R Tutorial: Elements of a sampling design

Want to learn more? Take the full course at https://learn.datacamp.com/courses/analyzing-survey-data-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that we understand survey weights, let's learn some common design structures and how they are specified using the popular survey package. Suppose we want to estimate the average amount of time Pennsylvania residents spend on social media. This means our target population is the residents of Pennsylvania and our study variable is the weekly hours spent on social media. Suppose we have a list of every Pennsylvania resident and we randomly picked 200 people to survey. These 200 people are denoted by the pink dots. This method of sampling is called simple random sampling. Everyone had an equal chance of being selected for our survey. Once we have collected the data and imported it into R, we now need to tell R about our sampling design. To do so, we will use the svydesign() function in Thomas Lumley's wonderful survey package. For simple random sampling, we need to specify the data frame, the column that stores the survey weights, the column that contains the population size, which statisticians call FPC, short for finite population correction, and the column that stores staging information. Since there is only one sampling stage in simple random sampling, we set id equal to tilde 1. The tildes in front of wts and N imply that these are the names of columns in the dataset paSample. To recap, the svydesign() function tells R what sampling design generated the data and creates an object that contains the data and important design information. Notice that if we randomly select 200 Pennsylvanians, many of these residents are concentrated around Pennsylvania's two largest cities, Philadelphia and Pittsburgh, since these places have a higher concentration of people. If we want to estimate the social media usage by county. We can't since some cou