fgitn

Author: libjohn

02_choropleth.Rmd

@@ -41,12 +41,11 @@ nc_pop <-
 #nc_pop
 ```
 
-```{}
-nc_pop <- 
-  get_acs(geography = "county",
-          variables = "B01003_001",
-          state = "NC",
-          geometry = TRUE)
+```{r eval=FALSE, echo=TRUE}
+nc_pop <- get_acs(geography = "county",
+                  variables = "B01003_001",
+                  state = "NC",
+                  geometry = TRUE)
 ```
 
 

038_facets_wrap_thematic_mapping.Rmd

@@ -3,7 +3,7 @@ title: "Faceted Thematic Mapping"
 ---
 
 ```{r load-libraries, message=FALSE, warning=FALSE}
-library(tidyverse)       # Tidyverse for Tidy Data
+library(tidyverse)       
 library(readxl)
 library(tigris)          # Get Census Geography Poloygons
 library(sf)
@@ -15,15 +15,16 @@ library(tidycensus)
 Using the `tigris` package get Census Tiger shapefiles for census geographies.  Tigris will return the shapefile in the `sf`, or simple features, format.
 
 ```{r get-data, message=FALSE, warning=FALSE}
-us_geo <- tigris::states(class = "sf") %>% 
-  shift_geometry()
+us_geo <- tigris::states(class = "sf", cb = TRUE) %>%   #cb is a more generalized, less detailed file.
+  shift_geometry() %>% 
+  filter(as.numeric(GEOID) < 60)
 ```
 
 ## Get BLS data
 
-I've already downloaded and stored some data from the Bureau of Labor Statistics.  Thoses data are stored in an excel file in the `data` directory of the repository:  `data/OES_Report.xlsx`.  **The goal is to attach this data to the previously downloaded shapefiles.**  
+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 github repository:  `data/OES_Report.xlsx`.  **The goal is to attach this data to the previously downloaded shapefiles.**  
 
-But you may be intersted in how I gathered the data. below are some summary notes documenting my steps of gathering the data from the Bureau of Labor Statistics.  
+But you may be interested in how I gathered the data. below are some summary notes documenting my steps of gathering the data from the Bureau of Labor Statistics.  
 
 https://data.bls.gov/oes/#/occGeo/One%20occupation%20for%20multiple%20geographical%20areas
 
@@ -49,11 +50,9 @@ my_df <- my_xl_files %>%
           col_types = c("text", "numeric"), 
           skip = 4, 
           .id = "sheet")
-```
 
-```{r}
 state_names <- us_geo %>% 
-  filter(REGION != "9") %>% 
+  # filter(REGION != "9") %>% jrljrl
   select(NAME) %>% 
   st_drop_geometry() 
 ```
@@ -69,8 +68,7 @@ my_df <- my_df %>%
          wages = "Annual mean wage(2)",
          type = sheet) %>% 
   mutate(State = str_extract(area, '.*(?=\\()')) %>% 
-  mutate(type = str_extract(type, "(?<=data/OES_)\\w+")) %>% 
-  filter(State != "Guam")
+  mutate(type = str_extract(type, "(?<=data/OES_)\\w+"))
 ```
 
 ### Missing data
@@ -106,16 +104,16 @@ my_df <- us_geo %>%
 Filter to the 50 states + D.C.
 
 ```{r}
-my_df <- my_df %>% 
-  filter(REGION != 9) 
+my_df <- my_df # %>%  jrljrl
+  # filter(REGION != 9) 
 ```
 
 ## Get Population Data -- Tidycensus
 
 ### identify and pick census variables
 
-B01003_001E = Total Population
-B06011_001E = Median income in the past 12 months
+- B01003_001E = Total Population  
+- B06011_001E = Median income in the past 12 months  
 
 ```{r identify_ACS_variables}
 variables_census <- load_variables(2015, "acs5", cache = TRUE)

039_facet_example.Rmd

@@ -7,7 +7,7 @@ title: "Example Faceted Thematic Mapping"
 Beyond the the data wrangling, this faceted map was created using the `ggplot2::facet_wrap()` function.  
 See Also: [full data wrangling code](038_facets_wrap_thematic_mapping.html) and workflow.
 
