Reproducible manuscripts with Quarto

R-SIG 15.07.2024

¹

Introduction

Quarto is an open-source scientific and technical publishing system. It allows us to combine code and markdown text to produce reproducible manuscripts that can automatically incorporate and evaluate our code when rendering. We can also build slides and even websites (like the one you are currently on).

Components

YAML header

Quarto documents have the ending .qmd.

The YAML header, which begins on the top of the document with ---, and also ends with ---, contains global options for the document:

---
title: "Reproducible manuscripts with Quarto"
description: "R-SIG 15.07.2024"
author: 
  - name: Nicklas Hafiz
  - affiliation: PhD student at the IQB, Methods team
categories: [R, quarto]
date: 07-15-2024
bibliography: references.bib
csl: apa7.csl
format: pdf
---

An overview of possible YAML-fields can be found here.
Note the format field, which lets us quickly convert our document between pdf, word and html, and also lets us use one of many templates.

Markdown text

The body of the document is written in markdown language. Some expressions:

• # for headers. Add as many # as you like for subheaders.
• **bold**: bold
• *italic*: italic
• ``code\
• Lists: - for bullet points, 1. for numbered lists (beware: the line above the list has to be empty)
• Linebreaks: Two spaces at the end of a line.
• Links: [text](url)

Code chunks

We can run code in different languages (like R, Julia, Python …) directly from our Quarto-file. In RStudio you can press Strg-Alt-i to insert a new r code chunk.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

athletes <- readRDS(file = here::here("raw_data", "athletes.rds"))
head(athletes)

  NOC     ID                  Name Sex Age Height Weight        Team
1 AFG 132181           Najam Yahya   M  NA     NA     NA Afghanistan
2 AFG  87371 Ahmad Jahan Nuristani   M  NA     NA     NA Afghanistan
3 AFG  44977     Mohammad Halilula   M  28    163     57 Afghanistan
4 AFG    502     Ahmad Shah Abouwi   M  NA     NA     NA Afghanistan
5 AFG 109153    Shakar Khan Shakar   M  24     NA     74 Afghanistan
6 AFG  29626  Sultan Mohammad Dost   M  28    168     73 Afghanistan
        Games Year Season      City     Sport
1 1956 Summer 1956 Summer Melbourne    Hockey
2 1948 Summer 1948 Summer    London    Hockey
3 1980 Summer 1980 Summer    Moskva Wrestling
4 1956 Summer 1956 Summer Melbourne    Hockey
5 1964 Summer 1964 Summer     Tokyo Wrestling
6 1960 Summer 1960 Summer      Roma Wrestling
                                    Event Medal      Region
1                     Hockey Men's Hockey  <NA> Afghanistan
2                     Hockey Men's Hockey  <NA> Afghanistan
3 Wrestling Men's Bantamweight, Freestyle  <NA> Afghanistan
4                     Hockey Men's Hockey  <NA> Afghanistan
5 Wrestling Men's Welterweight, Freestyle  <NA> Afghanistan
6 Wrestling Men's Welterweight, Freestyle  <NA> Afghanistan

We can tweak the code execution via different execution options, which are written on top of the chunk:

#| echo: false
#| message: false

library(tidyverse)
athletes <- readRDS(file = here::here(  "raw_data", "athletes.rds"))
head(athletes)

becomes:

  NOC     ID                  Name Sex Age Height Weight        Team
1 AFG 132181           Najam Yahya   M  NA     NA     NA Afghanistan
2 AFG  87371 Ahmad Jahan Nuristani   M  NA     NA     NA Afghanistan
3 AFG  44977     Mohammad Halilula   M  28    163     57 Afghanistan
4 AFG    502     Ahmad Shah Abouwi   M  NA     NA     NA Afghanistan
5 AFG 109153    Shakar Khan Shakar   M  24     NA     74 Afghanistan
6 AFG  29626  Sultan Mohammad Dost   M  28    168     73 Afghanistan
        Games Year Season      City     Sport
1 1956 Summer 1956 Summer Melbourne    Hockey
2 1948 Summer 1948 Summer    London    Hockey
3 1980 Summer 1980 Summer    Moskva Wrestling
4 1956 Summer 1956 Summer Melbourne    Hockey
5 1964 Summer 1964 Summer     Tokyo Wrestling
6 1960 Summer 1960 Summer      Roma Wrestling
                                    Event Medal      Region
1                     Hockey Men's Hockey  <NA> Afghanistan
2                     Hockey Men's Hockey  <NA> Afghanistan
3 Wrestling Men's Bantamweight, Freestyle  <NA> Afghanistan
4                     Hockey Men's Hockey  <NA> Afghanistan
5 Wrestling Men's Welterweight, Freestyle  <NA> Afghanistan
6 Wrestling Men's Welterweight, Freestyle  <NA> Afghanistan

