What data visualization techniques are best for presenting research data over time?

1 Line charts

Line charts are one of the most common and versatile data visualization techniques for presenting research data over time. They show how one or more variables change along a continuous axis, such as time, date, or age. Line charts are ideal for comparing trends, highlighting peaks and valleys, and revealing correlations or causations. For example, you can use a line chart to show how the sales of a product vary over months or years, or how the temperature affects the growth of a plant.

2 Bar charts

Bar charts are another popular data visualization technique for presenting research data over time. They show how one or more categorical variables change along a discrete axis, such as quarters, seasons, or years. Bar charts are suitable for comparing values, showing proportions, and displaying distributions. For example, you can use a bar chart to show how the market share of different brands changes over time, or how the population of a country is distributed by age groups.

3 Area charts

Area charts are similar to line charts, but they fill the area below the line with color or shading. They show how one or more variables change over time, and also emphasize the magnitude or volume of the change. Area charts are useful for showing cumulative effects, stacking values, and highlighting differences. For example, you can use an area chart to show how the total revenue of a company changes over time, or how the composition of a portfolio changes over different scenarios.

4 Scatter plots

Scatter plots are data visualization techniques that show the relationship between two or more variables, usually along two numerical axes. They show how the variables vary together over time, and reveal patterns, clusters, outliers, and correlations. Scatter plots are effective for exploring data, testing hypotheses, and finding insights. For example, you can use a scatter plot to show how the life expectancy and GDP per capita of different countries change over time, or how the weight and height of a sample of people correlate.

5 Heat maps

Heat maps are data visualization techniques that show the intensity or frequency of a variable over a grid or a map, using colors or shades. They show how the variable changes over time and space, and highlight hotspots, gradients, and anomalies. Heat maps are helpful for showing geographic data, displaying distributions, and identifying trends. For example, you can use a heat map to show how the crime rate or the air quality of a city changes over time, or how the traffic or the sales of a website varies by location.