loop through gtsummary table to pick out only significant variables - r

Question

I have a question. I am, relatively new to R. I am transitioning some code from another app to R. In that code, I was able to loop through a table and pick out only the significant variables based on the p-value and the size of the odds ratio for logistic regression. Then I was able to say something like "x had a significant link with y" when the p was less than or equal to 0.05 and the odds ratio as above 1.00 and do the converse "x had a significant negative link with " when the p value was less than 0.05 and the odds ration was below 1.00. Then, I was able to do what I understand from the gtsummary literature is inline_text these statements. As I am trying to get my bearings with R, I was wondering how I would I accomplish this with gtsummary tables? My reproducible code does not work, but it is below:
# install.packages("gtsummary")
library(gtsummary)
library(tidyverse)
#simulated data
gender <- sample(c(0,1), size = 1000, replace = TRUE)
age <- round(runif(1000, 18, 80))
xb <- -9 + 3.5*gender + 0.2*age
p <- 1/(1 + exp(-xb))
y <- rbinom(n = 1000, size = 1, prob = p)
mod <- glm(y ~ gender + age, family = "binomial")
summary(mod)
#create the gtsummary table
tab1 = mod %>%
tbl_regression(exponentiate = TRUE) %>%
as_gt() %>%
gt::tab_source_note(gt::md("*This data is simulated*"))
#attempt of going through the gtsummary table
for (i in 1:nrow(tab1[1:3,])) { # does one row at a time
pv = tab1[["_data"]]$p.value
num = tab1[i, "pv"]
name = tab1[i, "variable"]
if(pv <=0.05 ){
cat("The link between", name, "and is significant. ")
}
}
I ask about the gtsummary regression table because, I will have to do the same thing with the tbl_summary as well. I thought I would begin with the regression version. The idea is to get the gorgeous inline_text via an if else. All of this is triggered by the going down the p-value column, and then pulling the name of the variable and the amazing inline_text information into the sentence. I have looked through the available questions others have asked, but I haven't found anything that gets to the heart of this. If I have missed it, please, point me in the correct direction.

Answer 1

There is a data frame in every gtsummary table called x$table_body. I think it's easier to extract the information you need from there. Example below! (you could also wrap the last line in an inline_text() if that is better for you).
# install.packages("gtsummary")
library(gtsummary)
#> #BlackLivesMatter
library(tidyverse)
#simulated data
gender <- sample(c(0,1), size = 1000, replace = TRUE)
age <- round(runif(1000, 18, 80))
xb <- -9 + 3.5*gender + 0.2*age
p <- 1/(1 + exp(-xb))
y <- rbinom(n = 1000, size = 1, prob = p)
mod <- glm(y ~ gender + age, family = "binomial")
#create the gtsummary table
tab1 = mod %>% tbl_regression(exponentiate = TRUE)
# extract the variable names and the pvalues
tab1$table_body %>%
select(variable, p.value) %>%
filter(p.value <= 0.05) %>% # only keep the sig pvalues
deframe() %>%
imap(~str_glue("The link between 'y' and {.y} is significant ({style_pvalue(.x, prepend_p = TRUE)})."))
#> $gender
#> The link between 'y' and gender is significant (p<0.001).
#>
#> $age
#> The link between 'y' and age is significant (p<0.001).
Created on 2022-11-07 with reprex v2.0.2