用 "ID"将数据帧变量中的 NA 值替换为来自其他数据帧的值

Question

我想知道是否有比我在下面所做的更简洁的方法来替换数据框中变量的 NA 值。下面的代码似乎比我认为在 R 中可能的代码要长。例如，我不知道某些包/工具可以更简洁地完成此操作。

有没有一种方法可以仅在 NA 时替换或合并值？使用 all.x = T 合并两个数据帧后，我有一些 NA 值，我想用来自另一个数据帧的信息替换这些值，使用公共(public)变量链接替换.

# get dataframes
breaks <- structure(list(Break = 1:11, Value = c(2L, 13L, 7L, 9L, 40L, 
21L, 10L, 37L, 7L, 26L, 42L)), .Names = c("Break", "Value"), class = "data.frame", row.names = c(NA, 
-11L))

fsites <- structure(list(Site = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L), Plot = c(0L, 1L, 2L, 3L, 4L, 0L, 1L, 2L, 0L, 
1L, 2L, 3L, 4L, 5L), Break = c(1L, 5L, 7L, 8L, 11L, 1L, 6L, 11L, 
1L, 4L, 6L, 8L, 9L, 11L)), .Names = c("Site", "Plot", "Break"
), class = "data.frame", row.names = c(NA, -14L))

bps <- structure(list(Site = c(1L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L), Plot = c(0L, 1L, 2L, 3L, 1L, 2L, 0L, 1L, 2L, 3L, 4L), Value = c(0.393309653, 
0.12465733, 0.27380161, 0.027288989, 0.439712533, 0.289724079, 
0.036429062, 0.577460008, 0.820375917, 0.323217357, 0.28637503
)), .Names = c("Site", "Plot", "Value"), class = "data.frame", row.names = c(NA, 
-11L))

# merge fsites and bps
df1 <- merge(fsites, bps, by=c("Site", "Plot"), all.x=T)

# merge df1 and breaks to get values to eventually replace the NA values in 
# df1$Values.x, here "Break" is the ID by which to replace the NA values
df2 <- merge(df1, breaks, by=c("Break"))

# Create a new column 'Value' that uses Value.x, unless NA, then Value.y
df3 <- df2
df3$Value <- df2$Value.x
df2.na <- is.na(df2$Value.x)
df3$Value[df2.na] <- df2$Value.y[df2.na]

# get rid of unnecessary columns
cols <- c(1:3,6)
df4 <- df3[,cols]

Answer 1

在只有(breaks、fsites、bps 和)df1 的阶段:

df1$Value <- ifelse(is.na(df1$Value), 
                            breaks$Value[match(df1$Break, breaks$Break)], df1$Value)

#> df1
#   Site Plot Break       Value
#1     1    0     1  0.39330965
#2     1    1     5  0.12465733
#3     1    2     7  0.27380161
#4     1    3     8  0.02728899
#5     1    4    11 42.00000000
#6     2    0     1  2.00000000
#7     2    1     6  0.43971253
#8     2    2    11  0.28972408
#9     3    0     1  0.03642906
#10    3    1     4  0.57746001
#11    3    2     6  0.82037592
#12    3    3     8  0.32321736
#13    3    4     9  0.28637503
#14    3    5    11 42.00000000

#just to test with your `df4`
> sort(df1$Value) == sort(df4$Value)
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE