Plots in the R language

Key takeaways:

  • Plots are powerful tools for visualizing complex data. They help interpret data more easily and, hence, facilitate quicker decision-making by highlighting key trends and variations.

  • Each type of plot serves a unique purpose, allowing analysts to choose the most suitable visualization based on the data characteristics they wish to highlight

  • A scatter plot effectively represents the relationship between two continuous variables, highlighting correlations and outliers.

  • Line plots summarize data frequencies visually, offering a quick understanding of trends and values in a dataset.

  • Box plots illustrate the central tendency and dispersion of data, making it easier to identify outliers and understand the distribution.

  • Contour plots provide a two-dimensional representation of three-dimensional data, helping to visualize gradients and areas of interest in various fields like geography and meteorology.

  • Area plots illustrate cumulative values of multiple variables over a specific interval, enabling the identification of trends and patterns in the data.

  • Density plots reveal the distribution shape of a single variable, aiding in the identification of patterns such as symmetry, skewness, or multimodality.

Plot is the visual representation of complex data. It enables us to understand and interpret the trends of complex and large data sets. Data analysts use plots to help businesses grow by observing the data’s tendency. For instance, we can use the plots in R to analyze how well Uber is working in New York City or to explore stock market trends to make rational business decisions.

In this Answer, we’ll discuss a few examples of plots in the R language with illustrated examples.

Types of plots
Types of plots

Let’s look at each plot type with examples below:

Histogram

A histogram is a graphical representation that displays the distribution of a continuous variable by dividing the data into intervals or bins and counting the frequency of data points within each bin. Each bar in the histogram represents the number of data points within a specific range, allowing you to see how data is spread across different values. Histograms are useful for identifying patterns in data, such as skewness, modality (e.g., unimodal or bimodal), and the presence of any outliers. Below is an example of how to create a histogram in the R language.

# Generate random data
data <- rnorm(100)
# Create a histogram with colorful bars
hist(data, main = "Histogram", xlab = "Values", col = rainbow(10), border = "black")

The result of this code will be similar to the one shown in the following graph:

Histogram in R
Histogram in R

Scatter plot

A scatter plot represents the relationship between two continuous variables. It represents the set of data points in the graph, and each dot represents the value between two variables. A scatter plot helps interpret the correlation or outliers in the dataset. The code for a scatter plot in the R language is given below:

# Generate random data
x <- rnorm(100)
y <- rnorm(100)
# Create a scatter plot with colorful points
plot(x, y, main = "Scatter Plot", xlab = "X", ylab = "Y", col = rainbow(length(x)))

The result of this code is shown in the following graph:

Scatter plot in R
Scatter plot in R

Line plot

A line plot, also known as a strip plot, displays data points as individual markers along a number line. It is mainly used to show the frequency of the dataset and provides a quick visual summary of the dataset. If multiple points in the dataset share the same value, they will be stacked together. The R code to visualize a line plot is given below, along with its result.

# Generate example data
x <- 1:10  # x-values
y <- c(3, 5, 8, 4, 6, 2, 7, 9, 5, 4)  # y-values
# Create a line plot
plot(x, y, type = "o", main = "Line Plot", xlab = "X", ylab = "Y")

The result of this code is shown in the following graph:

Line plot in R
Line plot in R

Box plot

A box plot (or box-and-whisker plot) visualizes the distribution of a dataset. It is used to display the mean, mode, and median of the dataset and identify the outliers. A box plot provides the central tendency, spread, and dispersion of the dataset. Let’s look at the code and its result below to understand it better.

# Generate example data
x <- rnorm(100)  # Random data
# Create a box plot
boxplot(x, main = "Box Plot", ylab = "Values")

The result of this code is shown in the following graph:

Box plot in R
Box plot in R

Contour plot

A contour plot or level plot is the graphical representation of the 3-dimensional data to the 2-dimensional plane. It displays the variation of the variable over a defined plane. Contour plots are mainly used in geography, meteorology, and calculus to visualize a function’s behavior. They enable us to determine the area of high or low values, gradients, and boundaries of the function. The spacing between the contour plots indicates the rate of change in the variable. Here’s an example of a contour plot of three variables.

