如何解决如何通过将变量与另一个变量匹配来创建新列并重复它直到 R 中的第一个变量发生变化？
| 受体 | 年 | 月 | 天 | 小时 | hour.inc | lat | lon | 高度 | 压力 | 日期 |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2018 | 1 | 3 | 19 | 0 | 31.768 | -106.501 | 500.0 | 835.6 | 2018-01-03 19:00:00 |
| 1 | 2018 | 1 | 3 | 18 | -1 | 31.628 | -106.350 | 508.8 | 840.5 | 2018-01-03 18:00:00 |
| 1 | 2018 | 1 | 3 | 17 | -2 | 31.489 | -106.180 | 526.2 | 839.4 | 2018-01-03 17:00:00 |
| 1 | 2018 | 1 | 3 | 16 | -3 | 31.372 | -105.974 | 547.6 | 836.8 | 2018-01-03 16:00:00 |
| 1 | 2018 | 1 | 3 | 15 | -4 | 31.289 | -105.731 | 555.3 | 829.8 | 2018-01-03 15:00:00 |
| 1 | 2018 | 1 | 3 | 14 | -5 | 31.265 | -105.462 | 577.8 | 812.8 | 2018-01-03 14:00:00 |
| 1 | 2018 | 1 | 3 | 13 | -6 | 31.337 | -105.175 | 640.0 | 793.9 | 2018-01-03 13:00:00 |
| 1 | 2018 | 1 | 3 | 12 | -7 | 31.492 | -104.897 | 645.6 | 809.2 | 2018-01-03 12:00:00 |
| 1 | 2018 | 1 | 3 | 11 | -8 | 31.671 | -104.700 | 686.8 | 801.0 | 2018-01-03 11:00:00 |
| 1 | 2018 | 1 | 3 | 10 | -9 | 31.913 | -104.552 | 794.2 | 795.8 | 2018-01-03 10:00:00 |
| 2 | 2018 | 1 | 4 | 19 | 0 | 31.768 | -106.501 | 500.0 | 830.9 | 2018-01-04 19:00:00 |
| 2 | 2018 | 1 | 4 | 18 | -1 | 31.904 | -106.635 | 611.5 | 819.5 | 2018-01-04 18:00:00 |
| 2 | 2018 | 1 | 4 | 17 | -2 | 32.070 | -106.749 | 709.7 | 808.0 | 2018-01-04 17:00:00 |
| 2 | 2018 | 1 | 4 | 16 | -3 | 32.223 | -106.855 | 787.3 | 794.9 | 2018-01-04 16:00:00 |
上面是我的数据框的样子，但我正在尝试创建一个名为 date1 的新列，它看起来像下面的框架。
receptor year month day hour hour.inc lat lon height pressure date date1
1 1 2018 1 3 19 0 31.768 -106.501 500.0 835.6 2018-01-03 19:00:00 2018-01-03 19:00:00
2 1 2018 1 3 18 -1 31.628 -106.350 508.8 840.5 2018-01-03 18:00:00 2018-01-03 19:00:00
3 1 2018 1 3 17 -2 31.489 -106.180 526.2 839.4 2018-01-03 17:00:00 2018-01-03 19:00:00
4 1 2018 1 3 16 -3 31.372 -105.974 547.6 836.8 2018-01-03 16:00:00 2018-01-03 19:00:00
5 1 2018 1 3 15 -4 31.289 -105.731 555.3 829.8 2018-01-03 15:00:00 2018-01-03 19:00:00
6 1 2018 1 3 14 -5 31.265 -105.462 577.8 812.8 2018-01-03 14:00:00 2018-01-03 19:00:00
7 1 2018 1 3 13 -6 31.337 -105.175 640.0 793.9 2018-01-03 13:00:00 2018-01-03 19:00:00
8 1 2018 1 3 12 -7 31.492 -104.897 645.6 809.2 2018-01-03 12:00:00 2018-01-03 19:00:00
9 1 2018 1 3 11 -8 31.671 -104.700 686.8 801.0 2018-01-03 11:00:00 2018-01-03 19:00:00
10 1 2018 1 3 10 -9 31.913 -104.552 794.2 795.8 2018-01-03 10:00:00 2018-01-03 19:00:00
38 2 2018 1 4 19 0 31.768 -106.501 500.0 830.9 2018-01-04 19:00:00 2018-01-04 19:00:00
39 2 2018 1 4 18 -1 31.904 -106.635 611.5 819.5 2018-01-04 18:00:00 2018-01-04 19:00:00
40 2 2018 1 4 17 -2 32.070 -106.749 709.7 808.0 2018-01-04 17:00:00 2018-01-04 19:00:00
41 2 2018 1 4 16 -3 32.223 -106.855 787.3 794.9 2018-01-04 16:00:00 2018-01-04 19:00:00
忽略最左边的索引。我想将受体（例如：1,2）与第一次出现的日期（例如：2018-01-03 19:00:00,2018-01-04 19:00:00）匹配，然后重复直到受体变化。
我在 R 中工作，所以我想在 R 中找到解决方案，但我也可以使用 python 解决方案并利用 R 中的 Reticulate 包。
解决方法
使用 data.table 你可以试试
library(data.table)
setDT(df) #converting into data.frame
df[,date1 := date[1],receptor] # taking the first date per receptor
df
#Output
receptor year month day hour hour.inc lat lon height pressure date date1
1: 1 2018 1 3 19 0 31.768 -106.501 500.0 835.6 2018-01-03 19:00:00 2018-01-03 19:00:00
2: 1 2018 1 3 18 -1 31.628 -106.350 508.8 840.5 2018-01-03 18:00:00 2018-01-03 19:00:00
3: 1 2018 1 3 17 -2 31.489 -106.180 526.2 839.4 2018-01-03 17:00:00 2018-01-03 19:00:00
4: 1 2018 1 3 16 -3 31.372 -105.974 547.6 836.8 2018-01-03 16:00:00 2018-01-03 19:00:00
5: 1 2018 1 3 15 -4 31.289 -105.731 555.3 829.8 2018-01-03 15:00:00 2018-01-03 19:00:00
6: 1 2018 1 3 14 -5 31.265 -105.462 577.8 812.8 2018-01-03 14:00:00 2018-01-03 19:00:00
7: 1 2018 1 3 13 -6 31.337 -105.175 640.0 793.9 2018-01-03 13:00:00 2018-01-03 19:00:00
8: 1 2018 1 3 12 -7 31.492 -104.897 645.6 809.2 2018-01-03 12:00:00 2018-01-03 19:00:00
9: 1 2018 1 3 11 -8 31.671 -104.700 686.8 801.0 2018-01-03 11:00:00 2018-01-03 19:00:00
10: 1 2018 1 3 10 -9 31.913 -104.552 794.2 795.8 2018-01-03 10:00:00 2018-01-03 19:00:00
11: 2 2018 1 4 19 0 31.768 -106.501 500.0 830.9 2018-01-04 19:00:00 2018-01-04 19:00:00
12: 2 2018 1 4 18 -1 31.904 -106.635 611.5 819.5 2018-01-04 18:00:00 2018-01-04 19:00:00
13: 2 2018 1 4 17 -2 32.070 -106.749 709.7 808.0 2018-01-04 17:00:00 2018-01-04 19:00:00
14: 2 2018 1 4 16 -3 32.223 -106.855 787.3 794.9 2018-01-04 16:00:00 2018-01-04 19:00:00
尝试使用 np.nan 填充未更改值的位置，并使用 date（该索引的）填充更改值的位置，然后使用 .ffill() 简单地进行前向填充
df.receptor.shift().ne(df.receptor) 将为您提供受体值变化的位置。比较前一个值和当前值以查看变化。
df['date1'] = np.where(df.receptor.shift().ne(df.receptor),df.date,np.nan)
df.date1 = df.date1.ffill()
| 受体 | 年 | 月 | 天 | 小时 | hour.inc | lat | lon | 高度 | 压力 | 日期 | date1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2018 | 1 | 3 | 19 | 0 | 31.768 | -106.501 | 500.0 | 835.6 | 2018-01-03 19:00:00 | 2018-01-03 19:00:00 |
| 1 | 1 | 2018 | 1 | 3 | 18 | -1 | 31.628 | -106.350 | 508.8 | 840.5 | 2018-01-03 18:00:00 | 2018-01-03 19:00:00 |
| 2 | 1 | 2018 | 1 | 3 | 17 | -2 | 31.489 | -106.180 | 526.2 | 839.4 | 2018-01-03 17:00:00 | 2018-01-03 19:00:00 |
| 3 | 1 | 2018 | 1 | 3 | 16 | -3 | 31.372 | -105.974 | 547.6 | 836.8 | 2018-01-03 16:00:00 | 2018-01-03 19:00:00 |
| 4 | 1 | 2018 | 1 | 3 | 15 | -4 | 31.289 | -105.731 | 555.3 | 829.8 | 2018-01-03 15:00:00 | 2018-01-03 19:00:00 |
| 5 | 1 | 2018 | 1 | 3 | 14 | -5 | 31.265 | -105.462 | 577.8 | 812.8 | 2018-01-03 14:00:00 | 2018-01-03 19:00:00 |
| 6 | 1 | 2018 | 1 | 3 | 13 | -6 | 31.337 | -105.175 | 640.0 | 793.9 | 2018-01-03 13:00:00 | 2018-01-03 19:00:00 |
| 7 | 1 | 2018 | 1 | 3 | 12 | -7 | 31.492 | -104.897 | 645.6 | 809.2 | 2018-01-03 12:00:00 | 2018-01-03 19:00:00 |
| 8 | 1 | 2018 | 1 | 3 | 11 | -8 | 31.671 | -104.700 | 686.8 | 801.0 | 2018-01-03 11:00:00 | 2018-01-03 19:00:00 |
| 9 | 1 | 2018 | 1 | 3 | 10 | -9 | 31.913 | -104.552 | 794.2 | 795.8 | 2018-01-03 10:00:00 | 2018-01-03 19:00:00 |
| 10 | 2 | 2018 | 1 | 4 | 19 | 0 | 31.768 | -106.501 | 500.0 | 830.9 | 2018-01-04 19:00:00 | 2018-01-04 19:00:00 |
| 11 | 2 | 2018 | 1 | 4 | 18 | -1 | 31.904 | -106.635 | 611.5 | 819.5 | 2018-01-04 18:00:00 | 2018-01-04 19:00:00 |
| 12 | 2 | 2018 | 1 | 4 | 17 | -2 | 32.070 | -106.749 | 709.7 | 808.0 | 2018-01-04 17:00:00 | 2018-01-04 19:00:00 |
| 13 | 2 | 2018 | 1 | 4 | 16 | -3 | 32.223 | -106.855 | 787.3 | 794.9 | 2018-01-04 16:00:00 | 2018-01-04 19:00:00 |
在计算 ave 列以使用 Date 返回每个日期分组的第一个日期时间后考虑基本 R 的 head：
df <- within(df,{
date_short <- as.Date(substr(as.character(date),1,10),origin="1970-01-01")
first_dt_hour <- ave(date,date_short,FUN=function(x) head(x,1))
rm(date_short) # DROP HELPER COLUMN
})
print(df)
# receptor year month day hour hour.inc lat lon height pressure date first_dt_hour
# 1 1 2018 1 3 19 0 31.768 -106.501 500.0 835.6 2018-01-03 19:00:00 2018-01-03 19:00:00
# 2 1 2018 1 3 18 -1 31.628 -106.350 508.8 840.5 2018-01-03 18:00:00 2018-01-03 19:00:00
# 3 1 2018 1 3 17 -2 31.489 -106.180 526.2 839.4 2018-01-03 17:00:00 2018-01-03 19:00:00
# 4 1 2018 1 3 16 -3 31.372 -105.974 547.6 836.8 2018-01-03 16:00:00 2018-01-03 19:00:00
# 5 1 2018 1 3 15 -4 31.289 -105.731 555.3 829.8 2018-01-03 15:00:00 2018-01-03 19:00:00
# 6 1 2018 1 3 14 -5 31.265 -105.462 577.8 812.8 2018-01-03 14:00:00 2018-01-03 19:00:00
# 7 1 2018 1 3 13 -6 31.337 -105.175 640.0 793.9 2018-01-03 13:00:00 2018-01-03 19:00:00
# 8 1 2018 1 3 12 -7 31.492 -104.897 645.6 809.2 2018-01-03 12:00:00 2018-01-03 19:00:00
# 9 1 2018 1 3 11 -8 31.671 -104.700 686.8 801.0 2018-01-03 11:00:00 2018-01-03 19:00:00
# 10 1 2018 1 3 10 -9 31.913 -104.552 794.2 795.8 2018-01-03 10:00:00 2018-01-03 19:00:00
# 38 2 2018 1 4 19 0 31.768 -106.501 500.0 830.9 2018-01-04 19:00:00 2018-01-04 19:00:00
# 39 2 2018 1 4 18 -1 31.904 -106.635 611.5 819.5 2018-01-04 18:00:00 2018-01-04 19:00:00
# 40 2 2018 1 4 17 -2 32.070 -106.749 709.7 808.0 2018-01-04 17:00:00 2018-01-04 19:00:00
# 41 2 2018 1 4 16 -3 32.223 -106.855 787.3 794.9 2018-01-04 16:00:00 2018-01-04 19:00:00
数据
data <- ' receptor year month day hour hour.inc lat lon height pressure date
1 1 2018 1 3 19 0 31.768 -106.501 500.0 835.6 "2018-01-03 19:00:00"
2 1 2018 1 3 18 -1 31.628 -106.350 508.8 840.5 "2018-01-03 18:00:00"
3 1 2018 1 3 17 -2 31.489 -106.180 526.2 839.4 "2018-01-03 17:00:00"
4 1 2018 1 3 16 -3 31.372 -105.974 547.6 836.8 "2018-01-03 16:00:00"
5 1 2018 1 3 15 -4 31.289 -105.731 555.3 829.8 "2018-01-03 15:00:00"
6 1 2018 1 3 14 -5 31.265 -105.462 577.8 812.8 "2018-01-03 14:00:00"
7 1 2018 1 3 13 -6 31.337 -105.175 640.0 793.9 "2018-01-03 13:00:00"
8 1 2018 1 3 12 -7 31.492 -104.897 645.6 809.2 "2018-01-03 12:00:00"
9 1 2018 1 3 11 -8 31.671 -104.700 686.8 801.0 "2018-01-03 11:00:00"
10 1 2018 1 3 10 -9 31.913 -104.552 794.2 795.8 "2018-01-03 10:00:00"
38 2 2018 1 4 19 0 31.768 -106.501 500.0 830.9 "2018-01-04 19:00:00"
39 2 2018 1 4 18 -1 31.904 -106.635 611.5 819.5 "2018-01-04 18:00:00"
40 2 2018 1 4 17 -2 32.070 -106.749 709.7 808.0 "2018-01-04 17:00:00"
41 2 2018 1 4 16 -3 32.223 -106.855 787.3 794.9 "2018-01-04 16:00:00"'
df <- read.table(text=data,colClasses=c(rep("integer",7),rep("numeric",4),"POSIXct"),header=TRUE)
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