Terapkan fungsi tema baru ke plot

Setelah Anda membuat fungsi theme_ilo() sendiri, saatnya menerapkannya pada objek plot. Dalam video Anda melihat bahwa pemanggilan theme() dapat dirangkai. Anda akan memanfaatkannya dan menambahkan pemanggilan theme() lain untuk menyesuaikan beberapa kekhasan pada plot ber-facet.

Latihan ini adalah bagian dari kursus

Berkomunikasi dengan Data di Tidyverse

Petunjuk latihan

Mulai sekarang, Anda dapat langsung menambahkan theme_ilo() ke objek plot apa pun yang Anda inginkan. Cobalah! Timpa variabel ilo_plot agar theme_ilo() diterapkan secara permanen.
Padukan fungsi tema kustom Anda dengan pemanggilan theme() tambahan untuk menyesuaikan pengaturan plot tingkat lanjut secara fleksibel.
- Untuk melakukannya, tambahkan pemanggilan theme() lain setelah pemanggilan theme_ilo().
- Ubah fill latar belakang label facet menjadi "gray60" dan color-nya menjadi "gray95", menggunakan argumen strip.background.
- Ubah color label facet menjadi "white".

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Apply your theme function (dont't forget to call it with parentheses!)
ilo_plot <- ilo_plot +
  ___

# Examine ilo_plot
ilo_plot

ilo_plot +
  # Add another theme call
  ___(
    # Change the background fill and color
    strip.background = element_rect(___, ___),
    # Change the color of the text
    strip.text = element_text(___)
  )

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Berkomunikasi dengan Data di Tidyverse

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus 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: Memvisualisasikan aspek data dengan facet Exercise 2: Siapkan himpunan data untuk plot ber-facet Exercise 3: Tambahkan facet ke plot Exercise 4: Definisikan fungsi tema Anda sendiri Exercise 5: Terapkan fungsi tema baru ke plot

Latihan Saat Ini

Exercise 6: Plot kustom untuk menekankan perubahan Exercise 7: Dot plot dasar Exercise 8: Tambahkan panah pada garis di plot Exercise 9: Tambahkan beberapa label pada tiap negara Exercise 10: Memoles dot plot Exercise 11: Fungsi fct_reorder Exercise 12: Mengurutkan ulang elemen dalam plot Exercise 13: Perbaiki posisi label yang kurang rapi Exercise 14: Menyelesaikan plot untuk beragam audiens dan perangkat Exercise 15: Ubah viewport agar label tidak tumpang tindih dengan tepi plot Exercise 16: Mengoptimalkan plot untuk perangkat seluler

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