Closes #125 xportr 0.4.0 blog #126

sadchla-codes · 2024-02-28T22:14:22Z

No description provided.

bms63 · 2024-03-14T12:29:40Z

Hi @sadchla-codes - how goes the blog post? We should be moving to CRAN in the next couple of days

sadchla-codes · 2024-03-14T15:46:36Z

Hi @sadchla-codes - how goes the blog post? We should be moving to CRAN in the next couple of days

@bms63 It's ready for review!

bms63

Whoa... @sadchla-codes this is really good!! I like the JSON piece at the end. I have to review more tomorrow. I think xportr is going to CRAN on Monday or Tuesday

bms63 · 2024-03-24T17:39:31Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+- **Data Integrity**: [`xportr`](https://atorus-research.github.io/xportr/) preserves data integrity by retaining variable labels, formats, and other metadata during data export operations.
+- **User-Friendly Interface**: [`xportr`](https://atorus-research.github.io/xportr/) provides a user-friendly interface with intuitive functions for generating `xpt` datasets and performing data manipulation tasks.
+
+# About the other kid on the blog, (`JSON`)! Is The Future `XPT` or JSON?


Haha! Amazing! Can you make a reference to the datasetjson blog post on Atorus' site and the datasetjson package?

@mstackhouse maybe a follow up blog post on pharamverse is needed about datasetjson??

StefanThoma

Great post, @sadchla-codes !
I spotted some typos and added some suggestions.

StefanThoma · 2024-03-25T07:33:03Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+<!--------------- post begins here ----------------->
+
+# `XPT` Meets [`xportr`](https://atorus-research.github.io/xportr/)!
+In the pharmaceuticals and healthcare industries, it is crucial to maintain a standard structure for data exchange and regulatory submissions, enter `xpt` datasets! `XPT` datasets are binary files that are typically created by SAS software, they contain structured data, including variables, labels, and metadata. In order to develop `xpt` formatted files in R, let's introducing you to [`xportr`](https://atorus-research.github.io/xportr/).


Why is the second XPT all capital?

I think the last sentence should be rephrased, e.g.

Suggested change

In the pharmaceuticals and healthcare industries, it is crucial to maintain a standard structure for data exchange and regulatory submissions, enter `xpt` datasets! `XPT` datasets are binary files that are typically created by SAS software, they contain structured data, including variables, labels, and metadata. In order to develop `xpt` formatted files in R, let's introducing you to [`xportr`](https://atorus-research.github.io/xportr/).

In the pharmaceuticals and healthcare industries, it is crucial to maintain a standard structure for data exchange and regulatory submissions, enter `xpt` datasets! `XPT` datasets are binary files that are typically created by SAS software, they contain structured data, including variables, labels, and metadata. The [`xportr`](https://atorus-research.github.io/xportr/) package was created specifically to develop `xpt` formatted files in R.

StefanThoma · 2024-03-25T07:36:03Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+# `XPT` Meets [`xportr`](https://atorus-research.github.io/xportr/)!
+In the pharmaceuticals and healthcare industries, it is crucial to maintain a standard structure for data exchange and regulatory submissions, enter `xpt` datasets! `XPT` datasets are binary files that are typically created by SAS software, they contain structured data, including variables, labels, and metadata. In order to develop `xpt` formatted files in R, let's introducing you to [`xportr`](https://atorus-research.github.io/xportr/).
+
+[`xportr`](https://atorus-research.github.io/xportr/) is tools to build CDISC compliant data sets and check for CDISC compliance. By using  [`xportr`](https://atorus-research.github.io/xportr/), programmers are able to build these CDISC compliant files. 


Suggested change

[`xportr`](https://atorus-research.github.io/xportr/) is tools to build CDISC compliant data sets and check for CDISC compliance. By using [`xportr`](https://atorus-research.github.io/xportr/), programmers are able to build these CDISC compliant files.

[`xportr`](https://atorus-research.github.io/xportr/) is a tool to build CDISC compliant data sets and check for CDISC compliance. By using [`xportr`](https://atorus-research.github.io/xportr/), programmers are able to build these CDISC compliant files.

