Set a listwise complete-case filter for matching N across analyses

jcomplete() registers a set of variables and activates a listwise deletion filter that excludes any case with a missing value on any of the registered variables. This ensures that all subsequent analyses use the same set of complete cases, which is essential when preliminary analyses need to match the N of a final regression model.

The setting is stored per dataset, so switching juse() between datasets preserves each dataset's setting independently.

The jcomplete filter applies whenever the matching dataset is used, regardless of whether it was supplied via juse() or specified explicitly in a function call. To bypass temporarily without losing the setting, use jcomplete(off) before the analysis and jcomplete(on) afterward. This matches the SPSS USE ALL / FILTER convention.

If a variable in the setting is later dropped from the dataset or renamed, each analysis of that dataset stops, naming the variable, until jcomplete() is run again with the current names or the setting is cleared. jcomplete(on) and a preview of the set filter stop in the same way, and the status display (jcomplete()) names the variable in place of the complete-case count.

Usage

jcomplete(
  data,
  ...,
  preview = FALSE,
  console = FALSE,
  non.deletes = FALSE,
  clear.all = FALSE
)

Arguments

data: A data frame. If omitted, uses the default set by juse(). Instead of variable names, the call may carry one of three special values: the bare word off deactivates the setting but remembers the variables, on reactivates it, and NULL clears it entirely. Each acts on the juse() default dataset when data is omitted (jcomplete(off), jcomplete(NULL)), or on the named dataset when it is given (jcomplete(d, off), jcomplete(d, NULL)). With no default set, jcomplete(NULL) clears the one dataset that carries a setting, and asks you to name one when several do. A setting is stored under its dataset's name, so the named off and NULL still work after that dataset has been removed, or its name given to something that is not a data frame; on is refused there, since the setting cannot be checked without its dataset. Call with no arguments to check the current status.
...: Unquoted variable names to include in the listwise check.
preview: Logical. If TRUE, open a viewer (RStudio data tab) showing the rows the listwise filter will drop, with a leading Row column (original data position) and a trailing DeletionCheck flag (1 = the row will be dropped). May be used on its own to preview the already-set filter without re-listing the variables (jcomplete(preview = TRUE)). Default FALSE.
console: Logical or numeric. Print the dropped rows to the console. TRUE prints the first 10; a number prints that many. Independent of preview: on its own it prints to the console without opening the viewer; combine with preview = TRUE to get both. The console listing is always limited to the dropped rows so it cannot flood the console. Default FALSE.
non.deletes: Logical. If TRUE, the viewer shows every case (with DeletionCheck marking which will drop) rather than only the dropped rows. Affects the viewer only; the console listing stays deleted-rows-only. Default FALSE.
clear.all: Logical. If TRUE, clears the jcomplete setting on every dataset; use on its own, jcomplete(clear.all = TRUE). This is the same grammar as jsubset() and the registration functions (jdummy(), jnumeric(), jcount(), jlikert()): NULL clears one dataset, clear.all = TRUE clears them all.

Value

Invisibly returns NULL. When a preview is requested, invisibly returns the previewed data frame instead, so it can be captured (e.g. jcomplete_rows <- jcomplete(preview = TRUE)).

