Chapter 7 Reproducibility

There are several key cornerstones for doing rigorous and sound scientific research, two of which are reproducibility and replicability (Patil et al., 2016). Replicability is when a study is repeated by other independent research groups. [Reproducibility][sci-reproducibility] is when, given the same data and the same analytical/computational steps, a scientific result can be verified. Both of these concepts are surprisingly difficult to achieve.

This course is about data analysis, so we’ll be focusing solely on reproducibility rather than replicability. At present, there is little effort in science for having research be reproducible, likely due in many ways to a lack of training and awareness. Being reproducible isn’t just about doing better science, it can also:

  1. Make you much more efficient and productive, as you spend less time between coding and putting your results in the document.
  2. Make you more confident in your results, since what you report and show as figures or tables will be exactly what you get from your analysis. No copying and pasting required!

There are many aspects to reproducibility, such as:

  • Organized files and folder, preferably based on a standard or conventional structure.
  • Understandable and readable code that is documented and descriptive.
  • Results from analyses are identical to results presented in scientific output (e.g. article, poster, slides).
  • Results from analyses are identical when code is executed on other machines (results aren’t dependent on one computer).
  • Explicit description or instruction on the order that code and scripts need to be executed.

We’ll cover the first three items in this course.

7.1 Project organization

First off, what exactly does “project” mean? That depends a bit on the group, individual, or situation, but for our purposes, a “project” is anything related to one or more completed scientific “products” (e.g. poster, slides, manuscript, package, teaching material) related to a specific question or goal. This could be “one manuscript publication and associated conference presentations” per project. Confining a project to one “scientific output” facilitates keeping the project reproducible is kept reproducible, all files will relate to that “output,” and can be easily archived once the manuscript has been published. However, this definition could be different depending on your own situation and goals.

The ability to read, understand, modify, and write simple pieces of code is an essential skill for modern data analysis tasks and projects. Here we introduce you to some of the best practices one should have while writing their code. , many of which were taken from published “best practices” articles (Noble, 2009; Taschuk and Wilson, 2017; Wilson et al., 2017).

  • Organise all R scripts and files in a single parent directory using a common and consistent folder and file structure.
  • Use [version control][version-control] to track changes to files.
  • Make raw data “read-only” (don’t edit it directly) and use code to show what was done.
  • Write and describe code for people to read by being descriptive and using a [style guide][r-style-guide].
  • Think of code as part of your research product: Write for an audience or other reader.
  • Create functions to avoid repetition.
  • Whenever possible, use code to create output (figures, tables) rather than manualling creating or editing them.

Managing your projects in a reproducible fashion doesn’t just make your science reproducible, it also makes your life easier! RStudio is here to help us with that by using [R Projects][rstudio-r-projects]. RStudio projects make it straightforward to divide your work into multiple contexts, each with their own working directory, workspace, history, and source documents.

It is strongly recommended that you store all the necessary files that will be used in your code in the same parent directory. You can then use relative file paths to access them (we’ll talk about file paths below). This makes the directory and R Project a “product” or “bundle/package.” Like a tiny machine, that needs to have all its parts in the same place.

7.1.1 Creating your first project

There are many ways one could organise a project folder. We’ll set up a project directory folder using the prodigenr package:

# prodigenr::setup_project("ProjectName")

(You can also create a new project in RStudio by using “File -> New Project ->”Scientific Analysis Project using prodigenr".)

When we use the :: colon here, we are telling R “use setup_project function from the prodigenr package.” This function will then create the following folders and files:

├── R
│   ├──
│   ├── fetch_data.R
│   └── setup.R
├── data
│   └──
├── doc
│   └──
├── .gitignore
├── learningr.Rproj

This forces a specific, and consistent, folder structure to all your work. Think of this like the “introduction,” “methods,” “results,” and “discussion” sections of your paper. Each project is then like a single manuscript or report, that contains everything relevant to that specific project. There is a lot of power in something as simple as a consistent structure. Projects are used to make life easier. Once a project is opened within RStudio the following actions are taken:

  • A new R session (process) is started.
  • The current working directory is set to the project directory.
  • RStudio project options are loaded.