StefanThoma · 2024-03-25T07:37:40Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+
+# What is [`xportr`](https://atorus-research.github.io/xportr/)?
+
+[`xportr`](https://atorus-research.github.io/xportr/) has been developed through collaborations between developers from different parts of the industry [click here for a full list](https://atorus-research.github.io/xportr/authors.html). This collaborative effort ensures that [`xportr`](https://atorus-research.github.io/xportr/) meets the diverse needs and requirements of users across various domains. By leveraging insights and expertise from different stakeholders, [`xportr`](https://atorus-research.github.io/xportr/) continues to evolve and improve, maintaining its relevance and effectiveness in the ever-changing landscape of data management and analysis. With its meticulous design flow, users can expects to develop `xpt` files that has been vetted by many checks in the backend and CDISC compliant. 


Suggested change

[`xportr`](https://atorus-research.github.io/xportr/) has been developed through collaborations between developers from different parts of the industry [click here for a full list](https://atorus-research.github.io/xportr/authors.html). This collaborative effort ensures that [`xportr`](https://atorus-research.github.io/xportr/) meets the diverse needs and requirements of users across various domains. By leveraging insights and expertise from different stakeholders, [`xportr`](https://atorus-research.github.io/xportr/) continues to evolve and improve, maintaining its relevance and effectiveness in the ever-changing landscape of data management and analysis. With its meticulous design flow, users can expects to develop `xpt` files that has been vetted by many checks in the backend and CDISC compliant.

[`xportr`](https://atorus-research.github.io/xportr/) has been developed through collaborations between developers from different parts of the industry [click here for a full list](https://atorus-research.github.io/xportr/authors.html). This collaborative effort ensures that [`xportr`](https://atorus-research.github.io/xportr/) meets the diverse needs and requirements of users across various domains. By leveraging insights and expertise from different stakeholders, [`xportr`](https://atorus-research.github.io/xportr/) continues to evolve and improve, maintaining its relevance and effectiveness in the ever-changing landscape of data management and analysis. With its meticulous design flow, users can expect to develop `xpt` files that have been vetted by many checks in the backend and which are CDISC compliant.

StefanThoma · 2024-03-25T07:38:17Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+[`xportr`](https://atorus-research.github.io/xportr/) has been developed through collaborations between developers from different parts of the industry [click here for a full list](https://atorus-research.github.io/xportr/authors.html). This collaborative effort ensures that [`xportr`](https://atorus-research.github.io/xportr/) meets the diverse needs and requirements of users across various domains. By leveraging insights and expertise from different stakeholders, [`xportr`](https://atorus-research.github.io/xportr/) continues to evolve and improve, maintaining its relevance and effectiveness in the ever-changing landscape of data management and analysis. With its meticulous design flow, users can expects to develop `xpt` files that has been vetted by many checks in the backend and CDISC compliant. 
+![](xportr_design_flow.png){fig-align="center" width="500"}
+
+[`xportr`](https://atorus-research.github.io/xportr/) is currently on version 0.4.0 and you can keep up with all of its improvements by follow the [`Changelog here`](https://atorus-research.github.io/xportr/news/index.html)


Suggested change

[`xportr`](https://atorus-research.github.io/xportr/) is currently on version 0.4.0 and you can keep up with all of its improvements by follow the [`Changelog here`](https://atorus-research.github.io/xportr/news/index.html)

[`xportr`](https://atorus-research.github.io/xportr/) is currently on version 0.4.0 and you can keep up with all of its improvements by following the [`Changelog here`](https://atorus-research.github.io/xportr/news/index.html)

StefanThoma · 2024-03-25T07:39:22Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+
+# Why [`xportr`](https://atorus-research.github.io/xportr/)?
+
+Have I already mentioned that [`xportr`](https://atorus-research.github.io/xportr/) is an essential R package for generating `xpt` datasets! This package have functions to apply metadata information to your CDISC compliant datasets that were created in R, perform checks and provide useful feedbacks. In this blog post, we'll explore some high level of the capabilities of [`xportr`](https://atorus-research.github.io/xportr/), and its development.


Suggested change

Have I already mentioned that [`xportr`](https://atorus-research.github.io/xportr/) is an essential R package for generating `xpt` datasets! This package have functions to apply metadata information to your CDISC compliant datasets that were created in R, perform checks and provide useful feedbacks. In this blog post, we'll explore some high level of the capabilities of [`xportr`](https://atorus-research.github.io/xportr/), and its development.

