papaja: Prepare APA journal articles with R Markdown

papaja is an R package in the making including an 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.

If you are looking for an in-depth introduction to papaja, check out the current draft of the manual.

papaja is in active development and should be considered alpha. If you experience any problems, ask a question on Stack Overflow using the papaja tag or open an issue on Github.

Examples

Take a look at the R Markdown-file of the example manuscript in the folder example and the resulting PDF. The example document also contains some basic instructions. For an in-depth introduction to papaja, check out the current draft of the manual.

Installation

To use papaja you need either an up-to-date version of RStudio or pandoc. If you want to create PDF- in addition to DOCX-documents you additionally need a TeX distribution. If you have no use for TeX beyond rendering R Markdown documents, I recommend you use TinyTex. TinyTex can be installed from within R as follows.

if(!"tinytex" %in% rownames(installed.packages())) install.packages("tinytex")

tinytex::install_tinytex()

Otherwise consider 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
devtools::install_github("crsh/papaja")

# Install the latest development snapshot from GitHub
devtools::install_github("crsh/papaja@devel")

How to use 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.

Helper functions to report analyses

The functions apa_print() and apa_table() facilitate reporting results of your analyses. Take a look at the R Markdown-file 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.

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

Table. Iris regression table.

papaja currently provides methods for the following object classes:

* Not fully tested, don’t trust blindly!

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:

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().

Using papaja without RStudio

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")

Using papaja with CodeOcean

Seth Gree has kindly prepared a minimal papaja example capsule. If you want to use papaja in your next CodeOcean project you can use this capsule as a starting point.

Getting help

For an in-depth introduction to papaja, check out the current draft of the manual. If you have questions related to the use of papaja that are not answered in the manual, StackOverflow has a papaja-tag and is a great place to get answers. If you think you have found a bug, please open issues and provide a minimal complete verifiable example.

Contribute

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

Please cite papaja if you use it (citation('papaja') will provide the reference). Below are some peer-reviewed publications that used papaja. 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. https://doi.org/10.1080/13803395.2015.1125453

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. https://doi.org/10/gdxv8n (R Markdown and data files: https://osf.io/vnmby/)

Aust, F., & Stahl, C. (2019). The enhancing effect of caffeine on mnemonic discrimination is at best small. PsyArXiv. https://doi.org/10/gf6jwz (R Markdown and data files: https://osf.io/p7f4m/)

Barrett, T. S., Borrie, S. A., & Yoho, S. E. (2019). Automating with Autoscore: Introducing an R package for automating the scoring of orthographic transcripts. PsyArXiv. https://doi.org/10/gf4cqp (R Markdown and data files: https://osf.io/htqvr/)

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. https://doi.org/10/gdxv8m (R Markdown and data files: https://github.com/methexp/pdl2)

Bartlett, J. E. (2020). No Difference in Trait-Level Attentional Bias Between Daily and Non-Daily Smokers. PsyArXiv. https://doi.org/10/gg2c8f (R Markdown and data files: osf.io/am9hd/)

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. https://doi.org/10.1101/333005

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. https://doi.org/10.1111/cdev.13079 (R Markdown and data files: https://osf.io/uhv3d/)

Bol, N., Dienlin, T., Kruikemeier, S., Sax, M., Boerman, S. C., Strycharz, J., … de Vreese, C. H. (2018). Understanding the Effects of Personalization as a Privacy Calculus: Analyzing Self-Disclosure Across Health, News, and Commerce Contexts†. Journal of Computer-Mediated Communication, 23(6), 370–388. https://doi.org/10/gftcm6

Buchanan, E., Foreman, R., Johnson, B., Pavlacic, J., Swadley, R., & Schulenberg, S. (2018). Does the Delivery Matter? Examining Randomization at the Item Level. PsyArXiv. https://doi.org/10.17605/osf.io/p93df (R Markdown and data files: https://osf.io/gvx7s/)

