Plotting variations

The preparation is done; now let's explore stat_summary().

Summary statistics refers to a combination of location (mean or median) and spread (standard deviation or confidence interval).

These metrics are calculated in stat_summary() by passing a function to the fun.data argument. mean_sdl(), calculates multiples of the standard deviation and mean_cl_normal() calculates the t-corrected 95% CI.

Arguments to the data function are passed to stat_summary()'s fun.args argument as a list.

The position object, posn_d, and the plot with jittered points, p_wt_vs_fcyl_by_fam_jit, are available.

This exercise is part of the course

Intermediate Data Visualization with ggplot2

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

p_wt_vs_fcyl_by_fam_jit +
  # Add a summary stat of std deviation limits
  ___

Edit and Run Code

This exercise is part of the course

Intermediate Data Visualization with ggplot2

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A picture paints a thousand words, which is why R ggplot2 is such a powerful tool for graphical data analysis. In this chapter, you’ll progress from simply plotting data to applying a variety of statistical methods. These include a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.

Exercise 1: Stats with geoms Exercise 2: Smoothing Exercise 3: Grouping variables Exercise 4: Modifying stat_smooth Exercise 5: Modifying stat_smooth (2)Exercise 6: Stats: sum and quantile Exercise 7: Quantiles Exercise 8: Using stat_sum Exercise 9: Stats outside geoms Exercise 10: Preparations Exercise 11: Using position objects Exercise 12: Plotting variations

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