Introduction

This vignette demonstrates the standard workflow for comparing two or more groups when the input datasets do not contain a Type variable. Other vignettes describe the workflow for single-group analyses and for multi-group comparisons with a Type variable.

Starting from two or more datasets containing observed frequencies, the workflow prepares each dataset independently, fits discrete power-law models, computes the Relative Importance Factor (RIF), and generates publication-ready comparative tables and visualizations.

Most users will only need the high-level workflow functions described in this vignette. The Advanced usage section introduces lower-level functions for users who require additional customization.

Methodological foundation

The methods implemented in the RIFanalysis package are based on the following publication:

Llinas, B., Padilla, J., Llinas, H., Frydenlund, E., & Palacio, K. (2026). Modeling Rank Distribution and the Relative Importance Factor Index in Discrete Power-Law Models: Application to Social Resilience Using the Scopus Database. Mathematics, 14(6), 966..

Workflow overview

The complete analysis is organized into three main stages. First, each input dataset is standardized independently using rif_prepare(). Next, rif_workflow_z2() fits the discrete power-law models, computes the RIF measures for each group, and constructs the comparative RIF results. Finally, rif_workflow_z3() generates publication-ready visualizations from the combined results.

             Group 1 dataset       Group 2 dataset
                    │                     │
                    ▼                     ▼
             rif_prepare()         rif_prepare()
                    │                     │
                    └──────────┬──────────┘
                               ▼
                      rif_workflow_z2()
                               │
                ┌──────────────┼──────────────┐
                ▼              ▼              ▼
          Power-law fits   RIF comparison   Excel files
                               │
                               ▼
                      rif_workflow_z3()
                               │
                ┌──────────────┼──────────────┐
                ▼              ▼              ▼
         RIF matrices   RIF networks   Publication-ready figures

The workflow consists of the following steps:

1. Define user-specific objects and input parameters.

2. Import and prepare the datasets.

3. Fit discrete power-law models for each group.

4. Compute group-specific and comparative RIF measures.

5. Generate comparative tables and graphical outputs.

6. Export results to Excel and image files.

Load the package

library(RIFanalysis)

Input datasets

The comparative analysis begins by importing the datasets representing the groups to be compared. Each dataset should contain variables identifying the factors (or topics), their short labels, and their observed frequencies. The example below uses two datasets, although the comparative workflow may be extended to additional groups when supported by the corresponding function. The datasets should contain the same variables and represent comparable groups, since they will be analyzed jointly by rif_workflow_z2().

data_gr1 <- read.csv("0_data_gr1.csv")
data_gr2 <- read.csv("0_data_gr2.csv")

User-defined objects

Before running the workflow, define the objects that specify variable names, group labels, plot labels, output directories, and other user-specific settings. Most of these values can be left unchanged, but they may be customized for different applications.

Because the datasets used in this vignette do not contain Group or Type variables, the objects gr_value_name1, gr_value_name2, var_type_value_name1, and var_type_value_name2 do not refer to columns in the original datasets. Instead, they define labels assigned during the data preparation step through the group_value and type_value arguments of rif_prepare().

# Review required variable names (ALWAYS verify them in the dataset)
fact_lbl_prefix1        <- "gr1T"                # Choose according to your needs (gr=group, T=topic, C=concept, F=factor, I=index, etc.) 
var_factor_name1        <- "factor"              # Long/original FACTOR variable
var_factor_small1       <- "factor_small"        # Short FACTOR variable
var_factor_label_small1 <- "factor_label_small"  # FACTOR_label_small variable
var_count_name1         <- "count"               # COUNT variable

fact_lbl_prefix2        <- "gr2T"                # Choose according to your needs (gr=group, T=topic, C=concept, F=factor, I=index, etc.) 
var_factor_name2        <- "factor"              # Long/original FACTOR variable
var_factor_small2       <- "factor_small"        # Short FACTOR variable
var_factor_label_small2 <- "factor_label_small"  # FACTOR_label_small variable
var_count_name2         <- "count"               # COUNT variable

# Assign a common Type label to all observations because the datasets do not contain a Type variable.
var_type_value_name1  <- "corpus_type"  # Option B (abstract, index, etc.)
var_type_value_name2  <- "corpus_type"  # Option B (abstract, index, etc.)

