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app.R
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app.R
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suppressMessages({
library(shiny)
library(ggplot2)
library(dplyr)
library(tidyr)
library(stringr)
library(readr)
library(ggrepel)
library(RcppRoll)
library(countrycode)
})
max_na <- function(Count) {
if(all(is.na(Count))) {
NA
} else {
max(Count, na.rm = TRUE)
}
}
double_cases <- function(Confirmed0, doubling, actual, hidden, hospitalizing,
bedmax) {
case0 <- Confirmed0 * hidden
tibble(
dates = seq(as.Date("2020-03-16"), as.Date("2020-06-01"), by="days")) %>%
mutate(days = seq_along(dates) - 1,
Actual = case0 * 2 ^ (days / doubling),
Confirmed = Actual / hidden,
Hospitalized = Actual * hospitalizing / 100,
Max_Beds = bedmax) %>%
pivot_longer(Actual:Max_Beds,
names_to = "Cases",
values_to = "Count")
}
real_cases_jhu <- function() {
# These data were modified 3/22 to only have one line per unit and changed names.
# Province/State is no longer meaningful for USA.
dirpath <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series"
bind_rows(
Confirmed = read_csv(
file.path(dirpath, "time_series_covid19_confirmed_global.csv"),
col_types = cols()),
Death = read_csv(
file.path(dirpath, "time_series_covid19_deaths_global.csv"),
col_types = cols()),
Recovered = read_csv(
file.path(dirpath, "time_series_covid19_recovered_global.csv"),
col_types = cols()),
.id = "Type") %>%
rename(State = "Province/State",
Country = "Country/Region") %>%
pivot_longer(-(Type:Long), names_to = "Date", values_to = "Count") %>%
mutate(Date = as.POSIXct(Date, format="%m/%d/%y")) %>%
mutate(County = "All",
County = ifelse(str_detect(State, ","), str_remove(State, ",.*$"), County),
State = ifelse(str_detect(State, ","), str_remove(State, "^.*, "), State),
State = ifelse(State %in% state.name,
state.abb[match(State, state.name)],
State),
County = paste0(County, ", ", State),
Country = ifelse(Country == "US", "USA", Country),
Country = ifelse(str_detect(Country, "Taiwan"), "Taiwan", Country)) %>%
group_by(Type, County, State, Country, Date) %>%
summarize(Count = sum(Count, na.rm = TRUE)) %>%
ungroup %>%
# Remove any date for Country with 0 confirmed.
pivot_wider(names_from = Type, values_from = Count) %>%
filter(Confirmed > 0) %>%
pivot_longer(Confirmed:Recovered, names_to = "Type", values_to = "Count") %>%
filter(!is.na(Count)) %>%
weight_date()
}
read_local <- function(filename, dirpath) {
filepath <- file.path(dirpath, filename)
if(interactive()) {
localpath <- file.path("data", filename)
if(getfile <- file.exists(localpath)) {
# Is file recent?
getfile <- abs(as.numeric(as.POSIXct(Sys.Date()) -
as.POSIXct(file.info(localpath)$mtime))) < 24
}
if(!getfile) {
out <- read.csv(filepath, stringsAsFactors = FALSE)
write.csv(out, localpath, row.names = FALSE)
}
filepath <- localpath
}
filepath
}
real_cases_cds <- function() {
# data downloaded from "https://listaging-reportsbucket-1bjqfmfwopcdd.s3-us-west-1.amazonaws.com/beta/latest/timeseries-tidy.csv"
# takes too long in the shiny app to download the data each time (34 seconds). Need to
# manually update the file in the application for this one to work
#
# Note that around 5 April the timeseries format changed with more columns
# and update of tidy version seems to have broken down.
# See added line below to "tidy up"
filepath <- read_local("timeseries.csv",
dirpath = "https://coronadatascraper.com")
