Pharmaceutical Giants Stock Performance (2018-2025)

Log scale reveals growth patterns during pre-pandemic, pandemic, and recovery periods

A time series visualization examining pharmaceutical giants’ stock performance from 2018-2025 using a logarithmic scale to highlight relative growth patterns. This visualization reveals how major pharma companies navigated market volatility through the pre-pandemic period, COVID-19 disruption, and subsequent recovery years.

Published

April 23, 2025

Figure 1: Line chart showing pharmaceutical giants’ stock performance from 2018-2025 on a logarithmic scale. The chart displays five companies (LLY, ABBV, MRK, BMY, PFE) with Eli Lilly (LLY) showing the strongest growth, reaching nearly $1,000 by 2025. The COVID-19 pandemic period is highlighted with a pink vertical band in 2020. All companies experienced volatility during this time, with different recovery patterns afterward.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
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
  lubridate,      # Make Dealing with Dates a Little Easier
  tidyquant,      # Tidy Quantitative Financial Analysis # Tidy Quantitative Financial Analysis # Tidy Quantitative Financial Analysis # Tidy Quantitative Financial Analysis # Tidy Quantitative Financial Analysis
  ggrepel,        # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    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

# Define timeframe
end_date <- Sys.Date()
start_date <- end_date - years(7)

# Pharmaceutical companies
pharma_symbols <- c("PFE", "MRK", "ABBV", "LLY", "BMY")

# Get the stock data
pharma_data <- tq_get(
  pharma_symbols, 
  from = start_date,
  to = end_date,
  get = "stock.prices"
  )

3. Examine the Data

Show code

glimpse(pharma_data)
skim(pharma_data)

4. Tidy Data

Show code

### |- Tidy ----
# Get the last data point for each company for labeling
label_data <- pharma_data |>  
  group_by(symbol) |>
  filter(date == max(date)) |>
  ungroup()

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "PFE" = "#0000c6",           
    "MRK" = "#00857c",            
    "ABBV" = "#061d49",          
    "LLY" = "#d52b1e",        
    "BMY" = "#be2bba"
  )
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("Pharmaceutical Giants Stock Performance (2018-2025)",
                          width = 55) 

subtitle_text <- str_wrap("Log scale reveals growth patterns during pre-pandemic, pandemic, and recovery periods",
                          width = 85)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 23,
  source_text =  "Yahoo Finance via { tidyquant }" 
)

### |-  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$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    
    axis.line.x = element_line(color = "gray50", linewidth = .2),
    
    # Grid elements
    panel.grid.minor = element_line(color = "gray65", linewidth = 0.05),
    panel.grid.major = element_line(color = "gray65", linewidth = 0.05),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ggplot() +
  # Geoms
  geom_line(
    data = pharma_data, 
    aes(x = date, y = adjusted, color = symbol),
    linewidth = 0.6
  ) +
  geom_text_repel(
    data = label_data,
    aes(x = date, y = adjusted, label = symbol, color = symbol),
    nudge_x = 70,  
    hjust = 0,
    segment.size = 0.5,
    direction = "y",
    box.padding = 0.5,
    segment.alpha = 0.6,
    fontface = "bold",
    size = 3.5
  ) +
  # Annotate
  annotate(
    "rect", 
    xmin = as.Date("2020-03-01"), 
    xmax = as.Date("2020-12-01"),
    ymin = 10, 
    ymax = 1000,
    alpha = 0.1, 
    fill = "red"
  ) +
  annotate(
    "text", 
    x = as.Date("2020-07-01"), 
    y = 15, 
    label = "COVID-19\nPandemic", 
    color = "darkred",
    size = 3
  ) +
  annotate(
    "label", 
    x = as.Date("2018-04-01"), 
    y = 900, 
    label = "LLY = Eli Lilly\nABBV = AbbVie\nMRK = Merck\nBMY = Bristol Myers Squibb\nPFE = Pfizer",
    hjust = 0, 
    vjust = 1,
    size = 3,
    color = "gray30",
    fill = "white",
    alpha = 0.8,
    label.size = 0.5
  ) +
  # Scale
  scale_x_date(
    date_breaks = "1 year", 
    date_labels = "%Y",
    expand = expansion(mult = c(0.01, 0.08))
  ) +
  scale_y_log10(
    labels = scales::dollar_format(accuracy = 1)
  ) +
  scale_color_manual(values = colors$palette) +
  coord_cartesian(
    xlim = c(start_date, end_date + days(10))
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "Price (log scale)",
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.6),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code

### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 23, 
  width = 8, 
  height = 8
  )

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] here_1.0.1                 camcorder_0.1.0           
 [3] ggrepel_0.9.6              PerformanceAnalytics_2.0.8
 [5] quantmod_0.4.26            TTR_0.24.4                
 [7] xts_0.14.1                 zoo_1.8-12                
 [9] tidyquant_1.0.11           scales_1.3.0              
[11] skimr_2.1.5                janitor_2.2.0             
[13] showtext_0.9-7             showtextdb_3.0            
[15] sysfonts_0.8.9             ggtext_0.1.2              
[17] lubridate_1.9.3            forcats_1.0.0             
[19] stringr_1.5.1              dplyr_1.1.4               
[21] purrr_1.0.2                readr_2.1.5               
[23] tidyr_1.3.1                tibble_3.2.1              
[25] ggplot2_3.5.1              tidyverse_2.0.0           

loaded via a namespace (and not attached):
 [1] rlang_1.1.6         magrittr_2.0.3      furrr_0.3.1        
 [4] snakecase_0.11.1    compiler_4.4.0      systemfonts_1.1.0  
 [7] vctrs_0.6.5         quadprog_1.5-8      pkgconfig_2.0.3    
[10] fastmap_1.2.0       magick_2.8.5        utf8_1.2.4         
[13] rmarkdown_2.29      markdown_1.13       prodlim_2024.06.25 
[16] tzdb_0.5.0          ragg_1.3.3          xfun_0.49          
[19] jsonlite_1.8.9      recipes_1.2.1       parallel_4.4.0     
[22] R6_2.5.1            stringi_1.8.4       rsample_1.3.0      
[25] parallelly_1.43.0   rpart_4.1.23        Rcpp_1.0.13-1      
[28] knitr_1.49          future.apply_1.11.3 base64enc_0.1-3    
[31] pacman_0.5.1        Matrix_1.7-0        splines_4.4.0      
[34] nnet_7.3-19         timechange_0.3.0    tidyselect_1.2.1   
[37] rstudioapi_0.17.1   yaml_2.3.10         timeDate_4041.110  
[40] codetools_0.2-20    curl_6.0.0          listenv_0.9.1      
[43] lattice_0.22-6      withr_3.0.2         evaluate_1.0.1     
[46] timetk_2.9.0        future_1.34.0       survival_3.5-8     
[49] xml2_1.3.6          pillar_1.9.0        renv_1.0.3         
[52] generics_0.1.3      rprojroot_2.0.4     hms_1.1.3          
[55] commonmark_1.9.2    munsell_0.5.1       globals_0.16.3     
[58] class_7.3-22        glue_1.8.0          tools_4.4.0        
[61] data.table_1.16.2   gower_1.0.2         grid_4.4.0         
[64] RobStatTM_1.0.11    ipred_0.9-15        colorspace_2.1-1   
[67] repr_1.1.7          cli_3.6.4           textshaping_0.4.0  
[70] rsvg_2.6.1          fansi_1.0.6         svglite_2.1.3      
[73] lava_1.8.1          gtable_0.3.6        digest_0.6.37      
[76] farver_2.1.2        gifski_1.32.0-1     htmlwidgets_1.6.4  
[79] htmltools_0.5.8.1   lifecycle_1.0.4     hardhat_1.4.1      
[82] gridtext_0.1.5      MASS_7.3-60.2

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_23.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- Yahoo Finance via { tidyquant } { tidyquant }

