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Don't look at me…I do what he does — just slower. #rstats avuncular • ?Resistance Fighter • Cook • Christian • [Master] Chef des Données de Sécurité @ @rapid7

I’ve been (mostly) keeping up with annual updates for my R/{sf} U.S. foliage post which you can find on GH. This year, we have Quarto, and it comes with so many batteries included that you’d think it was Christmas. One of those batteries is full support for the Observable runtime. These are used in {ojs} Quarto blocks, and rendered versions can run anywhere.

The Observable platform is great for both tinkering and publishing (we’re using it at work for some quick or experimental vis work), and with a few of the recent posts, here, showing how to turn Observable notebooks into Quarto documents, you’re literally two clicks or one command line away from using any public Observable notebook right in Quarto.

I made a version of the foliage vis in Observable and then did the qmd conversion using the Chrome extension, tweaked the source a bit and published the same in Quarto.

The interactive datavis uses some foundational Observable/D3 libraries:

In the JS code we set some datavis-centric values:

foliage_levels = [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
foliage_colors = ["#83A87C", "#FCF6B4", "#FDCC5C", "#F68C3F", "#EF3C23", "#BD1F29", "#98371F"]
foliage_labels = ["No Change", "Minimal", "Patchy", "Partial", "Near Peak", "Peak", "Past Peak"]
week_label = ["Sept 5th", "Sept 12th", "Sept 19th", "Sept 26th", "Oct 3rd", "Oct 10th", "Oct 17th", "Oct 24th", "Oct 31st", "Nov 7th", "Nov 14th", "Nov 21st"]

We then borrow the U.S. Albers-projected topojson file from the Choropleth example notebook and rebuild the outline mesh and county geometry collections, since we need to get rid of Alaska and Hawaii (they’re not present in the source data). We do this by filtering out two FIPS codes:

counties = {
  var cty = topojson.feature(us, us.objects.counties);
  cty.features = cty.features.filter(
    (d) => (d.id.substr(0, 2) != "02") & (d.id.substr(0, 2) != "15")
  );
  return cty;
}

I also ended up modifying the source CSV a bit to account for missing counties.

After that, it was a straightforward call to our imported Choropleth function:

chart = Choropleth(rendered2022, {
  id: (d) => d.id.toString().padStart(5, "0"), // this is needed since the CSV id column is numeric
  value: (d) => d[week_label.indexOf(week) + 1], // this gets the foliage value based on which index the selected week is at
  scale: d3.scaleLinear, // this says to map foliage_levels to foliage_colors directly
  domain: foliage_levels,
  range: foliage_colors,
  title: (f, d) =>
    `${f.properties.name}, ${statemap.get(f.id.slice(0, 2)).properties.name}`, // this makes the county hover text the county + state names
  features: counties, // this is the counties we modified
  borders: statemesh, // this is the statemesh
  width: 975,
  height: 610
})

and placing the legend and scrubbing slider.

The only real difference between the notebook and qmd is the inclusion of the source functions rather than use Observable’s import (I’ve found that there’s a slight load delay for imports when network conditions aren’t super perfect and the inclusion of the source — WITH copyrights — makes up for that).

I’ve set up the Quarto project so that renders go to the docs/ directory, which makes it easy to publish as a GH page.

FIN

Drop issues on GH if anything needs clarifying or fixing and go experiment! You can’t break anything either on Observable or locally that version control can’t fix (yes, Observable has version control!).

Some things to consider modifying/adding:

  • have a click take you to a (selectable?) mapping service, so folks can get driving directions
  • turn the hover text into a proper tooltip
  • speed up or slow down the animation when ‘Play’ is tapped
  • use different colors
  • bring in older datasets (see the foliage GH repo) and make multiple maps or let the user select them or have them compare across years

My previous post announced a Rust-based command line tool for generating Quarto projects from Observable Notebooks.

Some folks may not want to use yet-another command line tool, and it dawned on me that it’d be more convenient to just do the conversion in-browser when one is already on a Notebook page. So, I dusted off some very creaky Chrome extension skills and put together an extension for doing just that.

It’s pretty straightforward:

  • navigate to a Notebook you want to serialize to a Quarto project
  • press the button
  • profit!

You can download individual resources by hand or just use the zip file that automagically downloaded.

screen capture of observable notebook showing how to press the quartize button

screencap showing the aftermath of the quartize process with annotations

vs code screencap showing the downloaded quarto project in source and render

The previous post had some hacky R code to grab seekrit JSON data in ObservableHQ (OHQ) Notebooks and spit out a directory with a Quarto qmd and any associated FileAttachments. Holding firm to my “no more generic public R packages” decree, that’s as far as the R code for that utility is going to get.

Quarto, by design, is not married to the R ecosystem. This should help it get more traction in and outside of the broader data science crowd than R Markdown was able to attain. One big element that makes Quarto enticing to me is that it ships with the Observable runtime and stdlib. Observable javascript tools make coding up reactive data visualizations and analyses fun, but I still really dislike using a browser for data science work. I’d much rather use Quarto’s {ojs} sections in a proper editor/IDE when iterating over a concept.

I also learn best by example-first -> experiment-second. Up until now, I’ve been doing the example-ing and experiment-ing over at OHQ. When I discovered that the OHQ notebook code and metadata ships with the HTML page of the notebook (in a JSON <script> block at the end of the document) I just had to build a tool to yank that out and turn it into a Quarto project.