We can also define global execution options for the whole ´.qmd´ file in the YAML header:

---
title: "Reproducible manuscripts with Quarto"
description: "R-SIG 15.07.2024"
author: 
  - name: Nicklas Hafiz
  - affiliation: PhD student at the IQB, Methods team
categories: [R, quarto]
date: 07-15-2024
bibliography: references.bib
csl: apa7.csl
format: pdf
execute:
  echo: false
  warning: false
  message: false
---

Tables

Tables can be a bit tricky. The great thing is: once you have styled them, they get updated automatically if some data changes. Also, there are a lot of different packages for building tables in markdown, here are some options:

Markdown

For some simple tables, the normal markdown syntax might be enough, see here.

| Column 1 | Column 2 | Column 3 |
|----------|----------|----------|
| A1       | B1       | C1       |
| A2       | B2       | C2       |

Column 1	Column 2	Column 3
A1	B1	C1
A2	B2	C2

Kable

With knitr::kable() you can build tables programmatically from code chunks:

judo_athletes_ger <- athletes %>%
  filter(Sport == "Judo", Region == "Germany", !is.na(Medal)) %>%
  select(Year, Name, Sex, Age, Height, Weight, Region, Medal) %>%
  arrange(Year, Sex)

knitr::kable(judo_athletes_ger)

Year	Name	Sex	Age	Height	Weight	Region	Medal
1964	Wolfgang Hofmann	M	23	177	80	Germany	Silver
1964	Klaus Glahn	M	22	187	101	Germany	Bronze
1972	Paul Barth	M	26	181	90	Germany	Bronze
1972	Klaus Glahn	M	30	187	101	Germany	Silver
1972	Dietmar Htger	M	25	172	70	Germany	Bronze
1976	Gnther Neureuther	M	20	186	95	Germany	Silver
1980	Detlef Ultsch	M	24	170	80	Germany	Bronze
1980	Dietmar Lorenz	M	30	180	93	Germany	Gold
1980	Harald Heinke	M	25	178	78	Germany	Bronze
1980	Dietmar Lorenz	M	30	180	93	Germany	Bronze
1980	Karl-Heinz Lehmann	M	23	169	71	Germany	Bronze
1984	Frank Wieneke	M	22	179	78	Germany	Gold
1984	Arthur Schnabel	M	35	182	104	Germany	Bronze
1984	Gnther Neureuther	M	28	186	95	Germany	Bronze
1988	Frank Wieneke	M	26	179	78	Germany	Silver
1988	Marcus “Marc” Meiling	M	26	194	95	Germany	Silver
1988	Henry Sthr	M	28	194	120	Germany	Silver
1988	Sven Loll	M	24	182	71	Germany	Silver
1988	Torsten Brcht (Oehmigen-)	M	24	178	78	Germany	Bronze
1992	Richard Trautmann	M	23	168	65	Germany	Bronze
1992	Udo Gnther Quellmalz	M	25	175	65	Germany	Bronze
1996	Johanna Hagn	F	23	168	NA	Germany	Bronze
1996	Udo Gnther Quellmalz	M	29	175	65	Germany	Gold
1996	Frank Mller	M	25	189	125	Germany	Bronze
1996	Richard Trautmann	M	27	168	65	Germany	Bronze
1996	Marko Spittka	M	25	179	88	Germany	Bronze
2000	Anna-Maria Gradante	F	23	154	48	Germany	Bronze
2004	Yvonne Bnisch	F	23	168	61	Germany	Gold
2004	Julia Matijass	F	30	161	48	Germany	Bronze
2004	Annett Bhm	F	24	179	83	Germany	Bronze
2004	Michael Jurack	M	25	190	100	Germany	Bronze
2008	Ole Bischof	M	28	180	81	Germany	Gold
2012	Kerstin Thiele	F	25	168	70	Germany	Silver
2012	Dimitri Peters	M	28	188	100	Germany	Bronze
2012	Ole Bischof	M	32	180	81	Germany	Silver
2012	Andreas Tlzer	M	32	192	131	Germany	Bronze
2016	Laura Vargas Koch	F	26	173	70	Germany	Bronze

