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README.Rmd
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---
title : "<img src='tools/images/papaja_hex.png' align='right' height='150' />papaja: Prepare APA Journal Articles<br />with R Markdown"
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
[]( https://cran.r-project.org/package=papaja) [](https://www.repostatus.org/#active)  [](https://github.com/crsh/papaja/actions) [](https://app.codecov.io/gh/crsh/papaja) [](https://github.com/crsh/papaja/issues?q=is%3Aopen+is%3Aissue+label%3Abug) [](https://stackoverflow.com/questions/tagged/papaja)
**papaja** is an [award-winning](https://improvingpsych.org/mission/awards/) R package that facilitates creating computationally reproducible, submission-ready manuscripts which conform to the American Psychological Association (APA) manuscript guidelines (6th Edition).
**papaja** provides
- an [R Markdown](https://rmarkdown.rstudio.com/) template that can be used with (or without) [RStudio](https://www.rstudio.com/) to create PDF documents (using the [apa6](http://www.ctan.org/pkg/apa6) LaTeX class) or Word documents (using a .docx-reference file).
- Functions to **typeset** the results from **statistical analyses**,
- functions to create **tables**, and
- functions to create **figures** in accordance with APA guidelines.
For a comprehensive introduction to **papaja**, see the current draft of the [manual](http://frederikaust.com/papaja_man/).
If you have a specific question that is not answered in the manual, feel free to ask a question on Stack Overflow [using the **papaja** tag](https://stackoverflow.com/questions/tagged/papaja).
If you believe you have found a bug or would like to request a new feature, [open an issue](https://github.com/crsh/papaja/issues) on Github and provide a [minimal complete verifiable example](https://stackoverflow.com/help/mcve).
## Example
Take a look at the [source file](https://github.com/crsh/papaja/blob/master/vignettes/papaja.Rmd) of the package vignette and the resulting [PDF](https://raw.githubusercontent.com/crsh/papaja/master/vignettes/papaja.pdf).
The vignette also contains some basic instructions.
## Installation
To use **papaja** you need either a recent version of [RStudio](https://www.rstudio.com/) or [pandoc](https://pandoc.org/).
If you want to create PDF- in addition to DOCX-documents you additionally need a [TeX](https://en.wikipedia.org/wiki/TeX) distribution.
We recommend you use [TinyTex](https://yihui.org/tinytex/), which can be installed from within R:
```{r eval = FALSE}
if(!requireNamespace("tinytex", quietly = TRUE)) install.packages("tinytex")
tinytex::install_tinytex()
```
You may also consider [MikTeX](http://miktex.org/) for Windows, [MacTeX](https://tug.org/mactex/) for Mac, or [TeX Live](https://tug.org/texlive/) for Linux.
Please refer to the [**papaja** manual](http://frederikaust.com/papaja_man/introduction.html#getting-started) for detailed installation instructions.
**papaja** is available on CRAN but you can also install it from the GitHub repository:
```{r install_papapja, eval = FALSE}
# Install latest CRAN release
install.packages("papaja")
# Install remotes package if necessary
if(!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
# Install the stable development version from GitHub
remotes::install_github("crsh/papaja")
# Install the latest development snapshot from GitHub
remotes::install_github("crsh/papaja@devel")
```
## Usage
Once **papaja** is installed, you can select the APA template when creating a new R Markdown file through the RStudio menus.
To add citations, specify your bibliography-file in the YAML front matter of the document (`bibliography: my.bib`) and start citing (for details, see pandoc manual on the [citeproc extension](https://pandoc.org/MANUAL.html#extension-citations). You may also be interested in [**citr**](https://github.com/crsh/citr), an R Studio addin to swiftly insert Markdown citations and [R Studio's visual editor](https://rstudio.github.io/visual-markdown-editing/), which also enables swiftly [inserting citations](https://rstudio.github.io/visual-markdown-editing/citations.html).
### Typeset analysis results
The functions `apa_print()` and `apa_table()` facilitate reporting results of your analyses.
When you pass the an output object of a supported class, such as an `htest`- or `lm`-object, to `apa_print()`, it will return a list of character strings that you can use to report the results of your analysis.
