ADAPTATION OVERVIEW This adaptation was implemented in a General Ecology course with the main goal of facilitating brief discussions on data visualization. The adaptation focuses on presenting students figures with all the information in their axes. However, such figures present a medium level of quality (Versions A) to encourage students to discuss conceptually in groups and come up with suggestions on how to make them better visualizations (Versions B). Here, I provide the R codes for these new figures so that instructors can manipulate them accordingly to their course activity.
This adaptation was framed under UDL guidelines provided by CAST to facilitate the module to a diverse audience and reinforce the learning outcomes. Because I envisioned the activity as an engagement “ice-breaking” tool during the first minutes of each class section, I focused on UDL guidelines for engagement and representation:
I carried out this activity during the first 3-5 minutes of each class section (twice a week). My classroom was organized in tables of ~6 students. Each table had a TV monitor where I projected the figure (Active Learning Classroom). Thus, it served as a routinized activity to make students talk to each other during those first minutes of class. After their brief discussion, I went to each table asking for a single observation, or more specific questions regarding patterns, statistics, and interpretation. I started the adaptation after implementing the original module for several weeks. I decided to add this adaptation in order to evaluate the students and see whether they have learned to identify ambiguity and biases in data figures and thus provide a discussion on how to avoid it when making their own figures.
Goal of Figure 1: Identify potential data misinterpretation when a line is drawn between two data points of a discrete variable.
Teaching notes: Best practices when plotting the group means of a categorical independent variable are bar plots. Categorical variables are factors used to test for mean differences among groups, as opposed to test for linear relationships (intercept and slope). In Version 1A of the figure, students realize that two dots will always be connected by a perfect straight line, thus, that line is meaningless in this context.
#Independent and dependent variables x <- c("Present","Absent") y <-c(mean(seq(1,5,1)),mean(seq(3,9,2))) # Version 1A plot(y, xaxt="n",pch=16,typ="o", xlab="Predator treatment", ylab = "Mean weight of prey (g)", lwd=2,cex.axis=1.3,cex.lab=1.5,ylim=c(2,8),xlim=c(0,3)) mtext(x, side=c(1,1), line=c(0,0), at=c(1.05,2)) # Version 1B barplot(y, names.arg=x, xlab="Predator treatment", ylab = "Mean weight of prey (g)", cex.axis=1.3,cex.lab=1.5,ylim=c(0,8))
Goal of Figure 2: Interpret the linear regression analysis and discuss whether drawing a line is appropriate.
Teaching notes: This one may depend on personal opinion and on publication venue. I state here my view. The answer to whether we should include fitted linear models models in data visualization depend on the statistical significance. If the model is significant, then we should include them in the visualization as it gives you the generalization of the pattern (line) and the observed variability (data points). In Version 2A of the figure, students interpret the linear regression result summary and ask themselves whether the fitted line should be depicted in the figure. After all, does the line tells us anything about the relationship between Parental survival and the number of offspring?
# Independent and dependent variables x <- seq(1,50,2) y <-sample(10:60,25) # Version 2A lm1 <- lm(y~x) lm2 <- lm1$coefficients plot(x,y, xlab="Number of offspring", ylab="Parental survival (%)", pch=16,ylim=c(0,95),cex.axis=1.3,cex.lab=1.5) abline(coef(lm1)) # Extracting model coefficients eqn <- paste("y =",paste(round(lm2[-1],2),names(lm2[-1])," + "),paste(round(lm2,2))) legend(-3,100,legend=eqn,bty="n") legend(-3,90,legend="p-value > 0.05",bty="n") # Version 2B plot(x,y, xlab="Number of offspring", ylab="Parental survival (%)", pch=16,ylim=c(0,80),cex.axis=1.3,cex.lab=1.5)
Goal of Figure 3: Look for biases in data visualization.
Teaching notes: Data visualization is just that, a visual of the data. It does not give you statistical outputs, it does not help with testing hypothesis. The main goal of figures is to facilitate, not mislead, the message from authors to readers. When preparing appropriate data visualizations, they should be done with high levels of ethics. More importantly, when reading data visualizations you need to do it critically. In Version 3A of the figure, students realize that the y-axis is being manipulated to show a large increase in available funding although the budget increased only from 3% to 4%.
# Independent and dependent variables x <- c("2018","2019") y <-c(mean(seq(1,5,1)),mean(seq(2,6,2))) # Version 3A barplot(y, names.arg=x, xlab="Year", ylab = "Research budget increase (%)", ylim=c(2.8,4.2),xpd=FALSE,cex.axis=1.2) # Version 3B barplot(y, names.arg=x, xlab="Year", ylab = "Research budget increase (%)", ylim=c(0,5),xpd=FALSE,cex.axis=1.2)