distilling

Author: libjohn

01_georeference.Rmd

@@ -1,20 +1,14 @@
 ---
 title: Latitude and Longitude Coordinates
-output: html_document
-#  html_document:
-#    toc: FALSE
 ---
- 
-
-To introduce the [`mapview` package](https://r-spatial.github.io/mapview/index.html), make an interactive map with Starbucks coffee shop locations in North Carolina (2012).  (Adapted from Machlis.)^[[5 Visualizations in 5 Minutes](http://www.computerworld.com/article/2893271/business-intelligence/5-data-visualizations-in-5-minutes-each-in-5-lines-or-less-of-r.html). ComputerWorld.com by Sharon Machlis]
+Use the [`mapview` package](https://r-spatial.github.io/mapview/index.html) to make interactive maps.  In this example, georeference Starbucks coffee shop locations in North Carolina (2012).  (Adapted from Machlis.)^[[5 Visualizations in 5 Minutes](http://www.computerworld.com/article/2893271/business-intelligence/5-data-visualizations-in-5-minutes-each-in-5-lines-or-less-of-r.html). ComputerWorld.com by Sharon Machlis]
 
 
 ## Load Libraries
 
 ```{r libraries, message=FALSE, warning=FALSE}
 library(tidyverse)
 library(sf)
-#library(leaflet)
 library(mapview)
 ```
 
@@ -31,7 +25,8 @@ starbucks <- read_csv("https://raw.githubusercontent.com/libjohn/mapping-with-R/
 starbucksNC <- starbucks  %>% 
   filter(State == "NC")
 
-starbucksNC
+starbucksNC %>% 
+  glimpse()
 ```
 
 ## Make the Map
@@ -61,12 +56,7 @@ mapview(sbux_sf)
 
 
 
-## Save the Map
-
-You can save a map as an interactive HTML page or a static `.png, .pdf, or .jpeg` file with the [`mapshot` function](https://r-spatial.github.io/mapview/reference/mapshot.html).  For example:
 
-- `mapshot(starNCmap, file = "map.png")`
-- `mapshot(starNCmap, url = "map.html")`
 
 
 

02_choropleth.Rmd

@@ -1,37 +1,36 @@
 ---
 title: "tidycensus"
-output: html_document
 ---
 
-The [tidycensus](https://walkerke.github.io/tidycensus/index.html) package, developed by [Kyle Walker](https://walkerke.github.io/), is very **convenient and easy to use package for making choropleth maps** from United States Department of **Census data**, specifically from the Decennial and ACS Census reports.  This package makes it possible to gather census variables and conveniently join those variables with "Census Geography" (i.e. aka "shapefiles", or polygons.)  Visualization, or plotting, maps can be done with separate packages.  
+The [tidycensus](https://walkerke.github.io/tidycensus/index.html) package, developed by [Kyle Walker](https://walkerke.github.io/), is very **convenient and easy to use package for making choropleth maps** from United States Department of **Census data**. Tidycensus uses the Decennial or ACS Census reports.  This package makes it possible to gather census variables and conveniently join those variables with "Census Geography" (i.e. aka "shapefiles", or polygons.)  Visualization can be done with separate packages such as `mapview`, `leaflet`, or `ggplot2::geom_sf()`.  
 
 
 ```{r libraries, message=FALSE, warning=FALSE}
 library(tidyverse)
 library(sf)
 library(tidycensus)
-#library(leaflet)
 library(mapview)
 ```
 
 ## Census API Key
 
-Use the tidycensus package to gather Census data and join that data with Census geography (i.e. geometry, i.e. shapefiles, i.e. polygons).  First, you will need to get a [free Census API key](https://api.census.gov/data/key_signup.html).  Kyle Walker's [*Basic usage of tidycensus*](https://walkerke.github.io/tidycensus/articles/basic-usage.html) documents this process. 
+you need a [free Census API key](https://api.census.gov/data/key_signup.html).  Kyle Walker's [*Basic usage of tidycensus*](https://walkerke.github.io/tidycensus/articles/basic-usage.html) documents this process. 
 