# Generate example Data
x <- -20:20
y <- -20:20
z <- sqrt(outer(x ^ 2, y ^ 2, "+"))
# Create a contour plot
contour(x=x, y=y, z=z)

The result of this code is shown in the following graph:

Contour plot in R
Contour plot in R

Area plot

An area plot visualizes multiple variables with their corresponding values or proportions over a specific interval. It is used to create a cumulative representation, where the value of each variable is added to the previous variable, making a stacked area. It is also used to identify the trends and patterns in the dataset.

# Create example data
x <- 1:10  # x-axis values
y1 <- c(3, 5, 8, 4, 6, 2, 7, 9, 5, 4)  # Data for variable 1
y2 <- c(2, 4, 6, 3, 5, 1, 6, 8, 4, 3)  # Data for variable 2
y3 <- c(1, 3, 5, 2, 4, 1, 4, 6, 3, 2)  # Data for variable 3
# Create an area plot
plot(x, y1, type = "n", ylim = c(0, max(y1+y2+y3)), ylab = "Values", main = "Area Plot")
polygon(c(x, rev(x)), c(y1, rev(y2)), col = "blue", border = NA)
polygon(c(x, rev(x)), c(y1+y2, rev(y1)), col = "green", border = NA)
polygon(c(x, rev(x)), c(y1+y2+y3, rev(y1+y2)), col = "red", border = NA)
legend("topright", legend = c("Variable 1", "Variable 2", "Variable 3"), fill = c("blue", "green", "red"))

The result of this code is shown in the following graph:

Area plot in R
Area plot in R

Density plot

A density plot (kernel-density plot) consists of a single variable to visualize a distribution. It is used to understand whether the data is symmetric, skewed, or multimodal. It helps in identifying the unusual patterns in the dataset. In the density plot, the horizontal axis represents the continuous variable’s values, whereas the vertical axis represents the estimated density or probability of the variable.

# Generate example data
x <- rnorm(100)  # Random data
# Create a density plot
density_plot <- density(x)  # Estimate the density
plot(density_plot, main = "Density Plot", xlab = "X", ylab = "Density")

The result of this code is shown in the following graph:

Density plot in R
Density plot in R

Comparison among different types of plots in R

Here’s a table that compares different plots with the perspective of R language usage while highlighting the purpose of each plot type, the type of data it is best suited for, the corresponding R function used, and some key parameters needed for plotting:

Plot Type

Purpose

Suitable Data

R Function

Key Parameters

Histogram

Displays the distribution of a continuous variable by counting values within intervals (bins)

One continuous variable

hist()

x, main, xlab, col, border, breaks

Scatter

Displays the relationship between two continuous variables

Two continuous variables

plot()

x, y, main, xlab, ylab, col

Line

Shows trends over time on ordered data

Sequential data (e.g., time series)

plot() with type = "l"

x, y, type, main, xlab, ylab

Box

Summarizes distribution through quartiles and outliers

Continuous variable

boxplot()

x, main, ylab

Contour

Displays 3D data in 2D with contour lines

Two continuous variables (with z-values)

contour()

x, y, z, main, xlab, ylab

Area

Shows cumulative data over time or categories

Stacked or time-based data

plot() with type = "n"

x, y, main, ylim, xlab, ylab, col

Density

Shows the distribution of a single continuous variable

Continuous variable

density() , plot()

x, main, xlab, ylab

Conclusion

In conclusion, plots give us a compelling visualization of the trends and patterns of the dataset. They allow us to understand variation across a given domain comprehensively and provide a visual representation of the data distribution by highlighting its shape, skewness, and multimodality. All this enables a deeper understanding of the underlying characteristics of the dataset.

Ready to dive deeper into the world of R data visualization? Here are some exciting project ideas to get hands-on experience:

Frequently asked questions

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What are the basic graphs used for representation in R?

In R, you can create various types of graphs, such as scatter plots, bar plots, line graphs, and histograms. These graphs help you visualize data points, trends, and distributions to better understand your data.

How to make different plots in R

To make different plots in R, you can use the built-in plot() function for simple graphs like scatter plots or line graphs, or use specialized functions like barplot() for bar charts and hist() for histograms. Additionally, the ggplot2 package offers more advanced plotting options with easy-to-use commands.

Which plot is the best for data visualization?

The best plot for data visualization depends on the specific insights you’re trying to highlight. Different plots are suited to different kinds of data, so choosing the right one depends on what aspects of the data you want to emphasize.

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