← SASnRFetching Frequencies

Introduction

A frequency count is usually the first summary we run on a new dataset — how many rows per category, and how are the values distributed?
Typical asks: count subjects per treatment, count adverse events per body system, cross-tabulate Sex by Age.
SAS uses proc freq with a tables statement, optionally writing the table out with out=.
Tidyverse uses count() from dplyr; base R uses table() wrapped in as.data.frame().
This lesson builds a one-way count of Sex on CLASS, then a Sex-by-Age cross tabulation on a slightly larger CLASS1 dataset.
After running, the counts should total back to the original row count so we know nothing was dropped.

Fetching Frequencies

SAS (Base SAS)+

R (tidyverse)+

class<-tribble(
~Name,~Sex,~Age,~Height,~Weight,
"Alfred","M",14,69,112.5,
"Alice","F",13,56.5,84,
"Barbara","F",13,65.3,98,
)

class1<-tribble(
~Name,~Sex,~Age,~Height,~Weight,
"Alfred","M",14,69,112.5,
"Alice","F",13,56.5,84,
"Barbara","F",13,65.3,98,
"Henry","M",14,63.5,102.5,
"James","M",12,57.3,83,
)


counts01<-count(class,Sex)


counts02<-count(class1,Sex,Age)

In tidyverse, count() from dplyr returns a tibble with one row per unique combination of the grouping variables and a column n for the count.
count(class, Sex) gives a one-way count of Sex — the direct equivalent of tables sex; in SAS.
We add more variables to build a cross tabulation: count(class1, Sex, Age) returns one row per Sex-Age combination.
The result is a tibble, so it pipes cleanly into downstream steps — no out= option needed.
For proportions, chain mutate(pct = n / sum(n)) on the result of count().
We run and compare the totals in COUNTS01 / COUNTS02 against the SAS run — they should line up row for row.

Happy learning!

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Introduction

Fetching Frequencies