Merge branch 'master' of https://github.com/sammo3182/regioncode

Author: sammo3182

.Rproj.user/shared/notebooks/paths

@@ -1,9 +1,4 @@
-D:/Seafile/WW_research/01_Research/R_package/regioncode/.Rbuildignore="63CD865F"
-D:/Seafile/WW_research/01_Research/R_package/regioncode/DESCRIPTION="25AF9A25"
-D:/Seafile/WW_research/01_Research/R_package/regioncode/NAMESPACE="96D5EBCA"
-D:/Seafile/WW_research/01_Research/R_package/regioncode/NEWS.md="00FBA817"
-D:/Seafile/WW_research/01_Research/R_package/regioncode/R/globals.R="56D29EEB"
-D:/Seafile/WW_research/01_Research/R_package/regioncode/R/regioncode.R="78A4CF23"
-D:/Seafile/WW_research/01_Research/R_package/regioncode/dev/citylevel2021.R="7C97FCDC"
-D:/Seafile/WW_research/01_Research/R_package/regioncode/vignettes/regioncode-vignette.Rmd="48AAAEC1"
-D:/Seafile/WW_research/01_Research/R_package/regioncode/vignettes/s_regioncode.bib="8CF3C239"
+/Users/xinyiye/Documents/GitHub/regioncode/R/regioncode.R="DC074D86"
+/Users/xinyiye/Documents/GitHub/regioncode/dev/citylevel2021.R="2E3C9D10"
+/Users/xinyiye/Documents/GitHub/regioncode/dev/cityranking.R="A45A71BD"
+/Users/xinyiye/Documents/博一下/空间分析/Lec2_visulizing_spatial_data/Lec2_visulizing_spatial_data.R="9BEF4761"

