Project report development can be a resource-consuming endeavor. During this demonstration, participants will learn how to create an R Markdown file in R that integrates both standard reporting text and R code that can be used to create Word, pdf, or HTML file reports at the click of the button.  Attendees will learn how to add visualizations, graphics, statistical analysis, and tables to these reports.  Standard R coding procedures and strategies that we have developed will be shared along with access to the code for everything shared during the demonstration.  Procedures on how to maintain participant anonymity as well as ways to positively highlight subgroup differences will also be shared. Using R Markdown, a free open source program is a great tool to have due to end users’ ability to share code with anyone around the world.  This eliminates the need to pay for expensive software and training.

For evaluation centers, project report development can be a resource-consuming endeavor. So, developing a report template is useful for an advanced start on the reporting process, and to create a standardized approach and look.  This collaborative process helps centers prepare a report structure early on in projects using predesigned templates and align data collection and analysis with the report content before data collection begins. This approach streamlines report development by enabling teams to design reports together and then hand them off to a colleague to complete the report independently. These templates can be created using R and R Markdown code (but the process universally applies to any software as well).  Report templates also give centers the information needed to develop dashboards, Adhoc reports, and needs; as well as a means for data team members to create a repository for commonly used scales, data sources, analysis methods, and metadata. 

Catch up with the ELRC Team from Purdue at AEA 2022 for one or more of their presentations:

Poster: Educational Equity in Somalia — (Re) shaping Evaluation to Inform Policy (Christiana Akande)

Online question system that's interactive.

Comparison of the number of observed vs expected responses in the DVS 2020 Census per country based on population.

Comparison of percentages of respondents who have indicated a given job frustration grouped into years of different experience.

Chart and map of the usage and relationship of data visualization tools in different regions.

Information on Purdue Libraries open access publication fund.

Likert is an R package designed to help analyzing and visualizing Likert type items. It has been developped by Jason Bryer and Kim Speerschneider.

This barplot comes from the demo page and has been sent by Carlos Ortega.

It allows to analyse the reading attitudes from a panel of people. Each line represents a question. The barplot explains the feeling of people concerning this question.

# library
library(likert) 
 
# Use a provided dataset
data(pisaitems) 
items28 <- pisaitems[, substr(names(pisaitems), 1, 5) == "ST24Q"] 
 
# Build plot
p <- likert(items28) 
plot(p)

Graphs of how groups of different experience levels differ in software use, job frustrations, and more.

Graphic of circles to visualize ways people learn data visualizations.

if you don't pay for an account you have to give credit somewhere on the infographic.

stock photos some do cost. Should choose high resolution.

build a color scheme Color brewer

use for study orientation.

study recruitment

study timeline

posters

This checklist is meant to be used as a diagnostic guide to identify elements of evaluation reports that could be enhanced using graphic design best practices and/or the assistance of a graphic design expert. Suggestions are best suited for those using standard Microsoft Word software.

This checklist is meant to be used as a guide for the development of high impact data visualizations. Rate each aspect of the data visualization by circling the most appropriate number, where 2 points means the guideline was fully met, 1 means it was partially met, and 0 means it was not met at all. n/a should not be used frequently, but reserved for when the guideline truly does not apply. For example, a pie chart has no axes lines or tick marks to rate. If the guidelines has been broken intentionally to make a point, rate it n/a and deduct those points from the total possible. Refer to the Data Visualization Anatomy Chart on the last page for guidance on vocabulary and the Resources at the end for more details.   by Stephanie Evergreen & Ann K. Emery May 2016

Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints—supported by research and the author’s extensive experience with clients in industries all over the world—for conveying data in an impactful way. Delivered in Evergreen’s humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel.