Data Visualization in Stata

Explore unequal bin histograms in stata, comparing custom bin widths to equal-probability histograms, and learn how adjusting bin sizes and colors highlights specific data ranges and distributions.

Learn to generate rootogram plots in Sta ta to visually test data against theoretical distributions, overlay normal or other distributions, adjust spike counts, and use confidence intervals to assess normality.

Violin plots combine a box plot with a rotated kernel density plot on each side to reveal the full distribution alongside summary statistics.

Explore how dot plots visualize a continuous distribution by displaying each observation as a dot, enabling easy group comparisons and highlighting clusters and outliers.

Assess why pie charts are often not useful for discrete data, note their visual appeal and limitations, and learn when exploding a slice better conveys a simple proportion.

Learn to create pie charts in Stata with graph pie, using the three modes and key options like explode, label, and sort for clear regional and demographic visuals.

Explore creating and customizing scatter plots in Stata, using two-way commands to overlay multiple variables, and fine-tune markers, colors, labels, connections, and multi-axis options.

Explore polar smoother plots, a nonparametric, circular visualization that transforms data to polar coordinates, smooths the central mass around 50%, and compares groups via ellipses and circles.

Learn to generate lines of best fit in Stata using two-way scatter plots, linear, quadratic, fractional polynomial, and non-parametric fits, with confidence intervals and by-group overlays.

Learn how line plots connect ordered data points with a line to show trends, distinguishing them from scatterplots and best-fit lines, especially for time-based data.

Learn to create line plots in Stata using the line command, overlay multiple lines, customize connections, colors, and axes, and optionally pair with scatter plots for clarity.

Rainbow plots use color gradation to encode the ordering of data across multiple plot types, not density, making many groups easier to compare.

Sparkline plots stack small line charts vertically to reduce clutter and highlight changing trends across many time series.

Explore how to create sparkling plots in Stata using the sparkling command, load the Grunfeld data, and compare multiple time series with options like over, buy, extremes, and white label.

Jitter plots visualize discrete data by randomly displacing values, revealing densities and relationships between categorical variables while keeping the overall pattern recognizable.

Learn how to create table plots in stata using the top plot command, explore two-way and three-way tabulations, and apply options like percent, missing data, horizontal bars, and cell labels.

Master mosaic plots in Stata, aka spike plots, to visualize two-variable data and relative frequencies using the weight qualifier; compare with bar graphs.

Explore how China faces visualize multivariate data for small samples by mapping variables to facial characteristics and comparing observations qualitatively.