papaja is a R-package in the making including a R Markdown template that can be used with (or without) RStudio to produce documents, which conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). The package uses the LaTeX document class apa6 and a .docx-reference file, so you can create PDF documents, or Word documents if you have to. Moreover, papaja supplies R-functions that facilitate reporting results of your analyses in accordance with APA guidelines.

Note, at this point papaja is in active development and should be considered alpha. If you experience any problems, please open an issue on Github.

Take a look at the .Rmd of the example manuscript in the folder example and the resulting .pdf. The example document also contains some basic instructions.

To use papaja you need to make sure the following software is installed on your computer:

• R (2.11.1 or later)
• RStudio (0.98.932 or later) is optional; if you don't use RStudio, you need to install pandoc using the instructions for your operating system
• A TeX distribution (2013 or later; e.g., MikTeX for Windows, MacTeX for Mac, obviously, or TeX Live for Linux).
  • Windows users should use MikTex if possible. Currently, pandoc and the Windows version of Tex Live don't seem to like each other. Make sure you install the complete---not the basic---version.
  • Ubuntu 14.04 users need a few additional TeX packages for the document class apa6 to work:

sudo apt-get install texlive texlive-publishers texlive-fonts-extra texlive-latex-extra texlive-humanities lmodern

Once all that is taken care of, install papaja from GitHub:

devtools::install_github("crsh/papaja")

Once papaja is installed, you can select the APA template when creating a new Markdown file through the RStudio menus.

If you want to add citations specify your BibTeX-file in the YAML front matter of the document (bibliography: my.bib) and you can start citing. If necessary, have a look at R Markdown's overview of the citation syntax. You may also be interested in citr, an R Studio addin to swiftly insert Markdown citations.

The functions apa_print() and apa_table() facilitate reporting results of your analyses. Take a look at the .Rmd of the example manuscript in the folder example and the resulting .pdf.

Drop a supported analysis result, such as an htest- or lm-object, into apa_print() and receive a list of possible character strings that you can use to report the results of your analysis.

my_lm <- lm(Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length, data = iris)
apa_lm <- apa_print(my_lm)

One element of this list is apa_lm$table that, in the case of an lm-object, will contain a complete regression table. Pass apa_lm$table to apa_table() to turn it into a proper table in your PDF or Word document (remember to set the chunk option results = "asis").

apa_table(apa_lm$table, caption = "Iris regression table.")

Table. Iris regression table.

papaja currently provides methods for the following object classes:

Be sure to also check out apa_barplot(), apa_lineplot(), and apa_beeplot() (or the general function apa_factorial_plot()) if you work with factorial designs:

apa_factorial_plot(
  data = npk
  , id = "block"
  , dv = "yield"
  , factors = c("N", "P", "K")
  , ylim = c(0, 80)
  , level = .34
  , las = 1
  , ylab = "Yield"
  , plot = c("swarms", "lines", "error_bars", "points")
)

If you prefer creating your plots with ggplot2 try theme_apa().

Don't use RStudio? No problem. Use the rmarkdown::render function to create articles:

# Create new R Markdown file
rmarkdown::draft(
  "mymanuscript.Rmd"
  , "apa6"
  , package = "papaja"
  , create_dir = FALSE
  , edit = FALSE
)

# Render manuscript
rmarkdown::render("mymanuscript.Rmd")

Like papaja and want to contribute? Take a look at the open issues if you need inspiration. Other than that, there are many output objects from analysis methods that we would like apa_print() to support. Any new S3-methods for this function are always appreciated (e.g., factanal, fa, lavaan, lmer, or glmer).

Although papaja is not yet on CRAN and is still undergoing a lot of changes, there are peer-reviewed publications that use it. If you have published a paper that was written with papaja, let me know and I will add it to this list.

Stahl, C., Barth, M., & Haider, H. (2015). Distorted estimates of implicit and explicit learning in applications of the process-dissociation procedure to the SRT task. Consciousness & Cognition, 37, 27–43. doi: 10.1016/j.concog.2015.08.003

Aust, F., & Edwards, J. D. (2016). Incremental validity of Useful Field of View subtests for the prediction of Instrumental Activities of Daily Living. Journal of Clinical and Experimental Neuropsychology, 38, 497-515. doi: 10.1080/13803395.2015.1125453

Stahl, C., Haaf, J., & Corneille, O. (2016). Subliminal Evaluative Conditioning? Above-Chance CS Identification May Be Necessary and Insufficient for Attitude Learning. Journal of Experimental Psychology: General, 145 (9), 1107-1131. doi: 10.1037/xge0000191

Stahl, C. & Heycke, T. (2016). Evaluative Conditioning with Simultaneous and Sequential Pairings Under Incidental and Intentional Learning Conditions. Social Cognition, 34, 382-412. doi: 10.1521/soco.2016.34.5.382

Papenberg, M., Willing, S. & Musch, J. (2017). Sequentially presented response options prevent the use of testwiseness cues in multiple-choice testing. Psychological Test and Assessment Modeling, 59, 245-266.

