通过填充缺失日期和对称迭代日期来找到 r 中可用的最接近值来进行平均插补
mean imputation by filling in missing dates and by symetrically iterating over dates up and down to find the closest value available in r
我需要估算每个 ID 的可用日期之间的所有缺失日期，然后对称地上下估算缺失日期。另外，我并不总是需要两个之间的平均值，例如：当我上下移动 2 个日期时，我只看到 1 个值，那么我会估算该值。
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21"),
price = c(NA, NA,100, NA, 50, NA, 200, NA)
)
@lovalery 有一个很好的解决对称迭代缺失插补的方法
在上面的解决方案中，使用了当前日期，但是当两者之间缺少大量日期时，这可能会成为一个问题。
因此，我想在两者之间插入所有缺失的日期，然后在两个方向上对称移动，直到我在任一方向上至少获得 1 个值，我需要保留它，如果 2 个值我需要平均值。
更新：我们还需要考虑价格仅出现在第一个日期或最后一个日期的情况。此外，如果相同的价格出现在多个日期
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11,
12,12,12,
13,13,13),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21",
"2021-07-01","2021-07-03","2021-07-05",
"2021-08-01","2021-08-03","2021-08-05"),
price = c(200, NA,100, NA, 50, NA, 200, NA,
10,NA,NA,
NA,NA,20)
)
我使用了@lovalery
的函数NA_imputations_dates_v2
df1 <- setDT(df1)
df2 <- NA_imputations_dates_v2(df1)
df3 <- merge(df1,df2,by = c("id","Date"),all.x = T)
请在下面找到一个使用 data.table 和 padr 库的 reprex 可能解决方案。
我构建了一个函数以使其更易于使用。
Reprex
- 你的数据集#1
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21"),
price = c(NA, NA,100, NA, 50, NA, 200, NA))
NA_imputations_dates() 函数的代码
library(data.table)
library(padr)
NA_imputations_dates <- function(x) {
setDT(x)[, Date := as.Date(Date)]
x <- pad(x, interval = "day", group = "id")
setDT(x)[, rows := .I]
z <- x[, .I[!is.na(price)]]
id_1 <- z[-length(z)]
id_2 <- z[-1]
values <- x[z, .(price = price, id = id)]
values_1 <- values[-nrow(values)]
names(values_1) <- c("price_1", "id_o1")
values_2 <- values[-1]
names(values_2) <- c("price_2", "id_o2")
subtract <- z[-1] - z[-length(z)]
r <- data.table(id_1, values_1, id_2, values_2, subtract)
r <- r[, `:=` (id_mean = fifelse(subtract > 2 & subtract %% 2 == 0, id_1+(subtract/2), (id_1+id_2)/2),
mean = fifelse(subtract >= 2 & subtract %% 2 == 0 & id_o1 == id_o2, (price_1+price_2)/2, NA_real_))
][, `:=` (price_1 = NULL, id_1 = NULL, id_o1 = NULL, id_2 = NULL, price_2 = NULL, id_o2 = NULL, subtract = NULL)
][x, on = .(id_mean = rows)][, dummy := cumsum(!is.na(mean)), by = .(id)]
h <- r[, .(price = na.omit(price)), by = .(dummy)]
Results <- r[, price := NULL
][h, on = .(dummy)
][, price := fifelse(!is.na(mean), mean, price)
][, `:=` (id_mean = NULL, mean = NULL, dummy = NULL)][]
return(Results)
}
NA_imputations_dates() 函数的输出
NA_imputations_dates(df1)
#> id Date price
#> 1: 11 2021-06-01 100
#> 2: 11 2021-06-02 100
#> 3: 11 2021-06-03 100
#> 4: 11 2021-06-04 100
#> 5: 11 2021-06-05 100
#> 6: 11 2021-06-06 100
#> 7: 11 2021-06-07 100
#> 8: 11 2021-06-08 100
#> 9: 11 2021-06-09 100
#> 10: 11 2021-06-10 100
#> 11: 11 2021-06-11 75
#> 12: 11 2021-06-12 50
#> 13: 11 2021-06-13 50
#> 14: 11 2021-06-14 50
#> 15: 11 2021-06-15 50
#> 16: 11 2021-06-16 50
#> 17: 11 2021-06-17 125
#> 18: 11 2021-06-18 200
#> 19: 11 2021-06-19 200
#> 20: 11 2021-06-20 200
#> 21: 11 2021-06-21 200
#> id Date price
由 reprex package (v2.0.1)
于 2021-12-12 创建
编辑函数以处理更通用的数据集 #2
作为您评论的后续，请在下方找到函数的修改版本（即 NA_imputations_dates_v2()）以处理新数据集提供的更一般情况（即 dataset #2 ).
