Short Attention Span Theatre: Reproducing Axios’ “1 Big Thing” Google Trends 2019 News In Review with {ggplot2} - Security Boulevard

Short Attention Span Theatre: Reproducing Axios’ “1 Big Thing” Google Trends 2019 News In Review with {ggplot2}

I woke up to Axios’ “1 Big Thing” ridgeline chart showing the crazy that was the 2019 news cycle:

and, I decided to reproduce it in {ggplot2}.

Getting The Data

First, I had to find the data. The Axios chart is interactive, so I assumed the visualization was built on-load. It was, but the data was embedded in a javascript file vs loaded as JSON via an XHR request:

which was easy enough to turn into JSON anyone can use.

library(ggalt)library(hrbrthemes) # hrbrmstr/hrbrthemeslibrary(tidyverse)jsonlite::fromJSON("https://rud.is/dl/2019-axios-news.json") %>%   as_tibble() -> xdfxdf## # A tibble: 31 x 3##    name                    avg data      ##    <chr>                 <dbl> <list>    ##  1 Gov't shutdown        20.5  <int [51]>##  2 Mexico-U.S. border    22.8  <int [51]>##  3 Green New Deal        11.3  <int [51]>##  4 Blackface              9.61 <int [51]>##  5 N. Korea-Hanoi Summit 11.2  <int [51]>##  6 Boeing 737 Max         4.79 <int [51]>##  7 Brexit                28.5  <int [51]>##  8 Israel                42.1  <int [51]>##  9 SpaceX                24.1  <int [51]>## 10 Game of Thrones       16.8  <int [51]>## # … with 21 more rows

This is pretty tidy already, but we’ll need to expand the data column and give each week an index:

unnest(xdf, data) %>%   group_by(name) %>%   mutate(idx = 1:n()) %>%   ungroup() %>%   mutate(name = fct_inorder(name)) -> xdf # making a factor foe strip/panel ordering xdf## # A tibble: 1,581 x 4##    name             avg  data   idx##    <fct>          <dbl> <int> <int>##  1 Gov't shutdown  20.5    69     1##  2 Gov't shutdown  20.5   100     2##  3 Gov't shutdown  20.5    96     3##  4 Gov't shutdown  20.5   100     4##  5 Gov't shutdown  20.5    19     5##  6 Gov't shutdown  20.5     9     6##  7 Gov't shutdown  20.5    17     7##  8 Gov't shutdown  20.5     3     8##  9 Gov't shutdown  20.5     2     9## 10 Gov't shutdown  20.5     1    10## # … with 1,571 more rows

We’ll take this opportunity to find the first week of each month (via rle()) so we can have decent axis labels:

# get index placement for each month axis labelsprintf("2019-%02s-1", 1:51) %>%   as.Date(format = "%Y-%W-%w") %>%   format("%b") %>%   rle() -> monsmons## Run Length Encoding##   lengths: int [1:12] 4 4 4 5 4 4 5 4 5 4 ...##   values : chr [1:12] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" ...month_idx <- cumsum(mons$lengths)-3month_idx##  [1]  1  5  9 14 18 22 27 31 36 40 44 48

We’ve got all we need to make a {ggplot2} version of the chart. Here’s the plan:

  • use geom_area() and map colour and fill to avg (like Axios did), using an medium alpha value so we can still see below the overlapped areas
  • also use an xspline() stat with geom_area() so we get smooth lines vs pointy ones
  • use geom_hline() vs an axis line so we can map a colour aesthetic to avg as well
  • make a custom x-axis scale so we can place the labels we just made
  • expand the y-axis upper limit to avoid cutting off any part of the geoms
  • use the inferno viridis palette, but not the extremes of it
  • make facets/panels on the name, positioning the labels on the left
  • finally, tweak strip positioning so we get overlapped charts
ggplot(xdf, aes(idx, data)) +  geom_area(alpha = 1/2, stat = "xspline", aes(fill = avg, colour = avg)) +  geom_hline(    data = distinct(xdf, name, avg),    aes(yintercept = 0, colour = avg), size = 0.5  ) +  scale_x_continuous(    expand = c(0,0.125), limits = c(1, 51),    breaks = month_idx, labels = month.abb  ) +  scale_y_continuous(expand = c(0,0), limits = c(0, 105)) +  scale_colour_viridis_c(option = "inferno", direction = -1, begin = 0.1, end = 0.9) +  scale_fill_viridis_c(option = "inferno", direction = -1, begin = 0.1, end = 0.9) +  facet_wrap(~name, ncol = 1, strip.position = "left", dir = "h") +  labs(    x = NULL, y = NULL, fill = NULL, colour = NULL,    title = "1 big thing: The insane news cycles of 2019",    subtitle = "Height is search interest in a given topic, indexed to 100.\nColor is average search interest between Dec. 30, 2018–Dec. 20, 2019",    caption = "Source: Axios <https://www.axios.com/newsletters/axios-am-1d9cd913-6142-43b8-9186-4197e6da7669.html?chunk=0#story0>\nData: Google News Lab. Orig. Chart: Danielle Alberti/Axios"  ) +  theme_ipsum_es(grid="X", axis = "") +  theme(strip.text.y = element_text(angle = 180, hjust = 1, vjust = 0)) +  theme(panel.spacing.y = unit(-0.5, "lines")) +  theme(axis.text.y = element_blank()) +  theme(legend.position = "none")

To produce this finished product:

FIN

The chart could be tweaked a bit more to get even closer to the Axios finished product.

Intrepid readers can also try to use {plotly} to make an interactive version.

Somehow, I get the feeling 2020 will have an even more frenetic news cycle.


*** This is a Security Bloggers Network syndicated blog from rud.is authored by hrbrmstr. Read the original post at: https://rud.is/b/2019/12/27/short-attention-span-theatre-reproducing-axios-1-big-thing-google-trends-2019-news-in-review-with-ggplot2/