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KingCountyCovid.Rmd
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KingCountyCovid.Rmd
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---
title: "King County Covid Cases"
author: "Beau Raines"
date: "7/4/2021"
output:
html_document: default
word_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(dplyr)
library(zoo)
library(hrbrthemes)
library(plotly)
library(emojifont)
```
```{r daily-and-rolling-14-day-average-cases, include = FALSE}
fips = read.csv("https://www2.census.gov/geo/docs/reference/codes/files/national_county.txt", header=FALSE)
nytimes= read.csv("https://github.com/nytimes/covid-19-data/blob/master/us-counties.csv?raw=true")
king_county = subset(nytimes,state =='Washington' & county == "King")
county_population = read.csv("https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/totals/co-est2019-alldata.csv")
data <- data.frame(date = as.Date(tail(king_county$date,-1)), new_cases = diff(king_county$cases), new_deaths = diff(king_county$deaths))
## missing very very first day
p <- ggplot(data, aes(x=date, y=new_cases)) +
# geom_line() +
geom_bar(stat = "identity") +
xlab("")+ylab("New Cases")
p+scale_x_date(date_labels = "%b-%y",date_breaks = "2 month")
p
x<-zoo(data$new_cases)
rolling_average = data.frame(date = as.Date(tail(king_county$date,-14)), rolling_new_cases_14d = rollmean(x,14))
p2 = ggplot(data=rolling_average, aes(x=date,y=rolling_new_cases_14d,group=1))+geom_line()+xlab("")
p2 +ylab("14 Day Rolling Average New Cases")
p2 +scale_x_date(date_labels = "%b-%y",date_breaks = "2 month")
combined_plot = ggplot() +
geom_bar(stat="identity",data=data, aes(x=date, y=new_cases),fill="#FF9999", colour="#FF9999") +
geom_line(data=rolling_average, colour= "darkblue", aes(x=date,y=rolling_new_cases_14d,group=1)) +
scale_x_date(date_labels = "%b-%y",date_breaks = "2 month") +
xlab("") + ylab("Cases")
combined_plot
# Add annotation
#combined_plot +
# annotate(geom="text",x =as.Date('2020-12-15'),subset(data,date=='2020-12-15')$new_cases,label="Christmas Day") +
# annotate(geom="point",x =as.Date('2020-12-15'),subset(data,date=='2020-12-15')$new_cases, size=10, shape=21, fill="transparent")
## Add titles
combined_plot = combined_plot +
labs(title="King County Covid-19 Cases",caption="Data sourced from NY Times GitHub")
cases_per_100K_14d = sum(tail(data$new_cases,14))/subset(county_population, STNAME == 'Washington' & CTYNAME == "King County" ,select=POPESTIMATE2019)*100000
if(cases_per_100K_14d >= 75) {
risk = "High"
} else if (cases_per_100K_14d >= 25 ){
risk = "Moderate"
} else {
risk = "Low"
}
## Write on plot
# combined_plot = combined_plot + annotate(geom="text",x=as.Date(Sys.Date()-21),y=2000,label=paste("Cases per 100k pop:",round(cases_per_100K_14d,2),sep="\n"))
# ipsum Theme
combined_plot = combined_plot +
theme_ipsum()
combined_plot
## Source runs, but doesn't display the plots.
# it seems you have to create them as an object then display it afterwards
source("hospitalizations.R", local = knitr::knit_global())
# beds
```
## Notice
This report continues to be updated, but is no longer under active development. Please refer to the [Covid Dashboard](CovidDashboard.html) as it is the recipient of active development.
## King County Covid Cases
Yesterday, `r tail(data$date,1)` there were `r tail(data$new_cases,1)` new cases reported. King County has a population of `r format(subset(county_population, STNAME == 'Washington' & CTYNAME == "King County" ,select=POPESTIMATE2019),big.mark = ",")`. This is `r format(cases_per_100K_14d,digits=3,decimal.mark=".", nsmall =1)` cases per 100k population over the last 14 days, which is `r risk` risk.
