5  Zeitgeist and Moodiness

At this point, I asked the AI engine to suggest additional analyses and those that follow were suggested by the AI engine. I find the analytical additions pretty remarkable. This suggests there are times when you need to let the AI engine take the lead.

1. The “Zeitgeist Score” (Consensus vs. Chaos)

We’ve looked at how companies change themselves (Volatility), but we haven’t looked at how much they agree with each other in a given year.

  • High Consensus: (e.g., 2022) Everyone chooses Green. The industry is aligned.

  • High Dissonance: (e.g., 2026) One chooses White, another Dark Brown, another Blue. The industry is fractured.

The Metric: We can calculate the Dispersion for each year. This is the average distance of all that year’s colors from the “Mean Color” of that year.

  • Hypothesis: 2022 (The Year of Green) will have a low score (High Agreement). 2026 might have a high score (Chaos).

We were working with the HSV values that define each color. The “V” component is called the “Value” or, more commonly, the “luminance.” That’s our focus here.

The AI engine describes the next analysis as follows.

Designers often talk about “Light & Airy” vs. “Dark & Moody.” We can quantify this by calculating the Luminance (Perceived Brightness) of the selections.

  • The Trend: Are we actually moving toward darker colors (the “Moody” trend), or is that just marketing hype? The chart will tell us if the 2020s are statistically darker than the 2010s.
Show the code 
library(ggplot2)
library(dplyr)
library(tidyr)
library(patchwork) # For combining plots

# 1. Setup Data (Cleaned & Accurate)
# We will filter out NAs for the calculation to be accurate
data_raw <- tribble(
  ~Year, ~Company, ~Hex,
  2026, "Pantone", "#F0EEE9", 2026, "Benj. Moore", "#4A413C", 2026, "Sherwin", "#BCA68E", 2026, "Behr", "#6A867F", 2026, "Valspar", "#7A8B78", 2026, "Dunn-Edw", "#2E4035", 2026, "Glidden", "#9D4A3C",
  2025, "Pantone", "#A47864", 2025, "Benj. Moore", "#AA8C96", 2025, "Sherwin", "#A3AF9D", 2025, "Behr", "#8A3324", 2025, "Valspar", "#2C5282", 2025, "Dunn-Edw", "#B58463", 2025, "Glidden", "#6A4B5F",
  2024, "Pantone", "#FFBE98", 2024, "Benj. Moore", "#5B6C91", 2024, "Sherwin", "#B4BEC3", 2024, "Behr", "#4E5052", 2024, "Valspar", "#97C5C9", 2024, "Dunn-Edw", "#7DA6B3", 2024, "Glidden", "#EFDECD",
  2023, "Pantone", "#BE3455", 2023, "Benj. Moore", "#D25A46", 2023, "Sherwin", "#AE8E7E", 2023, "Behr", "#F0ECE2", 2023, "Glidden", "#3A6A6C", 2023, "Dunn-Edw", "#A66E6A",
  2022, "Pantone", "#6667AB", 2022, "Benj. Moore", "#A3AA9E", 2022, "Sherwin", "#95978A", 2022, "Behr", "#B8CBC0", 2022, "Glidden", "#8C9C74", 2022, "Dunn-Edw", "#947B64",
  2021, "Pantone", "#C4BA4E", 2021, "Benj. Moore", "#647882", 2021, "Sherwin", "#545E60", 2021, "Behr", "#C09277", 2021, "Glidden", "#D0C5B3", 2021, "Dunn-Edw", "#7D8F9E",
  2020, "Pantone", "#0F4C81", 2020, "Benj. Moore", "#EBE1E1", 2020, "Sherwin", "#2F3D4C", 2020, "Behr", "#97A878", 2020, "Glidden", "#304B64", 2020, "Dunn-Edw", "#B6E0D2",
  2019, "Pantone", "#FF6F61", 2019, "Benj. Moore", "#AFB4B4", 2019, "Sherwin", "#D1866A", 2019, "Behr", "#4D6C8C", 2019, "Glidden", "#004746", 2019, "Dunn-Edw", "#8D4E3C",
  2018, "Pantone", "#5F4B8B", 2018, "Benj. Moore", "#AF2D2D", 2018, "Sherwin", "#195564", 2018, "Behr", "#7E9995", 2018, "Glidden", "#202226", 2018, "Dunn-Edw", "#3D4F46",
  2017, "Pantone", "#88B04B", 2017, "Benj. Moore", "#5F505F", 2017, "Sherwin", "#8C827D", 2017, "Behr", "#D5CDB5", 2017, "Glidden", "#7A5C6D", 2017, "Dunn-Edw", "#E3B668",
  2016, "Pantone", "#C4B9D0", 2016, "Benj. Moore", "#F3F4ED", 2016, "Sherwin", "#EDEAE0", 2016, "Glidden", "#7B9683"
)