# Following is future data source. Still in transition.
# dirpath = "https://listaging-reportsbucket-1bjqfmfwopcdd.s3-us-west-1.amazonaws.com/beta/latest")
# Probably need to rethink this, as there are records for whole country,
# whole state and county, and they may not add up.
# Also population is separate for each of these.
read.csv(filepath, stringsAsFactors = FALSE) %>%
# filter(city == "") %>% # remove any city level data
mutate(country = ifelse(country == "United States", "USA", country),
country = ifelse(country == "iso1:US", "USA", country),
country = ifelse(country == "Metropolitan France", "France", country)) %>%
pivot_longer(cases:tested, names_to = "type", values_to = "value") %>% # tidy up
filter(type %in% c("cases", "deaths", "recovered")) %>% # also have active, growthFactor for some
select(type, level, county, state, country, date, value, population) %>%
mutate(date = as.POSIXct(as.character(date), format="%Y-%m-%d")) %>%
rename(
Type = type,
County = county,
State = state,
Country = country,
Date = date,
Population = population,
Count = value
) %>%
mutate(Count = as.numeric(Count),
Type = case_when(
Type == "cases" ~ "Confirmed",
Type == "deaths" ~ "Death",
Type == "recovered" ~ "Recovered",
TRUE ~ NA_character_
)) %>%
as_tibble() %>%
# Fix USA state names.
mutate(State = str_remove(State, "^iso2:US-"),
State = ifelse(State %in% state.name,
state.abb[match(State, state.name)],
State)) %>%
weight_date()
}
real_cases_county_cds <- function(cases_cds) {
cases_cds %>%
filter(County != "",
level == "county") %>%
filter(!str_detect(County, "^,"),
!str_detect(County, "unassign")) %>%
mutate(County = str_remove(County, " County"),
County = paste0(County, ", ", State)) %>%
# mutate(County = paste0(County, ", ", State),
# County = ifelse(State %in% state.abb,
# str_replace(County,
# state.name[match(State, state.abb)],
# state.abb[match(State, state.abb)]),
# County)) %>%
select(-level)
}
real_cases_state_cds <- function(cases_cds) {
cases_cds %>%
filter(County == "",
State != "",
level == "state") %>%
select(-level, -County)
}
real_cases_country_cds <- function(cases_cds) {
cases_cds %>%
filter(State == "",
County == "",
level == "country") %>%
select(-level, -County, -State)
}
weight_date <- function(cases_state, skew = 0, days = 14) {
# Add weights skewed toward most recent dates.
# skew <- 20
sweight <- as.numeric(unique(cases_state$Date))
if(days > 0) {
pos <- (max(sweight) - sweight) < days * 86400
}
mweight <- min(sweight[pos])
eweight <- pmax(0, as.numeric(sweight - mweight))
sweight <- max(eweight)
eweight <- max(exp(skew * eweight / sweight))
# Add weights to states
cases_state %>%
mutate(Weight = pmax(0, as.numeric(Date - mweight) / sweight)) %>%
mutate(Weight = (Weight > 0) * exp(skew * Weight) / eweight)
}
real_cases_state_jhu <- function(cases_state) {
# Sum within Countrys
cases_state %>%
group_by(Type, Country, State, Date, Weight) %>%
summarize(Count = sum(Count, na.rm = TRUE)) %>%
ungroup
}
real_cases_country_jhu <- function(cases_state) {
# Sum within Countrys
cases_state %>%
group_by(Type, Country, Date, Weight) %>%
summarize(Count = sum(Count, na.rm = TRUE)) %>%
ungroup
}
dis_region <- function(cases) {
cases %>%
mutate(
Region = ifelse(is.na(Region) & Continent == "Africa",
"Saharan Africa", Region),
Region = ifelse(is.na(Continent),
"Europe Middle East", Region),
Continent = ifelse(is.na(Continent),
"Europe", Continent),
Region = ifelse(Continent == "Americas" & Region == "Europe & Central Asia",
"Europe", Region),
Region = ifelse(Continent == "Europe" & Region == "Europe & Central Asia",
"Europe", Region),
Region = ifelse(Continent == "Asia" & Region == "Europe & Central Asia",
"Central Asia", Region),
Region = ifelse(Continent == "Africa" & Region == "Middle East & North Africa",
"North Africa", Region),
Region = ifelse(Continent == "Europe" & Region == "Middle East & North Africa",
"Europe Middle East", Region),
Region = ifelse(Continent == "Asia" & Region == "Middle East & North Africa",
"Asia Middle East", Region),
Region = ifelse(Continent == "Oceania" & Region == "East Asia & Pacific",
"Pacific", Region),
Region = ifelse(Continent == "Asia" & Region == "East Asia & Pacific",
"East Asia", Region))
