The Flu Season Clock

Flu usually piles up in winter. In 2020–21, that seasonal pattern nearly disappeared.

30DayChartChallenge
2026
A circular distribution of weekly U.S. flu activity across the 52-week year, using CDC FluView ILINet data. The gray band shows the typical seasonal range from 2015–16 to 2019–20, with a sharp peak in winter months. The cyan shape shows the 2020–21 season, when COVID-19 pandemic measures nearly eliminated flu activity. Built with ggplot2 and coord_polar in R.
Author

Steven Ponce

Published

April 8, 2026

Figure 1: Circular chart showing the weekly distribution of U.S. flu activity across the 52-week year. A gray band represents the typical seasonal range from 2015–16 to 2019–20, with activity concentrated near January and February at the top of the clock face and near zero during summer months at the bottom. A small cyan ring near the center shows the 2020–21 season, when flu nearly disappeared due to COVID-19 pandemic measures.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false      
#| results: "hide"     

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse, ggtext, showtext, patchwork,     
  janitor, scales, glue, httr2, jsonlite  
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 7,
  height = 7,
  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"))

### |- helper: fetch one epiweek range from Delphi Epidata ----
# fetch_fluview <- function(epiweek_start, epiweek_end) {
#   resp <- request("https://api.delphi.cmu.edu/epidata/fluview/") |>
#     req_url_query(
#       regions  = "nat",
#       epiweeks = glue("{epiweek_start}-{epiweek_end}")
#     ) |>
#     req_perform()
#   
#   content <- resp |>
#     resp_body_string() |>
#     fromJSON(simplifyDataFrame = TRUE)
#   
#   if (content$result != 1) {
#     stop(glue("Epidata API error: {content$message}"))
#   }
#   
#   content$epidata |>
#     as_tibble() |>
#     clean_names()
# }

### |- cache path ----
# cache_path <- here::here("2026/data/fluview_national_ilinet.csv")

### |- read from cache or fetch from API ----
# if (file.exists(cache_path)) {
#   message("Cache found — reading from local CSV.")
#   flu_raw <- read_csv(cache_path, show_col_types = FALSE)
# } else {
#   message("No cache — fetching from CMU Delphi Epidata API...")
#   flu_raw <- tryCatch(
#     {
#       bind_rows(
#         fetch_fluview(201540, 202039),
#         fetch_fluview(202040, 202439)
#       )
#     },
#     error = function(e) {
#       stop("API fetch failed: ", e$message)
#     }
#   )
#   dir.create(here::here("2026/data"), showWarnings = FALSE, recursive = TRUE)
#   write_csv(flu_raw, cache_path)
#   message("Data cached to: ", cache_path)
# }
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

### |- RSF 2025 ----
flu_raw <- read_csv(
  here::here("data/30DayChartChallenge/2026/fluview_national_ilinet.csv"),
  show_col_types = FALSE
) |>
  clean_names()
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(flu_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

### |-column parsing ----
if ("epiweek" %in% names(flu_raw)) {
  epi_year <- as.integer(flu_raw$epiweek) %/% 100L
  epi_week <- as.integer(flu_raw$epiweek) %% 100L
} else {
  epi_year <- rep(NA_integer_, nrow(flu_raw))
  epi_week <- rep(NA_integer_, nrow(flu_raw))
}

flu_parsed <- flu_raw |>
  mutate(
    year = if ("year" %in% names(flu_raw)) coalesce(as.integer(.data$year), epi_year) else epi_year,
    week = if ("week" %in% names(flu_raw)) coalesce(as.integer(.data$week), epi_week) else epi_week
  ) |>
  filter(!is.na(wili), !is.na(year), !is.na(week))

flu_tidy <- flu_parsed |>
  mutate(
    season_start = if_else(week >= 40L, year, year - 1L),
    season_label = glue("{season_start}-{str_sub(season_start + 1L, 3, 4)}"),
    week_shifted = ((week - 1L + 13L) %% 52L) + 1L
  ) |>
  filter(season_start >= 2015L, season_start <= 2023L)

