Facet’li grafik için veri kümesini hazırla

Videoda belirtildiği gibi, bir sonraki egzersizde facet’li saçılım grafiğini oluşturmak için veri kümeni hazırlayacaksın.
Bunun için, veri kümesinde yalnızca 1996 ve 2006 yılları yer almalı; çünkü grafiğinde sadece iki facet olacak. ilo_data senin için önceden yüklendi.

Bu egzersiz, kursun bir parçasıdır

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Egzersiz talimatları

Veri kümesinde yalnızca 1996 ve 2006 yıllarını tutmak için filter() kullan.
filter() fonksiyon çağrında OR operatörünü kullan.

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# Filter ilo_data to retain the years 1996 and 2006
ilo_data <- ilo_data %>%
  ___

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

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Kursa Ücretsiz Başla

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

Geçerli egzersiz

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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

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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