#R MARKDOWN CHUNK OPTIONS CODE#

Knitr does the challenging work of processing other computing systems and interfaces to create a timeless, easy to read and interpret, output.

#R MARKDOWN CHUNK OPTIONS CODE#

The code chunks take the place of scripts in that they hold. Each line of code can be directly linked to the original source so that adjustments to content can be made easily and transparently. Well start with code chunks, since these are the distinguishing feature of R Markdown documents. and place all of these elements in one file.īy using code in Knitr, pdfs, Word documents, slide presentations, web pages and other document formats that are used in collaborative science, different pieces of information can be organized in one file. Knitr makes this process much easier by allowing the user to input text directly, access old text from outdated software, access data, graphs, html etc. It can be tedious, difficult and riddled with mistakes to attempt to locate and organize these elements one by one. Generally, the process of organizing these materials require accessing several different computing systems (software, graphs, animation etc.) and text. When research is reproduced, it is important that all notes, graphs, methods and all other details are clearly listed to be performed accurately.

There are several other code chunk options that allow you to manipulate graphing data, add images, create animation, code in other languages and many more. To name a code chunk, the name must be placed in the initial line of the code chunk.Īn unnamed code chunk looks like this, ``` ``` You have probably seen the odd chunk I often include in the top of my lecture documents, which has something like knitr::optschunk. Each code chunk must have a unique name (if it is named). It is of note that although code chunks do not need to be named, it may be useful to name them so that they are easy to locate if an error occurs. See chapter 5 for more on this.īeetle <- read.table( '', header = TRUE) #Load data plot(Beetle $taurus_pron, Beetle $taurus_horn, xlab = 'Pronotum width (mm)', ylab = 'Horn length (mm)', type = 'n') #Plot axes pred <- predict( loess(taurus_horn ~ taurus_pron, span = 0.5, data = Beetle), se = TRUE) #Generate predicted vales from loess smoother LCI <- pred $fit - 2 * pred $s #Lower confidence interval (approx) UCI <- pred $fit + 2 * pred $s #Upper confidence interval (approx) Xvec <- c(Beetle $taurus_pron, rev(Beetle $taurus_pron)) #'Wraparound' X values for drawing CI band Yvec <- c(LCI, rev(UCI)) #'Wraparound' Y values for drawing CI band polygon(Xvec, Yvec, col = 'lightblue', border = NA) #Draw the CI band lines(Beetle $taurus_pron, pred $fit, col = 'darkblue') #Draw the fitted loess smoother points(Beetle $taurus_pron, Beetle $taurus_horn) #Add the actual data pointsįigure: horn allometry in Onthophagus taurus showing a loess non-parametric smoother and approximate 95% confidence intervals.Knitr works directly with R Markdown by executing all the code chunks in the file.

Just to make things more exciting, if you’re using rmarkdown there are some options to do with graphics 4 which can also be set in the front matter of your document. Knitr::opt_chunk$set(comment=FALSE, tidy=TRUE)ĭon’t forget that double colon after knitr. 2.2 What is Pandoc and where is the installer?.1.4 Markup, markdown and the WYSIWYM philosophy.1 Introductory R Markdown: a beginner’s guide to creating reproducible documents, reports and presentations.

R markdown chunk options