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SAS PROC COMPARE-style output datasets

Source: R/sas-out.R
sas-out.Rd

These helpers reshape the long-format value_diff table inside a ks_comparison into the four classic SAS PROC COMPARE output datasets:

Usage

as_outbase(x)

as_outcomp(x)

as_outdif(x)

as_outnoequal(x)

Arguments

x

A ks_comparison object.

Value

A tibble.

Details

  • as_outbase() — values from the base frame, only for matched rows that contain at least one differing column; one row per matched key, one column per compared variable.

  • as_outcomp() — same shape as as_outbase() but values from the compare frame.

  • as_outdif() — numeric difference (base - comp) for every matched row that contains at least one differing column; non-numeric cells are reported as NA with a .note column.

  • as_outnoequal() — only the rows where at least one matched cell differs, in long format (one row per differing cell).

The shapes mirror SAS's OUTBASE=, OUTCOMP=, OUTDIF=, and OUTNOEQUAL= datasets in spirit; we do not replicate the exact metadata columns (_TYPE_, _OBS_, ...) but include key_id and the original key columns so the rows can be related back to the source frames.

Examples

a <- data.frame(id = 1:3, x = c(1, 2, 3), y = c("a", "b", "c"))
b <- data.frame(id = 1:3, x = c(1, 2, 4), y = c("a", "B", "c"))
cmp <- ks_compare(a, b, by = "id")
#> ⚠ a vs b — 2 value diffs across 2 columns
as_outbase(cmp)
#> # A tibble: 2 × 4
#>      id key_id x     y    
#>   <int>  <int> <chr> <chr>
#> 1     2      2 NA    b    
#> 2     3      3 3     NA   
as_outcomp(cmp)
#> # A tibble: 2 × 4
#>      id key_id x     y    
#>   <int>  <int> <chr> <chr>
#> 1     2      2 NA    B    
#> 2     3      3 4     NA   
as_outdif(cmp)
#> # A tibble: 2 × 4
#>      id key_id     x     y
#>   <int>  <int> <dbl> <dbl>
#> 1     2      2    NA    NA
#> 2     3      3    -1    NA
as_outnoequal(cmp)
#> # A tibble: 2 × 12
#>      id key_id base_row comp_row column_base column_comp kind  base  comp   diff
#>   <int>  <int>    <int>    <int> <chr>       <chr>       <chr> <chr> <chr> <dbl>
#> 1     3      3        3        3 x           x           doub… 3     4        -1
#> 2     2      2        2        2 y           y           char… b     B        NA
#> # ℹ 2 more variables: na_flow <chr>, note <chr>