Reprex
- 你的数据集 #2
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11,
12,12,12,
13,13,13),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21",
"2021-07-01","2021-07-03","2021-07-05",
"2021-08-01","2021-08-03","2021-08-05"),
price = c(NA, NA,100, NA, 50, NA, 200, NA,
10,NA,NA,
NA,NA,20))
NA_imputations_dates_v2() 函数的代码
library(data.table)
library(padr)
NA_imputations_dates_v2 <- function(x) {
setDT(x)[, Date := as.Date(Date)]
x <- pad(x, interval = "day", group = "id")
setDT(x)[, rows := .I]
z <- x[, .I[!is.na(price)]]
id_1 <- z[-length(z)]
id_2 <- z[-1]
values <- x[z, .(price = price, id = id)]
values_1 <- values[-nrow(values)]
names(values_1) <- c("price_1", "id_o1")
values_2 <- values[-1]
names(values_2) <- c("price_2", "id_o2")
subtract <- z[-1] - z[-length(z)]
r <- data.table(id_1, values_1, id_2, values_2, subtract)
r <- r[, `:=` (id_mean = fifelse(subtract > 2 & subtract %% 2 == 0 & id_o1 == id_o2, id_1+(subtract/2), NA_real_),
mean = fifelse(subtract >= 2 & subtract %% 2 == 0 & id_o1 == id_o2, (price_1+price_2)/2, NA_real_))
][, `:=` (price_1 = NULL, id_1 = NULL, id_o1 = NULL, id_2 = NULL, price_2 = NULL, id_o2 = NULL, subtract = NULL)
][x, on = .(id_mean = rows)][, dummy := cumsum(!is.na(mean)), by = .(id)]
h <- r[, .(price = na.omit(price)), by = .(dummy, id)]
Results <- r[, price := NULL
][h, on = .(dummy, id)
][, price := fifelse(!is.na(mean), mean, price)
][, `:=` (id_mean = NULL, mean = NULL, dummy = NULL)][]
return(Results)
}
NA_imputations_dates_v2() 函数的输出
NA_imputations_dates_v2(df1)
#> id Date price
#> 1: 11 2021-06-01 100
#> 2: 11 2021-06-02 100
#> 3: 11 2021-06-03 100
#> 4: 11 2021-06-04 100
#> 5: 11 2021-06-05 100
#> 6: 11 2021-06-06 100
#> 7: 11 2021-06-07 100
#> 8: 11 2021-06-08 100
#> 9: 11 2021-06-09 100
#> 10: 11 2021-06-10 100
#> 11: 11 2021-06-11 75
#> 12: 11 2021-06-12 50
#> 13: 11 2021-06-13 50
#> 14: 11 2021-06-14 50
#> 15: 11 2021-06-15 50
#> 16: 11 2021-06-16 50
#> 17: 11 2021-06-17 125
#> 18: 11 2021-06-18 200
#> 19: 11 2021-06-19 200
#> 20: 11 2021-06-20 200
#> 21: 11 2021-06-21 200
#> 22: 12 2021-07-01 10
#> 23: 12 2021-07-02 10
#> 24: 12 2021-07-03 10
#> 25: 12 2021-07-04 10
#> 26: 12 2021-07-05 10
#> 27: 13 2021-08-01 20
#> 28: 13 2021-08-02 20
#> 29: 13 2021-08-03 20
#> 30: 13 2021-08-04 20
#> 31: 13 2021-08-05 20
#> id Date price
由 reprex package (v2.0.1)
于 2021-12-14 创建
第二次编辑函数以处理更通用的数据集 #3
作为您第二条评论的后续，请在下方找到函数的修改版本（即 NA_imputations_dates_v3()），以处理新数据集提供的更一般情况（即 dataset #3).
