OpenRepGrid.ic {OpenRepGrid.ic}

OpenRepGrid.ic - Interpretive Clustering for Repertory Grids

Description

The OpenRepGrid.ic package implements Interpretive Clustering (IC) as outlined in Burr, King, and Heckmann (2020). The authors describe a variant of construct clustering which uses a procedure from graph theory called maximal cliques enumeration. Given a similarity measure, in our case the number of matching scores between two constructs, a network graph of relatedness between constructs is construed. A clique is a group of constructs which are all mutually related, given some cut-off criterion for relatedness (e.g. 6 matching scores in a grid with 7 elements). While the paper also describes an offline approach to identify the construct cliques, this software automates the process. Under the hood, the package uses the igraph package for clique identification.

The package also contains a shiny based UI you can start via the function ic(). Visit http://ic.openrepgrid.org for an online version. An introduction to the software is also available on YouTube. Below you find an example of how to process a repgrid in an Excel file using code only.

Author(s)

Maintainer: Mark Heckmann [e-mail] (ORCID) [copyright holder]

Authors:

• Viv Burr

• Nigel King

References

Burr, V. King, N. & Heckmann, M. (2020) The qualitative analysis of repertory grid data: Interpretive Clustering, Qualitative Research in Psychology, doi:10.1080/14780887.2020.1794088

See Also

Useful links:

• https://github.com/markheckmann/OpenRepGrid.ic

• Report bugs at https://github.com/markheckmann/OpenRepGrid.ic/issues

Examples

# The shiny package is just a small UI wrapper around the 
# the workhorse core functions. Here is how to call them.

library(tidyverse)
library(openxlsx)
library(igraph)
library(OpenRepGrid.ic)

file <- system.file("extdata", "sylvia.xlsx", package = "OpenRepGrid.ic")
file_out <- str_replace(file, ".xlsx$", " CLIQUES.xlsx") %>% basename

x <- read.xlsx(file)            # read grid
tests <- check_excel_input(x)   # check if input format is correct
l <- network_graph_images(x, min_clique_size = 3, 
                          show_edges = TRUE, 
                          min_matches = 6)    # produce images
file_tmp <- create_excel_output(file, l)      # create Excel file
# file.show(file_tmp)  # not run during tests

# open images saved as temp files (as shown in output Excel file)
file.show(l$img_all_constructs)
file.show(l$img_all_constructs_full_labels)
file.show(l$img_all_constructs_separate_poles)

file.show(l$img_cliques_only)
file.show(l$img_cliques_only_full_labels)
file.show(l$img_cliques_only_separate_poles)

# calculation results used in network_graph_images
# some of them are also contained in Excel file
s <- calculate_similarity(x)
s