Replace a subset of a data frame with dplyr join operations

Question

Suppose that I gave a treatment to some column values of a data frame like this:

  id animal weight   height ...
  1    dog     23.0
  2    cat     NA
  3   duck     1.2
  4  fairy     0.2
  5  snake     BAD


df <- data.frame(id = seq(1:5),
             animal = c("dog", "cat", "duck", "fairy", "snake"),
             weight = c("23", NA, "1.2", "0.2",  "BAD"))

Suppose that the treatment require to work in a separately table, and gave as the result, the following data frame that is a subset of the original:

  id animal weight
  2    cat    2.2
  5  snake    1.3

sub_df <- data.frame(id = c(2, 5),
             animal = c("cat", "snake"),
             weight = c("2.2", "1.3"))

Now I want to put all together again, so I use an operation like this:

> df %>%
   anti_join(sub_df, by = c("id", "animal")) %>%
   bind_rows(sub_df)

 id animal weight
 4  fairy    0.2
 1    dog   23.0
 3   duck    1.2
 2    cat    2.2
 5  snake    1.3

Exist some way to do this directly with join operations?

In the case that the subset is just the key column and the variable subject to give a treatment (id, animal weigth) and not the total variables of the original data frame (id, animal, weight, height), how could assemble the subset with the original set?

Answer 1

What you describe is a join operation in which you update some values in the original dataset. This is very easy to do with great performance using data.table because of its fast joins and update-by-reference concept (:=).

Here's an example for your toy data:

library(data.table)
setDT(df)             # convert to data.table without copy
setDT(sub_df)         # convert to data.table without copy

# join and update "df" by reference, i.e. without copy 
df[sub_df, on = c("id", "animal"), weight := i.weight]

The data is now updated:

#   id animal weight
#1:  1    dog   23.0
#2:  2    cat    2.2
#3:  3   duck    1.2
#4:  4  fairy    0.2
#5:  5  snake    1.3

You can use setDF to switch back to ordinary data.frame.

Answer 2

Remove the na's first, then simply stack the tibbles:

 bind_rows(filter(df,!is.na(weight)),sub_df)