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What is R and Why Should you Learn R? [White Paper]
Writing Your First R Program, Just the Basics [R Program, White Paper]
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 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% |
R Functions Common R Functions
Advanced R Solutions book Custom Functions
R4Stats - SAS and R code GitHub-Sheets R Project for Statistical Computing
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
Example 2: Summary Stats Table
| SAS Syntax | R Function |
| proc print data=d1; var _all_; run; | head(d1) , tail(d1) |
|
proc freq data=d1; tables sex*race; run; |
|
|
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() |
Libraries: SQLDF(), DPLY, DDPLYR()
| 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) |
Listen Data R blog Guru Blog Wikipedia R-blogger R-Tutor Joyti's R-blog
DataCamp CodeAcademy DataMentor R-Journal Data Table Study Trials blog
YouTube Videos R Studio (Webinars) GitHub-Submission R-Bootcamp Kickstarting R
R in Clinical Trial Data Analysis [YouTube]
R Tutorial: Designing and Analyzing Clinical Trials in R [YouTube]
R Consortium How to Create a Pie Chart in R using GGPLOT2
R Programming in a Clinical Trial Data Analysis blog
TFL programming in R versus SAS blog
Interesting packages taken from R/Pharma blog
UCLA: Introduction to R, Seminar, FAQs
A RISK-BASED APPROACH FOR ASSESSING R PACKAGE ACCURACY WITHIN A VALIDATED INFRASTRUCTURE Blog
Statistics Globe (Graph Gallery)
Geeks for Geeks - Task based examples
Learning Path: QC Tables > Create Tables > QC SDTMs/ADaMs > Create SDTMs/ADaMs
Clinical Reporting with R R/Pharma R for Clinical Study Reports and Submission book [Demo Project]
GitHub: Sample SDTM/ADaM Pharma Packages R4CDISC/R4DSXML Happy Git and GitHub for the useR
Using R in the Pharmaceutical Industry blog R Package Validation Framework [Phuse White Paper, Blog]
Using the Statistical Programming Language R in the Pharma Industry blog
R Programming in clinical trial data analysis Blog by Shrishaila Patil
The Rise of R-should SAS programmers get up to speed? blog
FDA: R OK for drug trials blog
OpenFDA: Github, FDA Site, R Package
GxP Compliance in Pharma Made Easier: Good Documentation Practices with R Markdown and {officedown}
Using R in a GxP Environment Microsoft R Validation Project Summary [GxP]
tidyverse, tidymodels, r-lib, and gt R packages: Regulatory Compliance and Validation Issues
R for Biostatisicians [Presentation] Github [IQ/OQ]Clinical Trials Package (R Packages for Clinical Trial Design, Monitoring, and Analysis)
Design and Monitoring, Design and Analysis, Analysis for Specific Designs, Analysis in General, Meta-Analysis- Atable - Creates Tables for Reporting Clinical Trials
- compareODM - Comparison of medical forms in CDISC ODM format
- CRTSize - Sample size estimation in cluster (group) randomized trials
- Blockrand - Creates randomizations for block random clinical trials
- DoseFinding - Supports design and analysis of dose-finding experiments
- Pact - Predictive Analysis of Clinical Trials
- SASxport - Read and write 'SAS' 'XPORT' Files
- ADCT - Adaptive Design in Clinical Trials
- ClinPK, cpk - Clinical Pharmacokinetics Toolkit
- randomizeR - Randomization for Clinical Trials
- Greport - Graphical Reporting for Clinical Trials
Tools for Clinical Data Management Package
atable: Create Tables for Reporting Clinical Trials Package [Documentation Article]
RShinyTLF OpenFDA RShiny DemoRShinyTLF (PhUSE)
R Shiny Lessions Towards Data Science blog
R Markdown (Validated Package from R Studio)
Introduction PDF Reports Cheetsheet Guide RTF: Example 1 Example 2
R Packages R for Data Science Efficient R R in Action Data Cleaning with R
DataQuest SAS and R R and Relational Databases A Little Book of R For Time Series [HTML]
Quick Start Guide for R Learn to Use R Flextable Modern Statistics with R
Hands-On Programming with R [Read and Write CSV, Dataframes, Debugging] Cookbook Mastering Shiny
Programming with R (Best Practices) Pirate’s Guide to R Data Analysis and Visualization in R for Ecologists
Beginner Level
UC of Riverside University of Georgia
Glossary: An Introduction to R book (PDF)
R Packages: A Beginner's Guide R Packages R Base and Other Packages Useful R Packages
DataCamp FAQs CRAN Glossary CRAN FAQs RStudio Project Templates
Functional Data Analysis (FDA) PURR (Functional Programming)
Tidyverse (Data Access, Data Management, Data Reporting and Analysis)
