Introduction

In this article, we’ll explore how to create multigroup frequency plots using ggplot in R. We’ll start by understanding the concept of multigroup frequency and then dive into the code. We’ll cover various aspects of data preparation, plot customization, and troubleshooting common issues.

What is Multigroup Frequency?

Multigroup frequency refers to a statistical technique used to analyze multiple groups or categories while examining their relationships with one or more variables. In this context, we’re interested in creating plots that display the proportion of subjects falling into each category for two variables (Belief and Choice) across different menutype categories.

Setting Up the Data

To create multigroup frequency plots, we first need to prepare our data. We’ll use a sample dataset with three variables: menutype, Belief, and Choice. We can generate this data using the set.seed() function for reproducibility.

# Set seed for reproducibility
set.seed(10)

# Example data frame
df <- data.frame(
  menutype = sample(c(1,2,4,5,6,8,12), 120, replace = T),
  Belief = sample(c(0,1), 120, replace = T),
  Choice = sample(c(0,1), 120, replace = T)
)

Calculating Metrics

We need to calculate the proportion of subjects falling into each category for both variables (Belief and Choice). We’ll use the group_by() function to group our data by menutype and then apply the count() function to count the number of subjects in each combination. The mutate() function will add new columns for these proportions.

# Calculate all metrics based on all variables you want to plot in a tidy way
df_plot <- df %>%
  group_by(Choice) %>%
  count(menutype, Belief) %>%
  mutate(prop = n / sum(n),
         prop_text = paste0(n, "/", sum(n))) %>%
  ungroup()

Plotting the Data

Now that we have our data prepared, let’s create a plot using ggplot. We’ll use the geom_col() function to create bar plots for each menutype category and facet it by Choice. The position = "dodge" argument ensures that the bars don’t overlap.

# Barplots using one variable and split plots using another variable
df_plot %>%
  mutate(Belief = factor(Belief),
         menutype = factor(menutype)) %>%
  ggplot(aes(menutype, prop, fill = Belief)) +
  geom_col(position = "dodge") +
  facet_wrap(~Choice, ncol=1) +
  geom_text(aes(label=prop_text), position = position_dodge(1), vjust = -0.5) +
  ylim(0,0.2)

Customizing the Plot

To further customize our plot, we can add a title, labels for x and y axes, and adjust other aspects as needed.

# Customize the plot
df_plot %>%
  mutate(Belief = factor(Belief),
         menutype = factor(menutype)) %>%
  ggplot(aes(menutype, prop, fill = Belief)) +
  geom_col(position = "dodge") +
  facet_wrap(~Choice, ncol=1) +
  geom_text(aes(label=prop_text), position = position_dodge(1), vjust = -0.5) +
  ylim(0,0.2) +
  labs(title = "Classification based on rank ordering",
       x = "",
       y = "") +
  theme_bw() +
  theme(legend.position="bottom", plot.title = element_text(hjust = 0.5))

Troubleshooting Common Issues

When working with ggplot, there are several common issues that can arise. Here are a few troubleshooting tips:

• Missing values: Make sure to check for missing values in your data and handle them accordingly.
• Scale issues: If the scales on your plot are not behaving as expected, try adjusting the scale_y_continuous() function or using other options like scales = "free_x".
• Positioning issues: Use the position argument to position elements in your plot correctly.

Conclusion

In this article, we explored how to create multigroup frequency plots using ggplot in R. We covered data preparation, plot customization, and troubleshooting common issues. By following these steps and adjusting parameters as needed, you can create informative and visually appealing plots that showcase the relationships between different variables across multiple groups.

Example Use Cases

• Medical Research: Use multigroup frequency plots to analyze the distribution of disease symptoms across different treatment groups.
• Marketing Analysis: Create plots to visualize customer behavior across different product categories.
• Social Science Research: Employ multigroup frequency plots to examine the relationships between demographic variables and social outcomes.

Next Steps

For further learning, explore other topics in data visualization using ggplot. Consider taking online courses or attending workshops to improve your skills and stay up-to-date with the latest developments in R programming and data science.

Last modified on 2024-09-04