Stéphane Guillou 2022-05-13
If you don’t have R and RStudio installed already, we have installation instructions
- If you are using your own laptop please open RStudio
- Make sure you have a working Internet connection
- On the Library’s training computers:
- Log in with your UQ username and password
- Make sure you have a working Internet connection
- Open the ZENworks application
- Look for “RStudio”
- Double click on RStudio, which will install both R and RStudio
With RStudio open, let’s make sure we have the necessary packages installed by running this command (this might take a few minutes):
install.packages(c("tidyverse", "knitr", "plotly", "htmltools"))This will install the Tidyverse packages, the knitr package to render reproducible reports, the plotly packages for interactive visualisations, and the htmltools for having all bases covered regarding rendering HTML documents.
If R asks about installing a binary or building from source, pick the binary option: it will be faster!
R is a great tool to go from importing to reporting. Today, we focus on the “reporting” part.
Using R, RStudio, the R Markdown syntax and the knitr package, we can create reproducible reports that mix code and prose. If the underlying data changes, we only need to replace the original data file and “knit” the report once more, which updates all its contents in one click.
Use the project menu (top right) to create a “New project…”. Let’s name this one “reports”.
We also need to create a new R Markdown file: “File > New File > R Markdown…”. We can change the title of the report, and the author as well. Let’s stick to “HTML document” as an output for now.
See how the document is already populated with a template? Scroll through and have a look at how it is structured. The three main elements are:
- a YAML header at the top, between the
---tags; - Markdown sections, where we can write prose, format text and add headers;
- and code chunks, in between
```where we can write R code.
But before we edit this document, let’s go straight to the “knit” button at the top of the source panel. Clicking that button will compile a document from the R Markdown file. You should see the process unfolding in the R Markdown tab, and the HTML document pop up in a separate window when it is finished.
See how the document contains a title, headers, code input and output, and explanations?
Let’s remove everything below our YAML header, and start writing our own report!
To add a header, we can start a line with ##: this will be a
header of level 2. The number of hash symbols corresponds to the level
of the header. See how the highlighting changes in the source editor?
We are going to deal with greenhouse gas emissions for Australia, so let’s add a header and some text about the source of the data and importing it. For example:
## National Greenhouse Gas Inventory data
Our data comes from the NGA, and is released under a [CC-BY](https://creativecommons.org/licenses/by/4.0/) licence. The latest release can be found on [this page](https://www.industry.gov.au/data-and-publications/national-inventory-reports)
The values are reported in Mt CO<sub>2</sub>-e.
Notice how we used a [text](link) syntax to add a link to a website?
We can also style our text by surrounding with other tags:
**for bold*for italic
Try to style your text, and add a header of level 3 for a section on “importing the data”. Knit the document to see if it works!
We can now add a code chunk to include some R code inside our
reproducible document. To add a code chunk, click the “Insert” button at
the top of the source panel, and click “R”. You can see that the
language of the code chunk is defined at the top, with {r}.
Let’s import the Tidyverse, by including this code in the chunk:
library(tidyverse)Notice that you can run your chunks of code one by one by clicking the green “play” button at the right of the chunk: you don’t have to knit the whole document every time you want to test your code.
Now, try to knit the document and see what it looks like.
Note that you can insert an r chunk by using the Ctrl+Alt+I (or Cmd+Alt+I on Mac).
Inside a new chunk, add some code to import the dataset located
here
into an object called ghg.
ghg <- read_csv("https://raw.githubusercontent.com/uqlibrary/technology-training/master/R/reports/aus_ghg_2017.csv")Clicking “Knit” will automatically save your .Rmd file as well as the HTML output.
Now, we can add a chunk to show the data, by including this code in it:
ghg## # A tibble: 28 × 8
## year `Agriculture, …` `Forestry - Ch…` Mining Manufacturing `Electricity, …`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1990 286. -19.1 44.6 68.0 148.
## 2 1991 266. -14.8 46.1 67.9 150.
## 3 1992 197. -19.7 48.5 68.1 153.
## 4 1993 178. -21.1 48.8 68.5 152.
## 5 1994 168. -17.6 46.7 68.3 153.
## 6 1995 144. -18.3 48.7 68.1 159.
## 7 1996 148. -18.1 51.0 66.2 161.
## 8 1997 156. -22.6 54.4 66.9 166.
## 9 1998 140. -22.5 55.4 67.5 178.
## 10 1999 151. -22.0 53.0 68.8 184.
## # … with 18 more rows, and 2 more variables:
## # `Services, Construction and Transport` <dbl>, Residential <dbl>
Note that the working directory for an R Markdown document will be the .Rmd file’s location by default (and not necessarily the working directory of the R project your are in). That is why it is a good idea to save your R Markdown file at the top of your R Project directory if you want consistency between your scripts and your R Markdown file.
In our example, we load a CSV file from the Internet, but if we had a data file stored locally, it is important to keep that in mind.