```{r echo = FALSE, message = FALSE, warning = FALSE, results = 'hide'}
library("papaja")
```
```{r}
my_lm <- lm(
Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length
, data = iris
)
apa_lm <- apa_print(my_lm)
apa_lm$full_result$Sepal_Length
```
**papaja** currently provides methods for the following object classes:
```{r echo = FALSE, results = "asis"}
print_classes <- gsub("apa_print\\.", "", as.character(utils::methods("apa_print")))
print_classes <- print_classes[!grepl(",", print_classes)]
print_classes <- c(print_classes, rep(NA, (4 - length(print_classes) %% 4) * (length(print_classes) %% 4 > 0)))
print_classes <- matrix(print_classes, ncol = 4)
colnames(print_classes) <- apply(print_classes, 2, function(x) {
first_letters <- tolower(substr(x, 1, 1))
first_letters <- c(first_letters[1], tail(first_letters, 1))
first_letters[is.na(first_letters)] <- "z"
col_names <- if(first_letters[1] == first_letters[2]) first_letters[1] else paste(first_letters, collapse = "-")
toupper(col_names)
})
print_classes[is.na(print_classes)] <- ""
knitr::kable(print_classes)
```
### Create tables
`apa_table()` may be used to produce publication-ready tables in an R Markdown document.
For instance, you might want to report some condition means (with standard errors).
```{r message = FALSE, eval = FALSE}
library("dplyr")
npk |>
group_by(N, P) |>
summarise(mean = mean(yield), se = sd(yield) / sqrt(length(yield)), .groups = "drop") |>
label_variables(N = "Nitrogen", P = "Phosphate", mean = "*M*", se = "*SE*") |>
apa_table(caption = "Mean pea yield (with standard errors)")
```
Table 1.
*Mean pea yield (with standard errors)*
```{r echo = FALSE, message = FALSE}
library("dplyr")
out <- npk %>%
group_by(N, P) %>%
summarise(mean = mean(yield), se = sd(yield) / sqrt(length(yield)), .groups = "drop")
colnames(out) <- c("Nitrogen", "Phosphate", "*M*", "*SE*")
knitr::kable(out, align = "c", digits = 2)
```
This is a fairly simple example, but `apa_table()` may be used to generate [more complex tables](https://osf.io/s4968/).
`apa_table()`, of course, plays nicely with the output from `apa_print()`.
Thus, it is possible to conveniently report complete regression tables, ANOVA tables, or the output from mixed-effects models.
```{r eval = FALSE}
lm(Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length, data = iris) |>
apa_print() |>
apa_table(caption = "Iris regression table.")
```
Table 2.
*Iris regression table.*
```{r echo = FALSE}
out <- lm(Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length, data = iris) |>
apa_print()
out$table$p.value <- gsub(" ", " ", out$table$p.value)
col_labels <- unlist(variable_labels(out$table))
col_labels["df"] <- "$df$"
colnames(out$table) <- gsub("\\$", "*", col_labels)
knitr::kable(out$table, align = "lrcrr")
```
### Create figures
**papaja** further provides functions to create publication-ready plots.
For example, you can use `apa_barplot()`, `apa_lineplot()`, and `apa_beeplot()` (or the general function `apa_factorial_plot()`) to visualize the results of factorial study designs:
```{r include = FALSE}
set.seed(42L)
x <- expand.grid(
id = seq_len(80L)
, congruency = c("Congruent", "Incongruent")
, load = c("High", "Low")
)
x[["response_time"]] <- rt(nrow(x), df = 20) * 80 +
(x$congruency == "Congruent") * - 80 +
(x$load == "High") * 40 +
500
variable_labels(x) <- list(
congruency = "Congruency"
, load = "Cognitive load"
, response_time = "Response time"
)
stroop_data <- x
```
```{r stroop-plot, fig.cap = "Response times from a simulated Stroop experiment. Large dots represent condition means, small dots represent individual participants' mean response time. Error bars represent 99% within-subjects confidence intervals."}
apa_beeplot(
data = stroop_data
, dv = "response_time"
, id = "id"
, factors = c("congruency", "load")
, ylim = c(0, 800)
, dispersion = wsci # within-subjects confidence intervals
, conf.level = .99
, las = 1
)
```
If you prefer `ggplot2`, try `theme_apa()`.
```{r stroop-ggplot}
library("ggplot2")
library("ggforce")
p <- ggplot(
stroop_data
, aes(x = congruency, y = response_time, shape = load, fill = load)
) +
geom_violin(alpha = 0.2, color = grey(0.6)) +
geom_sina(color = grey(0.6)) +
stat_summary(position = position_dodge2(0.95), fun.data = mean_cl_normal) +
lims(y = c(0, max(stroop_data$response_time))) +
scale_shape_manual(values = c(21, 22)) +
scale_fill_grey(start = 0.6, end = 1) +
labs(
x = "Congruency"
, y = "Response time"
, shape = "Cognitive load"
, fill = "Cognitive load"
)
p + theme_apa()
```
### Usage without RStudio
Don't use RStudio?