Heycke, T., Aust, F., & Stahl, C. (2017). Subliminal influence on preferences? A test of evaluative conditioning for brief visual conditioned stimuli using auditory unconditioned stimuli. Royal Society Open Science, 4, 160935. doi: 10.1098/rsos.160935 (Data & R Markdown files)

McHugh, C., McGann, M., Igou, E. R., & Kinsella, E. L. (2017). Searching for Moral Dumbfounding: Identifying Measurable Indicators of Moral Dumbfounding. Collabra: Psychology, 3(1), 23. doi: 10.1525/collabra.79 (Data & R Markdown files)

Haaf, J. M., & Rouder, J. N. (2017). Developing constraint in Bayesian mixed models. Psychological Methods, 22(4), 779-798. doi: 10.1037/met0000156 (R Markdown files)

Rouder, J. N., Haaf, J. M., & Aust, F. (2018). From theories to models to predictions: A Bayesian model comparison approach. Communication Monographs, 85(1), 41-56. doi: 10.1080/03637751.2017.1394581

Heycke, T., Gehrmann, S., Haaf, J. M., & Stahl, C. (2018). Of two minds or one? A registered replication of Rydell et al. (2006). Cognition and Emotion, 0(0), 1–20. doi: 10.1080/02699931.2018.1429389 (Data & R Markdown files)

Sauer, S. (in press). Observation oriented modeling revised from a statistical point of view. Behavior Research Methods. doi: 10.3758/s13428-017-0949-8 (Data & R Markdown files)

Aust, F., Haaf, J. M., & Stahl, C. (in press). A memory-based judgment account of expectancy-liking dissociations in evaluative conditioning. Journal of Experimental Psychology: Learning, Memory, and Cognition. (Data & R Markdown files)

Stahl, C., Henze, L., & Aust, F. (2016). False memory for perceptually similar but conceptually distinct line drawings. PsyArXiv. doi: 10.17605/OSF.IO/ZR7M8 (Data & R Markdown files)

Urry, H. L., Sifre, E., Song, J., Steinberg, H., Bornstein, M., Kim, J., … Andrews, M. (2017, March 13). Replication of Eskine, K. J., Kacinik, N. A., & Prinz, J. J. (2011) at Tufts University - Spring, 2017. Preprint retrieved from https://osf.io/fu384/ (Data & R Markdown files)

Buchanan, E. M, & Scofield, J. E. (2017, August 25). Bulletproof Bias? Considering the Type of Data in Common Proportion of Variance Effect Sizes. Preprint retrieved from https://osf.io/cs4vy/ (Data & R Markdown files)

Heycke, T., & Stahl, C. (2018). No Evaluative Conditioning Effects with Briefly Presented Stimuli. PsyArXiv. doi: 10.17605/OSF.IO/UJQ4G (Data & R Markdown files)

Barth, M., Stahl, C., & Haider, H. (2018). Assumptions of the process-dissociation procedure are violated in sequence learning. PsyArXiv. doi: 10.17605/OSF.IO/P7UXN (Data & R Markdown files)

By now, there are a couple of R packages that provide convenience functions to facilitate the reporting of statistics in accordance with APA guidelines.

• apa: Format output of statistical tests in R according to APA guidelines
• APAstats: R functions for formatting results in APA style and other stuff
• apaTables: Create American Psychological Association (APA) Style Tables
• pubprint: This package takes the output of several statistical tests, collects the characteristic values and transforms it in a publish-friendly pattern
• schoRsch: Tools for Analyzing Factorial Experiments
• sigr: Concise formatting of significances in R

Obviously, not all journals require manuscripts and articles to be prepared according to APA guidelines. If you are looking for other journal article templates, the following list of rmarkdown/pandoc packages and templates may be helpful.

• rticles: LaTeX Journal Article Templates for R Markdown
• Michael Sachs' pandoc journal templates: Pandoc templates for the major statistics and biostatistics journals

If you know of other packages and templates, drop us a note, so we can add them here.