Tidyverse Manifesto Design Guide Introduction to Tidyverse Book
Getting Started with Tidyverse (Storybencth) Tidy 101
Tidy Definitions and Functions (Study.com) David Robinson Blog to Learn Tidy
Tidyverse Cheat Sheet Data Transformation Cheat Sheet
Grammar of Tables (gt): GitHub Intro Examples Syntax Tutorial Blog
Presentation Book Workshop rstudio/gt YouTube
Debugging
1. Debug – Essential Principles and Functions that you can’t miss! [Data-Flair]
2. Debugging in R – How to Easily Overcome Errors in Your Code?
Papers (Rx Pharma Papers, R/Pharma Papers)
1. Exploring Use of R for Clinical Trials, Kalpesh Prajapati
3. Clinical Trial Datasets (CDISC - SDTM/ADaM) Using R, Prasanna Murugesan [Compare SAS Code, Presentation]
4. A Gentle Introduction to R From A SAS Programmer's Perspective, Saranya Duraisamy [Beginner Level]
5. Python and R made easy for the SAS® Programmer, Janet Li [Compare Code]
6. R for SAS programmers: It’s different, but friendly, Friedrich Schuster
7. SAS® and R - stop choosing, start combining and get benefits!, Diana Bulaienko
8. SAS and R Playing Nice Together, David Edwards, Bella Feng, Brian Schultheiss
9. Can clinical trial data sets (CDSIC - SDTM/ADaM) be generated using R? Blog
10. R: Validation Hub [FDA, IQ/OQ/PQ]
11. CRAN Task View: Clinical Trial Design, Monitoring, and Analysis (packages)
12. SAS® and R Working Together, Matthew Cohen [Date formats]
13. Best Practices for Reproducible Package Management in R
14. Techniques for writing robust R programs, Martin Gregory , Merck Serono
15. How do I select an R package for my clinical workflow?, Sean Lopp & Phil Bowsher
16. The Challenges of Validating R [Presentation]
17. Using R to Drive Agility in Clinical Reporting [GSK Presentation]
19. End to End Interactive TLF using R Shiny, Rohit Banga
20. Is there any better option than SAS for TLFs? Yes, there R!, Niccolo Bassani [Presentation - ddply(), Proc REPORT]
21. A quick introduction to plyr, Sean Anderson [PLYR package splits data]
22. Using R Programming for Clinical Trial Data Analysis Blog
23. The SAS® Versus R Debate in Industry and Academia, Chelsea Loomis Lofland, Rebecca Ottesen [Compare SAS and R]
24. Using R in a Regulatory Environment: some FDA perspectives [Presentation]
25. Using R: Perspectives of a FDA Statistial Reviewer [Presentation]
26. R-Pharma Papers
27. R for Clinical Reporting, Yes - Let's Explore It!, Hao Meng, Yating Gu, Yeshashwini Chenna (SASxport, sas7bdat, dplyr)
28. Generating ADaM Compliant ADSL Dataset Using R, Vipin Kumpawat
30. Using the R interface in SAS ® to Call R Functions and Transfer Data, Bruce Gilsen31. Expand Your Skills from SAS® to R with No Complications, Andrii Korchak [DPLYR]
32. Simulation in SAS with Comparisons to R, Chelsea Loomis Lofland, Rebecca Ottesen
37. The Power of Data Visualization in R, Babych Oleksandr
38. Open-NCA – R Scripts for CDISC-based Pharmacokinetic Analysis, Peter Schaefer
39. Statistical Computing Environments in CDER, Paul Schuette
41. R Package Oriented Software Development Life Cycle in Regulated Clinical Trial Environments, Yalin Zhu, Rinki Jajoo, Clare Bai, Sarad Nepal, Daniel Woodie, Keaven Anderson, Yilong Zhang [Presentation, GxP]
42. R for SDTM and ADaM Data [Presentation]
43. R syntax for SAS programmers, Max Cherny [Beginner, Tidyverse]
44. Creating Graphs Simply with SAS® or R, John O’Leary, Jaclyn Scholl
45. Techniques for writing robust R programs, Martin Gregory , Merck Serono
46. Seamless R And SAS: For Shiny Visualizations, Pragathi Kotha Venkata [Presentation]
47. R for the Analysis of Clinical Data, Greg Jones [Presentation]
48. Numerical validation as a critical aspect in bringing R to the Clinical Research, Adrian Olszewski [R and SAS differences]