Buchanan, E., Johnson, B., Miller, A., Stockburger, D., & Beauchamp, M. (2018a). Perceived Grading and Student Evaluation of Instruction. PsyArXiv. https://doi.org/10.17605/osf.io/7x4uf (R Markdown and data files: https://osf.io/jdpfs/)

Buchanan, E. M., & Scofield, J. E. (2018). Methods to detect low quality data and its implication for psychological research. Behavior Research Methods. https://doi.org/10.3758/s13428-018-1035-6 (R Markdown and data files: https://osf.io/x6t8a/)

Buchanan, E., & Scofield, J. (2018). Bulletproof Bias? Considering the Type of Data in Common Proportion of Variance Effect Sizes. PsyArXiv. https://doi.org/10.17605/osf.io/cs4vy (R Markdown and data files: https://osf.io/urd8q/)

Buchanan, E., Scofield, J., & Nunley, N. (2018b). The N400’s 3 As: Association, Automaticity, Attenuation (and Some Semantics Too). PsyArXiv. https://doi.org/10.17605/osf.io/6w2se (R Markdown and data files: https://osf.io/h5sd6/)

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. https://doi.org/10.31219/osf.io/k7dx5 (R Markdown and data files: https://osf.io/zs2qj/)

Buchanan, E., Valentine, K., & Maxwell, N. (2018c). English Semantic Feature Production Norms: An Extended Database of 4,436 Concepts. PsyArXiv. https://doi.org/10.17605/osf.io/gxbf4 (R Markdown and data files: https://osf.io/cjyzw/)

Buchanan, E., Valentine, K., & Maxwell, N. (2018d). The LAB: Linguistic Annotated Bibliography. PsyArXiv. https://doi.org/10.17605/osf.io/h3bwx (R Markdown and data files: https://osf.io/9bcws/)

Chen, S.-C., de Koning, B., & Zwaan, R. A. (2019). Does Object Size Matter with Regard to the Mental Simulation of Object Orientation? Experimental Psychology. https://doi.org/10/ggfzxw

Conigrave, J. H., Lee, K. K., Zheng, C., Wilson, S., Perry, J., Chikritzhs, T., … others. (2020). Drinking risk varies within and between Australian Aboriginal and Torres Strait Islander samples: A meta-analysis to identify sources of heterogeneity. Addiction. https://doi.org/10/ggsk3n

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. https://doi.org/10.1101/330134

Derringer, J. (2018). A simple correction for non-independent tests. PsyArXiv. https://doi.org/10/gdrbxc (R Markdown and data files: https://osf.io/re5w2/)

Faulkenberry, T. J., Cruise, A., & Shaki, S. (2018). Task instructions modulate unit–decade binding in two-digit number representation. Psychological Research. https://doi.org/10/gdxv8k (R Markdown and data files: https://github.com/tomfaulkenberry/twodigittaskmanip)

Field, A. P., Lester, K. J., Cartwright-Hatton, S., Harold, G. T., Shaw, D. S., Natsuaki, M. N., … Leve, L. D. (2020). Maternal and paternal influences on childhood anxiety symptoms: A genetically sensitive comparison. Journal of Applied Developmental Psychology, 68, 101123. https://doi.org/10/ggq38c (R Markdown and data files: https://osf.io/zgcg2/)

Flygare, O., Andersson, E., Ringberg, H., Hellstadius, A.-C., Edbacken, J., Enander, J., … Rück, C. (2018). Adapted cognitive behavior therapy for obsessive compulsive disorder with co-occuring autism spectrum disorder: A clinical effectiveness study. PsyArXiv. https://doi.org/10/gffjrb (R Markdown and data files: https://osf.io/gj87z/)

Garrison, H., Baudet, G., Breitfeld, E., Aberman, A., & Bergelson, E. (2020). Familiarity plays a small role in noun comprehension at 12-18 months. Infancy. https://doi.org/10/ggsnm2 (R Markdown and data files: https://osf.io/pb2g6/)