-```{r eval=FALSE, include=FALSE, echo=TRUE}
+```{r eval=FALSE, echo=TRUE}
 sf_data %>% 
   ggplot(aes(fill = wages, color = wages)) +
   geom_sf() +

docs/01_georeference.html

@@ -2115,7 +2115,7 @@ <h1>Faceted Thematic Mapping</h1>
 <div class="d-article">
 <div class="layout-chunk" data-layout="l-body">
 <div class="sourceCode">
-<pre class="sourceCode r"><code class="sourceCode r"><span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://tidyverse.tidyverse.org'>tidyverse</a></span><span class='op'>)</span>       <span class='co'># Tidyverse for Tidy Data</span>
+<pre class="sourceCode r"><code class="sourceCode r"><span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://tidyverse.tidyverse.org'>tidyverse</a></span><span class='op'>)</span>       
 <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://readxl.tidyverse.org'>readxl</a></span><span class='op'>)</span>
 <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://github.com/walkerke/tigris'>tigris</a></span><span class='op'>)</span>          <span class='co'># Get Census Geography Poloygons</span>
 <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
@@ -2127,14 +2127,15 @@ <h2 id="shapefiles-as-sf">Shapefiles as sf</h2>
 <p>Using the <code>tigris</code> package get Census Tiger shapefiles for census geographies. Tigris will return the shapefile in the <code>sf</code>, or simple features, format.</p>
 <div class="layout-chunk" data-layout="l-body">
 <div class="sourceCode">
-<pre class="sourceCode r"><code class="sourceCode r"><span class='va'>us_geo</span> <span class='op'>&lt;-</span> <span class='fu'>tigris</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/tigris/man/states.html'>states</a></span><span class='op'>(</span>class <span class='op'>=</span> <span class='st'>"sf"</span><span class='op'>)</span> <span class='op'>%&gt;%</span> 
-  <span class='fu'><a href='https://rdrr.io/pkg/tigris/man/shift_geometry.html'>shift_geometry</a></span><span class='op'>(</span><span class='op'>)</span>
+<pre class="sourceCode r"><code class="sourceCode r"><span class='va'>us_geo</span> <span class='op'>&lt;-</span> <span class='fu'>tigris</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/tigris/man/states.html'>states</a></span><span class='op'>(</span>class <span class='op'>=</span> <span class='st'>"sf"</span>, cb <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='op'>%&gt;%</span>   <span class='co'>#cb is a more generalized, less detailed file.</span>
+  <span class='fu'><a href='https://rdrr.io/pkg/tigris/man/shift_geometry.html'>shift_geometry</a></span><span class='op'>(</span><span class='op'>)</span> <span class='op'>%&gt;%</span> 
+  <span class='fu'><a href='https://rdrr.io/r/stats/filter.html'>filter</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span><span class='op'>(</span><span class='va'>GEOID</span><span class='op'>)</span> <span class='op'>&lt;</span> <span class='fl'>60</span><span class='op'>)</span>
 </code></pre>
 </div>
 </div>
 <h2 id="get-bls-data">Get BLS data</h2>
-<p>I’ve already downloaded and stored some data from the Bureau of Labor Statistics. Thoses data are stored in an excel file in the <code>data</code> directory of the repository: <code>data/OES_Report.xlsx</code>. <strong>The goal is to attach this data to the previously downloaded shapefiles.</strong></p>
-<p>But you may be intersted in how I gathered the data. below are some summary notes documenting my steps of gathering the data from the Bureau of Labor Statistics.</p>
+<p>I’ve already downloaded and stored some data from the Bureau of Labor Statistics. Those data are stored in an excel file in the <code>data</code> directory of the github repository: <code>data/OES_Report.xlsx</code>. <strong>The goal is to attach this data to the previously downloaded shapefiles.</strong></p>
+<p>But you may be interested in how I gathered the data. below are some summary notes documenting my steps of gathering the data from the Bureau of Labor Statistics.</p>
 <p><a href="https://data.bls.gov/oes/#/occGeo/One%20occupation%20for%20multiple%20geographical%20areas" class="uri">https://data.bls.gov/oes/#/occGeo/One%20occupation%20for%20multiple%20geographical%20areas</a></p>
 <ul>
 <li><p>One occupation for multiple geographical areas</p>
@@ -2160,13 +2161,9 @@ <h3 id="import-the-data-into-r">Import the Data into R</h3>
           col_types <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"text"</span>, <span class='st'>"numeric"</span><span class='op'>)</span>, 
           skip <span class='op'>=</span> <span class='fl'>4</span>, 
           .id <span class='op'>=</span> <span class='st'>"sheet"</span><span class='op'>)</span>
-</code></pre>
-</div>
-</div>
-<div class="layout-chunk" data-layout="l-body">
-<div class="sourceCode">
-<pre class="sourceCode r"><code class="sourceCode r"><span class='va'>state_names</span> <span class='op'>&lt;-</span> <span class='va'>us_geo</span> <span class='op'>%&gt;%</span> 
