英文:
Unable to use read.zoo due to the presence of NAs
问题
I have a large dataset of irregular multivariate timeseries that I want to convert with read.zoo.
一些最后几行填充了NAs。当我运行read.zoo包括带有NAs的行时，我收到以下错误消息："index has bad entries at data rows: 43 44 ..."
当我检查is.na()时，NA单元格显示为TRUE。我尝试了来自这里的na.fill解决方案，但它不起作用。
下面是一个包含两个变量Var1和Var2以及它们的日期date1和date2的数据集摘录：
2023-01-13 100.325 2023-01-11 99.7482023-01-16 100.378 2023-01-12 99.8322023-01-17 100.826 2023-01-13 99.878...（后续数据省略）...2023-03-10 99.489 2023-03-08 101.915NA NA 2023-03-09 101.927NA NA 2023-03-10 101.775NA NA NA NANA NA NA NANA NA NA NA
英文:
I have a large dataset of irregular multivariate timeseries that I want to convert with read.zoo.
Some of the last rows are populated with NAs. When I run read.zoo including the rows with the NAs, I get the following error message: "index has bad entries at data rows: 43 44 ...".
When I check is.na() the NA cells indicate TRUE. And I tried the na.fill solution from here, but it doesn't work.
Below is an extract of the dataset with two variables Var1 and Var2 with their respective dates date1 and date2:
date1 Var1 date2 Var22023-01-13 100.325 2023-01-11 99.7482023-01-16 100.378 2023-01-12 99.8322023-01-17 100.826 2023-01-13 99.8782023-01-18 100.933 2023-01-16 99.7622023-01-19 100.641 2023-01-17 99.4842023-01-20 100.148 2023-01-18 99.7432023-01-23 99.972 2023-01-19 99.4192023-01-24 100.256 2023-01-20 99.3642023-01-25 100.348 2023-01-23 99.5332023-01-26 100.146 2023-01-24 99.7112023-01-27 100.063 2023-01-25 99.7982023-01-30 99.649 2023-01-26 100.4812023-01-31 99.822 2023-01-27 100.7082023-02-01 99.885 2023-01-30 100.572023-02-02 101.121 2023-01-31 100.7732023-02-03 100.854 2023-02-01 100.9992023-02-06 100.5 2023-02-02 102.0372023-02-07 100.272 2023-02-03 102.1042023-02-08 100.372 2023-02-06 101.852023-02-09 100.659 2023-02-07 101.7652023-02-10 100.421 2023-02-08 101.8062023-02-13 100.418 2023-02-09 101.9052023-02-14 100.202 2023-02-10 101.6752023-02-15 99.913 2023-02-13 101.4912023-02-16 99.832 2023-02-14 101.3042023-02-17 99.911 2023-02-15 101.2422023-02-20 99.791 2023-02-16 101.6212023-02-21 99.451 2023-02-17 101.5812023-02-22 99.467 2023-02-20 101.5452023-02-23 99.642 2023-02-21 101.3342023-02-24 99.278 2023-02-22 101.2462023-02-27 99.114 2023-02-23 101.8572023-02-28 98.784 2023-02-24 101.712023-03-01 98.486 2023-02-27 101.7592023-03-02 98.396 2023-02-28 101.6492023-03-03 98.467 2023-03-01 101.5832023-03-06 98.276 2023-03-02 101.4262023-03-07 98.495 2023-03-03 101.6662023-03-08 98.572 2023-03-06 101.9192023-03-09 98.747 2023-03-07 102.0482023-03-10 99.489 2023-03-08 101.915NA NA 2023-03-09 101.927NA NA 2023-03-10 101.775NA NA NA NANA NA NA NANA NA NA NA
答案1
得分: 1
The solution was provided by @G. Grothendieck in another post here:
将 as.data.frame(x) 替换为 na.omit(as.data.frame(x))
英文:
The solution was provided by @G. Grothendieck in another post here:
Replace as.data.frame(x) with na.omit(as.data.frame(x))
答案2
得分: 0
首先，让我为您从您的数据创建一个数据框架：
lines <- "date1 Var1 date2 Var22023-01-13 100.325 2023-01-11 99.7482023-01-16 100.378 2023-01-12 99.8322023-01-17 100.826 2023-01-13 99.8782023-01-18 100.933 2023-01-16 99.7622023-01-19 100.641 2023-01-17 99.4842023-01-20 100.148 2023-01-18 99.7432023-01-23 99.972 2023-01-19 99.4192023-01-24 100.256 2023-01-20 99.3642023-01-25 100.348 2023-01-23 99.5332023-01-26 100.146 2023-01-24 99.7112023-01-27 100.063 2023-01-25 99.7982023-01-30 99.649 2023-01-26 100.4812023-01-31 99.822 2023-01-27 100.7082023-02-01 99.885 2023-01-30 100.572023-02-02 101.121 2023-01-31 100.7732023-02-03 100.854 2023-02-01 100.9992023-02-06 100.5 2023-02-02 102.0372023-02-07 100.272 2023-02-03 102.1042023-02-08 100.372 2023-02-06 101.852023-02-09 100.659 2023-02-07 101.7652023-02-10 100.421 2023-02-08 101.8062023-02-13 100.418 2023-02-09 101.9052023-02-14 100.202 2023-02-10 101.6752023-02-15 99.913 2023-02-13 101.4912023-02-16 99.832 2023-02-14 101.3042023-02-17 99.911 2023-02-15 101.2422023-02-20 99.791 2023-02-16 101.6212023-02-21 99.451 2023-02-17 101.5812023-02-22 99.467 2023-02-20 101.5452023-02-23 99.642 2023-02-21 101.3342023-02-24 99.278 2023-02-22 101.2462023-02-27 99.114 2023-02-23 101.8572023-02-28 98.784 2023-02-24 101.712023-03-01 98.486 2023-02-27 101.7592023-03-02 98.396 2023-02-28 101.6492023-03-03 98.467 2023-03-01 101.5832023-03-06 98.276 2023-03-02 101.4262023-03-07 98.495 2023-03-03 101.6662023-03-08 98.572 2023-03-06 101.9192023-03-09 98.747 2023-03-07 102.0482023-03-10 99.489 2023-03-08 101.915NA NA 2023-03-09 101.927NA NA 2023-03-10 101.775NA NA NA NANA NA NA NA"library(tidyverse)library(dplyr)DF <- read.table(text = lines, header = TRUE)
然后，让我将日期格式化为正确的格式：
library(zoo)# 将日期格式化为POSIXct格式DF$date1 <- as.POSIXct(DF$date1)DF$date2 <- as.POSIXct(DF$date2)
如果您想根据您的需求创建两个不同的数据集，可以这样做：
df1 <- DF %>% select(date1, Var1) %>% na.omit() %>% set_names(c("Date", "Var"))df2 <- DF %>% select(date2, Var2) %>% na.omit() %>% set_names(c("Date", "Var"))
然后，将这些分开的数据集创建成zoo对象：
zoo1 <- zoo(df1$Var, order.by = df1$Date)zoo2 <- zoo(df2$Var, order.by = df2$Date)
或者，如果您想合并这些变量，可以这样做：
# 合并上面创建的两个数据框架mergedDf <- merge(df1, df2, by = "Date")# 创建zoo对象zooObject <- zoo(mergedDf$Var.x, order.by = mergedDf$Date)
希望这有所帮助。
英文:
first let me create a dataframe from your data:
lines <- "date1 Var1 date2 Var22023-01-13 100.325 2023-01-11 99.7482023-01-16 100.378 2023-01-12 99.8322023-01-17 100.826 2023-01-13 99.8782023-01-18 100.933 2023-01-16 99.7622023-01-19 100.641 2023-01-17 99.4842023-01-20 100.148 2023-01-18 99.7432023-01-23 99.972 2023-01-19 99.4192023-01-24 100.256 2023-01-20 99.3642023-01-25 100.348 2023-01-23 99.5332023-01-26 100.146 2023-01-24 99.7112023-01-27 100.063 2023-01-25 99.7982023-01-30 99.649 2023-01-26 100.4812023-01-31 99.822 2023-01-27 100.7082023-02-01 99.885 2023-01-30 100.572023-02-02 101.121 2023-01-31 100.7732023-02-03 100.854 2023-02-01 100.9992023-02-06 100.5 2023-02-02 102.0372023-02-07 100.272 2023-02-03 102.1042023-02-08 100.372 2023-02-06 101.852023-02-09 100.659 2023-02-07 101.7652023-02-10 100.421 2023-02-08 101.8062023-02-13 100.418 2023-02-09 101.9052023-02-14 100.202 2023-02-10 101.6752023-02-15 99.913 2023-02-13 101.4912023-02-16 99.832 2023-02-14 101.3042023-02-17 99.911 2023-02-15 101.2422023-02-20 99.791 2023-02-16 101.6212023-02-21 99.451 2023-02-17 101.5812023-02-22 99.467 2023-02-20 101.5452023-02-23 99.642 2023-02-21 101.3342023-02-24 99.278 2023-02-22 101.2462023-02-27 99.114 2023-02-23 101.8572023-02-28 98.784 2023-02-24 101.712023-03-01 98.486 2023-02-27 101.7592023-03-02 98.396 2023-02-28 101.6492023-03-03 98.467 2023-03-01 101.5832023-03-06 98.276 2023-03-02 101.4262023-03-07 98.495 2023-03-03 101.6662023-03-08 98.572 2023-03-06 101.9192023-03-09 98.747 2023-03-07 102.0482023-03-10 99.489 2023-03-08 101.915NA NA 2023-03-09 101.927NA NA 2023-03-10 101.775NA NA NA NANA NA NA NANA NA NA NA"library(tidyverse)library(dplyr)DF <- read.table(text = lines, header = TRUE)