 ```{}
 census_api_key("YOUR API KEY GOES HERE")
 ```
 
 ### .Renviron File
 
-See also Kyle's [more detailed documentation](https://walkerke.github.io/tidycensus/reference/census_api_key.html) for putting the key into your environment file.  But skip that for the moment.
+See also Kyle's [more detailed documentation](https://walkerke.github.io/tidycensus/reference/census_api_key.html) for caching the API key in your R environment. 
 
 
 ## TidyCensus -- Get Data
 
-Create a Simple Features dataframe using `tidycensus::get_acs()`
+Create a Simple Features (i.e. `sf`) dataframe using `tidycensus::get_acs()`
 
 The Census population variable we'll use is "B01003_001".  More information about identifying Census variables is available at the [bottom of this page](#variables).
+
 ```{r load-data, message=FALSE, warning=FALSE, include=FALSE}
 nc_pop <- 
   get_acs(geography = "county",
@@ -41,6 +40,7 @@ nc_pop <-
 
 #nc_pop
 ```
+
 ```{}
 nc_pop <- 
   get_acs(geography = "county",
@@ -73,7 +73,8 @@ starbucks <- read_csv("data/All_Starbucks_Locations_in_the_US_-_Map.csv")
 ```
 
 
-Subset Starbucks Data to North Carolina
+Subset Starbucks Data to North Carolina  
+
 ```{r filter-dataset}
 starbucksNC <- starbucks  %>% 
   filter(State == "NC")
@@ -151,7 +152,7 @@ The Census is a very large collection of data.  Many casual users of Census data
 
 ## Shapefiles
 
-In the [*tmap* section](031_thematic_mapping.html) of the [Simple Features module](030_thematic_sf.html) we will use the `tigris` package to gather State level, Census geography shapefiles.  Shapefiles are an important GIS data standard.  There are many other standards, although shapefiles have a very broad user base.  If you need shapefiles for other geographies, please consult our [*GIS Data: Starting Points* guide](https://guides.library.duke.edu/gisdata) --  a very handy collection of GIS data sources, much of which will be in the shapefiles format.  
+Shapefiles are an important GIS data standard used frequently in thematic mapping.  There are many other standards, although shapefiles have a very broad user base.  If you need shapefiles for other geographies, please consult the [*guide to geospatial applications using the R programming language*](https://guides.library.duke.edu/r-geospatial) 
 
 ## End Notes
 

031_thematic_mapping.Rmd

@@ -1,6 +1,5 @@
 ---
 title: "Thematic Mapping with tmap"
-output: html_document
 ---
 
 
@@ -19,11 +18,11 @@ library(sf)
 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 <- tigris::states(class = "sf") 
 ```
 
 ```{r get-data, message=FALSE, warning=FALSE, include=FALSE, paged.print=FALSE}
-us_geo <- tigris::states(class = "sf")
+us_geo <- tigris::states(class = "sf") 
 ```
 
 ## Data Structure 
@@ -128,14 +127,12 @@ qtm(HelperShapeObject, fill = "wages")
 
 ## Contiguous 48 states
 
-Filter to only the contiguous 48 states + D.C.
+Filter to only the contiguous 50 states + D.C.  Note that `tigris::shift_geometry()` will shift and resize Alaska and Hawaii.
 
 ```{r subset-data}
 contiguous_states <- HelperShapeObject %>% 
-  filter(REGION != 9) %>%
-  filter(STUSPS != "AK") %>% 
-  filter(STUSPS != "HI")
-
+  filter(REGION != 9) %>% 
+  shift_geometry()
 ```
 
 

032_thematic_mapping_geom_sf.Rmd

@@ -1,6 +1,5 @@
 ---
 title: "Thematic Mapping with geom_sf"
-output: html_document
 ---
 
 
@@ -16,20 +15,19 @@ library(ggplot2)
 
 ## Shapefiles as sf 
 
-Repeating steps from the [previous section](031_thematic_mapping.html), using the `tigris` package, get Census Tiger shapefiles for census geographies.  Coastal boundaries can be gathered with the tigris argument:  `cb = TRUE`.
+Use the `tigris` package to get Census Tiger shapefiles for census geographies.  Coastal boundaries can be gathered with the tigris argument:  `cb = TRUE`.
 
