Public Radio Stations

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Last updated: 2022-11-19

Checks: 7 0

Knit directory: myTidyTuesday/

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---

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Ignored files:
    Ignored:    .Rhistory
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    Ignored:    data/.Rhistory
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    Ignored:    data/2022_11_18.png
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    Ignored:    data/weatherstats_toronto_daily.csv

Untracked files:
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    Untracked:  code/YammerReach.R
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Unstaged changes:
    Modified:   analysis/2021_01_19_tidy_tuesday.Rmd
    Modified:   analysis/2021_03_24_tidy_tuesday.Rmd
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    Deleted:    analysis/2022_02_11_tabular_playground.Rmd
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    Modified:   analysis/EnglishLanguageLearning.Rmd
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/2022_11_18_tidy_tuesday.Rmd) and HTML (docs/2022_11_18_tidy_tuesday.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	81ec898	opus1993	2022-11-19	wflow_publish("analysis/2022_11_18_tidy_tuesday.Rmd")
html	3be0fee	opus1993	2022-11-19	Build site.
Rmd	4bba3a9	opus1993	2022-11-19	wflow_publish("analysis/2022_11_18_tidy_tuesday.Rmd")
html	6664762	opus1993	2022-11-19	Build site.
Rmd	f3e8b10	opus1993	2022-11-19	wflow_publish("analysis/2022_11_18_tidy_tuesday.Rmd")
html	9298870	opus1993	2022-11-19	Build site.
Rmd	de73731	opus1993	2022-11-19	wflow_publish("analysis/2022_11_18_tidy_tuesday.Rmd")

---

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
And a link for data to reliably link the two TT data sets: https://t.co/QHS2Y6cItR pic.twitter.com/i7G7Y8jvz1

— DataFestND (@NdFest) November 13, 2022

We will start by loading a few R packages into memory:

suppressPackageStartupMessages({
library(tidyverse)
library(sf)
library(tigris)
})
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 site 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)
  
}

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

Let’s tweet the result

rtweet::post_tweet(
  status = "Last week's #TidyTuesday looked at radio stations around the United States and their reach. The code can be found here: https://opus1993.github.io/myTidyTuesday/. Credit to @frankiethull for the dataset and @kyle_e_walker for {tigris}. #rstats #r4ds",
  media = here::here("docs","figure", "plot_submission-1.png"),
  token = NULL,
  in_reply_to_status_id = NULL,
  destroy_id = NULL,
  retweet_id = NULL,
  auto_populate_reply_metadata = FALSE,
  media_alt_text = "A US map with shapefile polygons representing public radio station reach and labels for each station.",
  lat = NULL,
  long = NULL,
  display_coordinates = FALSE
)

tweetrmd::include_tweet("https://twitter.com/jim_gruman/status/1594044196322787329")

Last week's #TidyTuesday looked at radio stations around the United States and their reach. The code can be found here: https://t.co/Oa7V53k3uf. Credit to @frankiethull for the dataset and @kyle_e_walker for {tigris}. #rstats #r4ds pic.twitter.com/MiWnPkp04k

— 🧢📚🚵‍♂️⚙📈☕ (@jim_gruman) November 19, 2022

Session information

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] tigris_1.6.1    sf_1.0-8        forcats_0.5.2   stringr_1.4.1  
 [5] dplyr_1.0.10    purrr_0.3.5     readr_2.1.3     tidyr_1.2.1    
 [9] tibble_3.1.8    ggplot2_3.4.0   tidyverse_1.3.2 workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] fs_1.5.2            usethis_2.1.6       bit64_4.0.5        
 [4] lubridate_1.9.0     httr_1.4.4          rprojroot_2.0.3    
 [7] tools_4.2.2         backports_1.4.1     bslib_0.4.1        
[10] rgdal_1.5-32        utf8_1.2.2          R6_2.5.1           
[13] KernSmooth_2.23-20  DBI_1.1.3           colorspace_2.0-3   
[16] withr_2.5.0         sp_1.5-1            tidyselect_1.2.0   
[19] processx_3.8.0      bit_4.0.4           curl_4.3.3         
[22] compiler_4.2.2      git2r_0.30.1        cli_3.4.1          
[25] tweetrmd_0.0.9      rvest_1.0.3         xml2_1.3.3         
[28] sass_0.4.2          scales_1.2.1        arrow_10.0.0       
[31] classInt_0.4-8      callr_3.7.3         proxy_0.4-27       
[34] rappdirs_0.3.3      digest_0.6.30       foreign_0.8-83     
[37] rmarkdown_2.17      tidytuesdayR_1.0.2  pkgconfig_2.0.3    
[40] htmltools_0.5.3     highr_0.9           dbplyr_2.2.1       
[43] fastmap_1.1.0       ggthemes_4.2.4      rlang_1.0.6        
[46] readxl_1.4.1        rstudioapi_0.14     farver_2.1.1       
[49] jquerylib_0.1.4     generics_0.1.3      jsonlite_1.8.3     
[52] vroom_1.6.0         googlesheets4_1.0.1 magrittr_2.0.3     
[55] s2_1.1.0            Rcpp_1.0.9          munsell_0.5.0      
[58] fansi_1.0.3         lifecycle_1.0.3     stringi_1.7.8      
[61] whisker_0.4         yaml_2.3.6          grid_4.2.2         
[64] maptools_1.1-5      parallel_4.2.2      promises_1.2.0.1   
[67] crayon_1.5.2        lattice_0.20-45     haven_2.5.1        
[70] hms_1.1.2           knitr_1.40          ps_1.7.2           
[73] pillar_1.8.1        uuid_1.1-0          wk_0.7.0           
[76] reprex_2.0.2        glue_1.6.2          evaluate_0.18      
[79] getPass_0.2-2       modelr_0.1.9        selectr_0.4-2      
[82] vctrs_0.5.0         tzdb_0.3.0          httpuv_1.6.6       
[85] cellranger_1.1.0    gtable_0.3.1        assertthat_0.2.1   
[88] cachem_1.0.6        xfun_0.34           broom_1.0.1        
[91] e1071_1.7-12        later_1.3.0         class_7.3-20       
[94] googledrive_2.0.0   gargle_1.2.1        units_0.8-0        
[97] timechange_0.1.1    ellipsis_0.3.2      here_1.0.1