3-way crosstabs

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(pollster)
library(dplyr)
library(knitr)
library(ggplot2)

It's common to want to view a crosstab of two variables by a third variable, for instance educational attainment by sex and marital status. The function crosstab_3way accomplishes this. Row and cell percents are both supported; column percents are not.

illinois %>%
  # filter for recent years & limited ages
  filter(year > 2009,
         age > 39) %>%
  crosstab_3way(x = sex, y = educ6, z = maritalstatus, weight = weight,
                remove = c("widow/divorced/sep"),
                n = FALSE) %>%
  kable(digits = 0, caption = "Educational attainment by sex and marital status among Illinois residents ages 35+",
        format = "html")

Three-way crosstabs plot well as small multiples using ggplot facets.

illinois %>%
  # filter for recent years & limited ages
  filter(year > 2009,
         age > 34) %>%
  crosstab_3way(x = sex, y = educ6, z = maritalstatus, weight = weight,
                remove = c("widow/divorced/sep"), 
                format = "long") %>%
  ggplot(aes(educ6, pct, fill = maritalstatus)) +
  geom_bar(stat = "identity", position = position_dodge()) +
  facet_wrap(facets = vars(sex)) +
  labs("Educational attainment by sex and marital status",
       subtitle = "Illinois residents ages 40+") +
  theme(legend.position = "top")

The same plot can be made with margin of errors as well. (See the "crosstabs" vignette for a more detailed discussion of margin of errors.)

illinois %>%
  # filter for recent years & limited ages
  filter(year > 2009,
         age > 34) %>%
  moe_crosstab_3way(x = sex, y = educ6, z = maritalstatus, weight = weight,
                remove = c("widow/divorced/sep"), format = "long") %>%
  ggplot(aes(educ6, pct, fill = maritalstatus)) +
  geom_bar(stat = "identity", position = position_dodge(),
           alpha = 0.5) +
  geom_errorbar(aes(ymin = (pct - moe), ymax = (pct + moe),
                    color = maritalstatus),
                position = position_dodge()) +
  facet_wrap(facets = vars(sex)) +
  labs(title = "Educational attainment by sex and marital status",
       subtitle = "Illinois residents ages 35+",
       caption = "Current Population Survey, 2010-2018") +
  theme(legend.position = "top")

Special case, when the z-variable identifies survey waves

If the x-variable in your crosstab uniquely identifies survey waves for which the weights were independently generated, it is best practice to calculate the design effect independently for each wave. moe_wave_crosstab_3way does just that. All of the arguments remain the same as in moe_crosstab_3way.

moe_wave_crosstab_3way(df = illinois, x = sex, y = educ6, z = year, weight = weight)


Try the pollster package in your browser

Any scripts or data that you put into this service are public.

pollster documentation built on Aug. 25, 2020, 5:08 p.m.