Generate Dynamic Text Results with glue and lapply in R

🧩 Introduction

When presenting analysis results, it’s often necessary to embed specific values into structured sentences — for example, describing results for certain patients, countries, or hospitals.

The glue package in R provides a powerful solution for this. It allows you to integrate variable values into a fixed text template, making your code more efficient, readable, and less prone to errors.

In this post, I’ll demonstrate how to: - Use glue() to dynamically create descriptive text
- Use glue_collapse() to collapse grouped records
- Use lapply() to manage and prefix multiple datasets
- Integrate the results into Quarto or R Markdown reports

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📦 Required Packages

library(dplyr) 
library(glue) 

🪇 Step 1: Create and Prefix Multiple Datasetsrepresenting different hospitals’ medication records:

# Sample CM data from different hospitals
cm1 <- data.frame(USUBJID = c("001", "002"), CMTRT = c("DrugA", "DrugB"))
cm2 <- data.frame(USUBJID = c("003", "004"), CMTRT = c("DrugC", "DrugD"))

# Put into a named list
cm_list <- list(Hosp1 = cm1, Hosp2 = cm2)

# Prefix each dataset with its group name
cm_prefixed <- lapply(names(cm_list), function(name) {
  df <- cm_list[[name]]
  df$Group <- name
  df
})

# Merge all into one
cm_all <- bind_rows(cm_prefixed)

🧠 Step 2: Collapse Records per Subject

We want to describe each subject’s treatment history by combining multiple CMTRT values:

# Example: combine multiple treatments per subject
cm_text <- cm_all %>%
  group_by(USUBJID, Group) %>%
  summarise(cmx_CMTRT = glue_collapse(CMTRT, sep = "; "), .groups = "drop")

✨ Step 3: Use glue() to Format Sentences

# Create dynamic sentences for reporting
cm_text <- cm_text %>%
  mutate(sentence = glue("Subject {USUBJID} in {Group} was treated with: {cmx_CMTRT}."))

# Preview
cm_text$sentence

📝 Output Example

Subject 001 in Hosp1 was treated with: DrugA.
Subject 002 in Hosp1 was treated with: DrugB.
Subject 003 in Hosp2 was treated with: DrugC.
Subject 004 in Hosp2 was treated with: DrugD.

🧵 Conclusion

The combination of glue(), glue_collapse(), and lapply() offers a powerful workflow for:

• fficient text generation

• Flexible dataset processing

• Clean report integration

This approach not only reduces manual effort but also ensures consistency and clarity in reporting.

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