From 77f95ab9258bc6358a9d771c6a02e93ce55e2dae Mon Sep 17 00:00:00 2001 From: Xinyi Ye Date: Fri, 8 Nov 2024 16:39:19 +0800 Subject: [PATCH] =?UTF-8?q?Population=20Data=20for=20Yunnan=20Province=20C?= =?UTF-8?q?ities=20(1986=E2=80=932019)=20and=20National=20Cities=20(2020?= =?UTF-8?q?=E2=80=932022)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 【更新】 1.删除县级市 2.更正重庆市武隆区(2017年撤县设区) 3.处理重庆 、海南的错误代码 海南省1987 属于广东行政区, 1988成为省 重庆 96属于四川 97之后成为直辖市 4.处理云南缺失人口数据 5.新增20-22年全国人口数据 参考资料:https://www.mca.gov.cn/n156/n186/index.html 【待更新】
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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") + diff --git "a/inst/extdata/\344\272\221\345\215\227\344\272\272\345\217\243\346\236\204\346\210\220.xlsx" "b/inst/extdata/\344\272\221\345\215\227\344\272\272\345\217\243\346\236\204\346\210\220.xlsx" new file mode 100644 index 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