Have I already mentioned that [`xportr`](https://atorus-research.github.io/xportr/) is an essential R package for generating `xpt` datasets? This package includes functions to apply metadata information to your CDISC compliant datasets that were created in R, perform checks and provide useful feedbacks. In this blog post, we'll explore some high level capabilities of [`xportr`](https://atorus-research.github.io/xportr/), and its development.

StefanThoma · 2024-03-25T07:42:34Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+)
+```
+
+Users can use the [`xportr_metadata()`](https://atorus-research.github.io/xportr/reference/xportr_metadata.html) function along with [`xportr_type()`](https://atorus-research.github.io/xportr/reference/xportr_type.html) and [`xportr_order()`](https://atorus-research.github.io/xportr/reference/xportr_order.html) to not only apply metadata attributes to your data, receive feedbacks on your data based on your spec, all while also performing valuable checks.


Suggested change

Users can use the [`xportr_metadata()`](https://atorus-research.github.io/xportr/reference/xportr_metadata.html) function along with [`xportr_type()`](https://atorus-research.github.io/xportr/reference/xportr_type.html) and [`xportr_order()`](https://atorus-research.github.io/xportr/reference/xportr_order.html) to not only apply metadata attributes to your data, receive feedbacks on your data based on your spec, all while also performing valuable checks.

Users can use the [`xportr_metadata()`](https://atorus-research.github.io/xportr/reference/xportr_metadata.html) function along with [`xportr_type()`](https://atorus-research.github.io/xportr/reference/xportr_type.html) and [`xportr_order()`](https://atorus-research.github.io/xportr/reference/xportr_order.html) to not only apply metadata attributes to your data but also receive feedback on your data based on your spec – all while also performing valuable checks.

What do you mean by "valuable checks"? Could you be more specific?

StefanThoma · 2024-03-25T07:46:00Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+
+`XPT` datasets are standardized, structured files used for storing clinical trial data, making them essential for regulatory submissions and data analysis. They remain indispensable in certain industries and with advancements in data management and analysis tools, the future of `xpt` datasets lies in improved efficiency, compatibility, and ensuring seamless integration with emerging technologies and data formats.
+
+As data continues to play a central role in decision-making and innovation, the future of JavaScript Object Notation (JSON) datasets are worth exploring. JSON datasets offer flexibility, simplicity, and interoperability, making them increasingly popular for data storage and exchange. JSON's lightweight and human-readable format make it suitable for a wide range of applications, including web development, APIs, IoT, and data storage. While `xpt` datasets remain prevalent in certain industries, there's a growing interest in JSON datasets across various domains. Although we will not be exploring JSON datasets, this formatted data type was worth mentioning in this blog.


Suggested change

As data continues to play a central role in decision-making and innovation, the future of JavaScript Object Notation (JSON) datasets are worth exploring. JSON datasets offer flexibility, simplicity, and interoperability, making them increasingly popular for data storage and exchange. JSON's lightweight and human-readable format make it suitable for a wide range of applications, including web development, APIs, IoT, and data storage. While `xpt` datasets remain prevalent in certain industries, there's a growing interest in JSON datasets across various domains. Although we will not be exploring JSON datasets, this formatted data type was worth mentioning in this blog.

As data continues to play a central role in decision-making and innovation, the future of JavaScript Object Notation (JSON) datasets is worth exploring. JSON datasets offer flexibility, simplicity, and interoperability, making them increasingly popular for data storage and exchange. JSON's lightweight and human-readable format make it suitable for a wide range of applications, including web development, APIs, IoT, and data storage. While `xpt` datasets remain prevalent in certain industries, there's a growing interest in JSON datasets across various domains. Although we will not be exploring JSON datasets, this formatted data type was worth mentioning in this blog.

I have heard about JSON being discussed as a standard data format in the industry before. Would you be able to link here some further reading on this?

