Replace a subset of a data frame with dplyr join operations - r

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)

Answer 3

Isn't dplyr::rows_update exactly what we need here? The following code should work:
df %>% dplyr::rows_update(sub_df, by = "id")
This should work as long as there is a unique identifier (one or multiple variables) for your datasets.

Answer 4

For anyone looking for a solution to use in a tidyverse pipeline:
I run into this problem a lot, and have written a short function that uses mostly tidyverse verbs to get around this. It will account for the case when there are additional columns in the original df.
For example, if the OP's df had an additional 'height' column:
library(dplyr)
df <- tibble(id = seq(1:5),
animal = c("dog", "cat", "duck", "fairy", "snake"),
weight = c("23", NA, "1.2", "0.2", "BAD"),
height = c("54", "45", "21", "50", "42"))
And the subset of data we wanted to join in was the same:
sub_df <- tibble(id = c(2, 5),
animal = c("cat", "snake"),
weight = c("2.2", "1.3"))
If we used the OP's method alone (anti_join %>% bind_rows), this won't work because of the additional 'height' column in df. An extra step or two is needed.
In this case we could use the following function:
replace_subset <- function(df, df_subset, id_col_names = c()) {
# work out which of the columns contain "new" data
new_data_col_names <- colnames(df_subset)[which(!colnames(df_subset) %in% id_col_names)]
# complete the df_subset with the extra columns from df
df_sub_to_join <- df_subset %>%
left_join(select(df, -new_data_col_names), by = c(id_col_names))
# join and bind rows
df_out <- df %>%
anti_join(df_sub_to_join, by = c(id_col_names)) %>%
bind_rows(df_sub_to_join)
return(df_out)
}
Now for the results:
replace_subset(df = df , df_subset = sub_df, id_col_names = c("id"))
## A tibble: 5 x 4
# id animal weight height
# <dbl> <chr> <chr> <chr>
#1 1 dog 23 54
#2 3 duck 1.2 21
#3 4 fairy 0.2 50
#4 2 cat 2.2 45
#5 5 snake 1.3 42
And here's an example using the function in a pipeline:
df %>%
replace_subset(df_subset = sub_df, id_col_names = c("id")) %>%
mutate_at(.vars = vars(c('weight', 'height')), .funs = ~as.numeric(.)) %>%
mutate(bmi = weight / (height^2))
## A tibble: 5 x 5
# id animal weight height bmi
# <dbl> <chr> <dbl> <dbl> <dbl>
#1 1 dog 23 54 0.00789
#2 3 duck 1.2 21 0.00272
#3 4 fairy 0.2 50 0.00008
#4 2 cat 2.2 45 0.00109
#5 5 snake 1.3 42 0.000737
hope this is helpful :)