Source: https://www.rstudio.com/

Source: https://www.rstudio.com/

Slides

Here are the introduction slides for this practical on dynamic reports!

Overview

In this practical you’ll practice creating interactive reports using RMarkdown.

Cheatsheet

https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf

https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf

If you don’t have it already, you can access the Markdown cheatsheet here https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf

Examples

Tasks

  1. Create a new R project called dynamicreports.Rproj. Add three folders data, code, materials

  2. Go through the Examples above to create a new R Markdown document. Save the document under the name speffanalysis.Rmd in your main project working directory (next to the dynamicreports.Rproj file)

  3. At the top of the speffanalysis.Rmd document, add a new R chunk. You can do this by clicking the “Insert” button at the top of the console, or by using the “Option + Command + i” shortcut on Mac.

  4. In the chunk options, include echo = FALSE, message = FALSE, warning = FALSE.

  5. Inside of the chunk include the following code in the following chunk: The knitr::opts_knit$set(dir = "../") code will move your working directory one folder up to the project root, while knitr::opts_chunk$set() will set your global chunk options. Finally, options(digits = 2) makes sure that all of your numeric output is rounded to two decimal places.

knitr::opts_chunk$set(fig.width = 6,        # Figure width (in)
                      fig.height = 6,       # Figure height (in)
                      echo = TRUE,          # Repeat code
                      eval = TRUE,          # Evaluate chunks
                      message = FALSE,      # Don't print messages
                      warning = FALSE,      # Don't print warnings
                      fig.align = 'center') # Center figures

options(digits = 2)  # Round all output to 2 digits

  1. Now let’s load some packages. Inside your chunk, write the comment # Loading Packages -------------. Then, using the library() function, load the packages tidyverse, knitr and speff2trial.

  2. Knit the document to make sure it worked! If you have any errors, try to figure out how to solve them!

  3. For this practical we’ll use the ACTG175 dataset. This dataset originally comes from the speff2trial package. However, I saved a copy of the dataset as a comma–separated text file at "https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_data/ACTG175.csv". Open this link in a web-browser, and then save the ACTG175.csv file to the data folder of your project.

  4. Create a new code chunk and put it under the previous one. We will use this chunk to load the ACTG175.csv. In this chunk, load csv file with read_csv() and assign the result to the object ACTG175.

  5. Knit the document! Diagnose and correct any errors!

  6. Add the necessary text and markdown to your document to create the following two paragraphs. Pay attention to the header sizes, italics and code formats.

Write the necessary markdown to create this output!

Write the necessary markdown to create this output!

  1. Knit the document! Diagnose and correct any errors!

  2. Add the appropriate combination of text, markdown, code chunks, and R code to add the following output to your document. To report the number of patients, use an in-line chunk to access the number directly from the data using the nrow() function, – that is, don’t type 2139 directly! To create the table, create a new chunk, and inside that chunk, use the kable() function, with the appropriate arguments, to create the table.

  1. Knit the document! Diagnose and correct any errors!

  2. Write the necessary code to add the following output to your document. To do this, create a new chunk. In the chunk use dplyr code to create the summary table of data. Assign the result to the object trial_summary. Then, use kable() to render this dataframe as a table in the final document.

Here is some code you might find helpful in creating this table!

# Helpful code to create the summary table!

trial_summary <- ACTG175 %>% 
                 group_by(XX) %>% 
                 summarise(
                          N = n(),
                          Mean = mean(XX),
                          Median = median(XX),
                          SD = sd(XX),
                          Max = max(XX))

  1. Knit the document! Diagnose and correct any errors!

  2. Add the appropriate combination of text, markdown, code chunks, and R code to add the following output to your document. Be sure to include the figure caption (you can do this with the fig.cap argument to the chunk)

This code might help you to create the plot:

# Boxplot code template

ggplot(data = XX, 
       mapping = aes(x = factor(XX), y = XX)) +
  geom_boxplot() + 
  labs(x = "XX",
       y = "XX",
       title = "XX",
       subtitle = "XX",
       caption = "XX") + 
  theme_bw()

  1. Knit the document! Diagnose and correct any errors!

  2. A researcher wants to know if there is a correlation between patients’ CD4 T cell count at baseline (cd40) and the number of days until a major negative event. Include this information as a new subsection (with a second level header) in your analyses. To do this, run the following chunk. Then, write a sentence with the main outputs from the test, using inline chunks to directly access the correlation and the p-value. For example, a sentence could be: “The correlation between CD4 T cell count at baseline and number of days until a major negative event was r = XX, p = YY”.

# Correlation test between cd40 and days

cd4_cor <- cor.test(formula = ~ XX + XX,
                    data = XX)

cd4_cor_r <- cd4_cor$XX  # Get the correlation
cd4_cor_p <- cd4_cor$XX   # Get the p-value

  1. In addition to the correlation test, include a relevant scatterplot showing the relationship between CD4 T cell count at baseline (cd40) and number of days until a major negative event (days).

  2. Add a new section called “Conclusions”. Write the main conclusions of your analyses in one or two sentences. Feel free to add some formatting and/or in-line chunks to your content!

  3. (Optional) You can easily publish an HTML document online to Rpubs.com for free. To do this, Knit your document. Then, click the blue “Publish” button. Go through the process of signing up for a free Rpubs account and get your document online!

Slideshow

  1. Now it’s time to create a slideshow! To do this, we’ll use the Ninja template (click here for a demo) from the xaringan package (that’s what we use for all of our BaselRBootcamp slides). To install the xaringan package from GitHub, run the following code.
# Install the xaringan package from github
devtools::install_github("yihui/xaringan")

Once you’ve installed xaringan, open a new template with File – New File – R Markdown – From Template – Ninja Presentation. Give the presentation a title and your name as the author. Then click ok.

  1. You should see a new .Rmd document open. Save the document in your main directory as slideshow.Rmd.

  2. Knit the document to see the outline of the presentation!

  3. Play around with the presentation a bit. Change the existing content a bit and add a few slides. Try adding an image (maybe this one: https://actgnetwork.org/sites/all/themes/actg/images/actg_logo_275.png) by saving the image to your materials folder, and then loading the image into your document with include_graphics().

  4. Now, try to customize the presentation to include all of main analyses, outputs, and plots you have in your speffanalysis.Rmd document! Of course, there won’t be room for all of the text, so treat it like a normal presentation and put in what’s important.