You also might want to look at either gghtemr or ggthemes to find some "sexy" themes, as you requested. Geom_errorbar(aes(ymin = ypos - se, ymax = ypos + se, colour = grp), Geom_point(position = position_dodge(.2)) + The following plot adds error bars, but uses position_dodge instead of position_jitter, to have control of the position of both dots and error bars: help_3D <- structure(list("one"=c(10,9,8,7), "two"=c(8,7,6,5), Geom_point(position = position_jitter(.2)) +įinally, to add error bars, you need to have the standard error in your data set. Ggplot(help_3d_long, aes(x = treatment, y = ypos, colour = grp)) + Sjp.setTheme("scatter") # just for the theme However, in your case, you don't want to map y to the count of values, but to the value itself. In the upper cases, each group has some observations, and the count for each group is computed before plotting. You can easily change the plot type to dot plots or similar: sjp.grpfrq(efc$e42dep, efc$c172code, SjPlot makes it easy to produce ggplot figures - however, it requires the "raw" data, where the count (y-pos) is computed within the function. You can easily plot this with the sjPlot-package. Looks like a grouped bar chart, as Tal mentioned. Unless you tell us otherwise, their identifiers are arbitrary in terms of their response patterns 3 might be better placed between 1 and 2. Note: Implemented in Stata with code graph dot (asis) y, over(treatment) over(x) scheme(s1color) linetype(line) lines(lc(gs12) lw(vthin))ĮDIT: Regardless of whether these are real data, a further possibility is just to shuffle the individuals 1, 2, 3. Some of these threads above are especially pertinent here. The advantages of dot charts are more striking when each line contains two or more "dots" (more generally, markers or point symbols). In this case, there is a small functional difference between this display and similar bar charts, whether vertical or horizontal. How to best visualize differences in many proportions across three groups? Is there a better way than side-by-side barplots to compare binned data from different series How to add a third variable to a bar plot? Graph for relationship between two ordinal variablesĬhart for visualizing multi-dimensional data Another design has all treatments on the same line. Here treatments A B C D occur on the inside, which was found to show a simpler pattern. It's arbitrary which one categorical control nests inside another. Whenever points might occlude or obscure each other, open markers may be better. Solid markers here draw attention to magnitudes. Here it seems natural in other cases it can seem superfluous. There is no absolute reason for lines to start at zero. One candidate is the dot chart ably and energetically promoted by W.S.
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