Grafikteki çizgilere oklar ekle

Yılları etiketlemek yerine, değişimin yönünü göstermek için geom_path() çağrısındaki arrow argümanını kullan. Veri kümesi bu şekilde sıralandığı için oklar 1996'dan 2006'ya doğru işaret edecek. arrow() fonksiyonu iki argüman alır: İlki, önceki egzersizlerden hatırlayabileceğin unit() çağrısıyla belirtilebilen length. İkincisi ise ok ucunun nasıl görüneceğini tanımlayan type.

Bu egzersiz

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Kursu Görüntüle

Egzersiz talimatları

Her çizgiye ok eklemek için arrow argümanını ve bir arrow() fonksiyonu çağrısını kullan.
Oklar için, unit() çağrısıyla 1.5 "mm" length ve "closed" type belirt.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

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InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Verinin farklı yönlerini küçük çoklularla görselleştirme Exercise 2: Facet’li grafik için veri kümesini hazırla Exercise 3: Grafiğe facet ekle Exercise 4: Kendi tema fonksiyonunu tanımla Exercise 5: Yeni tema fonksiyonunu grafiğe uygula Exercise 6: Değişimi vurgulayan özel bir grafik Exercise 7: Temel bir nokta grafiği Exercise 8: Grafikteki çizgilere oklar ekle

Geçerli Egzersiz

Exercise 9: Her ülkeye birkaç etiket ekle Exercise 10: Nokta grafiğini cilalamak Exercise 11: fct_reorder fonksiyonu Exercise 12: Grafikteki öğeleri yeniden sıralama Exercise 13: Kötü etiket konumlarını düzelt Exercise 14: Grafiği farklı kitleler ve cihazlar için son hâline getirme Exercise 15: Görüntüleme alanını değiştir ki etiketler grafik kenarıyla çakışmasın Exercise 16: Grafiği mobil cihazlar için optimize etme

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