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Analyzing Survey Data in R

IntermediateSkill Level
4.8+
196 reviews
Updated 10/2022
Learn survey design using common design structures followed by visualizing and analyzing survey results.
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RProbability & Statistics
4 hr
14 videos
49 Exercises
3,950 XP
15,316
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Course Description

You've taken a survey (or 1000) before, right? Have you ever wondered what goes into designing a survey and how survey responses are turned into actionable insights? Of course you have! In Analyzing Survey Data in R, you will work with surveys from A to Z, starting with common survey design structures, such as clustering and stratification, and will continue through to visualizing and analyzing survey results. You will model survey data from the National Health and Nutrition Examination Survey using R's survey and tidyverse packages. Following the course, you will be able to successfully interpret survey results and finally find the answers to life's burning questions!

Prerequisites

Introduction to the TidyverseFoundations of Inference in R
1

Introduction to survey data

Our exploration of survey data will begin with survey weights. In this chapter, we will learn what survey weights are and why they are so important in survey data analysis. Another unique feature of survey data are how they were collected via clustering and stratification. We'll practice specifying and exploring these sampling features for several survey datasets.
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2

Exploring categorical data

Now that we have a handle of survey weights, we will practice incorporating those weights into our analysis of categorical data in this chapter. We'll conduct descriptive inference by calculating summary statistics, building summary tables, and constructing bar graphs. For analytic inference, we will learn to run chi-squared tests.
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3

Exploring quantitative data

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.
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4

Modeling quantitative data

To model survey data also requires careful consideration of how the data were collected. We will start our modeling chapter by learning how to incorporate survey weights into scatter plots through aesthetics such as size, color, and transparency. We'll model the survey data with linear regression and will explore how to incorporate categorical predictors and polynomial terms into our models.
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Analyzing Survey Data in R
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*4.8
from 196 reviews
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14%
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1%
  • Avinashkumar
    3 days ago

    Great course for understanding survey data analysis in R. It clearly explains concepts like sampling design, weighting and regression using real examples. The exercises are practical and helped me build confidence in using packages like survey and ggplot2

  • Abdullateef
    3 days ago

  • Eddie
    4 days ago

  • Lauren
    last week

  • Sandeep
    last week

    A clear and practical course that teaches survey-weighted analysis in R through hands-on examples. It effectively shows how weights impact visualizations and regression results. The svyglm() and ggplot2 exercises are especially helpful for building intuition. Some syntax can feel dense at first, but the structured progression makes it easy to follow overall.

  • Napaporn
    last week

"Great course for understanding survey data analysis in R. It clearly explains concepts like sampling design, weighting and regression using real examples. The exercises are practical and helped me build confidence in using packages like survey and ggplot2"

Avinashkumar

Abdullateef

Eddie

FAQs

What R packages are used to analyze survey data in this course?

You use the survey package for handling weighted survey data and tidyverse packages for manipulation and visualization. Together they support the full analysis workflow.

What real survey dataset does this course use?

You model and analyze data from the National Health and Nutrition Examination Survey (NHANES), a well-known large-scale survey conducted in the United States.

Does this course explain survey weights and why they matter?

Yes. The first chapter focuses entirely on survey weights, what they are, why they are critical for unbiased analysis, and how to specify them alongside clustering and stratification.

Is this course appropriate for someone with only basic R skills?

No. This is an advanced course with ten prerequisites covering statistics, regression, hypothesis testing, sampling, inference, and data visualization in R.

What statistical tests and models are covered?

You learn survey-weighted chi-squared tests for categorical data, survey-weighted t-tests for quantitative data, and survey-weighted linear regression with categorical predictors and polynomial terms.

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