}
# JHU Data. Only has State for a few countries.
cases_state <- real_cases_state_jhu(real_cases_jhu())
cases <- real_cases_country_jhu(cases_state) %>%
mutate(Continent =
countrycode(Country,
origin = "country.name",
destination = "continent",
warn = FALSE),
Region =
countrycode(Country,
origin = "country.name",
destination = "region",
warn = FALSE))
cases <- dis_region(cases)
# 5 continents
# 20 regions if used as destination
continents <- distinct(cases, Continent, Region, Country) %>%
filter(!is.na(Continent)) %>%
arrange(Continent, Region) %>%
mutate(Country.Code = countrycode(Country,
origin = "country.name",
destination = "iso3c",
warn = FALSE))
# This does not have population for VAT, ERI, ESH or TWN
population <- read.csv(
"https://raw.githubusercontent.com/datasets/population/master/data/population.csv",
stringsAsFactors = FALSE) %>%
filter(Year == 2018)
continents$Population <- NA
m <- match(continents$Country.Code, population$Country.Code, nomatch = 0)
continents$Population[m > 0] <- population$Value[m]
continents <- continents %>%
mutate(Population = ifelse(Country.Code == "TWN", 23.78 * 1e6, Population),
Population = ifelse(Country.Code == "VAT", 825, Population),
Population = ifelse(Country.Code == "ERI", 3.214 * 1e6, Population),
Population = ifelse(Country.Code == "ESH", 500000, Population))
cases <- cases %>%
mutate(Population = continents$Population[match(Country, continents$Country)])
cases_region <- cases %>%
filter(!is.na(Continent)) %>%
group_by(Type, Continent, Region, Date, Weight) %>%
summarize(Count = sum(Count),
Population = sum(Population)) %>%
ungroup
cases_continent <- cases_region %>%
filter(!is.na(Continent)) %>%
group_by(Type, Continent, Date, Weight) %>%
summarize(Count = sum(Count),
Population = sum(Population)) %>%
ungroup
# CovidAtlas Data. Using this for State and County
# Need to sort out County, State and Country better
cases_state <- real_cases_cds() %>%
mutate(Continent =
countrycode(Country,
origin = "country.name",
destination = "continent",
warn = FALSE),
Region =
countrycode(Country,
origin = "country.name",
destination = "region",
warn = FALSE))
cases_state <- dis_region(cases_state)
# For now, use CDS for state and county
cases_county <- real_cases_county_cds(cases_state)
cases_state <- real_cases_state_cds(cases_state)
counties <- sort(unique(cases_county$County))
countries <- sort(unique(cases$Country))
countries_state <- sort(unique(cases_state$Country))
countries_county <- sort(unique(cases_county$Country))
continent_names <- sort(unique(cases_state$Continent))
region_names <- sort(unique(cases_state$Region))
# Testing in USA
read_testing <- function(filename, by_state = FALSE,
dirpath = "https://covidtracking.com/api") {
filepath <- read_local(filename, dirpath)
out <- read_csv(filepath, col_types = cols()) %>%
mutate(date = as.Date(as.character(date), "%Y%m%d"))
if(by_state) {
out <- out %>%
select(date, state, positive:pending, recovered, death, hospitalized, total)
} else {
out <- out %>%
select(date, positive:pending, recovered, death, hospitalized, total)
}
out %>%
pivot_longer(positive:total, names_to = "status", values_to = "count") %>%
filter(status != "death")
}
test_us <- read_testing("us/daily.csv")
test_st <- read_testing("states/daily.csv", TRUE)
revlog <- scales::trans_new("revlog",
function(x) -log10(x),
function(x) 10^(-x),
scales::log_breaks(base=10),
domain = c(1e-100, Inf))
##########################################################################33
# Define UI for miles per gallon app ----
ui <- fluidPage(
# App title ----
titlePanel("Pandemic Cases"),
# Sidebar panel for inputs ----
tabsetPanel(
tabPanel("Reported Cases",
sidebarLayout(
sidebarPanel(
h4("Select data to view from the options below."),
radioButtons("states", "", c("Counties", "States","Countries","Regions","Continents"), inline = TRUE, selected = "States"),
conditionalPanel(
condition = 'input.states == "Countries" || input.states == "Regions"',
selectInput("continent", "Continents:",
continent_names, "",
multiple = TRUE),
uiOutput("region_cont")
),
conditionalPanel(
condition = 'input.states == "Countries"',
uiOutput("country_cont")
),
conditionalPanel(
condition = 'input.states == "States"',
selectInput("country_state", "Countries:",
countries_state,
c("USA"),
