• Sorting out your RStudio working environment.
• Getting you ready to write reproducible reports in RMarkdown.

• Results are communicated through reports such as peer reviewed scientific articles.
• Reported results should be reproducible!
• Data and code should be made available to others.
• It is difficult to relate code to figures, tables, numbers etc. especially where numbers were copy-pasted from software.
• Also manual processing of data and generation of reports is error prone.

• Create a new R chunk: try CTRL+ALT+I or CMD+ALT+I (i.e. the letter “i”)
• Create a chunk called packages and load libraries needed:

library(tidyverse)

• Create a new chunk called loaddata and load Blomkvist et al. (2017) data:

blomkvist <- read_csv("data/blomkvist.csv") %>% 
  select(id, age, smoker, sex, rt = rt_hand_d) %>% 
  drop_na()

• Note ```{r setup, echo=FALSE}
• setup is a label of this chunk (optional; useful for cross-referencing of figures and tables).
• Chunk configuration option echo = FALSE: don’t display chunk in output; echo = TRUE: display chunk.

knitr::opts_chunk$set(message = FALSE, # don't return messages
                      warning = FALSE, # don't return warnings
                      comment = NA, # don't comment output
                      echo = TRUE, # display chunk (is default)
                      eval = TRUE, # evaluate chunk (is default)
                      out.width = '45%',  # figure width
                      fig.align='center') # figure alignment

# This is a section header

## This is a subsection header

# This is another section header

## This is another subsection header

### This is a subsubsection header

• Create a new R chunk with label myscatterplot
• Set echo = F cause we only need the figure.
• Add a figure caption fig.cap = "A scatterplot." in the chunk configurations.

library(psyntur)
scatterplot(x = age, y = rt, data = blomkvist)

• Or using ggplot2

ggplot(blomkvist, aes(x = age, y = rt)) +
    geom_point() +
    theme_classic()

• Change the default size to out.width = 50%.
• Cross-reference figure using \ref{fig:myscatterplot} in the text.

"A scatterplot of age and reaction time can be found in Figure \ref{fig:myscatterplot}."

Cross-referencing figures requires the use of pdf_document2 as output format. In preamble replace output with:

output: 
  bookdown::pdf_document2:
      toc: false          # removes the table of contents
      number_sections: false   # removes section numbering

You may need to install bookdown. We also turning off the table of content (toc) and the section numbers.

header-includes:
- \usepackage{booktabs}

• We just need booktabs to improve type setting.

• Reporting results in tables formatted to a high standard.
• Calculate descriptives:

library(psyntur)
(smoker_age <- describe(data = blomkvist, by = smoker, mean = mean(age), sd = sd(age)))

# A tibble: 3 × 3
  smoker  mean    sd
  <chr>  <dbl> <dbl>
1 former  65.2  16.9
2 no      53.0  21.3
3 yes     50.6  17.5

• This format isn’t good enough for papers.

library(kableExtra)
smoker_age %>% 
  kable(format = 'latex',
        booktabs = TRUE,
        digits = 2,
        align = 'c', # centre value in each column
        caption = 'Descriptives of age by smoker.') %>% 
  kable_styling(position = 'center') # centre position of table

• Also, label your chunk “smoker” and cross-reference the table in the text using Table \ref{tab:smoker}.

• Add to the YAML preamble:

bibliography: refs.bib
biblio-style: apalike

• Create a file (in RStudio) called refs.bib (save in same working directory as your .Rmd file)

• Get the .bib entry for Blomkvist et al. (2017) from Google Scholar and paste it into refs.bib:
  • Copy the title “Reference Data on Reaction Time and Aging Using the Nintendo Wii Balance Board” into Google Scholar
  • Click cite and BibTeX
  • Copy the .bib entry into refs.bib
• Note the citation key blomkvist2017reference
• Cite Blomkvist et al. (2017) using @blomkvist2017reference or [@blomkvist2017reference].
• At the end of your document create a section “# References”

# Fit the model and get the summary
model <- lm(rt ~ sex, data = blomkvist)
model_summary <- summary(model)

# Extract R^2
r2 <- model_summary$r.sq

The $R^2$ for this model is `r round(r2, 2)`.

Renders “The \(R^2\) for this model is 0.03.”

# Extract F statistic
f_stat <- model_summary$fstatistic
p_value <- pf(f_stat[1], f_stat[2], f_stat[3], lower.tail = FALSE)

The model summary can be summarised like so: $F(`r round(f_stat[2])`, `r round(f_stat[3])`) =
`r round(f_stat[1],2)`$, $p `r format.pval(p_value, eps = 0.01)`$.

Renders “The model summary can be summarised like so: \(F(1, 263) = 7.48\), \(p <0.01\).”

p <- c(0.05, 0.02, 0.011, 0.005, 0.001)
format.pval(p, eps = 0.01)

[1] "0.05"  "0.02"  "0.01"  "<0.01" "<0.01"

• Strings are parsed using \(\LaTeX\) and typeset accordingly when used between '$' symbols for inline mode.
• For example $\beta$ renders \(\beta\).
• Subscripts: $\beta_0$ is \(\beta_0\) and using '{}' for more than one symbol as in $\beta_{01}$ which is \(\beta_{01}\)
• How would you write \(\beta_{0_1}\)?
• Superscripts: '^' as in $\sigma^2$ which is \(\sigma^2\).
• How would you write \(\sigma^{2^2}\)?
• How about \(\sigma_e^2\)?

install.packages("rmdformats")

or

install.packages("remotes")
remotes::install_github("juba/rmdformats")

• Create from template:
  • File > New File > R Markdown (e.g. readthedown or robobook for documents)
  • rmdshower::shower_presentations and ioslides_presentation for slides
• Rmarkdown example
• Online sharing on RPubs (“Publish”)

• Example: https://osf.io/vayhq/ published in Roeser et al. (2024)
• Installation: https://github.com/crsh/papaja

• You will use RMarkdown for your assignment (get started now).
• Create a list of continuous response variables that can be found in Psychology.
• Find a suitable data set (should have a mix of continuous and categorical variables).
• Use the RMarkdown we started and replace data with your own, calculate descriptives, and a visualisation.

• Read the lecture notes on RMarkdown.
• For formatting tables using kableExtra check here
• Check the rmarkdown website.
  
• And chapter 27 in “R for Data Science” (Wickham and Grolemund 2016).