Add column totals to summary_factorlist() output
ff_column_totals(
df.in,
.data,
dependent,
na_include_dependent = FALSE,
percent = TRUE,
digits = c(1, 0),
label = NULL,
prefix = "",
weights = NULL
)
finalfit_column_totals(
df.in,
.data,
dependent,
na_include_dependent = FALSE,
percent = TRUE,
digits = c(1, 0),
label = NULL,
prefix = "",
weights = NULL
)summary_factorlist() output.
Data frame used to create summary_factorlist().
Character. Name of dependent variable.
Logical. When TRUE, missing data in the dependent variable is included in totals.
Logical. Include percentage.
Integer length 2. Number of digits for (1) percentage, (2) weighted count.
Character. Label for total row.
Character. Prefix for column totals, e.g "N=".
Character vector of length 1: name of column to use for weights.
Data frame.
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.
#> 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_na_value_to_level(mort_5yr, level = "(Missing)")
)
colon_s %>%
summary_factorlist(dependent, explanatory) %>%
ff_column_totals(colon_s, dependent)
#> 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) 0 (0.0)