The README in each folder explains a bit about what should be placed there. But briefly:

  1. Documents like manuscripts, abstracts, and exploration type documents should be put in the doc/ directory (including [R Markdown][r-markdown] files which we will cover later).
  2. Data, raw data, and metadata should be in either the data/ directory or in data-raw/ for the raw data.
  3. All R files and code should be in the R/ directory.
  4. Name all new files to reflect their content or function. Follow the tidyverse [style guide for file naming][r-style-guide-files].

Note the DESCRIPTION file. This is used as metadata about the project and is useful when working on R projects. For any project, it is highly recommended to use version control. We’ll be covering version control in more detail later in the course.

7.1.2 Exercise: Better file naming

Look at the list of file names below. Which file names are good names and which shouldn’t you use?

fit models.R
Manuscript version 10.docx
new version of analysis.R

7.1.3 Should you keep your data under version control?

We have a data/ folder for a reason. But you might not want to keep the data under version control, for several reasons:

  1. It’s a large dataset (tens or more Mb in file size)
  2. There are sensitive and/or personally-identifying information in the data

As a rule of thumb, if you can send the data by an email attachment, you could probably put it into Git. Unless there is sensitive or personal data, then don’t. If it isn’t kept under version control, make sure you include a reference to how or where you got the data, either as an R script showing the code you used to import/clean/download it or described in the file.

7.2 Reusability

Part of reproducibility is also making sure your scripts and file organization is “reusable” meaning that others (or yourself) can run it again. So, for instance, making sure to use “relative file paths” compared to “absolute file paths” (we’ll cover these in a bit). Or indicating which other R packages your code depends on. So here we’ll cover how to make sure your scripts and project files are reusable.

7.2.1 Keeping a clean slate

When you finish writing your R code for the day and close the session, you probably will be asked about saving your session. What this does is everything kept in the [environment][environment] (e.g. all objects, functions, or datasets you created and used during the session) get saved to an .RData file. Then, the next time you open up your R session, R will see this .RData file and load everything in that file. Everything you did previously will be loaded into your environment. This seems like a good thing… but it’s not. Imagine eating your dinner on a really dirty plate… that’s not pleasant right? Loading a previous session is like that dirty plate.

So, to make sure you always use a clean slate, we’ll run a handy function from the [usethis][usethis] package to stop R from saving and loading this .RData file, ensuring you have a clean working environment. You only need to run this function once, as it will set the appropriate RStudio settings for you.


We’ll use the usethis package more throughout this chapter and others, as it provides several very useful functions when working with projects.

7.2.2 Packages, data, and file paths

A major strength of R is in its ability for others to easily create packages that simplify doing complex tasks (e.g. creating figures with the [ggplot2][ggplot2] package) and for anyone to easily install and use that package1. You load a package by writing:


Working with multiple R scripts and files, it quickly gets tedious to always write out each library function at the top of each script. A better way of managing this is to create a new file, keep all package loading code in that file, and sourcing that file in each R script. So, to create a new R file in the R/ folder, we’ll use this use_r() function from the usethis package:


This creates a file called package-loading.R in the R/ folder. In the package-loading.R file, add this code to it.


Then in other R scripts in the R/, include this code at the top the script:


The [here][r-here-pkg] package uses a function called here() that makes it easier to manage file paths. What is a file path and why is this necessary? A file path is the list of folders a file is found in. For instance, your resume may be found in /Users/Documents/personal_things/resume.docx. The problem with file paths in R is that when you run a script interactively (e.g. what we do in class and normally), the file path is located at the Project level (where the .Rproj file is found). You can see the file path by looking at the top of the “Console.”

But! When you source() an R script, it may likely run in the folder it is saved in, e.g. in the R/ folder. So your file path R/packages-loading.R won’t work because there isn’t a folder called R in the R/ folder. Often people use the function setwd(), but this is never a good idea since using it makes your script runnable only on your computer… which makes it no longer reproducible. We use the here() function to tell R to go to the project root (where the .Rproj file is found) and then use the file path from that point. This simple function can make your work more reproducible and easier for you to use later on.