49. How much is it? Validation of Open-Source-Software Using the example of R, Peter Bewerunge [IQ/OQ/PQ]
50. Introducing LearnR package.
51. An Automation Proof of Concept of Periodic Reporting via R Shiny [PSUR, Presentation]
52. Open-Source Development for Traditional Clinical Reporting, Mike Stackhouse, Nathan Kosiba
54. Validating R – Part of the Uphill Battle in the Pharmaceutical Industry, Peter Schaefer and Debra Fontana [Installation, Operational, Performance Qualifications]
GitHub r2rtf Overview Introduction Examples
55. A Beginner’s Babblefish: Basic Skills for Translation Between R and SAS, Sarah Woodruff
56. Using R to Validate Results of Other Programming Environments, Nikhil Abhyankar, Vidyagouri Prayag
57. Drug Safety Reporting- now and then, David Garbutt
1. What are common syntax for libname, filename and reading datasets? See R paper.
sdtm <- "c:/product/study/analysis/data/sdtm" # assign libname to object named sdtm
out <- "c:/product/study/analysis/data/adam" # assign out filename to path
library(haven) # required to read SAS datasets
dm <- read_sas(file.path(sdtm,"dm.sas7bdat")) # read sas file as a data frame
#'read_sas' function from the haven package (part of the tidyverse)
taadmin <- read_sas("H:/rproject/project_y_r2/taadmin.sas7bdt")
3. Is there a website to run example R programs? Yes, see site.
4. What is R? R is a programming language that uses the concept of functions to create objects to be linked together. There are many rules to understand and follow.
5. Why should you learn R? Since R is so vast and can be confusing quickly, I suggest you identify what you plan to use R for such as data access, data management, data reporting or data analysis. In the pharma industry, R got a jump start with graphs that were easy to create. I suggest you have patients learning R since may require more syntax than you may be expecting.
6. What are useful methods to learn R? Show and Tell is useful to see and run R syntax on a command by command basis. Taking your own notes helps to retain and build understanding. Cheat sheet are only helpful if you recognize the syntax and it's purpose. Since R is very technical, try to focus on your task and master the those few sets of commands. How-To step checklist and examples help to remind users how to run R syntax.
These are best practices for learning R: Get Familiar and Gain Confidence since concepts are similar to other programming languages. Know and understand starting and ending point and how best to get there. Learn R requirements vs R Nice-to-Know Functions. Just-in-time R training, Apply R right after Learning R in 15-30 min sessions which work best. Focus only on what is required for your role instead of learning all R. Instead of mapping SAS syntax to R directly, it is better to use SAS macro approach as R commands.
7. What is compariable R syntax for Data step KEEP and WHERE statements?
# complete cases and select
taadmin2 <- taadmin %>% filter(complete.cases(taadmin[["DOSECUN"]])) %>% select(INV, PT, DCMDATE, DOSECUN, DOSETL)
data taadmin2 (keep=inv pt dcmdate dosecun dosetl flag); set taadmin (where=(dosecun is not null)); run;
8. How can you get first. and last. records in R?
taadmin <- taadmin[order(taadmin$PT, taadmin$DCMDATE),]
taadmin <- cbind(taadmin, flagf=0, flagl=0)
taadmin[[6]] <- (!duplicated(taadmin$PT))
taadmin[[7]] <- (!duplicated(taadmin$PT,fromlast=TRUE))
proc sort data = taadmin2; by pt dcmdate; run;
data taadmin3; set taadmin2; by pt dcmdate; flagf=0; flagl=0; if first.pt then flagf=1; if last.pt then flagl=1; run;
9. What is an example of a simple R plot? Below syntax will create a cty by hwy plot.
g <- ggplot(data = mpg, aes(x = cty, y = hwy))
10. How can you read csv file in R?
data1 <- read.csv("./data/DiastolicBloodPressure_initial.csv")
11. What are useful R metadata functions to display data frame attributes?
tg <- ToothGrowth # save sample data frame to tg data frame
View(tg) # browse tg
str(tg) # display tg attributes and sample data
attributes(tg) # display tg attributes
head(tg) # display tg sample records
print(tg) # display tg all records
describe(tg) # run DESCRIBE command to yield more granular percentiles and extreme observations
stats <- summary(tg) # create stats object of continuous vars
print(stats) # display tg stats object
freq <- table(tg) # create freq of categorical vars
print(freq) # display tg freq object
12. What is a simple R function?
info <- function(d) { writeLines("First display the structure of the data frame.") str(d)
writeLines("Then print the first 6 observations to see the variables and values.") print(head(d)) }
# Call the new ‘info’ function:
info(data1)
%d for day of month, %a for 3 digit day, %A for full day, %m for short month, %b for 3 digit month, %B for full month, %y for 2 digit year, %Y for 4 digit year
14. What is the difference between package and library? A package is a like a book, a library is like a library; you use library() to check a package out of the library.