The R code in the previous post is fine for R folks (someone should 100% take that, make it nicer, and turn it into a small package with an RStudio addin and CLI wrapper; no credit back to me is required), but — as noted above — Quarto is not just for R folks. Rather than confine a conversion utility to some scripting language, I decided to port the R experiment over to Rust. That is how ohq2quarto was born.

You can grab Windows & macOS binaries from the Releases, or just:

cargo install --git https://github.com/hrbrmstr/ohq2quarto

to install it if you’re on Linux or already have a Rust environment setup. I’m working on the configuration of a GitHub Action that will make shipping binaries for all platforms stupid simple and automated.

When run in verbose mode, you’ll see something like this when converting a notebook:

$ ohq2quarto --ohq-ref @hrbrmstr/just-one-more-thing --output-dir ./examples --verbose 
      Title: Just One More Thing
       Slug: just-one-more-thing
  Author(s): boB Rudis
  Copyright: Copyright 2022 boB Rudis
    License: "mit"
 Observable: https://observablehq.com/@hrbrmstr/just-one-more-thing

A look at examples shows:

$ tree examples
├── _quarto.yml
├── columbo_data.csv
└── just-one-more-thing.qmd

The utility made the directory, created the qmd and downloaded the FileAttachment.

This is what the first few lines of the qmd look like:

$ head -16 examples/just-one-more-thing.qmd
---
title: 'Just One More Thing'
author: 'boB Rudis'
format: html
echo: false
observable: 'https://observablehq.com/@hrbrmstr/just-one-more-thing'
---

```{ojs}
md`# Just One More Thing`
```

```{ojs}
md`This week, Chris Holmes tweeted something super dangerous:`
```

FIN

I’ve tried it on some seriously complex OHQ notebooks and it seems to do what I’ve claimed it does on the tin’s label. If you run into issues, or have some feature requests, please drop an issue in the repo.

Quarto is amazing! And, it’s eating the world! OK. Perhaps not the entire world. But it’s still amazing!

If you browse around the HQ, you’ll find many interesting notebooks. You may even have a few yourself! Wouldn’t it be great if you could just import an Observable notebook right into Quarto? Well, now you can.

#' Transform an Observable Notebook into a Quarto project
#' 
#' This will yank the cells from a live Observable notebook and turn it into a Quarto project,
#' downloading all the `FileAttachments` as well.
#' 
#' @param ohq_ref either a short ref (e.g. `@@hrbrmstr/just-one-more-thing`) or a full
#'     URL to a published Observable notebook
#' @param output_dir quarto project directory (will be created if not already present)
#' @param quarto_filename if `NULL` (the default) the name will be the slug (e.g. `just-one-more-thing`
#'     as in the `ohq_ref` param eample) with `.qmd` suffix
#' @param echo set `echo` to `true` or `false` in the YAML
ohq_to_quarto <- function(ohq_ref, output_dir, quarto_filename = NULL, echo = FALSE) {

  ohq_ref <- ohq_ref[1]
  if (grepl("^@", ohq_ref)) ohq_ref <- sprintf("https://observablehq.com/%s", ohq_ref)

  output_dir <- output_dir[1]
  if (!dir.exists(output_dir)) dir.create(output_dir)

  quarto_filename <- quarto_filename[1]

  pg <- rvest::read_html(ohq_ref)

  pg |> 
    html_nodes("script#__NEXT_DATA__") |> 
    html_text() |> 
    jsonlite::fromJSON() -> x

  meta <- x$props$pageProps$initialNotebook
  nodes <- x$props$pageProps$initialNotebook$nodes

  if (is.null(quarto_filename)) quarto_filename <- sprintf("%.qmd", meta$slug)

  c(
    "---", 
    sprintf("title: '%s'", meta$title), 
    "format: html", 
    if (echo) "echo: true" else "echo: false",
    "---",
    "",
    purrr::map2(nodes$value, nodes$mode, ~{
      c(

        "```{ojs}",
        dplyr::case_when(
          .y == "md" ~ sprintf("md`%s`", .x),
          .y == "html" ~ sprintf("html`%s`", .x),
          TRUE ~ .x
        ),
        "```",
        ""
      )

    })
  ) |> 
    purrr::flatten_chr() |> 
    cat(
      file = file.path(output_dir, quarto_filename), 
      sep = "\n"
    )

  if (length(meta$files)) {
    if (nrow(meta$files) > 0) {
      purrr::walk2(
        meta$files$download_url,
        meta$files$name, ~{
          download.file(
            url = .x,
            destfile = file.path(output_dir, .y),
            quiet = TRUE
          )
        }
      )
    }
  }

}

You can try that out with my Columbo notebook:

ohq_to_quarto(
  ohq_ref = "@hrbrmstr/just-one-more-thing", 
  output_dir = "~/Development/columbo",
  quarto_filename = "columbo.qmd",
  echo = FALSE
)

That will download the CSV file into the specified directory and convert the cells to a .qmd. You can download that example file, but you’ll need the data to run it (or just run the converter).

This is what the directory tree looks like after the script is run and the document is rendered:

columbo/
├── columbo.html
├── columbo.qmd
├── columbo_data.csv
└── columbo_files
    └── libs
        ├── bootstrap
        │   ├── bootstrap-icons.css
        │   ├── bootstrap-icons.woff
        │   ├── bootstrap.min.css
        │   └── bootstrap.min.js
        ├── clipboard
        │   └── clipboard.min.js
        ├── quarto-html
        │   ├── anchor.min.js
        │   ├── popper.min.js
        │   ├── quarto-syntax-highlighting.css
        │   ├── quarto.js
        │   ├── tippy.css
        │   └── tippy.umd.min.js
        └── quarto-ojs
            ├── quarto-ojs-runtime.js
            └── quarto-ojs.css

The function has not been battle tested, and it’s limited to the current functionality, but it should do what it says on the tin.

