From 038de0a5b0cb15f3880ff8d0b2b3246fecd1c24b Mon Sep 17 00:00:00 2001 From: sadchla-codes Date: Wed, 28 Feb 2024 17:09:22 -0500 Subject: [PATCH 1/8] Closes #125 xportr 0.4.0 blog --- inst/WORDLIST.txt | 7 ++ media/xportr.png | Bin 0 -> 114626 bytes posts/2024-03-18_xportr_0_4_0/appendix.R | 78 +++++++++++++ posts/2024-03-18_xportr_0_4_0/xportr.png | Bin 0 -> 114626 bytes .../2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd | 107 ++++++++++++++++++ 5 files changed, 192 insertions(+) create mode 100644 media/xportr.png create mode 100644 posts/2024-03-18_xportr_0_4_0/appendix.R create mode 100644 posts/2024-03-18_xportr_0_4_0/xportr.png create mode 100644 posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd diff --git a/inst/WORDLIST.txt b/inst/WORDLIST.txt index 98398c87..074afecc 100644 --- a/inst/WORDLIST.txt +++ b/inst/WORDLIST.txt @@ -612,3 +612,10 @@ zhu Zhu zMachine zxqguo +deepdive +feedbacks +IoT +JSON's +magrittr +NotUsed +xport diff --git a/media/xportr.png b/media/xportr.png new file mode 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<- function(text, path) { + paste0("[", text, "](", path, ")") +} + + + +# worker functions -------------------------------------------------------- + +insert_source <- function(repo_spec, name, + collection = "posts", + branch = "main", + host = "https://github.com", + text = "source code") { + path <- paste( + host, + repo_spec, + "tree", + branch, + collection, + name, + "code_sections.qmd", + sep = "/" + ) + return(markdown_link(text, path)) +} + +insert_timestamp <- function(tzone = Sys.timezone()) { + time <- lubridate::now(tzone = tzone) + stamp <- as.character(time, tz = tzone, usetz = TRUE) + return(stamp) +} + +insert_lockfile <- function(repo_spec, name, + collection = "posts", + branch = "main", + host = "https://github.com", + text = "R environment") { + path <- paste( + host, + repo_spec, + "tree", + branch, + collection, + name, + "renv.lock", + sep = "/" + ) + return(markdown_link(text, path)) +} + + + +# top level function ------------------------------------------------------ + +insert_appendix <- function(repo_spec, name, collection = "posts") { + appendices <- paste( + markdown_appendix( + name = "Last updated", + content = insert_timestamp() + ), + " ", + markdown_appendix( + name = "Details", + content = paste( + insert_source(repo_spec, name, collection), + insert_lockfile(repo_spec, name, collection), + sep = ", " + ) + ), + sep = "\n" + ) + knitr::asis_output(appendices) +} diff --git a/posts/2024-03-18_xportr_0_4_0/xportr.png b/posts/2024-03-18_xportr_0_4_0/xportr.png new file mode 100644 index 0000000000000000000000000000000000000000..b439544c208f92451fcc8533737ca14718fbbc82 GIT binary patch literal 114626 zcmaG|1z45a(ne6ar3FDi5$W!bMoL6QrAxX~K|;C(6p$1_P*Fmo+R0s%MmGD ze7DjIqkEDU+tJ?1EX5D>Z{eK~S`y%5n>}|RGp^yh>}_UAu}TAMbv0pV`_i*>2yk==|h9!qMkpLrC<`FClF* zVM!Sonfih1c%HY~o3g%+b{uG&R*4m27iX~}w?7yv&B=i!Sn}yUK|u-_g^pO#Mg^4m zdJ4O_McyYU8w1luyA52S7m0|X+Plo?=;?cW7I*oX9xCy68ju8f(RMvw8&M6MoQH*H 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" +date: "2024-03-18" +# please do not use any non-default categories. +# You can find the default categories in the repository README.md +categories: [ADaMs, xportr] +# feel free to change the image +image: "xportr.png" + +--- + + + +```{r setup, include=FALSE} +long_slug <- "2024-03-18_xportr_0_4_0" +# renv::use(lockfile = "renv.lock") +``` + + + +# `XPT` Meets [`xportr`](https://atorus-research.github.io/xportr/)! + +`XPT` datasets are binary files typically created by SAS software. They contain structured data, including variables, labels, and metadata, and are crucial for data exchange and regulatory submissions in industries such as pharmaceuticals and healthcare. By using [`xportr`](https://atorus-research.github.io/xportr/), programmers are able to build these CDISC compliant files in R. + +# 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. 