Public Radio Stations
2022-11-18
Last updated: 2022-11-19
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Knit directory: myTidyTuesday/
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The data this week comes from Wikipedia and the Federal Communications Commission, with a credit to Frank Hull for proposing and cleaning the initial dataset.
An example of one of the submissions to Twitter:
tweetrmd::include_tweet("https://twitter.com/NdFest/status/1591803647520018432")Where is all the Public Radio? This week's #TidyTuesday data looked at radio stations around the United States and their reach. The code can be found here: https://t.co/M85kpXlEO8
— DataFestND (@NdFest) November 13, 2022
And a link for data to reliably link the two TT data sets: https://t.co/QHS2Y6cItR pic.twitter.com/i7G7Y8jvz1
We will start by loading a few R packages into memory:
suppressPackageStartupMessages({
library(tidyverse)
library(sf)
library(tigris)
library(geomtextpath)
})
options(tigris_use_cache = TRUE)We will load the data as provided by R4DS and use their cleaing script:
tuesdata <- tidytuesdayR::tt_load('2022-11-08')--- Compiling #TidyTuesday Information for 2022-11-08 ------- There are 2 files available ------ Starting Download ---
Downloading file 1 of 2: `state_stations.csv`
Downloading file 2 of 2: `station_info.csv`--- Download complete ---state_stations <- tuesdata$state_stations |>
right_join(tuesdata$station_info |>
select(-licensee),
by = c("call_sign")) |>
filter(stringr::str_detect(format, 'Public'))
contour_zip_url <- "https://transition.fcc.gov/Bureaus/MB/Databases/fm_service_contour_data/FM_service_contour_current.zip"
contour_zip_file <- here::here("data","FM_service_contour_current.zip")
if (!file.exists(contour_zip_file)) {
download.file(contour_zip_url,
destfile = contour_zip_file)
}
raw_contour_feather <- here::here("data","raw_contour.feather")
if (!file.exists(raw_contour_feather)) {
raw_contour <- read_delim(
contour_zip_file,
delim = "|",
show_col_types = FALSE
) |>
select(-last_col()) |>
set_names(nm = c(
"application_id", "service", "lms_application_id", "dts_site_number", "transmitter_site",
glue::glue("deg_{0:360}")
)) |>
separate(
transmitter_site,
into = c("site_lat", "site_long"),
sep = " ,") |>
pivot_longer(
names_to = "angle",
values_to = "values",
cols = deg_0:deg_360
) |>
mutate(
angle = str_remove(angle, "deg_"),
angle = as.integer(angle)
) |>
separate(
values,
into = c("deg_lat", "deg_lng"),
sep = " ,"
) |>
mutate(
across(c(application_id,
site_lat,
site_long,
deg_lat,
deg_lng),
as.numeric))
arrow::write_feather(raw_contour,
sink = raw_contour_feather)
} else {
raw_contour <- arrow::read_feather(raw_contour_feather,
as_data_frame = TRUE)
}
contour_sf <- raw_contour |>
na.omit() |>
st_as_sf(coords = c("deg_lng", "deg_lat"), crs = 4326) |>
group_by(application_id) |>
slice_tail(n = 360) |>
summarise(geometry = st_combine(geometry)) |>
st_cast("POLYGON")The following loops through the call letters belonging to the different application IDs so that the two datasets that were provided on Tidy Tuesday can be linked.
As above, we will cache results in a feather file to avoid hitting the fcc web page unnecessarily.
