Again using gtsummary it is easy to create tables of multiple univariate analyses at the same time. In this case we look at predictors of survival on the Titanic without adjusting for the impact of other variables on that outcome (e.g., the impact of sex on survival without considering age or class etc.)

Code

library(titanic)
library(tidyverse)
library(gtsummary)

theme_gtsummary_compact()

titanic_train %>% # Dataset used
  select(-c(Name, PassengerId, Ticket, Cabin, Embarked, Parch)) %>% # Keeping only the variables we want
  mutate( # Making death 1 and survival 0, easier to interpret in graph
    Survived = as.numeric(Survived!= '1'),
    Pclass = factor(Pclass) # Class should be categorical
  ) %>%
  tbl_uvregression(method=glm,
                   y = Survived,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    hide_n = T
  ) %>%
  add_nevent(location="level") %>%
  add_global_p() %>%    #omnibus test
  bold_p() %>% # bold p-values  (default 0.05)
  bold_labels() %>% 
  italicize_levels() %>% 
  modify_header(stat_nevent = "**Deaths**")

Characteristic	Deaths	OR1	95% CI1	p-value
Pclass				<0.001
1	80	—	—
2	97	1.90	1.27, 2.83
3	372	5.31	3.78, 7.53
Sex				<0.001
female	81	—	—
male	468	12.4	8.94, 17.2
Age	424	1.01	1.00, 1.02	0.038
SibSp	549	1.07	0.95, 1.22	0.3
Fare	549	0.98	0.98, 0.99	<0.001
1 OR = Odds Ratio, CI = Confidence Interval