 ```{r getuscb, message=FALSE, warning=FALSE, include=FALSE}
-us_geo <- tigris::states(class = "sf", cb = TRUE)
+us_geo <- tigris::states(class = "sf", cb = TRUE) %>% 
+  shift_geometry()
 ```
 ``` r 
 us_geo <- tigris::states(class = "sf", cb = TRUE)
 ```
 
 ## Get BLS data
 
-As mentioned before, the data are from the Bureau of Labor Statistics.  These 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`.  
-
-Again, from the previous section you will see how these data were gathered, loaded, transformed, and joined. 
+In additon to shapefiles, the wage data used in this example comes from from the Bureau of Labor Statistics.  These 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`.  
 
 ```{r getblsdata, message=FALSE, warning=FALSE}
 Salary4Helpers <- 
@@ -42,7 +40,7 @@ Salary4Helpers
 
 ## Wrangle the data
 
-As before...  
+Using the [`stringr` package](https://stringr.tidyverse.org) we can extract the text of the state names from the state code by leveraging regular expressions (i.e. regex) pattern matching techniques with stringr::str_extract().
 
 ```{r wrangleblsdata, message=FALSE, warning=FALSE}
 BlsWage_ToJoin <- Salary4Helpers %>% 
@@ -64,26 +62,26 @@ HelperShapeObject <- us_geo %>%
 ```
 
 
-## Contiguous 48 states
+## 50 states
+
+Filter to only the 50 US states + D.C.  
 
-Filter to only the contiguous 48 states + D.C.
+Alaska and Hawaii were shifted and rescaled using `tigris::shift_geometry()`, above.
 
 ```{r filterlower48, message=FALSE, warning=FALSE}
 contiguous_states <- HelperShapeObject %>% 
-  filter(GEOID < 60) %>% 
-  filter(STUSPS != "AK") %>% 
-  filter(STUSPS != "HI")
+  filter(GEOID < 60) 
 ```
 
 
 
 ## 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. ([Learn more](https://rfun.library.duke.edu/#portfolio) about ggplot2. 
+ggplot2 is one of the more popular and broadly distributed graphics packages used in the R community. ([Learn more](https://rfun.library.duke.edu/#portfolio) about ggplot2. 
 
-In this plot I 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.  
+In this plot I **reversed** the direction of the color scale.  My visualization colleagues alerted me that this is non-standard approach to visualization, but I kept the argument in this example to demonstrate the `direction` argument.  
 
-Use a pleasing projection, in this case assigned the crs projection to '5070' (`coord_sf(crs = 5070)`), and removed the gridlines (i.e [graticules](https://en.wikipedia.org/wiki/Graticule)) `coords_sf(datum = NA)` 
+Use a pleasing [projection](https://guides.library.duke.edu/r-geospatial/CRS), in this case assigned the crs projection to '5070' (`coord_sf(crs = 5070)`), and removed the gridlines (i.e [graticules](https://en.wikipedia.org/wiki/Graticule)) `coords_sf(datum = NA)` 
 
 ```{r with-ggplot-geom_sf, message=FALSE, warning=FALSE}
 contiguous_states %>% 

038_facets_wrap_thematic_mapping.Rmd

@@ -1,6 +1,5 @@
 ---
 title: "Faceted Thematic Mapping"
-output: html_document
 ---
 
 ```{r load-libraries, message=FALSE, warning=FALSE}
@@ -56,9 +55,10 @@ my_df <- my_xl_files %>%
 state_names <- us_geo %>% 
   filter(REGION != "9") %>% 
   select(NAME) %>% 
-  st_drop_geometry()
+  st_drop_geometry() 
 ```
 
+
 ### Wrangle the data
 
 Before we join the BLS data to the shapefile `us_geo` we need to transform BLS data
@@ -69,7 +69,8 @@ my_df <- my_df %>%
          wages = "Annual mean wage(2)",
          type = sheet) %>% 
   mutate(State = str_extract(area, '.*(?=\\()')) %>% 
-  mutate(type = str_extract(type, "(?<=data/OES_)\\w+")) 
+  mutate(type = str_extract(type, "(?<=data/OES_)\\w+")) %>% 
+  filter(State != "Guam")
 ```
 