Reprex
- 你的数据集#3
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11,
12,12,12,
13,13,13),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21",
"2021-07-01","2021-07-03","2021-07-05",
"2021-08-01","2021-08-03","2021-08-05"),
price = c(NA, NA,100, NA, 50, NA, 200, 200,
10,NA,NA,
NA,NA,20))
NA_imputations_dates_v3() 函数的代码
library(data.table)
library(padr)
NA_imputations_dates_v3 <- function(x) {
setDT(x)[, Date := as.Date(Date)]
x <- pad(x, interval = "day", group = "id")
setDT(x)[, rows := .I]
z <- x[, .I[!is.na(price)]]
id_1 <- z[-length(z)]
id_2 <- z[-1]
values <- x[z, .(price = price, id = id)]
values_1 <- values[-nrow(values)]
names(values_1) <- c("price_1", "id_o1")
values_2 <- values[-1]
names(values_2) <- c("price_2", "id_o2")
subtract <- z[-1] - z[-length(z)]
r <- data.table(id_1, values_1, id_2, values_2, subtract)
r <- r[, `:=` (id_mean = fifelse(subtract > 2 & subtract %% 2 == 0 & id_o1 == id_o2, id_1+(subtract/2), NA_real_),
mean = fifelse(subtract >= 2 & subtract %% 2 == 0 & id_o1 == id_o2, (price_1+price_2)/2, NA_real_))
][, `:=` (price_1 = NULL, id_1 = NULL, id_o1 = NULL, id_2 = NULL, price_2 = NULL, id_o2 = NULL, subtract = NULL)
][x, on = .(id_mean = rows)][, dummy := cumsum(!is.na(mean)), by = .(id)]
r <- r[, price_lag := shift(price, 1), by = .(dummy, id)]
h <- r[, .(price = na.omit(price)), by = .(dummy, id, price_lag)]
h <- h[h[,.I[is.na(price_lag)]]][, price_lag := NULL]
Results <- r[, `:=` (price = NULL, price_lag = NULL)
][h, on = .(dummy, id)
][, price := fifelse(!is.na(mean), mean, price)
][, `:=` (id_mean = NULL, mean = NULL, dummy = NULL)][]
return(Results)
}
NA_imputations_dates_v3() 函数的输出
NA_imputations_dates_v3(df1)
#> id Date price
#> 1: 11 2021-06-01 100
#> 2: 11 2021-06-02 100
#> 3: 11 2021-06-03 100
#> 4: 11 2021-06-04 100
#> 5: 11 2021-06-05 100
#> 6: 11 2021-06-06 100
#> 7: 11 2021-06-07 100
#> 8: 11 2021-06-08 100
#> 9: 11 2021-06-09 100
#> 10: 11 2021-06-10 100
#> 11: 11 2021-06-11 75
#> 12: 11 2021-06-12 50
#> 13: 11 2021-06-13 50
#> 14: 11 2021-06-14 50
#> 15: 11 2021-06-15 50
#> 16: 11 2021-06-16 50
#> 17: 11 2021-06-17 125
#> 18: 11 2021-06-18 200
#> 19: 11 2021-06-19 200
#> 20: 11 2021-06-20 200
#> 21: 11 2021-06-21 200
#> 22: 12 2021-07-01 10
#> 23: 12 2021-07-02 10
#> 24: 12 2021-07-03 10
#> 25: 12 2021-07-04 10
#> 26: 12 2021-07-05 10
#> 27: 13 2021-08-01 20
#> 28: 13 2021-08-02 20
#> 29: 13 2021-08-03 20
#> 30: 13 2021-08-04 20
#> 31: 13 2021-08-05 20
#> id Date price
由 reprex package (v2.0.1)
于 2021-12-14 创建
我需要估算每个 ID 的可用日期之间的所有缺失日期，然后对称地上下估算缺失日期。另外，我并不总是需要两个之间的平均值，例如：当我上下移动 2 个日期时，我只看到 1 个值，那么我会估算该值。
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21"),
price = c(NA, NA,100, NA, 50, NA, 200, NA)