Haaf, J. M., Klaassen, F., & Rouder, J. (2018). A Note on Using Systems of Orders to Capture Theoretical Constraint in Psychological Science. PsyArXiv. https://doi.org/10/gffjrf (R Markdown and data files: https://github.com/perceptionandcognitionlab/bf-order)

Haaf, J. M., & Rouder, J. (2018). Some do and some don’t? Accounting for variability of individual difference structures. PsyArXiv. https://doi.org/10.31234/osf.io/zwjtp (R Markdown and data files: https://github.com/perceptionandcognitionlab/ctx-mixture)

Haaf, J. M., & Rouder, J. N. (2017). Developing constraint in bayesian mixed models. Psychological Methods, 22(4), 779–798. https://doi.org/10.1037/met0000156 (R Markdown and data files: https://github.com/perceptionandcognitionlab/ctx-indiff)

Hardwicke, T. E., Mathur, M. B., MacDonald, K., Nilsonne, G., Banks, G. C., Kidwell, M. C., … Frank, M. C. (2018). Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal cognition. Royal Society Open Science, 5(8), 180448. https://doi.org/10/gdz63s (R Markdown and data files: https://osf.io/wn8fd/)

Hardwicke, T., & Ioannidis. (2018). Mapping the Universe of Registered Reports. PsyArXiv. https://doi.org/10.31222/osf.io/fzpcy (R Markdown and data files: https://osf.io/7dpwb/)

Harms, C., & Lakens, D. (2018). Making ’Null Effects’ Informative: Statistical Techniques and Inferential Frameworks. PsyArXiv. https://doi.org/10.17605/osf.io/48zca (R Markdown and data files: https://osf.io/wptju/)

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. https://doi.org/10.1080/21642850.2018.1428102 (R Markdown and data files: https://github.com/heinonmatti/baseline-visu)

Henderson, E. L., Vall’ee-Tourangeau, F., & Simons, D. J. (2019). The Effect of Concrete Wording on Truth Judgements: A Preregistered Replication and Extension of Hansen & Wänke (2010). Collabra: Psychology, 5. https://doi.org/10/gf9h3x

Heycke, T. (2018, July). Contingency Awareness in Evaluative Conditioning: Investigations Using Subliminal Stimulus Presentations (text.thesis.doctoral). Universität zu Köln. Retrieved from http://www.uni-koeln.de/

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. https://doi.org/10.1098/rsos.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, 32(8), 1708–1727. https://doi.org/10.1080/02699931.2018.1429389

Heycke, T., & Spitzer, L. (2019). Screen Recordings as a Tool to Document Computer Assisted Data Collection Procedures. Psychologica Belgica, 59(1), 269–280. https://doi.org/10/gf5t5c

Heycke, T., & Stahl, C. (2018). No evaluative conditioning effects with briefly presented stimuli. Psychological Research. https://doi.org/10.1007/s00426-018-1109-1

Heyman, T., & Heyman, G. (2018). Can prediction-based distributional semantic models predict typicality? PsyArXiv. https://doi.org/10.17605/osf.io/59xtd (R Markdown and data files: https://osf.io/nkfjy/)

Jordan, K., Buchanan, E., & Padfield, W. (2018). Focus on the Target: The Role of Attentional Focus in Decisions about War. PsyArXiv. https://doi.org/10.17605/osf.io/9fgu8 (R Markdown and data files: https://osf.io/r8qp2/)

Kothe, E. J., & Ling, M. (2019). Retention of participants recruited to a one-year longitudinal study via Prolific. PsyArXiv. https://doi.org/10.31234/osf.io/5yv2u (R Markdown and data files: https://osf.io/yjstk/)

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. https://doi.org/10/gdj7s9 (R Markdown and data files: https://osf.io/qamc6/)

Lewis, M., Braginsky, M., Tsuji, S., Bergmann, C., Piccinini, P. E., Cristia, A., & Frank, M. C. (2017). A Quantitative Synthesis of Early Language Acquisition Using Meta-Analysis. PsyArXiv. https://doi.org/10.31234/osf.io/htsjm

