R: Updating a data frame with another data frame

Question

Let\'s say our initial data frame looks like this:

df1 = data.frame(Index=c(1:6),A=c(1:6),B=c(1,2,3,NA,NA,NA),C=c(1,2,3,NA,NA,NA))

> df1
  Index A  B  C
         


        

Answer 1

Not sure what the general case or conditions would be, but this works for this instance without dplyr

df3 <- as.matrix(df1)
df3[which(is.na(df3))] <- as.matrix(df2)
df3 <- as.data.frame(df3)
df3

  A B C
1 1 1 1
2 2 2 2
3 3 3 3
4 4 4 5
5 5 4 5
6 6 4 5

Answer 2

We can use join from data.table. Convert the 'data.frame' to 'data.table' (setDT(df1), join on with 'df1' using "Index" and assign (:=), the values in 'B' and 'C' with 'i.B' and 'i.C'.

library(data.table)
setDT(df1)[df2, c('B', 'C') := .(i.B, i.C), on = "Index"]
df1
#   Index A B C
#1:     1 1 1 1
#2:     2 2 2 2
#3:     3 3 3 3
#4:     4 4 4 5
#5:     5 5 4 5
#6:     6 6 4 5

Answer 3

merge then aggregate:

aggregate(. ~ Index, data=merge(df1, df2, all=TRUE), na.omit, na.action=na.pass )

#  Index B C A
#1     1 1 1 1
#2     2 2 2 2
#3     3 3 3 3
#4     4 4 5 4
#5     5 4 5 5
#6     6 4 5 6

Or in dplyr speak:

df1 %>% 
    full_join(df2) %>%
    group_by(Index) %>%
    summarise_each(funs(na.omit))

#Joining by: c("Index", "B", "C")
#Source: local data frame [6 x 4]
#
#  Index     A     B     C
#  (dbl) (int) (dbl) (dbl)
#1     1     1     1     1
#2     2     2     2     2
#3     3     3     3     3
#4     4     4     4     5
#5     5     5     4     5
#6     6     6     4     5

Answer 4

For those interested, I've extended this problem to:

- handle updating a data frame with another data frame with new columns

- replace any existing entries regardless if they're NA or not.

Heres the solution I found using the aggregate function from @thelatemail :)

df1 = data.frame(Index=c(1:6),A=c(1:6),B=c(1,2,3,3,3,3),C=c(1,2,3,3,3,3))

df2 = data.frame(Index=c(4,5,6),B=c(4,4,4),C=c(5,5,5),D=c(6,6,6),E=c(7,7,7))

df3 = full_join(df1,df2)

# Create a function na.omit.last 
na.omit.last = function(x){
  x <- na.omit(x)
  x <- last(x)
}

# For the columns not in df1 
dfA = aggregate(. ~ Index, df3, na.omit,na.action = na.pass)
dfA = dfA[,-(1:ncol(df1))] 
dfA = data.frame(lapply(dfA,as.numeric))

dfB = aggregate(. ~ Index, df3[,1:ncol(df1)], na.omit.last, na.action = na.pass)

# If there are more columns in df2 append dfA
if (ncol(df2) > ncol(df1)) {
  df3 = cbind(dfB,dfA)
}  else {
    df3 = dfB
  }

print(df3)