affirm_dupe_free updates

[Meghansaha]

What changes are proposed in this pull request?

Updated affirm_no_dupes() to add record_id and flag_duplicate columns to the output, providing better visibility into duplicate detection logic.

If there is an GitHub issue associated with this pull request, please provide link.

Closes #42

---

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[Meghansaha]

Important

@shannonpileggi, please review this update to affirm_dupe_free() when time allows.

Per discussion in #42, I've modified affirm_dupe_free() (previously named affirm_no_dupes()) to add three new columns (flag_duplicate, duplicate_of, record_id) to both the data output and exported Excel report.

Key Files Changed:

• affirm_dupe_free.R - Diff Here
• tests/testthat/test-affirm_dupe_free.R - Diff Here
• NEWS.md - Diff Here
• DESCRIPTION for version bump, man (.Rd), and snap files for fx name change

---

Implementation Notes

• Renamed affirm_no_dupes() to affirm_dupe_free()and inverted the original function logic in the condition quosure to ensure accurate console messaging, since all affirm functions wrap affirm_true().

• Added duplicate_of variable showing the record_id of the first occurrence for each duplicate row. Since report listings only show flagged rows, this helps trace duplicates back to the original occurrence in the full dataset.

---

Validation

R CMD Check passes:

Test coverage maintained:

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Examples

With duplicates:

library(devtools)
load_all()
library(dplyr)

# Initialize affirm session
affirm_init(replace = TRUE)

df_test <-
  tibble::tibble(
    subject = c("123-45-678", "234-56-789", "345-67-890", "375-13-532", "123-45-678", "345-67-890"),
    event = c("nausea", "pain", "covid", "cardiac arrest", "nausea", "covid"),
    grade  = c(1,1,3,4,1,3),
    date = c("2025-02-01", "2025-01-23", "2024-05-08", "2023-06-07", "2025-02-01", "2024-05-08")
  )

# When duplicates are present
df_test |>
  affirm_dupe_free(
    label = "No duplicates event and dates fopr subjects",
    columns = everything()
  )

# Export the report
affirm_report_excel(
  "test.xlsx", affirmation_name = "df_1"
)

Returns:

• No duplicates event and dates for subjects
  2 issues identified.
# A tibble: 6 × 7
  subject    event          grade date       flag_duplicate duplicate_of record_id
  <chr>      <chr>          <dbl> <chr>      <lgl>                 <int>     <int>
1 123-45-678 nausea             1 2025-02-01 FALSE                    NA         1
2 234-56-789 pain               1 2025-01-23 FALSE                    NA         2
3 345-67-890 covid              3 2024-05-08 FALSE                    NA         3
4 375-13-532 cardiac arrest     4 2023-06-07 FALSE                    NA         4
5 123-45-678 nausea             1 2025-02-01 TRUE                      1         5
6 345-67-890 covid              3 2024-05-08 TRUE                      3         6

Without duplicates:

# Initialize affirm session
affirm_init(replace = TRUE)

# When duplicates are present
df_test |>
  distinct() |>
  affirm_dupe_free(
    label = "No duplicates event and dates for subjects",
    columns = everything()
  )

# Export the report
affirm_report_excel(
  "test2.xlsx", affirmation_name = "df_2"
)

Returns:

• No duplicates event and dates for subjects
  0 issues identified.
# A tibble: 4 × 7
  subject    event          grade date       flag_duplicate duplicate_of record_id
  <chr>      <chr>          <dbl> <chr>      <lgl>                 <int>     <int>
1 123-45-678 nausea             1 2025-02-01 FALSE                    NA         1
2 234-56-789 pain               1 2025-01-23 FALSE                    NA         2
3 345-67-890 covid              3 2024-05-08 FALSE                    NA         3
4 375-13-532 cardiac arrest     4 2023-06-07 FALSE                    NA         4

Excel report (with duplicates):

Excel report (without duplicates):

Returns no rows, consistent with other affirm functions.

[shannonpileggi]

hey @Meghansaha! this is looking better. however, it seems that the columns argument is used inconsistently across functions .

affirm_true, columns = represents "string of column names used in affirmation"

• it isn't super clear to me what this means and we dont have a lot of documentation/testing on this, would be good to flesh out

affirm_dupe_free, columns = represents "columns to check duplicates among"

affirm_class, columns = represents "columns to check class"

it is probably best to discuss

1. what should be expected behavior across functions
2. what can/should go in this PR, vs go in another PR

Example 1

df_test <- mtcars |>
  tibble::rownames_to_column() |> 
  dplyr::arrange(cyl, hp) 


options(
  'affirm.id_cols' =  c(
    "rowname", "mpg"
  )
)

affirm_init(replace = TRUE)

df_test |>
  affirm_dupe_free(
    label = "No duplicates in the number of cylinders & hp",
    columns = c(cyl,hp),
    id = 1,
    data_frames = "mtcars"
  )


df_test |> 
  affirm_true(
    label = "mpg <= 30",
    condition = mpg <= 30,
    id = 2,
    data_frames = "mtcars",
    # these do not work
    #columns = c("rowname", "mpg"),
    #columns = everything()
  )

affirm_report_excel("test.xlsx")

Affirmation 1 (affirm_dupe_free) - checks cyl & hp for duplicates; ignores affirm.id_cols in output creation; outputs flag_duplicate which is inconsistent with affirmation 2 (no flag output)

Affirmation 2 (affirm_true) - columns argument doesn't work; affirm.id_cols respected; specification of affirm.id_cols differs from columns specification in affirm_dupe_free (bare vs quoted variable names)

Example 2

affirm_init(replace = TRUE)

affirm_class(
  dplyr::as_tibble(iris),
  label = "all cols are numeric (but Species really isn't)",
  columns = everything(),
  data_frames = "iris",
  id = 1,
  class = "numeric"
)

affirm_report_excel("test_iris.xlsx")

affirm_class() behaves more like affirm_dupe_free(). all columns are tested but not output.

[shannonpileggi]

why did this get added here? this is something we push

[Meghansaha]

I had updated RStudio around the time I was working on this. That update added a projectID field to the .Rproj file that our current .gitignore isn't catching:

affirm/.gitignore

Lines 1 to 6 in 7b328b2

	.Rproj.user
	.Rhistory
	.RData
	.Ruserdata
	docs
	inst/doc

Should we commit this projectID or update the gitignore to exclude .Rproj files entirely? Tried to find some info on this, : rstudio/rstudio#15524

But still fuzzy on what the implications of projectID are.

Let's discuss.