Tidyverse Workflow: Grouping, Summarizing, Merging

Introduction

Transitioning from Clinical SAS to R opens up a world of powerful data manipulation tools, especially within the Tidyverse ecosystem. Developed by Hadley Wickham, the Tidyverse is a collection of R packages designed for data science, emphasizing readability, consistency, and efficiency.

If you're coming from SAS, where procedures like PROC SORT, PROC MEANS, and PROC SQL dominate data workflows, you'll find Tidyverse's dplyr and tidyr packages intuitive yet more expressive. This chapter will guide you through essential Tidyverse operations—grouping, summarizing, and merging datasets—with clear examples and comparisons to SAS for easier comprehension.

By the end of this chapter, you'll be able to:
- Group data using group_by()
- Summarize data with summarize()
- Merge datasets using joins like left_join()
- Understand how these operations compare to SAS

Let’s dive in!

1. The Tidyverse Workflow

Before jumping into specific operations, it's essential to understand the Tidyverse philosophy:

• Pipes (%>%) chain operations, making code readable.
• Consistent functions follow a structured naming convention (e.g., filter(), select()).
• Tibbles are enhanced data frames, similar to SAS datasets but with better printing and memory handling.

Installing and Loading Tidyverse

# Install (if not already installed)
install.packages("tidyverse")

# Load the core packages
library(tidyverse)

2. Grouping Data with group_by()

In SAS, you'd use PROC SORT followed by BY statements to group data. In R, the equivalent is group_by().

Example: Grouping Clinical Trial Data

Suppose we have a dataset trial_data with columns:
- patient_id
- treatment (e.g., "Drug A", "Placebo")
- visit_number
- response_value

# Group data by treatment and visit
grouped_data <- trial_data %>% 
  group_by(treatment, visit_number)

Comparison to SAS:

PROC SORT DATA=trial_data; 
  BY treatment visit_number; 
RUN;

Why group_by()?

• Lazy evaluation: No immediate computation until an action (like summarize()) is applied.
• Works seamlessly with other Tidyverse functions.

3. Summarizing Data with summarize()

In SAS, PROC MEANS or PROC SUMMARY generates summary statistics. In R, summarize() does this concisely.

Example: Calculating Mean Response by Treatment

summary_stats <- grouped_data %>% 
  summarize(
    mean_response = mean(response_value, na.rm = TRUE),
    sd_response = sd(response_value, na.rm = TRUE),
    n_patients = n()
  )

Comparison to SAS:

PROC MEANS DATA=trial_data MEAN STD N; 
  CLASS treatment visit_number; 
  VAR response_value; 
RUN;

Key Points:

• summarize() collapses grouped data into one row per group.
• Use na.rm = TRUE to exclude missing values (like PROC MEANS's default behavior).
• Helper functions: n() counts rows, mean(), sd(), min(), max(), etc.

4. Merging Datasets with Joins

In SAS, PROC SQL or DATA STEP merges datasets. In R, Tidyverse provides join functions:

Example: Merging Patient Demographics with Lab Results

# Patient demographics
demographics <- tibble(
  patient_id = c(1, 2, 3),
  age = c(45, 60, 38),
  sex = c("M", "F", "M")
)

# Lab results
lab_results <- tibble(
  patient_id = c(1, 2, 4),
  lab_value = c(120, 95, 110)
)

# Left join (keep all patients in demographics)
merged_data <- left_join(demographics, lab_results, by = "patient_id")

Result:

# A tibble: 3 × 4
  patient_id   age sex   lab_value
       <dbl> <dbl> <chr>     <dbl>
1          1    45 M           120
2          2    60 F            95
3          3    38 M            NA

Comparison to SAS:

PROC SQL;
  CREATE TABLE merged_data AS
  SELECT d.*, l.lab_value
  FROM demographics d
  LEFT JOIN lab_results l ON d.patient_id = l.patient_id;
QUIT;

5. Combining Operations: A Complete Workflow

Let’s simulate a real-world scenario:

Task:
- Group clinical trial data by treatment.
- Calculate the average response per treatment.
- Merge with a treatment reference table.

# Step 1: Group and summarize
treatment_summary <- trial_data %>% 
  group_by(treatment) %>% 
  summarize(avg_response = mean(response_value, na.rm = TRUE))

# Step 2: Merge with reference table (e.g., treatment codes)
treatment_ref <- tibble(
  treatment = c("Drug A", "Placebo"),
  treatment_code = c("A", "P")
)

final_data <- left_join(treatment_summary, treatment_ref, by = "treatment")

Output:

# A tibble: 2 × 3
  treatment avg_response treatment_code
  <chr>           <dbl> <chr>         
1 Drug A            78.2 A             
2 Placebo           65.5 P    

Summary & Key Takeaways

1. Grouping Data:
2. Use group_by() to define groups (similar to SAS BY statements).

3. No immediate changes to data until an action (e.g., summarize()) is applied.

4. Summarizing Data:

5. summarize() computes statistics per group (like PROC MEANS).

6. Helper functions (n(), mean(), sd()) make calculations easy.

7. Merging Data:

8. Join functions (left_join(), inner_join()) replace SAS PROC SQL or DATA STEP merges.

9. Explicit by argument ensures clarity in merges.

10. Workflow Efficiency:

11. Pipes (%>%) streamline multi-step operations.
12. Tidyverse code is often more readable than equivalent SAS code.

By mastering these Tidyverse functions, you’ll unlock efficient, reproducible workflows that are simpler to debug and maintain compared to legacy SAS procedures.

Next Steps:
- Practice with real clinical datasets.
- Explore tidyr for reshaping data (pivoting, spreading).
- Learn to visualize grouped summaries using ggplot2.

Happy coding! 🚀