Evolution of Business Priorities in Amazon’s Annual Reports

Tracking the relative importance of business areas from 2005 to 2023

TidyTuesday
Data Visualization
R Programming
2025
Analyzing Amazon’s transformation from e-commerce to tech giant through their annual reports. This visualization tracks how terminology shifts reflect changing business priorities, with AWS and cloud services becoming increasingly dominant while traditional retail language declined.
Author

Steven Ponce

Published

March 23, 2025

Figure 1: Line chart showing the evolution of business priorities in Amazon’s annual reports from 2005 to 2023. Five key categories are tracked: Financial Performance (highest line, around 7,000 mentions), Cloud Services (shows dramatic growth after 2015), Customer Experience, Operations & Logistics, and Innovation & Technology (lowest line). The chart includes vertical dashed lines marking key events: AWS Launch (2006), AWS Revenue Surpasses $1B (2010), AWS Becomes Profitable (2015), Whole Foods Acquisition (2017), and COVID-19 Pandemic (2020).

Steps to Create this Graphic

1. Load Packages & Setup

Show code 
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
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
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    camcorder,      # Record Your Plot History 
    tidytext,       # Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
    patchwork       # The Composer of Plots
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  8,
    units  = "in",
    dpi    = 320
)

# 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

Show code 
tt <- tidytuesdayR::tt_load(2025, week = 12) 

report_words_clean <- tt$report_words_clean |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code 
glimpse(report_words_clean)
skim(report_words_clean)

4. Tidy Data

Show code 
### |-  tidy data ----
text_df <- report_words_clean |>
    group_by(year, word) |>
    summarize(
        count = n(), 
        .groups = "drop"
    )

# Define word categories with targeted keywords
focus_categories <- list(
    innovation = c("innovation", "innovative", "invent", "patent", "technology", "research", 
                   "development", "ai", "machine", "learning", "automation"),
    customer = c("customer", "consumer", "satisfaction", "service", "experience", "value", 
                 "delivery", "prime", "shopping", "retail", "personalization"),
    finance = c("revenue", "profit", "margin", "earning", "cost", "expense", "financial", 
                "billion", "million", "investment", "growth", "cash", "flow", "capital"),
    cloud = c("aws", "cloud", "server", "compute", "storage", "database", "web", "services",
              "infrastructure", "platform", "hosting", "data"),
    operations = c("operations", "logistics", "fulfillment", "warehouse", "distribution", 
                   "supply", "chain", "inventory", "shipping", "transportation", "facility")
)

# Calculate category indices   
focus_indices <- map_dfr(names(focus_categories), function(category) {
    text_df |>
        filter(word %in% focus_categories[[category]]) |>
        group_by(year) |>
        summarize(
            word_count = sum(count),
            total_words = n(),
            index = word_count / total_words * 100,
            category = category,
            .groups = "drop"
        )
})
    
# Cleaner category label mapping
category_labels <- c(
    "innovation" = "Innovation & Technology",
    "customer" = "Customer Experience",
    "finance" = "Financial Performance",
    "cloud" = "Cloud Services (AWS)",
    "operations" = "Operations & Logistics"
)
    
# Key events/milestones for annotation
key_events <- tibble(
    year = c(2006, 2010, 2015, 2017, 2020),
    event = c("AWS\nLaunched", "AWS Revenue\nSurpasses $1B", "AWS Becomes\nProfitable", "Whole Foods\nAcquisition", "COVID-19\nPandemic"),
    index = c(5500, 3500, 5000, 4500, 4800),
    category = NA
)

5. Visualization Parameters

Show code 
colors <- get_theme_colors(
    palette = c(
        "innovation" = "#E69F00",
        "customer" = "#56B4E9", 
        "finance" = "#009E73",
        "cloud" = "#0072B2",
        "operations" = "#D55E00"
    )
)

### |-  titles and caption ----
title_text <- str_glue("Evolution of Business Priorities in Amazon's Annual Reports")
subtitle_text <- str_glue("Tracking the relative importance of business areas from 2005 to 2023")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 12,
    source_text =  "Amazon's Annual Reports" 
)

### |-  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(
        # Text styling 
        plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
        plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_line(color = "gray80", linewidth = 0.05),
        panel.grid.major = element_line(color = "gray80", linewidth = 0.02),
        
