CODEWITHSUNDEEP
rjoinreplacedataframedplyr
Zyferion
Zyferion

Reputation: 149

R: Updating a data frame with another data frame

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
1     1 1  1  1
2     2 2  2  2
3     3 3  3  3
4     4 4 NA NA
5     5 5 NA NA
6     6 6 NA NA

Another data frame contains new information for col B and C

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

> df2
  Index B C
1     4 4 5
2     5 4 5
3     6 4 5

How can you update the missing values in df1 so it looks like this:

  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

My attempt:

library(dplyr)

> full_join(df1,df2)
Joining by: c("Index", "B", "C")
  Index  A  B  C
1     1  1  1  1
2     2  2  2  2
3     3  3  3  3
4     4  4 NA NA
5     5  5 NA NA
6     6  6 NA NA
7     4 NA  4  5
8     5 NA  4  5
9     6 NA  4  5

Which as you can see has created duplicate rows for the 4,5,6 index instead of replacing the NA values.

Any help would be greatly appreciated!

Upvotes: 5

Views: 6560

Answers (5)

LMc
LMc

Reputation: 18612

As of dplyr >= 1.0.0 you can use rows_update:

library(dplyr)

df1 %>% 
  rows_update(df2, by = "Index")

  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

Alternatively, there is rows_patch:

rows_patch() works like rows_update() but only overwrites NA values.

Upvotes: 4

Zyferion
Zyferion

Reputation: 149

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)

Upvotes: 3

thelatemail
thelatemail

Reputation: 93803

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

Upvotes: 6

akrun
akrun

Reputation: 886928

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

Upvotes: 5

TomNash
TomNash

Reputation: 3288

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

Upvotes: 2

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