As mentioned in earlier lectures, libraries are collections of functions. Libraries can be installed in R using the function install.packages or in RStudio via the Tools > Install Packages menu. This practical session will make use of the “Tidyverse” libraries, that includes a number of different libraries, including knitr. The Make.R script includes the instruction to install the Tidyverse if not currently installed. You can check whether the Tidyverse is installed by clicking on the Packages tab in the bottom-right pane in RStudio, and search for “tidyverse”. If not installed, install it using the command below or via the Tools > Install Packages menu in RStudio.
install.packages("tidyverse")You can load a library using the function library, as shown below. Once a library is installed on a computer you don’t need to install it again, but every script needs to load all the library that it uses. Once a library is loaded all its functions can be used.
The main tool used to create this reproducible lecture and practical on reproducibility is RMarkdown That is an R library that allows you to create scripts that mix the Markdown mark-up language and R, to create dynamic documents. RMarkdown script can be compiled, at which point, the Markdown notation is interpreted to create the output files, while the R code is executed and the output incorporated in the document.
The core Markdown notation used in this session is presented below and its interpretation when compiled is further below.
# Header 1
## Header 2
### Header 3
#### Header 4
##### Header 5
**bold**
*italics*
[This is a link to the University of Leicester](http://le.ac.uk)
- Example list
- Main folder
- Analysis
- Data
- Utils
- Other bullet poit
- And so on
- and so forthbold italics
This is a link to the University of Leicester
R code can be embedded in RMarkdown documents as in the example below. That will result in the code chunk be displayed within the document (as echo=TRUE is specified), followed by the output from the execution of the same code.
```{r, echo=TRUE}
for (i in 1:4) {
if (i %% 2 == 0){
cat("even \n")
} else {
cat("odd \n")
}
}
```for (i in 1:4) {
if (i %% 2 == 0){
cat("even \n")
} else {
cat("odd \n")
}
}## odd
## even
## odd
## evenIn order to compile the RMarkdown scripts to .pdf documents, RMarkdown requires a LaTeX compiler.
If you are working on the University of Leicester Windows 10 computers, please check that MiKTeX is installed in the Software Centre. If not close RStudio if open and install it (this might take a few minutes). Once the installation is completed, re-open RStudio.
MiKTeX should work fine for any other Windows system. Make sure to select “Always install missing packages on-the-fly” in the general settings of the MiKTex console, so that the appropriate packages are installed when the .pdf document is compiled. On Linux, please install a TeX distribution, e.g. sudo apt-get install texlive-full on Ubuntu. On Mac OS, please install a TeX distribution such as MacTeX.
In RStudio, select Tools > Global Options…. In the Swave section, make sure that the following options are selected.
To create an RMarkdown document in RStudio, select File > New File > R Markdown …. On the RMarkdown document creation menu, specify a title and your name as author, and select PDF as default output format. The new document should contain only the core document information, as in the example below.
---
title: "Practical Session Instructions"
author: "Stefano De Sabbata"
date: "24 October 2018"
output: pdf_document
---Copy the following text below the document information and the click on the Knit button on the bar above the editor panel (top-left area) in RStudio, on the left side. Check the resulting pdf document. Try add some of your own code (e.g., from previous practical sessions) and Markdown text, and compile the document again.
# A nice heading
This is my first [RMarkdown](https://rmarkdown.rstudio.com/) document.
The code below will print:
- "even"
- if the number is even
- "odd"
- otherwise
```{r, echo=TRUE}
for (i in 1:4) {
if (i %% 2 == 0){
cat("even \n")
} else {
cat("odd \n")
}
}
```The term “repository” refers to the whole collection of code, data, and other files that compose a project, stored on a version-control system. This project is available on my GitHub ReproducibleResearch repository. Download the zipped repository from BlackBoard or GitHub, or clone the repository using git if you are familiar with the tool.
Once downloaded and un-zipped in a folder of your choice, observe the structure structure of the folder and sub-folders. The Data folders contains two scripts that will download some data in the same folder. A back-up version of those data can be found in the Backup folder. The Analysis folder contains a simple analysis of those data. The Materials section contains the lecture and practical session materials, and some additional folders containing the IOSlides templates and the images. The Utils folder contains a script to update the git branch connected to the GitHub Pages for this project.
In RStudio, click on File > Open Project… and navigate to the folder where the files have been un-zipped. Select the file ReproducibleResearch.Rproj (the extension .Rproj might be hidden on Windows, the file type is R Project) and click Open. RStudio will load the whole project and set the directory where ReproducibleResearch.Rproj is located as working directory.
The Make.R script in the main folder can be used to “build” all the scripts in the repository in the correct order. The Make_Clean.R script can be used to delete all the files created by the Make.R.
Build the project by running the Make.R script. That can be done by loading the file in RStudio and clicking on the Source button on bar above the editor panel (top-left area) in RStudio, right side.
Alternatively, you can run the Make.R file using the command below from the RStudio console.
source('Make.R')Once the project has been built, the Reproducible_analysis_in_R.pdf file will be available in the Analysis folder. Open the document, read and complete the exercise.
Once completed the practical, click on the project name ReproducibleResearch in the top-left corner in RStudio and then Close project to close the project. RStudio might show a prompt asking whether to save the .RData file before closing, in which case you can select to close without saving.