I’ll turn this into a Rust binary so it’s more usable outside of the R ecosystem.

You can try out a fledgling Rust version here.

Apple is in the final stages of shuttering the DarkSky service/API. They’ve replaced it with WeatherKit, which has both an xOS framework version as well as a REST API. To use either, you need to be a member of the Apple Developer Program (ADP) — $99.00/USD per-year — and calls to the service via either method are free up to 500K/month. After that, Apple has pricing tears.

As a result of the forced-ADP membership fee, I’m not sure how many folks are going to invest in building anything but freemium native or web apps. DarkSky had a generous free tier that only required an API key.

Since I had a {darksky} R package, I recently made a similar {weatherkit} package —https://rud.is/b/2022/07/07/introducing-weatherkit-the-eventual-replacement-r-package-for-darksky/ — complete with a CLI demo program.

Lots of R folks will disagree with the following, but R is a terrible language for CLI tools if you’re not already invested in the R ecosystem. CRAN makes it a pain to modify the user’s local system, and most R things have a ton of dependencies. So, while I generally code R-first, I do not code R-only, especially for CLI tools.

I like Rust more than Golang, and am also getting used to it over C/C++, so I threw together a Rust-based WeatherKit CLI tool shortly after the R one — https://github.com/hrbrmstr/weatherkit-rust. There’s documentation for how to cross all the t’s and dot all the i’s required to get authentication to work.

The GH releases have a signed macOS universal binary and I’m working on decomposing Starship’s wicked cool Rust release builder that uses the equally cool Google release-please to deal up binaries for virtually every platform.

I may make the Rust version a full WeatherKit API library, but I don’t know if I’m going to invest time into something that may just get shoved aside due to the hate I’m expecting to see pointed in Apple’s direction.

My {darksky} package has been around for years, now, and the service that powers it was purchased by Apple before the pandemic. The DarkSky API is slated to be shuttered in December of this year and is being replaced by Apple’s WeatherKit xOS Framework and REST API.

I’ve started work on a {weatherkit} package which uses the WeatherKit REST API. You’ll need an Apple Developer account and will also need to setup some items in said account, and locally so you can authenticate to the API. Once all the authentication bits are setup, it’s pretty easy to get the weather data:

