High-Performance Open-Source Archive
The goal of tidysummary is to streamlines the analysis of clinical data by automatically selecting appropriate statistical descriptions and inference methods based on variable types. See the vignette for more details.
You can install the development version of tidysummary like so:
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_github("htqqdd/tidysummary")library(tidysummary)
result <- iris %>%
add_var() %>%
add_summary() %>%
add_p()#Here is an prepared dataset
iris <- iris %>%
mutate(group = factor(rep(1:3, each = 50),
labels = c("group1", "group2", "group3")))
#Now use tidysummary
library(tidysummary)
result <- iris %>%
add_var() %>%
add_summary(binary_show = "all") %>%
add_p()View(result)kableExtra or others your
prefer)library(kableExtra)
result[is.na(result)] <- ""
result %>%
kbl(caption = "Table 1. Summary of Iris Dataset",
row.names = F,
align = "c") %>%
kable_classic(full_width = FALSE, html_font = "Cambria")
result %>%
writexl::write_xlsx("./test.xlsx")
Need mirroring services?
Contact our team at info@vpspulse.com.
Mirror powered by VPSpulse
Infrastructure sponsored by VPSPulse & Secure Payments by ArionPay.