.Rhistory

names(牍) <- gsub('\\(百万吨油当量|\\(百万吨', '（百万吨）', names(牍))
names(牍) <- gsub('\\(\\%|\\(\\‰', '（百分比）', names(牍))
names(牍) <- gsub('\\(十亿桶', '（十亿桶）', names(牍))
names(牍) <- gsub('\\(吨', '（吨）', names(牍))
names(牍) <- gsub('\\(元', '（元）', names(牍))
names(牍) <- gsub('\\(吨', '（吨）', names(牍))
names(牍) <- gsub('\\(亿美元', '（亿美元）', names(牍))
牍[grep('（亿美元）', names(牍))] <- round(牍[grep('（亿美元）', names(牍))] * 100000000)
names(牍) <- gsub('（美元）亿美元|（亿美元）', '（美元）', names(牍))
names(牍) <- gsub('\\/人|\\(辆|\\(张| 55056', '', names(牍))
names(牍) <- gsub(':', '：', names(牍))
names(牍) <- gsub('：：', '：', names(牍))
names(牍) <- gsub('^人民币', '', names(牍))
names(牍) <- gsub('中国实际使用外资金额', '外资实货量', names(牍))
names(牍) <- gsub('GDP|GDP：', '钱谷', names(牍))
names(牍) <- gsub('^人均钱谷（美元）美元$', '人均钱谷（美元）', names(牍))
names(牍) <- gsub('^钱谷*.+百万美元$', '钱谷（百万美元）', names(牍))
names(牍) <- gsub('\\(个|\\(所：|登记', '', names(牍))
names(牍) <- gsub('\\(亿\\)', '（亿）', names(牍))
牍[grep('（亿）', names(牍))] <- round(牍[grep('（亿）', names(牍))] * 100000000)
names(牍) <- gsub('（亿）', '', names(牍))
names(牍) <- gsub('\\(个：', '', names(牍))
names(牍) <- gsub('\\(所：|\\(所', '', names(牍))
names(牍) <- gsub('\\(港元', '（港元）', names(牍))
牍[grep('（百万港元）', names(牍))] <- round(牍[grep('（百万港元）', names(牍))] * 1000000)
names(牍) <- gsub('\\(百万港元', '（港元）', names(牍))
names(牍) <- gsub('：全球占比|占世界比重|：占全球比例|比重', '占全球', names(牍))
names(牍) <- gsub('占比', '', names(牍))
names(牍) <- gsub('高职\\(专科\\)', '专科', names(牍))
names(牍) <- gsub('\\(购买力平价\\)\\(国际元', '', names(牍))
names(牍) <- gsub('\\(亿千瓦时', '（亿千瓦时）', names(牍))
names(牍) <- gsub('（亿千瓦时）', '（亿千瓦时）', names(牍))
names(牍) <- gsub('老年人占总人口占全球', '老年人口占全球', names(牍))
names(牍) <- gsub('占钱谷占全球', '占全球钱谷', names(牍))
names(牍) <- gsub('\\(10亿立方米', '（十亿立方米）', names(牍))
names(牍) <- gsub('老年人人口总数', '老年人口总数', names(牍))
names(牍) <- gsub('高等学校在校学生数|高等学校本专科在校学生数', '高校在校学生数', names(牍))
names(牍) <- gsub('\\(平方公里', '（平方公里）', names(牍))
names(牍) <- gsub('\\(床', '', names(牍))
names(牍) <- gsub('\\(活产男婴/活产女婴', '（男/女）', names(牍))
names(牍) <- gsub('（元）/平方米', '（元/平方米）', names(牍))
names(牍) <- gsub('：|：：', '', names(牍))
names(牍) <- gsub('\\(本国货币单位', '（本国货币单位）', names(牍))
names(牍) <- gsub('\\(本币\\)\\(欧元', '（欧元）', names(牍))
names(牍) <- gsub('\\(医院和卫生院床位数\\(家|医院和卫生院床位数\\(家|^医院床位数和卫生院|^医院和卫生院床位数$|^医院床位数$|^医疗卫生机构床位数$|^病床数$|^医院、卫生院床位数$', '医院和诊疗所床数', names(牍))
names(牍) <- gsub('\\(含劳务派遣\\)', '（含劳务派遣）', names(牍))
names(牍) <- gsub('\\(本国货币单位', '（本国货币单位）', names(牍))
names(牍) <- gsub('男性占总人口占全球（百分比）', '男性占全球人口（百分比）', names(牍))
names(牍) <- gsub('女性占总人口占全球（百分比）', '女性占全球人口（百分比）', names(牍))
names(牍) <- gsub('总人口数|^总人口$|^户籍总人口$', '户籍人口', names(牍))
names(牍) <- gsub('人口总数|人数|人口总数|人口数量', '人口', names(牍))
names(牍) <- gsub('常住人口', '人口', names(牍))
names(牍) <- gsub(' $', '', names(牍))
names(牍) <- gsub('年平均', '平均', names(牍))
names(牍) <- gsub('职工（含劳务派遣）平均工资（元）|全部单位职工平均工资（元）|城镇单位职工平均工资（元）', '职工平均工资（元）', names(牍))
# lapply(牍, class)
names(牍) <- gsub('^钱谷增长率*.+$', '钱谷增长率（百分比）', names(牍))
names(牍) <- gsub('^在校小*.+$|小学*.+$', '在校小学生', names(牍))
names(牍) <- gsub('^高等*.+$|^高校*.+$', '在校高学生', names(牍))
names(牍) <- gsub('^专科*.+$|^本专科*.+$', '在校高专生', names(牍))
# 版[grep('.id|时间|农村|农牧|农民|城镇', names(版), value = TRUE)] %>%
#   .[rowSums(!is.na(版[grep('城镇化率', names(版), value = TRUE)])) > 0, ] # %>% names
# .[1001:1l500,] %>% data.frame
names(牍) <- gsub('农村居民|农牧民|农牧居民|渔农村居民', '农林牧渔民', names(牍))
