Sorting Likert Charts Based on Bar Plot Order in R

Mastering Likert Chart Customization: Sorting with Precision

Data visualization is an art, especially when dealing with survey responses. Imagine presenting insights from a survey where satisfaction levels vary across years. 🕵️‍♂️ A simple Likert chart may look compelling, but adding meaningful sorting can elevate your analysis significantly.

Sorting Likert charts based on an accompanying bar plot can help highlight trends more effectively. For instance, what if you wanted to showcase satisfaction levels for a specific group sorted by their relative frequency? With R's flexibility, this becomes achievable with the right approach.

Let’s consider an example: you’ve surveyed users across different years, capturing responses on a scale from "Very Dissatisfied" to "Very Satisfied." By combining the power of `gglikert` and data manipulation in R, we’ll explore how to align the Likert chart horizontally with the descending order of a bar plot. 📊

This guide walks you through sorting the Likert chart, step by step. Whether you're a data scientist presenting survey data or a beginner in R, you’ll find practical tips to create impactful visuals. Let’s dive in and bring clarity to your data storytelling!

Aligning Likert and Bar Charts: Step-by-Step Explanation

The first step in solving this problem involves generating a realistic dataset. Using R, the sample() function is employed to create random years and Likert responses. This dataset represents survey results where respondents express satisfaction levels over multiple years. The mutate(across()) function is then used to ensure the response columns adhere to the desired order of Likert levels, making the data ready for visual exploration. For example, imagine gathering customer feedback over the past five years and wanting to compare their satisfaction levels by year. 📊

Next, the script creates a bar plot that organizes the data in descending order based on response frequency. This is achieved using the count() function to tally responses, followed by reorder(), which ensures the responses are displayed in descending order of their counts. The result is a clear, intuitive chart that highlights the most common responses. Such a visualization can be critical for a product manager identifying trends in user satisfaction. By focusing on responses like "Very Satisfied," you can pinpoint what resonates most with your users. 😊

Once the bar plot is sorted, the Likert chart is created. This is where the data is transformed using pivot_longer(), which restructures the dataset into a long format ideal for plotting grouped responses. The data is then fed into a stacked bar chart using geom_bar(position = "fill"). Each bar represents proportions of satisfaction levels for a specific group, normalized to facilitate comparison across years. Think about an HR professional analyzing employee engagement scores; this visualization helps them easily spot shifts in satisfaction across departments over time.

The final step ensures the Likert chart aligns with the bar plot's sorting. By assigning the same factor levels determined in the bar plot to the Likert chart, the order is preserved across visualizations. This ensures clarity and consistency in presenting the data. For example, in a presentation to stakeholders, the alignment between charts simplifies the narrative and emphasizes critical insights. Using additional touches like facet_wrap() to create separate panels for each group (A, B, C), the visualization becomes even more intuitive, guiding the audience's focus seamlessly.

# Load necessary libraries
library(tidyverse)
library(ggplot2)
library(ggridges)
library(ggiraphExtra)

# Step 1: Generate sample data
set.seed(123)
likert_levels <- c("1" = "Very Dissatisfied",
                   "2" = "Dissatisfied",
                   "3" = "Neutral",
                   "4" = "Satisfied",
                   "5" = "Very Satisfied")

df <- data.frame(year = sample(c(2023, 2022, 2020, 2018), 50, replace = TRUE),
                 A = sample(likert_levels, 50, replace = TRUE),
                 B = sample(likert_levels, 50, replace = TRUE),
                 C = sample(likert_levels, 50, replace = TRUE)) %>%
  mutate(across(everything(), as.factor)) %>%
  as_tibble() %>%
  mutate(across(-year, ~factor(.x, levels = likert_levels)))

# Step 2: Create a bar plot with descending order
bar_data <- df %>%
  pivot_longer(-year, names_to = "group", values_to = "response") %>%
  count(response, group) %>%
  arrange(desc(n))

bar_plot <- ggplot(bar_data, aes(x = reorder(response, -n), y = n, fill = group)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Bar Plot of Responses", x = "Response", y = "Count") +
  theme_minimal()

print(bar_plot)

# Step 3: Create a Likert chart aligned to bar plot ordering
likert_data <- df %>%
  mutate(id = row_number()) %>%
  pivot_longer(-c(id, year), names_to = "group", values_to = "response") %>%
  mutate(response = factor(response, levels = levels(bar_data$response)))

likert_plot <- ggplot(likert_data, aes(x = response, fill = factor(year))) +
  geom_bar(position = "fill") +
  facet_wrap(~group) +
  labs(title = "Likert Chart Matched to Bar Plot", x = "Response", y = "Proportion") +
  theme_minimal()

print(likert_plot)

# Define a function for sorting and matching
create_sorted_charts <- function(df, likert_levels) {
  bar_data <- df %>%
    pivot_longer(-year, names_to = "group", values_to = "response") %>%
    count(response, group) %>%
    arrange(desc(n))

  bar_plot <- ggplot(bar_data, aes(x = reorder(response, -n), y = n, fill = group)) +
    geom_bar(stat = "identity", position = "dodge") +
    theme_minimal()

  likert_data <- df %>%
    mutate(id = row_number()) %>%
    pivot_longer(-c(id, year), names_to = "group", values_to = "response") %>%
    mutate(response = factor(response, levels = levels(bar_data$response)))

  likert_plot <- ggplot(likert_data, aes(x = response, fill = factor(year))) +
    geom_bar(position = "fill") +
    facet_wrap(~group) +
    theme_minimal()

  list(bar_plot = bar_plot, likert_plot = likert_plot)
}

# Use the function
plots <- create_sorted_charts(df, likert_levels)
print(plots$bar_plot)
print(plots$likert_plot)

Enhancing Data Visualizations: Sorting and Matching in R

When working with survey data, the alignment between different visualizations, such as a Likert chart and a bar plot, is crucial for delivering coherent insights. While previous examples focused on sorting and aligning the two charts, another critical aspect is enhancing the visual appeal and interpretability of the plots. This involves customizing colors, adding annotations, and ensuring the data story is accessible to your audience. For instance, using distinct color palettes for Likert levels can help distinguish satisfaction ranges at a glance. 🎨

Incorporating annotations into your visualizations is a powerful way to provide additional context. For example, you can use the geom_text() function in R to display percentage labels directly on the Likert chart. This addition helps audiences quickly interpret each segment's proportion without referring to external legends. Another way to enrich these charts is by applying interactive features with libraries such as plotly, which allows users to hover over elements to see detailed data points. Imagine a dashboard where stakeholders can explore satisfaction trends interactively—this can lead to more engaging and actionable insights. 📈

Lastly, consider adapting your visualizations for presentation or publication. Using the theme() function in R, you can fine-tune text size, font types, and axis labels for readability. Group-level comparisons can be further highlighted by adding vertical lines or shaded areas using geom_vline(). These small touches make a significant difference in professional settings, helping the audience focus on key takeaways effortlessly.