Dot plot is a type of data visualization that displays individual data points as dots along an axis.
To create a dot plot, use the dotchart(data, labels=) function, where data is a “numeric vector”, and “labels” is a vector of labels for each point. It helps compare values of different categories, spot trends, and identify outliers.
Here is the step-by-step guide:
Step 1: Install the necessary libraries
Install ggplot2 and tidyverse package for this project. If you have not installed it, then you can install it using this code:
install.packages("ggplot2")
install.packages("tidyverse")Step 2: Loading the Dataset
For this project, we will use the tcs_stock.csv dataset. You can also find this dataset on Kaggle.
tcs_data <- read.csv("./DataSets/tcs_stock.csv")
head(tcs_data)
Step 3: Creating the Dot Chart
Let’s visualize the closing prices over time using a dot chart.
library(ggplot2)
tcs_data <- read.csv("./DataSets/tcs_stock.csv")
# Sorting data by Date to ensure it's plotted chronologically
tcs_data_sorted <- tcs_data[order(as.Date(tcs_data$Date, format="%Y-%m-%d")),]
# Create the dot chart
dotchart(tcs_data_sorted$Close, labels = tcs_data_sorted$Date,
main="TCS Stock Closing Prices Over Time",
xlab="Closing Price",
ylab="Date")
You can see many dates, so the y-axis labels are crowded.
To make it more appealing, you could consider plotting only a subset of the data, adjusting the plot dimensions, or changing the label display frequency.
Step 5: Fine-tuning
Changing Point Color
You can change the color of the dots using the col parameter:
dotchart(tcs_data_sorted$Close, labels = tcs_data_sorted$Date,
main="TCS Stock Closing Prices Over Time",
xlab="Closing Price",
ylab="Date",
col="blue")
Changing Point Character
You can change the plot character of a dot plot using a pch argument.
dotchart(tcs_data_sorted$Close, labels = tcs_data_sorted$Date,
main="TCS Stock Closing Prices Over Time",
xlab="Closing Price",
ylab="Date",
pch=4) # Using a cross
Changing Label Display Frequency
If there are too many dates and it’s crowded, you can choose to display only every nth label:
library(ggplot2)
tcs_data <- read.csv("./DataSets/tcs_stock.csv")
# Sorting data by Date to ensure it's plotted chronologically
tcs_data_sorted <- tcs_data[order(as.Date(tcs_data$Date, format="%Y-%m-%d")),]
every_nth <- 10 # Display every 10th label
displayed_labels <- c(rep("", every_nth-1), tcs_data_sorted$Date)[1:nrow(tcs_data_sorted)]
dotchart(tcs_data_sorted$Close, labels = displayed_labels,
main="TCS Stock Closing Prices Over Time",
xlab="Closing Price",
ylab="Date")
Dot plot by group
In real-life projects, we often plot by group to analyze data thoroughly.
To demonstrate, I will assume that we want to create a dot plot of the closing prices of TCS stock, grouped by year. This will help us see the distribution of closing prices within each year.
library(ggplot2)
tcs_data <- read.csv("./DataSets/tcs_stock.csv")
# Sorting data by Date to ensure it's plotted chronologically
tcs_data_sorted <- tcs_data[order(as.Date(tcs_data$Date, format="%Y-%m-%d")),]
tcs_data$Year <- format(as.Date(tcs_data$Date, format="%Y-%m-%d"), "%Y")
tcs_data_sorted <- tcs_data[order(tcs_data$Year, as.Date(tcs_data$Date, format="%Y-%m-%d")),]
# Create a color palette
palette <- rainbow(length(unique(tcs_data_sorted$Year)))
dotchart(tcs_data_sorted$Close, labels = tcs_data_sorted$Date,
groups = tcs_data_sorted$Year,
main = "TCS Stock Closing Prices Grouped by Year",
xlab = "Closing Price",
ylab = "Date",
color = palette[tcs_data_sorted$Year])
legend("topright", legend = unique(tcs_data_sorted$Year), fill = palette, title = "Year")
Dumbbell dot plot
A dumbbell dot plot (or dumbbell chart) is a visualization used to compare two data points for different categories side-by-side.
The two data points are typically represented as dots connected by a line, resembling a dumbbell.
The geom_dumbbell() function comes from the ggalt package. So, you need to install this library.
install.packages("ggalt")
Now, you can create a dumbbell dot plot.
library(ggplot2)
library(ggalt)
tcs_data <- read.csv("./DataSets/tcs_stock.csv")
ggplot(tcs_data, aes(x=Open, xend=Close, y=Date)) +
geom_dumbbell(size=1.5, color="#555555",
point.colour.l = "red", point.colour.r = "blue",
point.size.l = 3, point.size.r = 3) +
labs(title="Comparison of Opening and Closing Prices for TCS Stock",
x="Stock Price",
y="Date") +
theme_minimal()Output
Conclusion
While dotchart() offers a simple way to create dot plots, it doesn’t have the extensive customization capabilities that ggplot2 offers.
However, for quick and simple plots, it can be quite handy.
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.