R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

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An Introduction to Mixed Models for Experimental Psychology (2019, unknown): “As for most modeling functions in R , the data need to be in a data.frame in the long format (also known as tidy data ; Wickham & Grolemund, 2017): Each observation corresponds to one row with one column containing the dependent variable and the other columns containing information pertaining to…”
MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus (2018, methodology): “In addition to facilitating analyses of model outputs, MplusAutomation allows researchers to leverage the outstanding capabilities of R to produce ﬁ gures and tables for SEM-based research (Wickham & Grolemund, 2017).”
Welcome to the Tidyverse (2019, methodology): “We use the model of data science tools from “R for Data Science” (Wickham & Grolemund, 2017): Wickham et al., (2019).”
Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019, methodology): “Descriptive data analysis was carried out with the statistics software R using the tidyr package (Wickham & Grolemund, 2016).”