Key Points

Before we Start

  • Navigate round RStudio and create an Rproj file.
  • Use RStudio to write and run R programs.
  • Install packages using the Packages tab or the install.packages() command.

Introduction to R

  • Assign values to object using the assignment operator <-. Remove existing objects using the rm() function.
  • Add comments in R scripts using the # operator.
  • Define and use R functions and arguments.
  • Getting help with the ?, ?? and help() functions.
  • Define the following terms as they relate to R: object, vector, assign, call, function.
  • Create or add new objects to a vector using the c() function. Subset vectors using [].
  • Deal with missing data in vectors using the is.na(), na.omit(), and complete.cases() functions.

Starting with Data

  • Use getwd() and setwd() to navigate between directories.
  • Use read_csv() from tidyverse to read tabular data into R.
  • Data frames are made up of vectors of equal length, with each vector representing each column of the data frame.
  • Summarise the dimension, content and variables in a data frame.
  • Using the square brackets [] and logical operators to subset data frames.

Data cleaning & transformation with dplyr

  • Use the dplyr package to manipulate dataframes.
  • Subset data frames using select() and filter().
  • Rename variables in a data frame using rename().
  • Recode values in a data frame using replace_values().
  • Use mutate() to create new variables.
  • Sort data using arrange().
  • Use group_by() and summarize() to work with subsets of data.
  • Use pipe (|>) to combine multiple commands.

Data Visualisation with ggplot2

  • ggplot2 is a flexible and useful tool for creating plots in R.
  • The data set and coordinate system can be defined using the ggplot function.
  • Additional layers, including geoms, are added using the + operator.
  • Boxplots are useful for visualizing the distribution of a continuous variable.
  • Barplot are useful for visualizing categorical data.
  • Faceting allows you to generate multiple plots based on a categorical variable.

Reproducible Reports with Quarto

  • Quarto allows you to mix code and text to create reproducible reports.
  • Use the YAML header to configure document metadata like title and output format.
  • Code chunks run R code and can display or hide input/output using options like #| echo: false.
  • Rendering the document executes the code and produces the final output (HTML, PDF, etc.).
  • This workflow saves time and reduces errors when reporting on data that changes over time.

Bibliometrics with OpenAlex

  • OpenAlex works without an account for casual use like this, and doesn’t need a personal email or API key for a workshop-scale query.
  • Deciding whether a patron’s request is an author, an organization with its own entry, or a department with no entry of its own is a reference-interview skill, not only an API choice.
  • API responses often include list-columns. tidyr::unnest(..., names_sep = "_") flattens them while avoiding name collisions with the outer table.
  • Unnesting a list-column multiplies rows, one per nested value, so totals should come from the original one-row-per-work table, not the unnested one.
  • OpenAlex’s current subject field is topics, not the older, unmaintained concepts field.
  • A verified ORCID confirms you have the right person, but not that OpenAlex’s own record for them is clean. Check actual works before trusting a count.
  • The same ggplot2 and reporting skills from earlier episodes transfer directly to new, API-sourced data, and any report should say plainly what it can’t tell you.