Bridging The Political [Polygons] Gap with ggplot2

The @pewresearch folks have been collecting political survey data for quite a while, and I noticed the [visualization below](http://www.people-press.org/2014/06/12/section-1-growing-ideological-consistency/#interactive) referenced in a [Tableau vis contest entry](https://www.interworks.com/blog/rrouse/2016/06/24/politics-viz-contest-plotting-political-polarization):

Cursor_and_Political_Polarization_and_Growing_Ideological_Consistency___Pew_Research_Center

Those are filled [frequency polygons](http://onlinestatbook.com/2/graphing_distributions/freq_poly.html), which are super-easy to replicate in ggplot2, especially since Pew even _kind of_ made the data available via their interactive visualization (it’s available in other Pew resources, just not as compact). So, we can look at all 5 study years for both the general population and politically active respondents with `ggplot2` facets, incorporating the use of `V8`, `dplyr`, `tidyr`, `purrr` and some R spatial functions along the way.

The first code block has the “data”, data transformations and initial plot code. The “data” is really javascript blocks picked up from the `view-source:` of the interactive visualization. We use the `V8` package to get this data then bend it to our will for visuals.

library(V8)
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)  # devtools::install_github("hadley/ggplot2)
library(hrbrmisc) # devtools::install_github("hrbrmstr/hrbrmisc)
library(rgeos)
library(sp)

ctx <- v8()
ctx$eval("
	var party_data = [
		[{
			name: 'Dem',
			data: [0.57,1.60,1.89,3.49,3.96,6.56,7.23,8.54,9.10,9.45,9.30,9.15,7.74,6.80,4.66,4.32,2.14,1.95,0.87,0.57,0.12]
		},{
			name: 'REP',
			data: [0.03,0.22,0.28,1.49,1.66,2.77,3.26,4.98,5.36,7.28,7.72,8.16,8.86,8.88,8.64,8.00,6.20,5.80,4.87,4.20,1.34]
		}],
		[{
			name: 'Dem',
			data: [1.22,2.78,3.28,5.12,6.15,7.77,8.24,9.35,9.73,9.19,8.83,8.47,5.98,5.17,3.62,2.87,1.06,0.75,0.20,0.15,0.04]
		}, {
			name: 'REP',
			data: [0.23,0.49,0.65,2.23,2.62,4.06,5.02,7.53,7.70,7.28,7.72,8.15,8.87,8.47,7.08,6.27,4.29,3.99,3.54,2.79,1.03]
		}],
		[{
			name: 'Dem',
			data: [2.07,3.57,4.21,6.74,7.95,8.41,8.58,9.07,8.98,8.46,8.47,8.49,5.39,3.62,2.11,1.98,1.00,0.55,0.17,0.17,0.00]
		}, {
			name: 'REP',
			data: [0.19,0.71,1.04,2.17,2.07,3.65,4.92,7.28,8.26,9.64,9.59,9.55,7.91,7.74,6.84,6.01,4.37,3.46,2.09,1.65,0.86]
		}],
		[{
			name: 'Dem',
			data: [2.97,4.09,4.28,6.65,7.90,8.37,8.16,8.74,8.61,8.15,7.74,7.32,4.88,4.82,2.79,2.07,0.96,0.78,0.41,0.29,0.02]
		}, {
			name: 'REP',
			data: [0.04,0.21,0.28,0.88,1.29,2.64,3.08,4.92,5.84,6.65,6.79,6.92,8.50,8.61,8.05,8.00,7.52,7.51,5.61,4.17,2.50]
		}],
		[{
			name: 'Dem',
			data: [4.81,6.04,6.57,7.67,7.84,8.09,8.24,8.91,8.60,6.92,6.69,6.47,4.22,3.85,1.97,1.69,0.66,0.49,0.14,0.10,0.03]
		}, {
			name: 'REP',
			data: [0.11,0.36,0.49,1.23,1.35,2.35,2.83,4.63,5.09,6.12,6.27,6.41,7.88,8.03,7.58,8.26,8.12,7.29,6.38,5.89,3.34]
		}],
	];

