chose to show only a single chart at once. For infrequent users, we felt
an extra step to change the sort was worth increased clarity. We also
realized users had diverse screen resolutions that could cramp a more
complex layout. With only one chart at a time, we were able to add a
second level of hierarchy with a parent bar for a category (like DNA
vaccines) and child bars for individual values (one of six individual
DNA vaccines).
Plot
The Plot supports y, x, and color variables (color and shape are varied
together). With only a y variable, data values are jittered horizontally
next to a box plot of their y distribution. Adding a categorical x
variable creates additional jittered data columns and box plots for each
x value (Figure 2). An interval x variable makes a scatterplot (Figure
4). A time x variable spreads the points by the study protocol day their
samples were collected (Figure 5). Studies usually define Day 0 as the
first vaccination. Data can be directly brushed to show all points from
the same subject and selected in either one or two dimensions to create
filters. Interestingly, design sessions with users have shown Plot can
be useful for understanding the nature of the available data in addition
to hypothesis generation. Plotting variables can be the most efficient
way to answer questions like, “which types of vaccine have been tested
against antigen X?” and also leads to natural follow-up questions about
performance. Below, we describe several other plot features that support
prioritized tasks.