The ultimate guide for importing, editing & analyzing real-world data files
If you start analyzing real-world data, which steps should you take in which order?
What you’ll learn
- Work fast and accurately with R Studio.
- Perform essential data editing and analysis skills in R.
- Screen data files for common issues and correct these if necessary.
- Create nicely detailed tables for frequencies, descriptives, correlations and more.
- Create decent bar charts, histogram, scatterplots and more.
Course Content
- Getting Started –> 3 lectures • 35min.
- Minimal Data Screening –> 8 lectures • 1hr 31min.
- Importing & Exporting Files –> 5 lectures • 59min.
- Univariate Data Analysis –> 2 lectures • 21min.
- Bivariate Data Analysis –> 4 lectures • 54min.
- Basic Data Editing –> 5 lectures • 59min.
Requirements
If you start analyzing real-world data, which steps should you take in which order?
And what’s a simple but solid way to perform these in R Studio?
This course teaches you exactly that with a minimal time investment.
We start off with a quick tour through the R Studio interface. Next up, we jump straight into a real-world data file. You’ll learn a minimal, step-by-step data screening routine that includes
- inspecting variable distributions with bar charts and histograms,
- checking for undesired Chr (string) variables,
- counting NA (missing) values
- and way more…
We’ll then walk you through some fundamental data analyses such as
- frequency tables with frequencies & column percentages,
- descriptive statistics over all observations & subgroups separately,
- contingency tables with frequencies and column percentages &
- Pearson correlations with listwise & pairwise exclusion of missing values.
Next up, you’ll learn how to import & export various file types into & from R Studio such as .R, .RData, .RDS, Excel, .CSV, .SAV & .PNG.
Finally, we’ll round off with some extra data editing skills. These include reordering and removing variables (columns) or observations (rows) and counting NA (missing) values within observations. Last but not least, we’ll cover computing means and sums over variables with & without NA values.
In short, you’ll learn exactly what you need for working with real-life data in R Studio.
Just do it.
Happy coding 😉