 ### Missing data
@@ -114,7 +115,7 @@ my_df <- my_df %>%
 ### identify and pick census variables
 
 B01003_001E = Total Population
-B06011_001E = Median income in the past 12 months --!!Total:
+B06011_001E = Median income in the past 12 months
 
 ```{r identify_ACS_variables}
 variables_census <- load_variables(2015, "acs5", cache = TRUE)
@@ -165,7 +166,7 @@ my_df <- my_df %>%
 ### Display Map
 
 ```{r}
-my_df %>% 
+my_df %>%
   ggplot(aes(fill = wages, color = wages)) +
   geom_sf() +
   coord_sf(crs = 5070, datum = NA) +

039_facet_example.Rmd

@@ -1,8 +1,5 @@
 ---
 title: "Example Faceted Thematic Mapping"
-output: 
-  html_document:
-    toc: FALSE
 ---
 
 ![](facet_map.png)

030_thematic_sf.Rmd _030_thematic_sf.Rmd030_thematic_sf.Rmd renamed to _030_thematic_sf.Rmd

@@ -1,6 +1,5 @@
 ---
 title: "Thematic Mapping with Simple Features"
-output: html_document
 ---
  
 

_CopyOfindex.Rmd.bak

@@ -0,0 +1,61 @@
+---
+output: 
+  html_document:
+    toc: FALSE
+---
+ 
+
+[**Slides**](/slides/)
+
+[Guide to geospatial applications using the R programming language](https://guides.library.duke.edu/r-geospatial)
+
+[Code for Hands-on Workshop -- Spring 2019](https://github.com/libjohn/map-spring2019)
+
+## Overview
+
+These learning and quick-reference resource pages support the face-to-face workshop, [Mapping with R](https://rfun.library.duke.edu/portfolio/mapping_workshop/), offered via the [Data & Visualization Services](https://library.duke.edu/data/) Department, Duke University Libraries. The exercises and workshop are designed to help you learn georeferencing, make a  [choropleth](https://en.wikipedia.org/wiki/Choropleth_map) with USA census data via the [tidycensus](https://walkerke.github.io/tidycensus/) package, and briefly introducing the [sf](http://r-spatial.github.io/sf/) package, simple features. 
+
+<div class = "picture1">
+<iframe height="315" width="500" src="https://warpwire.duke.edu/w/JZ8EAA/" frameborder="0" scrolling="0" allow="autoplay; encrypted-media; fullscreen;  picture-in-picture;" allowfullscreen></iframe>
+
+</div>
+
+During the Workshop we introduce
+
+- Georeferencing latitude and longitude
+- Choropleths
+- Polygons (shapes)
+- Census Geography
+- Briefly introduce ACS and Decennial census
+
+    - Census API keys 
+    
+- Thematic mapping using _Simple Features for R_
+
+## Workshop Resource Page
+
+1. Georeferencing, Plotting XY latitude & longitude: with [interactive `mapview` maps](01_georeference.html) and saving static maps.
+
+1. [Choropleths with tidycensus](02_choropleth.html)
+
+1. [Thematic Mapping](030_thematic_sf.html) with `ggplot2`, `geom_sf`, `tigris`, and `viridis`.  Additionally we show faceting mapping, and saving map images and files.  We also introduce `tmap` a full featured thematic mapping package.
+
+## Hands-on Exercises
+
+- [Exercises](exercises.html)
+
+
+## Supporting Materials
+
+- [**Slides**](slides)
+
+- [Software / Package Prequisites](software_prereqs.html)
+
+- [Next Steps](next.html): Moving toward GIS analysis, raster analytics, etc.
+
+
+&nbsp;
+
+<figure>![](https://imgs.xkcd.com/comics/2016_election_map.png)<figcaption>KXCD.  2016 Election Map:  https://xkcd.com/1939/</figcaption></figure><br>
+
+