StefanThoma · 2024-03-25T07:47:08Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+
+# Let's Recap!
+
+This blog post highlighted the benefits of using the [`xportr`](https://atorus-research.github.io/xportr/) package, showcased an example of [`xportr`](https://atorus-research.github.io/xportr/) function, and discussed the growing interest of JSON datasets in the data landscape. We've also discussed [`xportr`](https://atorus-research.github.io/xportr/)'s development through collaborations between developers from different parts of the industry, highlighting its versatility and relevance in various domains. Which gives me a nice transition to my call to action!! If you haven't used [`xportr`](https://atorus-research.github.io/xportr/) before, then consider this blog as your official invite to test it! Reap the benefit of collaborative efforts across industries and the pharma world. If are already a user of [`xportr`](https://atorus-research.github.io/xportr/) or you are experiencing some issues with a particular functions, then chances are someone else could either be experiencing a similar issue or have an answer so reach out to the team by creating an issue [`here`](https://github.com/atorus-research/xportr/issues).  


Suggested change

This blog post highlighted the benefits of using the [`xportr`](https://atorus-research.github.io/xportr/) package, showcased an example of [`xportr`](https://atorus-research.github.io/xportr/) function, and discussed the growing interest of JSON datasets in the data landscape. We've also discussed [`xportr`](https://atorus-research.github.io/xportr/)'s development through collaborations between developers from different parts of the industry, highlighting its versatility and relevance in various domains. Which gives me a nice transition to my call to action!! If you haven't used [`xportr`](https://atorus-research.github.io/xportr/) before, then consider this blog as your official invite to test it! Reap the benefit of collaborative efforts across industries and the pharma world. If are already a user of [`xportr`](https://atorus-research.github.io/xportr/) or you are experiencing some issues with a particular functions, then chances are someone else could either be experiencing a similar issue or have an answer so reach out to the team by creating an issue [`here`](https://github.com/atorus-research/xportr/issues).

This blog post highlighted the benefits of using the [`xportr`](https://atorus-research.github.io/xportr/) package, showcased an example of [`xportr`](https://atorus-research.github.io/xportr/) function, and discussed the growing interest of JSON datasets in the data landscape. We've also discussed [`xportr`](https://atorus-research.github.io/xportr/)'s development through collaborations between developers from different parts of the industry, highlighting its versatility and relevance in various domains. Which gives me a nice transition to my call to action!! If you haven't used [`xportr`](https://atorus-research.github.io/xportr/) before, then consider this blog as your official invite to test it! Reap the benefit of collaborative efforts across industries and the pharma world. If are already a user of [`xportr`](https://atorus-research.github.io/xportr/) or you are experiencing some issues with a particular functions, then chances are someone else could either be experiencing a similar issue or has an answer to it. So reach out to the team by creating an issue [`here`](https://github.com/atorus-research/xportr/issues).

bms63 · 2024-03-26T19:11:26Z

@sadchla-codes Could you add a short blurb about what xportr does so it appears on the front page

Please change date and shrink image as well

Thanks again!!

bms63 · 2024-03-26T19:24:56Z

posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd

+
+Users can use the [`xportr_metadata()`](https://atorus-research.github.io/xportr/reference/xportr_metadata.html) function along with [`xportr_type()`](https://atorus-research.github.io/xportr/reference/xportr_type.html) and [`xportr_order()`](https://atorus-research.github.io/xportr/reference/xportr_order.html) to not only apply metadata attributes to your data but also receive feedback on your data based on your spec. Additionally, some of those functions performing valuable checks like:
+
+  * Variable names must start with a letter (not an underscore), be comprised of only uppercase letters (A-Z), numerals (0-9) and be free of non-ASCII characters, symbols, and underscores.


Could we list out the other functions here. The xportr_label, xportr_format and xportr_write with the strict checks should get a mention here for sure.

oh and xportr_length

bms63 · 2024-03-26T19:27:38Z

media/xportr_design_flow.png

Can we increase the size of this image in the post please?

StefanThoma

LGTM!!

Closes #125 xportr 0.4.0 blog

038de0a

sadchla-codes requested a review from bms63 February 28, 2024 22:16

sadchla-codes linked an issue Feb 29, 2024 that may be closed by this pull request

Blog Post: xportr 0.4.0 blog post #125

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xportr 0.4.0 blog post atorus-research/xportr#212

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sadchla-codes and others added 2 commits March 14, 2024 10:25

Merge branch 'main' into 125_xportr_0_4_0

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Adding functions and elaborating the different section

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sadchla-codes requested review from StefanThoma and kaz462 March 14, 2024 15:46

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Can we increase the size of this image in the post please?

sadchla-codes added 2 commits March 28, 2024 12:55

Updating dates and adding description

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Closes #125 xportr 0.4.0 blog #126

Closes #125 xportr 0.4.0 blog #126

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	[`xportr`](https://atorus-research.github.io/xportr/) is tools to build CDISC compliant data sets and check for CDISC compliance. By using [`xportr`](https://atorus-research.github.io/xportr/), programmers are able to build these CDISC compliant files.
	[`xportr`](https://atorus-research.github.io/xportr/) is a tool to build CDISC compliant data sets and check for CDISC compliance. By using [`xportr`](https://atorus-research.github.io/xportr/), programmers are able to build these CDISC compliant files.