# Assign labels identifying the groups to be compared.
gr_value_name1  <- "gr1_NAME"  # Change (Colombia, Greece, Blue Economy, etc.)
gr_value_name2  <- "gr2_NAME"  # Change (Colombia, Greece, Blue Economy, etc.)
#prefix_name <- paste0(gr_name, "_")

#________________________________________________________________________
#

# If desired, change axis title  for RIF matrices (Topic, Concept, Factor, etc.) and labels. 
# IN rif_workflow_z3, SEE plot_matrix.R FUNCTION:

# Axis title: 
x_title_name  <-  "s: CHANGE_NAME at rank s" # default: Concept
y_title_name  <-  "r: CHANGE_NAME at rank r" # default: Concept
# Axis labels:  
factor_r_label_col_name <- "Factor_label" # default: factor_r_label_col = NULL
factor_s_label_col_name <- "Factor_label" # default: factor_s_label_col = NULL

#________________________________________________________________________
#
# Customize titles for power-law plots generated by rif_workflow_z2() and plot_zipf().
# Title default is "Observed and theoretical Zipf distributions..."
title_plotzipf_gr1_c2 <- "GROUPNAME1" # Change if desired
title_plotzipf_gr2_c2 <- "GROUPNAME2" # Change if desired

x_title_plotzipf_c2   <- "Position"  # Change if desired (default="Rank")
y_title_plotzipf_c2   <- "Frequency" # Change if desired (default="Count")

#________________________________________________________________________
#

# Change custom file names (if desired)
file_prefix_no_title     <- "zipf_notitle"
file_prefix_yes_title_c1 <- "zipf_yestitle_c1"
file_prefix_yes_title_c2 <- "zipf_yestitle_c2"

# Change output directories (if desired)
output_dir_personal   <- file.path(tempdir(), "Z0_personal")
output_dir_comparison <- file.path(tempdir(), "Z2_RIF_comparison")
output_dir_visual     <- file.path(tempdir(), "Z3_RIF_visual")

Descriptive Zipf analysis

Each dataset must be prepared independently before running the comparative workflow. The rif_prepare() function validates the input data, assigns Zipf ranks, and creates the standardized tables required by rif_workflow_z2().

The resulting objects, rif_data1 and rif_data2, represent the prepared datasets for Groups 1 and 2, respectively.

rif_data1 <- rif_prepare(data= data_gr1, 
    factor_col = var_factor_name1,
    count_col = var_count_name1,
    factor_small_col = var_factor_small1,
    group_col = NULL,
    type_col = NULL,
    group_value = gr_value_name1,
    type_value = var_type_value_name1,
    prefix = fact_lbl_prefix1,
    factor_small_label_style = "inline")

rif_data1
names(rif_data1)

rif_data2 <- rif_prepare(data= data_gr2, 
    factor_col = var_factor_name2,
    count_col = var_count_name2,
    factor_small_col = var_factor_small2,
    group_col = NULL,
    type_col = NULL,
    group_value = gr_value_name2,
    type_value = var_type_value_name2,
    prefix = fact_lbl_prefix2,
    factor_small_label_style = "inline")

rif_data2
names(rif_data2)

Z2: Multi-Group Power-Law Estimation and RIF Comparison

The comparative workflow is executed with rif_workflow_z2(). This function fits the discrete power-law model separately for each group, computes the corresponding RIF measures, constructs the comparative RIF results, generates diagnostic plots, and exports the resulting tables and figures.

z2 <- rif_workflow_z2(
    rif_data1 = rif_data1,
    rif_data2 = rif_data2,
    alpha_zipf = 1,
    no_of_sims = 1000,
    threads = 8,
    seed = 123,
    bootstrap_engine = "poweRlaw",
    output_dir = output_dir_comparison,
    plot_formats = c("png", "pdf")
    )

The workflow returns a single object containing both the individual analyses for each group and the combined comparative results. The table below summarizes its most important components.

The following commands illustrate how to access the principal components.

z2$input1
z2$input2

z2$data1
z2$data2

z2$zipf1
z2$zipf2

z2$analysis1
z2$analysis2

z2$rif_results1
z2$rif_results2

z2$rif_comparison
z2$plots
z2$files

Z3: RIF Visualization

The rif_workflow_z3() function generates publication-ready comparative visualizations from z2$rif_comparison. In this example, the combined results are used to create RIF matrices and networks representing relationships across the analyzed groups.

rif_workflow_z3(
  x = z2$rif_comparison,
  scope = "combined", 
  plot_types = c("matrix", "network"),
  formats = c("png", "pdf"),
  #plot_types = "matrix",
  output_dir = output_dir_visual,
  
  #SEE plot_matrix.R FUNCTION:
  matrix_args = list(
    #factor_r_label_col = factor_r_label_col_name,
    factor_s_label_col = factor_s_label_col_name,
    x_title = x_title_name,
    y_title = y_title_name 
    )
)

Advanced usage

Most low-level functions used in this workflow are identical to those described in Vignette 1: Single-Group Analysis. For this reason, they are not repeated here.

This section presents only the objects and functions that are specific to the multi-group comparison workflow implemented in rif_workflow_z2().

z2$plots$group1
z2$plots$group2

z2$files$group1
z2$files$group2

z2$files$plots
z2$files$excel