[BOT]Quarto Freeze by GH actions

.quarto/_freeze/posts/shinystandalone/index/execute-results/html.json

@@ -1,8 +1,8 @@
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-    "markdown": "---\ntitle: \"Quarto-shinylive & Quartolive Testdrive\"\nauthor: \"Yonghun Suh\"\ndate: \"Oct 17, 2024\"\ncategories: [Code]\nimage: https://hypebright.nl/wp-content/uploads/2023/09/shinylive-blog-2-930x620.png\n#engine: knitr\nformat: \n  live-html:\n    #resources:\n    #  - ./data/shinylive-sw.js\n    #page-layout: full\n    ##https://quarto.org/docs/authoring/article-layout.html\n    toc: false\n    number_sections: true\n    code-copy: true\n    code-fold: show\n    code-tools: true\n    code-overflow: scroll\n    code-link: true\n    number-sections: true\n    toc_depth: 3\n    lightbox: true\n    theme:\n      light: cosmo #sketchy\n      dark: [cosmo, ../../misc/theme-dark.scss]\n    css: ../../misc/styles.css\n＃webr: \n＃  show-startup-message: false\nfilters:\n  #- webr\n  - shinylive\n  \n---\n\n::: {.cell}\n\n:::\n\n\n\n\n\n# Quarto-Shinylive\n\n## Locations of Earthquakes off Fiji\n\n> The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964. There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.\n\nThis is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.\n\n\n:::{.column-screen-inset}\n\n\n```{shinylive-r}\n#| standalone: true\n#| viewerHeight: 1000\n\n\nlibrary(shiny)\nlibrary(leaflet)\nlibrary(RColorBrewer)\n\nui <- bootstrapPage(\n  tags$style(type = \"text/css\", \"html, body {width:100%;height:100%}\"),\n  leafletOutput(\"map\", width = \"100%\", height = \"100%\"),\n  absolutePanel(top = 10, right = 10,\n    sliderInput(\"range\", \"Magnitudes\", min(quakes$mag), max(quakes$mag),\n      value = range(quakes$mag), step = 0.1\n    ),\n    selectInput(\"colors\", \"Color Scheme\",\n      rownames(subset(brewer.pal.info, category %in% c(\"seq\", \"div\")))\n    ),\n    checkboxInput(\"legend\", \"Show legend\", TRUE)\n  )\n)\n\nserver <- function(input, output, session) {\n\n  # Reactive expression for the data subsetted to what the user selected\n  filteredData <- reactive({\n    quakes[quakes$mag >= input$range[1] & quakes$mag <= input$range[2],]\n  })\n\n  # This reactive expression represents the palette function,\n  # which changes as the user makes selections in UI.\n  colorpal <- reactive({\n    colorNumeric(input$colors, quakes$mag)\n  })\n\n  output$map <- renderLeaflet({\n    # Use leaflet() here, and only include aspects of the map that\n    # won't need to change dynamically (at least, not unless the\n    # entire map is being torn down and recreated).\n    leaflet(quakes) %>% addTiles() %>%\n      fitBounds(~min(long), ~min(lat), ~max(long), ~max(lat))\n  })\n\n  # Incremental changes to the map (in this case, replacing the\n  # circles when a new color is chosen) should be performed in\n  # an observer. Each independent set of things that can change\n  # should be managed in its own observer.\n  observe({\n    pal <- colorpal()\n\n    leafletProxy(\"map\", data = filteredData()) %>%\n      clearShapes() %>%\n      addCircles(radius = ~10^mag/10, weight = 1, color = \"#777777\",\n        fillColor = ~pal(mag), fillOpacity = 0.7, popup = ~paste(mag,\"진도\")\n      )\n  })\n\n  # Use a separate observer to recreate the legend as needed.\n  observe({\n    proxy <- leafletProxy(\"map\", data = quakes)\n\n    # Remove any existing legend, and only if the legend is\n    # enabled, create a new one.