README.md

In ideanetwork/ideanet: Ideanet: An application in R for Concept Mapping to compute and visualize a network of ideas

About concept mapping

Concept mapping a method for crowd-sourcing the understanding of and/or the design of interventions of complex challenges. Unlike other group processes which rely on consensus; this process elicits the many and diverse viewpoints on a complex issue. The methodology relies on member of a community to work invidually to:

• Generate ideas in response to a specific challenge,

• Organize all of the collected ideas into groups of similar ideas that they [participants] individually create and name, and,

• Optionally, assess (e.g., rate) the ideas on one or more measure of value (e.g., importance, feasibility).

Mathematical algorithms translate the contributions from multiple individuals into maps that shows the detailed ideas, themes and priorities; the collective wisdom of the group. While visualization is driven by sophisticated algorithms, the graphics are accessible without knowledge of the underlying mathematics. As such, community members that provided individual input can work together to interpret their results and plan actions.

About the Ideanet application

Ideanet is a software application to support concept mapping. The Ideanet application provides a menu-driven interface for data management, computation, and visualization for a concept mapping project.

The program distributed here is shared in the hope that this will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. This work is licensed under the GNU General Public License v3.0, a link to the details of the license can be found at: License

You are free to use this program, share with others and make and share changes to the code. If you do make changes, the software license requires that the code and any ensuing modifications be made publicly available, allowing the entire community to benefit.

Send suggestions, questions, improvements, etc. to: info\@ideanetworks.io{.email}

This is a development version, improvements to the code and additional functions are in the process of being created and tested. The code has been developed and tested in a Windows PC environment. This application has [not]{.underline} been tested in a macOS environment.

How to get started with Ideanet

1. Download supporting software

This code has been developed ad tested using version "4.2.0 patched" of R. At present the newest version of R (version 4.2) is causing problems unrelated to the application. I recommend either using an older version of R or using the patched version.

R version 4.1.3 can be downloaded at the URL below. Navigate to to R version 4.1.3 and the click on the link for R-4.1.3-win.exe to download and then open the file to install.

R version 4.1.3 can be downloaded at: https://cran.r-project.org/bin/windows/base/old/4.1.3/

R version 4.2.0 patched can be downloaded at: https://cran.r-project.org/bin/windows/base/rpatched.html

Rstudio desktop is recommended for running R code, a free version can be accessed here: Rstudio{style="color: blue"}

2. Install Ideanet application

Run the following code to install the application. This code only needs to be run once to install the application. Run this code in the future, as needed, to install newer versions of the Ideanet application. While I prefer using RStudio for editing and running code, launching Base R without RStudio will open an interface that can be used to install and run the software.

RStudio install

• If you downloaded the file "ideanet_install.R" then you can double-click on that file to open Rstudio with the code in the source window.

• If RStudio is already open then, on the menu click on File, Open file, and navigate to the file and click to open.

• If you do not have the the file "ideanet_install.R" then copy and paste the code below into RStudio source window.

• Once the code is visible in the RStudio source window, click on Ctrl+Alt+R to run the code or using the RStudio menu click on Code, Run region, Run all.

Base R install

• If you downloaded the file "ideanet_install.R" then from the menu, click on open script and navigate to the file.

• If you do not have the the file "ideanet_install.R" then from the menu, click on File, Open script and then copy and paste the code below into editor window.

• Once the code is available then in the menu click on Edit, Run All.

if (!require(rlang)){
  install.packages("rlang")
  suppressPackageStartupMessages(library(rlang))
}
if (!require(pacman)){
  install.packages("pacman")
  suppressPackageStartupMessages(library(pacman))
}
if (!require(devtools)){
  install.packages("devtools")
  suppressPackageStartupMessages(library(devtools))
}

pacman::p_unlock() # remove 00lock if present

remotes::install_github("ideanetwork/ideanet", force = TRUE) 

3. Run the Ideanet application

If you have a copy of "run_ideanet.R" on your computer then follow the same procedure as described above for installing Ideanet except, in this case use, run_ideanet.R. If you do not have the the file "run_ideanet.R" then copy and paste the code below following the same procedures described above.

Once executed the code will open the program. Sometimes the Ideanet app will open behind other windows applications. Look on your task bar for [feathericon]{.underline} or use the hot keys (Alt+Tab) to tab through open applications to find Ideanet.

Note that once the Ideanet application is open, R and RStudio will not accept input because the Ideanet application has control of R. To stop using the app and regain control of R or RStudio click Quit, Close app.

if (!require(pacman)){
  install.packages("pacman")
  suppressPackageStartupMessages(library(pacman))
}
pacman::p_unlock() # remove 00lock if present

Ideanet::ideanet() # open the application.

4. Use the Ideanet application

The following is a brief description of the menu options. Additional details can be found in application.

Data entry

Use the menu option to create a blank Excel template for data entry. The Ideanet software is structured to read data from this template. A concept map requires data in two worksheets. (1) Sorting data is required. Depending on how the sorting data was collected the data is added to either the racked worksheet if manually entered or stacked worksheet if downloaded from an online sorting program. (2) The text for ideas must be entered in the ideas worksheet. If rating data is collected the values worksheet accommodates up to three demographics and up to two measures (e.g., importance, feasibility).

Review & define data

Sorting data can be checked for errors such as missing cards and data entry errors so that these may be corrected. Rating data can be defined by entering details about demographics and the measures.

Compute maps

Multidimensional scaling (MDS) algorithm to compute the location of points in two dimensions and computes a stress value. Label analysis computes the location each label from each participant on the map. The map of items is partitioned with hierarchical cluster analysis using Ward's method and produces multiple cluster solutions. The app will create three output file; (1) an Excel workbook with cluster solutions and cluster labels, (2) a slide deck illustrating cluster membership and top labels for each cluster solution and (3) a slide deck illustrating cluster membership in a dendrogram. Output files are useful for choosing an optimal cluster solution for further analysis.

Create cluster report

Once a cluster solution is selected, this option creates a slide deck that is a detailed report for that cluster solution.

Analyze values

Pattern Analysis creates a cluster rating map to illustrate relative value among the clusters with a third (i.e., height) dimension. This option outputs a PowerPoint file. Pattern matching compares patterns in values between measures (e.g., importance & feasibility) or between demographic groups (e.g., management & staff) and saves output as a PowerPoint file. Two types of graphics are created for pattern matching; (1) a ladder graph compares values at the cluster level and a bivariate plot comparing the item means within each cluster.

Assess cluster solution

Once a cluster solution is chosen, in some cases, a review of the map may evoke assertions that a given point may be better placed in a nearby cluster instead of the cluster where the point is currently located. The analysis done here will validate cluster membership in the current map or indicate if the map may be improved by placing the item in neighboring cluster.

ideanetwork/ideanet documentation built on Dec. 8, 2022, 2:45 a.m.