)
@lovalery 有一个很好的解决对称迭代缺失插补的方法
在上面的解决方案中，使用了当前日期，但是当两者之间缺少大量日期时，这可能会成为一个问题。 因此，我想在两者之间插入所有缺失的日期，然后在两个方向上对称移动，直到我在任一方向上至少获得 1 个值，我需要保留它，如果 2 个值我需要平均值。
更新：我们还需要考虑价格仅出现在第一个日期或最后一个日期的情况。此外，如果相同的价格出现在多个日期
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11,
12,12,12,
13,13,13),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21",
"2021-07-01","2021-07-03","2021-07-05",
"2021-08-01","2021-08-03","2021-08-05"),
price = c(200, NA,100, NA, 50, NA, 200, NA,
10,NA,NA,
NA,NA,20)
)
我使用了@lovalery
的函数NA_imputations_dates_v2df1 <- setDT(df1)
df2 <- NA_imputations_dates_v2(df1)
df3 <- merge(df1,df2,by = c("id","Date"),all.x = T)
请在下面找到一个使用 data.table 和 padr 库的 reprex 可能解决方案。
我构建了一个函数以使其更易于使用。
Reprex
- 你的数据集#1
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21"),
price = c(NA, NA,100, NA, 50, NA, 200, NA))
NA_imputations_dates()函数的代码
library(data.table)
library(padr)
NA_imputations_dates <- function(x) {
setDT(x)[, Date := as.Date(Date)]
x <- pad(x, interval = "day", group = "id")
setDT(x)[, rows := .I]
z <- x[, .I[!is.na(price)]]
id_1 <- z[-length(z)]
id_2 <- z[-1]
values <- x[z, .(price = price, id = id)]
values_1 <- values[-nrow(values)]
names(values_1) <- c("price_1", "id_o1")
values_2 <- values[-1]
names(values_2) <- c("price_2", "id_o2")
subtract <- z[-1] - z[-length(z)]
r <- data.table(id_1, values_1, id_2, values_2, subtract)
r <- r[, `:=` (id_mean = fifelse(subtract > 2 & subtract %% 2 == 0, id_1+(subtract/2), (id_1+id_2)/2),
mean = fifelse(subtract >= 2 & subtract %% 2 == 0 & id_o1 == id_o2, (price_1+price_2)/2, NA_real_))
][, `:=` (price_1 = NULL, id_1 = NULL, id_o1 = NULL, id_2 = NULL, price_2 = NULL, id_o2 = NULL, subtract = NULL)
][x, on = .(id_mean = rows)][, dummy := cumsum(!is.na(mean)), by = .(id)]
h <- r[, .(price = na.omit(price)), by = .(dummy)]
Results <- r[, price := NULL
][h, on = .(dummy)
][, price := fifelse(!is.na(mean), mean, price)
][, `:=` (id_mean = NULL, mean = NULL, dummy = NULL)][]
return(Results)
}
NA_imputations_dates()函数的输出
NA_imputations_dates(df1)
#> id Date price
#> 1: 11 2021-06-01 100
#> 2: 11 2021-06-02 100
#> 3: 11 2021-06-03 100
#> 4: 11 2021-06-04 100
#> 5: 11 2021-06-05 100
#> 6: 11 2021-06-06 100
#> 7: 11 2021-06-07 100
#> 8: 11 2021-06-08 100
#> 9: 11 2021-06-09 100
#> 10: 11 2021-06-10 100
#> 11: 11 2021-06-11 75
#> 12: 11 2021-06-12 50
#> 13: 11 2021-06-13 50
#> 14: 11 2021-06-14 50
#> 15: 11 2021-06-15 50
#> 16: 11 2021-06-16 50
#> 17: 11 2021-06-17 125
#> 18: 11 2021-06-18 200
#> 19: 11 2021-06-19 200
#> 20: 11 2021-06-20 200
#> 21: 11 2021-06-21 200
#> id Date price
由 reprex package (v2.0.1)
于 2021-12-12 创建编辑函数以处理更通用的数据集 #2
作为您评论的后续，请在下方找到函数的修改版本（即 NA_imputations_dates_v2()）以处理新数据集提供的更一般情况（即 dataset #2 ).
