← SASnRMerging datasets- select unmatched records

Introduction

Sometimes we do not want the matched rows from a merge — we want the mismatches. For example, which subjects in the demographics file have no record in the vitals file, or vice versa.
SAS flags this by combining MERGE with the in= dataset option and an if condition on the flags.
Tidyverse provides anti_join(), which returns only the rows of the left input that do NOT have a match in the right input.
Base R achieves the same by subscripting with !(x %in% y) on the join key.
This lesson finds subjects that appear in one dataset but not the other, in both directions.
After running, we should have the names present in the sex dataset but missing from the ahw dataset, and vice versa.

Merging datasets- select unmatched records

SAS (Base SAS)+

R (tidyverse)+

library(tidyverse)

sex<-tribble(
~name,~sex,
"Alfred","M",
"Henry","M",
"Mary","F",
)

ahw<-tribble(
~name,~age,~height,~weight,
"Alfred",14,69,112.5,
"Henry",14,63.5,102.5,
"James",12,57.3,83,
)

unmatched<-anti_join(sex,ahw,by=c("name"))


unmatched<-anti_join(ahw,sex,by=c("name"))

In tidyverse, we use anti_join() for exactly this task.
anti_join(sex, ahw, by = "name") returns every row of sex whose name does NOT appear in ahw.
Swapping the arguments — anti_join(ahw, sex, by = "name") — returns every row of ahw not in sex.
No columns from the right side are added — the output shape matches the left side, just with fewer rows.
This is far more direct than an inner join + post-filter, and makes the intent obvious to a reviewer.

Happy learning!

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Introduction

Merging datasets- select unmatched records