An R Markdown or Quarto report contains text and code chunks. The text explains the purpose, methods, interpretation, and decisions. The code chunks import data, create summaries, generate figures, and produce tables. The rendered output combines both.
A minimal Quarto-style document may look like this:

\bigskip\hrule\bigskip
title: “Weekly Data Quality Report”
format: html
\bigskip\hrule\bigskip
## Purpose
This report summarizes enrollment, missingness, and query burden.
library(tidyverse)
prepared_data <- read_csv(”data_clean/prepared_participant_dataset_2026-06-01.csv”)
## Enrollment by Site

prepared_data |>
count(site, name = ”participants”)

The YAML header at the top defines document settings. The prose explains what the report is for. The code chunks run R code. When the document is rendered, the output appears in the report. This structure makes the report easier to review because the narrative and computation are in one place.
Clinical data management reports should include enough context for interpretation. A weekly report should state the data export date, report generation date, dataset version, and any important exclusions. If the report includes derived variables, their definitions should be provided or referenced.