No problem.
Use the `rmarkdown::render` function to create articles:
``` r
# Create new R Markdown file
rmarkdown::draft(
"mymanuscript.Rmd"
, "apa6"
, package = "papaja"
, create_dir = FALSE
, edit = FALSE
)
# Render manuscript
rmarkdown::render("mymanuscript.Rmd")
```
## Getting help
[](https://stackoverflow.com/questions/tagged/papaja)
For a comprehensive introduction to **papaja**, check out the current draft of the [**papaja** manual](http://frederikaust.com/papaja_man/).
If you have a specific question that is not answered in the manual, feel free to ask a question on Stack Overflow [using the **papaja** tag](https://stackoverflow.com/questions/tagged/papaja).
If you believe you have found a bug or you want to request a new feature, [open an issue](https://github.com/crsh/papaja/issues) on Github and provide a [minimal complete verifiable example](https://stackoverflow.com/help/mcve).
## Citation
Please cite **papaja** if you use it.
`r attr(unclass(citation("papaja"))[[1]], "textVersion")`
For convenience, you can [use `cite_r()`](http://frederikaust.com/papaja_man/writing.html#citing-r-and-its-packages) or copy the reference information returned by `citation('papaja')` to your BibTeX file:
~~~bibtex
```{r echo = FALSE, results = "asis"}
print(citation("papaja"), style = "Bibtex")
```
~~~
## papaja in the wild
If you are interested in seeing how others are using **papaja**, you can find a [collection of papers](http://frederikaust.com/papaja_man/published-manuscripts.html) and the corresponding R Markdown files in the manual.
If you have published a paper that was written with **papaja**, please add the reference to the [public Zotero group](https://www.zotero.org/groups/2202906/papaja) yourself or send us to me.
## Computational reproducibility
To ensure mid- to long-term computational reproducibility we highly recommend conserving the software environment used to write a manuscript (e.g. R and all R packages) either in a software container or a virtual machine.
This way you can be sure that your R code does not break because of updates to R or any R package.
For a brief primer on containers and virtual machines see [the supplementary material](https://psych-transparency-guide.uni-koeln.de/analytic-reproducibility.html#document-hardware-and-software-used-for-analyses) by Klein et al. (2018).
[Docker](https://www.docker.com/) is the most widely used containerization approach.
It is open source and free to use but requires some disk space.
[CodeOcean](https://codeocean.com/) is a commercial service that builds on Docker, facilitates setting up and sharing containers and lets you run computations in the cloud.
See the **papaja** manual on [how to get started using **papaja** with Docker or CodeOcean](http://frederikaust.com/papaja_man/tips-and-tricks.html#reproducible-software-environments) and [our Docker workflow](https://github.com/crsh/papaja_docker) tailored for easy use with **papaja**.
## Contribute
[](https://github.com/crsh/papaja/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) [](https://github.com/crsh/papaja/issues?q=is%3Aopen+is%3Aissue+label%3Adocumentation)
Like **papaja** and want to contribute?
We highly appreciate any contributions to the R package or its documentation.
Take a look at the [open issues](https://github.com/crsh/papaja/issues) if you need inspiration.
There are many additional analyses that we would like `apa_print()` to support.
Any new S3/S4-methods for this function are always appreciated (e.g., `factanal`, `fa`, `lavaan`).
For a primer on adding new `apa_print()`-methods, see the getting-started-vignette:
```r
vignette("extending_apa_print", package = "papaja")
```
Before working on a contribution, please review our brief [contributing guidelines](https://github.com/crsh/papaja/blob/master/.github/CONTRIBUTING.md) and [code of conduct](https://github.com/crsh/papaja/blob/master/CODE_OF_CONDUCT.md).
## 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**](https://github.com/dgromer/apa): Format output of statistical tests in R according to APA guidelines
- [**APAstats**](https://github.com/achetverikov/APAstats): R functions for formatting results in APA style and other stuff
- [**apaTables**](https://github.com/dstanley4/apaTables): Create American Psychological Association (APA) Style Tables
- [**rempsyc**](https://github.com/RemPsyc/rempsyc): Convenience functions for psychology
- [**sigr**](https://github.com/WinVector/sigr): Concise formatting of significances in R
If you are looking for other journal article templates, you may be interested in the [**rticles**](https://github.com/rstudio/rticles) package.
## Package dependencies
```{r dep-plot, echo = FALSE, fig.width = 10, fig.height = 9, message = FALSE, warning = FALSE}
depgraph::plot_dependency_graph()
```