multiple = TRUE)
),
conditionalPanel(
condition = 'input.states == "Counties"',
selectInput("country_county", "Countries:",
countries_county,
c("USA"),
multiple = TRUE)
),
conditionalPanel(
condition = 'input.states == "Counties" || input.states == "States"',
uiOutput("states_country")
),
conditionalPanel(
condition = 'input.states == "Counties"',
uiOutput("counties_states")
),
selectInput("casetypes", "Case Type:", c("Confirmed","Death","Recovered")),
selectInput("realscale", "Plot Scale:", c("raw","geometric","new_cases")),
checkboxInput("predict", "Add predict lines?", FALSE),
checkboxInput("rate", "Rate per 100,000?", FALSE),
conditionalPanel(
condition = 'input.states != "Continents"',
uiOutput("showallui")
),
checkboxInput("recent", "Focus on recent days?", FALSE),
hr(),
textOutput("latest"),
uiOutput("newhost")
),
mainPanel(
plotOutput(outputId = "case_plot"),
tableOutput("fitcase"),
uiOutput("onep3"),
conditionalPanel(
condition = 'input.realscale == "new_cases"',
textOutput("newcases")),
conditionalPanel(
condition = 'input.states != "Continents"',
uiOutput("topcasestxt"),
tableOutput("topcases"))
)
)
),
tabPanel("Testing",
plotOutput("testplot"),
fluidRow(
column(3,
radioButtons("testgroup", "", c("States","USA"), inline = TRUE),
conditionalPanel(
condition = 'input.testgroup == "States"',
selectInput("teststate", "State:",
state.abb,
c("WI","MI","IL","IA"),
multiple = TRUE)
),
selectInput("testscale", "Plot Scale:", c("raw","geometric")),
textOutput("testinfo")),
column(9,
tableOutput("testlast"))
)
),
tabPanel("Simulations",
sidebarLayout(
sidebarPanel(
# Input: Slider for initial settings and rates
sliderInput("Confirmed0", "Confirmed:", 0, 100, 10),
sliderInput("doubling", "Days to Double:", 0, 100, 6, 1),
sliderInput("hospitalizing", "Percent Hospitalized:", 0, 100, 10),
sliderInput("hidden", "Hidden per Confirmed:", 1, 10, 4),
sliderInput("bedmax", "Maximum Hospital Beds:", 0, 5000, 1000),
selectInput("scale", "Plot Scale:", c("raw","geometric"))
),
mainPanel(
plotOutput(outputId = "main_plot"),
uiOutput("simpenn"),
uiOutput("simharv"),
textOutput("space5"),
textOutput(outputId = "extra_text")
)
)
),
tabPanel("References",
textOutput("summary"),
# Reported Cases
textOutput("space1"),
textOutput("summary2"),
textOutput("jhudata"),
uiOutput("jhusource"),
uiOutput("cdsdata"),
# Testing
textOutput("space2"),
textOutput("summary3"),
uiOutput("testsource"),
textOutput("summary4"),
# Simulations
textOutput("space"),
textOutput("summary1"),
textOutput("main_text"),
uiOutput("url"),
uiOutput("pennmed"),
uiOutput("harvard"),
textOutput("agents"),
textOutput("space3"),
uiOutput("pointacres"),
textOutput("space4"),
uiOutput("sourceurl"),
uiOutput("epilist"),
uiOutput("dslist")
)
)
)
# Define server logic to plot various variables against mpg ----
server <- function(input, output) {
regions_reactive <- reactive({
sregions <- region_names
if(length(input$region)) {
sregions <- sort(input$region)
}
if(length(input$continent)) {
sregions <- sort(unique((continents %>%
filter(Continent %in% input$continent,
Region %in% sregions))$Region))
}
sregions
})
output$region_cont <- renderUI({
if(!isTruthy(selected <- input$region)) {
selected <- NULL
}
sregions <- region_names
if(length(input$continent)) {
sregions <- sort(unique((continents %>%
filter(Continent %in% input$continent))$Region))
}
choices <- sort(unique(c(selected, sregions)))
selectInput("region", "Regions:",
choices, selected,
multiple = TRUE)
})
output$country_cont <- renderUI({
if(!isTruthy(selected <- input$country)) {
selected <- c("USA", "France", "Iran", "Italy", "Spain")
}
choices <- sort(unique(c(selected, countries_reactive())))
selectInput("country", "Countries:",
choices,
selected,
multiple = TRUE)
})
output$main_text <- renderText("Simulation based on article by Liz Specht STAT (10 March 2020)")
url <- a("Simple Math, Alarming Answers",
href="https://www.statnews.com/2020/03/10/simple-math-alarming-answers-covid-19/")
output$url <- renderUI({
tagList("Article URL:", url)
})
sourceurl <- a("github.com/UW-Madison-DataScience/pandemic",
href="https://github.com/UW-Madison-DataScience/pandemic")
output$sourceurl <- renderUI({
tagList("Source URL:", sourceurl)
})
output$showallui <- renderUI({
units <- paste0("Show all ", req(input$states), "?")