\n    proxy %>% clearControls()\n    if (input$legend) {\n      pal <- colorpal()\n      proxy %>% addLegend(position = \"bottomright\",\n        pal = pal, values = ~mag\n      )\n    }\n  })\n}\n\nshinyApp(ui, server)\n\n```\nCredit: [Using Leaflet with Shiny](https://rstudio.github.io/leaflet/articles/shiny.html#modifying-existing-maps-with-leafletproxy){target=\"_blank\"}\n\n:::\n\n# Quarto-Live\n\n\n## Interactive R\n\n### Mergesort from scratch\n\nI coded this while teaching myself computer science.\n\n\nI think it is important to understand how the \"so-called\" the basis functions work when it comes to the programming.\n\nYou can play around with it. Plus, you can modify the code for a better understanding.\n\n\n\n\n::: {.cell}\n```{webr}\n### You can play ALL by YOURSELF!\n\n\nprint(\"Hello quarto-live world!\")\n\n\n\n{\n  # set seed for reproducability\n  set.seed(10)\n  givne_num <- sample(1:999, 20)\n}\n\n\n\n\n# merge two sorted array\nmerge <- function(a, b) {\n  \n  # create temp array\n  temp <- numeric(length(a) + length(b))\n  \n  \n  # array a, array b, setting the init value of temp array index i \n  a_i <- 1\n  b_i <- 1\n  temp_i <- 1\n  \n  \n  # loop through till the index temp_i reaches length of the temp array\n  for(temp_i in 1 : length(temp)) {\n    \n    # if\n    # the index of `a` does not exceedes the length of `a`(a present)\n    # a[a_i] < b[b_i]  or,\n    \n      \n    # the index of `b` does exceedes the length of `b`(i.e., b does not presnt)\n    if((a_i <= length(a) &&\n        a[a_i] < b[b_i]) ||\n       b_i > length(b)) {\n      \n      \n      # assign an element of `a[a_i]` into temp[a_i], then i++ the index of `a`\n      temp[temp_i] <- a[a_i]\n      a_i <- a_i + 1\n    }\n    \n    # else\n    else {\n      \n\n      # assign b[b_i] into temp[temp_i] then i++ the index\n      temp[temp_i] <- b[b_i]\n      b_i <- b_i + 1      \n    }\n    \n  }\n  \n  # return the sorted array (part of the divided/splitted)\n  return(temp)\n}\n\n\n\n\n# merge sort algorithm(splitting included) - recursive function\nmergesort <- function(arr) {\n  \n\n  # if length of the given array exceeds 1 then\n  if(length(arr) > 1) {\n    \n    # 분할할 중간 지점\n    # midpoint for the split\n    # e.g., an array size of 5: celing(5/2) = 3\n    half <- ceiling(length(arr)/2)\n    \n    \n    # calling recursive function: split untill the single element then sort\n    # 1 to midpoint\n    a <- mergesort(arr[1:half])\n    \n    \n    # midpoint+1 to last\n    b <- mergesort(arr[(half+1):length(arr)])\n    \n    # merge two sorted array and return\n    merge(a, b)\n  }\n  \n  # if the size of the array is 1\n  else {\n    # just return\n    return(arr)\n  }\n}\n\n\n\n\nminmax <- function(arr){\n  \n  temp <- mergesort(arr)\n  \n  # mimicing Python's dictionary... then return\n  return(list(\"max\" = temp[length(temp)], \"min\" = temp[1]))\n  \n}\n\n# function call\noutput <- minmax(givne_num)\n\n\n\n# max\noutput$max\n\n# min\noutput$min\n\n\n\n# Use the R built-in function to check the answer\nmax(givne_num)\nmin(givne_num)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n###\n```\n:::\n\n\n\n## Non-interactive\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncat(\"And, this is a non-interactive cell\", fill = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nAnd, this is a non-interactive cell\n```\n\n\n:::\n:::\n",