Then, let me format the dates in proper format:
library(zoo)# format dates to POSIXct formatDF$date1 <- as.POSIXct(DF$date1)DF$date2 <- as.POSIXct(DF$date2)
One way is to create two different datasets (looking at your requirement):
df1 <- DF %>% select(date1, Var1) %>% na.omit() %>% set_names(c("Date", "Var"))df2 <- DF %>% select(date2, Var2) %>% na.omit() %>% set_names(c("Date", "Var"))
The create the separate zoo objects out of these:
zoo1 <- zoo(df1$Var, order.by = df1$Date)zoo2 <- zoo(df2$Var, order.by = df2$Date)
Or if you want to merge these variables, you could do:
# merge both the dataframes created abovemergedDf <- merge(df1, df2, by = "Date")# create the zoo objectzooObject <- zoo(mergedDf$Var.x, order.by = mergedDf$Date)
Let me know if this helps.
答案3
得分: 0
以下是翻译好的部分：
在问题中，NA总是位于开头，因此使用Note末尾的Lines来定义N作为注释字符。
library(zoo)z <- read.zoo(text = Lines, header = TRUE, comment.char = "N")
注释
Lines <- "date1 Var1 date2 Var22023-01-13 100.325 2023-01-11 99.7482023-01-16 100.378 2023-01-12 99.832...(后续部分省略)
英文:
In the question NA is always at the beginning so using Lines from the Note at the end define N as a comment character.
library(zoo)z <- read.zoo(text = Lines, header = TRUE, comment.chaqr = "N")
Note
Lines <- "date1 Var1 date2 Var22023-01-13 100.325 2023-01-11 99.7482023-01-16 100.378 2023-01-12 99.8322023-01-17 100.826 2023-01-13 99.8782023-01-18 100.933 2023-01-16 99.7622023-01-19 100.641 2023-01-17 99.4842023-01-20 100.148 2023-01-18 99.7432023-01-23 99.972 2023-01-19 99.4192023-01-24 100.256 2023-01-20 99.3642023-01-25 100.348 2023-01-23 99.5332023-01-26 100.146 2023-01-24 99.7112023-01-27 100.063 2023-01-25 99.7982023-01-30 99.649 2023-01-26 100.4812023-01-31 99.822 2023-01-27 100.7082023-02-01 99.885 2023-01-30 100.572023-02-02 101.121 2023-01-31 100.7732023-02-03 100.854 2023-02-01 100.9992023-02-06 100.5 2023-02-02 102.0372023-02-07 100.272 2023-02-03 102.1042023-02-08 100.372 2023-02-06 101.852023-02-09 100.659 2023-02-07 101.7652023-02-10 100.421 2023-02-08 101.8062023-02-13 100.418 2023-02-09 101.9052023-02-14 100.202 2023-02-10 101.6752023-02-15 99.913 2023-02-13 101.4912023-02-16 99.832 2023-02-14 101.3042023-02-17 99.911 2023-02-15 101.2422023-02-20 99.791 2023-02-16 101.6212023-02-21 99.451 2023-02-17 101.5812023-02-22 99.467 2023-02-20 101.5452023-02-23 99.642 2023-02-21 101.3342023-02-24 99.278 2023-02-22 101.2462023-02-27 99.114 2023-02-23 101.8572023-02-28 98.784 2023-02-24 101.712023-03-01 98.486 2023-02-27 101.7592023-03-02 98.396 2023-02-28 101.6492023-03-03 98.467 2023-03-01 101.5832023-03-06 98.276 2023-03-02 101.4262023-03-07 98.495 2023-03-03 101.6662023-03-08 98.572 2023-03-06 101.9192023-03-09 98.747 2023-03-07 102.0482023-03-10 99.489 2023-03-08 101.915NA NA 2023-03-09 101.927NA NA 2023-03-10 101.775NA NA NA NANA NA NA NANA NA NA NA"
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