A typical research workflow

1. Prepare data (spreadsheet)

2. Analyse data (R)

3. Write report/paper (Word)

A typical research workflow

However, this workflow can unchain a maelstrom of email attachments …

Problems of a broken workflow

• How did you get these values? What analysis is behind this figure? Did you account for …?

• What dataset was used? Which individuals were left out? Where is the clean dataset?

• Oops, there is an error in the data. Can you repeat the analysis? And update figures/tables in Word!

Copy-pasting can be tedious & problematic

Transcribing numbers from stats software by hand was the largest source of errors - Eubank 2006

Reproducibility problems

You can find difficulties when resuming your own work and you can struggle to reproduce your own results from a few weeks/months/years ago…

Also, revising non-reproducible manuscripts can be very messy

Doing dynamic reports in R

Artwork by @allison_horst

Doing dynamic reports in R

Rmarkdown structure

Rmd = code (R, Python, etc) + text (Markdown)

Rmarkdown structure

Rmarkdown allows to track results

Where does this value come from?

Rmarkdown allows to track results

You can write inline code using the syntax `r `

surv.diff <- 30

In Rmarkdown:

Survival in population A was `r surv.diff` % higher

Output:

Survival in population A was 30 % higher

Rmarkdown allows to track results

You can write inline code using the syntax `r `

data <- iris
nrow(data)

[1] 150

In Rmarkdown:

We measured `r nrow(data)` individuals

Output:

We measured 150 individuals

Code chunk options

Chunk options can also be embbeded with #|

```{r}
#| echo = FALSE
#| eval = TRUE
#| fig.cap = "My figure caption"
plot(iris)
```

Naming chunks

```{r iris-plot1}
#| echo = FALSE
#| eval = TRUE
#| fig.cap = "My figure caption"
plot(iris)
```

Naming chunks

• It can help debugging and navigating long docs

Naming chunks:

• It can help debugging and navigating long docs
• Figure files take chunk name

Text formatting

# Header

## Subheader

*italic*

**bold**

[a link](https://example.com)

Can use Visual Markdown Editor

Or R package “Remedy” to facilitate writing in markdown with RStudio: https://thinkr-open.github.io/remedy/

Regenerate Word/PDF/HTML

With just one click (Knit):

Regenerate Word/PDF/HTML

Spotted error in the data? Want to make new changes? No problem!

Make changes in Rmarkdown document

Click Knit🧶 in Rstudio

The report will update automatically!