---
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", "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"
editor_options:
chunk_output_type: inline
execute:
freeze: true
cache: true
error: false
message: false
warning: false
eval: true
# filters:
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# 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
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---
{#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/)
:::
