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index.Rmd
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---
title: "COVID-19 Trends by UCI Statistics"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
<style type="text/css">
.main-container {
max-width: 1200px;
margin-left: auto;
margin-right: auto;
}
</style>
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = F, warning = F, message = F)
library(tidyverse)
library(lubridate)
library(scales)
library(TTR)
library(glue)
library(ggtext)
library(ckanr) # library to interact with CA Gov. Public Health Portal
library(cowplot)
# connect to CKAN instance
ckanr_setup(url="https://data.ca.gov")
ckan <- ckanr::src_ckan("https://data.ca.gov")
# get resources
resources <- rbind(resource_search("name:covid-19", as = "table")$results,
resource_search("name:hospitals by county", as = "table")$results)
resource_ids <- list(cases = resources$resource_id[resources$name == "COVID-19 Cases"],
tests = resources$resource_id[resources$name == "COVID-19 Testing"],
hosp = resources$resource_id[resources$name == "Hospitals By County"])
# pull resources into data frames (adds extra cols _id and _full_text)
cases <- tbl(src = ckan$con, from = resource_ids$cases) %>%
as_tibble( )%>%
select(-starts_with("_")) %>%
mutate(date = as.Date(date))
tests <- tbl(src = ckan$con, from = resource_ids$test) %>%
as_tibble() %>%
select(-starts_with("_")) %>%
mutate(date = as.Date(date))
hosp <- tbl(src = ckan$con, from = resource_ids$hosp) %>%
as_tibble() %>%
select(-starts_with("_")) %>%
mutate(date = as.Date(todays_date)) %>%
select(-todays_date)
county_pop <- read_csv("data/county_pop.csv") %>% rename_all(str_to_lower)
# stay at home order start and end dates
sah_start <- ymd("2020-03-19")
sah_end <- ymd("2020-05-04")
```
```{r tidy data, message=FALSE}
# Data Processing Options
counties_of_interest <- c("San Diego", "Los Angeles", "Orange", "Alameda", "Santa Clara")
per_n_people <- 1e6 # denominator for reporting counts (e.g, per million people)
ma_n <- 7 # moving average window length in days
# Tidy Data
hosp_tidy <- hosp %>%
filter(county %in% counties_of_interest) %>%
pivot_longer(cols = -c(date, county)) %>%
drop_na() %>%
left_join(county_pop) %>%
arrange(county, date)
cases_tidy <- cases %>%
filter(county %in% counties_of_interest) %>%
pivot_longer(cols = -c(date, county)) %>%
drop_na() %>%
left_join(county_pop) %>%
arrange(county, date)
# Theme options
theme_set(theme_bw(base_size = 14) +
theme(legend.position = "bottom",
legend.title = element_text(size = 12)))
cbPalette <- c("#56B4E9", "#009E73", "#E69F00", "#CC79A7", "#0072B2")
sah_alpha <- 0.2
gglayers = list(
geom_line(size = 1.5),
scale_color_manual(name = "County", values = cbPalette),
scale_x_date(name = "Date",
breaks = "14 day",
date_labels = "%b %d"),
scale_y_continuous(label = comma)
)
watermark <- function(plt) ggdraw(plt) + draw_label("UC Irvine COVID Awareness Group", color = "#0064A4", alpha=0.4, size = 15, angle = 0, x=0.25, y=0.85, hjust = 0, vjust = 0)
# Could also use stamp function
# stamp(plot1, label = "DRAFT", color = "red")
```
```{r make plots}
# Hospitalizations
hospitalizations_plot_data <- hosp_tidy %>%
filter(name == "hospitalized_covid_patients") %>%
group_by(county) %>%
mutate(value = runMean(value, ma_n)) %>%
drop_na()
hospitalizations_plot <- hospitalizations_plot_data %>%
ggplot(aes(date, value / population * per_n_people, group = county, color = county)) +
ggtitle(glue("Hospitalized Patients with COVID-19 <span style='font-size:14pt'>({ma_n} Day Moving Average)</span>")) +
#subtitle = glue("{ma_n} Day Moving Average"))
theme(
plot.title = element_markdown()
) +
gglayers +
ylab(glue("Hospitalized Patients with COVID-19\nper {comma(per_n_people)} People")) +
annotate("rect",
xmin = if_else(min(hospitalizations_plot_data$date) < sah_start,
sah_start,
min(hospitalizations_plot_data$date)),
