İki veri kümesini birleştir

Videoda, dplyr paketindeki inner_join() fonksiyonunun iki veri çerçevesini birleştirirken bir "anahtar" istemesi gerektiğini öğrendin. Aslında, eşleşmesi gereken birden fazla anahtar da belirtilebilir. Bu ilk egzersizde, iki veri kümesini iki anahtar üzerinden birleştireceksin. ilo_hourly_compensation ve ilo_working_hours veri çerçeveleri senin için önceden yüklendi ve çalışma alanında hazır.

Bu kursta unuttuğun pek çok kavrama değineceğiz; hızlıca tazelemen gerekirse, tidyverse Hızlı Başvuru Kağıdı dosyasını indirip elinin altında tut!

Bu egzersiz

Tidyverse ile Verilerle İletişim

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Her iki ILO veri çerçevesini ilo_hourly_compensation ve ilo_working_hours, dplyr'ın inner_join() fonksiyonunu kullanarak birleştir.
İki veri çerçevesini "country" ve "year" değişkenlerine göre birleştir.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Join both data frames
ilo_data <- ilo_hourly_compensation %>%
  ___(___, by = c(___, ___))

# Count the resulting rows
ilo_data  %>% 
    count()

# Examine ilo_data
ilo_data

Kodu Düzenle ve Çalıştır

Bu egzersiz

Tidyverse ile Verilerle İletişim

kursunun bir parçasıdır

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: Veriye giriş Exercise 2: İki veri kümesini birleştir

Geçerli Egzersiz

Exercise 3: Değişken türlerini değiştir Exercise 4: Veriyi filtreleme ve görselleştirme Exercise 5: Grafik için veriyi filtrele Exercise 6: Bazı özet istatistikler Exercise 7: Basit bir saçılım grafiği Exercise 8: Grafiğe etiketler ekle Exercise 9: Özel ggplot2 temaları Exercise 10: Varsayılan bir temayı uygula Exercise 11: Başlıkların görünümünü değiştir Exercise 12: Arka plan rengini değiştir ve kenar boşlukları ekle

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: 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