wx <- wxkit_weather(43.2683199, -70.8635506)
wx <- wx_tidy(wx)
str(wx)
## List of 4
##  $ currentWeather  :List of 18
##   ..$ name                  : chr "CurrentWeather"
##   ..$ metadata              :List of 8
##   .. ..$ attributionURL: chr "https://weather-data.apple.com/legal-attribution.html"
##   .. ..$ expireTime    : POSIXct[1:1], format: "2022-07-07 12:28:07"
##   .. ..$ latitude      : num 43.3
##   .. ..$ longitude     : num -70.9
##   .. ..$ readTime      : POSIXct[1:1], format: "2022-07-07 12:23:07"
##   .. ..$ reportedTime  : POSIXct[1:1], format: "2022-07-07 10:48:55"
##   .. ..$ units         : chr "m"
##   .. ..$ version       : int 1
##   ..$ asOf                  : POSIXct[1:1], format: "2022-07-07 12:23:07"
##   ..$ cloudCover            : num 0.29
##   ..$ conditionCode         : chr "MostlyClear"
##   ..$ daylight              : logi TRUE
##   ..$ humidity              : num 0.68
##   ..$ precipitationIntensity: num 0
##   ..$ pressure              : num 1018
##   ..$ pressureTrend         : chr "rising"
##   ..$ temperature           : num 19.5
##   ..$ temperatureApparent   : num 19.4
##   ..$ temperatureDewPoint   : num 13.5
##   ..$ uvIndex               : int 2
##   ..$ visibility            : num 29413
##   ..$ windDirection         : int 50
##   ..$ windGust              : num 12
##   ..$ windSpeed             : num 4.42
##  $ forecastDaily   :List of 3
##   ..$ name    : chr "DailyForecast"
##   ..$ metadata:List of 8
##   .. ..$ attributionURL: chr "https://weather-data.apple.com/legal-attribution.html"
##   .. ..$ expireTime    : POSIXct[1:1], format: "2022-07-07 13:23:07"
##   .. ..$ latitude      : num 43.3
##   .. ..$ longitude     : num -70.9
##   .. ..$ readTime      : POSIXct[1:1], format: "2022-07-07 12:23:07"
##   .. ..$ reportedTime  : POSIXct[1:1], format: "2022-07-07 10:48:55"
##   .. ..$ units         : chr "m"
##   .. ..$ version       : int 1
##   ..$ days    :'data.frame': 10 obs. of  26 variables:
##   .. ..$ forecastStart      : POSIXct[1:10], format: "2022-07-07 04:00:00" "2022-07-08 04:00:00" "2022-07-09 04:00:00" "2022-07-10 04:00:00" ...
##   .. ..$ forecastEnd        : POSIXct[1:10], format: "2022-07-08 04:00:00" "2022-07-09 04:00:00" "2022-07-10 04:00:00" "2022-07-11 04:00:00" ...
##   .. ..$ conditionCode      : chr [1:10] "PartlyCloudy" "PartlyCloudy" "MostlyClear" "MostlyClear" ...
##   .. ..$ maxUvIndex         : int [1:10] 7 7 7 8 7 6 7 4 5 4
##   .. ..$ moonPhase          : chr [1:10] "firstQuarter" "firstQuarter" "waxingGibbous" "waxingGibbous" ...
##   .. ..$ moonrise           : POSIXct[1:10], format: "2022-07-07 17:38:12" "2022-07-08 18:50:47" "2022-07-09 20:07:35" "2022-07-10 21:27:35" ...
##   .. ..$ moonset            : POSIXct[1:10], format: "2022-07-07 04:32:48" "2022-07-08 04:54:51" "2022-07-09 05:20:27" "2022-07-10 05:51:50" ...
##   .. ..$ precipitationAmount: num [1:10] 0 0.49 0 0 0 1.32 0.24 3.44 5.07 8.35
##   .. ..$ precipitationChance: num [1:10] 0.01 0.15 0.07 0 0.07 0.39 0.37 0.4 0.47 0.44
##   .. ..$ precipitationType  : chr [1:10] "clear" "rain" "clear" "clear" ...
##   .. ..$ snowfallAmount     : num [1:10] 0 0 0 0 0 0 0 0 0 0
##   .. ..$ solarMidnight      : POSIXct[1:10], format: "2022-07-07 04:48:29" "2022-07-08 04:48:39" "2022-07-09 04:48:49" "2022-07-10 04:48:58" ...
##   .. ..$ solarNoon          : POSIXct[1:10], format: "2022-07-07 16:48:26" "2022-07-08 16:48:35" "2022-07-09 16:48:44" "2022-07-10 16:48:53" ...
##   .. ..$ sunrise            : POSIXct[1:10], format: "2022-07-07 09:10:59" "2022-07-08 09:11:42" "2022-07-09 09:12:26" "2022-07-10 09:13:11" ...
##   .. ..$ sunriseCivil       : POSIXct[1:10], format: "2022-07-07 08:36:06" "2022-07-08 08:36:53" "2022-07-09 08:37:41" "2022-07-10 08:38:31" ...
##   .. ..$ sunriseNautical    : POSIXct[1:10], format: "2022-07-07 07:50:45" "2022-07-08 07:51:39" "2022-07-09 07:52:36" "2022-07-10 07:53:34" ...
##   .. ..$ sunriseAstronomical: POSIXct[1:10], format: "2022-07-07 06:55:17" "2022-07-08 06:56:30" "2022-07-09 06:57:46" "2022-07-10 06:59:04" ...
##   .. ..$ sunset             : POSIXct[1:10], format: "2022-07-08 00:25:50" "2022-07-09 00:25:26" "2022-07-10 00:24:59" "2022-07-11 00:24:30" ...
##   .. ..$ sunsetCivil        : POSIXct[1:10], format: "2022-07-08 01:00:39" "2022-07-09 01:00:10" "2022-07-10 00:59:38" "2022-07-11 00:59:04" ...
##   .. ..$ sunsetNautical     : POSIXct[1:10], format: "2022-07-08 01:46:01" "2022-07-09 01:45:23" "2022-07-10 01:44:42" "2022-07-11 01:43:58" ...
##   .. ..$ sunsetAstronomical : POSIXct[1:10], format: "2022-07-08 02:41:14" "2022-07-09 02:40:16" "2022-07-10 02:39:14" "2022-07-11 02:38:09" ...
##   .. ..$ temperatureMax     : num [1:10] 25.8 28.7 24.9 25.4 28.9 ...
##   .. ..$ temperatureMin     : num [1:10] 13.7 16.3 14.8 12.2 12.4 ...
##   .. ..$ daytimeForecast    :'data.frame':   10 obs. of  11 variables:
##   .. .. ..$ forecastStart      : POSIXct[1:10], format: "2022-07-07 11:00:00" "2022-07-08 11:00:00" "2022-07-09 11:00:00" "2022-07-10 11:00:00" ...
##   .. .. ..$ forecastEnd        : POSIXct[1:10], format: "2022-07-07 23:00:00" "2022-07-08 23:00:00" "2022-07-09 23:00:00" "2022-07-10 23:00:00" ...
##   .. .. ..$ cloudCover         : num [1:10] 0.39 0.45 0.33 0.11 0.42 0.69 0.39 0.95 0.87 0.88
##   .. .. ..$ conditionCode      : chr [1:10] "PartlyCloudy" "PartlyCloudy" "MostlyClear" "Clear" ...
##   .. .. ..$ humidity           : num [1:10] 0.57 0.58 0.54 0.47 0.49 0.63 0.64 0.71 0.7 0.66
##   .. .. ..$ precipitationAmount: num [1:10] 0 0.31 0 0 0 0.26 0.17 3.15 0.22 1.37
##   .. .. ..$ precipitationChance: num [1:10] 0 0.09 0.04 0 0.02 0.29 0.16 0.31 0.33 0.3
##   .. .. ..$ precipitationType  : chr [1:10] "clear" "rain" "clear" "clear" ...
##   .. .. ..$ snowfallAmount     : num [1:10] 0 0 0 0 0 0 0 0 0 0
##   .. .. ..$ windDirection      : int [1:10] 155 263 122 237 231 228 219 98 39 62
##   .. .. ..