papaja (Preparing APA Journal Articles) is an R package that provides document formats and helper functions to produce complete APA manscripts from RMarkdown-files (PDF and Word documents).
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papaja: Prepare APA journal articles with R Markdown

Project Status: WIP - Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. Build status

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 enable papaja's full set of features you need either an up-to-date version of RStudio or pandoc and a TeX distribution (e.g., MikTeX for Windows, MacTeX for Mac, or TeX Live for Linux).

Please refer to the papaja manual for detailed installation instructions.

papaja is not yet available on CRAN but you can install it from this repository:

# Install devtools package if necessary
if(!"devtools" %in% rownames(installed.packages())) install.packages("devtools")

# Install the stable development verions from GitHub

# Install the latest development snapshot from GitHub

How to use papaja

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

APA template selection

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.

Helper functions to report analyses

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.

Predictor b 95% CI t(146) p
Intercept 1.04 [0.51, 1.58] 3.85 < .001
Sepal Length 0.61 [0.48, 0.73] 9.77 < .001
Petal Width 0.56 [0.32, 0.80] 4.55 < .001
Petal Length -0.59 [-0.71, -0.46] -9.43 < .001

papaja currently provides methods for the following object classes:

afex_aov BFBayesFactorList list summary_emm
anova BFBayesFactorTop lm summary.glht
Anova.mlm emmGrid lsmobj summary.glm
aov glht summary.Anova.mlm summary.lm
aovlist glm summary.aov summary.ref.grid
BFBayesFactor htest summary.aovlist

Plot functions

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:

  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().

Using papaja without RStudio

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

# Create new R Markdown file
  , "apa6"
  , package = "papaja"
  , create_dir = FALSE
  , edit = FALSE

# Render manuscript


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/S4-methods for this function are always appreciated (e.g., factanal, fa, lavaan, lmer, or glmer).

Papers written with papaja

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, you can add the reference to the public Zotero group yourself or send it to me.

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(5), 497–515.

Aust, F., Haaf, J. M., & Stahl, C. (2018). A memory-based judgment account of expectancy-liking dissociations in evaluative conditioning. Journal of Experimental Psychology: Learning, Memory, and Cognition. (R Markdown and data files:

Barth, M., Stahl, C., & Haider, H. (2018). Assumptions of the process-dissociation procedure are violated in implicit sequence learning. Journal of Experimental Psychology: Learning, Memory, and Cognition. (R Markdown and data files:

Beaton, D., Sunderland, K. M., Levine, B., Mandzia, J., Masellis, M., Swartz, R. H., … Strother, S. C. (2018). Generalization of the minimum covariance determinant algorithm for categorical and mixed data types. bioRxiv.

Bergmann, C., Tsuji, S., Piccinini, P. E., Lewis, M. L., Braginsky, M., Frank, M. C., & Cristia, A. (2018). Promoting Replicability in Developmental Research Through Meta-analyses: Insights From Language Acquisition Research. Child Development. (R Markdown and data files:

Buchanan, E. M., & Scofield, J. E. (2018). Methods to detect low quality data and its implication for psychological research. Behavior Research Methods. (R Markdown and data files:

Buchanan, E., & Scofield, J. (2018). Bulletproof Bias? Considering the Type of Data in Common Proportion of Variance Effect Sizes. PsyArXiv. (R Markdown and data files:

Buchanan, E., & Valentine, K. (2018). An Extension of the QWERTY Effect: Not Just the Right Hand, Expertise and Typability Predict Valence Ratings of Words. PsyArXiv. (R Markdown and data files:

Buchanan, E., Foreman, R., Johnson, B., Pavlacic, J., Swadley, R., & Schulenberg, S. (2018). Does the Delivery Matter? Examining Randomization at the Item Level. PsyArXiv. (R Markdown and data files:

Buchanan, E., Johnson, B., Miller, A., Stockburger, D., & Beauchamp, M. (2018). Perceived Grading and Student Evaluation of Instruction. PsyArXiv. (R Markdown and data files:

Buchanan, E., Scofield, J., & Nunley, N. (2018). The N400’s 3 As: Association, Automaticity, Attenuation (and Some Semantics Too). PsyArXiv. (R Markdown and data files:

Buchanan, E., Valentine, K., & Maxwell, N. (2018a). English Semantic Feature Production Norms: An Extended Database of 4,436 Concepts. PsyArXiv. (R Markdown and data files:

Buchanan, E., Valentine, K., & Maxwell, N. (2018b). The LAB: Linguistic Annotated Bibliography. PsyArXiv. (R Markdown and data files:

Craddock, M., Klepousniotou, E., El-Deredy, W., Poliakoff, E., & Lloyd, D. M. (2018). Transcranial alternating current stimulation at 10 Hz modulates response bias in the Somatic Signal Detection Task. bioRxiv.

