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# R Reporting Part 4: Using Markdown for Presentations

This is the fourth of a series of articles on how to use R, RStudio and TexMaker to prepare presentations and batch jobs for automated reporting on a web server or Microsoft SharePoint server. The series is based upon the presentation that I did at the February 27, 2016 Dallas R User Group Meetup. Because the presentation was primarily a demonstration, there really isn’t a presentation to distribute; this series covers the topics from the presentation/demonstration. The series will eventually include the following articles as I complete them over the next couple of weeks:

The series of articles describes the process for daily batch jobs that generate the Daily Econometric Graphs web page which includes links to the same econometric charts in several formats, all generated through the same R code:

All of the examples are based upon the knitr R package; you should reference the knitr documentation, as this article is not a replacement for the knitr documentation.

Example source is available in images/documents/econometric_source.zip.

## Using Rmd for Interactive Presentations

The first step in creating the interactive presentation is to create the .Rmd file in RStudio as shown in Figure 1. RStudio will prompt you for the output type as shown in Figure 2, where there are three choices:

• HTML (ioslides)–this type allows interactive graphics and math markup (using MathJax). The math markup is not portable to other machines unless the machine has either an Internet connection or has MathJax installed.
• HTML (Slidy)–this type allows interactive graphics and math markup (using MathJax). The math markup is not portable to other machines unless the machine has either an Internet connection or has MathJax installed.
• PDF (Beamer)–this type does not allow interactive graphics and math markup (using LaTeX). The math markup in the PDF file is portable to other machines.

You can change the output type later if you want.

### Modify the Title and Other Header Information

The first step is to modify the header information in the Markdown file to set the title, author, output type and footer. The snippet below shows the major header settings that you are likely to need for a basic presentation. The incremental setting allows you to step through the bullet points in a presentation. The use of “:” and indention in the header is syntactically important; the “:” is a line end terminator.

 --- title: "Using RStudio for Presentations and Reports" author: "Bruce Moore" date: "February 26, 2016" output: ioslides_presentation: incremental: true footer: "Bruce Moore, Moore Software Services" --- 

The Markdown Reference Guide has complete information on the options that you can include in the header.

### Call all Needed Libraries in the Prologue knitr Chunk

Next, add the "prologue" in the file where all of the required R libraries are included and options are set:

 ‘‘‘{r,prologue,message=FALSE,echo=FALSE,error=FALSE,warning=FALSE} require(ggplot2) require(dplyr) require(magrittr) require(assertr) require(ggplot2) require(fImport) require(reshape2) require(scales) require(googleVis) #library(RCurl) setwd("~/svn_work/Consulting_Business/web_site/articles/econometric_charts/src/R") options('download.file.method'='wget') read_chunk("econometric_charts_chunks.R") ‘‘‘ 

All of this should be familiar except for the lines

 options('download.file.method'='wget') read_chunk("econometric_charts_chunks.R") 

The options line changes the download method used by the Rcurl package (a dependency loaded by fImport) to one that works on my Linux machine, as the default download method does not work on my machine. The read_chunk line is the one that is important for including code into Markdown presentations and reports.

When you create an R report that will be compiled by knitr, you can either put the code inline, or create it in a separate .R file that can be called from multiple .Rhtml, .Rmd, .RPres or .Rnw files; knitr reads these chunks from the file specified in the read_chunk("filename.R") call at the beginning of your .Rhtml or .Rnw file. From a re-use perspective, the chunk method is very helpful.

“Prologue” is not a reserved word nor does knitr require this label; this is just a personal standard for all of the .Rhtml and .Rnw files that I create.

### Create a Slide and call the R Chunk to Generate the Graphic

The next step is to add the ## tag followed by the slide heading and other Markdown tags for a slide. This is followed by the knitr markup to call our code chunk from the econometric_charts_chunks.R file:

 ‘‘‘{r, costOfFunds,fig.path="images/figures/",fig.width=7, fig.height=5,fig.dpi=100,message=FALSE,warning=FALSE,echo=FALSE,cache=FALSE} ‘‘‘ 

All R code in the .Rmd file must be enclosed with the knitr tags:

 ‘‘‘{r, } 

and

 ‘‘‘ 

This is a special Markdown code delimiters that knitr looks uses to identify and run any R code that is enclosed. Most of the knitr options in this example are self explanatory, but the fig.path option requires some discussion. By default, knitr will create a figure directory in whatever the root directory is–your home directory unless you otherwise set it, as was done in the prologue section in this example.

### Writing the Knitr Chunk File econometric_charts_chunks.R With an Interactive Graphic

The last step is to add the R code to the generate the interactive graphic to the econometric_charts_chunks.R so that knitr will generate the graphic. In the .R file, the costOfFundsInteractive

 ## ---- costOfFundsInteractive ---- 

label for the chuck must match the chunk label ## ---- costOfFundsInteractive ---- in the econometric_charts_chunks.R file:

 ## ---- costOfFundsInteractive ---- # # Make the figure with swap rates # swpTs<-fredSeries(c("MSWP5","MSWP3","MSWP2","MSWP1"),nDaysBack=365*10,to=Sys.timeDate()) swpTs$dateVal = as.Date(rownames(swpTs)) swpDf <- data.frame(swpTs) swpDf$dateVal <- as.Date(swpDf\$dateVal) # ggplot's scale_x_date doesn't work with character data types colnames(swpDf)[2] <- "Series" chart <- googleVis::gvisLineChart(data=swpDf ,xvar="dateVal",yvar=c("MSWP5","MSWP2","MSWP1") ,options=list(width=800,height=600,gvis.editor="Edit",title='Swap Rates')) #plot(chart) # use this for console debugging print(chart) # use this for output in Markdown 

In the .Rmd file

## Interactive graphic ‘‘‘{r, costOfFundsInteractive,fig.path="images/figures/",message=FALSE,warning=FALSE,echo=FALSE,cache=FALSE,results='asis'} ‘‘‘

While debugging in the console, uncomment the plot command; for output in knitr use the plot statement.

 ‘‘‘{r, epilogue,echo=FALSE,message=FALSE,warning=FALSE,cache=FALSE} system('optipng("images/figures/*.png")') ‘‘‘ 
The optipng utility compresses .png files and will improve the load time for your page. If you choose to generate .jpg or other file types, you can call other utilities to compress them.
### Running knitr to Generate the HTML File
The final step is to run knitr to process the file; to do this, click on the “Knit” icon at the top of the active tab in RStudio, as shown in Figure 2: