The gtsummary package lets you produce summary statistics for your numeric and categorical variables, formatted into a neat table. You can include p-values for pairwise comparisons and also split your summary across the categories of one of your covariates.
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library(tidyverse)
library(gtsummary)
data(CO2)
head(CO2)
# basic summary table
CO2 %>% select(!c(Plant,conc)) %>% tbl_summary()
# summary split by a categorical variable
CO2 %>% select(!c(Plant,conc)) %>% tbl_summary(by = Type)
# summary split by a categorical variable with p-value
CO2 %>% select(!c(Plant,conc)) %>%
tbl_summary(by = Type) %>% add_p()
# include overall, extra heading, custom stats
CO2 %>% select(!c(Plant,conc)) %>%
tbl_summary(
by = Type,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)"
), digits = all_continuous() ~ 2) %>%
add_p() %>% add_overall() %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Location**")
# crosstabs
CO2 %>%
tbl_cross(
row = Type,
col = Treatment,
percent = "cell"
) %>%
add_p()