names(牍) <- gsub('城镇居民', '邑民', names(牍)) #攻城堕邑，城镇故称「邑」
牍$`农林牧渔民可支配收入（人民币）` <- rowSums(牍[grep('农林牧渔民可支配收入', names(牍))], na.rm = TRUE) %>% na_if(0)
牍[grep('农林牧渔民可支配收入', names(牍))] <- NULL
牍$`邑民人均可支配收入（人民币）` <- rowSums(牍[grep('居民人均可支配收入', names(牍))], na.rm = TRUE) %>% na_if(0)
牍[grep('居民人均可支配收入', names(牍))] <- NULL
牍$地方财税 <- rowSums(牍[grep('地方|财政|税收收入', names(牍))], na.rm = TRUE) %>% na_if(0)
牍[grep('财政|税收收入', names(牍))] <- NULL
names(牍) <- gsub('存款余额个人储蓄存款|居民储蓄存款余额', '居民储蓄', names(牍))
names(牍) <- gsub('^居民储蓄存款余额（亿人民币）$|^存款余额个人储蓄存款（亿人民币）$|^城乡居民储蓄余额（亿人民币）$|^本外币居民储蓄（亿人民币）$|^城乡居民储蓄（亿人民币）$|^储蓄存款余额（亿人民币）$|^居民储蓄余额（亿人民币）$|^城乡居民储蓄（亿人民币）$|^本外币居民储蓄（亿人民币）$', '居民储蓄（亿人民币）', names(牍))
牍$`居民储蓄（亿人民币）` <- NULL
names(牍) <- gsub('^城乡居民储蓄余额$|居民储蓄存款余额|^城乡居民储蓄$|^居民储蓄余额$', '居民储蓄', names(牍))
names(牍) <- gsub('（元）', '（人民币）', names(牍))
names(牍) <- gsub('^存款余额住户存款$|^本外币存款余额境内住户存款$|^存款余额境内住户存款$|^本外币住户存款余额$|^本外币存款余额住户存款$|^住户存款余额$|^存款余额境内存款-个人存款$', '本外币余额（人民币）', names(牍))
names(牍) <- gsub('^住户存款余额（亿人民币）$|^存款余额境内住户存款（亿人民币）$|^本外币存款余额住户存款（亿人民币）$|^本外币住户存款余额（亿人民币）$|^存款余额住户存款（亿人民币）$|^本外币存款余额境内住户存款（亿人民币）$|^住户本外币存款余额（亿人民币）$|^存款余额境内存款住户存款（亿人民币）$|^个人存款余额（亿人民币）$|^本外币境内住户贷款余额（亿人民币）$', '本外币余额（亿人民币）', names(牍))
牍$`本外币余额（亿人民币）` <- NULL
牍[grep('（亿人民币）', names(牍))] <- round(牍[grep('（亿人民币）', names(牍))] * 100000000)
names(牍) <- gsub('^本外币居民储蓄$|^住户本外币存款余额$|^本外币境内住户贷款余额$|^储蓄存款余额$|^存款余额境内存款住户存款$|^个人存款余额$', '本外币余额（人民币）', names(牍))
names(牍) <- gsub('\\(本国货币|（本国货币单位）', '（本国币种）', names(牍))
names(牍) <- gsub('通货膨涨率\\(CPI\\)', '消费指数通胀率', names(牍))
names(牍) <- gsub('城镇', '城邑', names(牍))
names(牍) <- gsub('港货物吞吐量', '港口吞吐量', names(牍))
# 制造业和工业的区别和联系：https://worktile.com/kb/ask/11799.html
names(牍) <- gsub('制造业', '工业', names(牍))
names(牍) <- gsub('^全球', '', names(牍))
names(牍) <- gsub('时间', '年份', names(牍))
# names(牍) <- gsub('^城乡居民储蓄余额$|^住户本外币存款余额$|^本外币境内住户贷款余额$', '本外币余额（人民币）', names(牍))
# 牍$`存款余额住户存款（亿人民币）` %<>% replace(is.na(.), 'NA')
#牍乙 = 牍[-1][rowSums(!is.na(牍[-1])) > 0,]
#牍丙 = dplyr::inner_join(牍[c('时间', '人口')], 牍乙)
#牍 %<>% replace_na_dt(to = 'NA')
牍
}) %>%
plyr::ldply(.id = '天下') %>%
as.data.table() # as_tibble()
聚汇数据 <- 版
# 聚汇数据[, 年份 := data.table::year(年份)]
## ??data.table::year 中的year()函数数据矢量属于int数据类型
## ??integer
## ??round
# data.table::year(round(聚汇数据$年份))
# round(聚汇数据$年份)
聚汇数据 <- 聚汇数据[rowSums(!is.na(聚汇数据[,-c(1:2)])) > 0, ]
聚汇数据
聚汇数据 <- 聚汇数据[年份 != 199]
聚汇数据
saveRDS(聚汇数据, paste0(.蜀道, '诸子百家学府/聚汇数据.rds'))
if (!is.data.table(聚汇数据)) 聚汇数据 %<>% as.data.table
## 天下各别数据
天下数据 <- 聚汇数据[!grep('全世界', 天下)]
## 天下总和数据
总汇数据 <- 聚汇数据[grep('全世界', 天下)]
## 检验是否已设置途径。
# source('函数/...R')
# source('函数/....R')
## 秦国 China，秦人 Chinese
## 司马错得蜀既得楚
if (!exists('.蜀道')) {
.蜀道 <- getwd() |>
{\(.) str_split(., '/')}() |>
{\(.) c('/', .[[1]][2:5])}() |>
{\(.) c(., 'zhongkehongqi-cangku/')}() |>
{\(.) paste(., collapse = '/')}() |>
{\(.) substring(., 2)}()
}
if (!exists('聚汇数据')) {
聚汇数据 <- readRDS(paste0(.蜀道, '诸子百家学府/聚汇数据.rds'))
}
# conflicted::conflicts_prefer(data.table::`:=`)
if (!is.data.table(聚汇数据)) 聚汇数据 %<>% as.data.table
## 天下各别数据
天下数据 <- 聚汇数据[!grep('全世界', 天下)]
## 天下总和数据
总汇数据 <- 聚汇数据[grep('全世界', 天下)]
## 绘制聚汇数据图表
聚汇数据[c(1:3, (nrow(聚汇数据) - 3):nrow(聚汇数据)),] |>
{\(.) kbl(., caption = '聚汇数据：左氏春秋，鸟瞰天下', escape = FALSE)}() |>
## https://www.w3schools.com/cssref/css_colors.asp
## https://public.tableau.com/en-us/gallery/100-color-palettes?gallery=votd
{\(.) row_spec(., 0, background = 'DimGrey',
color = 'gold', bold = TRUE)}() |>
{\(.) column_spec(.,   1, background = '#991B00')}() |>
{\(.) column_spec(.,   2, background = '#D52600')}() |>
{\(.) column_spec(.,   3, background = 'DarkGrey')}() |>
{\(.) column_spec(.,   4, background = 'Gray')}() |>
{\(.) column_spec(.,   5, background = 'DarkGrey')}() |>
{\(.) column_spec(.,   6, background = 'Gray')}() |>
{\(.) column_spec(.,   7, background = 'DarkGrey')}() |>
{\(.) column_spec(.,   8, background = 'Gray')}() |>
{\(.) column_spec(.,   9, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  10, background = 'Gray')}() |>
{\(.) column_spec(.,  11, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  12, background = 'Gray')}() |>
{\(.) column_spec(.,  13, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  14, background = 'Gray')}() |>
{\(.) column_spec(.,  15, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  16, background = 'Gray')}() |>
{\(.) column_spec(.,  17, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  18, background = 'Gray')}() |>
{\(.) column_spec(.,  19, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  20, background = 'Gray')}() |>
{\(.) column_spec(.,  21, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  22, background = 'Gray')}() |>
{\(.) column_spec(.,  23, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  24, background = 'Gray')}() |>
{\(.) column_spec(.,  25, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  26, background = 'Gray')}() |>
{\(.) column_spec(.,  27, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  28, background = 'Gray')}() |>
{\(.) column_spec(.,  29, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  30, background = 'Gray')}() |>
{\(.) column_spec(.,  31, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  32, background = 'Gray')}() |>
{\(.) column_spec(.,  33, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  34, background = 'Gray')}() |>
{\(.) column_spec(.,  35, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  36, background = 'Gray')}() |>
{\(.) column_spec(.,  37, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  38, background = 'Gray')}() |>
{\(.) column_spec(.,  39, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  40, background = 'Gray')}() |>
{\(.) column_spec(.,  41, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  42, background = 'Gray')}() |>
{\(.) column_spec(.,  43, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  44, background = 'Gray')}() |>
{\(.) column_spec(.,  45, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  46, background = 'Gray')}() |>
{\(.) column_spec(.,  47, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  48, background = 'Gray')}() |>
{\(.) column_spec(.,  49, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  50, background = 'Gray')}() |>
{\(.) column_spec(.,  51, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  52, background = 'Gray')}() |>
{\(.) column_spec(.,  53, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  54, background = 'Gray')}() |>
{\(.) column_spec(.,  55, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  56, background = 'Gray')}() |>