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. [`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 reading the [`Changelog`](https://atorus-research.github.io/xportr/news/index.html) + +# Why [`xportr`](https://atorus-research.github.io/xportr/)? + +[`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 of the capabilities of [`xportr`](https://atorus-research.github.io/xportr/), its development, and the future trends of JSON (JavaScript Object Notation) and `xpt` datasets. + +# How does it work? + +You too can experience some of the benefits of [`xportr`](https://atorus-research.github.io/xportr/) by taking a [`deep dive`](https://atorus-research.github.io/xportr/articles/deepdive.html) at [`xportr`](https://atorus-research.github.io/xportr/)! + +There are data deliverables and supporting documentation needed for a successful submission to Health Authorities, while you can find a walk through on how you can use `xportr` [`here`](https://atorus-research.github.io/xportr/articles/deepdive.html#what-goes-in-a-submission-to-a-health-authority), let's look at a high level example: + +```{r, message = FALSE, echo = FALSE } +library(xportr) +library(magrittr) +``` + +Looking at an `adsl` and the define metadata information +```{r, message = FALSE, warnings = FALSE } +adsl <- data.frame( + Subj = as.character(123, 456, 789), + Different = c("a", "b", "c"), + Val = c("1", "2", "3"), + Param = c("param1", "param2", "param3") +) +``` + +and the define metadata information +```{r, message = FALSE, warnings = FALSE} +metadata <- data.frame( + dataset = "test", + variable = c("Subj", "Param", "Val", "NotUsed"), + type = c("numeric", "character", "numeric", "character"), + format = NA, + order = c(1, 3, 4, 2) +) +``` + +Users can use the [`xportr_metadata()`](https://atorus-research.github.io/xportr/reference/xportr_metadata.html) function in combination 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 the metadata to your data but also do valuable checks and receive feedbacks on your data based on your spec. + +```{r, warnings = FALSE} +adsl %>% + xportr_metadata(metadata, "test") %>% + xportr_type() %>% + xportr_order() +``` +You can also use additional functions like [`xportr_write()`](https://atorus-research.github.io/xportr/reference/xportr_write.html) to generate your `xpt` file. + +# Key Features of [`xportr`](https://atorus-research.github.io/xportr/): + +- **Efficient Handling**: [`xportr`](https://atorus-research.github.io/xportr/) offers efficient handling of `xpt` files, allowing users to export in `xpt` format, and manipulate large datasets with ease. +- **Compatibility**: [`xportr`](https://atorus-research.github.io/xportr/) ensures compatibility with various data types and formats commonly used in `xpt` files, ensuring seamless data exchange across different systems. +- **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. + +# Is The Future `XPT` or JSON? + +`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 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. + +# Let's Recap! + +This blog post highlighted the benefits of using the [`xportr`](https://atorus-research.github.io/xportr/) package, showcased some examples of [`xportr`](https://atorus-research.github.io/xportr/) functions, and discussed the growing significance 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. [`xportr`](https://atorus-research.github.io/xportr/). If you haven't used [`xportr`](https://atorus-research.github.io/xportr/) before, then consider this blog as you official invite to test it! If you have any 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 to the team by creating an issue [`here`](https://github.com/atorus-research/xportr/issues). + +As we look towards the future, embracing diverse data formats and technologies will be key to unlocking the full potential of data-driven innovation. By embracing tools like [`xportr`](https://atorus-research.github.io/xportr/) and staying abreast of emerging data formats, researchers, analysts, and regulatory professionals can unlock new opportunities for insights and innovation in their respective fields. + + + + + +```{r, echo=FALSE} +source("appendix.R") +insert_appendix( + repo_spec = "pharmaverse/blog", + name = long_slug +) +``` From e59e8cc2973f7168eea7592c8f1f750eaadef110 Mon Sep 17 00:00:00 2001 From: sadchla-codes Date: Thu, 14 Mar 2024 11:44:55 -0400 Subject: [PATCH 2/8] Adding functions and elaborating the different section --- media/xportr_design_flow.png | Bin 0 -> 152027 bytes .../2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd | 27 +++++++++++------- .../xportr_design_flow.png | Bin 0 -> 152027 bytes 3 files changed, 16 insertions(+), 11 deletions(-) create mode 100644 media/xportr_design_flow.png create mode 100644 posts/2024-03-18_xportr_0_4_0/xportr_design_flow.png diff --git a/media/xportr_design_flow.png b/media/xportr_design_flow.png new file mode 100644 index 0000000000000000000000000000000000000000..4ddf7ea9a5efc851de64e651d2cbeb2f1b611b37 GIT binary patch literal 152027 zcmeEuhg%cd);=90APOqd5vfWQ>4aiIIttRe^p1ociUJ3PN z+eQ==v~UUvs-aVKz&Fj6reYKnjOy;UZr#(qb&KzwzpsnCmoo*0*7FoIdUNBpiwLWv zH|jS*e0+l2Lh)K4KEc?F>S5=+Vg=MiF4H~vgWKYPpZEv!#%E=Jhyue)7oFXrpyHdY}B}PEjqfQiwIWqWeaq7qW+GXbOR~{5^uC%9pp*fKGCMYPQH9CcA;z^i_5PlWDAf&gV;VU&JXse0YRO3ZQT^KD^EEHf)DMfK}(_x<$mkNfmc!rvHl zcHwy?rvIeJ|IoLiE%nT|Pfzs4Z?Ujk$&x78JNRk1=PSefp%X z(LBFLNZaS-^^2A{LJpO%!_czZJZ2YH-rl%|$RCKxzC0~LlD2fvwC4YEK3@EEEa=?X ze!ig2jz5@;_g{*3+TUf|*UFQwI27l5eJ=jV(S?0;jZ6U%%_Y0cWO)TUFXLx#Sl@m* zAj-_%JWpz}46zP9_LCUC5FLL(?ZcJtoNTHJZ1sG6_oRbMtoZ&&Fj)>ii$7=;`8Iq0 zQ4p+;SDw|0z9U2?-e&9c!xYEO*znJM7xqIk71XX8BnsQ6w=umk`WYihJ@c4}ih^3p zL6;W8$9ifkj5BN|<(=>;zi65~YD&L**JG}*%!BzuUeZ3DHjJm?^L2wa)R|G7<~s?D zH{s$|&#bFfSLJ6=d8$Ak8|@V$o|3@AbVrxMknhUj-2e$Mm2_V@uZrWS9_ETE_KQx< zDK7)0_-{q@CSTHG5DCLaBnaxDfrGqbTspC!}%1x$zEoXFXL^%{iS{;aXcH=ZFT zmUIZ4H}9n+#lTQnN97kkUl<7Zdi2Qm!l$_I z({y!@c~8;2bh?C^v+3d|>Q%Y}4Qhqc2GNz% zET5@-9z*>|0nu!^%yrZ`=V&fpeh~Y5@rqEq;KKxkD|%n(LARR|FA0FOHTuq-31)xb++QPR2wv4t+{aN>}V_qC)ywff(2r-@&$$*8zWMN9}?ClZloF*nF zGA7J}ZYFP+tp{a4LQQ&#gFZ$+=r^h~b}ZJvm;6!u{#aR%5#jYH=hYOCJEZqQ#>67s z53&RO191b)H(zrEdbhq$Fwg^ikw>IPQSR85dI5AygLerT! zqBZlsIlQvsILZv@x}TYtshIgfHrT_qcXf#0-NK#LV-xiU3hkCX6sFg@xDX(heK*H& zN=NP1dY_Pol_#W12Gvm&>{;!;yX3a?c~M|dufJ$Ov7rC$izUk?|A5NCwBc|Ja36o4%!+pjh^&_#P`F*B+W=211bsdQ5iG8!|tonfZ?5T}*vUD*oL#w#%O}39-{oxU95@S-0Lddq(^wtdR z*E0=%!@2ySkLn7vdMDm5*AavzTu>h99+YS)>szY;d#t-iR@wPZsXmcvc8qnqO^UZV z<8ekTjw;SF&fu<1t6wXBD_v{a6>&|W1Z&l;@$bF{Wd`Zz8qeRO-!{{hz> z)UATo!LPh8dLN_N!#<&~pYj6xqR}~~!`rN8#T-Xl)>URVF+L&S6X*k8ySOI0X0leD zKH4d=@NglN`AJ+uD_6pM9$xT-3K6^uJ)WPRPMJsb7eil?E;Xf>I`TlXLLpdrXz(wo zaHDX!y?|et`~DHd5xjdD2b)LeJ+R|Q#dV8XbIGIZL&4*?!>LN}Nae6F^{2 zo~L6^b(^!ky>q7XT%5Bx|>U z+QyO&TIpZg$<}*5S&2;o$qQQ=!sRxb1!A;El3(ka=5%I<^F4i&lwKh-&mf;<)kQFz z&oj;6*&MDLtYZsTJJyLzXtcT~5t6!)ZHgGmUdyiM#|pQ|zvq8%_WfcRYkfOo+j#n( z>b2{h&>{7phdT(a6gUjt!Y_V5^@AWzA=uKVcG%XMe=U7h(RmQ2s-5m+#gim2jg+?I zJpNv-0medfZAA5}w3LH%s{QKpw3{2F5nr}Nb$>h)(3U4ptF zc8#iVW@IbXhQ8i|&nntLV(@F}cQevt*LV2~-)+6CCbHt?FK}My5sy67-H=2HRWJ^S z-Qgp#Yg7N;y0Y%m;pQLZ*Otw$o2Hh9*LrMyWVj%4fz!;`RM#xH1Z3C$dFUab0%bSk zTvB^~$qrN4{>kJ1-~s87WG4U1`@@)9Rk_z(p?&W3Ci&=_!fV)b?JTN};LlsX^j_nlg&5zz6=>x-&3fq2=WV8OaS6<&m z^U8uPL-p{(eRHt6udVb3=Mjr)qN*d%t49NK3vFGy1_{a$Sizcng5Gcj(KV(Uq1vhx zJX^QB_nu8flB@oE-LPL9!6>5GbLH)nO_bWe!jNgy$dUitg@kMR1jo??I}$c**|HH; ziBrT$PQ-uy5h-)LK(J!<2^?RTTB}cvzz8NY51c}$aL<@bHj4S}I!8#djwJ?pw^i&4XyiF=#lgOfzMY45^KKpQF*<#en8p(uD9+}FoA5oE!U8G9AI|35F8cZuSApYG6f~3^ z6tut*CGe|C$@$M?O-d08>Oa4yqM(R#r=aJp{kfCrQ$w@6A$NJ;*)Z{SiD^0!L& z+(VqbEN;7d12zNhp(=k}PUX+@|D)w!cl^th=Ks1<`i9KEUHX@%-!C`(4?F+)EnsNX(<+kxoHW(bCC^{+0Q1P@e%tT?@CkSs z`G?XI_($X)pXB2gjJVn|5(R}ih4yWY2O*TJ6Ck*lNre1bi_n2kg-FHsg?4^Ek>=ua zcQmLq)MIk_KivLmc0=dkfSc5%_maf+TYE?hYG>M`dZj~TAMh|d+trE*w5 z*+cBwfg$VJq@8VS3!zXo2h-ng6Zmnt=p#aTO6f?sgS7lWkLJ`_KJ~{GlvMwIV6PrM zPt#3(+7kXBO@E%8*_ani$hn-|_!VFOMmr&#{?D?#*|!Uqf0CSwp&# zn%O(VtH=5Ilj*vvry`*kVz+X~axt?0`%T2(jRWZ62g~P`vf6g=?zr)grDrL64g|05 z#W3U+GQ#^Cl~zh*EIoEo|9+Gg&;80d*~>|4zr;%0ZL=ZfXB*qa`tJ|=zmNXulR6bX zJU>qpR~ve`+bQX1Yu?rKf1Dbx&ZdAY|HeT;4FvG;Dpa?k(3-w8g2V?N3R4lg6FMGZ zFXvo;Gw46=^D>uwDSFg{fu;+DIG7>cj)s)5C=}1AJaK(xBlLGBY^0$A!IL=?nBivT z4o_*DT?J)@9uv?1K4JgpUreNzw!65j#-rgb@z-QmX<8*uH^aNV=l_R!E}T`6^q6J; z+o7H{XN=yAhR~f_4U-QhWs&@B86Uj(dtW(Ed+`jMRjKeeTWas*nX84~^vPo|)ZCQ^i46#VO8 z{%O5vX`nUz>D|AdX{pDL8$&5nUaeDI|h&!Oax0xQ$>N0cg zp9A@~14ceRKOTyncmKzzgn-t{jrhMeB;DDw3e1mP@Bbg8Vm+&nU=ecr?+r