### |- classify into layers ----
flu_classified <- flu_tidy |>
  mutate(
    layer = case_when(
      season_start %in% 2015:2019 ~ "historical",
      season_start == 2020L ~ "covid",
      TRUE ~ NA_character_
    )
  ) |>
  filter(!is.na(layer))

### |- historical band (10th-90th percentile) ----
historical_band <- flu_classified |>
  filter(layer == "historical") |>
  group_by(week_shifted) |>
  summarise(
    ili_lo     = quantile(wili, 0.10, na.rm = TRUE),
    ili_median = median(wili, na.rm = TRUE),
    ili_hi     = quantile(wili, 0.90, na.rm = TRUE),
    .groups    = "drop"
  )

### |- COVID season ----
covid_line <- flu_classified |>
  filter(layer == "covid") |>
  arrange(week_shifted)

### |- close polygons at the seam ----
historical_closed <- bind_rows(
  historical_band,
  historical_band |> slice(1) |> mutate(week_shifted = 53)
)

covid_closed <- bind_rows(
  covid_line,
  covid_line |> slice(1) |> mutate(week_shifted = 53)
)

### |- historical band as explicit polygon ----
historical_band_poly <- bind_rows(
  historical_closed |> transmute(week_shifted, y = ili_hi),
  historical_closed |> transmute(week_shifted, y = ili_lo) |> arrange(desc(week_shifted))
)

### |- scale helpers ----
y_max      <- ceiling(max(historical_closed$ili_hi, na.rm = TRUE)) + 1
inner_hole <- 0.8    

### |- reference ring data ----
ring_values <- c(1, 2, 3)
ring_values <- ring_values[ring_values < y_max]

ring_data <- expand_grid(
  week_shifted = seq(1, 53, by = 0.25),
  ring_val     = ring_values
)

### |- ring % labels  ----
ring_labels <- tibble(
  x     = 27,                         
  y     = inner_hole + ring_values + 0.08,
  label = paste0(ring_values, "%")
)

### |- month labels ----
month_labels <- tibble(
  x     = c(14, 27, 40,  1),
  y     = inner_hole + y_max * 1.00,
  label = c("Jan", "Oct", "Jul", "Apr")   
)
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    col_bg         = "#07111d",     
    col_band_fill  = "#7b8fa1",
    col_band_line  = "#a8bfcf",
    col_covid_fill = "#8fd3e8",
    col_covid_line = "#c8ecf5",
    col_text       = "#e9eef4",
    col_subtext    = "#a6b7c6",
    col_grid       = "#1f3d5c"
  )
)

### |- titles and caption ----
title_text    <- "The Flu Season Clock"

subtitle_text <- "Flu usually piles up in winter. In 2020\u201321, that seasonal pattern nearly disappeared."

caption_text  <- create_dcc_caption(
  dcc_year    = 2026,
  dcc_day     = 08,
  source_text = "CDC FluView ILINet \u00b7 CMU Delphi Epidata API"
)

### |- fonts ----
setup_fonts()
fonts <- get_font_families()

font_body    <- fonts$text    %||% ""
font_title   <- fonts$title   %||% ""
font_caption <- fonts$caption %||% ""
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |- main clock plot ----
p_clock <- ggplot() +

