# gt_summary tabulating mean/average counts with p-values

Hello there,

I'm trying to tabulate the mean or average counts daily (count/number_days), stratified by period and category, and calculate the p-value using the following data frame:

``````library(gtsummary)

df <-
data.frame(
number_days= c(120,  80, 120, 80), #number of days within the 2 period groups
count = c(1000, 400, 700, 600),
period = c("A", "B", "A", "B"),
category=c("Relevant", "Relevant", "Other", "Other"))
``````

I run the script below, using tbl_summary, to determine mean count, period and category:

``````df %>%
select(count, period) %>%
tbl_summary(by = period,
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)"),
digits = all_continuous() ~ 2,
type = list(count ~ "continuous")) %>%
``````

However, this doesn't work to produce what I'm intending to create, which is a 2 by 2 table with average counts and a p-value that tests for statistical significance:

I also have the row-level data (with with an exact date variable), not just the data frame with the aggregate data. How can I go about producing what I need to produce?

Any help would be appreciated! Thanks in advance!

Hi @R_Inquirer,
This works OK

``````library(dplyr)
library(gt)
library(gtsummary)

df <-
data.frame(
number_days= c(120,  80, 120, 80), #number of days within the 2 period groups
count = c(1000, 400, 700, 600),
period = c("A", "B", "A", "B"),
category=c("Relevant", "Relevant", "Other", "Other"))

df

df %>%
#select(count, period) %>%
select(-category) %>%
tbl_summary(by = period,
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)"),
digits = all_continuous() ~ 2,
type = list(count ~ "continuous")) %>%