Twitter in collage: a year in the life of a freshman Congresswoman

Introduction

A super simple post that summarizes R-based methods for visual summary & collage-building using image attachments on Twitter. In the process, a bit of a photo homage to Congresswoman Xochitl Torres Small in her first year representing New Mexico’s 2nd district.

if (!require("pacman")) install.packages("pacman")pacman::p_load(tidyverse, rtweet, tigris) options(tigris_use_cache = TRUE, tigris_class = "sf")

New Mexico’s 2nd District

The 2nd congressional district of New Mexico is a super fun district. It is not my district, but I have a few stomping grounds that way. Faywood is home to natural hot springs and a STAR-GAZING CHAIR!!!. Weed has an absolutely lovely frisbee golf course. Ruidoso has all things: including two disc golf courses and a horse track.

Folks in the district supported Trump in 2016 by a fair margin (+10.2%) and subsequently sent a freshman Democrat to the House in 2018. Only the second time the district has sent a Democrat to the House in the last 30 years. Also, it is one of only five districts that supported Trump by more than ten points, supported McCain in 2008 & Romney in 2012, and sent a Democrat to Congress in 2018. I have written some previously about Torres Small’s win in 2018 and the demographics of the district.

So, a complicated & ideologically diverse district. And Congresswoman Torres Small does an amazing job representing this diversity. She is one of the few House Dems that engages with Fox News, eg, writing op-eds and doing interviews. And she is a super positive Twitter follow if you are interested in feeling good about the folks that represent us in Congress.

The district is also geographically vast – and super-rural. Per the table below, NM-02 is the fifth largest district in the country – only the big rural states with at-large representation are bigger. So, lots of ground to cover.

cds <- tigris::congressional_districts(cb = TRUE)cds %>%  data.frame() %>%  arrange(desc(ALAND)) %>%  slice(1:5) %>%  mutate(ALAND = round(ALAND/ 2.59e+6,0), # SQUARE MILES         ALAND = format(ALAND,                        big.mark=",",                        scientific=FALSE),         geo = c('Alaska', 'Montana',                 'Wyoming', 'South Dakota',                 'New Mexico - 02')) %>%  select(geo, GEOID, ALAND)%>%  knitr::kable()

One hundred days into her term in the 116th Congress, Congresswoman Torres Small tweeted:

In #100Days, I’ve over 43,000 miles & met w/ constituents in

Bernalillo Catron Chaves Cibola De Baca Dona Ana Eddy Grant Guadalupe Hidalgo Lea Lincoln Luna McKinley Otero Roosevelt Sierra Socorro Valencia

And I’m just getting started

So, she is on the move. The map below highlights the district in geographical context. Lots of big districts in the Southwest. NM-02 is roughly bounded by Mexico, West Texas, ABQ/Santa Fe metros, and the Navajo Nation. A bit of cultural crossroads, as it were.

world <- rnaturalearth::ne_countries(scale = "medium", returnclass = "sf") %>%  filter(gu_a3 %in% c('USA', 'MEX'))states <- tigris::states(cb = TRUE)cds %>%  mutate(color = ifelse(GEOID == '3502', 'blue', 'gray')) %>%  ggplot() +   geom_sf(data = world,  color = 'darkgray', alpha = .75,            fill = '#dae2ba', size = 1.1) +   geom_sf(aes(fill = color), color = 'darkgray') +   scale_fill_manual(values = c('lightblue', 'gray')) +  ggsflabel::lims_bbox(cds %>%                         filter(STATEFP %in% c('04', '48', '35', '32'))) +  geom_sf(data = states,  color = 'darkgray', alpha = 0,  size = 1.1) +   theme(legend.position = 'none') +  ggtitle('NM-02 in context')

Collaging the year’s happenings

So, the goal here is to provide a visual summary (ie, collage) of Congresswoman Torres Small’s year representing NM-02 using images from Twitter. Via the rtweet package, we collect all of @RepTorresSmall tweets since she took office at the beginning of 2019.

xochitl_tweets <- rtweet::get_timeline(   "RepTorresSmall",   n = 1500,  check=FALSE) %>%  mutate(created_at = as.Date(gsub(' .*$', '', created_at))) %>%  filter(is_quote == 'FALSE' &            is_retweet == 'FALSE' &            created_at > '2019-01-02' &           display_text_width > 0)

Next, we identify tweets containing photo attachments. And then download these photos locally. The code presented here has been modified directly from this post. For a more detailed walk through of methods, I would recommend having a look.

pics <- xochitl_tweets %>%  filter(!is.na(media_url)) %>%  select(media_url, created_at)setwd(local_pics)lapply(pics$media_url, function (y) {  magick::image_read(y) %>%    magick::image_scale("1000") %>%    magick::image_border('white', '10x10') %>%    magick::image_write(gsub('^.*/', '', y)) #%>%    #magick::image_annotate(pics$created_at[y], font = 'Times', size = 50)  })

Next, we shuffle the photos some, and then stack photos as a collection of single column collages. Again, these intermediary files are stored locally.

files <- dir(local_pics, full.names = TRUE)set.seed(11)files <- sample(files, length(files))

files1 <- files[1:49]no_rows <- 7no_cols <- 7make_column <- function(i, files, no_rows){  magick::image_read(files[(i*no_rows+1):((i+1)*no_rows)]) %>%    magick::image_append(stack = TRUE) %>%    magick::image_write(paste0("cols", i, ".jpg"))}setwd(local_cols)walk(0:(no_cols-1),      make_column,      files = files1,     no_rows = no_rows)

Lastly, we piece together the column collages as a single collage. For good measure, I created three collages comprised of 7 x 7 = 49 photos. A busy year for the Congresswoman.

magick::image_read(dir(local_cols, full.names = TRUE)) %>%   magick::image_scale("500x1000") %>%  magick::image_append(stack = FALSE)