	var party_engaged_data = [
		[{
			name: 'Dem',
			data: [0.88,2.19,2.61,4.00,4.76,6.72,7.71,8.45,8.03,8.79,8.79,8.80,7.23,6.13,4.53,4.31,2.22,2.01,1.05,0.66,0.13]
		}, {
			name: 'REP',
			data: [0.00,0.09,0.09,0.95,1.21,1.67,2.24,3.22,3.70,6.24,6.43,6.62,8.01,8.42,8.97,8.48,7.45,7.68,8.64,7.37,2.53]
		}],
		[{
			name: 'Dem',
			data: [1.61,3.35,4.25,6.75,8.01,8.20,8.23,9.14,8.94,8.68,8.46,8.25,4.62,3.51,2.91,2.63,1.19,0.74,0.24,0.17,0.12]
		},{
			name: 'REP',
			data: [0.21,0.38,0.68,1.62,1.55,2.55,3.99,4.65,4.31,5.78,6.28,6.79,8.47,9.01,8.61,8.34,7.16,6.50,6.10,4.78,2.25]
		}],
		[{
			name: 'Dem',
			data: [3.09,4.89,6.22,9.40,9.65,9.20,8.99,6.48,7.36,7.67,6.95,6.22,4.53,3.79,2.19,2.02,0.74,0.07,0.27,0.27,0.00]
		}, {
			name: 'REP',
			data: [0.29,0.59,0.67,2.11,2.03,2.67,4.12,6.55,6.93,8.42,8.79,9.17,7.33,6.84,7.42,7.25,6.36,5.32,3.35,2.57,1.24]
		}],
		[{
			name: 'Dem',
			data: [6.00,5.24,5.11,7.66,9.25,8.25,8.00,8.09,8.12,7.05,6.59,6.12,4.25,4.07,2.30,1.49,0.98,0.80,0.42,0.16,0.06]
		}, {
			name: 'REP',
			data: [0.00,0.13,0.13,0.48,0.97,2.10,2.73,3.14,3.64,5.04,5.30,5.56,6.87,6.75,8.03,9.33,11.01,10.49,7.61,6.02,4.68]
		}],
		[{
			name: 'Dem',
			data: [9.53,9.68,10.35,9.33,9.34,7.59,6.67,6.41,6.60,5.21,4.84,4.47,2.90,2.61,1.37,1.14,0.73,0.59,0.30,0.28,0.06]
		}, {
			name: 'REP',
			data: [0.15,0.11,0.13,0.46,0.52,1.18,1.45,2.46,2.84,4.15,4.37,4.60,6.36,6.66,7.34,9.09,11.40,10.53,10.58,9.85,5.76]
		}],
	];
")

years <- c(1994, 1999, 2004, 2001, 2014)

# Transform the javascript data -------------------------------------------

party_data <- ctx$get("party_data")
map_df(1:length(party_data), function(i) {
  x <- party_data[[i]]
  names(x$data) <- x$name
  dat <- as.data.frame(x$data)
  bind_cols(dat, data_frame(x=-10:10, year=rep(years[i], nrow(dat))))
}) -> party_data

party_engaged_data <- ctx$get("party_engaged_data")
map_df(1:length(party_engaged_data), function(i) {
  x <- party_engaged_data[[i]]
  names(x$data) <- x$name
  dat <- as.data.frame(x$data)
  bind_cols(dat, data_frame(x=-10:10, year=rep(years[i], nrow(dat))))
}) -> party_engaged_data

# We need it in long form -------------------------------------------------

gather(party_data, party, pct, -x, -year) %>%
  mutate(party=factor(party, levels=c("REP", "Dem"))) -> party_data_long

gather(party_engaged_data, party, pct, -x, -year) %>%
  mutate(party=factor(party, levels=c("REP", "Dem"))) -> party_engaged_data_long

# Traditional frequency polygon plots -------------------------------------

gg <- ggplot()
gg <- gg + geom_ribbon(data=party_data_long,
                       aes(x=x, ymin=0, ymax=pct, fill=party, color=party), alpha=0.5)
gg <- gg + scale_x_continuous(expand=c(0,0), breaks=c(-8, 0, 8),
                              labels=c("Consistently\nliberal", "Mixed", "Consistently\nconservative"))
gg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 12))
gg <- gg + scale_color_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),
                              labels=c(Dem="Democrats", REP="Republicans"))
gg <- gg + guides(color="none", fill=guide_legend(override.aes=list(alpha=1)))
gg <- gg + scale_fill_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),
                             labels=c(Dem="Democrats", REP="Republicans"))
gg <- gg + facet_wrap(~year, ncol=2, scales="free_x")
gg <- gg + labs(x=NULL, y=NULL,
                title="Political Polarization, 1994-2014 (General Population)",
                caption="Source: http://www.people-press.org/2014/06/12/section-1-growing-ideological-consistency/iframe/")
gg <- gg + theme_hrbrmstr_an(grid="")
gg <- gg + theme(panel.margin=margin(t=30, b=30, l=30, r=30))
gg <- gg + theme(legend.position=c(0.75, 0.1))
gg <- gg + theme(legend.direction="horizontal")
gg <- gg + theme(axis.text.y=element_blank())
gg

