![]() In this way, slideshows in R are like Ogres they have layers. There’s a lot more to a slideshow than people think. Take note that much of what will occur in this chapter focuses on how to make a slideshow from R Markdown, and the implication is that you will use this process to insert plots, figures, and tables into a slideshow as well as occasionally using R Markdown to make references to values as inline code.Ī slide show created with R Markdown results from the integration of multiple programming and markup languages, and the introduction of several new terms can be confusing at first. You probably won’t actually do your data analysis inside a presentation Rmd file, but once you are comfortable with the process you will be able to convert csv files to charts and tables much faster and more flexibly than with a Powerpoint presentation. 14.3.8 Inserting plots into your slideshowīy now you have seen that R Markdown is a simple step in the quest for reproducible research and literate programming so in the words of Ron Popeil: but wait, there’s more! You can also use R Markdown to create HTML5 slide shows analogous to those created in Microsoft Powerpoint or LibreOffice Impress (don’t tell me this is the first time you’ve heard of it?).14.3.7 Specifying absolute location on the slide for text or images.14.3.6 Bootstrap widget for cycling images on a slide.14.3.4 Adding a slide background color or image.14.3.1 A quick overview of HTML syntax, and where to store your customizations.13.8.5 Adding Towns from Shapefile Attributed Table.13.8.3 Adding New Columns To A Data Frame. ![]() 13.8 Creating the Tauntaun Harvest Data CSV File.12.6.5 Documenting the TTestimators-package.12.6.4 Documenting the TauntaunHarvest Dataset.12.2 Load Required Packages and Programs.10.7 Controlling the Output and Metadata.10.5.4 Paragraph 4: Analysis of Age and Sex.10.5.2 Paragraph 2: Tauntaun Data Summary.10.4 Weaving Markdown with R (Section 3).10.3.9 Creating a Bibliography with Endnote.10.3.6 Paragraph 4: Analysis of Harvested Animals by Tauntaun Age and Sex.10.3.5 Paragraph 3: Hunter Demographics.10.3 Tauntaun Annual Report Outline and R Objects Needed (Section 2).10.1 HTML Markup vs. Markdown vs. R Markdown.7.8 Summarizing the Harvest with Aggregate.6.10 Read in the Hunter CSV file, and Save as hunter_clean.RData.6.9 Save the Cleaned Data as harvest_clean.RData.6.8 Stepping through Rows and Columns with Apply Functions.6.7.1 color = fur color of the reptomammalian Tauntaun.6.7 A Brief Interruption to Discuss NA and NULL.6.6.9 weight = weight of harvested animal (arbitrary units).6.6.8 length = length of harvested animal.6.6.7 town = town in which the animal was harvested.6.6.4 individual = the unique identifier of each harvested animal.6.6.3 sex = the sex of the harvested animal.6.6.2 age = the age of the harvested animal.6.6.1 hunter.id = the unique hunter identification number.3.4.7 Function Names from Different Packages.2.3 The Files, Plots, Package, Help Pane.
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