checkboxInput("showall", units, FALSE)
})
output$main_plot <- renderPlot({
dat <- double_cases(input$Confirmed0,
input$doubling,
input$actual,
input$hidden,
input$hospitalizing,
input$bedmax)
p <- suppressWarnings(ggplot(dat) +
aes(dates, Count / 1000, group = Cases, col = Cases) +
geom_line(size = 2) +
ggtitle("Thousands of Cases"))
if(input$scale == "geometric") {
p <- p +
scale_y_log10()
} else {
p <- p +
ylim(0, 5 * input$bedmax / 1000)
}
suppressWarnings(p)
})
output$extra_text <- renderText(
"Consider a community with ~500K people and ~2K beds. Suppose on 16 March 2020, there were 10 confirmed cases, which might represent 40 actual cases.
Consider a doubling every 6 days. Say 10% need hospitalization, which is only 4 at that time. By 1 April, that could jump to 60 confirmed cases, 250 actual cases, and 25 needing hospital beds. Doesn’t seem so bad, but the next jump is huge.
By 1 May, there would be over 2000 confirmed cases of 8000+ actual cases, with 800+ needing hospitalization.
Still manageable. However, by 1 June, there would be 70,000+ confirmed cases, ~300K actual cases (over half of the community),
and need for almost 30K beds. Put another way, by the first week of May, community would need 1-2K beds.
Slowing the days to double by social distancing buys time. Adjust sliders to view changes."
)
# Plot reported cases.
output$case_plot <- renderPlot({
req(input$states, input$casetypes)
if(isTruthy(input$showall)) {
dat <- allcases_reactive() %>%
filter(Type == input$casetypes)
} else {
dat <- cases_reactive() %>%
filter(Type == input$casetypes)
}
if(isTruthy(input$rate)) {
dat <- dat %>%
mutate(Count = Count * 100000 / Population)
}
if(isTruthy(input$recent)){
dat <- dat %>%
mutate(Date = 1 + (as.numeric(max(Date) - Date) / 86400))
}
if(req(input$realscale) == "new_cases") {
tmpfn <- function(Count) {
dc <- diff(Count)
c(rep(first(dc), 6),
roll_mean(c(first(Count), dc), 7))
}
switch(input$states,
States = {
dat <- dat %>%
group_by(State) %>%
mutate(Count = tmpfn(Count)) %>%
ungroup
},
Continents = {
dat <- dat %>%
group_by(Continent) %>%
mutate(Count = tmpfn(Count)) %>%
ungroup
},
Regions = {
dat <- dat %>%
group_by(Region) %>%
mutate(Count = tmpfn(Count)) %>%
ungroup
},
Countries = {
dat <- dat %>%
group_by(Country) %>%
mutate(Count = tmpfn(Count)) %>%
ungroup
},
Counties = {
dat <- dat %>%
group_by(County) %>%
mutate(Count = tmpfn(Count)) %>%
ungroup
})
}
# Color-blind friendly palette with grey:
cbPalette <- c("#DDDDDD",
rep_len(c("#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7"),
length(units_reactive())))
if(!isTruthy(input$showall))
cbPalette <- cbPalette[-1]
p <- ggplot(dat) +
scale_colour_manual(values=cbPalette)
p <- suppressWarnings(p +
aes(Date, Count) +
geom_line(size = 1.5) +
ggtitle(paste(input$casetypes, "cases")))
if(isTruthy(input$rate)) {
p <- p +
ylab("Rate per 100,000")
}
if(isTruthy(input$recent)) {
p <- p +
scale_x_continuous(trans = revlog) +
xlab("Recent Days")
latest_date <- 10
} else {
latest_date <- max(dat$Date)
}
if(length(units_reactive()) > 1 | isTruthy(input$showall)) {
switch(input$states,
States = {
p <- p +
aes(col = colgp, z = State) +
ggrepel::geom_label_repel(aes(label = colgp), color = "black",
data = . %>% filter(Date == latest_date)) +
theme(legend.position = "none")
},
Continents = {
p <- p +
aes(col = colgp, z = Continent) +
ggrepel::geom_label_repel(aes(label = colgp), color = "black",
data = . %>% filter(Date == latest_date)) +
theme(legend.position = "none")
},
Regions = {
p <- p +
aes(col = colgp, z = Region) +
ggrepel::geom_label_repel(aes(label = colgp), color = "black",
data = . %>% filter(Date == latest_date)) +
theme(legend.position = "none")
},
Countries = {
p <- p +
aes(col = colgp, z = Country) +
ggrepel::geom_label_repel(aes(label = colgp), color = "black",
data = . %>% filter(Date == latest_date)) +
theme(legend.position = "none")
},
Counties = {
p <- p +
aes(col = colgp, z = County) +
ggrepel::geom_label_repel(aes(label = colgp), color = "black",
data = . %>% filter(Date == latest_date)) +
theme(legend.position = "none")
})
} else {
p <- p +
aes(col = "#E69F00") +
theme(legend.position = "none")
}
if(input$realscale == "geometric") {
p <- suppressWarnings(p +
scale_y_log10())