application_id_feather <- here::here("data","application_id.feather")
if (!file.exists(application_id_feather)) {
application_id <- tibble(
application_id = unique(raw_contour$application_id),
call_sign = NA_character_)
site <- "https://licensing.fcc.gov/cgi-bin/ws.exe/prod/cdbs/pubacc/prod/app_det.pl?Application_id="
call_sign_extract <- function(application_id) {
Sys.sleep(sample(10, 1) * 0.02)
paste0(site, application_id) |>
rvest::read_html() |>
rvest::html_nodes("td") %>%
.[[20]] |>
rvest::html_text() |>
stringr::str_replace_all("\n", "") |>
stringr::str_squish()
}
application_id <- application_id |>
mutate(call_sign = map_chr(application_id, call_sign_extract))
arrow::write_feather(application_id,
sink = application_id_feather)
} else {
application_id <- arrow::read_feather(application_id_feather)
}state_stations |>
inner_join(application_id, by = c("call_sign")) |>
inner_join(contour_sf, by = "application_id") public_radio <- contour_sf %>%
inner_join(application_id, by = "application_id") |>
inner_join(state_stations, by = "call_sign") |>
shift_geometry()
ggplot() +
geom_sf(data = tigris::states(cb = TRUE) |>
filter(STUSPS %in% c(state.abb,"DC")) |>
shift_geometry(),
color = "gray70",
fill = "gray95") +
geom_sf(data = tigris::metro_divisions(),
color = "gray70",
fill = "orange" ) +
geom_sf(data = public_radio,
fill = NA,
color = "darkblue"
) +
geom_sf_text(data = public_radio ,
aes(label = call_sign),
size = 1.5,
fontface = "bold",
color = "darkblue",
check_overlap = TRUE) +
coord_sf(default_crs = sf::st_crs(public_radio)) +
ggthemes::theme_map() +
theme(plot.title = element_text(hjust = 0.5,
size = 20, face = "bold"),
plot.background = element_rect(fill = "lightblue")) +
labs(title = "Public Radio Station Coverage in the United States",
caption = "DataViz by @jim_gruman | Data from US FCC via Frank Hull | Based on TorvicialND @NdFest submission") Retrieving data for the year 2020
Retrieving data for the year 2020
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Let’s tweet the result
ggsave(here::here("data","2022_11_18.png"),
width = 6, height = 5, dpi = 300, bg = "black",
device = "png")tweetrmd::include_tweet("https://twitter.com/NdFest/status/1591803647520018432")Where is all the Public Radio? This week's #TidyTuesday data looked at radio stations around the United States and their reach. The code can be found here: https://t.co/M85kpXlEO8
— DataFestND (@NdFest) November 13, 2022