$ windSpeed          : num [1:10] 8.73 9.42 7.42 6.23 9.75 ...
##   .. ..$ overnightForecast  :'data.frame':   10 obs. of  11 variables:
##   .. .. ..$ forecastStart      : POSIXct[1:10], format: "2022-07-07 23:00:00" "2022-07-08 23:00:00" "2022-07-09 23:00:00" "2022-07-10 23:00:00" ...
##   .. .. ..$ forecastEnd        : POSIXct[1:10], format: "2022-07-08 11:00:00" "2022-07-09 11:00:00" "2022-07-10 11:00:00" "2022-07-11 11:00:00" ...
##   .. .. ..$ cloudCover         : num [1:10] 0.49 0.5 0.15 0.37 0.46 0.4 0.88 0.91 0.8 NA
##   .. .. ..$ conditionCode      : chr [1:10] "PartlyCloudy" "PartlyCloudy" "MostlyClear" "MostlyClear" ...
##   .. .. ..$ humidity           : num [1:10] 0.78 0.78 0.71 0.73 0.69 0.81 0.83 0.85 0.84 NA
##   .. .. ..$ precipitationAmount: num [1:10] 0.06 0.11 0 0 0 1.11 0.04 2.26 6.47 NA
##   .. .. ..$ precipitationChance: num [1:10] 0.05 0.07 0.01 0.02 0.1 0.27 0.24 0.31 0.31 NA
##   .. .. ..$ precipitationType  : chr [1:10] "rain" "rain" "clear" "clear" ...
##   .. .. ..$ snowfallAmount     : num [1:10] 0 0 0 0 0 0 0 0 0 NA
##   .. .. ..$ windDirection      : int [1:10] 192 341 347 223 218 242 276 13 49 NA
##   .. .. ..$ windSpeed          : num [1:10] 9.92 7.15 6.59 5.52 10.95 ...
##   .. ..$ restOfDayForecast  :'data.frame':   10 obs. of  11 variables:
##   .. .. ..$ forecastStart      : POSIXct[1:10], format: "2022-07-07 12:23:07" NA NA NA ...
##   .. .. ..$ forecastEnd        : POSIXct[1:10], format: "2022-07-08 04:00:00" NA NA NA ...
##   .. .. ..$ cloudCover         : num [1:10] 0.47 NA NA NA NA NA NA NA NA NA
##   .. .. ..$ conditionCode      : chr [1:10] "PartlyCloudy" NA NA NA ...
##   .. .. ..$ humidity           : num [1:10] 0.6 NA NA NA NA NA NA NA NA NA
##   .. .. ..$ precipitationAmount: num [1:10] 0 NA NA NA NA NA NA NA NA NA
##   .. .. ..$ precipitationChance: num [1:10] 0.01 NA NA NA NA NA NA NA NA NA
##   .. .. ..$ precipitationType  : chr [1:10] "clear" NA NA NA ...
##   .. .. ..$ snowfallAmount     : num [1:10] 0 NA NA NA NA NA NA NA NA NA
##   .. .. ..$ windDirection      : int [1:10] 163 NA NA NA NA NA NA NA NA NA
##   .. .. ..$ windSpeed          : num [1:10] 9.7 NA NA NA NA NA NA NA NA NA
##  $ forecastHourly  :List of 3
##   ..$ name    : chr "HourlyForecast"
##   ..$ metadata:List of 8
##   .. ..$ attributionURL: chr "https://weather-data.apple.com/legal-attribution.html"
##   .. ..$ expireTime    : POSIXct[1:1], format: "2022-07-07 13:23:07"
##   .. ..$ latitude      : num 43.3
##   .. ..$ longitude     : num -70.9
##   .. ..$ readTime      : POSIXct[1:1], format: "2022-07-07 12:23:07"
##   .. ..$ reportedTime  : POSIXct[1:1], format: "2022-07-07 10:48:55"
##   .. ..$ units         : chr "m"
##   .. ..$ version       : int 1
##   ..$ hours   :'data.frame': 243 obs. of  20 variables:
##   .. ..$ forecastStart         : POSIXct[1:243], format: "2022-07-07 02:00:00" "2022-07-07 03:00:00" "2022-07-07 04:00:00" "2022-07-07 05:00:00" ...
##   .. ..$ cloudCover            : num [1:243] 0.02 0.01 0.02 0.31 0.44 0.74 0.3 1 0.96 0.32 ...
##   .. ..$ conditionCode         : chr [1:243] "Clear" "Clear" "Clear" "MostlyClear" ...
##   .. ..$ daylight              : logi [1:243] FALSE FALSE FALSE FALSE FALSE FALSE ...
##   .. ..$ humidity              : num [1:243] 0.74 0.78 0.81 0.83 0.86 0.88 0.9 0.92 0.88 0.83 ...
##   .. ..$ precipitationAmount   : num [1:243] 0 0 0 0 0 0 0 0 0 0 ...
##   .. ..$ precipitationIntensity: num [1:243] 0 0 0 0 0 0 0 0 0 0 ...
##   .. ..$ precipitationChance   : num [1:243] 0 0 0 0 0 0 0 0 0 0 ...
##   .. ..$ precipitationType     : chr [1:243] "clear" "clear" "clear" "clear" ...
##   .. ..$ pressure              : num [1:243] 1014 1015 1016 1016 1016 ...
##   .. ..$ pressureTrend         : chr [1:243] "rising" "rising" "rising" "rising" ...
##   .. ..$ snowfallIntensity     : num [1:243] 0 0 0 0 0 0 0 0 0 0 ...
##   .. ..$ temperature           : num [1:243] 18.3 17 16.3 15.7 14.9 ...
##   .. ..$ temperatureApparent   : num [1:243] 18.2 16.9 16.1 15.5 14.7 ...
##   .. ..$ temperatureDewPoint   : num [1:243] 13.6 13.1 12.9 12.8 12.6 ...
##   .. ..$ uvIndex               : int [1:243] 0 0 0 0 0 0 0 0 0 1 ...
##   .. ..$ visibility            : num [1:243] 28105 26514 24730 23883 23669 ...
##   .. ..$ windDirection         : int [1:243] 315 302 315 308 310 298 307 316 319 6 ...
##   .. ..$ windGust              : num [1:243] 2.93 2.56 2.92 3.25 3.35 ...
##   .. ..$ windSpeed             : num [1:243] 2.93 2.56 2.92 3.25 3.35 3.09 3.51 2.91 2.36 4.55 ...
##  $ forecastNextHour:List of 6
##   ..$ name         : chr "NextHourForecast"
##   ..$ metadata     :List of 9
##   .. ..$ attributionURL: chr "https://weather-data.apple.com/legal-attribution.html"
##   .. ..$ expireTime    : POSIXct[1:1], format: "2022-07-07 13:23:07"
##   .. ..$ language      : chr "en-US"
##   .. ..$ latitude      : num 43.3
##   .. ..$ longitude     : num -70.9
##   .. ..$ providerName  : chr "US National Weather Service"
##   .. ..$ readTime      : POSIXct[1:1], format: "2022-07-07 12:23:07"
##   .. ..$ units         : chr "m"
##   .. ..$ version       : int 1
##   ..$ summary      :'data.frame':    1 obs. of  4 variables:
##   .. ..$ startTime             : POSIXct[1:1], format: "2022-07-07 12:24:00"
##   .. ..$ condition             : chr "clear"
##   .. ..$ precipitationChance   : num 0
##   .. ..$ precipitationIntensity: num 0
##   ..$ forecastStart: POSIXct[1:1], format: "2022-07-07 12:24:00"
##   ..$ forecastEnd  : POSIXct[1:1], format: "2022-07-07 13:45:00"
##   ..$ minutes      :'data.frame':    81 obs. of  3 variables:
##   .. ..$ startTime             : POSIXct[1:81], format: "2022-07-07 12:24:00" "2022-07-07 12:25:00" "2022-07-07 12:26:00" "2022-07-07 12:27:00" ...
##   .. ..$ precipitationChance   : num [1:81] 0 0 0 0 0 0 0 0 0 0 ...
##   .. ..$ precipitationIntensity: num [1:81] 0 0 0 0 0 0 0 0 0 0 ...

