Create beautiful histograms to visualize your data distributions. This interactive tool allows you to customize bin counts, define specific bin edges, and add density curves for smoother visualization. The calculator generates both visual representations and grouped frequency tables for comprehensive analysis. You can upload your own dataset or explore our sample datasets to get started immediately.
Try it out!
- Click Sample Data and select Restaurant Tips
- For Value column, select total_bill
- For Group/Color By column, select sex or leave it as None
- Choose bin type: Number of Bins or Custom Bin Edges
- For Density, check the box to show density line
- Click Generate Histogram to visualize the data
Calculator
1. Load Your Data
2. Select Column & Options
Learn More
Histograms: Understanding Data Distributions
What is a Histogram?
A histogram is a graphical representation that organizes a group of numerical data points into bins, displaying the frequency of data points that fall into each bin. Unlike bar charts, histograms are used for continuous data where bins represent ranges of values. The height of each bar shows how many observations fall into that range, helping visualize the distribution shape, central tendency, and variability of the data.
When to Use Histograms
- Visualizing the distribution of continuous numerical data
- Identifying patterns, skewness, and potential outliers in data
- Comparing distributions across different groups or categories
- Understanding the shape and spread of your data
- Checking assumptions of normality in statistical analyses
Best Practices
- Choose an appropriate number of bins to balance detail and smoothness
- Consider adding density lines for smoother distribution visualization
- Use consistent bin widths unless there's a specific reason not to
- Include clear labels for axes and legend when using groups
- Consider the scale of your y-axis (count vs. proportion)
- Use transparency when comparing multiple distributions
Creating Histogram in R
Here's a simple example of creating and customizing a histogram in R using theggplot2 package.
library(tidyverse)
tips <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/tips.csv")
# create histogram with 10 bins by setting bins = 10
ggplot(tips, aes(x = total_bill)) +
geom_histogram(bins = 10, fill = "steelblue", color = "white") +
labs(title = "Distribution of Total Bill",
x = "Total Bill Amount",
y = "Frequency") +
theme_minimal()
# customize bin edges
custom_breaks <- seq(0, 50, by = 5)
ggplot(tips, aes(x = total_bill)) +
geom_histogram(breaks = custom_breaks, fill = "steelblue", color = "white") +
labs(title = "Distribution of Total Bill",
x = "Total Bill Amount",
y = "Frequency") +
theme_minimal()
# showing density instead of count
ggplot(tips, aes(x = total_bill)) +
geom_histogram(aes(y = after_stat(density)), bins = 15,
fill = "steelblue", color = "white") +
labs(title = "Density Distribution of Total Bill",
x = "Total Bill Amount",
y = "Density") +
theme_minimal()
# histogram with density curve (figure below)
ggplot(tips, aes(x = total_bill)) +
geom_histogram(aes(y = after_stat(density)), bins = 15,
fill = "steelblue", color = "white") +
geom_density(aes(y = after_stat(density)), color = "red", linewidth = 1) +
labs(title = "Density Distribution of Total Bill with Density Curve",
x = "Total Bill Amount",
y = "Density") +
theme_minimal()
This code creates a histogram with 15 bins for the 'total_bill' variable from a restaurant tips dataset. The red line represents the density curve, showing the distribution of total bill amounts.