Energy Drink Brand Comparison: Activity Support Drives High Scores

‘Supports an active day’ ranks highest across all brands, while health claims show largest variation.

SWDchallenge

SWD Exercise

2025

A comparative analysis of three energy drink brands focusing on consumer attributes. The visualization highlights key performance metrics, showcasing how brands excel in activity support while revealing significant variations in health-related claims. Part of the #SWDchallenge exploring effective use of alignment and white space in data visualization.

Author

Steven Ponce

Published

January 15, 2025

Modified

October 14, 2026

Original

The goal of this month’s Storytelling with Data exercise is to use space and alignment effectively.

Additional information can be found HERE

Makeover

Figure 2: A connected dot plot comparing consumer ratings of three energy drink brands (Lime Rush, Neon Pulse, and Storm Fuel) across 10 attributes. The visualization shows ‘Supports an active day’ scoring highest (~95%) across all brands, while ‘Healthy energy source’ shows the largest variation between brands, with Lime Rush scoring significantly lower (4%) than its competitors.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  tidyverse,         # Easily Install and Load the 'Tidyverse'
  ggtext,            # Improved Text Rendering Support for 'ggplot2'
  showtext,          # Using Fonts More Easily in R Graphs
  scales,            # Scale Functions for Visualization
  glue,              # Interpreted String Literals
  here,              # A Simpler Way to Find Your Files
  janitor,           # Simple Tools for Examining and Cleaning Dirty Data
  camcorder          # Record Your Plot History
) 

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

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raw_data <- read_csv(
  here::here("data/lets_practice_exercise_037.csv")) |> clean_names()

3. Examine the Data

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glimpse(raw_data )

4. Tidy Data

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tidy_energy_drinks <- raw_data |>
  pivot_longer(
    cols = c(storm_fuel, neon_pulse, lime_rush),
    names_to = "brand",
    values_to = "score"
  ) |>
  mutate(
    brand = str_to_title(str_replace(brand, "_", " ")),
    # Reorder consumer likeability for more logical presentation
    consumer_likeability = factor(consumer_likeability,
      levels = c(
        "Won't buy", "Definitely buy", "Might buy",
        "Bold and exciting name", "Refreshing options",
        "Reliable energy boost", "Eye-catching design",
        "Reasonably priced", "Healthy energy source",
        "Supports an active day"
      )
    )
  )

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c("#2c3e50", "#e74c3c", "#3498db"))

### |-  titles and caption ----
title_text   <- str_glue("Energy Drink Brand Comparison: Activity Support Drives High Scores") 

subtitle_text <- str_glue("'Supports an active day' ranks highest across all brands, while health claims show largest variation")

# Create caption
caption_text <- create_swd_caption(
  year = 2025,
  month = "Jan",
  source_text = "Let's Practice! Exercise 3.7"
)


# |- fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)
            

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
      legend.position     = "top",
      plot.margin         = margin(t = 10, r = 20, b = 10, l = 20),
      axis.title.x        = element_text(margin = margin(10, 0, 0, 0), size = rel(1.1), 
                                         color = colors$text, family = fonts$text, face = "bold", hjust = 0.5),
      axis.title.y        = element_text(margin = margin(0, 10, 0, 0), size = rel(1.1), 
                                         color = colors$text, family = fonts$text, face = "bold", hjust = 0.5),
      axis.text           = element_text(size = rel(0.9), color = colors$text),
      axis.line.x         = element_line(color = "#252525", linewidth = .3),
      axis.ticks.x        = element_line(color = colors$text),  
      axis.title          = element_text(face = "bold"),
      panel.grid.minor    = element_blank(),
      panel.grid.major    = element_blank(),
      panel.grid.major.y  = element_line(color = "grey85", linewidth = .4)
      )
)
      

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

p <- ggplot(
  data = tidy_energy_drinks,
  aes(x = score, y = consumer_likeability, group = consumer_likeability, color = brand)
  ) +
  