The wx_tidy() function, for now, only does date-time string conversion to POSIXct objects, but it may do more in the future.

It doesn’t appear that historical weather data is available, yet, so you’re limited to using the API to get daily and hourly conditions and forecasts for the present plus some days. As such, I’ve focused a bit on some helper functions to show current conditions and forecasts in the R console/stdout:

current_conditions(wx)
##  Weather for (43.268, -70.864) as of 2022-07-07 08:23:07
## 
##  Conditions: Mostly Clear
## Temperature: 67.08°F
##  Feels like: 66.92°F
##   Dew Point: 56.28°F
##        Wind: 2.7 mph (NE)
##    Pressure: 1017.68 mb (rising)
##  Visibility: 18 miles
##    UV Index: 🟩 2 (Low)
## 
## https://weather-data.apple.com/legal-attribution.html
hourly_forecast(wx)
##  Weather forecast for (43.268, -70.864) as of 2022-07-07 08:23:07
## 
## Today @ 09:00 │ 🌡 69°F │ 💦 63% │ 1018 mb — │ 😎 │ Mostly Clear  │ 🟨
##       @ 10:00 │ 🌡 71°F │ 💦 58% │ 1018 mb — │ 😎 │ Mostly Clear  │ 🟨
##       @ 11:00 │ 🌡 74°F │ 💦 55% │ 1018 mb — │ 😎 │ Mostly Clear  │ 🟧
##       @ 12:00 │ 🌡 75°F │ 💦 53% │ 1017 mb — │ 😎 │ Partly Cloudy │ 🟧
##       @ 13:00 │ 🌡 77°F │ 💦 51% │ 1017 mb ↓ │ 😎 │ Partly Cloudy │ 🟧
##       @ 14:00 │ 🌡 78°F │ 💦 50% │ 1016 mb ↓ │ 😎 │ Partly Cloudy │ 🟧
##       @ 15:00 │ 🌡 78°F │ 💦 50% │ 1016 mb ↓ │ 😎 │ Partly Cloudy │ 🟨
##       @ 16:00 │ 🌡 77°F │ 💦 52% │ 1016 mb ↓ │ 😎 │ Partly Cloudy │ 🟨
##       @ 17:00 │ 🌡 76°F │ 💦 55% │ 1015 mb ↓ │ 😎 │ Partly Cloudy │ 🟩
##       @ 18:00 │ 🌡 75°F │ 💦 58% │ 1015 mb — │ 😎 │ Partly Cloudy │ 🟩
##       @ 19:00 │ 🌡 73°F │ 💦 62% │ 1015 mb — │ 😎 │ Mostly Clear  │ 🟩
##       @ 20:00 │ 🌡 70°F │ 💦 67% │ 1015 mb — │ 😎 │ Partly Cloudy │ 🟩
##       @ 21:00 │ 🌡 68°F │ 💦 71% │ 1015 mb — │ 🌕 │ Mostly Cloudy │ 🟩
##       @ 22:00 │ 🌡 67°F │ 💦 74% │ 1015 mb — │ 🌕 │ Mostly Cloudy │ 🟩
##       @ 23:00 │ 🌡 67°F │ 💦 74% │ 1015 mb — │ 🌕 │ Mostly Cloudy │ 🟩
##   Fri @ 00:00 │ 🌡 66°F │ 💦 74% │ 1015 mb — │ 🌕 │ Partly Cloudy │ 🟩
##       @ 01:00 │ 🌡 65°F │ 💦 78% │ 1015 mb — │ 🌕 │ Partly Cloudy │ 🟩
##       @ 02:00 │ 🌡 64°F │ 💦 81% │ 1015 mb — │ 🌕 │ Mostly Clear  │ 🟩
##       @ 03:00 │ 🌡 63°F │ 💦 83% │ 1015 mb — │ 🌕 │ Partly Cloudy │ 🟩
##       @ 04:00 │ 🌡 62°F │ 💦 85% │ 1015 mb — │ 🌕 │ Partly Cloudy │ 🟩
## 
## https://weather-data.apple.com/legal-attribution.html

