← SASnRLast dot concept

Introduction

The natural counterpart to "first of each group" is "last of each group" — the last visit per subject, the latest lab per test, the oldest age in each Sex.
In SAS, once a dataset is sorted by the grouping variable, the automatic flag last.var is TRUE on the last row of each group.
Tidyverse uses group_by() + slice(n()) or slice_tail() to keep the last row within each group.
Base R uses !duplicated(group, fromLast = TRUE) after sorting to keep the last occurrence per group.
This lesson picks the subject with the highest Height within each Sex on CLASS — the mirror image of lesson 2200.
As always, "last" is defined by the sort order, so we sort first and then take the last row.

Last dot concept

SAS (Base SAS)+

data CLASS;
 infile datalines dlm='|' dsd missover;
input Name : $8. Sex : $1. Age : best32. Height : best32. Weight : best32.;
label ;
 format ;
 datalines4;
Alfred|M|14|69|112.5
Alice|F|13|56.5|84
Barbara|F|13|65.3|98
Carol|F|14|62.8|102.5
Henry|M|14|63.5|102.5
James|M|12|57.3|83
Jane|F|12|59.8|84.5
Janet|F|15|62.5|112.5
Jeffrey|M|13|62.5|84
John|M|12|59|99.5
Joyce|F|11|51.3|50.5
Judy|F|14|64.3|90
Louise|F|12|56.3|77
Mary|F|15|66.5|112
Philip|M|16|72|150
Robert|M|12|64.8|128
Ronald|M|15|67|133
Thomas|M|11|57.5|85
William|M|15|66.5|112
;;;;
 run;

*------------------------------------------------------------------------------;
*subset of highest height;
*------------------------------------------------------------------------------;

proc sort data=class;
     by sex height;
 run;

data highestheight;
     set class;
     by sex height;
     if last.sex;
     keep sex height name;
 run;

In SAS, last.var is the automatic flag that turns TRUE on the last row of each group, once the dataset is sorted and the variable is named in a by statement.
proc sort data=class; by sex height; sorts ascending by Sex then Height, so within each Sex the tallest subject sits at the bottom of the group.
In the DATA step, set class; by sex height; reads the sorted data and exposes first.sex / last.sex / first.height / last.height.
if last.sex; on its own line is a subsetting IF — only rows where that flag is TRUE are written out.
keep sex height name; trims the output to the columns the report needs.
After running, HIGHESTHEIGHT has one row per Sex with the tallest subject; confirm the Name column lists the expected subjects.

R (tidyverse)+

library(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, 
"Carol","F",14,62.8,102.5, 
"Henry","M",14,63.5,102.5, 
"James","M",12,57.3,83,
 "Jane","F",12,59.8,84.5, 
"Janet","F",15,62.5,112.5, 
"Jeffrey","M",13,62.5,84, 
"John","M",12,59,99.5, 
"Joyce","F",11,51.3,50.5, 
"Judy","F",14,64.3,90,
 "Louise","F",12,56.3,77, 
"Mary","F",15,66.5,112, 
"Philip","M",16,72,150, 
"Robert","M",12,64.8,128, 
"Ronald","M",15,67,133, 
"Thomas","M",11,57.5,85, 
"William","M",15,66.5,112, 
) 

highestheight<-class %>% 
arrange(Sex,Height) %>% 
group_by(Sex) %>% 
slice(n()) %>% 
select(Name,Sex,Height)

In tidyverse, the "last row within each group" idea uses slice(n()) or slice_tail(n = 1) after group_by().
arrange(Sex, Height) %>% group_by(Sex) %>% slice(n()) %>% select(Name, Sex, Height) returns one row per Sex — the row with the largest Height after the sort.
n() gives the number of rows in the current group, so slice(n()) picks the final row of that group.
The sort direction matters: if we sort Height ascending, the last row is the tallest; if we sort descending, the last row becomes the shortest.
We can drop group_by() and the call returns only the overall last row instead of one per Sex.
We run and confirm one row per Sex with the tallest Height — the tidyverse counterpart of if last.sex in SAS.

Happy learning!

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Introduction

Last dot concept