Customizing a ggplot2 margin of error plot
You've hopefully identified some problems with the chart you created in the previous exercise. As the counties are not ordered, patterns in the data are difficult for a viewer to parse. Specifically, margin of error plots are much more effective when dots are ordered as the ordering allows viewers to understand the uncertainty in estimate values relative to other estimates. Additionally, the lack of plot formatting makes it difficult for chart viewers to understand the chart's content. In this exercise, you'll clean up your ggplot2 code to create a much more visually appealing margin of error chart.
This exercise is part of the course
Analyzing US Census Data in R
Exercise instructions
- Use the
str_replace()tidyverse function to remove" County, Maine"from the county name. - Reorder the counties in descending order of median household income in the plot.
- Set the subtitle to "Counties in Maine" to add information common to the data points with
subtitle.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Remove unnecessary content from the county's name
maine_inc2 <- maine_inc %>%
mutate(NAME = ___(NAME, " County, Maine", ""))
# Build a margin of error plot incorporating your modifications
ggplot(maine_inc2, aes(x = estimate, y = ___(NAME, estimate))) +
geom_errorbarh(aes(xmin = ___ - moe, xmax = estimate + ___)) +
geom_point(size = 3, color = "darkgreen") +
theme_grey(base_size = 14) +
labs(title = "Median household income",
___ = "Counties in Maine",
x = "ACS estimate (bars represent margins of error)",
y = "") +
scale_x_continuous(labels = scales::dollar)