Outliers can distort your analyses, but box plots can help you find potential outliers and gauge their influence on your work.
- Quantitative variables, … or scaled, measured, or continuous variables, … are usually graphed with both histograms, … and with box plots, … which are especially useful for showing you … the presence of outliers, … which can have a dramatic influence on your analyses. … So let's take a look at how to do a box plot. … We're going to use the same diamonds dataset, … that's from the tidyverse. … I'm going to load tidyverse. … You get a little information about the diamonds dataset … and we'll look at the first few rows. … Here they are right there. … And then let's make a box plot. … Now I'm going to be doing some of this with the generic … Let's get information on that. … It's called Generic X-Y Plotting. … It's a pretty smart graphing choice. … It often adapts very well to the kind of data that you're … showing to it. … And then there's the specific boxplot command. … I'm going to begin by creating a box plot … of the price of the diamonds … using the default boxplot command. … And so I start with boxplot …
- Installing R and RStudio
- Navigating the RStudio environment
- Importing data from a spreadsheet
- Working with the tidyverse
- Piping commands with %>%
- Visualizing data with R base graphics and ggplot2
- Visualizing hierarchical clusters
- Selecting cases and subgroups
- Recoding variables
- Calculating frequencies
- Calculating descriptives
- Calculating correlations
- Computing a linear regression
Skill Level Beginner
R for Data Science: Lunchbreak Lessonswith Mark Niemann-Ross10h 50m Intermediate
Integrating Tableau and R for Data Sciencewith Ben Sullins1h 10m Intermediate
1. What Is R?
R in context6m 54s
2. Getting Started
3. Data Visualization
4. Data Wrangling
5. Data Analysis
Next steps2m 45s
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