        # Legend elements
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # two-row legend
        legend.box.spacing = unit(0.4, "cm"),
        legend.key.width = unit(1.5, "cm"),
        legend.spacing.x = unit(0.2, "cm"),
 
        legend.box = "horizontal",
        legend.box.just = "left",
        
        # Plot margins 
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code 
### |-  Plot  ----
p <- ggplot() +
    # Geoms
    geom_line(
        data = focus_indices, 
        aes(x = year, y = index, color = category),
        linewidth = 1.2
    ) +
    geom_point(
        data = focus_indices, 
        aes(x = year, y = index, color = category),
        size = 3
    ) +
    geom_vline(
        data = key_events,
        aes(xintercept = year),
        linetype = "dashed",
        color = "gray50",
        alpha = 0.7
    ) +
    geom_text(
        data = key_events,
        aes(x = year, y = index, label = event),
        hjust = -0.1,
        vjust = 0,
        size = 3,
        fontface = "bold",
        color = "gray30"
    ) +
    # Annotate
    annotate(
        "text",
        x = max(focus_indices$year) + 0.5,
        y = filter(focus_indices, year == max(year) & category == "cloud")$index,
        label = "Cloud services\ndominate recent\nreporting",
        hjust = 0,
        size = 3.5,
        fontface = "italic",
        color = colors$palette[4]
    ) +
    annotate(
        "text",
        x = max(focus_indices$year) + 0.5,
        y = filter(focus_indices, year == max(year) & category == "finance")$index,
        label = "Financial focus\nremains strong",
        hjust = 0,
        size = 3.5,
        fontface = "italic",
        color = colors$palette[3]
    ) +
    # Scales
    scale_color_manual(
        values = colors$palette,
        labels = category_labels,
        name = NULL
    ) +
    scale_x_continuous(
        breaks = seq(min(focus_indices$year), max(focus_indices$year), by = 2),
        expand = expansion(mult = c(0.02, 0.15)) 
    ) +
    scale_y_continuous(
        labels = function(x) format(x, big.mark = ","),
        expand = expansion(mult = c(0.02, 0.02))
    ) +
    # Labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = "Year",
        y = "Focus Index (mentions per 100,000 words)"
    ) +
    # Legend
    guides(
        color = guide_legend(
            nrow = 2,
            byrow = TRUE,
            override.aes = list(size = 3)
        )
    ) +
    # Theme
    theme(
        plot.title = element_text(
            size = rel(2),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(1),
            family = fonts$subtitle,
            color = alpha(colors$subtitle, 0.9),
            lineheight = 1.2,
            margin = margin(t = 5, b = 30)
        ),
        plot.caption = element_markdown(
            size   = rel(0.65),
            family = fonts$caption,
            color  = colors$caption,
            hjust  = 0.5,
            margin = margin(t = 10)
        )
    )

7. Save

Show code 
### |-  plot image ----  
save_plot_patchwork(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 12, 
  width = 10,
  height = 8
)

8. 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] patchwork_1.3.0 tidytext_0.4.2  camcorder_0.1.0 here_1.0.1     
 [5] glue_1.8.0      scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      gh_1.4.1          
 [5] janeaustenr_1.0.0  digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4   
 [9] rsvg_2.6.1         tokenizers_0.3.0   magrittr_2.0.3     compiler_4.4.0    
[13] rlang_1.1.4        tools_4.4.0        utf8_1.2.4         yaml_2.3.10       
[17] knitr_1.49         labeling_0.4.3     htmlwidgets_1.6.4  bit_4.5.0         
[21] curl_6.0.0         xml2_1.3.6         repr_1.1.7         tidytuesdayR_1.1.2
[25] withr_3.0.2        grid_4.4.0         fansi_1.0.6        colorspace_2.1-1  
[29] gitcreds_0.1.2     cli_3.6.3          rmarkdown_2.29     crayon_1.5.3      
[33] generics_0.1.3     rstudioapi_0.17.1  tzdb_0.4.0         commonmark_1.9.2  
[37] parallel_4.4.0     ggplotify_0.1.2    yulab.utils_0.1.8  base64enc_0.1-3   
[41] vctrs_0.6.5        Matrix_1.7-0       jsonlite_1.8.9     gridGraphics_0.5-1
[45] hms_1.1.3          bit64_4.5.2        systemfonts_1.1.0  magick_2.8.5      
[49] gifski_1.32.0-1    codetools_0.2-20   stringi_1.8.4      gtable_0.3.6      
[53] munsell_0.5.1      pillar_1.9.0       rappdirs_0.3.3     htmltools_0.5.8.1 
[57] R6_2.5.1           httr2_1.0.6        rprojroot_2.0.4    vroom_1.6.5       
[61] evaluate_1.0.1     lattice_0.22-6     markdown_1.13      SnowballC_0.7.1   
[65] gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3         Rcpp_1.0.13-1     
[69] svglite_2.1.3      xfun_0.49          fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in tt_2025_12.qmd.

For the full repository, click here.

10. References

  1. Data Sources:

Back to top