+    "markdown": "---\ntitle: \"A Testdrive of Quarto-Shinylive & Quartolive\"\nauthor: \"Yonghun Suh\"\ndate: \"Oct 18, 2024\"\ncategories: [Code]\nimage: https://hypebright.nl/wp-content/uploads/2023/09/shinylive-blog-2-930x620.png\n#engine: knitr\nformat: \n  live-html:\n    #resources:\n    #  - ./data/shinylive-sw.js\n    #page-layout: full\n    ##https://quarto.org/docs/authoring/article-layout.html\n    toc: false\n    number_sections: true\n    code-copy: true\n    code-fold: show\n    code-tools: true\n    code-overflow: scroll\n    code-link: true\n    number-sections: true\n    toc_depth: 3\n    lightbox: true\n    theme:\n      light: cosmo #sketchy\n      dark: [cosmo, ../../misc/theme-dark.scss]\n    css: ../../misc/styles.css\n＃webr: \n＃  show-startup-message: false\nfilters:\n  #- webr\n  - shinylive\n  \n---\n\n::: {.cell}\n\n:::\n\n\n\n\n\n# Quarto-Shinylive\n\nShinylive allows you to run Shiny applications entirely in a web browser, without the need for a separate server running Python.\n\nThe traditional way of deploying Shiny involves in a separate server and client: the server runs Python and Shiny, and clients connect via the web browser. Each client keeps an open websocket connection as long as they are using the application.\n\n![](https://shiny.posit.co/py/docs/shinylive-shiny-deployment-model.png){width=100%}\n\n\nWhen an application is deployed with Shinylive, Python and Shiny run in the web browser: the browser is effectively both the client and server for the application. There is a web server that serves files, but it does not run Python or Shiny—it can be a “dumb” static web server.\n\n![](https://shiny.posit.co/py/docs/shinylive-shinylive-deployment-model.png){width=100%}\n\nHowever, there's a downside. The overhead **i.e.,** the speed for configuring the web browser of local machine is painfully slow.\n\nThis example takes up to `50 seconds` for the loading. So, please be patient.\n\n\nI render this post using [GitHub Actions](https://github.com/YONGHUNI/blog/actions/runs/11403350650){target=\"_blank\"} only. So you can reproduce this by just forking/cloning [this repository](https://github.com/YONGHUNI/blog){target=\"_blank\"}.\n\n\n## Locations of Earthquakes off Fiji\n\n> The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964. There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.\n\nThis is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.\n\n\n:::{.column-screen-inset}\n\n\n```{shinylive-r}\n#| standalone: true\n#| viewerHeight: 1000\n\n\nlibrary(shiny)\nlibrary(leaflet)\nlibrary(RColorBrewer)\n\nui <- bootstrapPage(\n  tags$style(type = \"text/css\", \"html, body {width:100%;height:100%}\"),\n  leafletOutput(\"map\", width = \"100%\", height = \"100%\"),\n  absolutePanel(top = 10, right = 10,\n    sliderInput(\"range\", \"Magnitudes\", min(quakes$mag), max(quakes$mag),\n      value = range(quakes$mag), step = 0.1\n    ),\n    selectInput(\"colors\", \"Color Scheme\",\n      rownames(subset(brewer.pal.info, category %in% c(\"seq\", \"div\")))\n    ),\n    checkboxInput(\"legend\", \"Show legend\", TRUE)\n  )\n)\n\nserver <- function(input, output, session) {\n\n  # Reactive expression for the data subsetted to what the user selected\n  filteredData <- reactive({\n    quakes[quakes$mag >= input$range[1] & quakes$mag <= input$range[2],]\n  })\n\n  # This reactive expression represents the palette function,\n  # which changes as the user makes selections in UI.\n  colorpal <- reactive({\n    colorNumeric(input$colors, quakes$mag)\n  })\n\n  output$map <- renderLeaflet({\n    # Use leaflet() here, and only include aspects of the map that\n    # won't need to change dynamically (at least, not unless the\n    # entire map is being torn down and recreated).\n    leaflet(quakes) %>% addTiles() %>%\n      fitBounds(~min(long), ~min(lat), ~max(long), ~max(lat))\n  })\n\n  # Incremental changes to the map (in this case, replacing the\n  # circles when a new color is chosen) should be performed in\n  # an observer. Each independent set of things that can change\n  # should be managed in its own observer.