To get more styling options, mainly for HTML table output, you can use kableExtra.

APA tables

For APA tables, I’ve found the rempsyc package which helps in building APA tables, but there are other options like flextable or gt as well:

#| label: tbl-judo
#| tbl-cap: Table with penguins species flipper length.

library(rempsyc)

nice_table(
  judo_athletes_ger
)

Table 1: German olympic medal winners in Judo.

Year	Name	Sex	Age	Height	Weight	Region	Medal
1,964	Wolfgang Hofmann	M	23	177	80.00	Germany	Silver
1,964	Klaus Glahn	M	22	187	101.00	Germany	Bronze
1,972	Paul Barth	M	26	181	90.00	Germany	Bronze
1,972	Klaus Glahn	M	30	187	101.00	Germany	Silver
1,972	Dietmar Htger	M	25	172	70.00	Germany	Bronze
1,976	Gnther Neureuther	M	20	186	95.00	Germany	Silver
1,980	Detlef Ultsch	M	24	170	80.00	Germany	Bronze
1,980	Dietmar Lorenz	M	30	180	93.00	Germany	Gold
1,980	Harald Heinke	M	25	178	78.00	Germany	Bronze
1,980	Dietmar Lorenz	M	30	180	93.00	Germany	Bronze
1,980	Karl-Heinz Lehmann	M	23	169	71.00	Germany	Bronze
1,984	Frank Wieneke	M	22	179	78.00	Germany	Gold
1,984	Arthur Schnabel	M	35	182	104.00	Germany	Bronze
1,984	Gnther Neureuther	M	28	186	95.00	Germany	Bronze
1,988	Frank Wieneke	M	26	179	78.00	Germany	Silver
1,988	Marcus "Marc" Meiling	M	26	194	95.00	Germany	Silver
1,988	Henry Sthr	M	28	194	120.00	Germany	Silver
1,988	Sven Loll	M	24	182	71.00	Germany	Silver
1,988	Torsten Brcht (Oehmigen-)	M	24	178	78.00	Germany	Bronze
1,992	Richard Trautmann	M	23	168	65.00	Germany	Bronze
1,992	Udo Gnther Quellmalz	M	25	175	65.00	Germany	Bronze
1,996	Johanna Hagn	F	23	168		Germany	Bronze
1,996	Udo Gnther Quellmalz	M	29	175	65.00	Germany	Gold
1,996	Frank Mller	M	25	189	125.00	Germany	Bronze
1,996	Richard Trautmann	M	27	168	65.00	Germany	Bronze
1,996	Marko Spittka	M	25	179	88.00	Germany	Bronze
2,000	Anna-Maria Gradante	F	23	154	48.00	Germany	Bronze
2,004	Yvonne Bnisch	F	23	168	61.00	Germany	Gold
2,004	Julia Matijass	F	30	161	48.00	Germany	Bronze
2,004	Annett Bhm	F	24	179	83.00	Germany	Bronze
2,004	Michael Jurack	M	25	190	100.00	Germany	Bronze
2,008	Ole Bischof	M	28	180	81.00	Germany	Gold
2,012	Kerstin Thiele	F	25	168	70.00	Germany	Silver
2,012	Dimitri Peters	M	28	188	100.00	Germany	Bronze
2,012	Ole Bischof	M	32	180	81.00	Germany	Silver
2,012	Andreas Tlzer	M	32	192	131.00	Germany	Bronze
2,016	Laura Vargas Koch	F	26	173	70.00	Germany	Bronze

Labels

Tables that are build programmatically can be labeled with #| label: tbl-judo at the top of the chunk. This will always put the correct number in the caption and also lets you cross-reference the table in your text by writing @tbl-judo Table 1.
The apaquarto template will take care of correctly rendering it to APA-style.
Captions can be written with #| tbl-cap: German olympic medal winners in Judo..

