Marges in grafieken

Facets zijn ideaal om deelverzamelingen binnen een variabele te zien, maar soms wil je zowel die deelverzamelingen als alle waarden in een variabele zien.

Hier komt het argument margins van facet_grid() van pas.

FALSE (standaard): geen marges.
TRUE: voeg marges toe voor elke variabele waarop je facetteert.
c("variable1", "variable2"): voeg alleen marges toe voor de opgegeven variabelen.

Om de facets makkelijker te volgen, hebben we twee factorvariabelen met duidelijke labels gemaakt — fam voor het type transmissie en fvs voor het type motor.

Zoom het grafiekvenster in om je plots beter te bekijken.

Deze oefening maakt deel uit van de cursus

Gevorderde datavisualisatie met ggplot2

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

ggplot(mtcars, aes(x = wt, y = mpg)) + 
  geom_point() +
  # Facet rows by fvs and cols by fam
  ___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Gevorderde datavisualisatie met ggplot2

SkillTag.level.intermediateSkillTag.label

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Begin de cursus gratis

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

The Coordinates layers offer specific and very useful tools for efficiently and accurately communicating data. Here we’ll look at the various ways of effectively using these layers, so you can clearly visualize lognormal datasets, variables with units, and periodic data.

Exercise 1: Coordinates Exercise 2: Zooming In Exercise 3: Aspect ratio I: 1:1 ratios Exercise 4: Aspect ratio II: setting ratios Exercise 5: Expand and clip Exercise 6: Coordinates vs. scales Exercise 7: Log-transforming scales Exercise 8: Adding stats to transformed scales Exercise 9: Double and flipped axes Exercise 10: Useful double axes Exercise 11: Flipping axes I Exercise 12: Flipping axes II Exercise 13: Polar coordinates Exercise 14: Pie charts Exercise 15: Wind rose plots

Facets let you split plots into multiple panes, each displaying subsets of the dataset. Here you'll learn how to wrap facets and arrange them in a grid, as well as providing custom labeling.

Exercise 1: De facet-laag Exercise 2: Basis van de facet-laag Exercise 3: Veel variabelen Exercise 4: Formulenotatie Exercise 5: Facet-labels en volgorde Exercise 6: Facetlabels toevoegen Exercise 7: Volgorde instellen Exercise 8: Facet-plotruimtes Exercise 9: Variabele plotruimtes I: continue variabelen Exercise 10: Variabele plotruimtes II: categorische variabelen Exercise 11: Facet wrap & marges Exercise 12: Wrap voor veel niveaus Exercise 13: Marges in grafieken

Huidige oefening

Now that you have the technical skills to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter, you’ll review three plot types that are commonly discouraged in the data viz community: heat maps, pie charts, and dynamite plots. You’ll learn the pitfalls with these plots and how to avoid making these mistakes yourself.

Exercise 1: Best practices: bar plots Exercise 2: Bar plots: dynamite plots Exercise 3: Bar plots: position dodging Exercise 4: Bar plots: Using aggregated data Exercise 5: Heatmaps use case scenario Exercise 6: Heat maps Exercise 7: Useful heat maps Exercise 8: Heat map alternatives Exercise 9: When good data makes bad plots Exercise 10: Suppression of the origin Exercise 11: Color blindness Exercise 12: Typical problems