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1. #RMARKDOWN RESUME UPDATE#
2. #RMARKDOWN RESUME FULL#
3. #RMARKDOWN RESUME PORTABLE#
4. #RMARKDOWN RESUME CODE#
5. #RMARKDOWN RESUME DOWNLOAD#

It also produces a convenient, aesthetically pleasing, lightweight document that’s easily shareable, and easily updatable over time.

#RMARKDOWN RESUME DOWNLOAD#

If you would like to see how I incorporated these techniques or take my resume project for a spin yourself, you can download the repo at my personal page.įor anyone with a similar background in R, programming, or data science, building your resume in your preferred language further shows your capabilities in it. You can find the new, R-ified resume here. That was when I happened on a Tweet from was very impressed with the easy to follow, accessible repo he made and highly recommend checking out his post and page over at .īy making use of rmarkdown and pagedown there is already a pretty nicely packaged template for resume development but the techniques incorporated by Strayer allow for greater customization and clear delineation of CSS styling manipulation (for those of us who may not have a strong CSS background). Prompted with the incentive for change, I wanted to make my resume a representation of the last few years of coding development, R capabilities, and creativity. My old resume was something I was pretty proud of, having mastered the real estate in a Word document and feeling like I added just the right amount of color to make it stand out from the rest. I admittedly had turned any attention to my resume since taking this position more than two years ago and so decided that if I was going to address my antiquated, out of date resume I might as well bring it into this shiny new era of Rstats programming I had at my disposal. These Shiny documents are created with the simplicity of R markdown, but have the same hosting requirements of a Shiny app and are not portable.At my job I was recently asked to submit an up to date resume for record keeping and for a biopic in an upcoming grant submission. You can include Shiny elements in an R Markdown document, which enables you create a report that responds interactively to user inputs. Many Shiny applications today would be better suited as parameterized R Markdown documents.įinally, Shiny and R Markdown are not mutually exclusive. If you host a document on RStudio Connect, then users can select inputs and generate new versions on demand. Additionally, adding parameters to your document makes it easy to generate multiple versions of that document.

#RMARKDOWN RESUME FULL#

It is a feature that would benefit a wide range of use cases, especially where the full power of Shiny is not required. This process is easy and powerful, yet remains underutilized by most R users. If you need to accept user input, but you don’t require the reactive framework of Shiny, you can add parameters to your R Markdown code. I use Shiny when I need an interactive user experience, but for everything else, I use R Markdown. Shiny is great – even “magical” – when you want your end users to have an interactive experience, but R Markdown documents are often simpler to program, easier to maintain, and can reach a wider audience.

#RMARKDOWN RESUME PORTABLE#

Have multiple output types such as HTML, Word, PDF, and many more.Īre not portable (i.e., users must visit the app).Īre files that can be sent via email or otherwise shared. Have an interactive and responsive user experience.Īre snapshots in time, rendered in batch. Knowing when to use Shiny and when to use R Markdown will increase your ability to influence decision makers. In previous posts, we discussed Dashboards with Shiny and Dashboards with R Markdown. They both depend on R, generate high-quality output, and can be designed to accept user inputs. Shiny and R Markdown are both used to communicate results.

#RMARKDOWN RESUME CODE#

So, even if your client insists on having Microsoft documents, by generating them with R Markdown, you can spend more time working on your code and less time maintaining reports. Moreover, R Markdown documents can be rendered in Word, PowerPoint, and many other output formats. Therefore, your documents should also be based on code! You can accomplish this with R Markdown, which produces documents that are generated by code, reproducible, and easy to maintain. In data science, your code - not your report or presentation - is the source of your results. They can be time-consuming to create and difficult to maintain.They are separate from the code you used to create your analysis.Although Microsoft Office documents are easy to share, they can be cumbersome for data scientists to write because they cannot be written with code. These tools, born in the 80’s and rising to prominence in the 90’s, are used everywhere for sharing reports, presentations, and dashboards.

#RMARKDOWN RESUME UPDATE#

The de facto tools for communication in the enterprise are still Microsoft Word, PowerPoint, and Excel. The vitae package leverages the dynamic nature of R Markdown to quickly produce and update CV entries from a variety of data sources.