Formatting model output

There are some packages out there that help you to format output of statistical models. Let’s fit a simple logistic regression model to predict an athletes medal win by country (Germany or Japan) in Judo:

library(tidyverse)

athletes_judo <- readRDS(file = here::here("raw_data", "athletes.rds")) %>%
  mutate(Medal_bi = ifelse(is.na(Medal), 0, 1)) %>%
  filter(Sport == "Judo", Region %in% c("Germany", "Japan"))


model <- glm(Medal_bi ~ Region, family = binomial(link = "logit"), data = athletes_judo)

Normally, the output looks something like this:

summary(model)

Call:
glm(formula = Medal_bi ~ Region, family = binomial(link = "logit"), 
    data = athletes_judo)

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -0.9740     0.1930  -5.048 4.46e-07 ***
RegionJapan   1.5982     0.2671   5.983 2.19e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 364.15  on 263  degrees of freedom
Residual deviance: 325.42  on 262  degrees of freedom
AIC: 329.42

Number of Fisher Scoring iterations: 4

Tidy up!

Now we have multiple options to easily extract the results of this regression. Most prominently, there is the broom package:

library(broom)

model_broom <- tidy(model)
model_broom

# A tibble: 2 × 5
  term        estimate std.error statistic       p.value
  <chr>          <dbl>     <dbl>     <dbl>         <dbl>
1 (Intercept)   -0.974     0.193     -5.05 0.000000446  
2 RegionJapan    1.60      0.267      5.98 0.00000000219

If we want to convert the estimates to Odds:

model_broom <- tidy(model, exponentiate = TRUE)
model_broom

# A tibble: 2 × 5
  term        estimate std.error statistic       p.value
  <chr>          <dbl>     <dbl>     <dbl>         <dbl>
1 (Intercept)    0.378     0.193     -5.05 0.000000446  
2 RegionJapan    4.94      0.267      5.98 0.00000000219

And if we want to extract the fit statistics:

glance(model)

# A tibble: 1 × 8
  null.deviance df.null logLik   AIC   BIC deviance df.residual  nobs
          <dbl>   <int>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
1          364.     263  -163.  329.  337.     325.         262   264

Tip

This works with many different model types, from t-tests over linear models to GLMs and multi level models and even lavaan output. They might be split out over multiple packages, multi level models for example can be tidied with broom.mixed.

Reporting

We can also let R write our report section for us ;):

library(report)

report(model)

We fitted a logistic model (estimated using ML) to predict Medal_bi with Region
(formula: Medal_bi ~ Region). The model's explanatory power is moderate (Tjur's
R2 = 0.14). The model's intercept, corresponding to Region = Germany, is at
-0.97 (95% CI [-1.36, -0.61], p < .001). Within this model:

  - The effect of Region [Japan] is statistically significant and positive (beta
= 1.60, 95% CI [1.08, 2.13], p < .001; Std. beta = 1.60, 95% CI [1.08, 2.13])

Standardized parameters were obtained by fitting the model on a standardized
version of the dataset. 95% Confidence Intervals (CIs) and p-values were
computed using a Wald z-distribution approximation.

If you want, take a look at report, it has many more functions to make the reporting workflow of your model a lot easier.

Automate in-text values

Even when not using report or some similar package, we can still extract the values automatically from a model and put them programmatically into the text. This can be achieved by simply adding an inline-codechunck into our markdown text:

The model’s intercept, corresponding to Region = Germany, is at 0.38.

Tip

Maybe put the data extraction into an own object to keep the text more readable:

model_intercept <- model_broom %>% filter(term == "(Intercept)") %>% pull(estimate) %>% round(2)

The model’s intercept, corresponding to Region = Germany, is at 0.38.

