Pengantar antarmuka latihan RMarkdown

Selamat datang di antarmuka latihan RMarkdown. Antarmuka ini sedikit berbeda dari yang telah Anda lihat sejauh ini: Saat Anda mengklik "Knit HTML", antarmuka akan merajut (knit) dokumen R Markdown Anda menjadi berkas HTML dan menampilkannya di panel baru. Anda juga bisa merajutnya menjadi berkas PDF, tetapi dalam kursus ini Anda akan tetap menggunakan HTML.

Untuk memudahkan, kami telah menyertakan beberapa kode R dasar yang dengan cepat menghasilkan plot dari paket R graphics, yang dimuat secara default, menggunakan himpunan data mtcars, yang juga dimuat secara default.

Latihan ini adalah bagian dari kursus

Berkomunikasi dengan Data di Tidyverse

Petunjuk latihan

Ubah judul menjadi "Communicating with Data in the Tidyverse", penulis menjadi nama Anda, dan tanggal pada header YAML di paling atas berkas RMarkdown.
Klik tombol "Knit HTML" untuk melihat berkas HTML yang telah dirajut, yang akan terbuka di panel baru.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

{"my_document.Rmd":"---\ntitle: \"___\"\nauthor: \"___\"\ndate: \"___\"\noutput: html_document\n---\n\nThe following plot shows the relationship between the number of cylinders and miles per gallon in the `mtcars` dataset.\n\n```{r}\nboxplot(mtcars$mpg ~ mtcars$cyl)\n```\n\nAs one can see, the more cylinders, the less miles per gallons.\n\n\n"}

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: Visualizing aspects of data with facets Exercise 2: Prepare the data set for the faceted plot Exercise 3: Add facets to the plot Exercise 4: Define your own theme function Exercise 5: Apply the new theme function to the plot Exercise 6: A custom plot to emphasize change Exercise 7: A basic dot plot Exercise 8: Add arrows to the lines in the plot Exercise 9: Add some labels to each country Exercise 10: Polishing the dot plot Exercise 11: The fct_reorder function Exercise 12: Reordering elements in the plot Exercise 13: Correct ugly label positions Exercise 14: Finalizing the plot for different audiences and devices Exercise 15: Change the viewport so labels don't overlap with plot border Exercise 16: Optimizing the plot for mobile devices

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: Apa itu RMarkdown?Exercise 2: Kapan sebuah dokumen dapat direproduksi?Exercise 3: Pengantar antarmuka latihan RMarkdown

Latihan Saat Ini

Exercise 4: Pemformatan dengan Markdown Exercise 5: Beri struktur pada dokumen Anda Exercise 6: Ubah pemformatan cuplikan teks Exercise 7: Kode R dalam dokumen RMarkdown Exercise 8: Tentukan paket di potongan kode pertama Exercise 9: Opsi chunk kode R Exercise 10: Pernyataan kode inline Exercise 11: Gambar dalam berkas RMarkdown Exercise 12: Menyesuaikan opsi figur, mengoptimalkannya untuk perangkat seluler.Exercise 13: Tambahkan gambar pendukung

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