7-Day Moving Average of Cases, Tests, and Positivity [Santa Clara County, CA]

.chart_ca-santa-clara_covid19_cases_tests_positivity.r

library(ggplot2)
library(slider)
library(scales)
library(dplyr)
library(readr)
library(tsibble)

sf <- 2
width <- 600 * sf
height <- 335 * sf

options(vsc.dev.args = list(width = width, height = height, res = 72 * sf))

# Load and process the COVID-19 data
df <- read_csv("https://data.sccgov.org/resource/t8ae-ku7k.csv",
  col_types = "ciicciiidd"
) |>
  select(end_date, case_count, test_count, positivity_rate) |>
  setNames(c("date", "cases", "tests", "positivity")) |>
  group_by(date) |>
  summarize(
    cases = sum(cases, na.rm = TRUE),
    tests = sum(tests, na.rm = TRUE),
    positivity = mean(positivity, na.rm = TRUE),
  ) |>
  mutate(
    date = as.Date(date),
    positivity = positivity / 100 # Convert to proportion for easier scaling
  )

# Calculate 7-day moving averages
ts <- df |> as_tsibble(index = date)

chart <- ggplot(ts, aes(x = date)) +
  geom_line(aes(y = cases, color = "Cases")) +
  geom_line(aes(y = tests, color = "Tests")) +
  geom_line(
    aes(
      y = positivity * max(tests, na.rm = TRUE),
      color = "Positivity"
    ),
    linetype = "dashed"
  ) +
  scale_y_continuous(
    name = "Cases / Tests",
    sec.axis = sec_axis(
      ~ . / max(ts$tests, na.rm = TRUE),
      name = "Positivity (%)",
      labels = percent_format(accuracy = 1)
    ),
    labels = scales::number_format(), # Fix for formatting
  ) +
  labs(
    title = paste0(
      "7-Day Moving Average of Cases, Tests, and Positivity ",
      "[Santa Clara County, CA]"
    ),
    x = "Date",
    color = "Metric"
  ) +
  geom_vline(
    xintercept = as.Date("2020-12-17"), linetype = "dashed",
    color = "black", size = 0.5
  ) +
  annotate(
    "text",
    x = as.Date("2020-12-17"), y = max(ts$cases, na.rm = TRUE),
    label = "First Vaccine\nGiven", vjust = -8, hjust = 1.1, size = 3.5
  ) +
  scale_color_manual(
    values = c(
      "Cases" = "green",
      "Tests" = "blue",
      "Positivity" = "red"
    )
  ) +
  theme_minimal() +
  theme(
    axis.title.y.right = element_text(color = "black"),
    legend.position = "bottom",
    legend.title = element_blank(),
    legend.direction = "horizontal"
  )

ggplot2::ggsave(
  filename = "chart1.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = c("cairo")
)

https://www.mortality.watch/charts/list.html#7-day-moving-average-of-cases-tests-and-positivity-santa-clara-county-ca

Long Term Care Cases:

library(readr)
df <- read_csv(
  paste0(
    "/Users/ben/Downloads/",
    "COVID-19_cases_at_Long_Term_Care_Facilities_by_date_20250228.csv"
  ),
  col_types = "iiD"
)
df |> ggplot(aes(x = dtepisode, y = New_cases)) +
  geom_line() +
  labs(
    title = "COVID-19 cases at Long Term Care Facilities by date",
    subtitle = "Source: https://data.sccgov.org/COVID-19"
  )