We also use the here() function when we import a dataset or save a dataset. So, let’s load in the NYC Dog License dataset. First, save the CSV in the data/ folder. Then create a new R file:


And write these lines in the file:

dog_license <- read_csv(here::here("data/nyc-dog-licenses.csv"))

That is how we will load data in from now on.

Here are a few other tips for keeping your code reusable:

  • When encountering a difficult problem, try to find R packages or functions that do your problem for you2.
  • Split up your analyses steps into individual files (e.g. “model” file, “plot” file). Then source those files as needed or save the output in data/ to use it in other files.
  • Try not to have R scripts be too long (e.g. more than 500 lines of code). Keep script sizes as small as necessary and as specific as possible (have a single purpose). A script should have an end goal.

7.3 Readability

There are two reasons we write code: to instruct the computer to do something and to record the steps we took to get a particular result for us or others to understand. For computers, how or what you write doesn’t matter, as long as the code is correct. Computers don’t need to understand the code. But humans do need to understand it. We need clear language and explicit meaning in order to understand what is going on. Humans write code, humans read code, and humans must maintain it and fix any errors. So, what you write and how you write it is extremely important.

Like natural human languages, R has a relaxed approached to how R code is written. This has some nice advantages, but also some major disadvantages, notably that writing styles can be quite different across the world or even within one’s own code. So, it’s important to stick to some guidelines, for instance, as laid out by the tidyverse [style guide][r-style-guide]. Some other tips include:

  • Write your code assuming other people will be reading it.
  • Stick to a [style guide][r-style-guide]. (We’re repeating this because it’s really important!)
  • Use full and descriptive words when typing and creating objects.
  • Use white space to separate concepts (empty lines between them, use spaces, and/or tabs).
  • Use RStudio R Script Sections ("Code->Insert Section" or Ctrl-Shift-R) to separate content in scripts.

Even though R doesn’t care about naming, spacing, and indenting, it really matters how your code looks. Coding is just like writing. Even though you may go through a brainstorming note-taking stage of writing, you eventually need to write correctly so others can understand, and read, what you are trying to say. Brainstorming and exploratory work is fine, but eventually you need to write code that will be legible. That’s why using a [style guide][r-style-guide] is really important.

7.3.1 Exercise: Make the code more readable

Using the [style guide][r-style-guide] found in the link, try to make these code more readable. Edit the code so it follows the correct style and so it is easier to understand and read. You don’t need to understand what the code does, just follow the guide.

# Object names
c <- 9
mean <- function(x) sum(x)

# Spacing
x[ ,1]
x[ , 1]
mean (x, na.rm = TRUE)
mean( x, na.rm = TRUE )
function (x) {}
mean(x, na.rm=10)
sqrt(x ^ 2 + y ^ 2)
df $ z
x <- 1 : 10

# Indenting
if (y < 0 && debug)
message("Y is negative")

FIXME: The below “details” will need to be dealt with since PDF doesn’t allow this FIXME: Maybe move to a solutions section at the end of chapter?

Click for a possible solution

The old code is in comments and the better code is below it.

# Object names

# Should be camel case
# DayOne
# dayone

# Should not over write existing function names
# T = TRUE, so don't name anything T
# T <- FALSE
false <- FALSE
# c is a function name already. Plus c is not descriptive
# c <- 9
number_value <- 9
# mean is a function, plus does not describe the function which is sum
# mean <- function(x) sum(x)
sum_vector <- function(x) sum(x)

# Spacing
# Commas should be in correct place
# x[,1]
# x[ ,1]
# x[ , 1]
x[, 1]
# Spaces should be in correct place
# mean (x, na.rm = TRUE)
# mean( x, na.rm = TRUE )
mean(x, na.rm = TRUE)
# function (x) {}
# function(x){}
function(x) {}
# height<-feet*12+inches
height <- feet * 12 + inches
# mean(x, na.rm=10)
mean(x, na.rm = 10)
# sqrt(x ^ 2 + y ^ 2)
sqrt(x^2 + y^2)
# df $ z
# x <- 1 : 10
x <- 1:10

# Indenting should be done after if, for, else functions
# if (y < 0 && debug)
# message("Y is negative")
if (y < 0 && debug)
    message("Y is negative")

7.3.2 Automatic styling with styler

You may have organised the exercise by hand, but it’s possible to do it automatically. The tidyverse [style guide][r-style-guide] has been implemented into the [styler][styler-pkg] package to automate the process of following the guide by directly re-styling selected code. The styler snippets can be found in the Addins function in the RStudio “Addins” menu after you have installed it.

