Package 'pcir'

Create a Bubble Plot for PCI Visualization

Description

This function generates a bubble plot to visualize the results of the PCI calculation. It shows the mean action acceptability on the y-axis and the PCI value as the size of the bubbles.

Create a bubble plot to visualize PCI results. This function generates a bubble plot to visualize PCI results. Each bubble represents an item, with size proportional to total responses, x-axis representing weighted mean, and color representing the PCI value.

Usage

bubble(
  data,
  scale_type = c("bipolar_with_neutral", "bipolar_without_neutral", "unipolar"),
  ylim_range = 4,
  unipolar_ylim = c(1, 9),
  xlab = "",
  ylab = "Action acceptability",
  title = NULL,
  bubble_color = "gray80",
  bubble_stroke = "black",
  x_line = NULL
)

bubble(
  data,
  scale_type = c("bipolar_with_neutral", "bipolar_without_neutral", "unipolar"),
  ylim_range = 4,
  unipolar_ylim = c(1, 9),
  xlab = "",
  ylab = "Action acceptability",
  title = NULL,
  bubble_color = "gray80",
  bubble_stroke = "black",
  x_line = NULL
)

Arguments

data	A data frame containing at least the columns name, Mean, and PCI.
scale_type	The type of response scale. One of 'bipolar_with_neutral', 'bipolar_without_neutral', or 'unipolar'.
ylim_range	Numeric value to define y-axis range for bipolar scales.
unipolar_ylim	A numeric vector of length 2 specifying y-axis limits for unipolar scale.
xlab	Label for the x-axis.
ylab	Label for the y-axis.
title	Title for the plot.
bubble_color	Fill color for the bubbles.
bubble_stroke	Border color for the bubbles.
x_line	Position of the horizontal reference line.

Details

This plot is useful for visualizing how polarized (PCI) and how acceptable (Mean) each action/item is.

Value

A ggplot2 object representing the bubble plot.

A ggplot2 object.

Examples

data <- data.frame(
  name = c('A', 'B', 'C'),
  Mean = c(0.5, -1, 1.2),
  PCI = c(0.2, 0.5, 0.8)
)
bubble(data, scale_type = 'bipolar_with_neutral')

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Create a Count Table with Percentages, Mean, and SD

Description

This function takes a data frame and computes the count and percentage of each unique value across specified columns. It also calculates the weighted mean and weighted standard deviation for each variable.

Create a count table with percentages, mean, and standard deviation. This function takes a data frame, computes summary statistics like counts, percentages, means, and standard deviations for each unique value in the selected columns.

Usage

counting(df1, cols)

counting(df1, cols)

Arguments

df1	A data frame containing the data to be processed.
cols	A vector of column names to be included in the calculation.

Details

The output is useful for summarizing responses from ordinal or Likert-type items, showing how values are distributed and summarized across variables.

Value

A data frame in wide format including:

• Counts and percentages for each unique value.

• Weighted mean and standard deviation.

A data frame in wide format including:

• Count and percentage for each value per variable

• Weighted mean

• Weighted standard deviation (SD)

• Total number of observations per variable

Examples

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)
)
counting(df1, cols = c('A', 'B', 'C', 'D', 'E'))

---

Calculate the Potential for Conflict Index (PCI2)

Description

This function computes the PCI2 (a generalized Potential for Conflict Index) for each item in a summarized data frame (typically from the counting() function), or for a single named numeric vector of counts. It works with three types of scales: bipolar_with_neutral, bipolar_without_neutral, and unipolar.

Calculate the Potential for Conflict Index (PCI). This function computes the PCI2 (a generalized Potential for Conflict Index) for each item in a summarized data frame (typically from the counting() function), or for a single named numeric vector of counts. It works with three types of scales: bipolar_with_neutral, bipolar_without_neutral, and unipolar.

Usage

pci(
  data,
  scale_type = c("bipolar_with_neutral", "bipolar_without_neutral", "unipolar"),
  min_scale_value,
  max_scale_value,
  exponent = 1
)

pci(
  data,
  scale_type = c("bipolar_with_neutral", "bipolar_without_neutral", "unipolar"),
  min_scale_value,
  max_scale_value,
  exponent = 1
)

Arguments

data	A data frame with columns named like 'Count X' (where X is a scale value), or a named numeric vector of counts.
scale_type	Type of scale: 'bipolar_with_neutral', 'bipolar_without_neutral', or 'unipolar'.
min_scale_value	The minimum value of the scale (e.g., -2 or 1).
max_scale_value	The maximum value of the scale (e.g., 2 or 9).
exponent	A number to raise distances to. Use 1 for linear distance, 2 for squared distance, etc. Default is 1.

Details

For unipolar scales, PCI2 is based on all pairwise distances between categories.

For bipolar scales, PCI2 considers distances between opposing poles, ignoring neutral (0) in the case of bipolar_without_neutral. In bipolar_with_neutral, 0 is included in the scale but not in the calculation of conflict.

The maximum possible polarization (used for normalization) assumes a perfectly split distribution.

For unipolar scales, PCI2 is based on all pairwise distances between categories.

For bipolar scales, PCI2 considers distances between opposing poles, ignoring neutral (0) in the case of bipolar_without_neutral. In bipolar_with_neutral, 0 is included in the scale but not in the calculation of conflict.

The maximum possible polarization (used for normalization) assumes a perfectly split distribution.

Value

If data is a data frame, it returns the same data frame with an added PCI column. If data is a named numeric vector, it returns the PCI value directly.

If data is a data frame, it returns the same data frame with an added PCI column. If data is a named numeric vector, it returns the PCI value directly.

Examples

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)
)
df2 <- counting(df1, cols = c('A', 'B', 'C', 'D', 'E'))
pci(df2, scale_type = 'bipolar_with_neutral', min_scale_value = -1, max_scale_value = 1)

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)
)
df2 <- counting(df1, cols = c('A', 'B', 'C', 'D', 'E'))
pci(df2, scale_type = 'bipolar_with_neutral', min_scale_value = -1, max_scale_value = 1)

---

Reinstall and Test the pcir Package

Description

This RStudio Addin reinstalls the 'pcir' package from the local source, reloads it, and runs all functions on example data for testing.

Usage

reinstall_and_test_addin()

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