Wrap-up
1. Wrap-up
So what did we cover in this course and what R packages did we use?2. R packages
Let's start with the R packages. We used survey for survey analysis, dplyr for data wrangling, and ggplot2 for data visualization. Okay. And, what concepts did we learn?3. Course summary
Recall that we started by tackling the fundamentals of a sampling design, such as clusters and stratification. We learned that the survey weight for a sampled element is the number of population units it represents. Handling these weights properly turned out to be a key theme of the course! In Chapter 1, we practiced telling R about the structure of a survey with svydesign(). In Chapter 2, we explored one and two categorical variables. We summarized with svytable(), graphed with geom_col(), and conducted hypothesis tests with svychisq().4. Course summary
We threw a quantitative variable into the mix in Chapter 3 and looked at useful summary stats, such as the mean, the total, and quantiles, computing these summary stats for different sub-populations using svyby(). We visualized the shape of a quantitative variable using geom_histogram() and geom_density(). We closed out Chapter 3 by testing for a difference in means with svyttest(). Chapter 4 was about modeling the trend between two quantitative variables. We produced survey-weighted scatter plots where we mapped the survey weights to the size, color, and opacity of the points. We added trend lines and then we built the linear regression model with svyglm().5. Extensions
But of course, there is still so much more exciting survey analysis we could do. We could estimate more complex quantities, such as ratios, or we could build more complex models, such as logistic regression models.6. Congratulations!
Sadly, we will have to save those topics for a future course! For now, it's time to go out and analyze your own survey data!Create Your Free Account
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