Data Visualization Objectives and chart selection
What are objectives of visualization and what popularly known charts serve these visualization objectives?
2: Distribution
Several types of charts are particularly relevant for visualizing data distributions and identifying characteristics such as normal tendency, range, outliers, percentiles, population distribution, clustering trends, and anomalies. Choosing the most relevant chart depends on the specific characteristics of your data and the insights you want to extract. Combining multiple visualizations can provide a comprehensive view of the distribution and help uncover patterns and anomalies:
Histograms: Display the distribution of a continuous variable. Histograms show the frequency distribution of data, making it easy to identify the central tendency, spread, and potential outliers.
Box-and-Whisker Plots (Boxplots): Summarize the distribution of a dataset and identify outliers. Boxplots provide a visual summary of the central tendency, spread, and skewness of the data. Outliers are explicitly highlighted.
Kernel Density Plots: Estimate the probability density function of a continuous variable. Kernel density plots provide a smoothed representation of the distribution, helping to identify trends and anomalies.
Violin Plots: Combine aspects of boxplots and kernel density plots to visualize the distribution. Violin plots provide a more detailed view of the distribution, offering insights into both central tendency and variability.
Cumulative Distribution Function (CDF) Plots: Show the cumulative probability of a continuous variable. CDF plots help assess the proportion of data below a certain threshold, making it easier to understand population distribution and percentiles.
Q-Q (Quantile-Quantile) Plots: Compare the distribution of a sample to a theoretical distribution (e.g., normal distribution). Q-Q plots help assess normality and identify deviations from expected distribution patterns.
Empirical Cumulative Distribution Function (ECDF) Plots: Display the cumulative distribution of observed data. ECDF plots are especially useful for comparing multiple datasets and understanding their distributional differences.
Scatter Plots: Visualize relationships between two variables. Scatter plots can reveal clustering trends, relationships, and outliers, providing insights into the distributional characteristics of data points.
Heatmaps: Display the distribution of values in a two-dimensional space. Heatmaps can reveal clustering patterns and anomalies, especially when applied to multivariate datasets.
3D Surface Plots: Visualize the distribution of three variables in a three-dimensional space. 3D surface plots provide insights into the joint distribution of three variables, revealing trends and anomalies.
Rug Plots: Add small lines to the axis to indicate individual data points. Rug plots complement other visualizations, providing a simple representation of individual data points along an axis.
