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Compare with SAS

C. Compare with SAS

With so many SAS programmers learning R, it makes sense that an R SAS package was created. With the SASSY package, SAS programmers can have a more seamless transition between R and SAS. With the SASSY package, SAS programmers can almost replicate reviewing logs, reviewing datasets, and program data steps, formats and reports. There are R packages and functions to replicate Proc Freq, Proc Means and Proc Report. It is important to realize that although SAS has tools to integrate with R and R has packages to replicate SAS programming, the objective of learning R is to consider it a standalone complete toolbox in its own right that can be used entirely independent of SAS.

From SAS to R Results Workshop: Derivations, Queries and Joins (R-Guru Cheat Sheet)

Create libnames [haven]
Import SAS Datasets into Data Frames [haven]
Pipe R Functions [%>%]
Data Management Operations [tidyverse, stringr]
Derive Variables [dplyr]
Query Data Frames [dplyr]
Join Data Frames [dplyr]
Transpose Data Frames [tidyverse]
Create SAS Data sets/Transport Files [xportr]

From SAS to R Results Workshop: Summary Tables (R-Guru Cheat Sheet)

Create libnames [haven]
Import SAS Datasets into Data Frames [haven]
Pipe R Functions [%>%]
Summarize Data (Sex and Age) from ADaMs [dplyr]
Perform T-Test Analysis (Sex by Treatment) from ADaMs
Create Summary Tables as RTF files [gt(), gtsummary()]
Create Plots [ggplot2]
Create SAS Data sets/Transport Files [xportr]

Similar to SAS, R functions resemble many features of the Data Step.

Compare and Contrast SAS Procedures and R Functions

proc print data=d1; var _all_; run; d1 displays all records, head

(d1) displays first 5 records, tail(d1) displays the last few records

To subset, first create R object based on subset and selected variables and then display all records

proc freq data=d1; tables sex*race; run;

table(d1$sex, d1$race)

proc univariate;

summary(d1)

proc sort data=dm2; by sex race; run;

dm2[order(dm1$sex, dm1$race)]

proc format;

In Vectors: sex_code <- c(‘M’, ‘M’, ‘M’, ‘F’, ‘M’) # 1. data values in simple vector to store data values

sex_decode <- c(‘M’=’Male’, ‘F’=’Female’) # 2. named vector data = ‘label’ for values similar to proc format

sex <- sex_decode[sex_code] # 3. converts values to labels sex_code vector is subset of sex_code vector

As Functions: age_cat <- vectorize(function(x) { # x is input value
if (x < 18) { # condition
ret <- "< 18" # return label
} else if (x >= 18 & x < 24) {
ret <- "18 to 24"
} else if (x >= 24 & x < 45) {
ret <- "24 to 45"
} else if (x >= 45 & x < 60) {
ret <- "45 to 60"
} else if (x >= 60) {
ret <- "> 60"
} else {
ret <- "Unknown"
}
return(ret) })

df$age_cat <- age_cat(df$age) # apply function to age variable to create age_cat variable

proc means;

summarise(AllPages = sum(Pages),
AvgLength = mean(Pages),
AvgRating = mean(MyRating),
AvgReadTime = mean(read_time),
ShortRT = min(read_time),
LongRT = max(read_time),
TotalAuthors = n_distinct(Author))

proc contents;

library(hmisc)

content(dm)

proc compare;

cmp <- comparedf(mockstudy, mockstudy2, by = "case", tol.vars = c("._ ", "case"), int.as.num = TRUE)

n.diffs(cmp)

proc report;

Section Objectives

Understand and be able to apply SASSY Package for Tables, Lists and Graphs
Know how to apply SQL methods in R programming
Understand the difference between SAS macros and R programming
Know how to apply Tidyverse Package

References

SAS 2 R Cheat Sheet (GitHub) (GitHub 2)

Bayer's R and SAS

How to learn R as a SAS user, Isabella Velasquez, Phil Bowsher

SAS vs R: What is Difference Between R and SAS?

SAS vs R Programming: Which to Choose and How to Switch

SAS Vs. R - Which Is Better?

Beyond SAS: How R is Revolutionizing Pharma and Life Sciences, Appsilon Blog

SAS vs. R: Definitions, Advantages and Differences

Integrate SAS with the R Language

An Introduction to SAS Data Steps [Blog, New to Programming]

Learning SAS by Diagrams and Examples [New to Programming]

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