RStudio also has its own automatic styling ability, through the menu item "Code -> Reformat Code" (or Ctrl-Shift-A). Try both methods of styling on the exercise code above. There are slight differences in how each method works and they both aren’t always perfect. For now, let’s stick with using the styler package. The styler functions work on R code within both .R script files as well as R code within .Rmd documents, which we will cover later in this lesson.

There are several styler RStudio addins, but we’ll focus on the two:

  • "Style selection": Highlight text and click this button to reformat the code.
  • "Style active file": Code in the .R or .Rmd file you have open and visible in RStudio will be reformatted when you click this button.

There are two other styler functions that are also useful:

  • styler::style_file("path/to/filename"): Styles the whole file as indicated by the file path in the first argument. Can be either an .R or .Rmd file.
  • styler::style_dir("directoryname"): Styles all files in the indicated directory in the first argument.

Let’s try the styler::style_file() function out. Inside a file called non-styled-code.R, it has:

# Spacing
mean (x, na.rm = TRUE)
function (x) {}
sqrt(x ^ 2 + y ^ 2)
df $ z
x <- 1 : 10

# Indenting
if (y < 0 && debug)
message("Y is negative")

Then we run:

Styling  1  files:
 testing-styler.R ℹ
Status  Count   Legend
✔   0   File unchanged.
ℹ   1   File changed.
✖   0   Styling threw an error.
Please review the changes carefully!

Which changes the file to be styled!

# Spacing
x[, 1]
mean(x, na.rm = TRUE)
function(x) {}
height <- feet * 12 + inches
sqrt(x^2 + y^2)
df$ z
x <- 1:10

# Indenting
if (y < 0 && debug) {
  message("Y is negative")

This is more or less everything that the styler package does.

7.3.3 Exercise: Use styler to fix code formatting

Use the styler package function on the code from the previous exercise by either running styler::style_file() or with the "Style selection" addin when highlighting the code.

7.4 Integrating text, code, and results

The most obvious demonstration of reproducibility is when the results obtained from executing the analysis code (by an independent entity) are indentical to the results presented in the scientific output such as in an article. When there is agreement between these two, reproducibility has been more or less acheived. In R, there are tools available to completely ensure that this happens by directly inserting the results from the code into the scientific ouput. This is done by using [R Markdown][r-markdown], which interweaves R code with text. So instead of, for example, manually inserting a figure, you write R code within the document to insert the figure for you! Using [R Markdown][r-markdown] can save so much time and get your work that much closer to being reproducible.

There are many other advantages to using R Markdown. From the single R Markdown format you can use it to create manuscripts, posters, slides, websites, books, and many more from simply using R Markdown. In fact, this book was written using R Markdown. As a bonus, switching between citation formats or Word templates for different journals is easier than doing it with Word.

7.4.1 Markdown

R Markdown uses, well, Markdown as the format to convert to multiple document types. Fun fact: This website is built based on Markdown! While there are many “flavours” of Markdown that have been developed over the years, R Markdown uses the [Pandoc][pandoc-markdown] version. Pandoc is a combination of pan which is Latin for “all” and doc which means document.

Markdown is a “markup language” meaning that special characters mean certain things, which we will cover below.

To format text, such as to bold or make a list, you use the special characters. You write Markdown as plain text (like R code), so you don’t need any special software (like you do with Word documents). Most features needed for writing a scientific document are available in Markdown, but not all. Tip: Try to fit your writing and document creation around what Markdown can acheive, rather than force or fight Markdown to do something it wasn’t designed to do.

