← SASnRSubset Observations

Introduction

Almost every analysis starts by filtering a dataset to a relevant subset — only males, only a certain age band, only a single site.
SAS typically does this inside a DATA step with a WHERE clause. R has two idiomatic flavours: filter() from dplyr, and [-subscript notation in base R.
This lesson shows two common filters on the CLASS dataset: keep only rows where sex is “M”, and keep only rows where age is 11 or 12 (the pre-teens).
After running, we should have two new datasets: males with just the male subjects, and preteen with just ages 11 and 12.

Subset Observations

SAS (Base SAS)+

R (tidyverse)+

library(tidyverse)

class<-tribble(
~Name,~Sex,~Age,~Height,~Weight,
"Alfred","M",14,69,112.5,
"Alice","F",13,56.5,84,
"Carol","F",14,62.8,102.5,
"Henry","M",14,63.5,102.5,
"James","M",12,57.3,83,
)


males <- filter(class, Sex=="M")


preteen<-filter(class,Age %in% c(11,12))

To filter rows in tidyverse, we use the filter() function from dplyr.
filter(class, Sex == "M") reads as “from class, keep rows where Sex equals M”. The comparison operator is == (double equals) — a single = is assignment in R.
For multi-value matches we use %in%: filter(class, Age %in% c(11, 12)). This is the direct analogue of the SAS in (11, 12).
filter() is aware of column names without quotes — we write Sex, not "Sex" — because dplyr evaluates the expression in the tibble’s data context.
The result is a new tibble with only the matching rows. The original class tibble is untouched.

Happy learning!

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Introduction

Subset Observations