TidyTuesday: SP500 Sector Performance

TidyTuesday
R
Finance
Visualizing SP500 sector ETF performance
Author

chokotto

Published

February 4, 2026

Overview

This TidyTuesday project visualizes S&P 500 sector performance, comparing daily returns with year-to-date (YTD) performance for each sector ETF using R and ggplot2.

Key metrics:

  • Daily Return: Single day price change (%)
  • YTD Return: Year-to-date cumulative return from January 1st (%)
  • Sectors: 11 S&P 500 sector ETFs + S&P 500 benchmark

Dataset

Code 
library(tidyverse)
library(arrow)
library(scales)
library(glue)
Code 
# Load pre-processed data (relative path from qmd file location)
data_file <- "data/sector_performance.parquet"

if (file.exists(data_file)) {
  sector_data <- read_parquet(data_file)
  cat("Data loaded successfully!\n")
  cat(glue("Records: {nrow(sector_data)}\n"))
  cat(glue("Data date: {unique(sector_data$data_date)}\n"))
} else {
  stop("Data file not found. Run prepare_data.py first.")
}
Data loaded successfully!
Records: 12Data date: 2026-02-20
Code 
# Convert to long format for ggplot
sector_long <- sector_data |>
  select(sector, daily_return, ytd_return, sort_order) |>
  pivot_longer(
    cols = c(daily_return, ytd_return),
    names_to = "metric",
    values_to = "value"
  ) |>
  mutate(
    metric = factor(
      metric,
      levels = c("daily_return", "ytd_return"),
      labels = c("Daily", "Year-to-date")
    ),
    sector = factor(sector, levels = sector_data$sector[order(sector_data$sort_order)])
  )

# Get data date for title
data_date <- unique(sector_data$data_date)
data_date_formatted <- format(as.Date(data_date), "%m/%d/%Y")

# Preview data
glimpse(sector_long)
Rows: 24
Columns: 4
$ sector     <fct> Energy, Energy, Health Care, Health Care, Utilities, Utilit…
$ sort_order <int> 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9,…
$ metric     <fct> Daily, Year-to-date, Daily, Year-to-date, Daily, Year-to-da…
$ value      <dbl> -0.54367386, 20.21905637, -0.27978329, 0.84239783, 0.477122…

Chart: S&P 500 Sector Performance

Code 
# Define colors matching the reference
colors <- c("Daily" = "#1e5aa8", "Year-to-date" = "#d4a012")

# Create the grouped bar chart
p <- ggplot(sector_long, aes(x = sector, y = value, fill = metric)) +
  geom_col(position = position_dodge(width = 0.7), width = 0.6) +
  geom_hline(yintercept = 0, color = "black", linewidth = 0.5) +
  geom_text(
    aes(
      label = sprintf("%.1f%%", value),
      vjust = ifelse(value >= 0, -0.5, 1.5),
      color = metric
    ),
    position = position_dodge(width = 0.7),
    size = 3,
    show.legend = FALSE
  ) +
  scale_fill_manual(values = colors) +
  scale_color_manual(values = colors) +
  scale_y_continuous(
    labels = label_percent(scale = 1, accuracy = 1),
    expand = expansion(mult = c(0.15, 0.15))
  ) +
  labs(
    title = glue("S&P 500 sector performance"),
    subtitle = glue("Date: {data_date_formatted} | Year-to-date"),
    x = NULL,
    y = NULL,
    fill = NULL
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16, hjust = 0),
    plot.subtitle = element_text(size = 12, color = "gray40", hjust = 0),
    axis.text.x = element_text(size = 10, angle = 0, hjust = 0.5),
    axis.text.y = element_text(size = 10),
    legend.position = "top",
    legend.justification = "left",
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    plot.margin = margin(20, 20, 20, 20)
  )

print(p)

Code 
# Save for social media
ggsave(
  "chart-1.png",
  plot = p,
  width = 14,
  height = 7,
  dpi = 150,
  bg = "white"
)

Data Table

Code 
# Display formatted table
sector_data |>
  select(Sector = sector, 
         `Daily Return` = daily_return, 
         `YTD Return` = ytd_return) |>
  mutate(
    `Daily Return` = sprintf("%+.2f%%", `Daily Return`),
    `YTD Return` = sprintf("%+.2f%%", `YTD Return`)
  ) |>
  knitr::kable(align = c("l", "r", "r"))
Sector Daily Return YTD Return
Energy -0.54% +20.22%
Health Care -0.28% +0.84%
Utilities +0.48% +7.30%
Materials +0.25% +14.83%
Real Estate +0.83% +7.88%
Financials +0.65% -4.44%
Cons. Staples +0.25% +13.13%
Industrials +0.50% +12.19%
S&P 500 +0.72% +0.92%
Comm. Serv. +1.44% -0.09%
Info. Tech. +0.48% -2.37%
Cons. Discr. +1.04% -0.76%

Alternative View: Lollipop Chart

Code 
# Create lollipop chart for YTD performance
ytd_data <- sector_data |>
  mutate(
    sector = factor(sector, levels = sector[order(ytd_return)]),
    color = ifelse(ytd_return >= 0, "#22c55e", "#ef4444")
  )

p_lollipop <- ggplot(ytd_data, aes(x = sector, y = ytd_return)) +
  geom_segment(
    aes(x = sector, xend = sector, y = 0, yend = ytd_return, color = color),
    linewidth = 1.5,
    show.legend = FALSE
  ) +
  geom_point(aes(color = color), size = 4, show.legend = FALSE) +
  geom_hline(yintercept = 0, color = "gray50", linewidth = 0.5) +
  geom_text(
    aes(label = sprintf("%+.1f%%", ytd_return)),
    hjust = ifelse(ytd_data$ytd_return >= 0, -0.3, 1.3),
    size = 3.5
  ) +
  scale_color_identity() +
  scale_y_continuous(
    labels = label_percent(scale = 1, accuracy = 1),
    expand = expansion(mult = c(0.1, 0.1))
  ) +
  coord_flip() +
  labs(
    title = "S&P 500 Sector YTD Performance",
    subtitle = glue("As of {data_date_formatted}"),
    x = NULL,
    y = "Year-to-Date Return (%)"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "gray40"),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank()
  )

print(p_lollipop)

Key Insights

  1. Sector Rotation: The chart reveals which sectors are leading market performance in the current year.

  2. Daily vs YTD Divergence: Sectors with strong YTD but weak daily returns may be experiencing profit-taking.

  3. Defensive vs Cyclical: Utilities and Consumer Staples are typically defensive, while Technology and Consumer Discretionary are more cyclical.

  4. Benchmark Reference: S&P 500 (SPY) provides context for relative sector performance.

Design Decisions

  1. Grouped Bars: Side-by-side comparison makes it easy to compare daily vs YTD for each sector.

  2. Color Scheme: Blue for daily (short-term), Gold for YTD (cumulative) - intuitive and consistent.

  3. Data Labels: Percentage values shown above/below bars for quick reading.

  4. Alternative View: Lollipop chart provides a different perspective focusing on YTD rankings.

Data source: Yahoo Finance. This post is part of the TidyTuesday weekly data visualization project.

CautionDisclaimer

This analysis is for educational and practice purposes only. Past performance is no guarantee of future results. Indexes are unmanaged, do not incur management fees, costs and expenses and cannot be invested in directly.