Calculating group-wise margins of error

One way to reduce margins of error in an ACS analysis is to combine estimates when appropriate. This can be accomplished using tidyverse group-wise data analysis tools. In this exercise, you'll combine estimates for male and female elderly poverty in Vermont, and use the moe_sum() function as part of this group-wise analysis. While you may lose some detail with this type of approach, your estimates will be more reliable relative to their margins of error than before you combined them.

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Analyzing US Census Data in R

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Instrucciones del ejercicio

Group the vt_eldpov dataset by the GEOID column.
In the summarize() call, use the moe_sum() function to calculate a derived margin of error.
Check the proportion of rows with margins of error that exceed their estimates.

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# Group the dataset and calculate a derived margin of error
vt_eldpov2 <- vt_eldpov %>%
  ___(___) %>%
  summarize(
    estmf = sum(estimate), 
    moemf = ___(___ = moe, estimate = estimate)
  )

# Filter rows where newly-derived margin of error exceeds newly-derived estimate
moe_check2 <- filter(vt_eldpov2, ___ > estmf)

# Check proportion of rows where margin of error exceeds estimate
nrow(moe_check2) / ___(vt_eldpov2)

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