# Knitr and R Markdown

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I’m late to the game, but have recently begun using R Markdown. I was motivated because my employer now has an open data/open code requirement for all data we generate.  My specific problem was that I am often using R code, but need to document what I am doing so that I may share my code. Hence, R Markdown was a perfect solution for me. As an added bonus, I have also switched over my R teaching materials to R Markdown and am now using Markdown to develop an online course on mixed-effect models with R.

Previously, I used sweave. Although powerful, sweave offers similar functionality to RMarkdown, but requires the file to be complied multiple times. Thus, sweave offers me no benefit compared to RMarkdown.

I usuallyRStudio as my editor and loving how it works. RStudio is easy to use and R Markdown is well documented. I was able to learn the program easily and get up to speed because of 3 factors. First, I previously used sweave. Second, I am familiar with Markdown from StackOverflow. Third, I am good with R. My only regret is that I did not start using it earlier.

As for time, learning R Markdown only required a couple of hours on Monday afternoon and I am now fully up to speed. The tutorials built into RStudio were fabulous! In summary, I would recommend RMarkdown for everybody wanting to create documents with R Code embedded within them. !

# tikz in LaTeX and Structural Equation Modeling

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During grad school, I attended an ESA Workshop on Structural Equation Modeling (SEM) let by Jim Grace. The approach allows for multivariate analysis with multiple predictors, multiple response variables, and latent variables. Up until now, my research never required using the method and I never bought the software he recommended at the time because the GUI program recommended by Grace was too expensive for my limited needs.

Recently, I had a need to use SEM at work. We had two response variables: environmental DNA (eDNA) and the ash-free dry weight of an aquatic organism (AFDW). Both were predicted by multiple environmental variables and AFDW predicted eDNA. A perfect problem for SEM.

To refresh myself of SEM, I revisited Grace’s work. I discovered that he maintains an excellent tutorial about SEM. The pages provide a nice introduction, as does his (slightly outdated) book, his classic book, and a recent Ecoshephere article.

However, I did not have a nice way to plot my results. I did not want to use a WYSIWYG tool like Inkscape or Power Point. But I remembered the tikz package in LaTeX. Here’s the figure I created:

Example SEM plot.

I created the figure using this LaTeX code:

 \documentclass{article}

 \usepackage[paperheight =11.3cm, paperwidth =9.5cm, margin = 0.1cm]{geometry} \usepackage{tikz} \usetikzlibrary{arrows} \usetikzlibrary{positioning} \begin{document} \pagenumbering{gobble} \begin{tikzpicture}[ -> , >=stealth',auto,node distance=3.5cm, thick,main node/.style={rectangle,draw, font=\sffamily}] \node[main node] (1) {Lake}; \node[main node] (2) [below of=1] {Depth}; \node[main node] (3) [below of=2] {Non-habitat}; \node[main node] (4) [below of=3] {Habitat}; \node[main node] (6) [below right of=2, align = center] {AFDW\\ $$r^2 = 0.223$$}; \node[main node] (7) [right of=6, align = center] {eDNA\\ $$r^2 = 0.384$$}; \path[every node/.style={font=\sffamily\small}] (1) edge node [above = 40pt] {\textbf{0.497}} (6) (2) edge node [left = 10pt] {\textbf{-0.370}} (6) (3) edge node [above] {0.094} (6) (4) edge node [left = 10pt] {0.116} (6) (1) edge[bend left] node [above = 10 pt] {\textbf{0.385}} (7) (2) edge[bend left] node [above = 5pt ] {0.197} (7) (3) edge[bend right] node [above = 0pt] {-0.298} (7) (4) edge[bend right] node [below = 5pt] {0.204} (7) (6) edge node [ ] {-0.180} (7); \end{tikzpicture} 

\end{document}