{\(.) column_spec(.,  57, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  58, background = 'Gray')}() |>
{\(.) column_spec(.,  59, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  60, background = 'Gray')}() |>
{\(.) column_spec(.,  61, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  62, background = 'Gray')}() |>
{\(.) column_spec(.,  63, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  64, background = 'Gray')}() |>
{\(.) column_spec(.,  65, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  66, background = 'Gray')}() |>
{\(.) column_spec(.,  67, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  68, background = 'Gray')}() |>
{\(.) column_spec(.,  69, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  70, background = 'Gray')}() |>
{\(.) column_spec(.,  71, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  72, background = 'Gray')}() |>
{\(.) column_spec(.,  73, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  74, background = 'Gray')}() |>
{\(.) column_spec(.,  75, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  76, background = 'Gray')}() |>
{\(.) column_spec(.,  77, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  78, background = 'Gray')}() |>
{\(.) column_spec(.,  79, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  80, background = 'Gray')}() |>
{\(.) column_spec(.,  81, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  82, background = 'Gray')}() |>
{\(.) column_spec(.,  83, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  84, background = 'Gray')}() |>
{\(.) column_spec(.,  85, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  86, background = 'Gray')}() |>
{\(.) column_spec(.,  87, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  88, background = 'Gray')}() |>
{\(.) column_spec(.,  89, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  90, background = 'Gray')}() |>
{\(.) column_spec(.,  91, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  92, background = 'Gray')}() |>
{\(.) column_spec(.,  93, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  94, background = 'Gray')}() |>
{\(.) column_spec(.,  95, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  96, background = 'Gray')}() |>
{\(.) column_spec(.,  97, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  98, background = 'Gray')}() |>
{\(.) column_spec(.,  99, background = 'DarkGrey')}() |>
{\(.) column_spec(., 100, background = 'Gray')}() |>
{\(.) column_spec(., 101, background = 'DarkGrey')}() |>
{\(.) column_spec(., 102, background = 'Gray')}() |>
{\(.) column_spec(., 103, background = 'DarkGrey')}() |>
{\(.) column_spec(., 104, background = 'Gray')}() |>
{\(.) column_spec(., 105, background = 'DarkGrey')}() |>
{\(.) column_spec(., 106, background = 'Gray')}() |>
{\(.) column_spec(., 107, background = 'DarkGrey')}() |>
{\(.) column_spec(., 108, background = 'Gray')}() |>
{\(.) column_spec(., 109, background = 'DarkGrey')}() |>
{\(.) column_spec(., 110, background = 'Gray')}() |>
{\(.) column_spec(., 111, background = 'DarkGrey')}() |>
{\(.) column_spec(., 112, background = 'Gray')}() |>
{\(.) column_spec(., 113, background = 'DarkGrey')}() |>
{\(.) column_spec(., 114, background = 'Gray')}() |>