### new Cases
```{r valuebox, echo=FALSE, message = FALSE, warning=FALSE,fig.height=2, fig.align='left'}
yesterday_new_cases <- tail(data$new_cases,1)
df <- data.frame(
x = 1,
y = 1,
h = 4.25,
w = 6.25,
value = c(yesterday_new_cases),
info = c("new cases"),
icon = fontawesome(search_fontawesome("chart"))[1],
font_family = c("fontawesome-webfont",
"EmojiOne"),
color = factor(1)
)
ggplot(df, aes(x, y, height = h, width = w, label = info)) +
## Create the tiles using the `color` column
geom_tile(aes(fill = color)) +
## Add the numeric values as text in `value` column
geom_text(color = "white", fontface = "bold", size = 10,
aes(label = value, x = x - 2.9, y = y + 1), hjust = 0) +
## Add the labels for each box stored in the `info` column
geom_text(color = "white", fontface = "bold",
aes(label = info, x = x - 2.9, y = y - 1), hjust = 0) +
coord_fixed() +
scale_fill_brewer(type = "qual",palette = "Dark2") +
## Use `geom_text()` to add the icons by specifying the unicode symbol.
geom_text(size = 20, aes(label = icon, family = font_family,
x = x + 1.5, y = y + 0.5), alpha = 0.25) +
theme_void() +
guides(fill = FALSE)
fig <- plot_ly(
domain = list(x = c(0, 1), y = c(0, 1)),
value = cases_per_100K_14d[,1],
title = list(text = "Cases per 100k Population"),
type = "indicator",
mode = "gauge+number+delta",
# delta = list(reference=52),
gauge = list(
bar = list(
color = "gray"
),
axis = list(
range = c(0,150),
dtick = 10
),
steps = list(
list(range = c(0,25),color = "lightgreen"),
list(range = c(25,75), color = "lightyellow"),
list(range = c(75,600), color = "red")
)
))
fig <- fig %>%
layout(margin = list(l=20,r=30))
fig
```
In the last 7 days, there were `r sum(tail(data$new_cases,7))` new cases and `r last_7_days_reported_deaths` deaths.
```{r combined_plot, echo=FALSE}
ggplotly(combined_plot) %>%
layout(
xaxis = list(
rangeslider = list(type = "date"))
)
```
```{r King County Last 14 Day Table, echo=FALSE, message = FALSE}
king_county_daily_cases <- tibble(Date = format(as.Date(tail(king_county$date,-1)),'%b %d'), `New Cases` = diff(king_county$cases), `New Deaths`=diff(king_county$deaths))
knitr::kable(king_county_daily_cases %>%
tail(14) %>% t(),
caption="King County 14 day History")
```
# Risk Factors
```{r King County Risk Factors, echo=FALSE, message = FALSE, warning=FALSE }
risk_data <- tibble(date = data$date,
new_cases = data$new_cases,
last_14_day_cases = NULL,
last_14_day_cases_per_100k_pop = NULL
)
for (i in 1:nrow(risk_data)) {
startdate = risk_data[i,'date']$date
enddate = risk_data[i,'date']$date -14
risk_data$last_14_day_cases[i] <- risk_data %>%
subset(date <= startdate & date >= enddate) %>%
select(new_cases) %>%
sum(na.rm=TRUE)
risk_data$last_14_day_cases_per_100k_pop[i] <- risk_data$last_14_day_cases[i] / 2252782 * 100000
if(risk_data$last_14_day_cases_per_100k_pop[i] >= 75) {
risk_data$risk[i] = "High"
} else if (cases_per_100K_14d >= 25 ){
risk_data$risk[i] = "Moderate"
} else {
risk_data$risk[i] = "Low"
}
}
ggplot(data = risk_data, mapping=aes(x=date,y=last_14_day_cases_per_100k_pop)) +
geom_area(fill="lightgray",aes()) +
geom_hline(yintercept=25,linetype="dashed",color="yellow")+
geom_hline(yintercept=75,linetype="dashed",color="red") +
labs(title="King County 14 Day Risk per 100k population",
subtitle="Using Washington state risk factors: Less than 25, low, 25 - 75 medium, greater than 75, high. Effective June 30, 2021, Washington state lifted their mask mandate and switched to the CDC Risk factors.",