Maxwell, N., & Buchanan, E. (2018a). Investigating the Interaction between Associative, Semantic, and Thematic Database Norms for Memory Judgments and Retrieval. PsyArXiv. https://doi.org/10.17605/osf.io/fcesn (R Markdown and data files: https://osf.io/y8h7v/)

Maxwell, N., & Buchanan, E. (2018b). Modeling Memory: Exploring the Relationship Between Word Overlap and Single Word Norms when Predicting Relatedness Judgments and Retrieval. PsyArXiv. https://doi.org/10.17605/osf.io/qekad (R Markdown and data files: https://osf.io/j7qtc/)

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). https://doi.org/10.1525/collabra.79 (R Markdown and data files: https://osf.io/wm6vc/)

McHugh, C., McGann, M., Igou, E. R., & Kinsella, E. L. (2020). Reasons or rationalizations: The role of principles in the moral dumbfounding paradigm. Journal of Behavioral Decision Making, bdm.2167. https://doi.org/10/ggf94x

Moors, P., & Hesselmann, G. (2019). Unconscious arithmetic: Assessing the robustness of the results reported by Karpinski, Briggs, and Yale (2018). Consciousness and Cognition, 68, 97–106. https://doi.org/10/gftmrj

Morin-Lessard, E., Poulin-Dubois, D., Segalowitz, N., & Byers-Heinlein, K. (2019). Selective attention to the mouth of talking faces in monolinguals and bilinguals aged 5 months to 5 years. PsyArXiv. https://doi.org/10.31234/osf.io/5pkne (R Markdown and data files: https://osf.io/ikvyr/)

Nalborczyk, L., Batailler, C., Lœvenbruck, H., Vilain, A., & Bürkner, P.-C. (2019). An Introduction to Bayesian Multilevel Models Using brms: A Case Study of Gender Effects on Vowel Variability in Standard Indonesian. Journal of Speech, Language, and Hearing Research, 62(5), 1225–1242. https://doi.org/10.1044/2018_JSLHR-S-18-0006 (R Markdown and data files: https://osf.io/dpzcb/)

Navarro, D. (2020). If mathematical psychology did not exist we might need to invent it: A comment on theory building in psychology. PsyArXiv. https://doi.org/10.31234/osf.io/ygbjp

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 http://www.psychologie-aktuell.com/fileadmin/download/ptam/2-2017_20170627/06_Papenberg_.pdf

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. https://doi.org/10.17605/osf.io/u98cw (R Markdown and data files: https://osf.io/4mjqt/)

Peterka-Bonetta, J., Sindermann, C., Sha, P., Zhou, M., & Montag, C. (2019). The relationship between Internet Use Disorder, depression and burnout among Chinese and German college students. Addictive Behaviors, 89, 188–199. https://doi.org/10/gd4rcw

Pollet, T. V., & Saxton, T. (2018). How diverse are the samples used in the journals “Evolution & Human Behavior” and “Evolutionary Psychology”? PsyArXiv. https://doi.org/10.17605/osf.io/7h24p

Robison, M. K., & Unsworth, N. (2018). Pupillometry tracks fluctuations in working memory performance. PsyArXiv. https://doi.org/10/gdz63r (R Markdown and data files: osf.io/vuw9h/)

Rouder, J., & Haaf, J. M. (2018). A Psychometrics of Individual Differences in Experimental Tasks. PsyArXiv. https://doi.org/10/gfdbw2 (R Markdown and data files: https://github.com/perceptionandcognitionlab/ctx-reliability)

Rouder, J., & Haaf, J. M. (2019). Optional Stopping and the Interpretation of The Bayes Factor. PsyArXiv. https://doi.org/10.31234/osf.io/m6dhw (R Markdown and data files: https://osf.io/uv456/)

