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Its great for learning. I am getting errors such as Deprecated etc. Have any updated one?
this is so cool!
Someone's an AEW Fan
Really great. Just wish you’d do things in base R. I’m an advanced user and avoid ggplot and tidyverse so wish you’d stop pushing those tools
I'm a newbie in R. Can you explain why would you avoid ggplot and tidyverse? Is there something inherently wrong in them?
@@suryoputr9344 As a developer, simpler is better. You want code that is easy to debug, easy to understand, and that will stand the test of time. Just using one command of tidyverse in an R package commits you to its ridiculously large number of dependencies which makes your code less stable. As for ggplot, it produces beautiful figures very quickly, but for EDA purposes you don't often need that and you will end up wasting time. Especially if you want to tweak your plot in any way that is different. As a newbie, you might appreciate the ease at which you can generate high quality graphics and the seemingly easy way to manipulate data with tidy verse, but as you get more advanced you will recognize them as being overly complicated.
@@suryoputr9344No reason. Learn and use the tidyverse.
What if you need to optimize two parameters from two different studies? For example, ka from oral bolus in vivo data and clearance from iv study? How do you optimize ka and clearance to fit both studies with one pbpk model?
Thanks for a great presentation!
O
That's fascinating! 🤩 Your team makes some of my dreams come true. I'll try my best to make the best use of it
cool
This is a very well constructed lecture and reproducible (all the data and source code is given on github). A nice scaffold to build your own lectures on and expand. My only complaint is that the transitions are often not clear and bit rugged (e.g. spend a lot time on LASSO but then use Logistic Regression).
If you are worried about which columns are picked in step_normalize() and you all want columns with values greater than 1 then I believe this code works: step_normalize(where(~is.numeric(.x) && any(.x > 1))). Now what the author uses in the video is more straight forward and thus simpler but if you have a lot of columns the first approach might be safer.
It is hard to sit there and look at a relatively small font (I know you can expand the view) on a light background and not get eye strain. I recommend using a dark background in RStudio.
If you are starting out with tidymodels then yo might be confused since a little of details are left out. Naturally you cannot cover such a big subject in a short lecture. Those needing more information might want to look at "Tidy Modeling with r" by Kuhn and Silge(2022). A free ebook version is available online.
Really nice talk. Thank you for pioneering this work!
Great video tutorial, looking forward for more {Teal} Tutorial
We hope to have more at R in Pharma 2024 October 29, 30, & 31st. Workshops will run the week before.
cool!
On running the following code snippet # Baseline Characteristics ---- adsl_bl <- pre_adsl %>% derive_vars_transposed( select(vs, USUBJID, VSTESTCD, VSSTRESN, VSBLFL), # Dataset to transpose and merge onto by_vars = vars(USUBJID), # Merge keys key = VSTESTCD, # Names of transposed variables value = VSSTRESN, # Values of transposed variables filter = VSTESTCD %in% c("HEIGHT", "WEIGHT") & VSBLFL == "Y" # Restrict records to just height and weight ) %>% # Do some cleanup rename(HEIGHTBL = HEIGHT, WEIGHTBL = WEIGHT) %>% select(-VSBLFL) %>% mutate(BMIBL = compute_bmi(HEIGHTBL, WEIGHTBL)) I am getting the following error. Error in `assert_list_of()`: ! Each element of `arg` must be an object of class/type 'symbol' but the following are not: ✖ Element 1 is an object of class 'quosure' --- Backtrace: ▆ 1. ├─pre_adsl %>% ... 2. └─admiral::derive_vars_transposed(...) 3. └─admiraldev::assert_vars(by_vars) 4. └─admiraldev::assert_list_of(arg, "symbol", named = expect_names, optional = optional) Run rlang::last_trace(drop = FALSE) to see 1 hidden frame. Can you please guide?
Great presentation. Thanks for sharing this.
good refresher, 😇
Can you add timestamps/sections to your video please?
This is excellent! I like the level of depth.
Great to hear! We hope to have more at R in Pharma 2024 October 29, 30, & 31st. Workshops will run the week before.
Thank you
Great demo, so informative! You explained Observable Plots really well.
This is amazing work!!
The reports are just amazing. Thank you for sharing!
I'm able to install ggsurvfit package, but cannot library it. any suggestions for the issue?
Hi @meredith0322 - you might ask the question here: github.com/pharmaverse/ggsurvfit/issues
This is outstanding presentation, I find it very useful. Thank you Daniel! Wojtek. W.
Nice one!
💡
🎉 wat een goede presentatie
In 1997 CDISC was formed to harmonise data standards across the industry and there were just as many nay-sayers for that initiative. Their work continues and admiral's is just beginning by comparison. admiral has the potential to make a similar impact on the industry.
Thank you! I signed up for this course when it first came out and Ilove it. I look forward to taking your new course. As a biostatistician in public health I was desperate to learn pharmaceuticals workflow and their reporting system, there isn't enough resources unfortunately especially for R.
Connect with Michael on LinkedIn, if you’d like to continue the discussion after r/pharma: www.linkedin.com/in/michaelrimler/
Project GitHub: github.com/phuse-org/OSTCDA Join the Discussions and leave your perspectives, opinions, links, references, presentations, to help us cultivate a comprehensive digest of the current state of the industry using OS tech for clinical data analytics and reporting
Great talk!
Wonderful presentation
Amazing
Thank you 🙏🏻
github.com/agstn/RPharma23
Lovely explanation John!
3rd Module starts at 1:14:20 4th Module starts at 1:31:05 5th 1:42:12 6th 2:11:54
love it!
Thank you Micheal, Atorus Research and R in Pharma for conducting this workshop and posting it online!! This is super helpful!!
Thanks for this instructive and worable presentation.
5:23
'Promosm' 🙈
This video puts the mind of SAS Programmer at ease to some extent, that creating a Dataset in R is not that Hard. But at the end of the video it does feel what's will be the future of Clinical Programming ?
Creating dataset is not hard at all in R, and SAS is so expensive, and all the certifications are extremely expensive too. R is a community of scientist, data scientist, and programmers that want people to really learn and understand. Also once you talk with a SAS representative and dig deeper they take a lot of the innovations of the R GNU community. The regular user of SAS does not realized that but the programmers do, and it is awful that a company make a lot of money taking what in reality is open source.
The first link in the dooblydoo is the talk slides.
Thanks for the video. May I got a github source code of that app in R? thanks so much
Interesting topic，but video quality not that good
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