{"id":4501,"date":"2016-07-07T16:27:25","date_gmt":"2016-07-07T21:27:25","guid":{"rendered":"http:\/\/rud.is\/b\/?p=4501"},"modified":"2018-03-07T16:42:22","modified_gmt":"2018-03-07T21:42:22","slug":"bridging-the-political-polygons-gap-with-ggplot2","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2016\/07\/07\/bridging-the-political-polygons-gap-with-ggplot2\/","title":{"rendered":"Bridging The Political [Polygons] Gap with ggplot2"},"content":{"rendered":"

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):<\/p>\n

\"Cursor_and_Political_Polarization_and_Growing_Ideological_Consistency___Pew_Research_Center\"<\/a><\/p>\n

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.<\/p>\n

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.<\/p>\n

library(V8)\r\nlibrary(dplyr)\r\nlibrary(tidyr)\r\nlibrary(purrr)\r\nlibrary(ggplot2)  # devtools::install_github("hadley\/ggplot2)\r\nlibrary(hrbrmisc) # devtools::install_github("hrbrmstr\/hrbrmisc)\r\nlibrary(rgeos)\r\nlibrary(sp)\r\n\r\nctx <- v8()\r\nctx$eval("\r\n\tvar party_data = [\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t},{\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t}, {\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t}, {\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t}, {\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t}, {\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t];\r\n\r\n\tvar party_engaged_data = [\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t}, {\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t},{\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t}, {\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t}, {\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t\t[{\r\n\t\t\tname: 'Dem',\r\n\t\t\tdata: [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]\r\n\t\t}, {\r\n\t\t\tname: 'REP',\r\n\t\t\tdata: [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]\r\n\t\t}],\r\n\t];\r\n")\r\n\r\nyears <- c(1994, 1999, 2004, 2001, 2014)\r\n\r\n# Transform the javascript data -------------------------------------------\r\n\r\nparty_data <- ctx$get("party_data")\r\nmap_df(1:length(party_data), function(i) {\r\n  x <- party_data[[i]]\r\n  names(x$data) <- x$name\r\n  dat <- as.data.frame(x$data)\r\n  bind_cols(dat, data_frame(x=-10:10, year=rep(years[i], nrow(dat))))\r\n}) -> party_data\r\n\r\nparty_engaged_data <- ctx$get("party_engaged_data")\r\nmap_df(1:length(party_engaged_data), function(i) {\r\n  x <- party_engaged_data[[i]]\r\n  names(x$data) <- x$name\r\n  dat <- as.data.frame(x$data)\r\n  bind_cols(dat, data_frame(x=-10:10, year=rep(years[i], nrow(dat))))\r\n}) -> party_engaged_data\r\n\r\n# We need it in long form -------------------------------------------------\r\n\r\ngather(party_data, party, pct, -x, -year) %>%\r\n  mutate(party=factor(party, levels=c("REP", "Dem"))) -> party_data_long\r\n\r\ngather(party_engaged_data, party, pct, -x, -year) %>%\r\n  mutate(party=factor(party, levels=c("REP", "Dem"))) -> party_engaged_data_long\r\n\r\n# Traditional frequency polygon plots -------------------------------------\r\n\r\ngg <- ggplot()\r\ngg <- gg + geom_ribbon(data=party_data_long,\r\n                       aes(x=x, ymin=0, ymax=pct, fill=party, color=party), alpha=0.5)\r\ngg <- gg + scale_x_continuous(expand=c(0,0), breaks=c(-8, 0, 8),\r\n                              labels=c("Consistently\\nliberal", "Mixed", "Consistently\\nconservative"))\r\ngg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 12))\r\ngg <- gg + scale_color_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),\r\n                              labels=c(Dem="Democrats", REP="Republicans"))\r\ngg <- gg + guides(color="none", fill=guide_legend(override.aes=list(alpha=1)))\r\ngg <- gg + scale_fill_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),\r\n                             labels=c(Dem="Democrats", REP="Republicans"))\r\ngg <- gg + facet_wrap(~year, ncol=2, scales="free_x")\r\ngg <- gg + labs(x=NULL, y=NULL,\r\n                title="Political Polarization, 1994-2014 (General Population)",\r\n                caption="Source: http:\/\/www.people-press.org\/2014\/06\/12\/section-1-growing-ideological-consistency\/iframe\/")\r\ngg <- gg + theme_hrbrmstr_an(grid="")\r\ngg <- gg + theme(panel.margin=margin(t=30, b=30, l=30, r=30))\r\ngg <- gg + theme(legend.position=c(0.75, 0.1))\r\ngg <- gg + theme(legend.direction="horizontal")\r\ngg <- gg + theme(axis.text.y=element_blank())\r\ngg\r\n\r\ngg <- ggplot()\r\ngg <- gg + geom_ribbon(data=party_engaged_data_long,\r\n                       aes(x=x, ymin=0, ymax=pct, fill=party, color=party), alpha=0.5)\r\ngg <- gg + scale_x_continuous(expand=c(0,0), breaks=c(-8, 0, 8),\r\n                              labels=c("Consistently\\nliberal", "Mixed", "Consistently\\nconservative"))\r\ngg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 12))\r\ngg <- gg + scale_color_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),\r\n                              labels=c(Dem="Democrats", REP="Republicans"))\r\ngg <- gg + guides(color="none", fill=guide_legend(override.aes=list(alpha=1)))\r\ngg <- gg + scale_fill_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),\r\n                             labels=c(Dem="Democrats", REP="Republicans"))\r\ngg <- gg + facet_wrap(~year, ncol=2, scales="free_x")\r\ngg <- gg + labs(x=NULL, y=NULL,\r\n                title="Political Polarization, 1994-2014 (Politically Active)",\r\n                caption="Source: http:\/\/www.people-press.org\/2014\/06\/12\/section-1-growing-ideological-consistency\/iframe\/")\r\ngg <- gg + theme_hrbrmstr_an(grid="")\r\ngg <- gg + theme(panel.margin=margin(t=30, b=30, l=30, r=30))\r\ngg <- gg + theme(legend.position=c(0.75, 0.1))\r\ngg <- gg + theme(legend.direction="horizontal")\r\ngg <- gg + theme(axis.text.y=element_blank())\r\ngg<\/code><\/pre>\n
\"genalpha\"<\/a><\/p>\n
\"engalpha\"<\/a><\/p>\n
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:<\/p>\n
– grouping each data frame by year
\n– turning each set of points (Dem & Rep) to R polygons
\n– computing the intersection of those polygons
\n– turning that intersection back into a data frame
\n– adding this new polygon to the plots while also removing the alpha blend<\/p>\n
Here’s what that looks like in code:<\/p>\n
# Setup a function to do the polygon intersection -------------------------\r\n\r\npolysect <- function(df) {\r\n\r\n  bind_rows(data_frame(x=-10, pct=0),\r\n            select(filter(df, party=="Dem"), x, pct),\r\n            data_frame(x=10, pct=0)) %>%\r\n    as.matrix() %>%\r\n    Polygon() %>%\r\n    list() %>%\r\n    Polygons(1) %>%\r\n    list() %>%\r\n    SpatialPolygons() -> dem\r\n\r\n  bind_rows(data_frame(x=-10, pct=0),\r\n            select(filter(df, party=="REP"), x, pct),\r\n            data_frame(x=10, pct=0)) %>%\r\n    as.matrix() %>%\r\n    Polygon() %>%\r\n    list() %>%\r\n    Polygons(1) %>%\r\n    list() %>%\r\n    SpatialPolygons() -> rep\r\n\r\n  inter <- gIntersection(dem, rep)\r\n  inter <- as.data.frame(inter@polygons[[1]]@Polygons[[1]]@coords)[c(-1, -25),]\r\n  inter <- mutate(inter, year=df$year[1])\r\n  inter\r\n\r\n}\r\n\r\n# Get the intersected area ------------------------------------------------\r\n\r\ngroup_by(party_data_long, year) %>%\r\n  do(polysect(.)) -> general_sect\r\n\r\ngroup_by(party_engaged_data_long, year) %>%\r\n  do(polysect(.)) -> engaged_sect\r\n\r\n\r\n# Try the plots again -----------------------------------------------------\r\n\r\ngg <- ggplot()\r\ngg <- gg + geom_ribbon(data=party_data_long,\r\n                       aes(x=x, ymin=0, ymax=pct, fill=party, color=party))\r\ngg <- gg + geom_ribbon(data=general_sect, aes(x=x, ymin=0, ymax=y), color="#666979", fill="#666979")\r\ngg <- gg + scale_x_continuous(expand=c(0,0), breaks=c(-8, 0, 8),\r\n                              labels=c("Consistently\\nliberal", "Mixed", "Consistently\\nconservative"))\r\ngg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 12))\r\ngg <- gg + scale_color_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),\r\n                              labels=c(Dem="Democrats", REP="Republicans"))\r\ngg <- gg + guides(color="none", fill=guide_legend(override.aes=list(alpha=1)))\r\ngg <- gg + scale_fill_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),\r\n                             labels=c(Dem="Democrats", REP="Republicans"))\r\ngg <- gg + facet_wrap(~year, ncol=2, scales="free_x")\r\ngg <- gg + labs(x=NULL, y=NULL,\r\n                title="Political Polarization, 1994-2014 (General Population)",\r\n                caption="Source: http:\/\/www.people-press.org\/2014\/06\/12\/section-1-growing-ideological-consistency\/iframe\/")\r\ngg <- gg + theme_hrbrmstr_an(grid="")\r\ngg <- gg + theme(panel.margin=margin(t=30, b=30, l=30, r=30))\r\ngg <- gg + theme(legend.position=c(0.75, 0.1))\r\ngg <- gg + theme(legend.direction="horizontal")\r\ngg <- gg + theme(axis.text.y=element_blank())\r\ngg\r\n\r\ngg <- ggplot()\r\ngg <- gg + geom_ribbon(data=party_engaged_data_long,\r\n                       aes(x=x, ymin=0, ymax=pct, fill=party, color=party))\r\ngg <- gg + geom_ribbon(data=engaged_sect, aes(x=x, ymin=0, ymax=y), color="#666979", fill="#666979")\r\ngg <- gg + scale_x_continuous(expand=c(0,0), breaks=c(-8, 0, 8),\r\n                              labels=c("Consistently\\nliberal", "Mixed", "Consistently\\nconservative"))\r\ngg <- gg + scale_y_continuous(expand=c(0,0), limits=c(0, 12))\r\ngg <- gg + scale_color_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),\r\n                              labels=c(Dem="Democrats", REP="Republicans"))\r\ngg <- gg + guides(color="none", fill=guide_legend(override.aes=list(alpha=1)))\r\ngg <- gg + scale_fill_manual(name=NULL, values=c(Dem="#728ea2", REP="#cf6a5d"),\r\n                             labels=c(Dem="Democrats", REP="Republicans"))\r\ngg <- gg + facet_wrap(~year, ncol=2, scales="free_x")\r\ngg <- gg + labs(x=NULL, y=NULL,\r\n                title="Political Polarization, 1994-2014 (Politically Active)",\r\n                caption="Source: http:\/\/www.people-press.org\/2014\/06\/12\/section-1-growing-ideological-consistency\/iframe\/")\r\ngg <- gg + theme_hrbrmstr_an(grid="")\r\ngg <- gg + theme(panel.margin=margin(t=30, b=30, l=30, r=30))\r\ngg <- gg + theme(legend.position=c(0.75, 0.1))\r\ngg <- gg + theme(legend.direction="horizontal")\r\ngg <- gg + theme(axis.text.y=element_blank())\r\ngg<\/code><\/pre>\n
\"genfull\"<\/a><\/p>\n
\"engfull\"<\/a><\/p>\n
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.) <\/p>\n
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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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): 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 […]<\/p>\n","protected":false},"author":1,"featured_media":4509,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"activitypub_content_warning":"","activitypub_content_visibility":"","activitypub_max_image_attachments":3,"activitypub_interaction_policy_quote":"anyone","activitypub_status":"","footnotes":""},"categories":[678,673,674,753,91],"tags":[810],"class_list":["post-4501","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-visualization","category-datavis-2","category-dataviz","category-ggplot","category-r","tag-post"],"yoast_head":"\nBridging The Political [Polygons] Gap with ggplot2 - 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Don't get me wrong, I can conjure up D3 maps pretty well [1] [2] and the utility of an interactive map\u2026","rel":"","context":"In "Charts & Graphs"","block_context":{"text":"Charts & Graphs","link":"https:\/\/rud.is\/b\/category\/charts-graphs\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":2846,"url":"https:\/\/rud.is\/b\/2013\/12\/27\/points-polygons-and-power-outages\/","url_meta":{"origin":4501,"position":3},"title":"Points, Polygons and Power Outages","author":"hrbrmstr","date":"2013-12-27","format":false,"excerpt":"Most of my free coding time has been spent tweaking a D3-based live power outage tracker for Central Maine Power customers (there's also a woefully less-featured Shiny app for it, too). 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This reaction surprised me, then someone pointed me to this link\u2026","rel":"","context":"In "Charts & Graphs"","block_context":{"text":"Charts & Graphs","link":"https:\/\/rud.is\/b\/category\/charts-graphs\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2016\/03\/supremes.png?fit=1200%2C987&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2016\/03\/supremes.png?fit=1200%2C987&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2016\/03\/supremes.png?fit=1200%2C987&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2016\/03\/supremes.png?fit=1200%2C987&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2016\/03\/supremes.png?fit=1200%2C987&ssl=1&resize=1050%2C600 3x"},"classes":[]}],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/posts\/4501","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/comments?post=4501"}],"version-history":[{"count":0,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/posts\/4501\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/media\/4509"}],"wp:attachment":[{"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/media?parent=4501"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/categories?post=4501"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/tags?post=4501"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}