  # Geoms
  geom_line(
    data = ring_data,
    aes(x = week_shifted, y = inner_hole + ring_val, group = ring_val),
    color = colors$palette$col_grid,
    linewidth = 0.4
  ) +
  geom_polygon(
    data  = historical_band_poly,
    aes(x = week_shifted, y = inner_hole + y),
    fill  = colors$palette$col_band_fill,
    alpha = 0.45
  ) +
  geom_line(
    data = historical_closed,
    aes(x = week_shifted, y = inner_hole + ili_median),
    color = colors$palette$col_band_line,
    linewidth = 0.55,
    alpha = 0.6,
    linetype = "dashed"
  ) +
  geom_polygon(
    data  = covid_closed,
    aes(x = week_shifted, y = inner_hole + wili),
    fill  = colors$palette$col_covid_fill,
    alpha = 0.35
  ) +
  geom_line(
    data = covid_closed,
    aes(x = week_shifted, y = inner_hole + wili),
    color = colors$palette$col_covid_line,
    linewidth = 0.7,
    alpha = 0.8
  ) +
  geom_text(
    data = ring_labels,
    aes(x = x, y = y, label = label),
    color = colors$palette$col_grid,
    size = 2.3,
    hjust = 0.5,
    family = font_body
  ) +
  geom_text(
    data = month_labels,
    aes(x = x, y = y, label = label),
    color = colors$palette$col_subtext,
    size = 3.8,
    hjust = 0.5,
    family = font_body
  ) +

  # Annotate
  annotate(
    "label",
    x = 40,
    y = inner_hole + 0.30,
    label  = "2020\u201321 (COVID disruption):\nflu nearly vanished",
    color = colors$palette$col_covid_fill,
    fill = alpha(colors$palette$col_bg, 0.90),
    label.size = 0,
    size  = 2.8,
    hjust  = 0.5,
    lineheight = 1.05,
    family = font_body
  ) +
  annotate(
    "label",
    x   = 14,
    y  = inner_hole + y_max * 0.82,
    label  = "Typical winter peak\n(Dec\u2013Feb)",
    color = colors$palette$col_subtext,
    fill = alpha(colors$palette$col_bg, 0.88),
    label.size = 0,
    size = 2.7,
    hjust  = 0.5,
    family = font_body
  ) +
  coord_polar(start = -pi / 2, direction = -1, clip = "off") +

  # Scales
  scale_x_continuous(limits = c(1, 53), breaks = NULL, expand = c(0, 0)) +
  scale_y_continuous(limits = c(0, inner_hole + y_max * 1.02), breaks = NULL, expand = c(0, 0)) +

  # Labs
  labs(title = title_text, subtitle = subtitle_text) +

  # Theme
  theme_void() +
  theme(
    plot.background = element_rect(fill = colors$palette$col_bg, color = NA),
    panel.background = element_rect(fill = colors$palette$col_bg, color = NA),
    plot.title = element_text(
      family = font_title,
      face   = "bold",
      color = colors$palette$col_text,
      size  = 20,
      hjust  = 0.5,
      margin = margin(t = 14, b = 4)
    ),
    plot.subtitle = element_text(
      family = font_body,
      color = colors$palette$col_subtext,
      size = 10,
      hjust  = 0.5,
      lineheight = 1.3,
      margin = margin(b = 2)
    ),
    plot.margin = margin(5, 5, 0, 5)
  )

### |- caption + legend panel ----
legend_line <- glue(
  "<span style='color:{colors$palette$col_band_fill};'><b>Historical band</b></span>",
  " (10th\u201390th percentile, 2015\u201316 to 2019\u201320) &nbsp;\u2022&nbsp; ",
  "<span style='color:{colors$palette$col_covid_fill};'><b>2020\u201321 disruption</b></span>"
)

p_caption <- ggplot() +
  annotate(
    "richtext",
    x = 0.5, y = 0.75,
    label = legend_line,
    color = colors$palette$col_subtext,
    fill = NA,
    label.color = NA,
    size = 2.8,
    hjust = 0.5,
    family = font_body
  ) +
  annotate(
    "richtext",
    x = 0.5, y = 0.20,
    label = caption_text,
    color = colors$palette$col_subtext,
    fill = NA,
    label.color = NA,
    size = 2.5,
    hjust = 0.5,
    family = font_caption
  ) +
  scale_x_continuous(limits = c(0, 1)) +
  scale_y_continuous(limits = c(0, 1)) +
  theme_void() +
  theme(
    plot.background = element_rect(fill = colors$palette$col_bg, color = NA),
    plot.margin     = margin(0, 10, 8, 10)
  )

