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Can you be more specific? What are you struggling with? Is it about local installation (Build - Install)? Do you use version control, do you have it on Git / GitHub? Is it about updating documentation in the actual package (Build - Document and Build - Load All)?

Not sure if I fully understand your use case. Reading your comment my first thought was parameterized reports. These are possible in Quarto just like in RMarkdown - only exception being that up to now (as far as I know), you can't use the interactive shiny screen in Quarto that "Knit with parameters" gives you in RMarkdown. But programmatically, which I believe is your application, you can just as well pass parameters to Quarto for rendering. Have you checked all the options in quarto::quarto_render()? (quarto is a small R package that bridges to the Quarto software.) So you could make the categories to split your data by a parameter (or several, if needed) in the call to quarto_render(execute_params = ...). Re-reading your comment, it seems the decisive argument you need is envir. That indeed seems to available in rmarkdown::render(), but not in quarto::quarto_render(). So basically you'd load the full dataset in your document and then filter by a parameter. I believe this is also the idea behind rmarkdown: to run in a new session. Using data from the user's Global Environment may break reproducibility of reports (e. g. manual data cleaning steps outside of the .Rmd not reproducible for colleagues who only have access to raw data).

An idea: use a data format that allows for filtering while loading data, e. g. a database format, or maybe parquet (which I don't have much experience with). Then a parameter determines which part of the data is loaded for a specific report, so you don't repeatedly load the full data for report creation.

I recommend a speaking name for each dataset and a corresponding name for each .R file. Example (from my chartmusicdata package, which you can find at github: fjodor/chartmusicdata): Datasets: topsongs.Rda, topalbums.Rda, albums2000.Rda, songs2000.Rda Documentation: topsongs.R, topalbums.R, albums2000.R, songs2000.R

@@StatistikinDD Thank you very much!

@@mikiallen7733 I'd say esquisse is not meant for such settings, e.g. regular data updates. I'd use it to get started writing ggplot2 code. In shiny apps I wouldn't embed esquisse.

@@StatistikinDD thanks for the prompt response sir

You could try to get it from GitHub. Do you have the latest R Version installed?

@@StatistikinDD no. I think Now a have R version 4.3.2 I would like to update, do I need to Remove old version or I can try it: install.packages ("installr") library(installr) updateR()

@@StatistikinDD after that I Would like to Join two columns become unique column in a dataframe. So the new column might be character and have two categories corresponding to the old two columns names. While the old two columns had number of test observed and another the number of tests expected in the same health facility. These numbers is grouped in week (15 weeks). I want to keep this new column and another new column that have number of tests, and another the week identifications (1,2,3...15) If you have some video about basic r skills with dataframe manipulation, please suggest me

​@@albinonhabanga8066 Yes, installr is a good option. However, going over the 4.4 threshold you might need to re-install your packages. No need to remove the old R version.

​@@albinonhabanga8066 Other channels offer more on data manipulation, e. g. Statistics Globe (Joachim Schork). We have collaborated in the past.

Im Modell mit Interaktionsterm sind die Einzeleffekte nicht separat zu beurteilen. Ein Teil des Effekts steckt im einzelnen Koeffizienten, ein weiterer Teil im Interaktionsterm. Anschaulicher ist es, konkrete Werte für beide Prädiktoren einzusetzen und Vorhersagen dafür zu berechnen anhand der Regressionsformel.

@@StatistikinDD Danke! Mit p-Wert von 0,199 betrachte und interpretiere ich also auch nicht dessen Regressionskoeffizienten (-0,457), sondern nur den des Interaktionsterms? Gibt es eine Quelle dafür, dass man sich nur Variable des Interaktionsterms ansieht? Wie kann das zustande kommen, dass Lernstunden (x) auf Lernerfolg (y) in diesem Moderationsmodell nicht signifikant ist, wenn angenommen in einem linearen Modell eine Signifikanz zwischen Lernstunden und Lernerfolg (gleicher Datensatz) vorhanden wäre?

@@JessiJ-zz7rp Die Effekte verändern sich, wenn die Moderation mit berücksichtigt wird. Es ist schließlich ein anderes Modell! Alle Kombinationen sind möglich: einzeln signifikant, im Moderationsmodell nicht, oder umgekehrt, oder beide Mal sig. oder nicht sig. Die grafische Interpretation ist oft einfacher als das Grübeln über die (nicht mehr von einander unabhängigen!) Koeffizienten. Das Modell mit Interaktionsterm testet in erster Linie, ob eine (statistisch signifikante) Wechselwirkung vorliegt.

You can add any data that you can get in the Global Environment. If it is already in a package, you may also add that package to Suggests or Imports.

So how: load data into Global Environment. Then proceed as in video. I'm just not sure if I'd like the same data in different packages. But there may be situations where it makes sense. E. g. to avoid dependency on that other package, maybe because it contains a lot more than the data, or because it is difficult to access in your environment.

Can you give more details? Where do you get stuck?