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b/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd index 3408fe3a..1b701bb8 100644 --- a/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd +++ b/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd @@ -22,16 +22,20 @@ long_slug <- "2024-03-18_xportr_0_4_0" # `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/). -`XPT` datasets are binary files typically created by SAS software. They contain structured data, including variables, labels, and metadata, and are crucial for data exchange and regulatory submissions in industries such as pharmaceuticals and healthcare. By using [`xportr`](https://atorus-research.github.io/xportr/), programmers are able to build these CDISC compliant files in R. +[`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. # 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. 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. [`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 reading the [`Changelog`](https://atorus-research.github.io/xportr/news/index.html) +[`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) # Why [`xportr`](https://atorus-research.github.io/xportr/)? -[`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 of the capabilities of [`xportr`](https://atorus-research.github.io/xportr/), its development, and the future trends of JSON (JavaScript Object Notation) and `xpt` datasets. +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. # How does it work? @@ -65,7 +69,7 @@ metadata <- data.frame( ) ``` -Users can use the [`xportr_metadata()`](https://atorus-research.github.io/xportr/reference/xportr_metadata.html) function in combination 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 the metadata to your data but also do valuable checks and receive feedbacks on your data based on your spec. +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. ```{r, warnings = FALSE} adsl %>% @@ -73,29 +77,30 @@ adsl %>% xportr_type() %>% xportr_order() ``` + You can also use additional functions like [`xportr_write()`](https://atorus-research.github.io/xportr/reference/xportr_write.html) to generate your `xpt` file. # Key Features of [`xportr`](https://atorus-research.github.io/xportr/): -- **Efficient Handling**: [`xportr`](https://atorus-research.github.io/xportr/) offers efficient handling of `xpt` files, allowing users to export in `xpt` format, and manipulate large datasets with ease. -- **Compatibility**: [`xportr`](https://atorus-research.github.io/xportr/) ensures compatibility with various data types and formats commonly used in `xpt` files, ensuring seamless data exchange across different systems. +- **Efficient Handling**: [`xportr`](https://atorus-research.github.io/xportr/) offers efficient avenues to produce `xpt` files, allowing users to manipulate clinical datasets with ease and export in `xpt` format in R. - **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. -# Is The Future `XPT` or JSON? +# About the other kid on the blog, (`JSON`)! Is The Future `XPT` or JSON? `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 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. +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 some examples of [`xportr`](https://atorus-research.github.io/xportr/) functions, and discussed the growing significance 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. [`xportr`](https://atorus-research.github.io/xportr/). If you haven't used [`xportr`](https://atorus-research.github.io/xportr/) before, then consider this blog as you official invite to test it! If you have any 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 to the team by creating an issue [`here`](https://github.com/atorus-research/xportr/issues). - -As we look towards the future, embracing diverse data formats and technologies will be key to unlocking the full potential of data-driven innovation. By embracing tools like [`xportr`](https://atorus-research.github.io/xportr/) and staying abreast of emerging data formats, researchers, analysts, and regulatory professionals can unlock new opportunities for insights and innovation in their respective fields. +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). +As we look towards the