# {\(.) column_spec(., 12, background = 'LightGray',
#                   color = 'goldenrod')}() |>
{\(.) kable_styling(., bootstrap_options =
c('striped', 'hover', 'condensed', 'responsive'))}() |>
##`full_width = FALSE`是将每列设置为伸缩性自动调整宽度。
{\(.) kable_material(., full_width = FALSE)}() |>
{\(.) scroll_box(., width = '100%', fixed_thead = TRUE,
height = '490px')}()
## 绘制天下数据图表
总汇数据 |>
{\(.) kbl(., caption = '聚汇数据：左氏春秋，鸟瞰天下', escape = FALSE)}() |>
## https://www.w3schools.com/cssref/css_colors.asp
## https://public.tableau.com/en-us/gallery/100-color-palettes?gallery=votd
{\(.) row_spec(., 0, background = 'DimGrey',
color = 'gold', bold = TRUE)}() |>
{\(.) column_spec(.,   1, background = '#991B00')}() |>
{\(.) column_spec(.,   2, background = '#D52600')}() |>
{\(.) column_spec(.,   3, background = 'DarkGrey')}() |>
{\(.) column_spec(.,   4, background = 'Gray')}() |>
{\(.) column_spec(.,   5, background = 'DarkGrey')}() |>
{\(.) column_spec(.,   6, background = 'Gray')}() |>
{\(.) column_spec(.,   7, background = 'DarkGrey')}() |>
{\(.) column_spec(.,   8, background = 'Gray')}() |>
{\(.) column_spec(.,   9, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  10, background = 'Gray')}() |>
{\(.) column_spec(.,  11, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  12, background = 'Gray')}() |>
{\(.) column_spec(.,  13, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  14, background = 'Gray')}() |>
{\(.) column_spec(.,  15, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  16, background = 'Gray')}() |>
{\(.) column_spec(.,  17, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  18, background = 'Gray')}() |>
{\(.) column_spec(.,  19, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  20, background = 'Gray')}() |>
{\(.) column_spec(.,  21, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  22, background = 'Gray')}() |>
{\(.) column_spec(.,  23, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  24, background = 'Gray')}() |>
{\(.) column_spec(.,  25, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  26, background = 'Gray')}() |>
{\(.) column_spec(.,  27, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  28, background = 'Gray')}() |>
{\(.) column_spec(.,  29, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  30, background = 'Gray')}() |>
{\(.) column_spec(.,  31, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  32, background = 'Gray')}() |>
{\(.) column_spec(.,  33, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  34, background = 'Gray')}() |>
{\(.) column_spec(.,  35, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  36, background = 'Gray')}() |>
{\(.) column_spec(.,  37, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  38, background = 'Gray')}() |>
{\(.) column_spec(.,  39, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  40, background = 'Gray')}() |>
{\(.) column_spec(.,  41, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  42, background = 'Gray')}() |>
{\(.) column_spec(.,  43, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  44, background = 'Gray')}() |>
{\(.) column_spec(.,  45, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  46, background = 'Gray')}() |>
{\(.) column_spec(.,  47, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  48, background = 'Gray')}() |>
