ff_row_totals {finalfit} | R Documentation |
summary_factorlist()
outputThis adds a total and missing count to variables. This is useful for
continuous variables. Compare this to summary_factorlist(total_col =
TRUE)
which includes a count for each dummy variable as a factor and mean
(sd) or median (iqr) for continuous variables.
ff_row_totals( df.in, .data, dependent, explanatory, missing_column = TRUE, percent = TRUE, digits = 1, na_include_dependent = FALSE, na_complete_cases = FALSE, total_name = "Total N", na_name = "Missing N" ) finalfit_row_totals( df.in, .data, dependent, explanatory, missing_column = TRUE, percent = TRUE, digits = 1, na_include_dependent = FALSE, na_complete_cases = FALSE, total_name = "Total N", na_name = "Missing N" )
df.in |
|
.data |
Data frame used to create |
dependent |
Character. Name of dependent variable. |
explanatory |
Character vector of any length: name(s) of explanatory variables. |
missing_column |
Logical. Include a column of counts of missing data. |
percent |
Logical. Include percentage. |
digits |
Integer length 1. Number of digits for percentage. |
na_include_dependent |
Logical. When TRUE, missing data in the dependent variable is included in totals. |
na_complete_cases |
Logical. When TRUE, missing data counts for variables are for compelte cases across all included variables. |
total_name |
Character. Name of total column. |
na_name |
Character. Name of missing column. |
Data frame.
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = 'mort_5yr' colon_s %>% summary_factorlist(dependent, explanatory) %>% ff_row_totals(colon_s, dependent, explanatory)