.~region_data.xlsx

@@ -1,73 +1,147 @@
-# citylevel_population
-# 目的：在sysdata.rda中增加城市规模，根据城市的人口计算出城市规模。
-# 数据：中国城市统计年鉴、中国人口统计年鉴
-rm(list = ls())
-setwd("C:/Users/amand/Documents/GitHub/regioncode_new")
-
-if (!requireNamespace("dplyr", quietly = TRUE)) {
-  install.packages("dplyr")
-}
-library(dplyr)
-
-if (!requireNamespace("pacman", quietly = TRUE)) {
-  install.packages("pacman")
-}
-library(pacman)
-p_load("rio",
-       "tidyverse")
-
-# import---------------------------------------------------------------------------
-
-load("R/sysdata.rda")
-df_citylevel<- import("data/cityranking.xls")
-
-library(tidyverse)
-
-# 定义函数，根据population生成cityranking变量-----------------------------------------
-generate_cityranking <- function(df) {
-  year <- unique(df$year)
-  year_cityranking <- paste0(year, "_cityranking")
-  if (year %in% 1986:2013) {
-    df[[year_cityranking]] <- case_when(
-      df$population > 100 ~ "特大城市",
-      df$population > 50 & df$population <= 100 ~ "大城市",
-      df$population > 20 & df$population <= 50 ~ "中等城市",
-      df$population <= 20 ~ "小城市",
-      TRUE ~ NA_character_
-    )
-  } else if (year %in% 2014:2019) {
-    df[[year_cityranking]] <- case_when(
-      df$population > 1000 ~ "超大城市",
-      df$population > 500 & df$population <= 1000 ~ "特大城市",
-      df$population > 300 & df$population <= 500 ~ "I型大城市",
-      df$population > 100 & df$population <= 300 ~ "II型大城市",
-      df$population > 50 & df$population <= 100 ~ "中等城市",
-      df$population > 20 & df$population <= 50 ~ "I型小城市",
-      df$population <= 20 ~ "II型小城市",
-      TRUE ~ NA_character_
-    )
-  }
-  df <- select(df, -year, -population,-population_original)
-  df
-}
-
-# 按年份拆分并生成cityranking变量--------------------------------------------------------
-dfs <- map(1986:2019, ~df_citylevel %>%
-             filter(year == .x) %>%
-             generate_cityranking())
-
-# 合并86-19年的数据集-----------------------------------------------------------------
-cityranking<- reduce(dfs, full_join, by = c("city_code", "city_name"))
-
-# 删除city_code为空值的观测-----------------------------------------------------------
-cityranking <- cityranking[!is.na(cityranking$city_code), ]
-
-# region_table新建一列city_code-------------------------------------------------------
-region_table$city_code <- ifelse(region_table$zhixiashi == "TRUE", region_table$prov_code, region_table$`2019_code`)
-
-#合并---------------------------------------------------------------------------------
-region_data <- merge(region_table,cityranking, by = "city_code")
-region_data <- select(region_data, -city_code,-city_name)
-
-save(region_data, region_table, corruption,file = "~/R/sysdata.rda")
-
+# citylevel_population
+# 目的：在sysdata.rda中增加城市规模，根据城市的人口计算出城市规模。
+# 数据：中国城市统计年鉴、中国人口统计年鉴
+if (!requireNamespace("dplyr", quietly = TRUE)) {
+  install.packages("dplyr")
+}
+library(dplyr)
+
+if (!requireNamespace("pacman", quietly = TRUE)) {
+  install.packages("pacman")
+}
+library(pacman)
+p_load("rio",
+       "tidyverse")
+
+# import---------------------------------------------------------------------------
+
+load("R/sysdata.rda")
+df_citylevel<- import("inst/extdata/cityranking.xls")
+yunnan<- import("inst/extdata/云南人口构成.xlsx")
+
+yunnan <- yunnan %>%
+  filter(!is.na(year))%>%
+  mutate(population= as.numeric(population))
+
+new<- import("inst/extdata/全国各市城镇人口构成.xlsx")
+
+new <- new %>%
+  mutate(population = as.numeric(population)) %>%
+  arrange(code, year)
+
+
+# 定义函数，根据population生成cityranking变量-----------------------------------------
+
+generate_cityranking <- function(df) {
+  year <- unique(df$year)
+  year_cityranking <- paste0(year, "_cityranking")
+  if (year %in% 1986:2013) {
+    df[[year_cityranking]] <- case_when(
+      df$population > 100 ~ "特大城市",
+      df$population > 50 & df$population <= 100 ~ "大城市",
+      df$population > 20 & df$population <= 50 ~ "中等城市",
+      df$population <= 20 ~ "小城市",
+      TRUE ~ NA_character_
+    )
+  } else if (year %in% 2014:2019) {
+    df[[year_cityranking]] <- case_when(
+      df$population > 1000 ~ "超大城市",
+      df$population > 500 & df$population <= 1000 ~ "特大城市",
+      df$population > 300 & df$population <= 500 ~ "I型大城市",
+      df$population > 100 & df$population <= 300 ~ "II型大城市",
+      df$population > 50 & df$population <= 100 ~ "中等城市",
+      df$population > 20 & df$population <= 50 ~ "I型小城市",
+      df$population <= 20 ~ "II型小城市",
+      TRUE ~ NA_character_
+    )
+  }
+  df <- select(df, -year, -population,-population_original)
+  df
+}
+
+# 按年份拆分并生成cityranking变量--------------------------------------------------------
+dfs <- map(1986:2019, ~df_citylevel %>%
+             filter(year == .x) %>%
+             generate_cityranking())
+
+# 合并86-19年的数据集-----------------------------------------------------------------
+cityranking<- reduce(dfs, full_join, by = c("city_code", "city_name"))
+
+# 删除city_code为空值的观测-----------------------------------------------------------
+cityranking <- cityranking[!is.na(cityranking$city_code), ]
+
+# region_table新建一列city_code-------------------------------------------------------
+region_table$city_code <- ifelse(region_table$zhixiashi == "TRUE", region_table$prov_code, region_table$`2019_code`)
+
+#合并---------------------------------------------------------------------------------
+region_data <- merge(region_table,cityranking, by = "city_code")
+region_data <- select(region_data, -city_code,-city_name)
+
+save(region_data, region_table, corruption,file = "~/R/sysdata.rda")
+
+
+# 云南 -----------------------------------------------------------------------------
+
+new_cityranking <- function(df, year) {
+  year_cityranking <- paste0(year, "_cityranking")
+
+  if (year %in% 1986:2013) {
+    df[[year_cityranking]] <- case_when(
+      df$population > 100 ~ "特大城市",
+      df$population > 50 & df$population <= 100 ~ "大城市",
+      df$population > 20 & df$population <= 50 ~ "中等城市",
+      df$population <= 20 ~ "小城市",
+      TRUE ~ NA_character_
+    )
+  } else if (year > 2013) {
+    df[[year_cityranking]] <- case_when(
+      df$population > 1000 ~ "超大城市",
+      df$population > 500 & df$population <= 1000 ~ "特大城市",
+      df$population > 300 & df$population <= 500 ~ "I型大城市",
+      df$population > 100 & df$population <= 300 ~ "II型大城市",
+      df$population > 50 & df$population <= 100 ~ "中等城市",
+      df$population > 20 & df$population <= 50 ~ "I型小城市",
+      df$population <= 20 ~ "II型小城市",
+      TRUE ~ NA_character_
+    )
+  }
+
+  df <- df %>% select(-year, -population)
+  return(df)
+}
+
+
+dfs <- map(1986:2019, ~yunnan %>%
+             filter(year == .x) %>%
+             new_cityranking(., .x))
+
+yunnan_cityranking<- reduce(dfs, full_join, by = c("name"))
+
+yunnan_cityranking <- yunnan_cityranking %>%
+  select(-contains("code"))
+
+write_xlsx(yunnan_cityranking, "yunnan_cityranking.xlsx")
+
+
+# new 20-22 ----------------------------------------------------------------------------
+
+dfs <- map(2020:2022, ~new %>%
+             filter(year == .x) %>%
+             new_cityranking(., .x))
+
+
+new_cityranking<- reduce(dfs, full_join, by = c("code"))
+
+new_cityranking <- new_cityranking %>%
+  select(name,code,contains("cityrank"))
+
+region_data <- region_data %>%
+  mutate(join_key = case_when(
+    zhixiashi == TRUE ~ prov_code,
+    zhixiashi != TRUE ~ `2022_code`
+  ))
+
+region_data  <- region_data %>%
+  left_join(new_cityranking, by = c("join_key" = "code")) %>%
+  select(-"join_key")
+