatVectors objects created with the `terra` package. It also adds specific `geom_spat*()` functions for plotting rasters with `ggplot2`.
 
@@ -262,7 +262,7 @@ sm_conus= ggplot() +
 print(sm_conus)
 ```
 
-## Plotting vector data
+### Plotting vector data
 
 Importing and plotting shapefiles is equally easy in R. We will import the shapefile of the updated global `IPCC climate reference regions` and global `coastline`as Simple Feature (`sf`) Object. Terra can also be used to import shapefiles as vectors using the function `vect`. However, `sf` is more versatile, especially for shapefiles. Notice how the attributes of the `sf` objects resemble an Excel data sheet.
 
@@ -326,7 +326,7 @@ points(cbind(Long,Lat),                # Lat-Long as Spatial Points
        col="blue", pch=16, cex=1.2)    # Shape, size and color of point
 ```
 
-## Reprojection of rasters using `terra::project`
+### Reprojection of rasters using `terra::project`
 
 A coordinate reference system (CRS) is used to relate locations on Earth (which is a 3-D spheroid) to a 2-D projected map using coordinates (for example latitude and longitude). Projected CRSs are usually expressed in Easting and Northing (x and y) values corresponding to long-lat values in Geographic CRS.
 
@@ -344,21 +344,20 @@ A good description of coordinate reference systems and their importance can be f
 
 In R, the coordinate reference systems or `CRS` are commonly specified in `EPSG` (European Petroleum Survey Group) or PROJ4 format (See: <https://epsg.io/>). Few commonly used projection systems and their codes are summarized below:
 
-+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+------------+
-| Projection system                                                                                 | PROJ.4 code                                                                                                                  | EPSG code  |
-+===================================================================================================+==============================================================================================================================+============+
-| NAD83 (North American Datum 1983)                                                                 | "+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs +type=crs"                                                       | EPSG:4269  |
-+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+------------+
-| Mercator                                                                                          | "+proj=merc +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +R=6371000 +units=m +no_defs +type=crs"                                         | ESRI:53004 |
-+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+------------+
-| WGS 84 (World Geodetic System 1984)                                                               | "+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +type=crs"                                            | EPSG:3395  |
-+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+------------+
-| WGS 84 / Pseudo-Mercator -- Spherical Mercator ( used in Google Maps, OpenStreetMap)              | "+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs" | EPSG:3857  |
-+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+------------+
-| Robinson (better for plotting global data due to minimal distortion, except for higher latitudes) | "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +type=crs"                                                | ESRI:54030 |
-+---------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+------------+
-
-: The `SpatRaster` reprojection process is done with `project()` from the `terra` package.
+------------------------------------------------------------------------
+
+|                                                                                                   |                                                                                                                                   |             |
+|---------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-------------|
+| *Projection system*                                                                               | *PROJ.4 code*                                                                                                                     | *EPSG code* |
+| NAD83 (North American Datum 1983)                                                                 | "+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs +type=crs"                                                            | EPSG:4269   |
+| Mercator                                                                                          | "+proj=merc +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +R=6371000 +units=m +no_defs +type=crs"                                              | ESRI:53004  |
+| WGS 84 (World Geodetic System 1984)                                                               | "+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +type=crs"                                                 | EPSG:3395   |
+| WGS 84 / Pseudo-Mercator -- Spherical Mercator ( used in Google Maps, OpenStreetMap)              | "+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=`@null` +wktext +no_defs +type=crs" \| | EPSG:3857   |
+| Robinson (better for plotting global data due to minimal distortion, except for higher latitudes) | "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +type=crs"                                                     | ESRI:54030  |
+
+------------------------------------------------------------------------
+
+The `SpatRaster` reprojection process is done with `project()` from the `terra` package.
 
 ```{r, message = FALSE, warning = FALSE,results='asis'}
 # Importing SMAP soil moisture data
@@ -417,18 +416,18 @@ RobinsonPlot <- ggplot() +
 print(RobinsonPlot)
 
 # Save high resolution plot to disk
-ggsave(
-  "globalSM.png",          # Name of the file to be created
-  plot = RobinsonPlot,     # Plot to be exported
-  bg = "white",            # Plot background
-  height = 6,              # Height 
-  width = 10,              # Width  
-  units = c("in")          # Units ("in", "cm", "mm" or "px")
-)
+  # ggsave(
+  #   "globalSM.png",          # Name of the file to be created
+  #   plot = RobinsonPlot,     # Plot to be exported
+  #   bg = "white",            # Plot background
+  #   height = 6,              # Height 
+  #   width = 10,              # Width  
+  #   units = c("in")          # Units ("in", "cm", "mm" or "px")
+  # )
 
