Creating Bar Plots with Frequency of “Yes” Values Across Multiple Variables in R

In this tutorial, we will explore how to create bar plots of the frequency of “Yes” values across multiple variables using the ggplot2 package in R. We will provide an example using a dataset containing presence of various chemicals across multiple waterbodies.

Background

The ggplot2 package is a popular data visualization library in R that provides a grammar-based approach to creating beautiful and informative plots. The package uses the long format for data, which makes it easier to create bar plots, scatter plots, box plots, and more.

When working with multiple variables, we often want to visualize the frequency of specific values across those variables. In this tutorial, we will show how to achieve this using ggplot2.

Data Format

Before we dive into creating bar plots, it’s essential to understand the data format required for ggplot2. The data should be in a long format, which means each observation is represented as one row with one variable for the x-axis and another variable for the y-axis.

The dataset provided has multiple variables, but the values are not yet in a long format. To create a bar plot of the frequency of “Yes” values across multiple variables, we need to convert the data to a long format using pivot_longer.

Converting Data to Long Format

To convert the data to a long format, we use the pivot_longer function from the tidyr package. This function takes in the dataset and selects all columns except for one specified column (in this case, “value”).

library(tidyverse)

df %>% 
  pivot_longer(everything()) %>% 
  filter(value == "Yes") %>% 
  ggplot(aes(name)) + 
  geom_bar(stat = "count")

In the code above, pivot_longer is used to convert the dataset into a long format. The filter function then selects only the rows where the value is equal to “Yes”. Finally, we create the bar plot using ggplot.

Sub-Variables and Multiple Variables

When working with multiple variables, it’s common to want to visualize the frequency of specific values across those variables. In this case, we can use the aes function in ggplot2 to map the sub-variable id or a singular variable.

To select the range of columns (chem1 through chem5) to display, we can use the syntax x = 3:7, where 3 and 7 represent the column names. However, this will not work directly because ggplot2 expects the data in a long format.

One workaround is to create separate plots for each sub-variable using aes(x = chem1) + geom_bar() or aes(x = chem5) + geom_bar(). This approach can be time-consuming and inefficient, especially when working with large datasets.

A better approach is to use the gather function from the tidyr package to create a new column for each sub-variable. Then, we can use the pivot_wider function to create a long format dataset that includes all columns.

library(tidyverse)

df_long %>% 
  pivot_wider(names_from = chem, values_from = value) %>% 
  filter(value == "Yes") %>% 
  ggplot(aes(name)) + 
  geom_bar(stat = "count")

In this code, gather is used to create a new column for each sub-variable (chem). Then, we use pivot_wider to create a long format dataset where the values are stored in the same column.

Example

Let’s create an example using a sample dataset containing presence of various chemicals across multiple waterbodies. We will then convert the data to a long format and create a bar plot of the frequency of “Yes” values across multiple variables.

# Load required libraries
library(tidyverse)

# Create a sample dataset
df <- structure(list(chem1 = c("Yes", "Yes", "Yes", "No", "Yes", "No", 
                             "Yes", "No", "Yes", "No"), chem2 = c("No", "Yes", 
                                                                 "No", "Yes", "No", "Yes", "No", "Yes", "No", "Yes")), 
               class = "data.frame", row.names = c(NA, 10L))

# Convert the data to a long format
df_long <- df %>% 
  pivot_longer(everything()) %>% 
  filter(value == "Yes") %>% 
  ggplot(aes(name)) + 
  geom_bar(stat = "count")

In this example, we create a sample dataset df containing the presence of various chemicals across multiple waterbodies. We then convert the data to a long format using pivot_longer. Finally, we create a bar plot of the frequency of “Yes” values across multiple variables.

Output

The output of the code will be a bar plot showing the frequency of “Yes” values across multiple variables. The x-axis represents the sub-variable name, and the y-axis represents the count of “Yes” values.

# Output:

Note: This is just an example, and you may need to adjust the code based on your specific dataset and requirements.

Conclusion

In this tutorial, we explored how to create bar plots of the frequency of “Yes” values across multiple variables using ggplot2 in R. We provided an example using a sample dataset containing presence of various chemicals across multiple waterbodies. The key takeaways are:

• Data should be in a long format for ggplot2
• Use pivot_longer to convert data to a long format
• Use aes function to map sub-variable id or singular variable
• Use gather and pivot_wider functions to create a long format dataset

By following these steps, you can easily create bar plots of the frequency of “Yes” values across multiple variables using ggplot2.

Last modified on 2024-12-04