Shinya Uryu uribo

## scrapbox_api.R
library(httr2)
library(rvest)

# ページリスト ------------------------------------------------------------------
req <-
  request("https://scrapbox.io/") |>
  req_url_path_append("api/pages/") |>
  req_url_path_append("mandaRa")
x <-
  req |>

## r_intro.ipynb

      
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                uribo
                / r_intro.ipynb
            
            
              Created
              November 22, 2023 00:43
            
          
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## estat_toukei_00500215.R
##############################
# 作物統計調査のデータをダウンロードして綺麗な形でRに読み込む
# 表分類: 品目別結果樹面積、収穫量及び出荷量累年統計（昭和48年～）
##############################
# 1. 対象のデータを一覧にする ---------------------------------------------------------
base_url <- "https://www.e-stat.go.jp"

df_item <-
  seq.int(2) |>
  purrr::map(

## URL
https://mybinder.org/v2/gh/uribo/binder221202/main?urlpath=git-pull?repo=https://github.com/uribo/cue2022aw_r104

## zip_to_administration.R
read_zipcode_oogaki <-
  memoise::memoise(
    function(.path) {
      dplyr::select(zipangu::read_zipcode(.path,
                                          type = "oogaki"),
                    jis_code, zip_code, prefecture, city, street)
    }
  )

zip_to_administration <- function(x, value = "jis_code", .path = NULL) {

## apple_book_marker.R
apple_book_name_fix <- function(data) {
  purrr::set_names(data,
                   stringr::str_to_lower(
                     stringr::str_remove(names(data), "^Z")
                   ))
}

apple_book_markers <- function() {
  con <- DBI::dbConnect(RSQLite::SQLite(),
                   "~/Library/Containers/com.apple.iBooksX/Data/Documents/AEAnnotation/AEAnnotation_v10312011_1727_local.sqlite",

## hadley_ds_cycle.R
library(DiagrammeR)
library(remoji)

nodes <-
  create_nodes(
    nodes = c(
      paste0(emoji("floppy_disk"), "Import"),
      paste0(emoji("repeat"), "Tidy"),
      paste(
        paste0(

## vis_tide_level.R
library(dplyr)
library(jmastats)
library(ggplot2)
library(ggforce)


# 対象地点のデータを取得 -------------------------------------------------------------
# 東京
df_tk <-
  read_tide_level(.year = 2020, .month = 8, .stn = "TK")

## maff_disaster1964-2020.R
######################################
# 農林水産省 過去の主な異常災害等と農林水産被害
# http://www.maff.go.jp/j/saigai/arc/index-1.html
######################################
# 1/3 Setup -------------------------------------------------------------------
library(dplyr)
library(purrr)
library(stringr)
bind_line2df <- function(x, index) {
  x[index] %>%

## read_senkyo_s_shikuchouson.R
# 市区町村別得票数
read_senkyo_s_shikuchouson <- function(path, sheet = 1) {
  df_raw <-
    readxl::read_excel(path,
                       sheet = sheet,
                       skip = 3)
  df_raw <-
    df_raw %>%
    dplyr::filter(!is.na(`候補者名`))
  x_candidate <-
	library(httr2)
	library(rvest)

	# ページリスト ------------------------------------------------------------------
	req <-
	request("https://scrapbox.io/") \|>
	req_url_path_append("api/pages/") \|>
	req_url_path_append("mandaRa")
	x <-
	req \|>
	##############################
	# 作物統計調査のデータをダウンロードして綺麗な形でRに読み込む
	# 表分類: 品目別結果樹面積、収穫量及び出荷量累年統計（昭和48年～）
	##############################
	# 1. 対象のデータを一覧にする ---------------------------------------------------------
	base_url <- "https://www.e-stat.go.jp"

	df_item <-
	seq.int(2) \|>
	purrr::map(
	read_zipcode_oogaki <-
	memoise::memoise(
	function(.path) {
	dplyr::select(zipangu::read_zipcode(.path,
	type = "oogaki"),
	jis_code, zip_code, prefecture, city, street)
	}
	)

	zip_to_administration <- function(x, value = "jis_code", .path = NULL) {
	apple_book_name_fix <- function(data) {
	purrr::set_names(data,
	stringr::str_to_lower(
	stringr::str_remove(names(data), "^Z")
	))
	}

	apple_book_markers <- function() {
	con <- DBI::dbConnect(RSQLite::SQLite(),
	"~/Library/Containers/com.apple.iBooksX/Data/Documents/AEAnnotation/AEAnnotation_v10312011_1727_local.sqlite",
	library(DiagrammeR)
	library(remoji)

	nodes <-
	create_nodes(
	nodes = c(
	paste0(emoji("floppy_disk"), "Import"),
	paste0(emoji("repeat"), "Tidy"),
	paste(
	paste0(
	library(dplyr)
	library(jmastats)
	library(ggplot2)
	library(ggforce)


	# 対象地点のデータを取得 -------------------------------------------------------------
	# 東京
	df_tk <-
	read_tide_level(.year = 2020, .month = 8, .stn = "TK")
	######################################
	# 農林水産省過去の主な異常災害等と農林水産被害
	# http://www.maff.go.jp/j/saigai/arc/index-1.html
	######################################
	# 1/3 Setup -------------------------------------------------------------------
	library(dplyr)
	library(purrr)
	library(stringr)
	bind_line2df <- function(x, index) {
	x[index] %>%
	# 市区町村別得票数
	read_senkyo_s_shikuchouson <- function(path, sheet = 1) {
	df_raw <-
	readxl::read_excel(path,
	sheet = sheet,
	skip = 3)
	df_raw <-
	df_raw %>%
	dplyr::filter(!is.na(`候補者名`))
	x_candidate <-