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.
Este exercício faz parte do curso
Analyzing US Census Data in R
Instruções do exercício
- 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.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# 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)