R 按日期合并数据帧生成NA

我正在学习R，目前正在尝试用缺失的日期和NA值填充数据框

数据样本：

Date <- c("23-01-19", "24-01-19", "25-01-19",  "30-01-19", "31-01-19" )
Open <- c("69.849998", "69.440002", "69.540001", "70.32", "69.559998")
High <- c("69.849998", "69.440002", "69.540001", "70.32", "69.559998")
Low <- c("69.849998", "69.440002", "69.540001", "70.32", "69.559998")
Close <- c("69.849998", "69.440002", "69.540001", "70.32", "69.559998")
Adj_Close <- c("69.849998", "69.440002", "69.540001", "70.32", "69.559998")
Volume <- c("0", "0", "0", "0","0")

InvescoDf <- data.frame(Date, Open, High, Low, Close, Adj_Close, Volume)

我不知道如何将

complete

状态设置为

lappy

---

请提供帮助。

您可以将

日期

转换为日期对象，然后使用

完成
从
tidyr
填充缺少的日期

library(dplyr)
library(tidyr)

InvescoDf %>%
  mutate(Date = as.Date(Date, "%d-%m-%y")) %>%
  complete(Date = seq(min(Date), max(Date), by = "day"))

#  Date        Open  High   Low Close Adj_Close Volume
#  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>  <int>
#1 2019-01-23  69.8  69.8  69.8  69.8      69.8      0
#2 2019-01-24  69.4  69.4  69.4  69.4      69.4      0
#3 2019-01-25  69.5  69.5  69.5  69.5      69.5      0
#4 2019-01-26  NA    NA    NA    NA        NA       NA
#5 2019-01-27  NA    NA    NA    NA        NA       NA
#6 2019-01-28  NA    NA    NA    NA        NA       NA
#7 2019-01-29  NA    NA    NA    NA        NA       NA
#8 2019-01-30  70.3  70.3  70.3  70.3      70.3      0
#9 2019-01-31  69.6  69.6  69.6  69.6      69.6      0

库（dplyr）
图书馆（tidyr）
投资组合%>%
变异（日期=as.Date（日期，“%d-%m-%y”））%>%
完成（日期=序号（最短（日期），最长（日期），截止日期）
#日期开高关低调整关量
#                 
#1 2019-01-23  69.8  69.8  69.8  69.8      69.8      0
#2 2019-01-24  69.4  69.4  69.4  69.4      69.4      0
#3 2019-01-25  69.5  69.5  69.5  69.5      69.5      0
#4 2019-01-26不适用
#5 2019-01-27不适用
#6 2019-01-28不适用
#7 2019-01-29不适用
#8 2019-01-30  70.3  70.3  70.3  70.3      70.3      0
#9 2019-01-31  69.6  69.6  69.6  69.6      69.6      0


要对列表中的多个数据帧执行此操作，我们可以

new_list <- lapply(new_list, function(dat) {
    dat[[1]] <- as.Date(dat[[1]], "%d-%m-%y")
    # change the other variables to num 
    dat[-1] <- lapply(dat[-1],  function(x) as.numeric(as.character(x)))
    # complete the dates?
    dat <- complete(dat, Date = seq(min(Date), max(Date), by = "day"))
    #OR
    #dat <- complete(dat, Date = seq(min(dat[[1]]), max(dat[[1]]), by = "day"))
    dat
 })

新列表非常感谢！我可以用lappy在列表中的多个数据帧中进行迭代吗？@ToniR您的意思是，您有多个这样的数据帧，并且希望对所有这些数据帧使用相同的代码？是的，你可以使用
lappy
。我似乎不清楚，你正在使用mutate来更改变量，但我认为mutate创建了一个新变量。你能帮我理解吗？
mutate
如果数据中没有变量，则创建一个新变量。如果变量已经存在，则更改变量。@ToniR更新了答案，以便在列表中执行此操作。请查收。
library(dplyr)
library(tidyr)

InvescoDf %>%
  mutate(Date = as.Date(Date, "%d-%m-%y")) %>%
  complete(Date = seq(min(Date), max(Date), by = "day"))

#  Date        Open  High   Low Close Adj_Close Volume
#  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>  <int>
#1 2019-01-23  69.8  69.8  69.8  69.8      69.8      0
#2 2019-01-24  69.4  69.4  69.4  69.4      69.4      0
#3 2019-01-25  69.5  69.5  69.5  69.5      69.5      0
#4 2019-01-26  NA    NA    NA    NA        NA       NA
#5 2019-01-27  NA    NA    NA    NA        NA       NA
#6 2019-01-28  NA    NA    NA    NA        NA       NA
#7 2019-01-29  NA    NA    NA    NA        NA       NA
#8 2019-01-30  70.3  70.3  70.3  70.3      70.3      0
#9 2019-01-31  69.6  69.6  69.6  69.6      69.6      0

new_list <- lapply(new_list, function(dat) {
    dat[[1]] <- as.Date(dat[[1]], "%d-%m-%y")
    # change the other variables to num 
    dat[-1] <- lapply(dat[-1],  function(x) as.numeric(as.character(x)))
    # complete the dates?
    dat <- complete(dat, Date = seq(min(Date), max(Date), by = "day"))
    #OR
    #dat <- complete(dat, Date = seq(min(dat[[1]]), max(dat[[1]]), by = "day"))
    dat
 })

InvescoDf <- type.convert(InvescoDf)