US Broadband Access
Jim Gruman
May 5, 2021
Last updated: 2021-10-07
Checks: 7 0
Knit directory: myTidyTuesday/
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The #TidyTuesday data this week is on Internet Access, from Microsoft by way of The Verge.
If broadband access was a problem before 2020, the pandemic turned it into a crisis. As everyday businesses moved online, city council meetings or court proceedings became near-inaccessible to anyone whose connection couldn’t support a Zoom call. Some school districts started providing Wi-Fi hotspots to students without a reliable home connection. In other districts, kids set up in McDonald’s parking lots just to get a reliable enough signal to do their homework. After years of slowly widening, the broadband gap became impossible to ignore.
First, let’s load libraries and set a ggplot theme:
options(tigris_use_cache = TRUE)
suppressPackageStartupMessages({
library(tidyverse)
library(hrbrthemes)
library(tidycensus)
library(sf)
library(patchwork)
})
source(here::here("code","_common.R"),
verbose = FALSE,
local = knitr::knit_global())Registered S3 method overwritten by 'tune':
method from
required_pkgs.model_spec parsnipggplot2::theme_set(theme_jim(base_size = 12))Loading the data
broadband_il <- tidytuesdayR::tt_load("2021-05-11")$broadband %>%
janitor::clean_names() %>%
filter(st == "IL") %>%
transmute(
GEOID = as.character(county_id),
usage = parse_number(broadband_usage, na = "-")
) %>%
left_join(get_acs(
geography = "county",
variables = "B19013_001",
state = "IL",
geometry = TRUE
))
Downloading file 1 of 2: `broadband.csv`
Downloading file 2 of 2: `broadband_zip.csv`plot_il <- function(variable) {
broadband_il %>%
st_as_sf() %>%
ggplot(aes(fill = {{ variable }})) +
geom_sf(color = "gray80", size = 0.2) +
coord_sf(
label_axes = "----",
label_graticule = "----"
) +
theme(panel.grid.major = element_blank())
}
plot_il(estimate) +
scale_fill_viridis_b(
option = "plasma",
n.breaks = 8,
labels = scales::comma
) +
labs(
subtitle = "Median Household Income",
fill = "$US"
) +
plot_il(usage) +
scale_fill_viridis_b(
n.breaks = 8,
labels = scales::percent_format(accuracy = 1)
) +
labs(
subtitle = "High Speed Broadband Usage",
fill = NULL
) +
plot_annotation(
title = "High Speed Broadband in Illinois",
caption = "Data Sources: Microsoft and US Census ACS 2019 \n
Percent of people per county that use the internet at more than 25 Mbps/3 Mbps"
) &
theme(
plot.margin = margin(0.5, 0, 0, 0, "cm"),
plot.title = element_text(size = 30)
)
Related tweets and inspirations:
tweetrmd::include_tweet("https://twitter.com/jrosecalabrese/status/1392299000401858563")My first contribution to #TidyTuesday — I compared Internet usage in my home state versus my current statehttps://t.co/Hh0yHclKvP pic.twitter.com/FjHmyonpEm
— Julianna Calabrese (@jrosecalabrese) May 12, 2021
tweetrmd::include_tweet("https://twitter.com/juliasilge/status/1392324410082689026")This week's #TidyTuesday is about internet access across the US 👩🏽💻🧑🏻💻👨🏿💻 and I can hardly resist making maps of my home state of TX!!
