Library Carpentry: Introduction to R and litsearchr: Glossary

R and litsearchr can help automate repetitive tasks of a systematic review and add to the reproducibility of the the search development process.

Systematic reviews differ from traditional literature reviews in a number of significant ways

Systematic review methods strive to reduce bias and increase reproducibility and transparency

Automation and coding software like R can be used to facilitate parts of the systematic review process

RStudio is a user interface for working with R.

The Script Pane is sort of like a text editor, or a place to write and save code.

To execute code you use Ctrl+Enter (Cmd+Enter on a Mac).

If you type an incomplete command and press Enter, R will display a + prompt, which means R is waiting for you to type the rest of your command.

If you type a command that R doesn’t recognize, R will return an error message.

The Environment Pane will display the objects that you’ve read into and objects you have defined.

The Navigation Pane has many functions: files, plots, packages, viewer, and help.

The working directory is very important, as it is the place where you will store, save, and retrieve your files.

Using consistent filing naming and folder structure across your projects will help keep things organized.

Download the file for this lesson if you haven’t done so already.

There are many ways to get data into R. You can import data from a .csv file using the read.csv(…) function.

R is primarily a scripting language, and written line-by-line.

There is no need to rerun the whole script in order to change a line of code.

The strength of R is in it’s interactive data analysis.

You can use R to assign values, such as a numeric value like 4, and create objects.

A data frame is the term in R for a spreadsheet style of data: a grid of rows and columns.

Lists are the R objects which contain elements of different types like − numbers, strings, vectors and another list inside it.

Matrices behave as two-dimensional vectors

litsearchr helps identify search terms and write Boolean searches for systematic reviews.

A good naive search provides a good basis for identifying potential search terms on a topic.

Search results should be deduplicated to avoid terms used in some papers being overrepresented.

litsearchr helps identify potential search terms related to the topic of a systematic review.

litsearchr helps identify important search terms related to the topic of a systematic review.

litsearchr assumes keywords are grouped into distinct concept categories.

Given a list of terms grouped by concept, litsearchr can write full Boolean search string from them.

litsearchr can help test the recall (sensitivity) of search strategies.

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