All right, let’s create and save an R Markdown file. In RStudio, go to File -> New File -> R Markdown. A dialog box will pop up. In the “Title” section, type in Reproducible documents and in the “Author” section type in your name. Choose the HTML output format. Save this file as learning-rmarkdown.Rmd in the doc/ folder.

Inside the file, there is a bunch of text that shows some basic formatting you can use for writing Markdown. For now, delete everything except the top part of the file (the part surrounded by ---). This part is called the YAML header, which we will cover more a bit later. Try converting the file to HTML by hitting the “Knit” button at the top or by typing out Ctrl-Shift-K.

“Knit” button. Headers

Creating headers (like chapters or sections) is indicated by using one or more # at the beginning of a line, prefixing some text:

# Header level 1


## Header level 2


### Header level 3


This creates the section headers as seen directly above (“Headers”) or below (“Text formatting”). The header text must be on one line, otherwise the next line is interpreted as paragraph text.

See how it looks by “Knitting” the document (“Knit” button or Ctrl-Shift-K). Text formatting

To format text individually, surround the text with the special characters, as shown here:

  • **bold** gives bold.
  • *italics* gives italics.
  • super^script^ gives superscript.
  • sub~script~ gives subscript.

What if you want to use the special character as simple text? Prefix it with an \, so \* becomes *, \^ becomes ^, and \~ becomes ~. Lists

To create an unnumbered list, do:

- item 1
- item 2
- item 3

which gives…

  • item 1
  • item 2
  • item 3

Notice the empty lines above and below the line, those are important. To create a numbered list, do:

1. item 1
2. item 2
3. item 3

which gives…

  1. item 1
  2. item 2
  3. item 3

See how it looks by “Knitting” the document (“Knit” button or Ctrl-Shift-K). Blockquotes

Sometimes (probably not too commonly), you may need to quote someone by using “blockquotes.” To do that, do:

> Blockquote

which gives…


Blockquotes can be as many lines as you want. To stop a paragraph from being in the blockquote, separate the text with an empty line:

> Bockquote paragraph

Regular paragraph Adding footnotes

Footnotes can be added by using [^some-text-label], such as:


[^1]: Footnote content.

which gives…


So you can write some text, add some footnotes within, and include the footnote content right below the paragraph:

Paragraph text[^1], with some more text[^reference].

[^1]: This is the first footnote.
[^reference]: This is the next footnote.

More paragraphs.

Notice the empty lines in between. The footnote should also be on one line, though it isn’t strictly necessary. See how it looks by “Knitting” the document (“Knit” button or Ctrl-Shift-K). Inserting pictures, images, or figures

You can include externally created (i.e. not by an R code chunk, discussed later on) png, jpeg, or pdf image file by adding (:

![Image caption here.](path/to/image/file.png)

So something like this:

![Steps to being more reproducible. Source DOI: 10.1038/d41586-018-05990-5](figures/reproducibility/code-sharing-steps.png)

which gives…

Steps to being more reproducible. Source DOI: 10.1038/d41586-018-05990-5

Tip: Can also include links to images from the Internet, as a URL link.

If you want to modify the width or sizing, append something like {width=##%} to the end of the image insertion:


which gives…

Caption. Inserting (simple) tables

You can insert tables with Markdown too. We wouldn’t recommend doing it for complicated tables though, as it can get tedious fast! (A recommended approach for more complex or bigger tables is to make the table contents as a data frame in R first and then use the knitr::kable() function to create the table, as we’ll cover in the R Markdown section below). You can even include Markdown text formatting inside the table:

|   | Fun | Serious |
| **Happy** | 1234 | 5678 |
| **Sad** | 123 | 456 |

which gives…

Fun Serious
Happy 1234 5678
Sad 123 456

The |--:| or |:--| tell the table to right-align or left-align, respectively, the values in the table column. Center-align is |:--:|. See how it looks by “Knitting” the document (“Knit” button or Ctrl-Shift-K). Exercise: Try to re-create a document using Markdown

  1. Open this link. This is a HTML file that has been created by using Markdown formatting.
  2. Create a new R Markdown file, with output type “HTML,” and save it as mimic-html-file.Rmd.
  3. Delete all the automatically added text except the top part Inside the R Markdown file
  4. Write text using Markdown formatting so that you can create a html_document that looks exactly like the linked file.
  5. Knit the R Markdown document. Confirm that your version looks the same as the above version.