gg <- ggplot()
gg <- gg + geom_ribbon(data=party_engaged_data_long,
                       aes(x=x, ymin=0, ymax=pct, fill=party, color=party), alpha=0.5)
gg <- gg + scale_x_continuous(expand=c(0,0), breaks=c(-8, 0, 8),
                              labels=c("Consistently\nliberal", "Mixed", "Consistently\nconservative"))
gg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 12))
gg <- gg + scale_color_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),
                              labels=c(Dem="Democrats", REP="Republicans"))
gg <- gg + guides(color="none", fill=guide_legend(override.aes=list(alpha=1)))
gg <- gg + scale_fill_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),
                             labels=c(Dem="Democrats", REP="Republicans"))
gg <- gg + facet_wrap(~year, ncol=2, scales="free_x")
gg <- gg + labs(x=NULL, y=NULL,
                title="Political Polarization, 1994-2014 (Politically Active)",
                caption="Source: http://www.people-press.org/2014/06/12/section-1-growing-ideological-consistency/iframe/")
gg <- gg + theme_hrbrmstr_an(grid="")
gg <- gg + theme(panel.margin=margin(t=30, b=30, l=30, r=30))
gg <- gg + theme(legend.position=c(0.75, 0.1))
gg <- gg + theme(legend.direction="horizontal")
gg <- gg + theme(axis.text.y=element_blank())
gg

genalpha

engalpha

It provides a similar effect to the Pew & Interworks visuals using alpha transparency to blend the point of polygon intersections. But I _really_ kinda like the way both Pew & Interworks did their visualizations without alpha blending yet still highlighting the intersected areas. We can do that in R as well with a bit more work by:

– grouping each data frame by year
– turning each set of points (Dem & Rep) to R polygons
– computing the intersection of those polygons
– turning that intersection back into a data frame
– adding this new polygon to the plots while also removing the alpha blend

Here’s what that looks like in code:

# Setup a function to do the polygon intersection -------------------------

polysect <- function(df) {

  bind_rows(data_frame(x=-10, pct=0),
            select(filter(df, party=="Dem"), x, pct),
            data_frame(x=10, pct=0)) %>%
    as.matrix() %>%
    Polygon() %>%
    list() %>%
    Polygons(1) %>%
    list() %>%
    SpatialPolygons() -> dem

  bind_rows(data_frame(x=-10, pct=0),
            select(filter(df, party=="REP"), x, pct),
            data_frame(x=10, pct=0)) %>%
    as.matrix() %>%
    Polygon() %>%
    list() %>%
    Polygons(1) %>%
    list() %>%
    SpatialPolygons() -> rep

  inter <- gIntersection(dem, rep)
  inter <- as.data.frame(inter@polygons[[1]]@Polygons[[1]]@coords)[c(-1, -25),]
  inter <- mutate(inter, year=df$year[1])
  inter