caption="") +
xlab("") +
ylab("Cases per 100k population in last 14 days") +
scale_x_date(date_labels = "%b-%y",date_breaks = "2 month")
```
## County Level Risk
```{r Washington county map, echo=FALSE, message = FALSE }
outlist = list()
washington = subset(nytimes,state =='Washington')
#Remove unknown county
washington <- subset(washington,county != 'Unknown')
nytimes <- subset(nytimes,county != 'Unknown')
for (c in unique(washington$county)) {
county_info <-subset(washington,county == c & county != 'Unknown')
county_fips = unique(county_info$fips)
fooSTATE = as.numeric(substr(county_fips, 1, 2))
fooCOUNTY = as.numeric(substr(county_fips, nchar(county_fips)-3+1, nchar(county_fips)))
population = subset(county_population,STATE == fooSTATE & COUNTY == fooCOUNTY,select=c('POPESTIMATE2019'),na=0)$POPESTIMATE2019
first_case_reported <- min(county_info$date)
new_cases =data.frame(date = tail(county_info$date,-1), new_cases = diff(county_info$cases))
last_14d_cases <- sum(subset(new_cases,date >= Sys.Date()-14)$new_cases)
last_7d_cases <- sum(subset(new_cases,date >= Sys.Date()-7)$new_cases)
out <- data.frame(state = unique(county_info$state),
county = unique(county_info$county),
county_fips = county_fips,
region = unique(county_info$fips),
first_case_reported = first_case_reported,
last_14d_cases = last_14d_cases,
last_7d_cases = last_7d_cases,
population = coalesce(population,0),
last_14d_cases_per_100k = coalesce(last_14d_cases / population *100000,0) ,
last_7d_cases_per_100k = coalesce(last_7d_cases / population *100000,0))
outlist[[unique(county_info$fips)]] <- out
}
metrics = do.call(rbind,outlist)
library(choroplethr)
metrics$value = metrics$last_7d_cases_per_100k
choroplethr::county_choropleth(metrics,title="Last 7 Days Cases per 100k population",num_colors = 4,state_zoom = 'washington')+ scale_fill_brewer(palette=7)
```
```{r Washington Last 14 Day Table, echo=FALSE, message = FALSE}
last_14_day_cases <- nytimes %>%
filter( date >= Sys.Date() - 15) %>%
group_by(state, county, fips) %>%
summarise(last_14_days_cases = sum(diff(cases)))
last_14_day_cases = left_join(last_14_day_cases,county_population,by=c("state"="STNAME","county"= "CTYNAME"))
last_14_day_cases = subset(last_14_day_cases, select = c("state","county","fips","last_14_days_cases","POPESTIMATE2019"))
last_14_day_cases$cases_per_100K_14d = last_14_day_cases$last_14_days_cases / last_14_day_cases$POPESTIMATE2019 * 100000
knitr::kable(subset(last_14_day_cases,state=='Washington',select=c('county','last_14_days_cases')),caption="Cases by County, Last 14 Days")
```
### County New Case History
```{r County New Case History, echo=FALSE, fig.align="center", fig.height=6, fig.width=10, message=FALSE, warning=FALSE, results=FALSE}
library(hrbrthemes)
#Remove unknown county
washington <- subset(washington,county != 'Unknown')
nytimes <- subset(nytimes,county != 'Unknown')
county_data = tibble()
for (c in unique(washington$county)) {
county_info <-subset(washington,county == c & county != 'Unknown')
county_fips = unique(county_info$fips)
county_data <- bind_rows(county_data,tibble(state ='Washington',
county = c,
date = tail(county_info$date,-1),
new_cases = diff(county_info$cases),
new_deaths = diff(county_info$deaths) )
)
}
county_data$date <- as.Date(county_data$date)
county_data %>%
# filter(date >='2021-01-01') %>%
ggplot(aes(x=date,y=new_cases))+
geom_line(group=1) +
facet_wrap(~county,scales = "free") +
labs(x="", y="",
title="New Cases by county") +
theme_ipsum()
```