APA tables

For example with rempsyc:

library(rempsyc)

nice_table(model_broom, broom = "glm")

Term	estimate	SE	t	p
(Intercept)	0.38	0.19	-5.05	< .001***
RegionJapan	4.94	0.27	5.98	< .001***

Plots

Plots can be very easily created from within a code chunk:

best_by_sport <- athletes %>%
  ## Get all gold medalists
  filter(Medal == "Gold") %>%
  ## Group them by sport and region
  group_by(Sport, Region) %>%
  ## count the number of medals each country has per sport category
  count(Medal) %>%
  ## Now only group by sport, so we can extract the maximum medal row by sport, and not by sport and country
  group_by(Sport) %>%
  ## Extract the country with the most medals
  slice(which.max(n))



p1 <- ggplot(
  data = best_by_sport,
  aes(
    x = Sport,
    y = n
  )
) +
  geom_col(aes(fill = Region, x = reorder(Sport, n))) +
  geom_text(aes(label = Region), hjust = -0.3, angle = 90, size = 2.5) +
  theme_classic() +
  ## And turn the axis labels again, because the new theme has overwritten our theme
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  ## Specify which colors are used for the filling. They are from the package viridsLite, so you might need to install it.
  scale_fill_manual(values = viridisLite::viridis(19)) +
  ggtitle("Country with the most Olympic gold medal winners by sport") +
  xlab("Sport") +
  ylab("Number of gold medal winners")

Again, we can tweak the layout, captions etc. via the chunk options, see here for an overview.

#| fig-height: 8
#| fig-width: 11

p1

Citations

Citations are saved in .bib files. The .bib format can be chosen to download or copy on almost every website, often next to APA and others. It looks like this:

@article{allport1936trait,
  title={Trait-names: A psycho-lexical study.},
  author={Allport, Gordon W and Odbert, Henry S},
  journal={Psychological monographs},
  volume={47},
  number={1},
  pages={i},
  year={1936},
  publisher={Psychological Review Company}
}

@book{darwin1859,
  added-at = {2008-05-27T04:02:47.000+0200},
  address = {London},
  author = {Darwin, Charles},
  biburl = {https://www.bibsonomy.org/bibtex/2d70d713c717fb28384fb073c9f6dfbc2/neilernst},
  citeulike-article-id = {2376343},
  interhash = {c738acbb887362be5b0e6abc51be42d3},
  intrahash = {d70d713c717fb28384fb073c9f6dfbc2},
  keywords = {evolution},
  note = { or the Preservation of Favored Races in the Struggle for Life},
  priority = {2},
  publisher = {Murray},
  timestamp = {2008-05-27T04:02:47.000+0200},
  title = {On the Origin of Species by Means of Natural Selection},
  year = 1859
}

Of course, you should still check if the fields are filled in correctly. If you have created a references.bib file in your project directory, you can include it in your quarto document by adding bibliography: references.bib to your YAML header.
To cite a reference in you text, you can use an @ in front of the tag like @darwin1859 Darwin (1859) , or [@allport1936trait] (Allport & Odbert, 1936).

Referencing R packages

R packages are an important part of your analysis, and should be cited as such. The package grateful helps you with that.
To use it, put a grateful-refs.bib file into your YAML header:

---
title: "Reproducible manuscripts with Quarto"
description: "R-SIG 15.07.2024"
author: 
  - name: Nicklas Hafiz
  - affiliation: PhD student at the IQB, Methods team
categories: [R, quarto]
date: 07-15-2024
bibliography: 
  - references.bib
  - grateful-refs.bib
csl: apa7.csl
format: pdf
---

By using this list syntax, we can add multiple .bib-files to our quarto document. Then we only have to call the citation function in a code chunk:

grateful::cite_packages(output = "paragraph", out.dir = ".")

We used R version 4.4.1 (R Core Team, 2024) and the following R packages: flextable v. 0.9.6 (Gohel & Skintzos, 2024), here v. 1.0.1 (Müller, 2020), knitr v. 1.46 (Xie, 2014, 2015, 2024), rempsyc v. 0.1.8 (Thériault, 2023), report v. 0.5.8 (Makowski et al., 2023), rmarkdown v. 2.27 (Allaire et al., 2024; Xie et al., 2018, 2020), tidyverse v. 2.0.0 (Wickham et al., 2019), viridisLite v. 0.4.2 (Garnier et al., 2023).

