Learn all 20+ Excel Charts and Graphs| Data Storytelling | Become a Data Visualization Expert.
Excel is likely the underutilized tool when it comes to storytelling and data visualization, especially since the advent of other visualization tools — Power BI and Tableau.
But, Excel remains everyone’s go-to spreadsheet for quick, easy, and efficient data analysis, data visualization, and storytelling.
The pretty cool part of Excel is the constant updates and improvement of its feature by Microsoft which makes Excel everyone’s favorite.
In this article, we’ll walk through all the charts and graphs types in Microsoft Excel.
- What it is commonly used for.
- Types of datasets for each chart and graphs
- Pro-Tips from Top Excel Expert.
Bar and Column Charts: Commonly used for comparing numerical data from different categories.
Examples of datasets or variables.
- Total sales by product type.
- Revenue by department, by quarter.
- Population by country.
Pro-Tip: Use stacked or clustered bars or columns to group by subcategory or compare multiple metrics.
Histograms and Pareto Charts: Commonly used for showing the distribution of a continuous data set.
Examples of datasets or variables.
- Frequency of test scores among students.
- Distribution of heights or weights. Io
- Distribution of population or age group.
Pro-Tip: Adjust the bin size to customize the grouping of values.
Area Chart: This shows changes in data composition over time.
Examples of datasets or variables.
- Population by continent, by decade.
- Percentage of total downloads by browser, by week.
- Sales by department, by month.
Pro-Tip: Keep the number of unique categories relatively low to maintain clarity.
Line Chart: Commonly used for visualizing trends over time.
Examples of datasets or variables.
- Stock price by the hour.
- Average temperature by month.
- Profit by quarter.
Pro-Tip: Use linear or polynomial trendlines to visualize patterns or forecast future periods. To trend two variables on different scales, combine line and column charts ( a.k.a Combo Charts).
Pie and Donut Charts: Commonly used for comparing proportions that total 100%.
Examples of datasets or variables.
- Percentage of budget spent by the department.
- Breakdown of site traffic by source.
- The proportion of internet users by age range.
Pro-Tip: Keep the number of slices small to maximize readability. Use a donut chart to visualize more than one series at once.
Scatter Plots: Commonly used for exploring correlations or relationships between series.
Examples of datasets or variables.
- Hours of television watched by age.
- Ice cream sales and average temperature by day.
- The Number of goals scored and salary by player.
Pro-Tip: Add a trendline or line of best fit to quantify the correlation between variables. Remember that correlation does not imply causation.
Bubble chart: This chart type is a scatter plot with a third dimension (size).
Examples of datasets or variables.
- Product sales (X), Revenue (Y), and Market Share (size) by Company.
- The income per Capital (X), Life Expectancy (Y), and Population (size) by Country.
Pro-Tip: To distinguish across categories, add color as a fourth dimension.
Box and Whisker charts: Commonly used for visualizing statistical properties across data sets. It plays a similar role and functions like a histogram.
Examples of datasets or variables.
- Comparing historical annual rainfall across cities.
- Comparing mean and median height or weight by country.
- Analyzing distributions of values and identifying outliers.
Pro-Tip: Use the Design and Format tabs to customize the look of your chart.
TreeMaps and Sunburst charts: Commonly for visualizing hierarchical data with natural groups and sub-groups.
Examples of datasets or variables.
- Revenue by Book Title, Sub-Genre, and Genre.
- Population by City, State, and Region.
- Number of Employees by Department and Office.
Pro-Tip: Use TreeMaps when you are only visualizing 1 or 2 hierarchical levels (i.e., topic and sub-topic) or when relative sizes are important, and Sunburst charts to visualize the depth of multiple hierarchical levels. Make sure your raw source data is grouped and sorted before creating hierarchical charts.
Waterfall chart: This shows the net value after a series of positive and negative contributions.
Examples of datasets or variables.
- Tracking Personal income and spending.
- Corporate balance sheet analysis.
Pro-Tip: Sub-totals can be used to construct "checkpoints" to separate different types of profits and losses.
Funnel chart: Commonly used to show progress through the stages of a funnel.
Examples of datasets or variables.
- The volume of views, clicks, and sales on an eCommerce website.
- The number of runners who reach each checkpoint in a marathon.
Pro-Tip: To illustrate the percent of users (rather than the number) in each funnel step, use "percent of total" calculations.
Radar chart: Using a two-dimensional chart to plot three or more quantitative variables in relation to a focal point.
Examples of datasets or variables.
- Comparing test scores across multiple subjects.
- Sales of different types of vegetables, by month.
- Visualizing personality test results across subjects.
Pro-Tip: Normalize each statistic to the same scale (e.g., 0-1, 1–10, 1-100). To reduce noise and increase impact, limit the number of categories or data series.
Surface and Contour charts: Commonly used to Plotting data in three dimensions to find optimum combinations of values.
Examples of datasets or variables.
- Accident rates by the hour of day and day of the week.
- Elevation by latitude and longitude.
- Cookie deliciousness by oven temp and baking time.
Pro-Tip: Don’t use surface charts if a simple heat map will tell the same story.
Avoid using wireframe chart types when possible, as they can be difficult to interpret.
Stock chart: Commonly used for visualizing stock market data, including volume, high, low, open, and closing prices.
Examples of datasets or variables.
- Facebook’s daily stock performance in 2021.
- High, low, and closing prices for Google in Q1.
- Relative performance across multiple stocks.
Pro-Tip: Manually set axis minimum/maximum values to enhance readability. Switch from a date to a text axis to eliminate gaps when markets are closed.
Heat Maps: Commonly used for visualizing trends or relationships using color scales.
Examples of datasets or variables.
- Accident rates by time of day and day of the week.
- Average temperature by city, by month.
- Average sentiment by hashtag.
Pro-Tip: Use easily understood color scales (i.e. red to green) and apply custom formatting to hide cell values (;;;).
Maps: Also known as the Choropleth map, is commonly used for visualizing location-based data, there is also an option to use a 3D map in Excel also called Power Map.
Examples of datasets or variables.
- Frequency of accidents by street address.
- Unemployment rate by country.
- Average rainfall by state.
Pro-Tip: Use Excel’s Power Map plug-in to create geospatial visualizations and animate changes over time. Use attributes like color and size to visualize multiple attributes at once.
Combo Chart: Commonly used to trend two variables on different scales. A combo chart is a combined line and column chart to visualize two metrics at once.
Examples of datasets or variables.
- Any combination of volume and rate metrics.
- Especially metrics on entirely different scales.
Pro-Tip: To create a combination chart, you must use a 2-D chart, such as a 2-D Line, 2-D Column, Scatter, or Bubble chart.
Spark Line: It’s a miniature version of a line chart or column chart packed into a single cell.
Examples of datasets or variables
- Average temperature by city, by month.
- Profit by quarter.
Pro-Tip: When you change the data source, sparklines update automatically.
Conclusion
There you have it, all excel charts and graphs for data visualization and storytelling explained. Support: SUBSCRIBE to my YouTube channel — Excel Island
