Create informative dot plots to visualize the distribution of your data points. Upload your own data or try our sample datasets.
Auto mode uses the Freedman-Diaconis rule to automatically determine the optimal number of bins based on your data's distribution and sample size.
A dot plot is a statistical chart that displays data points as dots positioned along an axis. Each dot represents a single data point, making it an excellent visualization for showing the distribution of values in a dataset. Dot plots are particularly useful when dealing with smaller datasets, as they preserve the visibility of individual data points.
When interpreting a dot plot, consider the following:
Dot plots offer several advantages over similar chart types:
The ggplot2 package provides the geom_dotplot() function.
library(tidyverse)
tips <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/tips.csv")
# use default binning
ggplot(tips, aes(x = tip)) +
geom_dotplot() +
theme_minimal()
# specify binwidth since some bin contains too many points
ggplot(tips, aes(x = tip)) +
geom_dotplot(binpositions = "all", binwidth = 0.2) +
theme_minimal()
# if you don't want to see the y ticks
ggplot(tips, aes(x = tip)) +
geom_dotplot(binpositions = "all", binwidth = 0.2) +
theme_minimal() +
theme(axis.ticks.y = element_blank(), axis.text.y = element_blank())
While Python's popular libraries like Matplotlib and Seaborn do not have a dedicated dot plot function, you can create dot plots using scatter plots or by stacking points vertically. Here's an example using Matplotlib:
import numpy as np
tips = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/tips.csv")
x = tips["tip"]
counts, bins = np.histogram(x, bins=20)
for i, count in enumerate(counts):
for j in range(count):
plt.plot(bins[i] + (bins[i+1]-bins[i])/2, j+1, "o", color="steelblue")
plt.xlabel("Tip amount")
plt.ylabel("Count (in dots)")
plt.title("Dot histogram of tips")
plt.show()
Create informative dot plots to visualize the distribution of your data points. Upload your own data or try our sample datasets.
Auto mode uses the Freedman-Diaconis rule to automatically determine the optimal number of bins based on your data's distribution and sample size.
A dot plot is a statistical chart that displays data points as dots positioned along an axis. Each dot represents a single data point, making it an excellent visualization for showing the distribution of values in a dataset. Dot plots are particularly useful when dealing with smaller datasets, as they preserve the visibility of individual data points.
When interpreting a dot plot, consider the following:
Dot plots offer several advantages over similar chart types:
The ggplot2 package provides the geom_dotplot() function.
library(tidyverse)
tips <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/tips.csv")
# use default binning
ggplot(tips, aes(x = tip)) +
geom_dotplot() +
theme_minimal()
# specify binwidth since some bin contains too many points
ggplot(tips, aes(x = tip)) +
geom_dotplot(binpositions = "all", binwidth = 0.2) +
theme_minimal()
# if you don't want to see the y ticks
ggplot(tips, aes(x = tip)) +
geom_dotplot(binpositions = "all", binwidth = 0.2) +
theme_minimal() +
theme(axis.ticks.y = element_blank(), axis.text.y = element_blank())While Python's popular libraries like Matplotlib and Seaborn do not have a dedicated dot plot function, you can create dot plots using scatter plots or by stacking points vertically. Here's an example using Matplotlib:
import numpy as np
tips = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/tips.csv")
x = tips["tip"]
counts, bins = np.histogram(x, bins=20)
for i, count in enumerate(counts):
for j in range(count):
plt.plot(bins[i] + (bins[i+1]-bins[i])/2, j+1, "o", color="steelblue")
plt.xlabel("Tip amount")
plt.ylabel("Count (in dots)")
plt.title("Dot histogram of tips")
plt.show()