  # Geoms
  geom_line(color = "gray85", linewidth = 0.8) +
  geom_point(size = 3.5) +

  # Scales
  scale_x_continuous(
    limits = c(0, 100),
    breaks = seq(0, 100, by = 20)
  ) +
  scale_y_discrete() +
  scale_color_manual(values = colors$palette) +
  coord_cartesian(clip = "off") +

  # Labs
  labs(
    x = "Score",
    y = NULL,
    color = "Brand: ",
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
  ) + 
  
  # Theme
  theme(
    plot.title = element_markdown(
      size = rel(1.7),
      family = "title",
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(1.05),
      family = "subtitle",
      color = colors$subtitle,
      lineheight = 1.1,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.65),
      family = "caption",
      color = colors$caption,
      lineheight = 1.1,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 15, b = 5)
    )
  )

7. Save

Show code

### |-  plot image ----  

source(here::here("R/image_utils.R"))
save_plot(p, type = 'swd', year = 2025, month = 01, exercise = 37, 
                    width = 10, height = 10)

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 janitor_2.2.0   here_1.0.1      glue_1.8.0     
 [5] scales_1.3.0    showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     codetools_0.2-20  snakecase_0.11.1  htmltools_0.5.8.1
[21] yaml_2.3.10       pillar_1.9.0      crayon_1.5.3      magick_2.8.5     
[25] commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37     stringi_1.8.4    
[29] rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0       
[33] colorspace_2.1-1  cli_3.6.3         magrittr_2.0.3    utf8_1.2.4       
[37] withr_3.0.2       bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29   
[41] bit_4.5.0         ragg_1.3.3        hms_1.1.3         evaluate_1.0.1   
[45] knitr_1.49        markdown_1.13     rlang_1.1.4       gridtext_0.1.5   
[49] Rcpp_1.0.13-1     xml2_1.3.6        renv_1.0.3        svglite_2.1.3    
[53] rstudioapi_0.17.1 vroom_1.6.5       jsonlite_1.8.9    R6_2.5.1         
[57] systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in swd_2025_01 - Ex_037.qmd. For the full repository, click here.

10. References

Expand for References

Data Sources:
- Storytelling with Data Excercise | use space and alignment effectively: Download the data

