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Survey-weighted density plots

We can also explore the shape of a variable with the smooth curve of a density plot. Let's create a density plot of nightly sleep where we facet by gender. Whereas the height of the histogram bars represent counts, the height of the density curve represents probabilities. Therefore, we will need to do some data wrangling before we create the plot.

This is a part of the course

“Analyzing Survey Data in R”

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Exercise instructions

  • For NHANESraw, remove rows that are missing SleepHrsNight or Gender.
  • Grouping by Gender, add the column WTMEC4YR_std which equals WTMEC4YR/sum(WTMEC4YR).
  • Pipe your wrangled data directly into a ggplot() where SleepHrsNight is mapped to x and WTMEC4YR_std is mapped to weight. Include a density layer with bw = 0.6 and fill = "gold" and a facetting layer where you facet by Gender.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Density plot of sleep faceted by gender
NHANESraw %>%
    ___(!is.na(___), !is.na(___)) %>%
    group_by(___) %>%
    mutate(WTMEC4YR_std = ___) %>%
    ggplot(mapping = aes(x = ___, weight = ___)) + 
        geom____(bw = 0.6,  fill = "gold") +
        labs(x = "Hours of Sleep") + 
        ____wrap(~___, labeller = "label_both")
Edit and Run Code

This exercise is part of the course

Analyzing Survey Data in R

IntermediateSkill Level
4.4+
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Learn survey design using common design structures followed by visualizing and analyzing survey results.

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Of course not all survey data are categorical and so in this chapter, we will explore analyzing quantitative survey data. We will learn to compute survey-weighted statistics, such as the mean and quantiles. For data visualization, we'll construct bar-graphs, histograms and density plots. We will close out the chapter by conducting analytic inference with survey-weighted t-tests.

Exercise 1: Summarizing quantitative dataExercise 2: Survey statisticsExercise 3: Estimating quantilesExercise 4: Visualizing quantitative dataExercise 5: Bar plots of survey-weighted meansExercise 6: Output of svyby()Exercise 7: Bar plots with errorExercise 8: Survey-weighted histogramsExercise 9: Survey-weighted density plots
Exercise 10: Inference for quantitative dataExercise 11: Survey-weighted t-testExercise 12: Tying it all together!

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