		# What is [`xportr`](https://atorus-research.github.io/xportr/)?

		[`xportr`](https://atorus-research.github.io/xportr/) has been developed through collaborations between developers from different parts of the industry [click here for a full list](https://atorus-research.github.io/xportr/authors.html). This collaborative effort ensures that [`xportr`](https://atorus-research.github.io/xportr/) meets the diverse needs and requirements of users across various domains. By leveraging insights and expertise from different stakeholders, [`xportr`](https://atorus-research.github.io/xportr/) continues to evolve and improve, maintaining its relevance and effectiveness in the ever-changing landscape of data management and analysis. With its meticulous design flow, users can expects to develop `xpt` files that has been vetted by many checks in the backend and CDISC compliant.


		# Why [`xportr`](https://atorus-research.github.io/xportr/)?

		Have I already mentioned that [`xportr`](https://atorus-research.github.io/xportr/) is an essential R package for generating `xpt` datasets! This package have functions to apply metadata information to your CDISC compliant datasets that were created in R, perform checks and provide useful feedbacks. In this blog post, we'll explore some high level of the capabilities of [`xportr`](https://atorus-research.github.io/xportr/), and its development.


		`XPT` datasets are standardized, structured files used for storing clinical trial data, making them essential for regulatory submissions and data analysis. They remain indispensable in certain industries and with advancements in data management and analysis tools, the future of `xpt` datasets lies in improved efficiency, compatibility, and ensuring seamless integration with emerging technologies and data formats.

		As data continues to play a central role in decision-making and innovation, the future of JavaScript Object Notation (JSON) datasets are worth exploring. JSON datasets offer flexibility, simplicity, and interoperability, making them increasingly popular for data storage and exchange. JSON's lightweight and human-readable format make it suitable for a wide range of applications, including web development, APIs, IoT, and data storage. While `xpt` datasets remain prevalent in certain industries, there's a growing interest in JSON datasets across various domains. Although we will not be exploring JSON datasets, this formatted data type was worth mentioning in this blog.


		# Let's Recap!

		This blog post highlighted the benefits of using the [`xportr`](https://atorus-research.github.io/xportr/) package, showcased an example of [`xportr`](https://atorus-research.github.io/xportr/) function, and discussed the growing interest of JSON datasets in the data landscape. We've also discussed [`xportr`](https://atorus-research.github.io/xportr/)'s development through collaborations between developers from different parts of the industry, highlighting its versatility and relevance in various domains. Which gives me a nice transition to my call to action!! If you haven't used [`xportr`](https://atorus-research.github.io/xportr/) before, then consider this blog as your official invite to test it! Reap the benefit of collaborative efforts across industries and the pharma world. If are already a user of [`xportr`](https://atorus-research.github.io/xportr/) or you are experiencing some issues with a particular functions, then chances are someone else could either be experiencing a similar issue or have an answer so reach out to the team by creating an issue [`here`](https://github.com/atorus-research/xportr/issues).


		Users can use the [`xportr_metadata()`](https://atorus-research.github.io/xportr/reference/xportr_metadata.html) function along with [`xportr_type()`](https://atorus-research.github.io/xportr/reference/xportr_type.html) and [`xportr_order()`](https://atorus-research.github.io/xportr/reference/xportr_order.html) to not only apply metadata attributes to your data but also receive feedback on your data based on your spec. Additionally, some of those functions performing valuable checks like:

		* Variable names must start with a letter (not an underscore), be comprised of only uppercase letters (A-Z), numerals (0-9) and be free of non-ASCII characters, symbols, and underscores.

Closes #125 xportr 0.4.0 blog #126

Closes #125 xportr 0.4.0 blog #126

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