Add column totals to summary_factorlist() output

ff_column_totals(df.in, .data, dependent, na_include_dependent = FALSE,
  percent = TRUE, digits = 1, label = NULL, prefix = "")

finalfit_column_totals(df.in, .data, dependent,
  na_include_dependent = FALSE, percent = TRUE, digits = 1,
  label = NULL, prefix = "")

Arguments

df.in

summary_factorlist() output.

.data

Data frame used to create summary_factorlist().

dependent

Character. Name of dependent variable.

na_include_dependent

Logical. When TRUE, missing data in the dependent variable is included in totals.

percent

Logical. Include percentage.

digits

Integer length 1. Number of digits for percentage.

label

Character. Label for total row.

prefix

Character. Prefix for column totals, e.g "N=".

Value

Data frame.

Examples

explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = 'mort_5yr' colon_s %>% summary_factorlist(dependent, explanatory) %>% ff_column_totals(colon_s, dependent)
#> Note: dependent includes missing data. These are dropped.
#> Warning: Factor `obstruct.factor` contains implicit NA, consider using `forcats::fct_explicit_na`
#> label levels Alive Died #> 1 Total N (%) 511 (55.8) 404 (44.2) #> 2 Age <40 years 31 (6.1) 36 (8.9) #> 3 40-59 years 208 (40.7) 131 (32.4) #> 4 60+ years 272 (53.2) 237 (58.7) #> 5 Sex Female 243 (47.6) 194 (48.0) #> 6 Male 268 (52.4) 210 (52.0) #> 7 Obstruction No 408 (82.1) 312 (78.6) #> 8 Yes 89 (17.9) 85 (21.4) #> 9 Perforation No 497 (97.3) 391 (96.8) #> 10 Yes 14 (2.7) 13 (3.2)
# Ensure works with missing data in dependent colon_s = colon_s %>% dplyr::mutate( mort_5yr = forcats::fct_explicit_na(mort_5yr) ) colon_s %>% summary_factorlist(dependent, explanatory) %>% ff_column_totals(colon_s, dependent)
#> Warning: Factor `obstruct.factor` contains implicit NA, consider using `forcats::fct_explicit_na`
#> label levels Alive Died (Missing) #> 1 Total N (%) 511 (55.0) 404 (43.5) 14 (1.5) #> 2 Age <40 years 31 (6.1) 36 (8.9) 3 (21.4) #> 3 40-59 years 208 (40.7) 131 (32.4) 5 (35.7) #> 4 60+ years 272 (53.2) 237 (58.7) 6 (42.9) #> 5 Sex Female 243 (47.6) 194 (48.0) 8 (57.1) #> 6 Male 268 (52.4) 210 (52.0) 6 (42.9) #> 7 Obstruction No 408 (82.1) 312 (78.6) 12 (85.7) #> 8 Yes 89 (17.9) 85 (21.4) 2 (14.3) #> 9 Perforation No 497 (97.3) 391 (96.8) 14 (100.0) #> 10 Yes 14 (2.7) 13 (3.2)