![]() ![]() What pandoc has done is parse your markdown file and mapped the elements of each type (heading 1, heading 2, list etc.) to corresponding element classes in the HTML document. Restyling the HTML output ( for those unfamiliar with CSS) ¶ If you aren't familiar with CSS don't worry you can still tweak, that's what this next section is for. Props to the guy who originally wrote this CSS sheet (I've modified it only slightly), but if you're like me then you are going to immediately want to tweak some things. You can inspect the resulting HTML file in your browser. The -o switch specifies the output file name, the -c switch specifies the css file to use for html creation and the input to the command, resume.md, is at the very end. tex template, but it's more efficient to just borrow the styling from the HTML. The pdf could alternatively be generated from the markdown using pandoc with a LaTex engine and a. Pandoc: the swiss army knife of interconverting file formats, and wkhtmtopdf the precision scalpel (?) of printing HTML to PDFs. Still trying to decide if this is the workflow for you? Here are the original markdown files and the three output files for your persual: css sheet and one reference Word doc, and even if you aren't familiar with such things I can give you some basic steps for re-styling. Styling of the outputs requires maintaining one. docx output formats is automated with a few lines of code. This post outlines the simplest possible workflow where resume content is maintained in a simple markdown file and generating. ¶Ĭhanging the content or styling of a resume or CV is a relatively common event that can be frustrating andtime-consuming. 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.In which I cast off the shackles of messy LaTex templates and embrace slightly less messy CSS templates. 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. 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. ![]() 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. 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. The de facto tools for communication in the enterprise are still Microsoft Word, PowerPoint, and Excel. ![]()
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