Save & Load RData Workspace Files in R (3 Examples) | save(), save.image(), and saveRDS() Functions 

That's great to hear, thank you very much for the feedback!

Hey Ramon, thank you very much for the positive feedback! Glad you like it!

Hi Alice, Thanks a lot for the wonderful response, I’m happy to hear that you like our instructions.

Hey, thanks a lot for the kind words! Unfortunately, I'm not an expert on this topic. In case you have questions on this topic, you may ask in the Statistics Globe Facebook group. This way, others can contribute/read as well: facebook.com/groups/statisticsglobe

Hi Aliou, thanks for the compliment! Hope the video was helpful!

Hey Haraldur, you may either save certain elements or the entire environment. This can not only be used to save data sets, but also to save functions, vector objects etc. Hence, this method is more flexible.

I have a data set that required me to delete two questions (I was running EFA for an assignment). Given the original data set was randomly generated from a massive df, I cannot easily go back to the original set. Hope this makes sense?

Hello! It is possible that you receive this error since you are trying to load the RMD file, not an RData file. RMD files are R Markdown documents that contain a mix of text and code chunks. They are intended for reporting and can be rendered into various output formats like HTML, PDF, or Word using the markdown package. They are not like Rdata files, which store R objects such as data frames, matrices, etc. Hence, make sure that you have the right file extension while using the load() function. Regards, Cansu

You are welcome Amir! :)

Sorry for my late response, Adarsh! I've been on vacation... Do you still have trouble with the problem?

Hey, unfortunately I'm not an expert on this topic. In case you have questions on this topic, you may ask in the Statistics Globe Facebook group. This way, others can contribute/read as well: facebook.com/groups/statisticsglobe

@@StatisticsGlobe still ok. As the Facebook link still very helpful

Glad you like the group! :)

Thanks for the feedback Vicente! Glad you like our way of machine learning ;)

Hello, I was on vacation. That's why I couldn't respond earlier. You should be able to use the given code also for built-in datasets. Best, Cansu

@cansustatisticsglobe I mean which 📂 folder of R to Edit,so that I can have my personalised Datasets as inbuilt ones ❗️

Hello @@Hassan_MM., Do you like to create an R package where you can find your data as you find a built-in dataset? I am not sure if I get you well. Could you please give some more details? Best, Cansu

@cansustatisticsglobe Ya❗️ at least can I edit any Existing Package for the same

Hello @@Hassan_MM., Creating a package in R involves several steps. Below is a basic step-by-step guide to creating a package that allows you to access user datasets as if they were built-in ones. Let's assume you have a dataset named mydata and you want to include it in a package named mypackage. Setup: First, make sure you have the required tools installed. This typically means installing the devtools and usethis packages: install.packages(c("devtools", "usethis")) library(devtools) library(usethis) Create a New Package: Create a new package structure in your desired directory. create_package("~/mypackage") Documenting the Package: This step involves creating the necessary documentation for your package, so it's recognized by R and can be installed. Use the roxygen2 package to help with this. use_roxygen_md() Add Your Dataset: Save your dataset as an .RData file inside the data directory of the package you've just created: use_data(mydata, overwrite = TRUE) Document Your Dataset: It's good practice to document your datasets so users (including future you) know what they're about. use_r("mydata") This will open a script. Document your data following roxygen2 conventions. Build & Install: Now, you'll build and install your package: install("~/mypackage") Load Your Package and Access Your Dataset: After building and installing, you can now load your package like any other in R: library(mypackage) data(mydata) These are what I found after research, but please remember that creating a robust, full-fledged R package involves more steps, such as writing unit tests, maintaining version control with Git and GitHub, and more. Best, Cansu