In lab 1, we go over some of the basics of working in RStudio, writing in RMarkdown, and working in R. We also discuss why we may want to conduct data ethnographies.
In lab 2, we begin to map out a research plan, anticipate some of the social, political, and economic contexts our research topics are embedded within, and begin to search for relevant public data.
In lab 3, we examine data documentation and other sources where a dataset is cited to unpack how the dataset was produced and what it was designed to represent.
In lab 4, we learn how to import data into R and how to clean data for analysis. We then explore the data, documenting its structure, its observational unit, how values are categorized, and why there may be missing values.
In lab 5, we learn how to zoom into to analyze specific observations in the data and to zoom out summarizing statistics across grouped data. We also learn how to visualize variation and co-variation in a dataset.
In lab 6, we learn how and when it is appropriate to gather quantiative insights from a dataset. We also learn how to visualize measures of central tendency and measures of dispersion.
In lab 7, we learn how and why place and time matters when interpreting data. We learn how to facet data visualizations to track changes across place and over time.
In lab 8, we reflect on our data practice to document some of the knowledge gaps in our data.
In lab 9, we consider how to present findings in our data as well as how to document uncertainty.