R-Guru Pharma

The pharmaceutical industy has quickly adapted to embrace R! The Pharmaverse concept is created as a collaboration amoung top pharma and industy organizatins for open-source solutions. Organizations now have the option to continue programming in R using common packages or use the packages from Pharmaverse to get a jump start. With Pharmaverse R package compliance standards, organizations can feel more confident to apply these packages. Smarter organizations make time to confirm packages behave as expected with expected results. Up to 50% of Pharmaverse is built using Tidyverse. This page is designed to help guide you using Pharmaverse packages.

Pharmaverse has R packages that work as modules to help in the CDISC submission process. Organizations can plan to understand and start to incoporate R packages as needed to grow. See new to clinical data and new to CDISC to learn about the basics. Note that the SDTMs and ADaMs pages within R-Guru utilize base R and other non-Pharmaverse packages.

Pharmaverse Common FAQs - Your Guide to Leveraging Pharmaverse Packages

Where do you start? What is your task/objective?
- As pharmaverse continues to grow, there are packages for the full clinical trial workflow process:
- Metadata > OAK > Admiral > Define.xml > TLGs (rtf/pdf) > Submissions > Shiny
Who are the key players?

Pharma industry leaders setting ‘new standards in R and submissions’ since they are key developers of pharmaverse packages
Genentech, GSK, Merk, J&J
CDISC
PHUSE, Atorus, Appsilon, ProCogia

Do you have to use all of the pharmaverse packages?
- No, organizations can choose and priority packages to test and install
- Organizations can continue to use tidyverse and dplyr for in-house developed R scripts
How different are pharmaverse packages to tidyverse?
- Pharmaverse packages are built from up to 50% of tidyverse packages so can easily learn pharmaverse packages
What methods can I learn and apply pharmaverse packages?
- R-Guru.com/pharma is a designed for the collecdtion and curation of:
- Videos
- Examples
- Case Studies
- Documentation
What about pharmaverse shiny apps?
- tidyCDISC
Why should i use pharmaverse packages?
- Organizations can save development time, money and SMEs
Are there risks in using pharmaverse packages?
- Yes, organizations are responsible for their own risk management
- Pharmaverse packages undergo rigorous tests to help assure robust and reliable results

CDISC R Packages

Atorus R Packages

- Tplyr (Summary Tables, Examples)
- pharmaRTF (Enhance RTF, GitHub), xportr (XPTs), logrx (Log Scan)
- OpenVal

# Metadata functions: is.character(), is.numeric, is.Date(), contents(), names(), rename(), label(), contains(), starts_with(), ends_with(), nchar(), mode()
print(contents(df), maxlevels=10, levelType='table') # requires hmisc package
names(adsl) <- tolower(names(adsl)) # lower case all variable names, toupper()
df <- df %>% rename(vr_new = vr_old) # rename variables
label(df$vr1) <- 'My Label' # assign variable labels
label(df[["vr1"]]) <- "My Label" # data frame options method
attr(data[["age"]], "label") # attr function to assign labels
df2 <- df1 %>% select(-contains('vr1')) # drop variables names that contain vr1
df <- select (vr1, vr10:vr15, starts_with("L")) # select vr1, vr10 to vr15 variables by order and variables that start with L, ends_with()
intersect(names(df1), names(df2)) # list common variables between df1 and df2
setdiff(names(df1), names(df2)) # list of df1 unique variables that are not in df2
nms <- c(names(df1), names(df2)) # list of unique variable names from two or more data frames to compare
nms[duplicated(nms)]
dt1 <- as.Date("2021/01/25")
log1 <- TRUE
mode(2) # numeric variable
mode('a') # character variable
mode(dt1) # numeric date variable
mode(log1) # logical variable

# SAS vs R Checking SDTM Compliance
# create exposure data
exposure <- data.frame(
USUBJID = c("1001", "1002", "1003", "1004"),
EXTRT = c("TreatmentA", "TreatmentB", "TreatmentA", "TreatmentC"),
EXDOSE = c(50, 100, 75, 25),
EXDOSU = c("mg", "mg", "mg", "IU"),
EXROUTE = c("Oral", "IV", "Topical", "Nasal"),
EXSTART = as.Date(c("2024-01-01", "2024-01-05", "2024-01-10", "2024-01-15")),
EXENDTC = as.POSIXct(c("2024-01-01 12:00:00", "2024-01-05 10:30:00", NA, "2024-01-15 12:30:00")),
EXDUR = c(2, 2, NA, 3),
EXDURU = c("days", "hours", NA, "hours"),
EXSTAT = c("Ongoing", "Completed", "Discontinued", "Ongoing")
)
# EX vars
ex_vars <- c("USUBJID", "EXTRT", "EXDOSE", "EXDOSU", "EXROUTE", "EXSTART", "EXENDTC", "EXDUR", "EXDURU", "EXSTAT")
# error counter variable
error_count <- 0
for (var in names(exposure)) { # loop over all ex variable names
if (!(var %in% ex_vars)) { # check each ex var with list of ex_vars
cat("Error: Variable", var, "does not comply with SDTM EX naming standards.\n")
error_count <- error_count + 1
}
if (nchar(var) > 8) { # check each ex var for name length > 8
cat("Error: Variable", var, "length exceeds SDTM EX naming standards.\n")
error_count <- error_count + 1
}
}
if (error_count == 0) { # message for no or any errors
cat("All variables comply with SDTM EX naming standards.\n")
} else {
cat("Errors found in variable names in the data.\n")
}