-  <span class='fu'><a href='https://rdrr.io/r/stats/filter.html'>filter</a></span><span class='op'>(</span><span class='va'>REGION</span> <span class='op'>!=</span> <span class='st'>"9"</span><span class='op'>)</span> <span class='op'>%&gt;%</span> 
+
+<span class='va'>state_names</span> <span class='op'>&lt;-</span> <span class='va'>us_geo</span> <span class='op'>%&gt;%</span> 
+  <span class='co'># filter(REGION != "9") %&gt;% jrljrl</span>
   <span class='fu'>select</span><span class='op'>(</span><span class='va'>NAME</span><span class='op'>)</span> <span class='op'>%&gt;%</span> 
   <span class='fu'><a href='https://r-spatial.github.io/sf/reference/st_geometry.html'>st_drop_geometry</a></span><span class='op'>(</span><span class='op'>)</span> 
 </code></pre>
@@ -2181,8 +2178,7 @@ <h3 id="wrangle-the-data">Wrangle the data</h3>
          wages <span class='op'>=</span> <span class='st'>"Annual mean wage(2)"</span>,
          type <span class='op'>=</span> <span class='va'>sheet</span><span class='op'>)</span> <span class='op'>%&gt;%</span> 
   <span class='fu'>mutate</span><span class='op'>(</span>State <span class='op'>=</span> <span class='fu'>str_extract</span><span class='op'>(</span><span class='va'>area</span>, <span class='st'>'.*(?=\\()'</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>%&gt;%</span> 
-  <span class='fu'>mutate</span><span class='op'>(</span>type <span class='op'>=</span> <span class='fu'>str_extract</span><span class='op'>(</span><span class='va'>type</span>, <span class='st'>"(?&lt;=data/OES_)\\w+"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>%&gt;%</span> 
-  <span class='fu'><a href='https://rdrr.io/r/stats/filter.html'>filter</a></span><span class='op'>(</span><span class='va'>State</span> <span class='op'>!=</span> <span class='st'>"Guam"</span><span class='op'>)</span>
+  <span class='fu'>mutate</span><span class='op'>(</span>type <span class='op'>=</span> <span class='fu'>str_extract</span><span class='op'>(</span><span class='va'>type</span>, <span class='st'>"(?&lt;=data/OES_)\\w+"</span><span class='op'>)</span><span class='op'>)</span>
 </code></pre>
 </div>
 </div>
@@ -2200,12 +2196,12 @@ <h3 id="missing-data">Missing data</h3>
 </code></pre>
 </div>
 <pre><code>                 State       type
-1       North Carolina legislator
-2              Vermont legislator
-3             Kentucky legislator
-4                Maine legislator
-5         South Dakota legislator
-6 District of Columbia legislator</code></pre>
+1                Maine legislator
+2 District of Columbia legislator
+3         South Dakota legislator
+4              Vermont legislator
+5             Kentucky legislator
+6       North Carolina legislator</code></pre>
 </div>
 <div class="layout-chunk" data-layout="l-body">
 <div class="sourceCode">
@@ -2227,14 +2223,18 @@ <h3 id="states">50 states</h3>
 <p>Filter to the 50 states + D.C.</p>
 <div class="layout-chunk" data-layout="l-body">
 <div class="sourceCode">
-<pre class="sourceCode r"><code class="sourceCode r"><span class='va'>my_df</span> <span class='op'>&lt;-</span> <span class='va'>my_df</span> <span class='op'>%&gt;%</span> 
-  <span class='fu'><a href='https://rdrr.io/r/stats/filter.html'>filter</a></span><span class='op'>(</span><span class='va'>REGION</span> <span class='op'>!=</span> <span class='fl'>9</span><span class='op'>)</span> 
+<pre class="sourceCode r"><code class="sourceCode r"><span class='va'>my_df</span> <span class='op'>&lt;-</span> <span class='va'>my_df</span> <span class='co'># %&gt;%  jrljrl</span>
+  <span class='co'># filter(REGION != 9) </span>
 </code></pre>
 </div>
 </div>
 <h2 id="get-population-data-tidycensus">Get Population Data – Tidycensus</h2>
 <h3 id="identify-and-pick-census-variables">identify and pick census variables</h3>
-<p>B01003_001E = Total Population B06011_001E = Median income in the past 12 months</p>
+<ul>
+<li>B01003_001E = Total Population<br />
+</li>
+<li>B06011_001E = Median income in the past 12 months</li>
+</ul>
 <div class="layout-chunk" data-layout="l-body">
 <div class="sourceCode">
 <pre class="sourceCode r"><code class="sourceCode r"><span class='va'>variables_census</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/tidycensus/man/load_variables.html'>load_variables</a></span><span class='op'>(</span><span class='fl'>2015</span>, <span class='st'>"acs5"</span>, cache <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
@@ -2298,7 +2298,7 @@ <h3 id="display-map">Display Map</h3>
              nrow <span class='op'>=</span> <span class='fl'>3</span>, ncol <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span>
 </code></pre>
 </div>
-<p><img src="038_facets_wrap_thematic_mapping_files/figure-html5/unnamed-chunk-10-1.png" width="624" /></p>
+<p><img src="038_facets_wrap_thematic_mapping_files/figure-html5/unnamed-chunk-9-1.png" width="624" /></p>
 </div>
 <h3 id="save-map">Save Map</h3>
 <div class="layout-chunk" data-layout="l-body">

