You have clean data, you've tested your hypotheses, and now you need to show your findings. But which chart do you use? A bar chart? A line chart? A pie chart (gulp)?
Choosing the wrong chart can obscure your message or even mislead your audience. Choosing the right one makes your data sing.
1. To Show a Trend Over Time 📈
Best For: Seeing how something changes day-to-day, month-to-month, year-to-year.
Chart Types:
- Line Chart: Classic, great for continuous data. Shows direction.
- Area Chart: Like a line chart, but the area under the line is filled. Good for showing total volume over time.
- Bar Chart (Time Series): Use if you have discrete time periods (e.g., yearly sales) and want to compare exact values.
# Example Use Case: Monthly Website Traffic
# Chart: Line Chart
2. To Compare Categories 📊
Best For: Showing differences in size or value across distinct groups.
Chart Types:
- Bar Chart (Vertical/Column): Most common. Great for comparing quantities across groups. Easy to read exact values.
- Bar Chart (Horizontal): Better when you have many categories or long category names.
- Grouped Bar Chart: Compares sub-categories within main categories.
- Stacked Bar Chart: Shows total for a category AND how it's made up of sub-categories.
# Example Use Case: Sales per Region
# Chart: Horizontal Bar Chart
3. To Show Composition (Part-to-Whole) 🍕
Best For: Displaying how a total is divided into parts. Use with caution!
Chart Types:
- Pie Chart: Only use if you have few categories (max 5-6) and you want to show proportions of a whole. The *largest* slice is easiest to read.
- Donut Chart: Similar to pie, but the center is cut out (can sometimes display a total value).
- Stacked Bar Chart (100%): Shows proportions across categories, but as bars, which are often easier to compare than pie slices.
# Example Use Case: Market Share (if only 3 companies)
# Chart: Pie Chart (if few companies) or 100% Stacked Bar
Warning: Humans are bad at comparing slice angles. Bar charts are usually better for precise comparisons.
4. To Show Relationships (Correlation) 🔗
Best For: Seeing if two numerical variables are connected and how strongly.
Chart Types:
- Scatter Plot: The go-to. Each dot is an observation, showing the values of two variables. Look for patterns (linear, curved, clusters).
- Bubble Chart: A scatter plot where the size of the "bubble" (dot) represents a third numerical variable.
# Example Use Case: Does Experience correlate with Salary?
# Chart: Scatter Plot
5. To Show Distribution 📦
Best For: Understanding the range, spread, and central tendency of a single numerical variable.
Chart Types:
- Histogram: Shows frequency counts within bins (ranges) of your data. Great for spotting skewness or multi-modal distributions.
- Box Plot (Whisker Plot): Shows median, quartiles, and potential outliers. Excellent for comparing distributions across categories.
# Example Use Case: Distribution of customer ages
# Chart: Histogram or Box Plot (if comparing age by product)
💡 The Ultimate Rule:
Keep it simple. The chart should tell the story quickly. If your audience has to stare at it for five minutes to figure out what's going on, it's not working.
🎯 Today's Goal(What you should do)
✔️ Know which chart excels at showing trends vs. comparisons vs. relationships.
✔️ Use bar charts for categories and line charts for time.
✔️ Be very cautious with pie charts!
✔️ Use scatter plots to find connections.