\n  observe({\n    pal <- colorpal()\n\n    leafletProxy(\"map\", data = filteredData()) %>%\n      clearShapes() %>%\n      addCircles(radius = ~10^mag/10, weight = 1, color = \"#777777\",\n        fillColor = ~pal(mag), fillOpacity = 0.7, popup = ~paste(mag,\"진도\")\n      )\n  })\n\n  # Use a separate observer to recreate the legend as needed.\n  observe({\n    proxy <- leafletProxy(\"map\", data = quakes)\n\n    # Remove any existing legend, and only if the legend is\n    # enabled, create a new one.\n    proxy %>% clearControls()\n    if (input$legend) {\n      pal <- colorpal()\n      proxy %>% addLegend(position = \"bottomright\",\n        pal = pal, values = ~mag\n      )\n    }\n  })\n}\n\nshinyApp(ui, server)\n\n```\nCredit: [Using Leaflet with Shiny](https://rstudio.github.io/leaflet/articles/shiny.html#modifying-existing-maps-with-leafletproxy){target=\"_blank\"}\n\n:::\n\n# Quarto-Live\n\n\n## Interactive R\n\n### Mergesort from scratch\n\nI coded this while teaching myself computer science.\n\n\nI think it is important to understand how the \"so-called\" the basis functions work when it comes to the programming.\n\nYou can play around with it. Plus, you can modify the code for a better understanding.\n\n\n\n\n::: {.cell}\n```{webr}\n### You can play ALL by YOURSELF!\n\nprint(\"Hello quarto-live world!\")\n\n\n# set seed for reproducability\nset.seed(10)\ngivne_num <- sample(1:999, 20)\n\n\n# merge two sorted array\nmerge <- function(a, b) {\n    \n  # create temp array\n  temp <- numeric(length(a) + length(b))\n  \n  # array a, array b, setting the init value of temp array index i \n  a_i <- 1\n  b_i <- 1\n  temp_i <- 1\n  \n  # loop through till the index temp_i reaches length of the temp array\n  for(temp_i in 1 : length(temp)) {\n      \n    # if\n    # the index of `a` does not exceedes the length of `a`(a present)\n    # a[a_i] < b[b_i]  or,\n    \n    # the index of `b` does exceedes the length of `b`(i.e., b does not presnt)\n    if((a_i <= length(a) && a[a_i] < b[b_i]) || b_i > length(b)) {\n      \n      # assign an element of `a[a_i]` into temp[a_i], then i++ the index of `a`\n      temp[temp_i] <- a[a_i]\n      a_i <- a_i + 1\n    }\n    \n    # else\n    else {\n      \n      # assign b[b_i] into temp[temp_i] then i++ the index\n      temp[temp_i] <- b[b_i]\n      b_i <- b_i + 1      \n    }\n    \n  }\n  \n  # return the sorted array (part of the divided/splitted)\n  return(temp)\n}\n\n# merge sort algorithm(splitting included) - recursive function\nmergesort <- function(arr) {\n \n  # if length of the given array exceeds 1 then\n  if(length(arr) > 1) {\n    \n    # midpoint for the split\n    # e.g., an array size of 5: celing(5/2) = 3\n    half <- ceiling(length(arr)/2)\n    \n    # calling recursive function: split untill the single element then sort\n    # 1 to midpoint\n    a <- mergesort(arr[1:half])\n    \n    # midpoint+1 to last\n    b <- mergesort(arr[(half+1):length(arr)])\n    \n    # merge two sorted array and return\n    merge(a, b)\n  }\n  \n  # if the size of the array is 1\n  else {\n    # just return\n    return(arr)\n  }\n}\n\n\nminmax <- function(arr){\n  \n  temp <- mergesort(arr)\n  \n  # mimicing Python's dictionary... then return\n  return(list(\"max\" = temp[length(temp)], \"min\" = temp[1]))\n  \n}\n\n# function call\noutput <- minmax(givne_num)\n\n# max\noutput$max\n# min\noutput$min\n\n# Use the R built-in function to check the answer\nmax(givne_num)\nmin(givne_num)\n\n###\n```\n:::\n\n\n\n## Non-interactive\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncat(\"And, this is a non-interactive cell\", fill = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nAnd, this is a non-interactive cell\n```\n\n\n:::\n:::\n",
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