Note that the attribution is required by Apple.

I’ll likely add a daily forecast console printer soon.

There are a few helper functions in the package for value conversion between unit systems, iconifying some values, and working with time zones. {weatherkit} uses lutz::tz_lookup_coords() in places to auto-determine the time zone from lat/lng pairs, and also includes a function to intuit lat/lng from an IP address using ipapi (they have a generous free tier).

As of the timestamp on this blog post, Apple’s WeatherKit provides up to 500,000 API calls a month per Apple Developer Program membership. If you need additional API calls, monthly subscription plans will be available for purchase sometime after the beta is officially over. This is the expected pricing:

  • 500,000 calls/month: Included with membership
  • 1 million calls/month: US$ 49.99
  • 2 million calls/month: US$ 99.99
  • 5 million calls/month: US$ 249.99
  • 10 million calls/month: US$ 499.99
  • 20 million calls/month: US$ 999.99

Apple’s WeatherKit documentation consistently says “Apple Developer Program membership”, which seems to indicate you need to give them money every year to use the REST API. We’ll see if that’s truly the case after the service leaves beta status.

FIN

Kick the tyres & drop issues/PRs as one may be wont to do.

I realize you have to be living under a rock in the U.S. to not know that yesterday, was Juneteenth (a portmanteau of “June Nineteenth”). Still, I feel compelled to explain that said date marks the day when federal troops arrived in Galveston, Texas in 1865 to take control of the state and ensure that all enslaved people be freed. (See, Texas has always been kinda horrible when it comes to basic human decency). The arrival of said troops came ~2.5 years after the signing of the Emancipation Proclamation. The day honors the end to slavery in the United States.

GreyNoise (my employer) — like an increasing number of organizations in the U.S. — observes Juneteenth as a company-wide holiday, and I’ll be spending part of today pondering that word, “end”, in the last sentence of the previous paragraph.

There are two major political parties in the U.S. and one just decided, this past Juneteenth weekend, we no longer need the Voting Rights Act of 1965. March 7th of that year was a Sunday, known today as “Bloody Sunday” (it turns out there have been far too many of those kinds of Sundays). It was a day when Alabama state troopers beat & whipped voting rights advocates with nightsticks, and also used chemical weapons on them, in an effort to keep in place discriminatory practices that were extensively used to prevent African Americans from exercising their right to vote.

The act banned literacy tests for voting, required federal oversight over voter registration in areas where less than 50 percent of the non-white population had registered to vote, and authorized the U.S. attorney general to investigate the use of poll taxes in state and local elections. (In 1964, the 24th Amendment made poll taxes illegal in federal elections; poll taxes in state elections were banned in 1966 by the U.S. Supreme Court.)

I’m fairly certain (~95%) — regardless of the party in power — we’ll see the current Supreme Court overturn the state poll tax decision within the next 5-10 years (thankfully, constitutional amendments are a bit harder to wipe way). You can definitely say goodbye to the voting rights act if Republicans gain control of Congress and especially if they regain Congress and the POTUS seat.

Both of those events will make it possible for ~21 states to become even more evil than they already are, and set the stage for a few, awful decades.

I know gas is expensive.

I know food is more expensive than ever.

I know some shelves are bare.

I know lines are longer.

I know we’re still in a pandemic.

I also know that I don’t want to see the devolution of America back to when it was so “great” that we cheered on police when they were treating innocent, peaceful citizens like armed, enemy combatants, and ensured the reign of evil men by making it impossible for large swaths of Americans to vote.

In the fall of 2022 and fall of 2024, we’ll know if we, as a nation, care more about convenience than conscience.

Which side of Juneteenth will you be on when the time comes to choose the path forward?

In today’s newsletter Leonardo, an open source project and free online too from Adobe that lets you make great and accessible color palettes for use in UX/UI design and data visualizations! You can read the one newsletter section to get a feel for Leonardo, then go play with it a bit.

The app lets you download the palettes in many forms, as well as just copy the values from the site. Two of the formats are SVG: one for discrete mappings (so, a small, finite number of colors) and another for continuous mappings (so, a gradient). I’ll eventually add the following to my {swatches} package, but, for now, you can tuck these away into a snippet if you do end up working with Leonardo on-the-regular.