Citation styles

Again, because we have our references in a plain text format, we can easily convert between different citation styles. One way to do this is to provide th csl argument in the YAML header, like so:

---
title: "Reproducible manuscripts with Quarto"
description: "R-SIG 15.07.2024"
author: 
  - name: Nicklas Hafiz
  - affiliation: PhD student at the IQB, Methods team
categories: [R, quarto]
date: 07-15-2024
bibliography: references.bib
csl: apa7.csl
format: pdf
---

.csl files can just be put into your project folder. They define the citation style, and can for example be downloaded from Zoteros style repository.

Reference section

The references will be automatically generated at the end of your document. Alternatively, you can include it where you want with

::: {#refs}
:::

Allaire, J., Xie, Y., Dervieux, C., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., & Iannone, R. (2024). rmarkdown: Dynamic documents for r. https://github.com/rstudio/rmarkdown

Allport, G. W., & Odbert, H. S. (1936). Trait-names: A psycho-lexical study. Psychological Monographs, 47(1).

Darwin, C. (1859). On the origin of species by means of natural selection. Murray.

Garnier, Simon, Ross, Noam, Rudis, Robert, Camargo, Pedro, A., Sciaini, Marco, Scherer, & Cédric. (2023). viridis(Lite) - colorblind-friendly color maps for r. https://doi.org/10.5281/zenodo.4678327

Gohel, D., & Skintzos, P. (2024). flextable: Functions for tabular reporting. https://ardata-fr.github.io/flextable-book/

Makowski, D., Lüdecke, D., Patil, I., Thériault, R., Ben-Shachar, M. S., & Wiernik, B. M. (2023). Automated results reporting as a practical tool to improve reproducibility and methodological best practices adoption. CRAN. https://easystats.github.io/report/

Müller, K. (2020). here: A simpler way to find your files. https://here.r-lib.org/

R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Thériault, R. (2023). rempsyc: Convenience functions for psychology. Journal of Open Source Software, 8(87), 5466. https://doi.org/10.21105/joss.05466

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

Xie, Y. (2014). knitr: A comprehensive tool for reproducible research in R. In V. Stodden, F. Leisch, & R. D. Peng (Eds.), Implementing reproducible computational research. Chapman; Hall/CRC.

Xie, Y. (2015). Dynamic documents with R and knitr (2nd ed.). Chapman; Hall/CRC. https://yihui.org/knitr/

Xie, Y. (2024). knitr: A general-purpose package for dynamic report generation in r. https://yihui.org/knitr/

Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown

Xie, Y., Dervieux, C., & Riederer, E. (2020). R markdown cookbook. Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown-cookbook

Templates & APA

Own Templates

You can always create your own template Luckily, some people have done a lot of the work for us, and therefore we can use templates provided for specific journals. Oooor more generally, APA templates:

Papaja

There are some markdown templates that format your text in APA style. Most famously, the papaja package lets you write APA conform manuscripts. Sadly only in R Markdown, not in Quarto.

apaquarto

Alternatively, I’ve found (but not yet tested in a whole project) the apaquarto extension. You can install via the Terminal (not the R Console):

## Set the working directory
cd path/to/my/folder

## Use the Quarto Template
quarto use wjschne/apaquarto

Via R Console

Alternativley, you can also try to install via the R Console, however, it didn’t work for quite some people:

setwd("home/my_project") # Make sure the folder is empty
quarto::quarto_use_template("wjschne/apaquarto")

This will create the necessary files in your folder. Update the .qmd file with the same name as your folder. I’d suggest to also create a RStudio-project there, and maybe structure your files into multiple folders (data, R-Scripts …).
The template will label your tables and figures correctly, and format the bibliography as well as the whole document.
The tables can be build with the packages presented in the last chapter.