You can change the default behaviour by using the Knit dropdown menu and choosing an option in “Knit directory”.
Notice how our two first chunks show some messages as an output? We might want to remove that if it is not important and we don’t want to include it in the report. At the top of your chunk, you can modify the options like so:
{r message=FALSE}
The code will be executed and the output (if there is any) will be shown, but the messages won’t!
There are many options to choose from, depending on what you want to do and show with your chunk of code. For example, to hide both messages and warnings, and only show the output of the code (without showing the underlying code), you can use these options, separated by commas:
{r message=FALSE, warning=FALSE, echo=FALSE}
It also is a good idea to label your chunks, especially in longer documents, so you can spot issues more easily. It won’t be shown in the report, but will be used in the R Markdown console and can be used to navigate your script (with the dropdown menu at the bottom of the source panel). For example, for our first chunk:
{r load_packages, message=FALSE}
It is also possible to include a chunk at the top of your document, that will detail the default options you want to use for all you chunks. That is particularly useful if you want to define a default size for all your figures, for example.
Here is an example of a chunk you might use to change default options:
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
```
That would make sure that, by default:
- The code is shown, but
- the messages and warnings are hidden.
It should be straight forward to find where an issue comes from when knitting a report does not work.
Try changing a chunk code so the code is not valid. What can you see in the R Markdown console?
Double-click on the error message to jump the problem.
Let’s keep populating our report with more code. Our data is not respecting the tidy data principles, so let’s fix that first with a tidyr function:
ghg_tidy <- pivot_longer(ghg,
-year,
names_to = "sector",
values_to = "emissions")We can also include code that will be executed inside Markdown text. For example, you can write the following sentence:
The dataset contains GHG emissions for the period
`r min(ghg$year)`to`r max(ghg$year)`. The maximum GHG emissions recorded for the mining sector is`r round(max(ghg$Mining), 2)`.
We can also use this feature to auto-update the date of your report
every time it is knitted. Replace the date line in the YAML header
with this one:
date: "`r Sys.Date()`"
Now, try knitting the report again.
We can also include a visualisation using, for example, ggplot2:
ggplot(ghg_tidy, aes(x = year, y = emissions, colour = sector)) +
geom_line() +
ylab("emissions (Mt CO2e)")
If you want to hide the code that created an output, like for this plot, you can add the option
echo=FALSEto it.
Finally, let’s create an interactive version of our plot:
library(plotly)
p <- ggplot(ghg_tidy, aes(x = year, y = emissions, colour = sector)) +
geom_line() +
ylab("emissions (Mt CO2e)")
ggplotly(p)This will work in a HTML document, but will most likely fail in other output formats.
If you want to change the size of your visualisations, you can tweak the
width and height with chunk options. However, you make that consistent
for all your figures, by using an extra default option in the setup
chunk (the one that contains the {r setup, include=FALSE} header, at
the top of the document). For example:
knitr::opts_chunk$set(fig.width = 8)
We have an updated version of the dataset. The only thing we need to do to update the whole report is point the data import code to the new file, at the top of our document, changing the year to “2019”:
ghg <- read_csv("https://raw.githubusercontent.com/uqlibrary/technology-training/master/R/reports/aus_ghg_2019.csv")Knitting again will update all the objects and visualisations for us! This is the power of reproducible reports in R.
With reproducible reports, you can potentially structure and write (most of) a report even before you have your research project’s final dataset. (Well, at least the data analysis part, maybe not so much the conclusions!)
The benefits of using HTML documents are multiple:
- figures won’t break the flow of the document by jumping to the next page and leaving a large blank space;
- you can include interactive visualisations making use of the latest HTML features;
- they can be directly integrated into a website.
However, other output formats are available. Here are some examples:
pdf_documentfor a non-editable, widespread, portable formatword_documentandodt_documentto open and edit with Microsoft Word and LibreOffice Writermd_documentfor a Markdown file that can easily be published on GitHub or GitLab- and more, including for creating slides.
In some cases, you might be required to share your report as a PDF. Knitting your document to PDF can generate very professional-looking reports, but it will require having extra software on your computer.
You can install the necessary LaTeX packages with an R package called TinyTeX, which is a great alternative to very big LaTeX distributions that can be several gigabytes-big.
install.packages("tinytex")
tinytex::install_tinytex()After this, try to change your YAML header’s output value to
pdf_document and knit it.
At the top of the Rmd editor window, you have the option to switch between Source and Visual. If you change to the visual editor, you will see that the document now formats itself to be slightly more like the final output. This includes creating the code boxes, and showing text formatting, such as bold and italics.
You can also use this much more easily insert tables and other items without having to code so much by hand.
Related to R Markdown and knitr:
- R Markdown Cookbook, by Yihui Xie and Christophe Dervieux
- Official R Markdown website by RStudio
- R Markdown cheatsheet
We also have a list of recommended R resources.