}

# Get the intersected area ------------------------------------------------

group_by(party_data_long, year) %>%
  do(polysect(.)) -> general_sect

group_by(party_engaged_data_long, year) %>%
  do(polysect(.)) -> engaged_sect


# Try the plots again -----------------------------------------------------

gg <- ggplot()
gg <- gg + geom_ribbon(data=party_data_long,
                       aes(x=x, ymin=0, ymax=pct, fill=party, color=party))
gg <- gg + geom_ribbon(data=general_sect, aes(x=x, ymin=0, ymax=y), color="#666979", fill="#666979")
gg <- gg + scale_x_continuous(expand=c(0,0), breaks=c(-8, 0, 8),
                              labels=c("Consistently\nliberal", "Mixed", "Consistently\nconservative"))
gg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 12))
gg <- gg + scale_color_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),
                              labels=c(Dem="Democrats", REP="Republicans"))
gg <- gg + guides(color="none", fill=guide_legend(override.aes=list(alpha=1)))
gg <- gg + scale_fill_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),
                             labels=c(Dem="Democrats", REP="Republicans"))
gg <- gg + facet_wrap(~year, ncol=2, scales="free_x")
gg <- gg + labs(x=NULL, y=NULL,
                title="Political Polarization, 1994-2014 (General Population)",
                caption="Source: http://www.people-press.org/2014/06/12/section-1-growing-ideological-consistency/iframe/")
gg <- gg + theme_hrbrmstr_an(grid="")
gg <- gg + theme(panel.margin=margin(t=30, b=30, l=30, r=30))
gg <- gg + theme(legend.position=c(0.75, 0.1))
gg <- gg + theme(legend.direction="horizontal")
gg <- gg + theme(axis.text.y=element_blank())
gg

gg <- ggplot()
gg <- gg + geom_ribbon(data=party_engaged_data_long,
                       aes(x=x, ymin=0, ymax=pct, fill=party, color=party))
gg <- gg + geom_ribbon(data=engaged_sect, aes(x=x, ymin=0, ymax=y), color="#666979", fill="#666979")
gg <- gg + scale_x_continuous(expand=c(0,0), breaks=c(-8, 0, 8),
                              labels=c("Consistently\nliberal", "Mixed", "Consistently\nconservative"))
gg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 12))
gg <- gg + scale_color_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),
                              labels=c(Dem="Democrats", REP="Republicans"))
gg <- gg + guides(color="none", fill=guide_legend(override.aes=list(alpha=1)))
gg <- gg + scale_fill_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),
                             labels=c(Dem="Democrats", REP="Republicans"))
gg <- gg + facet_wrap(~year, ncol=2, scales="free_x")
gg <- gg + labs(x=NULL, y=NULL,
                title="Political Polarization, 1994-2014 (Politically Active)",
                caption="Source: http://www.people-press.org/2014/06/12/section-1-growing-ideological-consistency/iframe/")
gg <- gg + theme_hrbrmstr_an(grid="")
gg <- gg + theme(panel.margin=margin(t=30, b=30, l=30, r=30))
gg <- gg + theme(legend.position=c(0.75, 0.1))
gg <- gg + theme(legend.direction="horizontal")
gg <- gg + theme(axis.text.y=element_blank())
gg

genfull

engfull

Without much extra effort/work we now have what I believe to be a more striking set of visuals. (And, I should probably makes a `points_to_spatial_polys()` convenience function.)

You’ll find the “overall” group data as well as the party median values in [the Pew HTML source code](view-source:http://www.people-press.org/2014/06/12/section-1-growing-ideological-consistency/iframe/) if you want to try to fully replicate their visualizations.

Cover image from Data-Driven Security
Amazon Author Page

8 Comments Bridging The Political [Polygons] Gap with ggplot2

  1. timothyjkiely

    I like the use case for finding and plotting the intersection of two polygons; I think this could be especially applicable for anything geo-spatial related, like finding overlapping areas on a map.

    Separate from that, forgive me for saying but the above seems like a lot of extra work just to come up with a plot that is nearly the same as with geom_density()? I suppose the advantage to the intersection technique is that you can color the middle polygon anything you like.

    Reply
    1. hrbrmstr

      Except that (a) we only have the frequency points from the data (b) they’re frequency polygons vs kernel density estimates (which are, in reality, very different) and (c) as you pointed out, we get full control over the fill value for the overlapping region. For a diagnostic plot, I wouldn’t bother, but for a publication or news story, I’d def bother to generate the region, now.

      Reply
      1. timothyjkiely

        Interesting. All great points! Could you explain (b) a bit more? At the surface, it seems like a frequency polygon is just the result of performing a kernel density estimate (?).

        Reply
        1. hrbrmstr
          library(ggplot2)
          library(gridExtra)
          
          grid.arrange(
            ggplot(diamonds, aes(carat)) + geom_histogram(),
            ggplot(diamonds, aes(carat)) + geom_freqpoly(),
            ggplot(diamonds, aes(carat)) + geom_density(),
            ncol=1
          )
          

          shld give a fairly quick view of the diff.

          Reply
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  5. Charles

    Wow, that’s a lot of work, but clearly shows the power of visualization to make data more readily understandable!

    (Shakes fist at people/organizations that don’t make their data easily available/accessible).

    Reply

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