7.4.2 R Markdown

[R Markdown][r-markdown] is an extension of Markdown that weaves together R code with Markdown formatted text all together in a single document. Output from the R code gets inserted directly into the document for a seamless integration of document writing and analysis reporting. YAML header/metadata

Most Markdown documents (especially for R Markdown) include YAML metadata at the top of the document, surrounded by ---. YAML is a data format, like CSV, that contains the metadata and various options that R Markdown uses for the entire document. Data in YAML is stored in the form variable: value. For instance, title or author is paired with their respective “values” and then used by Markdown when creating the document. Other options are also included here, such as what the converted output document should be, such as Word. There are many more output formats to choose from (e.g. slides, websites, books). The YAML header looks something like this:

title: "Document title"
author: Your Name
output: html_document

Here, there are three variable-value pairings: title, author, and output. In the output variable, the R Markdown function html_document is given so that the output document format is converted to HTML. There are also word_document and pdf_document settings. For now, we’ll focus on the html_document output. Usually when you create the R Markdown file, this YAML header gets added automatically. There are additional options you can set in the output field, which we will cover later on. Using R code chunks

R Markdown’s primary function is to allow combining text and R code in the same document, which is incredibly powerful and useful! All R code chunks have the appearance:

```{r chunk-label-name, chunk.option="...", chunk.option=...}
...R code...

Notice that chunk options need to be on the same line (one single line). Any R code in the code chunk gets evaluated and any output gets inserted into the document. So if the code prints to the console, it will get inserted into the document.

The r tells R Markdown to run R on the code chunk, while the [chunk label][chunk-label] differentiates the chunk from other chunks. Using a chunk label also helps navigate a document when you use RStudio’s “Document outline” (Code -> Show Document Outline). It also does a few other things.

Note: Standard practice is that code [chunk labels][chunk-label] should be named without _, spaces, or . and instead should be one word or be separated by -.

Let’s load in some packages and data:

```{r setup}
co2_data <- read_csv(here::here("data/co2.csv")) %>%
  filter(co2_standard > 0)

When running your code chunks interactively as you develop and write your document, R Markdown will look for a code chunk labeled setup to run first (for instance, to load all packages used in a document). Hence we name this chunk “setup.” Inserting figures

One of the most obvious benefits to using R Markdown is to automatically insert a plot. To do that we do:

```{r co2-time-plot}
ggplot(co2_data, aes(x = year, y = co2_standard)) +

What if we want to include the plot but not the code? Easy! Set the chunk option echo to FALSE:

```{r co2-time-plot, echo=FALSE}
ggplot(co2_data, aes(x = year, y = co2_standard)) +

Or what if you don’t want R to run the code chunk, but still show the code? Set the chunk option eval (for evaluate) to FALSE:

```{r co2-time-plot, eval=FALSE}
ggplot(co2_data, aes(x = year, y = co2_standard)) +

Since we have a figure, we can change some width, height, and alignment options, as well as add a caption with the fig.width, fig.height, fig.align, and fig.cap (respectively):

```{r co2-time-plot, fig.cap="Add your figure title here.", fig.height=4, fig.width=7}
ggplot(co2_data, aes(x = year, y = co2_standard)) +
ggplot(co2_data, aes(x = year, y = co2_standard)) +
Add your figure title here.

Figure 7.1: Add your figure title here.

