名为d的数据框包含以下数据:
timestamp,value
"2013-06-02 00:00:00",70
"2013-06-02 00:02:00",70
"2013-06-02 00:07:00",60
"2013-06-02 00:15:00",70
"2013-06-02 00:12:00",60
"2013-06-02 00:30:00",70
"2013-06-02 00:45:00",70
"2013-06-02 01:00:00",70
我的代码是:
d = read.csv(path, header=TRUE, sep=",")
d2 <- xts(x = d[c("value")], order.by = as.POSIXct(d[, "timestamp"], tz = "GMT", format = "%Y-%m-%d %H:%M:%S"))
ends <- endpoints(d2, on = "minutes", k = 15)
d3 <- period.apply(d2, ends, mean)
之后我想将xts对象转换为数据帧,我正在使用它:
d3$timestamp = rownames(d3)
rownames(d3) = NULL
d3$timestamp = strptime(d3$timestamp, "%Y-%m-%d %H:%M:%S")
然而,在最后一步中,它会向此输出错误:
Error in NextMethod(.Generic) :
number of items to replace is not a multiple of replacement length
当我观察整个命令后输入d3时,对象具有这种格式的数据:
timestamp
2013-06-02 00:15:00 65
2013-06-02 00:30:00 70
2013-06-02 00:45:00 70
2013-06-02 01:00:00 70
但是,在列名中,它必须具有名称值,而第二列的时间戳必须为here。可能有什么不对?
正确的输出必须是:
value
65 2013-06-02 00:15:00
70 2013-06-02 00:30:00
70 2013-06-02 00:45:00
70 2013-06-02 01:00:00
答案 0 :(得分:9)
您可以像这样创建data.frame:
data.frame(value=coredata(d3),timestamp=index(d3))
# value timestamp
# 1 65 2013-06-02 00:12:00
# 2 70 2013-06-02 00:15:00
# 3 70 2013-06-02 00:30:00
# 4 70 2013-06-02 00:45:00
# 5 70 2013-06-02 01:00:00
我建议您使用read.zoo
将您的数据作为动物园对象读取,并避免手动强制xts。例如:
dat <- read.zoo(text='timestamp,value
"2013-06-02 00:00:00",70
"2013-06-02 00:02:00",70
"2013-06-02 00:07:00",60
"2013-06-02 00:15:00",70
"2013-06-02 00:12:00",60
"2013-06-02 00:30:00",70
"2013-06-02 00:45:00",70
"2013-06-02 01:00:00",70',tz ='' , format = "%Y-%m-%d %H:%M:%S",header=TRUE,
sep=',')
d2 <- as.xts(dat)
答案 1 :(得分:1)
另一个选项是tidyquant
包,它作为两个函数用于向xts对象强制转换(转换)数据帧:即as_xts()
用于将数据帧转换为xts,以及as_tibble()
用于将xts(和其他时间序列或矩阵对象)转换为“整洁”的数据帧。
这是一个简单的例子。我使用tribble()
函数重新创建您的示例。在转换过程中,我使用as_datetime()
中的lubridate
函数(tidyquant
自动加载此函数)将字符转换为日期时间类。其他一切都应该非常简单。
library(tidyquant)
# Recreate data frame
data_df <- tribble(
~timestamp, ~value,
"2013-06-02 00:00:00", 70,
"2013-06-02 00:02:00", 70,
"2013-06-02 00:07:00", 60,
"2013-06-02 00:15:00", 70,
"2013-06-02 00:12:00", 60,
"2013-06-02 00:30:00", 70,
"2013-06-02 00:45:00", 70,
"2013-06-02 01:00:00", 70
)
data_df
#> # A tibble: 8 × 2
#> timestamp value
#> <chr> <dbl>
#> 1 2013-06-02 00:00:00 70
#> 2 2013-06-02 00:02:00 70
#> 3 2013-06-02 00:07:00 60
#> 4 2013-06-02 00:15:00 70
#> 5 2013-06-02 00:12:00 60
#> 6 2013-06-02 00:30:00 70
#> 7 2013-06-02 00:45:00 70
#> 8 2013-06-02 01:00:00 70
# Convert data frame to xts
data_xts <- data_df %>%
mutate(timestamp = as_datetime(timestamp, tz = Sys.timezone())) %>%
as_xts(date_col = timestamp)
data_xts
#> value
#> 2013-06-02 00:00:00 70
#> 2013-06-02 00:02:00 70
#> 2013-06-02 00:07:00 60
#> 2013-06-02 00:12:00 60
#> 2013-06-02 00:15:00 70
#> 2013-06-02 00:30:00 70
#> 2013-06-02 00:45:00 70
#> 2013-06-02 01:00:00 70
# Convert xts to data frame
data_df_2 <- data_xts %>%
as_tibble(preserve_row_names = TRUE) %>%
rename(timestamp = row.names) %>%
mutate(timestamp = as_datetime(timestamp, tz = Sys.timezone()))
data_df_2
#> # A tibble: 8 × 2
#> timestamp value
#> <dttm> <dbl>
#> 1 2013-06-02 00:00:00 70
#> 2 2013-06-02 00:02:00 70
#> 3 2013-06-02 00:07:00 60
#> 4 2013-06-02 00:12:00 60
#> 5 2013-06-02 00:15:00 70
#> 6 2013-06-02 00:30:00 70
#> 7 2013-06-02 00:45:00 70
#> 8 2013-06-02 01:00:00 70