15. What is your working directory? R is always pointed at a directory on your computer. Often this will be your home directory, ex. C:/Users/Sunil/Documents . When you work within a RStudio project, the working directory will be the head of that directory. You can find out which directory by running: getwd(). You can use setwd("C:/study/analysis/output") to change the default directory.
16. What packages are installed? installed.packages()
install.packages(“SASxport”)
library(SASxport)
library(sas7bdat)
library(dplyr)
dat1 <- read.sas7bdat(“dm.sas7bdat”)
select(dat1,”PROJECT”,”SUBJECT”,”SITEID”,”RACE”,”ETHNIC”,”SEX”,”AGE”)
dm1 <- merge(x=dat2,y=dat3, by=”SUBJECT”,all.x=TRUE)
write.xport(dm_f2,file=paste(getwd(), "dm.xpt", sep="/"),autogen.formats =FALSE
library(Hmisc)
adsl <- sasxport.get("adsl.xpt", lowernames=FALSE)
18. How do write and run R code? Writing your first R code. Enter code in code editor window. Click Run to execute current line and Source to execute all statements.
19. What are examples of applying proc freq in R?
table(cars$Type) # proc freq data=cars; tables type; run;
table(cars$Type,cars$Cylinders) # proc freq data=cars; tables cars*Cylinders; run;
20. What are examples of appying proc means in R?
ASL_mean <- ASL %>% group_by(ARMCD) %>% summarise(avg_age = mean(AGE), avg_bmi = mean(BMI))
# %>% is pipe to connect R functions, see magrittr package
# proc means data=ASL; by ARMCD; var mean bmi; run;
21. What are examples of proc sql in R? See R paper.
adsl <- dm %>% select(studyid, subjid, age, sex, height, weight, race, scrfl) %>% mutate(bmi = (weight*703)/height^2 ) %>%
filter(scrfl == “Y”) %>% select(-scrfl) %>% arrange(studyid, subjid)
# apply %>% to connect R functions
# select 8 dm variables
# derive bmi variable from weight and height
# subset for scrfl = ‘Y’
# sort by studyid and subjid
22. How do you convert excel files to R data frame? library(readxl) # required for read_excel function
l_cancer <- read_excel(“C:/data/l_cancer.xlsx”)
23. How do you display a list of functions once a package/library is opened? lsf.str("package:dplyr")
24. What is the syntax for keeping and dropping variables? df = subset(mydata, select = -c(x,z) ) # by variable name
df <- mydata[ -c(1,3:4) ] # by column index number
25. How best can you sort data? newdata <- mtcars[order(mpg, cyl),] # sort by mpg and cyl
26. What is the syntax to create R arrays? Array_name = array(data,dim = c(row_size,column_size,matrices), dimnames = list(row_names,column_names,matrices_names))
27. What are useful metadata functions and to check for missing data? Does the data frame exists? What is the data frame class? Does the data frame contain values?
28. How best to create data frames from vectors? hospital <- c("New York", "California") patients <- c(150, 350) df <- data.frame(hospital, patients)
29. How do you name values in vectors? Access defined islands named vector > str(islands)
Named num [1:48] 11506 5500 16988 2968 16 ...
- attr(*, "names")= chr [1:48] "Africa" "Antarctica" "Asia" "Australia" ..
30. %>% - R Pipe Function, Sequence by passing the left hand side of the operator to the first argument of the right hand side of the operator, requires installing magrittr package, library(magrittr) iris %>% head()
31. What is R Shiny? R Shiny is a web application framework for R and R Studio's Shiny server which makes shiny applications available over the web. See R Studio blog on R Shiny. See how to create a simple R Shiny app.
32. Can you write macros in R? With R, you can create custom and complex functions that are similar to macros. This give you flexibility in multiple methods for calling the R function. See Doing Macros in R blog. See Macros In R blog.
33. What options are there to export R? Export to CSV, Excel and SAS. See export to RTF. See other examples.
34. What interfaces are their between R and SharePoint? See blog.
35. How exactly is R an object-oriented programming language? By using symbols (<-, $, [], etc.), R language directly access data frames, variables and values. R symbols have built-in meanings that save time in writing code. All R functions are concise and process objects as input and outputs. These features help to make R programs 'cryptic' and more 'syntax heavy'.
