Joining / merging two data frames by symmetric differences in rows and columns - r

Question

I would like to join / merge two data frames, but ignoring similarities in rows and columns in the resulting data frame. Consider the following example:
df1 <- data.frame(
id = c("a","b","c"),
a = runif(3,1,9),
b = runif(3,1,9)
)
df2 <- data.frame(
df1[1:2,],
c = runif(2,1,9)
)
Results in two data frames that have exactly four cells in common (not counting id), so df1[1:2,2:3] == df2[1:2,2:3]. However, they do differ in regard that df1 as an additional row and df2 has an additional column:
> print(df1)
id a b
1 a 6.396168 4.037320
2 b 4.119025 8.181253
3 c 5.608775 4.219469
> print(df2)
id a b c
1 a 6.396168 4.037320 2.444122
2 b 4.119025 8.181253 6.444280
I want a new data frame to consist of the symmetric differences between these two, so no duplicates in rows or columns. The closest result I have achieved is by using dplyr::full_join(df1, df2, by = "id"), but this results in duplicated columns.
The result should look like this:
id a b c
1 a 6.396168 4.037320 2.444122
2 b 4.119025 8.181253 6.444280
3 c 5.608775 4.219469 NA
What's the best way of achieving this dynamically? Thanks

Answer 1

With data.table we can join on the 'id' and assign the 'c' from the second dataset to create the 'c' column in the first data. By default, the non-matching elements will be assigned as NA
library(data.table)
setDT(df1)[df2, c := c, on = .(id)]
df1
# id a b c
#1: a 4.601639 1.065642 7.476494
#2: b 6.065758 6.234421 8.929932
#3: c 4.000351 7.365717 NA
NOTE: The values are different as there was not set seed
In base R, an option would be match
df1$c <- df2$c[match(df1$id, df2$id)]
Regarding the OP's use of full_join (left_join would be fine based on the example), the trick is to remove the columns that are not needed in the second dataset
library(dplyr)
nm1 <- c("id", setdiff(names(df2), names(df1)))
left_join(df1, select(df2, nm1), by = 'id')

Answer 2

Another approach if one of the data frames has all the rows you want (df2 here):
library(dplyr)
bind_rows(df2, anti_join(df1, df2))
#Joining, by = c("id", "a", "b")
# id a b c
#1 a 1.912298 5.792475 6.899253
#2 b 2.537666 1.495075 1.186120
#3 c 5.947766 6.594028 NA

Answer 3

In this particular case this would be sufficient
library(sqldf)
sqldf("select * from df1 left natural join df2")
## id a b c
## 1 a 6.396168 4.037320 2.444122
## 2 b 4.119025 8.181253 6.444280
## 3 c 5.608775 4.219469 NA
or with dplyr:
library(dplyr)
left_join(df1, df2)
but in general you might need the following. Note this is perfectly general. We did not need to specify the column or row names in either the above or following code and in the following code it is symmetric in df1 and df2 so it does not rely on knowing the structure of either.
sqldf("select * from df1 left natural join df2
union
select * from df2 left natural join df1")
## id a b c
## 1 a 6.396168 4.037320 2.444122
## 2 b 4.119025 8.181253 6.444280
## 3 c 5.608775 4.219469 NA
or with dplyr. This will give a warning but still works. You can avoid the warning if id were character rather than factor or if you convert it to character first.
library(dplyr)
rbind(left_join(df1, df2), left_join(df2, df1)) %>% distinct
Note
Because the question did not use set.seed the code to generate the input is
not reproducible but we can copy the particular df1 and df2 so that we have the same data as in the question.
Lines1 <- "
id a b
1 a 6.396168 4.037320
2 b 4.119025 8.181253
3 c 5.608775 4.219469"
df1 <- read.table(text = Lines1)
Lines2 <- "
id a b c
1 a 6.396168 4.037320 2.444122
2 b 4.119025 8.181253 6.444280"
df2 <- read.table(text = Lines2)