# 2. Extract RGB & Luminance
get_vals <- function(hex) {
  rgb <- col2rgb(hex)
  # Standard Luminance formula (Perceived Brightness)
  lum <- (0.299 * rgb[1] + 0.587 * rgb[2] + 0.114 * rgb[3])
  c(R = rgb[1], G = rgb[2], B = rgb[3], Lum = lum)
}

df_vals <- cbind(data_raw, t(sapply(data_raw$Hex, get_vals)))

# 3. ANALYSIS A: CONSENSUS (Dispersion)
# Calculate the "Centroid" (Average Color) of each year, then avg distance from it
df_dispersion <- df_vals %>%
  group_by(Year) %>%
  summarise(
    Mean_R = mean(R), Mean_G = mean(G), Mean_B = mean(B)
  ) %>%
  inner_join(df_vals, by = "Year") %>%
  mutate(
    dist_from_mean = sqrt((R - Mean_R)^2 + (G - Mean_G)^2 + (B - Mean_B)^2)
  ) %>%
  group_by(Year) %>%
  summarise(Dispersion = mean(dist_from_mean))

# Plot A: The "Confusion" Chart
p1 <- ggplot(df_dispersion, aes(x = Year, y = Dispersion)) +
  geom_line(color = "purple", size = 1) +
  geom_point(size = 3, color = "purple") +
  scale_x_continuous(breaks = 2016:2026) +
  labs(title = "Industry Consensus vs. Chaos",
       subtitle = "Higher = Companies disagreed more (High Dissonance)",
       y = "Color Dispersion Score") +
  theme_minimal()

# 4. ANALYSIS B: LUMINANCE (Moodiness)
# Plot B: The "Light vs Dark" Chart
p2 <- ggplot(df_vals, aes(x = Year, y = Lum)) +
  # Add individual points
  geom_jitter(width = 0.1, size = 2, alpha = 0.3) +
  # Add trend line (Mean Luminance)
  stat_summary(fun = mean, geom = "line", color = "darkblue", size = 1.2, group = 1) +
  stat_summary(fun = mean, geom = "point", color = "darkblue", size = 3) +
  scale_x_continuous(breaks = 2016:2026) +
  scale_y_continuous(limits = c(0, 255), labels = function(x) paste0(round(x/2.55), "%")) +
  labs(title = "The 'Moodiness' Index",
       subtitle = "Trend of Average Brightness (0% = Black, 100% = White)",
       y = "Luminance (Brightness)") +
  theme_minimal()

# Combine plots (Requires 'patchwork' library)
p1 / p2

What to expect from these charts:

  1. The 2022 “Singularity”: You will likely see a dip in the Consensus chart around 2022. Because almost everyone chose a variation of “Sage Green,” the mathematical distance between companies should be very low.

  2. The 2026 “Fragmentation”: I expect the 2026 dispersion score to be very high. We have Cloud Dancer (White) and Silhouette (Dark Brown) in the same year. Geometrically, these are on opposite ends of the color cube, which will drive the “Chaos Score” through the roof.

  3. The “Darkening” Trend: In the Luminance chart, look for a downward slope starting around 2023/2024. The era of “Simply White” (2016) and “First Light” (2020) is likely giving way to lower luminance values as “Moody” interiors become fashionable.