future, embracing diverse data formats and technologies will be key to unlocking the full potential of data-driven innovation. Championing tools like [`xportr`](https://atorus-research.github.io/xportr/) and staying abreast of emerging data formats, researchers, analysts, and regulatory professionals can unlock new opportunities for insights and innovation in their respective fields. +**Disclaimer** +This blog contains opinions that are of the authors alone and do not necessarily reflect the strategy of their respective organizations. ```{r, echo=FALSE} diff --git a/posts/2024-03-18_xportr_0_4_0/xportr_design_flow.png b/posts/2024-03-18_xportr_0_4_0/xportr_design_flow.png new file mode 100644 index 0000000000000000000000000000000000000000..4ddf7ea9a5efc851de64e651d2cbeb2f1b611b37 GIT binary patch literal 152027 zcmeEuhg%cd);=90APOqd5vfWQ>4aiIIttRe^p1ociUJ3PN z+eQ==v~UUvs-aVKz&Fj6reYKnjOy;UZr#(qb&KzwzpsnCmoo*0*7FoIdUNBpiwLWv zH|jS*e0+l2Lh)K4KEc?F>S5=+Vg=MiF4H~vgWKYPpZEv!#%E=Jhyue)7oFXrpyHdY}B}PEjqfQiwIWqWeaq7qW+GXbOR~{5^uC%9pp*fKGCMYPQH9CcA;z^i_5PlWDAf&gV;VU&JXse0YRO3ZQT^KD^EEHf)DMfK}(_x<$mkNfmc!rvHl zcHwy?rvIeJ|IoLiE%nT|Pfzs4Z?Ujk$&x78JNRk1=PSefp%X z(LBFLNZaS-^^2A{LJpO%!_czZJZ2YH-rl%|$RCKxzC0~LlD2fvwC4YEK3@EEEa=?X ze!ig2jz5@;_g{*3+TUf|*UFQwI27l5eJ=jV(S?0;jZ6U%%_Y0cWO)TUFXLx#Sl@m* zAj-_%JWpz}46zP9_LCUC5FLL(?ZcJtoNTHJZ1sG6_oRbMtoZ&&Fj)>ii$7=;`8Iq0 zQ4p+;SDw|0z9U2?-e&9c!xYEO*znJM7xqIk71XX8BnsQ6w=umk`WYihJ@c4}ih^3p zL6;W8$9ifkj5BN|<(=>;zi65~YD&L**JG}*%!BzuUeZ3DHjJm?^L2wa)R|G7<~s?D zH{s$|&#bFfSLJ6=d8$Ak8|@V$o|3@AbVrxMknhUj-2e$Mm2_V@uZrWS9_ETE_KQx< zDK7)0_-{q@CSTHG5DCLaBnaxDfrGqbTspC!}%1x$zEoXFXL^%{iS{;aXcH=ZFT zmUIZ4H}9n+#lTQnN97kkUl<7Zdi2Qm!l$_I z({y!@c~8;2bh?C^v+3d|>Q%Y}4Qhqc2GNz% zET5@-9z*>|0nu!^%yrZ`=V&fpeh~Y5@rqEq;KKxkD|%n(LARR|FA0FOHTuq-31)xb++QPR2wv4t+{aN>}V_qC)ywff(2r-@&$$*8zWMN9}?ClZloF*nF zGA7J}ZYFP+tp{a4LQQ&#gFZ$+=r^h~b}ZJvm;6!u{#aR%5#jYH=hYOCJEZqQ#>67s z53&RO191b)H(zrEdbhq$Fwg^ikw>IPQSR85dI5AygLerT! zqBZlsIlQvsILZv@x}TYtshIgfHrT_qcXf#0-NK#LV-xiU3hkCX6sFg@xDX(heK*H& zN=NP1dY_Pol_#W12Gvm&>{;!;yX3a?c~M|dufJ$Ov7rC$izUk?|A5NCwBc|Ja36o4%!+pjh^&_#P`F*B+W=211bsdQ5iG8!|tonfZ?5T}*vUD*oL#w#%O}39-{oxU95@S-0Lddq(^wtdR z*E0=%!@2ySkLn7vdMDm5*AavzTu>h99+YS)>szY;d#t-iR@wPZsXmcvc8qnqO^UZV z<8ekTjw;SF&fu<1t6wXBD_v{a6>&|W1Z&l;@$bF{Wd`Zz8qeRO-!{{hz> z)UATo!LPh8dLN_N!#<&~pYj6xqR}~~!`rN8#T-Xl)>URVF+L&S6X*k8ySOI0X0leD zKH4d=@NglN`AJ+uD_6pM9$xT-3K6^uJ)WPRPMJsb7eil?E;Xf>I`TlXLLpdrXz(wo zaHDX!y?|et`~DHd5xjdD2b)LeJ+R|Q#dV8XbIGIZL&4*?!>LN}Nae6F^{2 zo~L6^b(^!ky>q7XT%5Bx|>U z+QyO&TIpZg$<}*5S&2;o$qQQ=!sRxb1!A;El3(ka=5%I<^F4i&lwKh-&mf;<)kQFz z&oj;6*&MDLtYZsTJJyLzXtcT~5t6!)ZHgGmUdyiM#|pQ|zvq8%_WfcRYkfOo+j#n( z>b2{h&>{7phdT(a6gUjt!Y_V5^@AWzA=uKVcG%XMe=U7h(RmQ2s-5m+#gim2jg+?I zJpNv-0medfZAA5}w3LH%s{QKpw3{2F5nr}Nb$>h)(3U4ptF zc8#iVW@IbXhQ8i|&nntLV(@F}cQevt*LV2~-)+6CCbHt?FK}My5sy67-H=2HRWJ^S z-Qgp#Yg7N;y0Y%m;pQLZ*Otw$o2Hh9*LrMyWVj%4fz!;`RM#xH1Z3C$dFUab0%bSk zTvB^~$qrN4{>kJ1-~s87WG4U1`@@)9Rk_z(p?&W3Ci&=_!fV)b?JTN};LlsX^j_nlg&5zz6=>x-&3fq2=WV8OaS6<&m z^U8uPL-p{(eRHt6udVb3=Mjr)qN*d%t49NK3vFGy1_{a$Sizcng5Gcj(KV(Uq1vhx zJX^QB_nu8flB@oE-LPL9!6>5GbLH)nO_bWe!jNgy$dUitg@kMR1jo??I}$c**|HH; ziBrT$PQ-uy5h-)LK(J!<2^?RTTB}cvzz8NY51c}$aL<@bHj4S}I!8#djwJ?pw^i&4XyiF=#lgOfzMY45^KKpQF*<#en8p(uD9+}FoA5oE!U8G9AI|35F8cZuSApYG6f~3^ z6tut*CGe|C$@$M?O-d08>Oa4yqM(R#r=aJp{kfCrQ$w@6A$NJ;*)Z{SiD^0!L& z+(VqbEN;7d12zNhp(=k}PUX+@|D)w!cl^th=Ks1<`i9KEUHX@%-!C`(4?F+)EnsNX(<+kxoHW(bCC^{+0Q1P@e%tT?@CkSs z`G?XI_($X)pXB2gjJVn|5(R}ih4yWY2O*TJ6Ck*lNre1bi_n2kg-FHsg?4^Ek>=ua zcQmLq)MIk_KivLmc0=dkfSc5%_maf+TYE?hYG>M`dZj~TAMh|d+trE*w5 z*+cBwfg$VJq@8VS3!zXo2h-ng6Zmnt=p#aTO6f?sgS7lWkLJ`_KJ~{GlvMwIV6PrM zPt#3(+7kXBO@E%8*_ani$hn-|_!VFOMmr&#{?D?#*|!Uqf0CSwp&# zn%O(VtH=5Ilj*vvry`*kVz+X~axt?0`%T2(jRWZ62g~P`vf6g=?zr)grDrL64g|05 z#W3U+GQ#^Cl~zh*EIoEo|9+Gg&;80d*~>|4zr;%0ZL=ZfXB*qa`tJ|=zmNXulR6bX zJU>qpR~ve`+bQX1Yu?rKf1Dbx&ZdAY|HeT;4FvG;Dpa?k(3-w8g2V?N3R4lg6FMGZ zFXvo;Gw46=^D>uwDSFg{fu;+DIG7>cj)s)5C=}1AJaK(xBlLGBY^0$A!IL=?nBivT z4o_*DT?J)@9uv?1K4JgpUreNzw!65j#-rgb@z-QmX<8*uH^aNV=l_R!E}T`6^q6J; z+o7H{XN=yAhR~f_4U-QhWs&@B86Uj(dtW(Ed+`jMRjKeeTWas*nX84~^vPo|)ZCQ^i46#VO8 z{%O5vX`nUz>D|AdX{pDL8$&5nUaeDI|h&!Oax0xQ$>N0cg zp9A@~14ceRKOTyncmKzzgn-t{jrhMeB;DDw3e1mP@Bbg8Vm+&nU=ecr?+r

