In Pakillo/rmdTemplates: A collection of Rmarkdown templates

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^1^ Estación Biológica de Doñana (CSIC)

^2^ Universidad de Sevilla

^3^ Second Author affiliation

* Corresponding author: [e-mail]

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Write your abstract here.

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Keywords: Rmarkdown, reproducible science

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library("knitr")

### Chunk options: see http://yihui.name/knitr/options/ ###
## Text results
opts_chunk$set(echo = FALSE, warning = TRUE, message = TRUE)

## Code decoration
opts_chunk$set(tidy = TRUE, comment = NA, highlight = TRUE)

## Cache
# opts_chunk$set(cache = 2, cache.path = "output/cache/")

## Plots
opts_chunk$set(fig.path = "output/figures/")

library(knitcitations)
cleanbib()   
cite_options(citation_format = "pandoc")

INTRODUCTION

Write your introduction here.

You can cite bibliography like this [@Yan2011; @Sutherland2011] if you provide a BibTeX file with references. You can also search for references on PubMed, DataCite or Crossref, cite by DOI, or read them from your Zotero library or a shared Zotero group (see https://rstudio.github.io/visual-markdown-editing/#/citations and https://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html for more information).

You can also specify the desired output format for your bibliography (see csl field in the YAML above). Many different bibliography styles (CSL files) can be obtained at https://zotero.org/styles or https://github.com/citation-style-language/styles.

METHODS

Study Area

We worked in a beautiful place with lots of trees, like Quercus suber and Laurus nobilis.

Data collection and analysis

dataset <- read.csv("mydata.csv")

We applied a linear model where

$$ y_{i} = \alpha + \beta*x_{i} $$

model <- lm(y ~ x)

We used the statistical language R r citep(citation()) for all our analyses. These were implemented in dynamic rmarkdown documents using knitr r citep(citation("knitr")) and rmarkdown r citep(citation("rmarkdown")) packages. All the multilevel models were fitted with lme4 r citep(citation("lme4")).

RESULTS

Trees in forest A grew taller than those in forest B (mean height: r mean(25, 31, 28) versus r mean(13, 19, 16) m).

And many more cool results that get updated dynamically, e.g. see Table \@ref(tab:Table-mtcars) and Fig. \@ref(fig:scatterplot). Note Tables and Figures are cross-linked and numbered automatically. They could also appear in the middle of the document, not necessarily at the end.

See also Fig. \@ref(fig:SupplFig) and Table \@ref(tab:SupplTable) in the Supplementary Material.

DISCUSSION

Discuss.

CONCLUSIONS

Wrap up

ACKNOWLEDGEMENTS

On the shoulders of giants.

REFERENCES

write.bibtex(file = "knitcitations.bib")

::: {#refs} :::

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\clearpage

\listoftables

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kable(head(iris), caption = "A glimpse of the famous Iris dataset.", booktabs = TRUE)

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kable(mtcars[10:16, ], caption = "Now a subset of mtcars dataset.", booktabs = TRUE)

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\listoffigures

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x <- rnorm(100)
y <- jitter(x, 1000)
plot(x, y)

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a <- sort(rnorm(100))
b <- c(rep("Group Small", 35), rep("Group Big", 65))
boxplot(a ~ b)

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\clearpage

SUPPLEMENTARY MATERIAL {-}

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kable(head(iris), caption = "A supplementary table.", booktabs = TRUE)

\clearpage

x <- rnorm(100)
y <- jitter(x, 1000)
plot(x, y)

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# set eval = FALSE if you don't want this info (useful for reproducibility) to appear 
sessionInfo()
# sessioninfo::session_info()

Pakillo/rmdTemplates documentation built on Sept. 16, 2022, 3:04 p.m.