pcir is an R package developed to assist researchers and practitioners
in calculating, comparing, and visualizing the Potential for Conflict
Index (PCI) among stakeholders. PCI is a descriptive statistical method
designed to enhance the understanding of outcomes in human dimensions
research (Manfredo et
al. 2003;
Vaske et
al. 2010).
The concepts of consensus, disagreement, and conflict are relevant across a wide range of disciplines, including economics, political science, psychology, sociology, and natural resource management. Although PCI can be calculated using software such as Excel, SPSS, and SAS, no dedicated R package existed for this analysis — until now.
This package was developed as part of my training in the rOpenSci Champions Program, supported by the Chan Zuckerberg Initiative.
Additional information:
Figure 1. Likert scales used in the Potential for Conflict Index (PCI).
Steps implemented in the ‘pcir’ package:
Read the input data from interviews – see example dataset (Spreadsheet);
Count the frequency of responses for each question – see (Table 1);
Calculate the PCI for each question – see (Table 2);
Generate a bubble chart to visualize the results – see (Figure).
Figure 2. Workflow of the pcir package.
counting(): Summarizes data by calculating counts, percentages,
means, and standard deviations.
pci(): Computes the Potential for Conflict Index from summary data.
bubble(): Visualizes PCI results using a bubble plot.
Install the development version of pcir directly from GitHub:
# Uncomment the line below if devtools is not installed
# install.packages("devtools")
devtools::install_github("fblpalmeira/pcir")
After installation, load the package:
library(pcir)
Example dataset:
df1 <- data.frame(
A = c(-1, -1, -1, 0, -1),
B = c(-1, 1, 0, -1, 1),
C = c(0, 0, 1, 0, -1),
D = c(0, -1, 1, 1, 1),
E = c(1, 1, 0, -1, -1)
)
Count responses:
data <- counting(df1, cols = names(df1)[2:6])
data
Calculate PCI:
data <- pci(data,
scale_type = 'bipolar_with_neutral',
min_scale_value = -1,
max_scale_value = 1)
Visualize with a bubble plot:
# The bubble function creates a bubble plot to visualize the PCI results.
# You can customize the colors and title as per your preferences.
plot <- bubble(data = df3,
scale_type = 'bipolar_with_neutral',
ylim_range = 3,
bubble_color = 'lightblue',
bubble_stroke = 'darkblue',
title = 'Custom Bubble Colors')
plot
Figure 3. Bubble chart illustrating the Potential for Conflict Indices.
Manfredo, M., Vaske, J., Teel, T. (2003). The potential for conflict index: A graphic approach to practical significance of human dimensions research. Human Dimensions of Wildlife, 8(3), 219-228.
Vaske, J. J., Beaman, J., Barreto, H., Shelby, L. B. (2010). An extension and further validation of the potential for conflict index. Leisure Sciences, 32(3), 240-254.
If you use the pcir package in your work, please cite it as follows:
citation(package = 'pcir')
Example output:
To cite the 'pcir' package in publications, use:
Palmeira F, Wundervald B (2025). _pcir: Potential for
Conflict Index in R_. R package version 0.0.0.9000,
<https://github.com/fblpalmeira/pcir>.
The BibTeX entry for LaTeX users is
@Manual{,
title = {pcir: Potential for Conflict Index in R},
author = {Francesca Belem Lopes Palmeira and Bruna Wundervald},
year = {2025},
note = {R package version 0.0.0.9000},
url = {https://github.com/fblpalmeira/pcir},
}
This package is licensed under the MIT License file for more details.
If you encounter any bugs or issues, please report them on the GitHub Issues page.
Note: To report a bug, you need a GitHub account, which you can join for free.
The Discussions
section is the space
for the pcir community to ask questions, share ideas, or get help
without opening formal issues.
For any questions or inquiries, please contact Francesca Palmeira at francesca@alumni.usp.br.