Unlocking the Power of FIRST. and LAST. in SAS vs R: A Programmer's Guide

 Data manipulation and analysis often involve dealing with special cases and specific data structures. One such scenario is understanding the concept of the "first dot" and "last dot" within datasets. Both SAS and R have their unique ways to handle these concepts, and knowing the differences can significantly enhance your data processing capabilities.

In this blog post, we will explore how SAS and R handle the "first dot" and "last dot" concepts, providing side-by-side examples for better understanding.

What is the "First Dot" and "Last Dot" Concept?

The "first dot" and "last dot" concepts refer to identifying the first and last occurrence of a particular condition or value within a dataset. This is particularly useful for tasks like data cleaning, summarization, or tracking events over time.

Implementing First Dot and Last Dot in SAS

In SAS, the FIRST. and LAST. variables are used within the BY statement to identify the first and last occurrence of a group within a sorted dataset.

Example in SAS

Let's consider a dataset of patients with multiple visit records. We want to identify the first and last visit for each patient.

/* Sample Data */ DATA visits; LENGTH PatientID $ 3 Visit $ 20; INPUT PatientID $ VisitDate : date9. Visit $; FORMAT VisitDate date9.; DATALINES; 101 01JAN2023 Screening 101 15JAN2023 Follow-up 101 01FEB2023 Treatment 102 05JAN2023 Screening 102 20JAN2023 Treatment 103 10JAN2023 Screening ; RUN; /* Sorting the Data */ PROC SORT DATA=visits; BY PatientID VisitDate; RUN; /* Identifying First and Last Visit */ DATA first_last_visits; SET visits; BY PatientID; /* Identify first visit */ IF FIRST.PatientID THEN FirstVisit = 'First Visit'; /* Identify last visit */ IF LAST.PatientID THEN LastVisit = 'Last Visit'; RUN; PROC PRINT DATA=first_last_visits; VAR PatientID VisitDate Visit FirstVisit LastVisit; RUN;


Explanation:

  1. The dataset visits contains records of patients with their visit dates and reasons.
  2. The PROC SORT step sorts the data by PatientID and VisitDate.
  3. In the DATA step, FIRST.PatientID and LAST.PatientID are used to flag the first and last visit for each patient.
  4. The PROC PRINT step displays the results, showing which visits are the first and last for each patient.

Implementing First Dot and Last Dot in R

In R, the dplyr package provides functions like first and last to achieve similar functionality.

Example in R

We will use the same dataset concept of patients with multiple visit records and identify the first and last visit for each patient using R.

# Load necessary library library(dplyr) # Sample Data visits <- data.frame( PatientID = c("101", "101", "101", "102", "102", "103"), VisitDate = as.Date(c("2023-01-01", "2023-01-15", "2023-02-01", "2023-01-05", "2023-01-20", "2023-01-10")), VisitReason = c("Screening", "Follow-up", "Treatment", "Screening", "Treatment", "Screening") ) # Identifying First and Last Visit first_last_visits <- visits %>% group_by(PatientID) %>% mutate(FirstVisit = if_else(VisitDate == first(VisitDate), "First Visit", NA_character_), LastVisit = if_else(VisitDate == last(VisitDate), "Last Visit", NA_character_))



Explanation:

  1. The dataset visits contains records of patients with their visit dates and reasons.
  2. The group_by function groups the data by PatientID.
  3. The mutate function creates new columns FirstVisit and LastVisit, using if_else to flag the first and last visit based on the VisitDate.
  4. The print function displays the results, showing which visits are the first and last for each patient.

Summary

Both SAS and R provide powerful tools to handle the "first dot" and "last dot" concepts, which are essential for various data analysis tasks. SAS uses FIRST. and LAST. variables within the BY statement, while R leverages the dplyr package with functions like first and last.

Understanding these techniques allows you to efficiently manage and analyze your data, regardless of whether you prefer SAS or R. By mastering these concepts, you can ensure that your data processing is both accurate and efficient.

Happy coding!

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