法規年齡

問題

有人宣稱台灣有很多老舊過時的法規

是否可以在資料上看到法規更新的狀況？

library(jsonlite)
library(dplyr)

## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(lubridate)
library(tidyr)
library(stringr)
library(ggplot2)
library(knitr)

df <- jsonlite::fromJSON("../mojLawSplitJSON/index.json", flatten = T) %>% tbl_df()

mutate_update_entry <- function(entry) {
  ifelse(is.null(entry), "" , str_c(entry, collapse = ","))
}

paste_updates_and_lastupdate <- function(entry){
  ifelse(is.na(entry), lastUpdate, str_c(entry,  lastUpdate,sep = ","))
}

df2 <- df %>% 
  mutate(updates = lapply(updates, mutate_update_entry)) %>%
  rowwise() %>% 
  mutate(
    updates = ifelse(
      updates == "", 
      lastUpdate, 
      str_c(updates, lastUpdate, sep = ",")
      )
    )

df3 <- df2 %>%
  select(PCode, updates) %>%
  transform(updates = str_split(updates, ",")) %>%
  unnest(updates)

df4 <- df3 %>% 
  mutate(updates = ymd(updates)) %>%
  mutate(year = year(updates))

## Warning: 3 failed to parse.

df4$year %>% hist

最多編修

df4 %>% count(PCode, sort = T) %>% top_n(10) %>% left_join(df, by = "PCode") %>%
  select(name, n) %>% kable()

## Selecting by n

name	n
商業團體分業標準	61
道路交通安全規則	53
建築技術規則建築設計施工編	44
農藥殘留容許量標準	41
證券商管理規則	33
汽車運輸業管理規則	31
固定通信業務管理規則	30
違反道路交通管理事件統一裁罰基準及處理細則	29
食品添加物使用範圍及限量暨規格標準	27
工業及礦業團體分業標準	26

每年更新

df4 %>% count(year) %>%
  filter(year >1992) %>%
  ggplot(aes(x = year, y = n)) + geom_bar(stat = "identity")

法規年齡

base_year <- 2005
age_2005 <- df4 %>% filter(year <= base_year) %>%
  group_by(PCode) %>%
  summarize(lastUpdated = max(year)) %>%
  mutate(age = base_year - lastUpdated) 
age_2005$age %>% hist()

未完待續…