Derringer, J. (2018). A simple correction for non-independent tests. PsyArXiv. (R Markdown and data files:

Faulkenberry, T. J., Cruise, A., & Shaki, S. (2018). Task instructions modulate unit–decade binding in two-digit number representation. Psychological Research. (R Markdown and data files:

Haaf, J. M., & Rouder, J. N. (2017). Developing constraint in bayesian mixed models. Psychological Methods, 22(4), 779–798. (R Markdown and data files:

Hardwicke, T., & Ioannidis. (2018). Mapping the Universe of Registered Reports. PsyArXiv. (R Markdown and data files:

Hardwicke, T., Mathur, M., MacDonald, K., Nilsonne, G., Banks, G., Kidwell, M., … Frank, M. (2018). Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal Cognition. PsyArXiv. (R Markdown and data files:

Harms, C., & Lakens, D. (2018). Making ’Null Effects’ Informative: Statistical Techniques and Inferential Frameworks. PsyArXiv. (R Markdown and data files:

Heino, M. T. J., Vuorre, M., & Hankonen, N. (2018). Bayesian evaluation of behavior change interventions: A brief introduction and a practical example. Health Psychology and Behavioral Medicine, 6(1), 49–78. (R Markdown and data files:\#.wvy3h4jovgm)

Heycke, T., & Stahl, C. (2018). No Evaluative Conditioning Effects with Briefly Presented Stimuli. PsyArXiv. (R Markdown and data files:

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(9), 160935.

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. (R Markdown and data files:

Heyman, T., & Heyman, G. (2018). Can prediction-based distributional semantic models predict typicality? PsyArXiv. (R Markdown and data files:

Jordan, K., Buchanan, E., & Padfield, W. (2018). Focus on the Target: The Role of Attentional Focus in Decisions about War. PsyArXiv. (R Markdown and data files:

Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence Testing for Psychological Research: A Tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. (R Markdown and data files:

Maxwell, N., & Buchanan, E. (2018a). Investigating the Interaction between Associative, Semantic, and Thematic Database Norms for Memory Judgments and Retrieval. PsyArXiv. (R Markdown and data files:

Maxwell, N., & Buchanan, E. (2018b). Modeling Memory: Exploring the Relationship Between Word Overlap and Single Word Norms when Predicting Relatedness Judgments and Retrieval. PsyArXiv. (R Markdown and data 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). (R Markdown and data files:

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(2), 245–266. Retrieved from

Pavlacic, J., Buchanan, E., Maxwell, N., Hopke, T., & Schulenberg, S. (2018). A Meta-Analysis of Expressive Writing on Positive Psychology Variables and Traumatic Stress. PsyArXiv. (R Markdown and data files:

Pollet, T. V., & Saxton, T. (2018). How diverse are the samples used in the journals “Evolution & Human Behavior” and “Evolutionary Psychology”? PsyArXiv.

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.

Sauer, S. (2017). Observation oriented modeling revised from a statistical point of view. Behavior Research Methods. (R Markdown and data files:

Stahl, C., & Heycke, T. (2016). Evaluative Conditioning with Simultaneous and Sequential Pairings Under Incidental and Intentional Learning Conditions. Social Cognition, 34(5), 382–412.

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 and Cognition, 37, 27–43.

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, 1107–1131.

Stahl, C., Henze, L., & Aust, F. (2016). False memory for perceptually similar but conceptually distinct line drawings. PsyArXiv. (R Markdown and data files:

Stevens, J. R., & Soh, L.-K. (2018). Predicting similarity judgments in intertemporal choice with machine learning. Psychonomic Bulletin & Review, 25(2), 627–635.

Urry, H. L., Sifre, E., Song, J., Steinberg, H., Bornstein, M., Kim, J., … Andrews, M. (2018). Effect of Disgust on Judgments of Moral Wrongness: A Replication of Eskine, Kacinik, and Prinz (2011). At Tufts University - Spring, 2017. Retrieved from (R Markdown and data files:

Valentine, K., Buchanan, E., Scofield, J., & Beauchamp, M. (2018). Beyond p-values: Utilizing Multiple Estimates to Evaluate Evidence. PsyArXiv. (R Markdown and data files:

Other related R packages

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.

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

Package dependencies