Read a qualitative leonardo SVG palette

This is a pretty straightforward format to read and transform into something usable in R:

<svg xmlns="http://www.w3.org/2000/svg" version="1.1" width="616px" height="80px" aria-hidden="true" id="svg">
    <rect x="0" y="0" width="80" height="80" rx="8" fill="#580000"></rect>
    <rect x="88" y="0" width="80" height="80" rx="8" fill="#a54d15"></rect>
    <rect x="176" y="0" width="80" height="80" rx="8" fill="#edc58d"></rect>
    <rect x="264" y="0" width="80" height="80" rx="8" fill="#ffffe0"></rect>
    <rect x="352" y="0" width="80" height="80" rx="8" fill="#b9d6c7"></rect>
    <rect x="440" y="0" width="80" height="80" rx="8" fill="#297878"></rect>
    <rect x="528" y="0" width="80" height="80" rx="8" fill="#003233"></rect>
</svg>

which means {xml2} can make quick work of it:

read_svg_palette <- \(path) {
  xml2::read_xml(path) |> 
    xml2::xml_find_all(".//d1:rect") |> 
    xml2::xml_attr("fill")
}

pal <- read_svg_palette("https://rud.is/dl/diverging.svg")

scales::show_col(pal)

Read a gradient leonardo SVG palette

The continuous one is only slightly more complex:

<svg xmlns="http://www.w3.org/2000/svg" version="1.1" width="800px" height="80px" aria-hidden="true" id="gradientSvg">
    <rect id="gradientRect" width="800" height="80" fill="url(#gradientLinearGrad)" rx="8"></rect>
    <defs id="gradientDefs">
        <linearGradient id="gradientLinearGrad" x1="0" y1="0" x2="800" y2="0" gradientUnits="userSpaceOnUse">
            <stop offset="0" stop-color="rgb(88, 0, 0)"></stop>
            <stop offset="0.04081632653061224" stop-color="rgb(123, 37, 6)"></stop>
            <stop offset="0.08163265306122448" stop-color="rgb(153, 65, 16)"></stop>
            <stop offset="0.12244897959183673" stop-color="rgb(179, 90, 25)"></stop>
            <stop offset="0.16326530612244897" stop-color="rgb(203, 115, 34)"></stop>
            <stop offset="0.20408163265306123" stop-color="rgb(222, 139, 51)"></stop>
            <stop offset="0.24489795918367346" stop-color="rgb(230, 166, 94)"></stop>
            <stop offset="0.2857142857142857" stop-color="rgb(236, 190, 130)"></stop>
            <stop offset="0.32653061224489793" stop-color="rgb(240, 210, 160)"></stop>
            <stop offset="0.3673469387755102" stop-color="rgb(245, 227, 184)"></stop>
            <stop offset="0.40816326530612246" stop-color="rgb(249, 241, 204)"></stop>
            <stop offset="0.4489795918367347" stop-color="rgb(252, 250, 217)"></stop>
            <stop offset="0.4897959183673469" stop-color="rgb(254, 254, 222)"></stop>
            <stop offset="0.5306122448979592" stop-color="rgb(251, 252, 222)"></stop>
            <stop offset="0.5714285714285714" stop-color="rgb(242, 248, 220)"></stop>
            <stop offset="0.6122448979591837" stop-color="rgb(229, 240, 216)"></stop>
            <stop offset="0.6530612244897959" stop-color="rgb(210, 229, 209)"></stop>
            <stop offset="0.6938775510204082" stop-color="rgb(188, 216, 201)"></stop>
            <stop offset="0.7346938775510204" stop-color="rgb(160, 202, 189)"></stop>
            <stop offset="0.7755102040816326" stop-color="rgb(126, 186, 178)"></stop>
            <stop offset="0.8163265306122449" stop-color="rgb(74, 170, 167)"></stop>
            <stop offset="0.8571428571428571" stop-color="rgb(53, 147, 146)"></stop>
            <stop offset="0.8979591836734694" stop-color="rgb(42, 122, 121)"></stop>
            <stop offset="0.9387755102040817" stop-color="rgb(28, 94, 95)"></stop>
            <stop offset="0.9795918367346939" stop-color="rgb(9, 65, 66)"></stop>
        </linearGradient>
    </defs>
</svg>

Which means we have to do a tad bit more work in R:

read_svg_gradient <- \(path) {

  xml2::read_xml(path) |> 
    xml2::xml_find_all(".//d1:stop") -> stops

  stringi::stri_replace_last_fixed(
    str = xml2::xml_attr(stops, "stop-color"),
    pattern = ")",
    replacement = ", alpha = 255, maxColorValue = 255)"
  ) -> rgbs

  list(
    colours = lapply(rgbs, \(rgb) parse(text = rgb)) |> 
      sapply(eval) |> 
      stringi::stri_replace_last_regex("FF$", ""),
    values = as.numeric(xml2::xml_attr(stops, "offset"))
  )

}

svg_grad <- read_svg_gradient("https://rud.is/dl/diverging-gradient.svg")

scales::show_col(svg_grad$colours)

We can use the continuous palette with ggplot2::scale_color_gradientn():

df <- data.frame(
  x = runif(100),
  y = runif(100),
  z1 = rnorm(100),
  z2 = abs(rnorm(100))
)

ggplot2::ggplot(df, ggplot2::aes(x, y)) +
  ggplot2::geom_point(ggplot2::aes(colour = z1)) +
  ggplot2::scale_color_gradientn(
    colours = svg_grad$colours,
    values = svg_grad$values
  ) +
  hrbrthemes::theme_ft_rc(grid="XY") 

FIN

Short post, but hopefully a few folks are inspired to try Leonardo out.