Rouder, J., Haaf, J. M., & Snyder, H. K. (2018a). Minimizing Mistakes In Psychological Science. PsyArXiv. https://doi.org/10/gfdb27 (R Markdown and data files: https://github.com/perceptionandcognitionlab/lab-transparent)

Rouder, J., Haaf, J. M., Stober, C., & Hilgard, J. (2017). Beyond Overall Effects: A Bayesian Approach to Finding Constraints Across A Collection Of Studies In Meta-Analysis. PsyArXiv. https://doi.org/10/gffjrd (R Markdown and data files: https://github.com/perceptionandcognitionlab/meta-planned)

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

Samaey, C., Wagemans, J., & Moors, P. (2020). Individual differences in processing orientation and proximity as emergent features. Vision Research, 169, 12–24. https://doi.org/10/ggnc6w (R Markdown and data files: https://osf.io/vgxja/)

Sauer, S. (2017). Observation oriented modeling revised from a statistical point of view. Behavior Research Methods. https://doi.org/10.3758/s13428-017-0949-8 (R Markdown and data files: https://osf.io/6vhja/)

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. https://doi.org/10.1016/j.concog.2015.08.003

Stahl, C., & Corneille, O. (2020). Evaluative conditioning in the Surveillance paradigm is moderated by awareness exclusion criteria. PsyArXiv. https://doi.org/10.31234/osf.io/3xsbu (R Markdown and data files: https://osf.io/qs35v)

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

Stahl, C., Henze, L., & Aust, F. (2016b). False memory for perceptually similar but conceptually distinct line drawings. PsyArXiv. https://doi.org/10.17605/osf.io/zr7m8 (R Markdown and data files: https://osf.io/jxm7z/)

Stahl, C., & Heycke, T. (2016). Evaluative Conditioning with Simultaneous and Sequential Pairings Under Incidental and Intentional Learning Conditions. Social Cognition, 34(5), 382–412. https://doi.org/10.1521/soco.2016.34.5.382

Stevens, J. R., & Soh, L.-K. (2018). Predicting similarity judgments in intertemporal choice with machine learning. Psychonomic Bulletin & Review, 25(2), 627–635. https://doi.org/10/gdfghk

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 https://osf.io/fu384/ (R Markdown and

data files: https://osf.io/ddmkm)

Valentine, K., Buchanan, E., Scofield, J., & Beauchamp, M. (2018). Beyond p-values: Utilizing Multiple Estimates to Evaluate Evidence. PsyArXiv. https://doi.org/10.17605/osf.io/9hp7y (R Markdown and data files: https://osf.io/u9hf4/)

Vuorre, M., & Curley, J. P. (2018). Curating Research Assets: A Tutorial on the Git Version Control System. PsyArXiv. https://doi.org/10.31234/osf.io/6tzh8 (R Markdown and data files: https://github.com/mvuorre/reproguide-curate)

Xu, R., DeShon, R. P., & Dishop, C. R. (2019). Challenges and Opportunities in the Estimation of Dynamic Models. Organizational Research Methods, 109442811984263. https://doi.org/10/gf3vbj

Zhang, H., Miller, K. F., Sun, X., & Cortina, K. S. (2020). Wandering eyes: Eye movements during mind wandering in video lectures. Applied Cognitive Psychology, acp.3632. https://doi.org/10/ggjvfp

Zhang, H., Qu, C., Miller, K. F., & Cortina, K. S. (2019). Missing the joke: Reduced rereading of garden-path jokes during mind-wandering. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10/gf68nd

Zhang, T., Hu, G., Yang, Y., Wang, J., & Zhou, Y. (2019). All-Atom Knowledge-Based Potential for RNA Structure Discrimination Based on the Distance-Scaled Finite Ideal-Gas Reference State. Journal of Computational Biology. https://doi.org/10/ggcp6w

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.

• rticles: LaTeX Journal Article Templates for R Markdown
• chi-proc-rmd-template: ACM CHI Proceedings R Markdown Template
• 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.