Kualitatif, sekuensial, divergen

Ada tiga jenis skala warna, masing-masing dirancang untuk menonjolkan hal yang berbeda dalam sebuah visualisasi.

Jenis	Tujuan
kualitatif	Membedakan kategori tak berurutan
sekuensial	Menunjukkan pengurutan
divergen	Menunjukkan di atas atau di bawah titik tengah

Di sini Anda dapat melihat hasil survei tentang kecemasan matematika dari sekelompok siswa. Setiap pertanyaan memiliki barisnya sendiri, dan jawaban berkisar dari "Sangat Tidak Setuju" hingga "Netral" hingga "Sangat Setuju".

Pilih skala warna yang tepat untuk plot tersebut.

Sumber data: Bai et al. (2009)

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In this chapter you’ll learn the value of visualizations, using real-world data on British monarchs, Australian salaries, Panamanian animals, and US cigarette consumption, to graphically represent the spread of a variable using histograms and box plots.

Exercise 1: A plot tells a thousand words Exercise 2: Motivating visualization Exercise 3: Continuous vs. categorical variables Exercise 4: Histograms Exercise 5: Interpreting histograms Exercise 6: Adjusting bin width Exercise 7: Box plots Exercise 8: Interpreting box plots Exercise 9: Ordering box plots

You’ll learn how to interpret data plots and understand core data visualization concepts such as correlation, linear relationships, and log scales. Through interactive exercises, you’ll also learn how to explore the relationship between two continuous variables using scatter plots and line plots. You'll explore data on life expectancies, technology adoption, COVID-19 coronavirus cases, and Swiss juvenile offenders. Next you’ll be introduced to two other popular visualizations—bar plots and dot plots—often used to examine the relationship between categorical variables and continuous variables. Here, you'll explore famous athletes, health survey data, and the price of a Big Mac around the world.

Exercise 1: Scatter plots Exercise 2: Interpreting scatter plots Exercise 3: Trends with scatter plots Exercise 4: Line plots Exercise 5: Interpreting line plots Exercise 6: Logarithmic scales for line plots Exercise 7: Bar plots Exercise 8: Interpreting bar plots Exercise 9: Interpreting stacked bar plots Exercise 10: Dot plots Exercise 11: Interpreting dot plots Exercise 12: Sorting dot plots

It’s time to make your insights even more impactful. Discover how you can add color and shape to make your data visualizations clearer and easier to understand, especially when you find yourself working with more than two variables at the same time. You'll explore Los Angeles home prices, technology stock prices, math anxiety, the greatest hiphop songs, scotch whisky preferences, and fatty acids in olive oil.

Exercise 1: Dimensi yang lebih tinggi Exercise 2: Dimensi lain untuk sebaran (scatter plot)Exercise 3: Dimensi lain untuk plot garis Exercise 4: Menggunakan warna Exercise 5: Warna yang mencolok Exercise 6: Kualitatif, sekuensial, divergen

Latihan Saat Ini

Exercise 7: Menyorot data Exercise 8: Memplot banyak variabel sekaligus Exercise 9: Menafsirkan pair plot Exercise 10: Menafsirkan heatmap korelasi

In this final chapter, you’ll learn how to identify and avoid the most common plot problems. For example, how can you avoid creating misleading or hard to interpret plots, and will your audience understand what it is you’re trying to tell them? All will be revealed! You'll explore wind directions, asthma incidence, and seats in the German Federal Council.

Exercise 1: Polar coordinates Exercise 2: Pie plots Exercise 3: Rose plots Exercise 4: Axes of evil Exercise 5: Bar plot axes Exercise 6: Dual axes Exercise 7: Sensory overload Exercise 8: Chartjunk Exercise 9: Multiple plots Exercise 10: Congratulations