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2001 From: sadchla-codes Date: Mon, 25 Mar 2024 15:25:05 -0400 Subject: [PATCH 3/8] Fixing typos and adding references --- .../2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd | 30 ++++++++++++------- 1 file changed, 20 insertions(+), 10 deletions(-) diff --git a/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd b/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd index 1b701bb8..8809da8d 100644 --- a/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd +++ b/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd @@ -21,21 +21,21 @@ long_slug <- "2024-03-18_xportr_0_4_0" -# `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/). +# `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. +[`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. +[`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. ![](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) +[`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) # 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. +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. # How does it work? @@ -69,8 +69,18 @@ metadata <- data.frame( ) ``` -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. 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. + * Allotted length for each column containing character (text) data should be set to the maximum length of the variable used across all data sets (≤ 200) + * Coerces variables to only numeric or character types + * Display format support for numeric float and date/time values + * Variables names are ≤ 8 characters. + * Variable labels are ≤ 40 characters. + * Data set labels are ≤ 40 characters. + * Presence of non-ASCII characters in Variable Names, Labels or data set labels. + +For example: ```{r, warnings = FALSE} adsl %>% xportr_metadata(metadata, "test") %>% @@ -86,11 +96,11 @@ You can also use additional functions like [`xportr_write()`](https://atorus-res - **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? +# About the other kid on the blog, (`JSON`)! Is The Future `xpt` or JSON? -`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. +`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. +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. In fact, R users can refer to [`this article`](https://www.atorusresearch.com/datasetjson-0-0-1-release/) for a summary about JSON and the R package [`datasetjson`](https://atorus-research.github.io/datasetjson/) available on [`CRAN`](https://cloud.r-project.org/web/packages/datasetjson/index.html). 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! From 00ba0823c0c71be79b0144b8232f9beb240c7072 Mon Sep 17 00:00:00 2001 From: sadchla-codes Date: Mon, 25 Mar 2024 15:46:58 -0400 Subject: [PATCH 4/8] addressing link issue --- .lycheeignore | 1 + R/CICD.R | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.lycheeignore b/.lycheeignore index 364a3dbc..d93dec0b 100644 --- a/.lycheeignore +++ b/.lycheeignore @@ -14,3 +14,4 @@ https://github.com/pharmaverse/blog/pkgs/container/blog https://stackoverflow.com/* https://stackoverflow.com/questions/12688717/round-up-from-5#comment110611119_12688836 https://appsilon.com/docker-vs-podman-vs-singularity/amp/ +file:///home/runner/work/blog/blog/posts/2024-03-01_rhino_shiny_app_validation/Appsilon.com diff --git a/R/CICD.R b/R/CICD.R index ac144b9f..f2a4cb90 100644 --- a/R/CICD.R +++ b/R/CICD.R @@ -8,7 +8,7 @@ spelling::spell_check_files(list.files(pattern = ".*.qmd$", recursive = TRUE), ) # now check those words and whether or not they are really mistakes. -# once you fixed all 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a/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd b/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd similarity index 68% rename from posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd rename to posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd index 8809da8d..679c0c86 100644 --- a/posts/2024-03-18_xportr_0_4_0/xportr_0_4_0.qmd +++ b/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd @@ -2,11 +2,11 @@ title: "xportr 0.4.0" author: - name: Sadchla Mascary -description: "Let's Talk About xportr 0.4.0, An R Package Within Pharmaverse Universe! " -date: "2024-03-18" +description: "xportr is a tool tailor-made for clinical programmers, enabling them to generate CDISC-compliant XPT files for SDTM and/or ADaM. It streamlines the preparation on xpt files, ensuring they are primed for submission to clinical data validation applications or regulatory agencies." +date: "2024-03-29" # please do not use any non-default categories. # You can find the default categories in the repository README.md -categories: [ADaMs, xportr] +categories: [ADaMs, SDTMs, xportr] # feel free to change the image image: "xportr.png" @@ -15,7 +15,7 @@ image: "xportr.png" ```{r setup, include=FALSE} -long_slug <- "2024-03-18_xportr_0_4_0" +long_slug <- "2024-03-29_xportr_0_4_0" # renv::use(lockfile = "renv.lock") ``` @@ -24,18 +24,14 @@ long_slug <- "2024-03-18_xportr_0_4_0" # `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 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 expect to develop `xpt` files that have been vetted by many checks in the backend and which are 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 following the [`Changelog here`](https://atorus-research.github.io/xportr/news/index.html) +[`xportr`](https://atorus-research.github.io/xportr/) is the result of collaboration among developers from various domains within the pharmaceutical 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. [`xportr`](https://atorus-research.github.io/xportr/) is currently on version 0.4.0 (you can keep up with all of its improvements by following the [`Changelog here`](https://atorus-research.github.io/xportr/news/index.html)) and 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. +![](xportr_design_flow.png){fig-align="center" width="800"} # 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 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. +Did I mention that [`xportr`](https://atorus-research.github.io/xportr/) is an indispensable R package for creating `xpt` datasets? This toolkit offers functions to apply metadata information to your CDISC-compliant datasets created in R, conduct strict checks, and offer valuable feedbacks about formats, format lengths, etc. In this blog post, we'll explore some high level capabilities of [`xportr`](https://atorus-research.github.io/xportr/), and its development. # How does it work? @@ -88,7 +84,30 @@ adsl %>% xportr_order() ``` -You can also use additional functions like [`xportr_write()`](https://atorus-research.github.io/xportr/reference/xportr_write.html) to generate your `xpt` file. +In addition to the functions mentioned above, users have access to other functionalities such as [`xportr_write()`](https://atorus-research.github.io/xportr/reference/xportr_write.html), which facilitates the conversion of a local data frame into an `xpt` file. For instance, if we utilize the ADSL dataframe defined earlier, we can create a variable `var_spec` using the following syntax. +```{r eval=FALSE} +var_spec <- data.frame( + dataset = "adsl", + label = "Subject-Level Analysis Dataset", + data_label = "ADSL" +) +``` + +Users can then create their ADSL `xpt` file in the following manner: +```{r eval=FALSE} +xportr_write(adsl, + path = paste0(tempdir(), "/adsl.xpt"), + domain = "adsl", + metadata = var_spec, + strict_checks = FALSE +) +``` + +[`xportr`](https://atorus-research.github.io/xportr/) also includes other important functions such as: + * [`xportr_length()`](https://atorus-research.github.io/xportr/reference/xportr_length.html) to assign the SAS length to a specified data frame. + * [`xportr_label()`](https://atorus-research.github.io/xportr/reference/xportr_length.html) to enable users to assign variable labels from metadata at the variable level to a specified data frame. + * [`xportr_format()`](https://atorus-research.github.io/xportr/reference/xportr_format.html), which allow users to apply a SAS format from a variable level metadata to a given data frame +Further details on all [`xportr`](https://atorus-research.github.io/xportr/) functions can be found [`here`](https://atorus-research.github.io/xportr/reference/index.html). # Key Features of [`xportr`](https://atorus-research.github.io/xportr/): diff --git a/posts/2024-03-18_xportr_0_4_0/xportr_design_flow.png b/posts/2024-03-29_xportr_0_4_0/xportr_design_flow.png similarity index 100% rename from posts/2024-03-18_xportr_0_4_0/xportr_design_flow.png rename to posts/2024-03-29_xportr_0_4_0/xportr_design_flow.png From 9c184f9bb001531f386e1b91f4a898b4625c7965 Mon Sep 17 00:00:00 2001 From: sadchla-codes Date: Thu, 28 Mar 2024 13:05:26 -0400 Subject: [PATCH 6/8] Fixing links issues --- posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd b/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd index 679c0c86..eb5bd9b1 100644 --- a/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd +++ b/posts/2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd @@ -65,7 +65,7 @@ metadata <- data.frame( ) ``` -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: +Users can use the [`xportr_metadata()`](https://atorus-research.github.io/xportr/reference/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. * Allotted length for each column containing character (text) data should be set to the maximum length of the variable used across all data sets (≤ 200) From cba723f98d747ff85188161ff917b9cbba5a719e Mon Sep 17 00:00:00 2001 From: Ben Straub Date: Tue, 2 Apr 2024 14:27:52 +0000 Subject: [PATCH 7/8] docs: bullet points and image shrinking (fail) --- media/xportr.png | Bin 1978 -> 51488 bytes posts/2024-03-29_xportr_0_4_0/xportr.png | Bin 114626 -> 51488 bytes .../2024-03-29_xportr_0_4_0/xportr_0_4_0.qmd | 10 +++++++--- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/media/xportr.png b/media/xportr.png index 91d483bfee8a20d18fc3f180fcd611e6f57526a5..4440ece1c2a08ee693dcf2408decf85fd866214f 100644 GIT binary patch literal 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[`xportr_length()`](https://atorus-research.github.io/xportr/reference/xportr_length.html) to assign the SAS length to a specified data frame. - * [`xportr_label()`](https://atorus-research.github.io/xportr/reference/xportr_length.html) to enable users to assign variable labels from metadata at the variable level to a specified data frame. - * [`xportr_format()`](https://atorus-research.github.io/xportr/reference/xportr_format.html), which allow users to apply a SAS format from a variable level metadata to a given data frame + +* [`xportr_length()`](https://atorus-research.github.io/xportr/reference/xportr_length.html) to assign the SAS length to a specified data frame. + +* [`xportr_label()`](https://atorus-research.github.io/xportr/reference/xportr_length.html) to enable users to assign variable labels from metadata at the variable level to a specified data frame. + +* [`xportr_format()`](https://atorus-research.github.io/xportr/reference/xportr_format.html), which allow users to apply a SAS format from a variable level metadata to a given data frame Further details on all [`xportr`](https://atorus-research.github.io/xportr/) functions can be found [`here`](https://atorus-research.github.io/xportr/reference/index.html). + # Key Features of [`xportr`](https://atorus-research.github.io/xportr/): - **Efficient Handling**: [`xportr`](https://atorus-research.github.io/xportr/) offers efficient avenues to produce `xpt` files, allowing users to manipulate clinical datasets with ease and export in `xpt` format in R. From e7367aa0766833a8bd02fa6203b8e462185592ec Mon Sep 17 00:00:00 2001 From: Ben Straub Date: Tue, 2 Apr 2024 14:32:29 +0000 Subject: [PATCH 8/8] chore: ignoring daids link check - 403 --- .lycheeignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.lycheeignore b/.lycheeignore index d93dec0b..255af40b 100644 --- a/.lycheeignore +++ b/.lycheeignore @@ -15,3 +15,4 @@ https://stackoverflow.com/* https://stackoverflow.com/questions/12688717/round-up-from-5#comment110611119_12688836 https://appsilon.com/docker-vs-podman-vs-singularity/amp/ file:///home/runner/work/blog/blog/posts/2024-03-01_rhino_shiny_app_validation/Appsilon.com +https://rsc.niaid.nih.gov/clinical-research-sites/daids-adverse-event-grading-tables