@@StatistikinDD I previously wrote an article introducing my r package, submitted it to the journal of statistical software, and received the following comment: 2 sample tests are run using TEST_FUN(group1, group2) and no formula version is available (it might be less natural in the multidimensional setting but e.g., Hotelling::hotelling.test implements one which is very clear and easy to use). test_fun is mentioned here, but I am not clearly how to use it

@@YEHE-ju7wm Is TEST_FUN an actual function or maybe just a placeholder for more specific functions?

Gut aufgepasst, danke für den Hinweis!

Thanks, happy to read that!

Have you specified a dataset? Is the data suitable? Sometimes there may be conflicts with other packages. In such cases, closing Rcmdr, starting a new R session and reopening Rcmdr worked for me.

Thanks, Joachim! On the one hand, it's fun to dive into new technology. On the other hand, it was another case of "Well, I guess I can throw away my training material on Reporting" when Quarto appeared on the scene ... 🙂

@@StatistikinDD haha I know what you mean! :D

Thanks!

Thanks for the kind comment, glad you find the topic interesting! :)

Thank you so much for the kind words Raoul, glad you find our content helpful! :)

Can you post the full error message? At what exact moment does it appear?

Hört sich nach Apple an. Habe keine Erfahrung mit Rcmdr auf iOS. Vielleicht helfen die Hinweise hier: www.john-fox.ca/RCommander/installation-notes.html

Hi, I'm on holidays, just a quick idea: maybe an invalid file path? I think I got similar problems when my file path contained spaces.

To install from a private repo, you (i. e. potential users of your package) need to provide authentication. I wouldn't spread authentication information, so if I wanted others to access my package, I'd make the repo public. Maybe it helps if you have a private repo where you try out things, and a public repo containing a version you feel ok about being public?

Hi, haven't noticed that. What is your setup? Operating system, R version, quarto version, RStudio (?) version. For the quarto version, you can install the quarto R package. (Not a replacement for the quarto software, just a small interface.) Then use quarto::quarto_version().

@@StatistikinDD Thanks a lot. Problem solved by updating Quarto. Thanks man. But one more question: is there a way to automatically update the Quarto? I don't even know my Quarto is out of date if you did not mention it.

​@@CanDoSo_org Not aware of an automatic update, not from within R anyway. Quarto gets bundled with RStudio, so when you update that, you may get a new quarto version as well. You can install daily builds of RStudio to get quicker updates. (I don't do that.) Apart from that, I sometimes check the quarto website and manually download quarto.

@@StatistikinDD Thanks a lot.

Do you have a DESCRIPTION text file at the top level of the package? Did you create the package using RStudio - New Project - Package?

@@StatistikinDD Thanks for your prompt reply! yes i do have a DESCRIPTION text file at the top level and I am creating the package using RStudio - New Project - Package.

@@LisaMok-w8k Looks like a known issue. github.com/rstudio/rstudio/issues/12945 github.com/r-lib/devtools/issues/2512 Maybe try updating as much as possible, e. g. RStudio, devtools, pkgbuild

@@StatistikinDD Thank you!

Good question. The DV is hard-coded in the function and so not passed on as a function parameter. Line 14: model <- as.formula(paste("result ~", IV)) DV = result for all the models calculated here. Only the IV varies.

@@StatistikinDD so the functions have access to local memory meaning they "know" where the result variable is stored?

@@anmolpardeshi3138 Functions can access R's Global Environment. The result variable (DV) is part of the dataset which is specifiied in the call to lm(), which accesses Global Environment. Note that, however, the workers for running the loops in parallel do not automatically have access to the Global Environment in this approach, at least on Windows. Each worker starts in an empty environment. So the data (and the reg function) are exported to the workers using the clusterExport function in this line of code: clusterExport(cl, c("reg", "data")) cl is the object resulting from the makeCluster call. I'd now recommend this approach: cl <- makeCluster(parallelly::availableCores(omit = 1)) See comment below and a later video on why to avoid detectCores(). ruclips.net/video/EYpuUKsGqPQ/видео.html

Currently I'm lacking experience in that. Read that fixed width and toggling with self-contained / embed-resources may help in PDF output stability. Thanks to Nicola Rennie.

Hi, thanks for your feedback! For CRAN submission, you upload a tar.gz file to CRAN, which contains the vignette. From the R Packages book at r-pkgs.org/vignettes.html#sec-vignettes-how-built-checked Prepare built vignettes for a CRAN submission: Don’t try to do this by hand or in advance. Allow vignette (re-)building to happen as part of devtools::submit_cran() or devtools::release(), both of which build the package.

Thanks for your comment, Robert. I'll try to stick more to the point next time.

@@StatistikinDD Clearly explained and straight to point. Saved me a lot of time just by watching this video. Thank you!

You have a very logical point but some information about other stuff comes with main topic's wind. You don't just go from point a to b. You also see where the supermarket on your way to point b. This kind of informations is crucial for some people, and showing it with a realistic and alive example is also important. You can simply skip it. Anyways. Great tutorial @StatistikinDD keep it rooling!