{\(.) column_spec(.,  49, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  50, background = 'Gray')}() |>
{\(.) column_spec(.,  51, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  52, background = 'Gray')}() |>
{\(.) column_spec(.,  53, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  54, background = 'Gray')}() |>
{\(.) column_spec(.,  55, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  56, background = 'Gray')}() |>
{\(.) column_spec(.,  57, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  58, background = 'Gray')}() |>
{\(.) column_spec(.,  59, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  60, background = 'Gray')}() |>
{\(.) column_spec(.,  61, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  62, background = 'Gray')}() |>
{\(.) column_spec(.,  63, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  64, background = 'Gray')}() |>
{\(.) column_spec(.,  65, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  66, background = 'Gray')}() |>
{\(.) column_spec(.,  67, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  68, background = 'Gray')}() |>
{\(.) column_spec(.,  69, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  70, background = 'Gray')}() |>
{\(.) column_spec(.,  71, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  72, background = 'Gray')}() |>
{\(.) column_spec(.,  73, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  74, background = 'Gray')}() |>
{\(.) column_spec(.,  75, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  76, background = 'Gray')}() |>
{\(.) column_spec(.,  77, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  78, background = 'Gray')}() |>
{\(.) column_spec(.,  79, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  80, background = 'Gray')}() |>
{\(.) column_spec(.,  81, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  82, background = 'Gray')}() |>
{\(.) column_spec(.,  83, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  84, background = 'Gray')}() |>
{\(.) column_spec(.,  85, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  86, background = 'Gray')}() |>
{\(.) column_spec(.,  87, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  88, background = 'Gray')}() |>
{\(.) column_spec(.,  89, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  90, background = 'Gray')}() |>
{\(.) column_spec(.,  91, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  92, background = 'Gray')}() |>
{\(.) column_spec(.,  93, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  94, background = 'Gray')}() |>
{\(.) column_spec(.,  95, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  96, background = 'Gray')}() |>
{\(.) column_spec(.,  97, background = 'DarkGrey')}() |>
{\(.) column_spec(.,  98, background = 'Gray')}() |>
{\(.) column_spec(.,  99, background = 'DarkGrey')}() |>
{\(.) column_spec(., 100, background = 'Gray')}() |>
{\(.) column_spec(., 101, background = 'DarkGrey')}() |>
{\(.) column_spec(., 102, background = 'Gray')}() |>
{\(.) column_spec(., 103, background = 'DarkGrey')}() |>
{\(.) column_spec(., 104, background = 'Gray')}() |>
{\(.) column_spec(., 105, background = 'DarkGrey')}() |>
{\(.) column_spec(., 106, background = 'Gray')}() |>
{\(.) column_spec(., 107, background = 'DarkGrey')}() |>