Reprex
- 你的数据集 #2
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11,
12,12,12,
13,13,13),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21",
"2021-07-01","2021-07-03","2021-07-05",
"2021-08-01","2021-08-03","2021-08-05"),
price = c(NA, NA,100, NA, 50, NA, 200, NA,
10,NA,NA,
NA,NA,20))
NA_imputations_dates_v2()函数的代码
library(data.table)
library(padr)
NA_imputations_dates_v2 <- function(x) {
setDT(x)[, Date := as.Date(Date)]
x <- pad(x, interval = "day", group = "id")
setDT(x)[, rows := .I]
z <- x[, .I[!is.na(price)]]
id_1 <- z[-length(z)]
id_2 <- z[-1]
values <- x[z, .(price = price, id = id)]
values_1 <- values[-nrow(values)]
names(values_1) <- c("price_1", "id_o1")
values_2 <- values[-1]
names(values_2) <- c("price_2", "id_o2")
subtract <- z[-1] - z[-length(z)]
r <- data.table(id_1, values_1, id_2, values_2, subtract)
r <- r[, `:=` (id_mean = fifelse(subtract > 2 & subtract %% 2 == 0 & id_o1 == id_o2, id_1+(subtract/2), NA_real_),
mean = fifelse(subtract >= 2 & subtract %% 2 == 0 & id_o1 == id_o2, (price_1+price_2)/2, NA_real_))
][, `:=` (price_1 = NULL, id_1 = NULL, id_o1 = NULL, id_2 = NULL, price_2 = NULL, id_o2 = NULL, subtract = NULL)
][x, on = .(id_mean = rows)][, dummy := cumsum(!is.na(mean)), by = .(id)]
h <- r[, .(price = na.omit(price)), by = .(dummy, id)]
Results <- r[, price := NULL
][h, on = .(dummy, id)
][, price := fifelse(!is.na(mean), mean, price)
][, `:=` (id_mean = NULL, mean = NULL, dummy = NULL)][]
return(Results)
}
NA_imputations_dates_v2()函数的输出
NA_imputations_dates_v2(df1)
#> id Date price
#> 1: 11 2021-06-01 100
#> 2: 11 2021-06-02 100
#> 3: 11 2021-06-03 100
#> 4: 11 2021-06-04 100
#> 5: 11 2021-06-05 100
#> 6: 11 2021-06-06 100
#> 7: 11 2021-06-07 100
#> 8: 11 2021-06-08 100
#> 9: 11 2021-06-09 100
#> 10: 11 2021-06-10 100
#> 11: 11 2021-06-11 75
#> 12: 11 2021-06-12 50
#> 13: 11 2021-06-13 50
#> 14: 11 2021-06-14 50
#> 15: 11 2021-06-15 50
#> 16: 11 2021-06-16 50
#> 17: 11 2021-06-17 125
#> 18: 11 2021-06-18 200
#> 19: 11 2021-06-19 200
#> 20: 11 2021-06-20 200
#> 21: 11 2021-06-21 200
#> 22: 12 2021-07-01 10
#> 23: 12 2021-07-02 10
#> 24: 12 2021-07-03 10
#> 25: 12 2021-07-04 10
#> 26: 12 2021-07-05 10
#> 27: 13 2021-08-01 20
#> 28: 13 2021-08-02 20
#> 29: 13 2021-08-03 20
#> 30: 13 2021-08-04 20
#> 31: 13 2021-08-05 20
#> id Date price
由 reprex package (v2.0.1)
于 2021-12-14 创建第二次编辑函数以处理更通用的数据集 #3
作为您第二条评论的后续，请在下方找到函数的修改版本（即 NA_imputations_dates_v3()），以处理新数据集提供的更一般情况（即 dataset #3).