docs/02_choropleth.html

@@ -2116,6 +2116,19 @@ <h1>Example Faceted Thematic Mapping</h1>
 <p><img src="facet_map.png" /></p>
 <p>Beyond the the data wrangling, this faceted map was created using the <code>ggplot2::facet_wrap()</code> function.<br />
 See Also: <a href="038_facets_wrap_thematic_mapping.html">full data wrangling code</a> and workflow.</p>
+<div class="layout-chunk" data-layout="l-body">
+<div class="sourceCode">
+<pre class="sourceCode r"><code class="sourceCode r"><span class='va'>sf_data</span> <span class='op'>%&gt;%</span> 
+  <span class='fu'>ggplot</span><span class='op'>(</span><span class='fu'>aes</span><span class='op'>(</span>fill <span class='op'>=</span> <span class='va'>wages</span>, color <span class='op'>=</span> <span class='va'>wages</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>+</span>
+  <span class='fu'>geom_sf</span><span class='op'>(</span><span class='op'>)</span> <span class='op'>+</span>
+  <span class='fu'>coord_sf</span><span class='op'>(</span>crs <span class='op'>=</span> <span class='fl'>5070</span>, datum <span class='op'>=</span> <span class='cn'>NA</span><span class='op'>)</span> <span class='op'>+</span>
+  <span class='fu'>scale_fill_viridis_c</span><span class='op'>(</span><span class='op'>)</span> <span class='op'>+</span> 
+  <span class='fu'>scale_color_viridis_c</span><span class='op'>(</span><span class='op'>)</span> <span class='op'>+</span>
+  <span class='fu'>facet_wrap</span><span class='op'>(</span><span class='op'>~</span> <span class='va'>category</span>, 
+             nrow <span class='op'>=</span> <span class='fl'>3</span>, ncol <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span>
+</code></pre>
+</div>
+</div>
 <div class="sourceCode" id="cb1"><pre class="sourceCode r distill-force-highlighting-css"><code class="sourceCode r"></code></pre></div>
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