--- title: "Pharmaceutical Giants Stock Performance (2018-2025)" subtitle: "Log scale reveals growth patterns during pre-pandemic, pandemic, and recovery periods" description: "A time series visualization examining pharmaceutical giants' stock performance from 2018-2025 using a logarithmic scale to highlight relative growth patterns. This visualization reveals how major pharma companies navigated market volatility through the pre-pandemic period, COVID-19 disruption, and subsequent recovery years." date: "2025-04-23" categories: ["30DayChartChallenge", "Data Visualization", "R Programming", "2025"] tags: [ "tidyquant", "ggplot2", "financial-data", "log-scale", "time-series", "pharmaceutical-industry", "stock-performance", "pandemic-impact", "market-trends", "directlabels", "data-storytelling" ] image: "thumbnails/30dcc_2025_23.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_23.html" # description: "Day 23 of #30DayChartChallenge: Exploring pharmaceutical giants' stock performance using logarithmic scaling to reveal growth patterns across pandemic and recovery periods" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![Line chart showing pharmaceutical giants' stock performance from 2018-2025 on a logarithmic scale. The chart displays five companies (LLY, ABBV, MRK, BMY, PFE) with Eli Lilly (LLY) showing the strongest growth, reaching nearly $1,000 by 2025. The COVID-19 pandemic period is highlighted with a pink vertical band in 2020. All companies experienced volatility during this time, with different recovery patterns afterward.](30dcc_2025_23.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ 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 lubridate, # Make Dealing with Dates a Little Easier tidyquant, # Tidy Quantitative Financial Analysis # Tidy Quantitative Financial Analysis # Tidy Quantitative Financial Analysis # Tidy Quantitative Financial Analysis # Tidy Quantitative Financial Analysis ggrepel, # Automatically Position Non-Overlapping Text Labels with 'ggplot2' camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 8, 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 ```{r} #| label: read #| include: true #| eval: true #| warning: false # Define timeframe end_date <- Sys.Date() start_date <- end_date - years(7) # Pharmaceutical companies pharma_symbols <- c("PFE", "MRK", "ABBV", "LLY", "BMY") # Get the stock data pharma_data <- tq_get( pharma_symbols, from = start_date, to = end_date, get = "stock.prices" ) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(pharma_data) skim(pharma_data) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Get the last data point for each company for labeling label_data <- pharma_data |> group_by(symbol) |> filter(date == max(date)) |> ungroup() ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c( "PFE" = "#0000c6", "MRK" = "#00857c", "ABBV" = "#061d49", "LLY" = "#d52b1e", "BMY" = "#be2bba" ) ) ### |- titles and caption ---- # text title_text <- str_wrap("Pharmaceutical Giants Stock Performance (2018-2025)", width = 55) subtitle_text <- str_wrap("Log scale reveals growth patterns during pre-pandemic, pandemic, and recovery periods", width = 85) caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 23, source_text = "Yahoo Finance via { tidyquant }" ) ### |- 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$text, size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), axis.line.x = element_line(color = "gray50", linewidth = .2), # Grid elements panel.grid.minor = element_line(color = "gray65", linewidth = 0.05), panel.grid.major = element_line(color = "gray65", linewidth = 0.05), # Plot margins plot.margin = margin(t = 10, r = 20, b = 10, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot() + # Geoms geom_line( data = pharma_data, aes(x = date, y = adjusted, color = symbol), linewidth = 0.6 ) + geom_text_repel( data = label_data, aes(x = date, y = adjusted, label = symbol, color = symbol), nudge_x = 70, hjust = 0, segment.size = 0.5, direction = "y", box.padding = 0.5, segment.alpha = 0.6, fontface = "bold", size = 3.5 ) + # Annotate annotate( "rect", xmin = as.Date("2020-03-01"), xmax = as.Date("2020-12-01"), ymin = 10, ymax = 1000, alpha = 0.1, fill = "red" ) + annotate( "text", x = as.Date("2020-07-01"), y = 15, label = "COVID-19\nPandemic", color = "darkred", size = 3 ) + annotate( "label", x = as.Date("2018-04-01"), y = 900, label = "LLY = Eli Lilly\nABBV = AbbVie\nMRK = Merck\nBMY = Bristol Myers Squibb\nPFE = Pfizer", hjust = 0, vjust = 1, size = 3, color = "gray30", fill = "white", alpha = 0.8, label.size = 0.5 ) + # Scale scale_x_date( date_breaks = "1 year", date_labels = "%Y", expand = expansion(mult = c(0.01, 0.08)) ) + scale_y_log10( labels = scales::dollar_format(accuracy = 1) ) + scale_color_manual(values = colors$palette) + coord_cartesian( xlim = c(start_date, end_date + days(10)) ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = "Price (log scale)", ) + # Theme theme( plot.title = element_text( size = rel(1.6), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 15) ), plot.caption = element_markdown( size = rel(0.55), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 10, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 23, width = 8, height = 8 ) ``` #### 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 [`30dcc_2025_23.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_23.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: - Yahoo Finance via { tidyquant } [{ tidyquant }](https://business-science.github.io/tidyquant/) :::