What if we want to first run some code before running the plot, but don’t want to include the output of the other code? Make sure that the output doesn’t “print.” So letting R evaluate both dataframe and plot, the code chunk will output and insert both into the knitted document.

```{r co2-time-plot-after-2000-1, fig.cap="Add your figure title here.", fig.height=4, fig.width=7}
co2_after_2000 <- co2_data %>%
    filter(year >= 2000)

# Print dataframe

# Print plot
ggplot(co2_after_2000, aes(x = year, y = co2_standard)) +
## # A tibble: 680 x 5
##     year month date_numeric co2_standard station               
##    <dbl> <dbl>        <dbl>        <dbl> <chr>                 
##  1  2000     1        2000.         369. Mauna Loa, Hawaii, USA
##  2  2000     2        2000.         369. Mauna Loa, Hawaii, USA
##  3  2000     3        2000.         371. Mauna Loa, Hawaii, USA
##  4  2000     4        2000.         372. Mauna Loa, Hawaii, USA
##  5  2000     5        2000.         372. Mauna Loa, Hawaii, USA
##  6  2000     6        2000.         372. Mauna Loa, Hawaii, USA
##  7  2000     7        2001.         370. Mauna Loa, Hawaii, USA
##  8  2000     8        2001.         368. Mauna Loa, Hawaii, USA
##  9  2000     9        2001.         367. Mauna Loa, Hawaii, USA
## 10  2000    10        2001.         367. Mauna Loa, Hawaii, USA
## # … with 670 more rows
Add your figure title here.

Figure 7.2: Add your figure title here.

Compare to this next code chunk, which will only output (“print”) the plot. See how we don’t include code that would send anything to the console to be “printed?” Only things that get “printed” will be included in the R Markdown output document. Also notice how we renamed the chunk label? In R Markdown you can’t have duplicate code chunk labels.

```{r co2-time-plot-after-2000-2, fig.cap="Add your figure title here.", fig.height=4, fig.width=7}
co2_after_2000 <- co2_data %>%
    filter(year >= 2000)

# Print plot
ggplot(co2_after_2000, aes(x = year, y = co2_standard)) +
Add your figure title here.

Figure 7.3: Add your figure title here. Exercise: Add some figures to a R Markdown document

  1. Create a new R Markdown file (“File -> New File -> R Markdown”), providing the title, author name (your name), and setting the output to HTML.
  2. Save the file as using-rmarkdown.Rmd in the doc/ folder.
  3. Delete all text except the YAML header.
  4. Create an R code chunk and call the label “setup.” Write code so the packages and data are loaded.
  5. Create another code chunk and call it “plot-licenses-by-year.” Write R code to create a point plot (geom_point()) of the year on the x axis and number of licenses on the y axis.
  6. Knit the document and see what the output looks like.
  7. Change the theme of the plot to another builtin theme (hint: themes start with theme_). Using R code chunks to insert tables

