如何为规则时间序列转换具有假天数的不规则表？

Question

我有很多表格，每天都有气候观测。如您所见，月份的所有列都有 31 天。我想正确地组织这些数据，排除所有错误的日子并按常规时间序列进行转换。

数据视图：

输入（头（数据，31））：

structure(list(NA. = 1:31, JAN = c(NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_
), FEV = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), MAR = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 12.5, 0), ABR = c(0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, NA), MAI = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 2.5, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0), JUN = c(0, 
0, 0, 0, 0, 3.4, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 
0, 6.8, 2.4, 2.1, 0, 0, 0, 0, 0, NA), JUL = c(0, 0, 4.4, 0, 0, 
15.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 1.3, 0, 0, 1.7, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0), AGO = c(0, 0, 0, 0, 1.9, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.1, 4, 2.6, 0, 0, 
0, 0, 0), SET = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA), OUT = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0), NOV = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17.7, 0, 0, 0, 0, 0, 0, 
0, NA), DEZ = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, 
31L), class = "data.frame")

我想喜欢这个例子：

原始数据：https://www.dropbox.com/s/0xizr6fhbriieds/data.csv?dl=0

请任何人帮助我。是为了我的科学启蒙。

Answer 1

从您的问题中并不清楚您要如何过滤数据，但要转换为长格式并删除 NAs，一种方法是使用 dplyr、tidyr 和 lubridate。

我正在使用data.table 加载您的数据，因为由于某种原因，即使文件扩展名是.csv，它也是用分号分隔的。

您可能不重命名月份，因为您当地可能接受西班牙语月份的缩写。

libary(data.table)
data <- fread("data.csv")
names(data) <- c("Day",month.abb)

library(dplyr)
library(tidyr)
library(lubridate)
Year <- 2000
data %>% 
  pivot_longer(-Day,names_to = "Month") %>%
  mutate(Date = dmy(paste(Day,Month,Year))) %>%
  filter(!is.na(value)) %>%
  select(Date,value)
#   Date       value
#   <date>     <dbl>
# 1 2000-07-03   4.4
# 2 2000-08-05   1.9
# 3 2000-06-06   3.4
# 4 2000-07-06  15.4
# 5 2000-04-12   1.9
# 6 2000-06-15   7  
# 7 2000-07-16   2.2
# 8 2000-07-18   1.3
# 9 2000-05-21   2.5
#10 2000-07-21   1.7