Voeg pijlen toe aan de lijnen in de plot

In plaats van jaren te labelen, gebruik je het argument arrow in de aanroep van geom_path() om de richting van verandering te laten zien. De pijlen wijzen van 1996 naar 2006, omdat de gegevensset zo is geordend. De functie arrow() neemt twee argumenten: het eerste is length, dat je kunt opgeven met een aanroep van unit(), die je je misschien herinnert van eerdere oefeningen. Het tweede is type, dat bepaalt hoe de pijlpunten eruitzien.

Deze oefening maakt deel uit van de cursus

Communiceren met data in de Tidyverse

Cursus bekijken

Oefeninstructies

Gebruik het argument arrow en een aanroep van arrow() om pijlen aan elke lijn toe te voegen.
Geef voor de pijlen een length van 1.5 "mm" (met een aanroep van unit()) en een type van "closed" op.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

ggplot(ilo_data) +
  geom_path(aes(x = working_hours, y = country),
  # Add an arrow to each path
  arrow = ___(___ = ___(___, "___"), ___ = "___"))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Communiceren met data in de Tidyverse

SkillTag.level.beginnerSkillTag.label

4.8+

Begin de cursus gratis

In this chapter, you will have a first look at the data you're going to work with throughout this course: the relationship between weekly working hours and monetary compensation in European countries, according to the International Labour Organization (ILO). After that, you'll dive right in and discover a stunning correlation by employing an exploratory visualization. You will then apply a custom look to that graphic – you'll turn an ordinary plot into an aesthetically pleasing and unique data visualization.

Exercise 1: Introduction to the data Exercise 2: Join the two data sets together Exercise 3: Change variable types Exercise 4: Filtering and plotting the data Exercise 5: Filter the data for plotting Exercise 6: Some summary statistics Exercise 7: A basic scatter plot Exercise 8: Add labels to the plot Exercise 9: Custom ggplot2 themes Exercise 10: Apply a default theme Exercise 11: Change the appearance of titles Exercise 12: Alter background color and add margins

Barcharts, scatter plots, and histograms are probably the most common and effective data visualizations. Yet, sometimes, there are even better ways to visually highlight the finding you want to communicate to your audience. So-called "dot plots" make us better grasp and understand changes in data: development over time, for example. In this chapter, you'll build a custom and unique visualization that emphasizes and explains exactly one aspect of the story you want to tell.

Exercise 1: Aspecten van data visualiseren met facetten Exercise 2: Bereid de gegevensset voor op de gefacetteerde plot Exercise 3: Voeg facets toe aan de plot Exercise 4: Definieer je eigen theme-functie Exercise 5: Pas de nieuwe themabfunctie toe op de plot Exercise 6: Een aangepaste plot om verandering te benadrukken Exercise 7: Een eenvoudige dotplot Exercise 8: Voeg pijlen toe aan de lijnen in de plot

Huidige oefening

Exercise 9: Voeg labels toe aan elk land Exercise 10: De dotplot verfijnen Exercise 11: De functie fct_reorder Exercise 12: Elementen in de plot herordenen Exercise 13: Corrigeer lelijke labelposities Exercise 14: De plot finaliseren voor verschillende doelgroepen en apparaten Exercise 15: Pas de viewport aan zodat labels niet overlappen met de plotrand Exercise 16: De plot optimaliseren voor mobiele apparaten

Back in the old days, researchers and data analysts used to generate plots in R and then tediously copy them into their LaTeX or Word documents. Nowadays, whole reports can be produced and reproduced from within R and RStudio, using the RMarkdown language – combining R chunks, formatted prose, tables and plots. In this chapter, you'll take your previous findings, results, and graphics and integrate them into such a report to tell the story that needs to be told.

Exercise 1: What is RMarkdown?Exercise 2: When is a document reproducible?Exercise 3: Introduction to the RMarkdown exercise interface Exercise 4: Formatting with Markdown Exercise 5: Give your document a structure Exercise 6: Change formatting of text snippets Exercise 7: R code in RMarkdown documents Exercise 8: Specify packages in the first code chunk Exercise 9: R code chunk options Exercise 10: Inline code statements Exercise 11: Images in RMarkdown files Exercise 12: Adjusting figure options, optimizing them for mobile devices.Exercise 13: Add auxiliary images

Your boss, your client, or your professor usually expects your results to be accurate and presented in a clear and concise structure. However, coming up with a nicely formatted and unique report on top of that is certainly a plus and RMarkdown can be customized to accomplish this. In this last chapter, you'll take your report from the last chapter and brand it with your own custom and unique style.

Exercise 1: Advanced YAML settings Exercise 2: Change the overall appearance of your report Exercise 3: Add a table of contents Exercise 4: More YAML hacks Exercise 5: Custom stylesheets Exercise 6: CSS selectors Exercise 7: Change style attributes of text elements Exercise 8: Reference the style sheet Exercise 9: Beautiful tables Exercise 10: Beautify a table with kable Exercise 11: Repetition: CSS Exercise 12: Summary