— Julia Silge (@juliasilge) May 12, 2021
I used tidycensus to get median household income data 💰 for comparison; #rstats code is here:https://t.co/2vc5sXjwD7 pic.twitter.com/snHcGRnRAs
tweetrmd::include_tweet("https://twitter.com/jim_gruman/status/1392486129786572801")My contribution to this week's #TidyTuesday on broadband internet access 🖥️💻🧑🎓👩🌾 in Illinoishttps://t.co/rQmigbCZCI pic.twitter.com/JFrFAQkQPn
— Jim Gruman📚🚵♂️⚙ (@jim_gruman) May 12, 2021
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.1.1 sf_1.0-2 tidycensus_1.1 hrbrthemes_0.8.0
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[9] readr_2.0.2 tidyr_1.1.4 tibble_3.1.4 ggplot2_3.3.5
[13] tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 uuid_0.1-4 backports_1.2.1
[4] systemfonts_1.0.2 workflows_0.2.3 selectr_0.4-2
[7] plyr_1.8.6 tidytuesdayR_1.0.1 sp_1.4-5
[10] splines_4.1.1 listenv_0.8.0 usethis_2.0.1
[13] digest_0.6.28 foreach_1.5.1 htmltools_0.5.2
[16] yardstick_0.0.8 viridis_0.6.1 parsnip_0.1.7.900
[19] fansi_0.5.0 magrittr_2.0.1 tune_0.1.6
[22] tzdb_0.1.2 recipes_0.1.17 globals_0.14.0
[25] modelr_0.1.8 gower_0.2.2 extrafont_0.17
[28] vroom_1.5.5 R.utils_2.11.0 extrafontdb_1.0
[31] hardhat_0.1.6 rsample_0.1.0 dials_0.0.10
[34] colorspace_2.0-2 rvest_1.0.1 rappdirs_0.3.3
[37] textshaping_0.3.5 haven_2.4.3 xfun_0.26
[40] rgdal_1.5-27 crayon_1.4.1 jsonlite_1.7.2
[43] survival_3.2-11 tigris_1.5 iterators_1.0.13
[46] glue_1.4.2 gtable_0.3.0 ipred_0.9-12
[49] R.cache_0.15.0 tweetrmd_0.0.9 Rttf2pt1_1.3.8
[52] future.apply_1.8.1 scales_1.1.1 infer_1.0.0
[55] DBI_1.1.1 Rcpp_1.0.7 viridisLite_0.4.0
[58] units_0.7-2 bit_4.0.4 GPfit_1.0-8
[61] foreign_0.8-81 proxy_0.4-26 lava_1.6.10
[64] prodlim_2019.11.13 httr_1.4.2 wk_0.5.0
[67] ellipsis_0.3.2 farver_2.1.0 R.methodsS3_1.8.1
[70] pkgconfig_2.0.3 nnet_7.3-16 sass_0.4.0
[73] dbplyr_2.1.1 janitor_2.1.0 utf8_1.2.2
[76] here_1.0.1 labeling_0.4.2 tidyselect_1.1.1
[79] rlang_0.4.11 DiceDesign_1.9 later_1.3.0
[82] cachem_1.0.6 munsell_0.5.0 cellranger_1.1.0
[85] tools_4.1.1 cli_3.0.1 generics_0.1.0
[88] broom_0.7.9 evaluate_0.14 fastmap_1.1.0
[91] ragg_1.1.3 yaml_2.2.1 bit64_4.0.5
[94] knitr_1.36 fs_1.5.0 workflowsets_0.1.0
[97] s2_1.0.7 future_1.22.1 whisker_0.4
[100] R.oo_1.24.0 xml2_1.3.2 compiler_4.1.1
[103] rstudioapi_0.13 curl_4.3.2 e1071_1.7-9
[106] reprex_2.0.1 lhs_1.1.3 bslib_0.3.0
[109] stringi_1.7.5 highr_0.9 gdtools_0.2.3
[112] lattice_0.20-44 Matrix_1.3-4 styler_1.6.2
[115] classInt_0.4-3 conflicted_1.0.4 vctrs_0.3.8
[118] tidymodels_0.1.4 pillar_1.6.3 lifecycle_1.0.1
[121] furrr_0.2.3 jquerylib_0.1.4 maptools_1.1-2
[124] httpuv_1.6.3 R6_2.5.1 promises_1.2.0.1
[127] KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.28.1
[130] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1
[133] rprojroot_2.0.2 withr_2.4.2 parallel_4.1.1
[136] hms_1.1.1 grid_4.1.1 rpart_4.1-15
[139] timeDate_3043.102 class_7.3-19 snakecase_0.11.0
[142] rmarkdown_2.11 git2r_0.28.0 pROC_1.18.0
[145] lubridate_1.7.10