You can also create tables by using the kable() function from the knitr package. Let’s create a table of the mean CO2 concentration over the years at each monthly period for each station.

```{r mean-co2-table}
co2_data %>%
    select(station, month, co2_standard) %>%
    group_by(station, month) %>%
    summarise(MeanCO2 = round(mean(co2_standard, na.rm = TRUE), 2)) %>%
    spread(station, MeanCO2) %>%
    knitr::kable(caption = "Table caption here.")
## `summarise()` has grouped output by 'station'. You can override using the `.groups` argument.
Table 7.1: Table caption here.
month Alert Station, NWT, Canada Cape Grim, Tasmania, Australia Mauna Loa, Hawaii, USA
1 378.54 364.63 354.61
2 380.06 364.65 355.96
3 380.75 364.73 356.11
4 381.07 364.89 357.47
5 380.36 363.34 356.57
6 378.43 363.70 356.64
7 371.33 364.16 354.50
8 365.98 364.60 352.51
9 365.11 364.85 350.85
10 369.21 364.90 351.49
11 374.77 364.80 352.24
12 376.94 364.68 353.53 Inline R code

Often you might have results inside the text you are writing. Here you can include R code within the text so that the results are inserted directly into the document. It looks like:

The mean of CO2 is `r round(mean(co2_data$co2_standard, na.rm = TRUE), 2)`.

Which gives…

The mean of CO2 is round(mean(co2_data$co2_standard, na.rm = TRUE), 2).

Keep in mind that inline R code can only insert a single number or character value, nothing more. Citing literature with R Markdown

No scientific writing is complete without being able to include references. If you want to insert a citation, use the Markdown key [@Cone2016], which will look like (Conery, 2016). The text Cone2016 is the key that the bibliography manager uses to identify a specific reference. Adding more references is done by separating by a ;, so like [@AuthorYear; @Author2Year; @Author3Year]. The resulting citation reference will be inserted at the bottom of the document. To get the bibliography to work, you’ll also need to add a line to the YAML header like this:

title: "My report"
author: "Me!"
bibliography: my_references.bib

The my_references.bib is a .bib file found in the same folder as the .Rmd file. So in our case, the .bib file is in the doc/ folder. You can also use other bibliography manager files, such as EndNote. See this documentation for which bibliography managers can be used.

Since all references are appended to the bottom of the document, it’s good to add a final “Reference” section header to the end of your file, like so:

# References Making your report prettier

This part mostly applies to HTML-based and PDF4 outputs, since programmatically modifying or setting templates in Word documents is rather difficult5. Changing broad features of a document can be done by setting the “theme” of the document. Add an option in the YAML metadata like:

title: "My report"
        theme: sandstone

Check out the R Markdown [documentation][rmd-themes] for more types of themes you can use for HTML documents, and advanced topics such as parameterized R Markdown documents. Most of the [Bootswatch][bootswatch] themes are available for use in R Markdown to HTML conversion.

Want to add a Table of Contents? Easy! Add toc: true to the YAML header:

title: "My report"
        theme: sandstone
        toc: true

Adding a toc only works for PDF and HTML, but not Word documents. Exercise: Add a summary table, inline results, and a prettier theme

  1. Use the R Markdown file from the previous exercise (using-rmarkdown.Rmd).
  2. Create three new header 1 # sections: Objective, Results, Conclusion.
  3. Write in the “Objective” section an idea you have about the Dog License dataset. It can be as simple as “How many dogs are there in New Year City?” Include an italics in this sentence.
  4. Create three new code chunks in the “Results” section: One for setup, one for plot-dogs, and one for table-dogs.
  5. Write R code to load the packages and data in the setup chunk. Knit the document to see what it looks like.
  6. Write R code to create a simple ggplot2 plot in the plot-dogs chunk related to your “Objective.” Knit the document to see what it looks like.
  7. Write R code to create a simple summary table using kable() in the table-dogs chunk related to your “Objective.” Knit the document to see what it looks like.
  8. Write an observation you made about the data from the plot and table in the “Conclusions” section. Include a bold text in this section.
  9. Check out the [Bootswatch][bootswatch] themes and change your HTML theme to something else and add a Table of Contents.

7.5 Key Points

  • A structured and standard project folder and file layout is the first step to having a reproducible data analysis project.
  • Writing documents in R Markdown can reduce the time spent on manual tasks since results can be easily re-generated and inserted into the final document to improve reproducibility.
  • usethis has several helper functions for managing data analysis projects.
  • Following a style guide and emphasizing readable code can lead to better quality code and to code that is more likely to be reproducible and reusable.
  • Using Markdown to write documents is a great way to improve accessibility (since it is plain text only) and allows you to generate multiple types of output (HTML, PDF, slides, etc) from a single document source.

7.6 Additional learning resources and material

For learning:

For help:

Note: Source material for this chapter was modified from, as well as many other resources (see

  1. You may encounter some who say you shouldn’t rely on packages and to only use base R functions. However, this is seriously bad advice since the ecosystem of R packages can greatly simplify your life doing data analysis. Plus, packages greatly expand and enhance the capability of R, so make use of packages! If someone invents a wheel, why wouldn’t you use it?↩︎

  2. You may hear some people say “oh, don’t bother with R packages, do everything in base R”… don’t listen to them. Do you build a computer from scratch everytime you want to do any type of work? Or a car when you want to drive somewhere? No, you don’t. Make use of other people’s hard work to make tools that simplify your life.↩︎

  3. Footnote content.↩︎

  4. Knitting to PDF requires LaTeX, which you can install from [tinytex][tinytex]. After you install LaTeX you can create truly beautifully typeset PDF documents.↩︎

  5. If you really want to do it, the best way is to create your template in the .odt, and then convert to .docx.↩︎