Word templates

If you want a word file as output (maybe some journal or co-author requires you to), you can do that easily by setting format: docx in your yaml header. If you don’t use a template like apaquarto, you can also style your own word template simply by changing the styles in a template word file and then load the template file into your quarto document with

format:
  docx:
    reference-doc: custom-reference-doc.docx

in the YAML header. Take a look at the official documentation for more detailled info.

Split your project

I recommend that you split up your project into many subfiles, that get merged in the end in a main document. In my opinion, this makes everything a lot easier to overview, because working with quarto makes it easy to just put everything from data preperation to reporting into one huge, confousing document. For example, you can put R-Functions into a own script and source it with source(functions.R). You can also put each chapter into a own .qmd file, and merge them together in a main document. Take a look at include for some input on how to do that.

Exercises

Markdown Exercises

0. Set up a new RStudio project in a new directory.
1. Create an empty Quarto file.
2. Fill it with a “mini-Analysis” of the characters and psych_stats data sets. You can use the code from the Plotting Exercise, for now not in APA style (we will do this next week). This Quarto file should contain:

• A YAML header.
• Invisible code chunks for data preparation (you will have to merge the characters and psych_stats data sets, take a look at Tip 1).
• Some little text body (not important whats written there).
• A little labeled table that also gets mentioned in the text body.
• At least one plot that gets mentioned in the text body.
• Own references.

Don’t spent too much time on what to write and on the code, you can just copy the code we wrote in the previous plotting exercises.

Tip 1: Hint

Prepare your data with:

# install.packages("tidyverse")
# install.packages("here")

library(tidyverse)
library(here)

here() starts at /home/runner/work/IQB-Methods/IQB-Methods

## Load the data
characters <- readRDS(file = here::here("raw_data", "characters.rds"))
psych_stats <- read.csv(
  file = here::here("raw_data", "psych_stats.csv"),
  sep = ";"
)

## Reshape into long format:
psych_stats <- psych_stats %>%
  pivot_longer(
    cols = messy_neat:innocent_jaded,
    names_to = "question",
    values_to = "rating"
  )

## Merge it
characters_stats <- merge(
  x = characters,
  y = psych_stats,
  by.x = "id",
  by.y = "char_id"
)

3. Add a small “mini-analysis”, where you fit some kind of model and describe it in the text and/or in a table.

Hint

One idea would be a linear mixed model investigating the influence of the notability rating on the rating on a specific item, like messy_neat

library(lme4)
library(lmerTest) # for pvalues of fixed effects. 

mod_dat <- characters_stats %>%
  dplyr::filter(question == "messy_neat") %>%
  dplyr::group_by(uni_name) %>%
  ## center for interpretability:
  mutate(notability_uni_mean = mean(notability, na.rm = TRUE)) %>%
  mutate(notability_centered = notability - notability_uni_mean)

mod1 <- lmer(rating ~ notability_centered + (1|name) + (1|uni_name), data = mod_dat)

4. Render your file into pdf, word and html.

APA Exercises

0. Create a fresh, empty folder for this exercise.
1. Install the apaquarto template.

Hint

• Make sure you have a current version of quarto installed. If not, download from here and install. Restart RStudio, it should find the new quarto installation automatically.

• Make sure to set the working directory to an empty folder before installing:

setwd("home/my_project") # Make sure the folder is empty
quarto::quarto_use_template("wjschne/apaquarto")

2. Create a RStudio project in your directory and open it.

3. Repeat the analysis you’ve done in the previous exercise (just copy the code), but this time focus on the APA formating aspect of everything. Try to use as many features we’ve talked about before in your script, especially the automatic table and figure captions and numeration.

4. Convert your manuscript into a word file and a PDF.

Bonus

5. Split up your document into multiple files. This is kind of a preference thing, however I find it easier to keep an overview if I split up my chapters and R code and don’t have a huge .qmd file containing everything. So try it! Create a folder called docs or something similar. It can contain files for functions, for data preperation etc, and can be loaded into your main script with source(). Create another folder called chapters where you put the .qmd files for the single chapters of your work. In the parent directory use a main .qmd file that brings everything together (either with source, or, in the case of other .qmd files, with {{< include _content.qmd >}} ).

Footnotes

1. Image by Towfiqu barbhuiya on Unsplash.