rebuild website & update pages

Author: libjohn

01_ggmap_georef.Rmd

@@ -6,7 +6,7 @@ output: html_document
 ---
  
 
-This page is deprecated.  The `mapview` packages seems to do much of the same thing, and with less effort.  Explore our introduction to the [plotting coordinates with mapview](01_georeference.html).
+> This page is **deprecated**.  The `mapview` packages deliveres much of the same functionality, and with less effort.  Please explore the introduction to the [plotting coordinates with mapview](01_georeference.html).
 
 Using the same data as in the [previous exercise](01_georeference.html), **build a static map quickly and easily using `ggmap`**. The `ggmap` package enables the integration of popular raster base-maps with ggplot2 syntax.  
 

030_thematic_sf.Rmd

@@ -10,35 +10,31 @@ output: html_document
 
 1. Thematic Mapping with [sf & ggplot::geom_sf](032_thematic_mapping_geom_sf.html): creating choropleth with `ggplot2`, `sf`, and `viridis` packages
 
-1. [Interactive thematic mapping](033_thematic_leaflet_example.html) with leaflet.  This is largely a repeat of the Choropleth section (tidyverse), but focused on other interactive and stylistics features not previously discussed.
-
-1. [Sharing ouput files](036_thematic_mapping-outputs-fullwebpage.html): saving maps.
-
 1. [Faceted Thematic Maps](039_facet_example.html) with **ggplot2::geom_sf**
 
+1. [Saving ouput files](01_georeference.html#save_the_map).
+
 ## Simple Features
 
-The initial modules of *Mapping in R* introduce plotting latitude and longitude with an interactive map (`leaflet`), a static map (`ggmap`), and generating a thematic map using `tidycensus.`  The focus of this module is to introduce other customized thematic map packages: `tmap`, `sf`, `ggplot2`.  
+The initial modules of *Mapping in R* introduce plotting latitude and longitude with an interactive map (`mapview`) and generating a choropleth (a thematic map) using `tidycensus.`  The focus of this module is to introduce other customized thematic map packages: `tmap`, `sf`, `ggplot2`.  
 
-**The backbone of this type of mapping data manipulation is the `sf` package** -- sf for simple features -- a modern tidyverse compatible package for encoding spatial vector data. `sf` is the successor to `sp`. Since `sf` is tidyverse compatible, among the advantages is the fact that you can see your numeric data in a more familiar (and easier-to-view) data frame .  
+**The backbone of this type of mapping data manipulation is the [`sf` package](https://r-spatial.github.io/sf/)** -- sf for simple features -- a modern tidyverse compatible package for encoding spatial vector data. `sf` is the successor to `sp`. Since `sf` is tidyverse compatible, among the advantages is the fact that you can see your numeric data in a more familiar (and easier-to-view) data frame .  
 
 > The `sf` simple features package affords the R coder an easy and familiar method of **storing data** vectors and **shapefile** polygons in a **tidy data frame**.
 
-In these *Thematic Mapping with Simple Features* sections, we introduce the `tmap` package first.  Through the the `tmap` package you will learn an easy-to-use package for building spatial distributions on geographic maps.  These maps are typically `.png` image files.  (.png files are easy to produce and share.)  In this section we'll also use the `tigris` package -- to gather Census shapefiles -- and store those shape objects as in the Simple Features data structure.  (In the [*Chropleth* module](02_choropleth.html) we used `tidycensus` to gather census Geography shape objects, as well as to gather census data variables.)  `tigris` simply gathers the shapefile polygons.  Later we will join the polygon object to a data frame using `tmaptools`.  
+In these *Thematic Mapping with Simple Features* sections, we introduce the `tmap` package first.  Through the the `tmap` package you will learn an easy-to-use package for building spatial distributions on geographic maps.  These maps are typically `.png` image files.  (.png files are easy to produce and share.)  In this section we'll also use the `tigris` package -- to gather Census shapefiles -- and store those shape objects in the Simple Features data structure.  (In the [*Chropleth* module](02_choropleth.html) we used `tidycensus` to gather census Geography shape objects, as well as to gather census data variables.)  `tigris` simply gathers the shapefile polygons.  Later we will join the polygon object to a data frame using `tmaptools`.  
 
 Next, we'll use the `ggplot::geom_sf` function to create our mapping visualiations using the widely adopted ggplot2 syntax.
 