{\(.) column_spec(., 108, background = 'Gray')}() |>
{\(.) column_spec(., 109, background = 'DarkGrey')}() |>
{\(.) column_spec(., 110, background = 'Gray')}() |>
{\(.) column_spec(., 111, background = 'DarkGrey')}() |>
{\(.) column_spec(., 112, background = 'Gray')}() |>
{\(.) column_spec(., 113, background = 'DarkGrey')}() |>
{\(.) column_spec(., 114, background = 'Gray')}() |>
# {\(.) column_spec(., 12, background = 'LightGray',
#                   color = 'goldenrod')}() |>
{\(.) kable_styling(., bootstrap_options =
c('striped', 'hover', 'condensed', 'responsive'))}() |>
##`full_width = FALSE`是将每列设置为伸缩性自动调整宽度。
{\(.) kable_material(., full_width = FALSE)}() |>
{\(.) scroll_box(., width = '100%', fixed_thead = TRUE,
height = '490px')}()
## 绘制天下数据图表
绘图甲 <- 天下数据 |>
highchart() |>
hc_chart('line', hcaes(x = 年份, y = 钱谷, group = 天下)) |>
hc_title(text = '天下') |>
hc_subtitle(text = '钱谷')
tagList(绘图甲)
group_by
highchater::group_by
highcharter::group_by
dplyr::group_by
## 绘制天下数据图表
天下数据 %>%
dplyr::group_by(天下) %>%
e_charts(x = 年份) %>%
e_line(钱谷, smooth = TRUE) %>%
e_datazoom(
type = 'slider',
toolbox = FALSE,
bottom = -5) %>%
e_tooltip() %>%
e_title(text = '天下', subtext = '钱谷', left = 'center') %>%
e_axis_labels(x = '年份', y = '钱谷') %>%
e_x_axis(年份, axisPointer = list(show = TRUE)) %>%
e_legend(
orient = 'vertical',
type = c('scroll'),
#selectedMode = 'multiple', #https://echarts.apache.org/en/option.html#legend
#selected = list('天下'),
left = 0, top = 80) %>%
e_grid(left = 150, top = 90) %>%
#e_theme('shine') %>%
e_toolbox_feature('saveAsImage', title = '截图')
季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1.rds")
日内指数平滑数据_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/日内指数平滑数据_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1.rds")
季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1
日内指数平滑数据_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1
unique(日内指数平滑数据_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1)
unique(日内指数平滑数据_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1)
季节性自回归自动化_差分阶数0_季节性差分阶数0_zoo1200 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_zoo1200.rds")
unique(季节性自回归自动化_差分阶数0_季节性差分阶数0_zoo1200)
`自动季回归_差分阶数_0_季节性差分阶数_0_数据量1200_频率1_预测时间单位1_2018-01-02 00:01:00CST` <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/1/自动季回归_差分阶数_0_季节性差分阶数_0_数据量1200_频率1_预测时间单位1_2018-01-02 00:01:00CST.rds")
`自动季回归_差分阶数_0_季节性差分阶数_0_数据量1200_频率1_预测时间单位1_2018-01-02 00:02:00CST` <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/1/自动季回归_差分阶数_0_季节性差分阶数_0_数据量1200_频率1_预测时间单位1_2018-01-02 00:02:00CST.rds")
自动季回归_差分阶数_0_季节性差分阶数_0_数据量1200_频率1_预测时间单位1_2018-01-02 00:01:00CST
`自动季回归_差分阶数_0_季节性差分阶数_0_数据量1200_频率1_预测时间单位1_2018-01-02 00:01:00CST`
`自动季回归_差分阶数_0_季节性差分阶数_0_数据量1200_频率1_预测时间单位1_2018-01-02 00:02:00CST`
日内指数平滑数据_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/日内指数平滑数据_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1.rds")
季节性自回归自动化_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1.rds")
日内指数平滑数据_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1
季节性自回归自动化_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1
季节性自回归自动化_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_msts_数据量1200_频率1.rds")
季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1.rds")
