New release: Choroplethr v3.2.0

August 27, 2015
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Today I am happy to announce that a new version of choroplethr, v3.2.0, is now available. You can get it by typing the following from an R console:

install.packages("choroplethr")

Note that it sometimes takes a few days for new packages to get copied to each CRAN mirror. If install.packages(“choroplethr”) only gets you version 3.1.0, please try again tomorrow.

This version contains three changes.

Change #1: Better Default Projection

The most significant change is the addition of a better default map projection. I have discussed the issue of map projections in R at length here and here, among other places. Today I simply want to thank Hadley Wickham for pointing me to  coord_quickmap(), which gives you a nice projection that also works with insets. Here is a comparison between the old and new maps:

old-new-projection

library(choroplethr)
 
data(df_pop_county)
df_pop_county$value=NA

new = county_choropleth(df_pop_county, 
                        title = "New Default")

old            = CountyChoropleth$new(df_pop_county)
old$title      = "Old Default"
old$projection = element_blank()
old            = old$render()

library(gridExtra)
grid.arrange(new, old, ncol=2)

 

Change #2: Better Border Control

On maps with many small regions, the borders can obscure information. Look at the map below, which shows all the zip codes in New York state.

ny-zip-border-comp

In previous version of choroplethr it was hard to make the right-hand map. Choroplethr 3.2.0 simplifies this process quite a bit. It adds a ggplot_polygon member variable to the base Choropleth object. If you set it to geom_polygon(aes(fill=value), color=NA), as shown below, then the borders disappear:

library(choroplethrZip)
library(ggplot2)
data(df_pop_zip)

default = zip_choropleth(df_pop_zip, 
                         state_zoom = "new york",
                         title = "With Border") + 
            theme(legend.position="none")

c = ZipChoropleth$new(df_pop_zip)
c$title = "Without Border"
c$ggplot_polygon = geom_polygon(aes(fill = value), 
                                color = NA)
c$set_zoom_zip(state_zoom  = "new york", 
               county_zoom = NULL, 
               msa_zoom    = NULL, 
               zip_zoom    = NULL)

no_border = c$render() + 
              theme(legend.position = "none")

grid.arrange(default, no_border, ncol = 2)

Change #3: Continental US States

In my own work I frequently zoom in on the contiguous United States. I got tired of continually computing this vector of state names, so I added it as the variable continental_us_states. Here is an example:

continental

data(df_pop_county)        
data(continental_us_states)

county_choropleth(df_pop_county, 
                  state_zoom = continental_us_states)            

In Other News

On Tuesday I wrote a guest blog post titled Following up on news stories with choroplethr and R on the Revolution Analytics blog. In it I describe how I used R and choroplethr to research how fracking has changed the demographics of North Dakota. I first learned about this issue in an interview that Russ Roberts did with Gregory Zuckerman on the EconTalk podcast. The post is of technical interest because I demonstrate how to calculate the percentage change in census demographics.

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The post New release: Choroplethr v3.2.0 appeared first on AriLamstein.com.

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