 Later, in the *interactive thematic mapping* section, you will make a different interactive choropleth using `leaflet`.  This section is largely a repeat of the earlier choropleth module. The level of interactivity is sparse, but functional.  Interactivity demonstrates how you can generate maps which move beyond traditional 2D map-on-a-page images. Just know that this zoom-in/zoom-out interactivity feature is possible because we use `leaflet` to draw the map.  By extension, you can add many interactive features, e.g. pop-up windows.  This section is a good set-up for the last section which covers how to save maps as files.  
 
-[Sharing ouput files](036_thematic_mapping-outputs-fullwebpage.html), introduces the file saving techniques for saving maps.  Even interactive maps can be shared and distributed.  You can share maps either on the web, as an HTML file, or as [shapefile.](next.html#shapefiles)
+[Saving / sharing ouput files](01_georeference.html#save_the_map).  Even interactive maps can be shared and distributed.  You can share maps either on the web, as an HTML file.
 
 
 ## Converting between `sf` and `sp`
 
 `sf` is developed by the same [person](https://github.com/edzer)/people who developed `sp`.  `sf` is the modern approach.  However, if you need to convert between the two formats, it's easy! See [Reading, Writing and Converting Simple Features](https://r-spatial.github.io/sf/articles/sf2.html) 
 
 
-## Hands-on Exercises
 
-Head over to the [exercises section](exercises.html) and practice making your own thematic map.
 

031_thematic_mapping.Rmd

@@ -16,11 +16,10 @@ library(sf)
 
 ## Shapefiles as sf 
 
-Using the `tigris` package, get Census Tiger shapefiles for census geographies.  Tigris will return the shapefile in the `sf`, or simple features, format.  (See also, [more information on Shapefiles](02_choropleth.html#shapefiles).)
+Using the `tigris` package, get Census Tiger shapefiles for census geographies.  Tigris will return the shapefile in the `sf`, or simple features, format.  
 
 ```{}
 us_geo <- tigris::states(class = "sf")
-# us_geo_spdf <- states()  # spdf uses @data slots -- old school
 ```
 
 ```{r get-data, message=FALSE, warning=FALSE, include=FALSE, paged.print=FALSE}
@@ -30,7 +29,7 @@ us_geo <- tigris::states(class = "sf")
 
 ## Data Structure 
 
-The advantage of managing your spatial data as "Simple Features" (i.e. sf) over `sp` is that `sf` data can easily be viewed and manipulated as a rectangular data frame, before visualizing.   `sp` is the predecessor to `sf`, but uses the old school @data slots method.  If you're used to this it can be a highly functional data structure.  But if you're inclined toward the tidyverse method, you'll see your data in a more familiar and easy-to-view format.  Below are two methods of viewing the structure of the downloaded shapefiles.
+The "Simple Features" (`sf`) data structure can easily be viewed and manipulated as a rectangular data frame, before visualizing.   As an historical note -- an `sf` predecessor -- the `sp` data structure uses `@data` slots to hold data.  We'll focus on the `sf` package.  Below are two methods of viewing the structure of the downloaded shapefiles.
 
 ```{r data-structure}
 class(us_geo)
@@ -46,6 +45,18 @@ And as noted, here's the data frame view.  Notice the geometry (polygon shape) i
 as_tibble(us_geo)
 ```
 
+### Quick Plotting
+
+If you want to see a very quick view of your mapping data, you can plot the geometry data with the `plot` function.  In this case we use the `sf::st_geometry()` function to plot only the geometry.  You can quickly generate a faceted map by excluding the `st_geometry` function:  e.g. `plot(us_geo)` but that will consume computation cycles (i.e. wait time).  Therefore, I recommend trying the smaller layer for now.
+
+> **Note:** Census geography for the USA will span the globe in part becuase Region 9 includes a multitude of pacific islands.  Later we will limit to simply the "lower 48" states.
+
+```{r}
+plot(st_geometry(us_geo))
+```
+
+
+
 ## Get BLS data
 
 I've already downloaded and stored some data from the Bureau of Labor Statistics.  Those data are stored in an excel file in the `data` directory of the [repository](https://github.com/libjohn/mapping-with-R):  `data/OES_Report.xlsx`.  **The goal is to attach this data to the previously downloaded shapefiles.**  
@@ -147,7 +158,7 @@ Mark likes the USA_Contiguous_Albers_Equal_Area_Conic_USGS_version projection fo
 contiguous_states %>% 
   st_transform(5070) %>% 
   tm_shape() +
-  tm_polygons("wages", id = "Name")
+  tm_polygons("wages", id = "Name") 
 ```
 