--- title: "Energy Drink Brand Comparison: Activity Support Drives High Scores" subtitle: "'Supports an active day' ranks highest across all brands, while health claims show largest variation." description: "A comparative analysis of three energy drink brands focusing on consumer attributes. The visualization highlights key performance metrics, showcasing how brands excel in activity support while revealing significant variations in health-related claims. Part of the #SWDchallenge exploring effective use of alignment and white space in data visualization." author: "Steven Ponce" date: "2025-01-15" date-modified: last-modified categories: ["SWDchallenge", "SWD Exercise", "2025"] tags: [ "Data Visualization", "SWD Challenge", "R", "ggplot2", "Brand Analysis", "Consumer Research", "Market Analysis" ] image: "thumbnails/swd_2025_01-Ex_0037.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/SWD Challenge/2025/swd_2025_01 - Ex_037.html" # description: "Analysis comparing energy drink brands' consumer ratings across key attributes, revealing strong performance in activity support but varying health perceptions. #SWDchallenge" # linkedin: true # twitter: true # email: true --- ### Original The goal of this month's Storytelling with Data exercise is to use space and alignment effectively. ![Original chart](https://swd-community-media.s3.amazonaws.com/media/Screenshot_2025-01-15_at_09.16.11.png){#fig-1} Additional information can be found [HERE](https://community.storytellingwithdata.com/exercises/use-space-and-alignment-effectively) ### **Makeover** ![A connected dot plot comparing consumer ratings of three energy drink brands (Lime Rush, Neon Pulse, and Storm Fuel) across 10 attributes. The visualization shows 'Supports an active day' scoring highest (~95%) across all brands, while 'Healthy energy source' shows the largest variation between brands, with Lime Rush scoring significantly lower (4%) than its competitors.](swd_2025_01-Ex_0037.png){#fig-1} ### <mark> __Steps to Create this Graphic__ </mark> #### 1. Load Packages & Setup ```{r} #| label: load if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files janitor, # Simple Tools for Examining and Cleaning Dirty Data camcorder # Record Your Plot History ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read raw_data <- read_csv( here::here("data/lets_practice_exercise_037.csv")) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(raw_data ) ``` #### 4. Tidy Data ```{r} #| label: tidy tidy_energy_drinks <- raw_data |> pivot_longer( cols = c(storm_fuel, neon_pulse, lime_rush), names_to = "brand", values_to = "score" ) |> mutate( brand = str_to_title(str_replace(brand, "_", " ")), # Reorder consumer likeability for more logical presentation consumer_likeability = factor(consumer_likeability, levels = c( "Won't buy", "Definitely buy", "Might buy", "Bold and exciting name", "Refreshing options", "Reliable energy boost", "Eye-catching design", "Reasonably priced", "Healthy energy source", "Supports an active day" ) ) ) ``` #### 5. Visualization Parameters ```{r} #| label: params ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = c("#2c3e50", "#e74c3c", "#3498db")) ### |- titles and caption ---- title_text <- str_glue("Energy Drink Brand Comparison: Activity Support Drives High Scores") subtitle_text <- str_glue("'Supports an active day' ranks highest across all brands, while health claims show largest variation") # Create caption caption_text <- create_swd_caption( year = 2025, month = "Jan", source_text = "Let's Practice! Exercise 3.7" ) # |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( legend.position = "top", plot.margin = margin(t = 10, r = 20, b = 10, l = 20), axis.title.x = element_text(margin = margin(10, 0, 0, 0), size = rel(1.1), color = colors$text, family = fonts$text, face = "bold", hjust = 0.5), axis.title.y = element_text(margin = margin(0, 10, 0, 0), size = rel(1.1), color = colors$text, family = fonts$text, face = "bold", hjust = 0.5), axis.text = element_text(size = rel(0.9), color = colors$text), axis.line.x = element_line(color = "#252525", linewidth = .3), axis.ticks.x = element_line(color = colors$text), axis.title = element_text(face = "bold"), panel.grid.minor = element_blank(), panel.grid.major = element_blank(), panel.grid.major.y = element_line(color = "grey85", linewidth = .4) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot p <- ggplot( data = tidy_energy_drinks, aes(x = score, y = consumer_likeability, group = consumer_likeability, color = brand) ) + # Geoms geom_line(color = "gray85", linewidth = 0.8) + geom_point(size = 3.5) + # Scales scale_x_continuous( limits = c(0, 100), breaks = seq(0, 100, by = 20) ) + scale_y_discrete() + scale_color_manual(values = colors$palette) + coord_cartesian(clip = "off") + # Labs labs( x = "Score", y = NULL, color = "Brand: ", title = title_text, subtitle = subtitle_text, caption = caption_text, ) + # Theme theme( plot.title = element_markdown( size = rel(1.7), family = "title", face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(1.05), family = "subtitle", color = colors$subtitle, lineheight = 1.1, margin = margin(t = 5, b = 20) ), plot.caption = element_markdown( size = rel(0.65), family = "caption", color = colors$caption, lineheight = 1.1, hjust = 0.5, halign = 0.5, margin = margin(t = 15, b = 5) ) ) ``` #### 7. Save ```{r} #| label: save ### |- plot image ---- source(here::here("R/image_utils.R")) save_plot(p, type = 'swd', year = 2025, month = 01, exercise = 37, width = 10, height = 10) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`swd_2025_01 - Ex_037.qmd`](https://github.com/poncest/personal-website/tree/master/data_visualizations/SWD%20Challenge/2025/swd_2025_01 - Ex_037.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - Storytelling with Data Excercise | use space and alignment effectively: [Download the data](https://community.storytellingwithdata.com/exercises/use-space-and-alignment-effectively) :::