36. Are there any symbols for ending R statements like ';' in SAS? There is no end of statements in R. Line breaks generally are used for readability of code. R executes line by line or in batch mode during console window.
37. Is it possible to run R programs within SAS? Yes, see SAS Programming for R Users book for examples.
38. How can you create log file as in SAS? Load the logr library within the SASSY package.
39. What is R not? Easy to Learn, Technically Friendly, Only for Plots, Only for Statistical Modeling, Similar to SAS, Only for Data Science, Case Insensitive, Missing Data Friendly. R has functions and tools for data access, data management, statistical modeling, analysis datasets TLGs and data visualization.
40. Is there an R function similar to SAS's proc compare? Yes, see comparedf function.
41. Does FDA use R? Yes, R is installed at FDA. FDA does not recommend specific software packages. FDA only requires sponsors to assure software used is validated. See Using R: Perspectives of a FDA Statistical RevieweR.
42. What are the options for saving R data? You can use save() function to save multiple objects as RData object or the saveRDS() function to save one object as RDS object. See examples.
a <- 1
b <- 2
c <- 3
save(a, b, c, file = "stuff.RData")
load("stuff.RData")
saveRDS(a, file = "stuff.RDS")
a <- readRDS("stuff.RDS")
43. How does R handle missing data?44. What are useful R debugging functions? traceback(), debug(), browser(), trace(), recover()
45. What are common R errors?
46. What are possible R error types? syntax, symbols, program logic, parameters (input / output), data (input / output / missing / invalid), object / vector type incompatible, invalid object / data assignments / data type conversions.
47. What are best practices for debugging R programs? Review data frame metadata, missing/unique/range of data, program logic, display intermediate object values.
48. What is a useful R package to compare two data frames? The diffdf package is useful to compare two data frames.
49. What is a useful R package for project management? The packrat package is useful to manage projects.
50. What is the difference between '<-' and '='? See StackOverflow.com.
51. How can you display all objects created? objects() or ls() displays all object names.
52. How do you navigate within workspace? See blog1 and blog2.
53. What tools expect for creating a configuration management system for R programs? Github with security and restricted access allows organizations to check in and out for version control.
54. Is it best to only use validated R packages for pharma companies? In general, it is best to only use validated R packages. If you do use any user developed R package that is not officially validated by an R organization, then users and organizations can self validate these R packages using rigerous testing and documentation. R is supported many fortune 500 organizations and has over 2 million R users. The availability of source code helps to make over 18,000 R packages to date in the 4.1.0 is the latest version. Graduates are learning R and Python in school so the pool of talent is large. Users can test R packages with known data and results from publication. The higher the # of downloads and the frequency of updated also helps to determine who widely it is used.
55. Is R backwards compatible? Yes, in general.
56. How can you display all four panels (source, console, environment and files)? See blog 1. See blog 2.
57. What is the syntax for saving data frames? See Hands-On Programming with R examples.
setwd("C:/study/analysis/output") # change from C:/Users/Sunil/Documents
write.csv(dm, "C:/study/analysis/output/mydm.csv", row.names = FALSE) # R function to write csv file
saveRDS(asl2, file = "asl2.RDS") # R function to save R data frame
myasl <- readRDS("asl2.RDS") # R function to read R data frame
58. What is the syntax for displaying all variables and objects created so far?
is() # display all variables
objects() to display all objects in workspace (data frames, vectors, etc.)
59. What are comparable R functions for SAS's INPUT() and PUT() functions? The AS.NUMERIC() and AS.CHARACTER() functions.
mychar = '10'
mynewnum=as.numeric(mychar) to save char as numeric var
mynewnum=10
mynewchar=as.character(mynewnum)
mynewchar='10'
60. What R function is useful to display variable type? The CLASS() function.
class(mynewchar)
"character"
class(mynewchar)
"numeric"
61. What are R limitations and challenges?
R is a great tool for the validation needed in the clinical trials domain. However, there could be some challenges in more wide spread use. For example, Help in R is more technical and not organized as compared that of other software. The numerous packages in R provide the latest in theory. However, the user needs to be aware of the packages and functions available. Frequent updation of the R packages might disturb the stability of the validator and make it difficult to maintain. The regulatory agencies and review officers need to be equipped with the necessary knowledge and the required software. There is a market perception to disregard free software as not being up to the mark. Performance wise, R is slower than other programming languages such as C++. R is inefficient in handling huge datasets and complex looping.