 

032_thematic_mapping_geom_sf.Rmd

@@ -90,17 +90,15 @@ contiguous_states %>%
 
 ## ggplot2 with geom_sf and viridis
 
-In this section we introduce making shapefiles with ggplot2.  ggplot2 is one of the more popular and broadly distributed graphics packages used in the R community. I also reveresed the direction of the color scale.  After consulting with my visualization colleagues it seems this may have been a non-standard action on my part.  But I leave the `direction` argument here for the curious.
+In this section we introduce making shapefiles with ggplot2.  ggplot2 is one of the more popular and broadly distributed graphics packages used in the R community. I also reversed the direction of the color scale.  After consulting with my visualization colleagues it seems this may have been a non-standard action on my part.  But, I leave the `direction` argument here for the curious.  Finally, please note the easiest way to remove the [graticules](https://en.wikipedia.org/wiki/Graticule) is as follows:  `coords_sf(datum = NA)` 
 
 ```{r with-ggplot-geom_sf}
 contiguous_states %>% 
   ggplot(aes(fill = wages, color = wages)) +
   geom_sf() +
-  coord_sf(crs = 5070) +
+  coord_sf(crs = 5070, datum = NA) +
   scale_fill_viridis(option = "viridis", direction = -1) + 
   scale_color_viridis(option = "viridis", direction = -1)
-
-
 ```
 
 

033_thematic_leaflet_example.Rmd

@@ -5,6 +5,9 @@ output:
     toc: FALSE
 ---
 
+> This page is **deprecated**.  The `mapview` packages deliveres much of the same functionality, and with less effort.  Please explore the introduction to the [plotting coordinates with mapview](01_georeference.html).
+
+
 ```{r}
 library(leaflet)
 ```

036_thematic_mapping-outputs-fullwebpage.Rmd

@@ -3,6 +3,9 @@ title: "Thematic Mapping: Saving Ouput Files"
 output: html_document
 ---
 
+
+> This page is **deprecated**.  The `mapview` packages deliveres much of the same functionality, and with less effort.  Please explore the introduction to the [saving maps with mapview](01_georeference.html#save_the_map).
+
 On this page we demonstrate two methods of saving map files for sharing.  The first method develops an interactive map that uses the entire web browser as the delivery mechanism.  It's a nice way to maximize screen estate, a desirable feature for maps.  The second method saves the file as a **shapefile**, a broadly portable file standard in GIS analysis.
 
 Please note that I disabled some of the code chunks for efficiency of processing.  The process documented below does work, despite reported feedback errors:  [See the example output](mymap_sasw.html).  This method should work for you as well, all the code is below.  If you download this file from the repository, you'll need to make a simple change to the last two code chunks: re-insert the `r` into the curley braces in the last two code chunks.  

038_facets_wrap_thematic_mapping.Rmd

@@ -342,4 +342,3 @@ tall %>%
 ```{r save_it}
 ggsave("facet_map.png", width = 8, height = 8, units = "in")
 ```
-

039_facet_example.Rmd

@@ -23,4 +23,4 @@ Produced this faceted map
 
 ![](facet_map.png)
 
-Full disclosure.  The actual code was produced on the garbage-spaghetti plan.  The quality of the code is not something I'm proud of.  Therefore, I sanitized the code by showing the example,  above.  All the [actual code is available](038_facets_wrap_thematic_mapping.html).  You can explore and/or reproduce this process from the actual code, as you see fit.  If you don't mind, rewrite the code, using `purrr` then send me a cleaned and literate version.  'prec.
+Full disclosure.  The actual code was produced on the garbage-spaghetti plan.  The quality of the code is not something I'm proud of.  Therefore, I sanitized the code by showing the example,  above.  All the [actual code is available](038_facets_wrap_thematic_mapping.html).  You can explore and/or reproduce this process from the actual code, as you see fit.  (If you don't mind, rewrite the code, using `purrr` then send me a cleaned and literate version.)

_site.yml

@@ -28,10 +28,8 @@ navbar:
         href: 032_thematic_mapping_geom_sf.html
       - text: "ggplot2 & facet mapping"
         href: 039_facet_example.html
-      - text: "Interactive"
-        href: 033_thematic_leaflet_example.html
       - text: "Saving / Sharing"
-        href: 036_thematic_mapping-outputs-fullwebpage.html
+        href: 01_georeference.html#save_the_map
   - text: Exercises
     href: exercises.html
   right: