提取前r中

Question

固定日期我有一个数据集的最近的日期，df如下：

df <- read.table(text = " 
       ID INDEX_DATE DATE VALUE 
       1 14/06/2017 16/02/2015 7 
       1 14/06/2017 16/02/2015 6.5 
       1 14/06/2017 21/07/2015 7 
       1 14/06/2017 08/09/2015 9.5 
       1 14/06/2017 04/12/2015 8.9 
       1 14/06/2017 28/01/2016 8.4 
       1 14/06/2017 30/03/2016 8.2 
       1 14/06/2017 25/05/2016 7.6 
       1 14/06/2017 17/08/2016 8.2 
       1 14/06/2017 16/11/2016 8 
       1 14/06/2017 18/01/2017 8.8 
       1 14/06/2017 24/03/2017 9.1 
       1 14/06/2017 14/06/2017 7.9 
       1 14/06/2017 14/06/2017 8.0 
       2 10/01/2017 26/03/2015 7.4 
       2 10/01/2017 26/03/2015 7.7 
       2 10/01/2017 14/04/2016 7 
       2 10/01/2017 03/04/2017 7.6 
       3 14/01/2015 06/05/2015 7.2 
       3 14/01/2015 29/07/2015 7.1 
       3 14/01/2015 18/11/2015 7.2 
       3 14/01/2015 17/02/2016 7.3 
       3 14/01/2015 03/08/2016 7.3 
       4 08/12/2016   NA NA 
       ", header = TRUE, stringsAsFactors = FALSE) 

我想以提取对应于所述DATE之前INDEX_DATE最接近为每个ID的VALUE 。如果在INDEX_DATE之前没有DATE，则将使用INDEX_DATE之后最接近的DATE。

有三种情况：

1. ID == 1：只有具有（或相同的日期）的INDEX_DATE
2. ID == 2DATE s到事先：前后INDEX_DATE
3. ID == 3之后DATE S：仅有DATE s后INDEX_DATE

如果所选的DATE有多个值，我会随机选择其中的任何一个。

我想要的输出：

df2 <- read.table(text = " 
       ID INDEX_DATE DATE VALUE 
       1 14/06/2017 14/06/2017 7.9 
       2 10/01/2017 14/04/2016 7 
       3 14/01/2015 06/05/2015 7.2 
       4 08/12/2016   NA NA 
       ", header = TRUE, stringsAsFactors = FALSE) 

我试着写我的代码，但它不会在第二种情况下（即ID == 2）工作：

library(lubridate); library(dplyr) 
df2 <- df %>% 
    mutate_at(vars(INDEX_DATE, DATE), funs(dmy)) %>% 
    mutate(DATEDIFF = (INDEX_DATE - DATE)) %>% 
    group_by(ID) %>% 
    mutate(PRIORPOST = if_else(any(DATEDIFF >= 0), "PRIOR", "POST_ONLY"), 
     CHOSEN_VALUE = if_else(PRIORPOST == "PRIOR" & DATEDIFF >= 0 & DATEDIFF == min(DATEDIFF), VALUE, 
         if_else(PRIORPOST == "POST_ONLY" & DATEDIFF < 0 & DATEDIFF == max(DATEDIFF), VALUE, NA_real_))) %>% 
    filter(!is.na(CHOSEN_VALUE)) 

Answer 1

试试这个，希望这帮助：

library(dplyr) 
df$INDEX_DATE <- as.Date(df$INDEX_DATE, format="%d/%m/%Y") 
df$DATE <- as.Date(df$DATE, format="%d/%m/%Y") 
df$minDay <- abs(difftime(df$INDEX_DATE, df$DATE, units="days")) #Calculate the nearest day. 

index <- 1 
for (i in 1:length(unique(df$ID))) { 
    temp <- subset(df, ID==unique(df$ID)[i]) 
    temp <- temp[temp$minDay == min(temp$minDay),] #Extract the minimum day of each ID. 
    if (nrow(temp) > 1) {temp <- temp[sample(nrow(temp),1),]} else {temp <- temp} #Randomly pick 1 row. 
    temp <- temp[, -5] #Remove minDay 
    if (index == 1) { #Combine each ID's minimum row 
    output <- temp 
    index <- 0 
    } else { 
    output <- rbind(output, temp) 
    } 
} 
output