### |- combined plots ----
combined_plots <- p_clock / p_caption +
  plot_layout(heights = c(10, 1.1)) +
  plot_annotation(
    theme = theme(plot.background = element_rect(fill = colors$palette$col_bg, color = NA))
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  combined_plots, 
  type = "30daychartchallenge", 
  year = 2026, 
  day = 08, 
  width = 7, 
  height = 7
  )
```

8. Session Info

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)

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 utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      jsonlite_2.0.0  httr2_1.2.2     glue_1.8.0     
 [5] scales_1.4.0    janitor_2.2.1   patchwork_1.3.2 showtext_0.9-7 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.5
[13] forcats_1.0.1   stringr_1.6.0   dplyr_1.2.0     purrr_1.2.1    
[17] readr_2.2.0     tidyr_1.3.2     tibble_3.2.1    ggplot2_4.0.2  
[21] tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.56          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.2.4  vctrs_0.7.1        tools_4.3.1        generics_0.1.4    
 [9] curl_7.0.0         parallel_4.3.1     gifski_1.32.0-2    pacman_0.5.1      
[13] pkgconfig_2.0.3    ggplotify_0.1.3    RColorBrewer_1.1-3 S7_0.2.0          
[17] lifecycle_1.0.5    compiler_4.3.1     farver_2.1.2       codetools_0.2-19  
[21] snakecase_0.11.1   litedown_0.9       htmltools_0.5.9    yaml_2.3.12       
[25] pillar_1.11.1      crayon_1.5.3       camcorder_0.1.0    magick_2.8.6      
[29] commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7     
[33] labeling_0.4.3     rsvg_2.6.2         rprojroot_2.1.1    fastmap_1.2.0     
[37] grid_4.3.1         cli_3.6.5          magrittr_2.0.3     withr_3.0.2       
[41] rappdirs_0.3.4     bit64_4.6.0-1      timechange_0.4.0   rmarkdown_2.30    
[45] bit_4.6.0          otel_0.2.0         hms_1.1.4          evaluate_1.0.5    
[49] knitr_1.51         markdown_2.0       gridGraphics_0.5-1 rlang_1.1.7       
[53] gridtext_0.1.6     Rcpp_1.1.1         xml2_1.5.2         svglite_2.1.3     
[57] rstudioapi_0.18.0  vroom_1.7.0        R6_2.6.1           fs_1.6.7          
[61] systemfonts_1.3.2

9. GitHub Repository

The complete code for this analysis is available in 30dcc_2026_08.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
    • Centers for Disease Control and Prevention (CDC). (2026). FluView: ILINet — U.S. Outpatient Influenza-like Illness Surveillance Network. Retrieved April 2026 from https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html
    • Reinhart, A., Brooks, L., Jahja, M., Rumack, A., Tang, J., Saeed, W. A. I., … & Tibshirani, R. J. (2021). An open repository of real-time COVID-19 indicators. PNAS, 118(51). https://doi.org/10.1073/pnas.2111452118
    • CMU Delphi Group. (2026). Delphi Epidata API — FluView endpoint. Retrieved April 2026 from https://api.delphi.cmu.edu/epidata/fluview/

11. Custom Functions Documentation

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

  • fonts.R: setup_fonts(), get_font_families() - Font management with showtext
  • social_icons.R: create_social_caption() - Generates formatted social media captions
  • image_utils.R: save_plot() - Consistent plot saving with naming conventions
  • base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

Back to top

Citation

BibTeX citation:
@online{ponce2026,
  author = {Ponce, Steven},
  title = {The {Flu} {Season} {Clock}},
  date = {2026-04-08},
  url = {https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2026/30dcc_2026_08.html},
  langid = {en}
}
For attribution, please cite this work as:
Ponce, Steven. 2026. “The Flu Season Clock.” April 8. https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2026/30dcc_2026_08.html.