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isLandscape = Generators.observe(next => {
  // yield initial value
  next(screen.orientation?.angle == 90 || screen.orientation?.angle == 180);

  // define event listener
  const listener = () => next(screen.orientation?.angle == 90 || screen.orientation?.angle == 180);

  // attach event listener
  screen.orientation.addEventListener("change", listener);

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gdp <- data.frame(
  name = c("us", "cn", "jp", "de", "in", "gb", "fr", "it", "ca"),
  gdp = c(26854, 19374, 4410, 4309, 3740, 3160, 2924, 2170, 2090),
  x = c(1,2,3,1,2,3,1,2,3),
  y = c(3,3,3,2,2,2,1,1,1))


p1 <- ggplot(gdp) +
  aes(x, y) +
  geom_flag(aes(country = name, size= gdp)) +

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my_func <- function(alpha, beta, gamma) alpha + beta + gamma

# no
tibble(a = 1:5, b = 6:10, c = 11:15) %>%
  mutate(z = pmap_int(., my_func))

# yes
tibble(a = 1:5, b = 6:10, c = 11:15) %>%
  mutate(z = pmap_int(
    dplyr::select(., alpha = a, beta = b, gamma = c),

jimjam-slam

In Svelte:

• export each Illustrator layer as a separate SVG (using the same artboard)
• modify the SVG files:
  • remove the xml parent node;
  • rename the extension to .svelte
  • if using several layers, rename the classes to avoid collisions between them when inlining
• import the components in svelte: `import FileName from "./FileName.svelte"
• use the layer components sequentially in a parent component with <FileName>. wrap them in a parent div.
• stack the layers visually by giving the parent div position: relative and the map layers position: absolute; top: 0; left: 0;. use :global([selector]) to inject the styles into the svgs
• insert other svelte components, like buttons, in between or on top of the layers

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project:
  type: website

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flutter_pytorch_mobile

lib/model.dart:

• Defines:
  • getPrediction: calls the predict method
  • getImagePrediction: calls the predictImage method and then compares the maximum score agains tthe label list
  • getImagePredictionList: calls the predictImage method, returns all scores

android/src/main/java/io/funn/pytroch_mobile/PyTorchMobilePlugin.java:

jimjam-slam

library(tidyverse)
library(rtweet)
library(jsonlite)
library(lubridate)
library(here)

# let's pull it in from the zip file, get the essential stats fairly tidy
here("twitter-2022-11-06", "data", "tweets.js") |>
  readLines() |>
  str_replace("window.YTD.tweets.part0 = ", "") |>

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library(sf)
library(httr2)
library(ggplot2)

# based on https://community.esri.com/t5/gis-blog/
# accessing-arcgis-rest-services-using-r/ba-p/898451

api_query <- url_parse("https://geo.abs.gov.au")

api_root <- "/arcgis/rest/services"

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tl;dr

Shapefile(s) to vector tile set

graph LR
    A[Vector source - eg. shapefile] --> |ogr2ogr| B[geoJSON]
    B --> |tippecanoe| C[Set of .pbf tiles]

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# in hindsight i probably should've used a network analysis package to do this

library(tidyverse)
set.seed(1)

# create a sample data frame of origins and destinations
testdf <-
  tibble(
    origin = sample(letters, 10),
    destination = sample(letters, 10),