update documentation

R/data.R

@@ -1,11 +1,11 @@
 #' GPT-4-generated free associations to 'intelligence'
 #'
-#' A data set of free associations generated by GPT-4. This data set includes free associations to the cue word 'intelligence' (response_level == 1), as well as associations to each of the responses by the same participant (response_level == 2). The data set further includes two participant covariables (gender and education) simulated by using a respective system prompt when generating the data with OpenAI's GPT-4.
+#' A data set of free associations generated by GPT-4. This data set includes free associations to the cue word 'intelligence' (response_level == 1), as well as associations to each of the responses by the same participant (response_level == 2). The data set further includes two participant covariables (gender and education) simulated by using a respective prompt when generating the data with OpenAI's GPT-4.
 #'
 #' Inspect and use the data by calling \code{intelligence} in your R session.
 #'
 #' @format `intelligence`:
-#' A tibble with 29,820 rows and 7 coulumns:
+#' A tibble with 29,882 rows and 7 coulumns:
 #' \describe{
 #'  \item{participant_id}{Unique id for each participant.}
 #'  \item{gender}{Gender of the participant.}
@@ -16,6 +16,6 @@
 #'  \item{response_level}{Response level of the trial. (1: first-level response to the cue 'intelligence', 2: second-level response to first-level responses of the same participant as a cue).}
 #' }
 #'
-#' @references Aeschbach, S., Mata, R., Wulff, D. U. (in preparation). associatoR.
-#' @source \url{https://doi.org}
+#' @references Aeschbach, S., Mata, R., & Wulff, D. U. (2024, March 22). Mapping the Mind With Free Associations: A Tutorial Using the R Package associatoR. \url{https://doi.org/10.31234/osf.io/ra87s}
+#' @source \url{https://github.com/samuelae/GPT4-intelligence/tree/main}
 "intelligence"

README.md

@@ -1,26 +1,24 @@
 # associatoR
 
-Associations offer a window into the mind, revealing how words, concepts, and topics are represented. They can be used to map how humans and artificial intelligence think about key social and technological issues.
+Associations offer a window into the mind, revealing how words, concepts, and topics are represented. They can be used to map how people think about key social and technological issues.
 
 `associatoR` is an R package for analyzing free association data. It covers the entire analysis pipeline, from importing and processing to embedding, clustering, and visualizing the data.
 
-## Team
-
-The `associatoR` package is developed by [Samuel Aeschbach](https://github.com/samuelae) and [Dirk U. Wulff](https://github.com/dwulff), with contributions from [Rui Mata](https://github.com/matarui).
-
 ## Installation
 
 The latest development version on GitHub can be installed via `devtools::install_github("samuelae/associatoR")`. This may require prior installation of the `devtools` package via `install.packages("devtools")`.
 
-## Citation
+## Team and Citation
+
+The `associatoR` package is developed by [Samuel Aeschbach](https://github.com/samuelae) and [Dirk U. Wulff](https://github.com/dwulff), with contributions from [Rui Mata](https://github.com/matarui).
 
 Please cite the package as:
 
 Aeschbach, S., Mata, R., & Wulff, D. U. (2024, March 22). Mapping the Mind With Free Associations: A Tutorial Using the R Package associatoR. <https://doi.org/10.31234/osf.io/ra87s>
 
 ## Overview
 
-The Figure provides an overview of the functionality of the associatoR package based on the example presented in [Aeschbach, Mata, & Wulff (2024)](https://doi.org/10.31234/osf.io/ra87s).
+The figure below provides an overview of core functions and workflows of the `associatoR` package based on the example presented in [Aeschbach, Mata, and Wulff (2024)](https://doi.org/10.31234/osf.io/ra87s).
 
 ![](man/figures/overview.png)