 ```
 
-Let us view the exported plot saved on the disk.
+Run the commented script above, and view the exported plot saved on the disk.
 
 **Other resources:**
 

ch1.html → docs/ch1.html

@@ -485,13 +485,13 @@ <h3 data-number="2.1.3" class="anchored" data-anchor-id="data-types"><span class
 <span id="cb86-8"><a href="#cb86-8" aria-hidden="true" tabindex="-1"></a>out[[<span class="dv">2</span>]] <span class="ot">=</span> data2</span>
 <span id="cb86-9"><a href="#cb86-9" aria-hidden="true" tabindex="-1"></a>out[[<span class="dv">1</span>]]          <span class="co"># Contains data1 at this location</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code> [1] 25.42772 29.18901 25.48689 29.03675 26.95134 22.81134 27.13496 20.82484
- [9] 20.45778 27.15116 22.15038 27.13918 24.84337 22.05860 27.02549 22.00617
-[17] 23.91949 29.00458 26.12511 22.17041 25.64904 23.62808 27.23896 24.77415
-[25] 22.89046 25.18251 29.38348 21.34865 22.70105 23.00579 20.93994 27.69340
-[33] 21.31076 23.77950 25.41240 29.26983 23.38066 23.83741 27.28841 21.66094
-[41] 29.24796 24.89167 25.24554 25.89850 29.06924 25.31701 26.74200 22.79233
-[49] 29.94284 20.00816</code></pre>
+<pre><code> [1] 27.11756 28.68166 29.86901 26.28753 25.28627 24.00654 29.89054 22.37843
+ [9] 21.85850 21.36041 20.25304 22.18784 24.24517 27.52384 23.89215 28.42054
+[17] 28.24869 23.03340 20.12828 27.27353 27.32140 25.28569 27.57224 23.66329
+[25] 26.56781 21.45258 24.02670 24.29696 26.98421 26.07914 23.56593 25.70500
+[33] 24.99104 24.12146 26.56791 20.89452 27.42495 20.88284 21.90780 20.82813
+[41] 26.17926 25.82521 21.80011 29.14641 26.02670 24.44777 22.62200 25.04881
+[49] 25.64245 22.97770</code></pre>
 </div>
 <div class="sourceCode cell-code" id="cb88"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb88-1"><a href="#cb88-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Data frame</span></span>
 <span id="cb88-2"><a href="#cb88-2" aria-hidden="true" tabindex="-1"></a>out<span class="ot">=</span><span class="fu">data.frame</span>(<span class="at">x=</span>data1, <span class="at">y=</span>data2)</span>
@@ -844,12 +844,12 @@ <h3 data-number="2.3.3" class="anchored" data-anchor-id="multivariate-plots"><sp
 <pre><code># A tibble: 6 × 4
    Year January Month  Value
   &lt;dbl&gt;   &lt;dbl&gt; &lt;chr&gt;  &lt;dbl&gt;
-1  1913    78.4 Jan    -6.35
-2  1913    78.4 Feb   -15.6 
-3  1913    78.4 Mar    -6.51
-4  1914    40.6 Jan    -3.69
-5  1914    40.6 Feb    -4.96
-6  1914    40.6 Mar    -5.17</code></pre>
+1  1913    65.1 Jan    -7.94
+2  1913    65.1 Feb    -4.96
+3  1913    65.1 Mar    -8.86
+4  1914    73.4 Jan   -14.7 
+5  1914    73.4 Feb    -2.37
+6  1914    73.4 Mar   -10.6 </code></pre>
 </div>
 </div>
 <p><strong>Line plot</strong></p>