xmax = sah_end, ymin = -Inf, ymax = Inf, alpha = sah_alpha) +
annotate("text", y = 175, x = sah_end+8, label = "Stay at Home\nOrder Ended")
# ICU Occupancy
icu_plot_data <- hosp_tidy %>%
filter(name %in% c("icu_covid_confirmed_patients", "icu_suspected_covid_patients")) %>%
group_by(county, date, population) %>%
summarize(value = sum(value)) %>%
group_by(county) %>%
mutate(value = runMean(value, ma_n)) %>%
drop_na() %>%
filter(date >= min(hospitalizations_plot_data$date))
icu_plot <- icu_plot_data %>%
ggplot(aes(date, value / population * per_n_people, group = county, color = county)) +
ggtitle(glue("ICU Patients with COVID-19 <span style='font-size:14pt'>({ma_n} Day Moving Average)</span>")) +
# subtitle = glue("{ma_n} Day Moving Average")) +
theme(
plot.title = element_markdown()
) +
gglayers +
ylab(glue("ICU Patients with COVID-19\nper {comma(per_n_people)} People")) +
annotate("rect",
xmin = if_else(min(icu_plot_data$date) < sah_start, sah_start, min(icu_plot_data$date)),
xmax = sah_end, ymin = -Inf, ymax = Inf, alpha = sah_alpha) +
annotate("text", y = 51, x = sah_end+8, label = "Stay at Home\nOrder Ended")
# Deaths
deaths_plot_data <- cases_tidy %>%
filter(name == "newcountdeaths") %>%
group_by(county) %>%
mutate(value = runMean(value, ma_n)) %>%
drop_na() %>%
filter(date >= min(hospitalizations_plot_data$date))
deaths_plot <- deaths_plot_data %>%
ggplot(aes(date, value / population * per_n_people, group = county, color = county)) +
ggtitle(glue(" New Daily Deaths due to COVID-19 <span style='font-size:14pt'>({ma_n} Day Moving Average)</span>")) +
# subtitle = glue("{ma_n} Day Moving Average")) +
theme(
plot.title = element_markdown()
) +
gglayers +
ylab(glue("New Daily Deaths due to COVID-19\nper {comma(per_n_people)} People")) +
annotate("rect",
xmin = if_else(min(deaths_plot_data$date) < sah_start,
sah_start,
min(deaths_plot_data$date)),
xmax = sah_end, ymin = -Inf, ymax = Inf, alpha = sah_alpha) +
annotate("text", y = 3, x = sah_end+8, label = "Stay at Home\nOrder Ended")
# Cases
cases_plot_data <- cases_tidy %>%
filter(name == "newcountconfirmed") %>%
group_by(county) %>%
mutate(value = runMean(value, ma_n)) %>%
drop_na() %>%
filter(date >= min(hospitalizations_plot_data$date))
cases_plot <- cases_plot_data %>%
ggplot(aes(date, value / population * per_n_people, group = county, color = county)) +
ggtitle(glue("New Daily COVID-19 Cases <span style='font-size:14pt'>({ma_n} Day Moving Average)</span>")) +
# subtitle = glue("{ma_n} Day Moving Average")) +
theme(
plot.title = element_markdown()
) +
gglayers +
ylab(glue("New Daily Confirmed COVID-19 Cases\nper {comma(per_n_people)} People")) +
annotate("rect",
xmin = if_else(min(cases_plot_data$date) < sah_start,
sah_start,
min(cases_plot_data$date)),
xmax = sah_end, ymin = -Inf, ymax = Inf, alpha = sah_alpha) +
annotate("text", y = 250, x = sah_end+8, label = "Stay at Home\nOrder Ended")
```
<div class = "row">
<div class = "col-md-6">
```{r hospitalizations_plot}
watermark(hospitalizations_plot)
```
</div>
<div class = "col-md-6">
```{r icu_plot}
watermark(icu_plot)
```
</div>
</div>
<div class = "row">
<div class = "col-md-6">
```{r cases_plot}
watermark(cases_plot)
```
</div>
<div class = "col-md-6">
```{r deaths_plot}
watermark(deaths_plot)
```
</div>
</div>
<div class = "col-md-6">
<img src = "assets/MarchIncid.png"/>
</div>
<div class = "col-md-6">
<img src = "assets/AprilIncid.png"/>
</div>
<div class = "col-md-6">
<img src = "assets/MayIncid.png"/>
</div>
<div class = "col-md-6">
<img src = "assets/JuneIncid.png"/>
</div>
<div class = "col-md-6">
<img src = "assets/JulyIncid.png"/>
</div>
Data collected from the [California Open Data Portal](https://data.ca.gov/group/covid-19).
Last updated `r as.Date(max(resources$last_modified[resources$name %in% c("COVID-19 Cases", "COVID-19 Testing", "Hospitals By County")]))`.