Reprex
- 你的数据集#3
df1 <- data.frame(id = c(11,11,11,11,11,11,11,11,
12,12,12,
13,13,13),
Date = c("2021-06-01", "2021-06-05", "2021-06-08", "2021-06-09", "2021-06-14", "2021-06-16", "2021-06-20", "2021-06-21",
"2021-07-01","2021-07-03","2021-07-05",
"2021-08-01","2021-08-03","2021-08-05"),
price = c(NA, NA,100, NA, 50, NA, 200, 200,
10,NA,NA,
NA,NA,20))
NA_imputations_dates_v3()函数的代码
library(data.table)
library(padr)
NA_imputations_dates_v3 <- function(x) {
setDT(x)[, Date := as.Date(Date)]
x <- pad(x, interval = "day", group = "id")
setDT(x)[, rows := .I]
z <- x[, .I[!is.na(price)]]
id_1 <- z[-length(z)]
id_2 <- z[-1]
values <- x[z, .(price = price, id = id)]
values_1 <- values[-nrow(values)]
names(values_1) <- c("price_1", "id_o1")
values_2 <- values[-1]
names(values_2) <- c("price_2", "id_o2")
subtract <- z[-1] - z[-length(z)]
r <- data.table(id_1, values_1, id_2, values_2, subtract)
r <- r[, `:=` (id_mean = fifelse(subtract > 2 & subtract %% 2 == 0 & id_o1 == id_o2, id_1+(subtract/2), NA_real_),
mean = fifelse(subtract >= 2 & subtract %% 2 == 0 & id_o1 == id_o2, (price_1+price_2)/2, NA_real_))
][, `:=` (price_1 = NULL, id_1 = NULL, id_o1 = NULL, id_2 = NULL, price_2 = NULL, id_o2 = NULL, subtract = NULL)
][x, on = .(id_mean = rows)][, dummy := cumsum(!is.na(mean)), by = .(id)]
r <- r[, price_lag := shift(price, 1), by = .(dummy, id)]
h <- r[, .(price = na.omit(price)), by = .(dummy, id, price_lag)]
h <- h[h[,.I[is.na(price_lag)]]][, price_lag := NULL]
Results <- r[, `:=` (price = NULL, price_lag = NULL)
][h, on = .(dummy, id)
][, price := fifelse(!is.na(mean), mean, price)
][, `:=` (id_mean = NULL, mean = NULL, dummy = NULL)][]
return(Results)
}
NA_imputations_dates_v3()函数的输出
NA_imputations_dates_v3(df1)
#> id Date price
#> 1: 11 2021-06-01 100
#> 2: 11 2021-06-02 100
#> 3: 11 2021-06-03 100
#> 4: 11 2021-06-04 100
#> 5: 11 2021-06-05 100
#> 6: 11 2021-06-06 100
#> 7: 11 2021-06-07 100
#> 8: 11 2021-06-08 100
#> 9: 11 2021-06-09 100
#> 10: 11 2021-06-10 100
#> 11: 11 2021-06-11 75
#> 12: 11 2021-06-12 50
#> 13: 11 2021-06-13 50
#> 14: 11 2021-06-14 50
#> 15: 11 2021-06-15 50
#> 16: 11 2021-06-16 50
#> 17: 11 2021-06-17 125
#> 18: 11 2021-06-18 200
#> 19: 11 2021-06-19 200
#> 20: 11 2021-06-20 200
#> 21: 11 2021-06-21 200
#> 22: 12 2021-07-01 10
#> 23: 12 2021-07-02 10
#> 24: 12 2021-07-03 10
#> 25: 12 2021-07-04 10
#> 26: 12 2021-07-05 10
#> 27: 13 2021-08-01 20
#> 28: 13 2021-08-02 20
#> 29: 13 2021-08-03 20
#> 30: 13 2021-08-04 20
#> 31: 13 2021-08-05 20
#> id Date price
由 reprex package (v2.0.1)
于 2021-12-14 创建