You have been warned. That is a bummer for me because the original PowerPoint slides I am trying to replicate using R Markdown have multiple images and tables on them. Making it perfect is impossible. Above, we created images by specifying the exact number of pixels. You might see something like the following. The first plot I’m going to make is a bar chart of life expectancy by country for the most recent year of data, 2007. Around this time, I was reading tweets about new development on the patchwork package for arranging ggplot objects, and thought that instead of including multiple images on one slide, I could just create a multi-image layout in R and then render that layout to the PowerPoint slide. In the end, you may have very little to do to make the document look great the first time you knit it! To do this, I take the elements created above and describe their arrangement using the fun patchwork syntax. There are some things that I run into fairly frequently (and some not so much) when I’m rendering my rmarkdown documents. RStudio Connect takes advantage of this metadata, allowing output files, custom email subjects, and additional email attachments. If you start using R Markdown a lot, and there is a good chance of that, once you get some settings you use often, you’ll not want to start from scratch, but simply reuse them. Then I can do crossprod and the text of the function name, or any text of class func, will have the appropriate color and weight. In this case, you can set the size of the image using the width and/or height attributes, e.g., First, create a new R Markdown document and specify powerpoint_presentation as the output format in the YAML header: If you want to use a PowerPoint template, you can specify a reference document in the header. In this post, I show how to combine plots and tables using patchwork to create multi-image PowerPoint slides using R Markdown. Alright, I’ve got the three graphical elements I want to combine into one layout on a single slide. Is markdown, as we discussed in the earlier section, It provides a simple way to mark up text - bullet list - bullet list - bullet list 1. numbered list 2. numbered list 3. numbered list __bold__, **bold**, _italic_, *italic* > quote of something profound ```r # computer code goes in … The default layout if layout is not specified is l-body, which will cause content to span the width of the main article body: [ alt text here ] ( path-to-image-here) However, when you knit the report, R will only be able to find your image if you have placed it in the right place - RELATIVE to your .Rmd file. Over time, these files and settings will grow, especially as you learn new options and want to tweak old. There a lot more available too, as YAML is a programming syntax all its own, so how deep you want to get into it is up to you. One limitation they note is: Images and tables will always be placed on new slides. If we want to change the color from the default setting for all links, we go into our CSS file. Images, in particular, are a powerful means of communication in a report, whether they be data visualizations, diagrams, or pictures. If your code ran properly, the plot output should look like the image above. This sentence is tyrian purple, bold, and has bigger font because I put before it and after it. 5.1 Font color. For some of you, if you aren’t careful, you’ll spend an afternoon on an already finished document trying to make it look perfect. Figure sizes are specified in inches and can be included as a global option of the document output format. Then, I’ll add a title and caption to finish off the layout. R Markdown provides an useful framework for including images and figures in reproducible reports. Of course, it is possible to just use markdown for that: ! The rmarkdown package allows report authors to emit additional output metadata from their report. Then you have to add the path to the image in brackets. All browsers have this, making it easy to see exactly what’s going on with any webpage. 2. 5.1.1 Using an R function to write raw HTML or LaTeX code; 5.1.2 Using a Pandoc Lua filter (*) 5.2 Indent text; 5.3 Control the width of text output; 5.4 Control the size of plots/images; 5.5 Figure alignment; 5.6 Verbatim code chunks. I haven’t yet figured out the utility in having figures and output breaking up the flow of code and text along with everything else going on, especially since they’ll be precisely where they need to be in the final product. Here’s anotherexample. Key considerations include: User-generated images and R-generated figures are handled differently. In CSS, tags like a, table, and img, have no marker, classes are denoted with .classname, and IDs are note with #idname. A plot: ```{r} hist(co2) ``` A report. Your R markdown syntax seems to be correct, and it will render correctly in RMD file in R Studio, should you put it there. For example, the following code chunk computes a data summary and renders a plot as a PNG image: Writing reports in R Markdown allows you to skip painful and error-prone copy-paste in favor of dynamically-generated reports written in R and markdown that are easily reproducible and updateable. Within an R Markdown file, R Code Chunks can be embedded with the native Markdown syntax for fenced code regions. If you have headings in your current document, go ahead and turn on table of contents. • These chunk options (out.width, fig.cap, fig.pos, fig.align) work with plots as well. Turn your analyses into high quality documents, reports, presentations and dashboards with R Markdown. An alternative to using PowerPoint would be xaringan and doing some fancy layout using CSS, but I’m not very good at CSS and we need the slides in PowerPoint format because other folks at the office add additional slides to the presentations I generate. Here’s a way, well actually a number of ways, some good, some … not. CSS, like HTML, has a fairly simple syntax, but is very flexible and can do a ton of stuff you wouldn’t think of. Here is an example of Chrome Developer Tools, which you can access through its menus. There is a lot of other stuff too. Your `R Markdown` file will have more code in it. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. This layout is controlled by a set of layout classes, which are in turn applied to R Markdown chunks using the layout chunk option.. [caption](path/to/image) . The g argument is whatever you want to plot – for instance, the object returned from a ggplot()call. So be familiar with your options. In the pop up window, go to the last slide (5), The title is "Slide with Plot", but the image is missing (I can only see a blue question mark icon,
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