Tendências com gráficos de dispersão

GlobalHealth Insights construiu um explorador de dispersão para entender a relação entre a riqueza nacional e os resultados de saúde. O gráfico mostra a expectativa de vida de cada país plotada em relação à Renda Nacional Bruta (RNB) per capita — uma medida de quão rico o país é.

você tem a opção entre escalas linear e logarítmica no eixo x, e pode adicionar linhas de tendência lineares ou curvas (LOESS).

Explore diferentes combinações e, em seguida, determine qual afirmação melhor descreve a tendência.

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Entendendo a visualização de dados

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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: Gráficos de dispersão Exercise 2: Interpretação de gráficos de dispersão Exercise 3: Tendências com gráficos de dispersão

Exercicio Atual

Exercise 4: Gráficos de linhas Exercise 5: Interpretação de gráficos de linhas Exercise 6: Escalas logarítmicas para gráficos de linhas Exercise 7: Gráficos de barras Exercise 8: Interpretação de gráficos de barras Exercise 9: Interpretação de gráficos de barras empilhadas Exercise 10: Gráficos de pontos Exercise 11: Interpretação de gráficos de pontos Exercise 12: Classificação de gráficos de pontos

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: Higher dimensions Exercise 2: Another dimension for scatter plots Exercise 3: Another dimension for line plots Exercise 4: Using color Exercise 5: Eye-catching colors Exercise 6: Qualitative, sequential, diverging Exercise 7: Highlighting data Exercise 8: Plotting many variables at once Exercise 9: Interpreting pair plots Exercise 10: Interpreting correlation heatmaps

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