季节性自回归自动化_差分阶数0_季节性差分阶数0_zoo1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_zoo1.rds")
季节性自回归自动化_差分阶数0_季节性差分阶数0_ts1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_ts1.rds")
getwd()
列表1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/列表1.rds")
季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1.rds")
季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1 <- readRDS("/home/englianhu/文档/猫城/binary.com-interview-question-data/诸子百家学府/fx/USDJPY/仓库/季节性自回归自动化_差分阶数0_季节性差分阶数0_xts_数据量1200_频率1.rds")
conflicts_prefer(Ipaper::is_empty, .quiet = TRUE)
conflicts_prefer(git2r::reset, .quiet = TRUE)
conflicts_prefer(Ipaper::llply, .quiet = TRUE)
conflicts_prefer(Ipaper::`%->%`, .quiet = TRUE)
conflicts_prefer(tibble::view, .quiet = TRUE)
conflicts_prefer(lubridate::year, .quiet = TRUE)
conflicts_prefer(gtools::permutations, .quiet = TRUE)
conflicts_prefer(tidyft::complete, .quiet = TRUE)
conflicts_prefer(tidyft::nth, .quiet = TRUE)
conflicts_prefer(tidyft::fill, .quiet = TRUE)
conflicts_prefer(tidyft::nest, .quiet = TRUE)
conflicts_prefer(tidyft::unnest, .quiet = TRUE)
conflicts_prefer(tidyft::cummean, .quiet = TRUE)
conflicts_prefer(tidyft::group_by, .quiet = TRUE)
conflicts_prefer(tidyft::distinct, .quiet = TRUE)
conflicts_prefer(tidyft::filter, .quiet = TRUE)
conflicts_prefer(tidyft::select, .quiet = TRUE)
conflicts_prefer(tidyft::rename, .quiet = TRUE)
conflicts_prefer(tidyft::count, .quiet = TRUE)
conflicts_prefer(tidyft::arrange, .quiet = TRUE)
conflicts_prefer(tidyft::summarise, .quiet = TRUE)
conflicts_prefer(tidyft::separate, .quiet = TRUE)
conflicts_prefer(tidyft::lead, .quiet = TRUE)
conflicts_prefer(tidyft::lag, .quiet = TRUE)
conflicts_prefer(tidyft::unite, .quiet = TRUE)
conflicts_prefer(tidyft::left_join, .quiet = TRUE)
conflicts_prefer(tidyft::right_join, .quiet = TRUE)
conflicts_prefer(tidyft::inner_join, .quiet = TRUE)
conflicts_prefer(tidyft::full_join, .quiet = TRUE)
conflicts_prefer(tidyft::anti_join, .quiet = TRUE)
conflicts_prefer(tidyft::semi_join, .quiet = TRUE)
conflicts_prefer(tidyft::select_dt, .quiet = TRUE)
conflicts_prefer(tidyft::transpose, .quiet = TRUE)
conflicts_prefer(tidyft::setDT, .quiet = TRUE)
conflicts_prefer(tidyft::setnames, .quiet = TRUE)
conflicts_prefer(dplyr::mutate, .quiet = TRUE)
conflicts_prefer(dplyr::collapse, .quiet = TRUE)
conflicts_prefer(lubridate::year, .quiet = TRUE)
conflicts_prefer(data.table::first, .quiet = TRUE)
conflicts_prefer(data.table::last, .quiet = TRUE)
conflicts_prefer(data.table::between, .quiet = TRUE)
conflicts_prefer(data.table::set, .quiet = TRUE)
conflicts_prefer(data.table::`:=`, .quiet = TRUE)
conflicts_prefer(hms::hms, .quiet = TRUE)
conflicts_prefer(fabletools::accuracy, .quiet = TRUE)
conflicts_prefer(base::load, .quiet = TRUE)
conflicts_prefer(base::merge, .quiet = TRUE)
save.image("~/文档/猫城/RedFlag-Linux/.RData")
rm(list = ls())
rm(list = ls()); save.image("~/文档/猫城/binary.com-interview-question/.RData")
save.image("~/文档/猫城/RedFlag-Linux/.RData")
library(readxl)
fenci <- read_excel("礼逆袭应用仓库/红旗音乐/TongJi/fenci.xls")
View(fenci)
reticulate::repl_python()
#http://music.163.com/api/song/lyric?' + 'id=' + str(song_Id) + '&lv=1&kv=1&tv=-1
library(readxl)
fenci <- read_excel("礼逆袭应用仓库/红旗音乐/TongJi/fenci.xls")
View(fenci)