如何与“参考日期”进行比较,然后在R中填写缺失的数据? [英] How to compare with a "reference date", then fill missing data in R?
问题描述
有关此主题的另外两个问题:
library(tidyverse)
#示例数据
dt_A = data.frame(Date = c( 2016年1月4日10:53 , 02/04/2016 10:54, 03/04/2016 10:55),
A_Value = c(5,6,7))
dt_B = data.frame(日期= c( 2016年1月4日10:53, 2016年3月4日10:55),
B_Value = c(1,3))
#使用数据A的日期完成数据B的日期
dt_B%>%已完成(Date = dt_A $ Date)
##小技巧:3 x 2
#日期B_Value
#< chr> < dbl>
#1 01/04/2016 10:53 1
#2 02/04/2016 10:54 NA
#3 03/04/2016 10:55 3
Two more questions about this topic: A B
First, let me show the example data (Data A & B):
(1) Data A:
Date_Collected A_Value
01/04/2016 10:53 0.137
01/20/2016 13:13 0.204
01/25/2016 11:09 0.199
02/01/2016 12:55 0.441
02/01/2016 12:56 0.215
02/01/2016 13:11 0.397
02/03/2016 09:19 0.377
02/10/2016 08:11 1.45
02/15/2016 13:04 2.63
(2) Data B:
Date_Collected B_Value
01/04/2016 10:53 0.108
01/20/2016 13:13 0.404
02/01/2016 13:11 0.594
02/15/2016 13:04 1.99
Second, I will tell what I want to do with R. You can see that "Data A" has 9 records, while "Data B" has only 4 records. As these values are so precious, I will not delete "Data A" to fit the rows of "Data B". Instead, I will fill the "missing" data in "Data B". The things need to do can be separated into two parts:
(Part Ⅰ) ① To add blank rows for "Data B" in the corresponding location, according to "Data A"; ② In these blank rows (blue in Fig.1), copy the corresponding date. The result at the end of Part Ⅰ is like Fig.1.
(Part Ⅱ) To interpolate the missing data in "B_Value". This part has been solved. You can see the solution in here of Stack Overflow.
Could someone give me advice about it (especially Part Ⅰ)? Thanks.
library(tidyverse)
# example data
dt_A = data.frame(Date = c("01/04/2016 10:53", "02/04/2016 10:54", "03/04/2016 10:55"),
A_Value = c(5,6,7))
dt_B = data.frame(Date = c("01/04/2016 10:53", "03/04/2016 10:55"),
B_Value = c(1,3))
# complete dates of data B using dates of data A
dt_B %>% complete(Date = dt_A$Date)
# # A tibble: 3 x 2
# Date B_Value
# <chr> <dbl>
# 1 01/04/2016 10:53 1
# 2 02/04/2016 10:54 NA
# 3 03/04/2016 10:55 3
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