動態更新/合併兩個data.frames在R

Question

我還沒有找到一個在網絡上的解決方案，因爲它是不容易拿出正確的問題。 我有兩個data.frames，x和y，並且想要合併它們以獲得z：

棘手的是，z比較x和y的日期值，並採用最近的觀察來更新A， B，C和D.因此「動態」更新/組合。

x=data.frame(c("2000-01-01","2000-06-01","2001-01-01"),c("100","100","100"),c("200","200","200")) 
colnames(x)=c("Date","A","B") 

y=data.frame(c("2000-01-05","2000-04-09"),c("10","0"),c("0","35")) 
colnames(y)=c("Date","C","D") 

z=data.frame(c("2000-01-01","2000-01-05","2000-04-09","2000-06-01","2001-01-01"),c("100","100","100","100","100"),c("200","200","200","200","200"),c("0","10","10","0","0"),c("0","0","35","0","0")) 
colnames(z)=c("Date","A","B","C","D") 

x$Date = as.Date(x$Date) 
y$Date = as.Date(y$Date) 

問題：如何通過高效的代碼到達z？

舉例說明：

> x 
     Date A B 
1 2000-01-01 100 200 
2 2000-06-01 100 200 
3 2001-01-01 100 200 
> y 
     Date C D 
1 2000-01-05 10 0 
2 2000-04-09 0 35 
> z 
     Date A B C D 
1 2000-01-01 100 200 0 0 
2 2000-01-05 100 200 10 0 
3 2000-04-09 100 200 10 35 
4 2000-06-01 100 200 10 35 
5 2001-01-01 100 200 10 35 
> 

編輯： 感謝下面的答案。 的解決方案似乎是一個簡單的完全連接，然後在一個循環的循環（我想通了，第二個步驟）：

x$Date = as.Date(x$Date) 
y$Date = as.Date(y$Date) 

tt=merge(x,y,by='Date',all=TRUE) 

for (i in 2:(ncol(x)+ncol(y)-1)){ 
    for (j in 2:(nrow(x)+nrow(y))){ 
    if (is.na(tt[j,i])==TRUE & is.na(tt[j-1,i])==FALSE){ 
     tt[j,i]=tt[j-1,i]} 
    } 
} 

EDIT2：他人發佈下文進一步的解決方案似乎是更有效的。

y=data.frame(c("2000-01-05","2000-04-09"),c("10",NA),c(NA,"35")) 
colnames(y)=c("Date","C","D") 

，然後更換來港Z中的最後一步：只是爲了完整性，如果Y中的0被替換NA，即定義爲Ÿ我再解決方案的工作原理。

我從我的第一個編輯中學到了東西，我不是在編輯上面的原始問題以避免混淆。

非常感謝您的幫助！

Answer 1

一種可能的解決方案可以是使用從zoo packackages的data.table的組合和na.locf功能：

# loading the needed packages 
library(data.table) 
library(zoo) 

# converting x & y to datatables 
setDT(x) 
setDT(y) 

# merge x & y into z 
z <- merge(x, y, by="Date", all=TRUE) # this works in base R as well 

# fill the NA's with the last observation 
cols <- c("A","B","C","D") # in this specific case, you can also use: LETTERS[1:4] 
z[, (cols) := lapply(.SD, na.locf, rule = 1, na.rm=FALSE), .SDcols= cols] 

這給出：

> z 
     Date A B C D 
1: 2000-01-01 100 200 NA NA 
2: 2000-01-05 100 200 10 0 
3: 2000-04-09 100 200 0 35 
4: 2000-06-01 100 200 0 35 
5: 2001-01-01 100 200 0 35 

該結果也可以在基礎R來實現正如@Tensibai在評論中提到的那樣（由於某些原因，我的系統一開始並不適用）：

z <- merge(x, y, by="Date", all=TRUE) 
z <- na.locf(z) 

---

得到確切所需的輸出，你需要一些額外的步驟（省略，因爲它們是相同的第一個步驟）：

# merge x & y into z 
z <- merge(x, y, by="Date", all=TRUE) # this works in base R as well 

# replace the zero with NA 
z[z==0] <- NA 

# fill the NA's with the last observation 
cols <- LETTERS[1:4] 
z[, (cols) := lapply(.SD, na.locf, rule = 1, na.rm=FALSE), .SDcols= cols] 

# replace the remaining NA's with zero's 
z[is.na(z)] <- 0 

這給：

> z 
     Date A B C D 
1: 2000-01-01 100 200 0 0 
2: 2000-01-05 100 200 10 0 
3: 2000-04-09 100 200 10 35 
4: 2000-06-01 100 200 10 35 
5: 2001-01-01 100 200 10 35 

在你會做的基地R：

z <- merge(x, y, by="Date", all=TRUE) 
z[z==0] <- NA 
z <- na.locf(z) 
z[is.na(z)] <- 0 

得到相同的結果。

Answer 2

使用dplyr和一些功能的另一種方法：

library(lubridate) 
library(dplyr) 

# dataset 
x=data.frame(c("2000-01-01","2000-06-01","2001-01-01"), 
      c("100","100","100"), 
      c("200","200","200"), stringsAsFactors = F) 
colnames(x)=c("Date","A","B") 

y=data.frame(c("2000-01-05","2000-04-09"), 
      c("10","0"), 
      c("0","35"), stringsAsFactors = F) 
colnames(y)=c("Date","C","D") 

# update date columns 
x$Date = ymd(x$Date) 
y$Date = ymd(y$Date) 

# function that replaces NAs with 0s 
ff = function(x){x[is.na(x)]=0 
       return(as.numeric(x))} 

# function that updates zero elements with the previous ones 
ff2 = function(x){ 

    for (i in 2:length(x)){x[i] = ifelse(x[i]==0, x[i-1], x[i])} 

    return(x) 

} 

# create the full dataset 
xy = 
    x %>% 
    full_join(y, by="Date") %>% 
    arrange(Date) 

xy 

#   Date A B C D 
# 1 2000-01-01 100 200 <NA> <NA> 
# 2 2000-01-05 <NA> <NA> 10 0 
# 3 2000-04-09 <NA> <NA> 0 35 
# 4 2000-06-01 100 200 <NA> <NA> 
# 5 2001-01-01 100 200 <NA> <NA> 


    xy %>% 
    group_by(Date) %>% 
    mutate_each(funs(ff)) %>% 
    ungroup %>% 
    select(-Date) %>% 
    mutate_each(funs(ff2)) %>% 
    bind_cols(data.frame(Date=xy$Date)) %>% 
    select(Date,A,B,C,D) 

#   Date A B C D 
# 1 2000-01-01 100 200 0 0 
# 2 2000-01-05 100 200 10 0 
# 3 2000-04-09 100 200 10 35 
# 4 2000-06-01 100 200 10 35 
# 5 2001-01-01 100 200 10 35