Examples

juse(community)
#> Default data frame set to: community
jcomplete(Income, Education, Age)
#> Listwise Case Filter
#> Using default data frame: community
#> 
#> Variable    N   Missing  % Missing
#> ---------  ---  -------  ---------
#> Income     103     6        5.8%
#> Education  103     6        5.8%
#> Age        103     0        0.0%
#> 
#>   Complete cases: 91 of 103 (88.3%)
#>   Listwise filter activated -- 12 cases will be excluded from
#>   subsequent analyses.
jcomplete(community, Income, Education, Age)         # Explicit dataset
#> Listwise Case Filter
#> Variable    N   Missing  % Missing
#> ---------  ---  -------  ---------
#> Income     103     6        5.8%
#> Education  103     6        5.8%
#> Age        103     0        0.0%
#> 
#>   Complete cases: 91 of 103 (88.3%)
#>   Listwise filter activated -- 12 cases will be excluded from
#>   subsequent analyses.
jcomplete(data = community, Income, Education, Age)  # ... given by name
#> Listwise Case Filter
#> Variable    N   Missing  % Missing
#> ---------  ---  -------  ---------
#> Income     103     6        5.8%
#> Education  103     6        5.8%
#> Age        103     0        0.0%
#> 
#>   Complete cases: 91 of 103 (88.3%)
#>   Listwise filter activated -- 12 cases will be excluded from
#>   subsequent analyses.
jdesc(Age)                     # Uses only complete cases on those 3 vars
#> Descriptive Statistics
#> Using default data frame: community
#> 
#> Case Processing  Excluded  Remaining
#>     Original           --        103
#>     jcomplete()        12         91  Income, Education, +1 more
#>     Remaining N        --         91
#> ----------------------------------------------------------------
#> 
#> Variable  Total  Non_missing  Min  Max   Mean     SD
#> --------  -----  -----------  ---  ---  ------  ------
#> Age         91        91       18   71  40.484  11.933
#> 
jcomplete(Income, Education, Age, preview = TRUE)  # Set and preview together
#> Listwise Case Filter
#> Using default data frame: community
#> 
#> Variable    N   Missing  % Missing
#> ---------  ---  -------  ---------
#> Income     103     6        5.8%
#> Education  103     6        5.8%
#> Age        103     0        0.0%
#> 
#>   Complete cases: 91 of 103 (88.3%)
#>   Listwise filter activated -- 12 cases will be excluded from
#>   subsequent analyses.
#> jcomplete Preview -- rows scheduled for deletion
#> Row  Income  Education  Age  DeletionCheck
#> ---  ------  ---------  ---  -------------
#>   6                  4   48              1
#>  16   34000              32              1
#>  27                  2   49              1
#>  31                  5   31              1
#>  39   27000              40              1
#>  43                  1   50              1
#>  55   64000              45              1
#>  61                  2   56              1
#>  68   48000              46              1
#>  72                  3   47              1
#> 
#>   Showing the first 10 of 12 dropped rows.
#> (The preview viewer needs an interactive RStudio session; showing the first 10
#> in the console instead.)
jcomplete(preview = TRUE)      # Preview the already-set filter (viewer)
#> jcomplete Preview -- rows scheduled for deletion
#> Row  Income  Education  Age  DeletionCheck
#> ---  ------  ---------  ---  -------------
#>   6                  4   48              1
#>  16   34000              32              1
#>  27                  2   49              1
#>  31                  5   31              1
#>  39   27000              40              1
#>  43                  1   50              1
#>  55   64000              45              1
#>  61                  2   56              1
#>  68   48000              46              1
#>  72                  3   47              1
#> 
#>   Showing the first 10 of 12 dropped rows.
#> (The preview viewer needs an interactive RStudio session; showing the first 10
#> in the console instead.)
jcomplete(preview = TRUE, non.deletes = TRUE)  # Viewer shows all cases
#> jcomplete Preview -- rows scheduled for deletion
#> Row  Income  Education  Age  DeletionCheck
#> ---  ------  ---------  ---  -------------
#>   6                  4   48              1
#>  16   34000              32              1
#>  27                  2   49              1
#>  31                  5   31              1
#>  39   27000              40              1
#>  43                  1   50              1
#>  55   64000              45              1
#>  61                  2   56              1
#>  68   48000              46              1
#>  72                  3   47              1
#> 
#>   Showing the first 10 of 12 dropped rows.
#> (The preview viewer needs an interactive RStudio session; showing the first 10
#> in the console instead.)
jcomplete(console = 10)        # Console only -- first 10 dropped rows
#> jcomplete Preview -- rows scheduled for deletion
#> Row  Income  Education  Age  DeletionCheck
#> ---  ------  ---------  ---  -------------
#>   6                  4   48              1
#>  16   34000              32              1
#>  27                  2   49              1
#>  31                  5   31              1
#>  39   27000              40              1
#>  43                  1   50              1
#>  55   64000              45              1
#>  61                  2   56              1
#>  68   48000              46              1
#>  72                  3   47              1
#> 
#>   Showing the first 10 of 12 dropped rows. Use preview = TRUE to see them all in the viewer.
jcomplete(preview = TRUE, console = 25)        # Viewer and console
#> jcomplete Preview -- rows scheduled for deletion
#> Row  Income  Education  Age  DeletionCheck
#> ---  ------  ---------  ---  -------------
#>   6                  4   48              1
#>  16   34000              32              1
#>  27                  2   49              1
#>  31                  5   31              1
#>  39   27000              40              1
#>  43                  1   50              1
#>  55   64000              45              1
#>  61                  2   56              1
#>  68   48000              46              1
#>  72                  3   47              1
#>  80   52000              30              1
#> 101   46000              29              1
jcomplete(off)                 # Deactivate
#> jcomplete deactivated for community.
jcomplete(on)                  # Reactivate
#> jcomplete reactivated for community: Income, Education, Age
jcomplete(community, off)      # Deactivate on a named dataset
#> jcomplete deactivated for community.
jcomplete(community, on)       # ... and reactivate it
#> jcomplete reactivated for community: Income, Education, Age
jcomplete()                    # Check status
#> jcomplete active for community: Income, Education, Age
#> (91 of 103 complete cases)
jcomplete(NULL)                # Clear the default dataset's setting
#> jcomplete cleared for community (had: Income, Education, Age).
jcomplete(community, NULL)     # Clear a named dataset's setting
#> No jcomplete filter set for community.
#> Nothing to clear.
jcomplete(clear.all = TRUE)    # Clear every dataset's setting
#> No jcomplete settings to clear.

# juse(NULL) clears the default dataset -- not normally needed. You'd
# clear a default or registration only to undo a mistake, or -- as in
# this example -- to reset state for testing.
juse(NULL)
#> Default data frame cleared.

Set a listwise complete-case filter for matching N across analyses

Usage

Arguments

Value

See also

Examples