How to add a row to a dataframe modifying only some columns

Question

In order to prepare the data for plotting I need to add a new row to the data:

I have this dataframe:

df <- data.frame(
  test_id = c(1, 1, 1, 1),
  test_nr = c(1, 1, 1, 1),
  region = c("A", "B", "C", "D"),
  test_value = c(3, 1, 1, 2)
)

  test_id test_nr region test_value
1       1       1      A          3
2       1       1      B          1
3       1       1      C          1
4       1       1      D          2

I would like to add a row to this dataframe so the desired output should be:

  test_id test_nr region test_value
1       1       1      A       3.00
2       1       1      B       1.00
3       1       1      C       1.00
4       1       1      D       2.00
5       1       1   mean       1.75

As you can see: column 1 and column 2 are the same value, column 3 changed to 'mean' and column 4 is the mean of row1-4.

I have tried to use add_row from tibble package which gives an error:

library(dpylr)
library(tibble)

df %>% 
  mutate(mean1 = mean(test_value)) %>% 
  add_row(test_id = test_id[1], test_nr=test_nr[1],region="mean", test_value=mean(test_value))

Error in eval_tidy(xs[[j]], mask) : object 'test_id' not found

Answer 1

Coming in with a base R approach, you can build off of an existing row and then rbind to row bind two objects.

# save a new vector from any row you like
row_to_add <- df[1,] 

# alter the values want one at a time
row_to_add$region <- "mean"
row_to_add$test_value <- mean(df$test_value)

# OR alter them at once if you prefer...
row_to_add[,c("region","test_value")] <- c("mean",mean(df$test_value))

# finally use rbind to add to the bottom of the data.frame
rbind(df,row_to_add)

Answer 2

If we want to use the OP's method without calling the .$, use exposition pipe (%$%) from magrittr. This have similarity using the with (from base R)

library(magrittr)
library(dplyr)
df %$%
  add_row(., test_id = first(test_id), test_nr = first(test_nr),
    region = "mean", test_value = mean(test_value))
  test_id test_nr region test_value
1       1       1      A       3.00
2       1       1      B       1.00
3       1       1      C       1.00
4       1       1      D       2.00
5       1       1   mean       1.75