R Programming

See R-Guru.com for R Training for SAS Programmers (Outline)

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Basics
Advance
Compare with SAS
R and SQL
Blogs, Classes and References
Pharma Industry
Books
Popular R Packages
R Shiny
R Markdown

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Webinars

What is R and Why Should you Learn R? [White Paper]

Writing Your First R Program, Just the Basics [R Program, White Paper]

Basics (Top)

R Programming Style Tidy Programming Style Basic Concepts for Beginners

RStudio Cheat Sheets R Reference Card

Hardware Configuration: Workbench, Connect, Package Manager

Required Steps: 1. Install R 4.0.4 for Windows, 2. Install RStudio 3. Click R icon 4. Select US option for CRAN mirror

Installation Steps Videos(Install R Packages)

Tutorials Point Quick Guide The Analysis Factor Tutorials

R Examples

R Programming Style Types

Data Access and Data Management

Data Processing (Wrangling) book Data Cleaning book

Data Reporting, Analysis and Plotting

Common Operations	R Symbols
Variable Assignment	<- (most popular), <<-, ->, ->>
Arthritic	+, -, *, /
Comparison Operators return logic	>, <, ==, !=, <=, >=
Logical	&, \|\|, !
Other	:, %in%

Common R Functions

Advance (Top)

Advanced R Solutions book Custom Functions

R4Stats - SAS and R code GitHub-Sheets R Project for Statistical Computing

Advanced R book

Compare with SAS (Top)

Run SAS in R: R Markdown, Example 1, Example 2

Run R in SAS: SAS Programming for R Users book, Example 1, Example 2

Guru99 Blog EDUCBA Blog SAS and R Examples Read/Write SAS Datasets

R Package for SAS Programmers

Example 1: Listing

Example 2: Summary Stats Table

Example 3: Figure

flextable() vs Proc Report

SAS Syntax	R Function
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) prop.table()
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;	flextable()

R and SQL (Top)

Libraries: SQLDF(), DPLY, DDPLYR()

CRAN package SQL Server

SQLDF() rdrr.io

SQLDF() in R SQLDF() Tutorial

SQLDF() Examples

Introduction to DPLY

SQL Feature	SQL Clause
Select variables	select()
Create new variables definition	mutate() & chg=
Create new variable conditional logic	ifelse()
Where condition	filter(),
Group by	group_by()
Sort by	arrange()

R DataFrame Joins	SQL Function
left join to keep all dm1, dm1 and dm2 with different key variables merge left join to keep all dm1, dm1 and dm2 with same key variables merge right join to keep all dm2, dm1 and dm2 with same key variables merge inner join for only dm1 and dm2 matching with same key variables merge outer join for all dm1 and dm2, dm1 and dm2 with same key variables, similar to all.x=TRUE, all.y=TRUE	dm3 = left_join(dm1, dm2, by = c(‘usubjid’=’usubjid’, ‘race’=’race1’)) dm3 = merge(x=dm1, y=dm2, by = ‘usubjid’, all.x=TRUE) dm3 = merge(x=dm1, y=dm2, by = ‘usubjid’, all.y=TRUE) dm3 = merge(x=dm1, y=dm2, by = ‘usubjid’) dm3 = merge(x=dm1, y=dm2, by = ‘usubjid’, all=TRUE)