And a link for data to reliably link the two TT data sets: https://t.co/QHS2Y6cItR pic.twitter.com/i7G7Y8jvz1
sessionInfo()R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22621)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] geomtextpath_0.1.1 tigris_1.6.1 sf_1.0-8 forcats_0.5.2
[5] stringr_1.4.1 dplyr_1.0.10 purrr_0.3.5 readr_2.1.3
[9] tidyr_1.2.1 tibble_3.1.8 ggplot2_3.4.0 tidyverse_1.3.2
[13] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 colorspace_2.0-3 selectr_0.4-2
[4] ellipsis_0.3.2 class_7.3-20 rgdal_1.5-32
[7] rprojroot_2.0.3 fs_1.5.2 rstudioapi_0.14
[10] proxy_0.4-27 farver_2.1.1 bit64_4.0.5
[13] fansi_1.0.3 lubridate_1.9.0 xml2_1.3.3
[16] cachem_1.0.6 knitr_1.40 jsonlite_1.8.3
[19] broom_1.0.1 dbplyr_2.2.1 compiler_4.2.2
[22] httr_1.4.4 backports_1.4.1 assertthat_0.2.1
[25] fastmap_1.1.0 gargle_1.2.1 cli_3.4.1
[28] later_1.3.0 s2_1.1.0 htmltools_0.5.3
[31] tools_4.2.2 gtable_0.3.1 glue_1.6.2
[34] wk_0.7.0 ggthemes_4.2.4 rappdirs_0.3.3
[37] Rcpp_1.0.9 cellranger_1.1.0 jquerylib_0.1.4
[40] vctrs_0.5.0 xfun_0.34 ps_1.7.2
[43] rvest_1.0.3 timechange_0.1.1 lifecycle_1.0.3
[46] googlesheets4_1.0.1 getPass_0.2-2 scales_1.2.1
[49] vroom_1.6.0 hms_1.1.2 promises_1.2.0.1
[52] parallel_4.2.2 yaml_2.3.6 curl_4.3.3
[55] sass_0.4.2 tweetrmd_0.0.9 stringi_1.7.8
[58] highr_0.9 maptools_1.1-5 e1071_1.7-12
[61] rlang_1.0.6 pkgconfig_2.0.3 systemfonts_1.0.4
[64] evaluate_0.18 lattice_0.20-45 bit_4.0.4
[67] processx_3.8.0 tidyselect_1.2.0 here_1.0.1
[70] magrittr_2.0.3 tidytuesdayR_1.0.2 R6_2.5.1
[73] generics_0.1.3 DBI_1.1.3 pillar_1.8.1
[76] haven_2.5.1 whisker_0.4 foreign_0.8-83
[79] withr_2.5.0 units_0.8-0 sp_1.5-1
[82] modelr_0.1.9 crayon_1.5.2 uuid_1.1-0
[85] KernSmooth_2.23-20 utf8_1.2.2 tzdb_0.3.0
[88] rmarkdown_2.17 usethis_2.1.6 grid_4.2.2
[91] readxl_1.4.1 callr_3.7.3 git2r_0.30.1
[94] reprex_2.0.2 digest_0.6.30 classInt_0.4-8
[97] httpuv_1.6.6 arrow_10.0.0 textshaping_0.3.6
[100] munsell_0.5.0 bslib_0.4.1 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IDAuMDIpDQogIA0KICBwYXN0ZTAoc2l0ZSwgYXBwbGljYXRpb25faWQpICB8Pg0KICAgIHJ2ZXN0OjpyZWFkX2h0bWwoKSAgfD4NCiAgICBydmVzdDo6aHRtbF9ub2RlcygidGQiKSAgJT4lDQogICAgLltbMjBdXSB8Pg0KICAgIHJ2ZXN0OjpodG1sX3RleHQoKSB8Pg0KICAgIHN0cmluZ3I6OnN0cl9yZXBsYWNlX2FsbCgiXG4iLCAiIikgfD4NCiAgICBzdHJpbmdyOjpzdHJfc3F1aXNoKCkNCn0NCg0KYXBwbGljYXRpb25faWQgPC0gYXBwbGljYXRpb25faWQgfD4gDQogIG11dGF0ZShjYWxsX3NpZ24gPSBtYXBfY2hyKGFwcGxpY2F0aW9uX2lkLCBjYWxsX3NpZ25fZXh0cmFjdCkpIA0KDQphcnJvdzo6d3JpdGVfZmVhdGhlcihhcHBsaWNhdGlvbl9pZCwNCiAgICAgICAgICAgICAgICAgICAgIHNpbmsgPSBhcHBsaWNhdGlvbl9pZF9mZWF0aGVyKQ0KfSBlbHNlIHsNCg0KYXBwbGljYXRpb25faWQgPC0gYXJyb3c6OnJlYWRfZmVhdGhlcihhcHBsaWNhdGlvbl9pZF9mZWF0aGVyKQ0KICANCn0NCg0KYGBgDQoNCg0KYGBge3J9DQpzdGF0ZV9zdGF0aW9ucyB8PiAgDQogIGlubmVyX2pvaW4oYXBwbGljYXRpb25faWQsIGJ5ID0gYygiY2FsbF9zaWduIikpIHw+IA0KICBpbm5lcl9qb2luKGNvbnRvdXJfc2YsIGJ5ID0gImFwcGxpY2F0aW9uX2lkIikgDQogIA0KcHVibGljX3JhZGlvIDwtIGNvbnRvdXJfc2YgJT4lIA0KICBpbm5lcl9qb2luKGFwcGxpY2F0aW9uX2lkLCBieSA9ICJhcHBsaWNhdGlvbl9pZCIpIHw+IA0KICBpbm5lcl9qb2luKHN0YXRlX3N0YXRpb25zLCBieSA9ICJjYWxsX3NpZ24iKSB8PiANCiAgc2hpZnRfZ2VvbWV0cnkoKSANCg0KDQpnZ3Bsb3QoKSArDQogIGdlb21fc2YoZGF0YSA9IHRpZ3Jpczo6c3RhdGVzKGNiID0gVFJVRSkgfD4NCiAgICAgICAgICAgIGZpbHRlcihTVFVTUFMgJWluJSBjKHN0YXRlLmFiYiwiREMiKSkgfD4NCiAgICAgICAgICAgICBzaGlmdF9nZW9tZXRyeSgpLA0KICAgICAgICAgICAgY29sb3IgPSAiZ3JheTcwIiwNCiAgICAgICAgICAgIGZpbGwgPSAiZ3JheTk1IikgKw0KICBnZW9tX3NmKGRhdGEgPSB0aWdyaXM6Om1ldHJvX2RpdmlzaW9ucygpLA0KICAgICAgICAgIGNvbG9yID0gImdyYXk3MCIsDQogICAgICAgICAgZmlsbCA9ICJvcmFuZ2UiICkgKw0KICBnZW9tX3NmKGRhdGEgPSBwdWJsaWNfcmFkaW8sDQogICAgICAgICAgIGZpbGwgPSBOQSwNCiAgICAgICAgICAgY29sb3IgPSAiZGFya2JsdWUiDQogICAgICAgKSArDQogIGdlb21fc2ZfdGV4dChkYXRhID0gcHVibGljX3JhZGlvICwNCiAgICBhZXMobGFiZWwgPSBjYWxsX3NpZ24pLA0KICAgIHNpemUgPSAxLjUsDQogICAgZm9udGZhY2UgPSAiYm9sZCIsDQogICAgY29sb3IgPSAiZGFya2JsdWUiLA0KICAgIGNoZWNrX292ZXJsYXAgPSBUUlVFKSArDQogIGNvb3JkX3NmKGRlZmF1bHRfY3JzID0gc2Y6OnN0X2NycyhwdWJsaWNfcmFkaW8pKSAgKw0KICBnZ3RoZW1lczo6dGhlbWVfbWFwKCkgKw0KICB0aGVtZShwbG90LnRpdGxlID0gZWxlbWVudF90ZXh0KGhqdXN0ID0gMC41LA0KICAgICAgICBzaXplID0gMjAsIGZhY2UgPSAiYm9sZCIpLA0KICAgICAgICBwbG90LmJhY2tncm91bmQgPSBlbGVtZW50X3JlY3QoZmlsbCA9ICJsaWdodGJsdWUiKSkgKw0KICBsYWJzKHRpdGxlID0gIlB1YmxpYyBSYWRpbyBTdGF0aW9uIENvdmVyYWdlIGluIHRoZSBVbml0ZWQgU3RhdGVzIiwNCiAgICAgICBjYXB0aW9uID0gIkRhdGFWaXogYnkgQGppbV9ncnVtYW4gfCBEYXRhIGZyb20gVVMgRkNDIHZpYSBGcmFuayBIdWxsIHwgQmFzZWQgb24gVG9ydmljaWFsTkQgQE5kRmVzdCBzdWJtaXNzaW9uIikgDQoNCmBgYA0KDQpMZXQncyB0d2VldCB0aGUgcmVzdWx0DQoNCmBgYHtyfQ0KI3wgbGFiZWw6IHR3ZWV0IHRoZSByZXN1bHQNCiN8IGV2YWw6IGZhbHNlDQoNCmdnc2F2ZShoZXJlOjpoZXJlKCJkYXRhIiwiMjAyMl8xMV8xOC5wbmciKSwNCiAgICAgIHdpZHRoID0gNiwgaGVpZ2h0ID0gNSwgZHBpID0gMzAwLCBiZyA9ICJibGFjayIsDQogICAgICBkZXZpY2UgPSAicG5nIikNCg0KYGBgDQoNCg0KYGBge3J9DQp0d2VldHJtZDo6aW5jbHVkZV90d2VldCgiaHR0cHM6Ly90d2l0dGVyLmNvbS9OZEZlc3Qvc3RhdHVzLzE1OTE4MDM2NDc1MjAwMTg0MzIiKQ0KYGBgDQoNCg0K