} else {
if(isTruthy(input$predict)) {
# Set vertical range by data.
p <- p +
ylim(min(dat$Count), max(dat$Count))
if(input$states == "States") {
p <- suppressWarnings(p +
geom_smooth(method="glm", mapping = aes(weight = Weight), se = FALSE,
formula = as.integer(round(y)) ~ x,
method.args = list(family = "poisson"),
linetype = "dashed"))
} else {
p <- suppressWarnings(p +
geom_smooth(method="glm", mapping = aes(weight = Weight), se = FALSE,
formula = as.integer(round(y)) ~ x,
method.args = list(family = "poisson"),
linetype = "dashed"))
}
}
}
suppressWarnings(p)
})
units_reactive <- reactive({
switch(
req(input$states),
Counties = {
sort(req(input$county))
},
States = {
sort(req(input$state))
},
Countries = {
sort(req(input$country))
},
Regions = {
if(!isTruthy(regions <- input$region)) {
regions <- regions_reactive()
}
sort(regions)
},
Continents = {
sort(continent_names)
})
})
allcases_reactive <- reactive({
units <- req(units_reactive())
switch(
req(input$states),
Counties = {
cases_county %>%
filter(State %in% input$state) %>%
mutate(colgp = ifelse(County %in% units,
County,
""))
},
States = {
cases_state %>%
filter(Country %in% countries_reactive()) %>%
mutate(colgp = ifelse(State %in% units,
State,
""))
},
Continents = {
cases_continent %>%
mutate(colgp = Continent)
},
Regions = {
cases_region %>%
mutate(colgp = ifelse(Region %in% units,
Region,
""))
},
Countries = {
continents_in <- continent_names
if(length(input$continent)) {
continents_in <- input$continent
}
cases %>%
filter(Continent %in% continents_in) %>%
mutate(colgp = ifelse(Country %in% units,
Country,
""))
})
})
cases_reactive <- reactive({
units <- req(units_reactive())
switch(
req(input$states),
Counties = {
cases_county %>%
filter(County %in% units,
State %in% input$state) %>%
mutate(colgp = County)
},
States = {
cases_state %>%
filter(Country %in% countries_reactive(),
State %in% units) %>%
mutate(colgp = State)
},
Continents = {
cases_continent %>%
mutate(colgp = Continent)
},
Regions = {
cases_region %>%
filter(Region %in% units) %>%
mutate(colgp = Region)
},
Countries = {
cases %>%
filter(Country %in% units) %>%
mutate(colgp = Country)
})
})
countries_reactive <- reactive({
switch(
req(input$states),
Countries =,
Regions = {
sort((continents %>%
filter(Region %in% regions_reactive()))$Country)
},
Counties = {
sort(req(input$country_county))
},
States = {
sort(req(input$country_state))
})
})
# States in a Country
output$states_country <- renderUI({
states_country <-
sort(
unique((
cases_state %>%
filter(Country %in% countries_reactive()))$State))
if(!isTruthy(selected <- input$state)) {
selected <- c("WI","MI","IL","IA")
}
states_country <- sort(unique(c(selected, states_country)))
selectInput("state", "States:",
states_country,
selected,
multiple = TRUE)
})
# Counties in a State
output$counties_states <- renderUI({
req(input$state)
counties_states <-
sort(
unique((
cases_county %>%
filter(Country %in% countries_reactive(),
State %in% input$state))$County))
if(!isTruthy(selected <- input$county)) {
selected <- c("Dane, WI","Milwaukee, WI", "Waukesha, WI", "Brown, WI", "Racine, WI",
"Walworth, WI", "Kenosha, WI")
}
selectInput("county", "Counties:",
counties_states,
selected,
multiple = TRUE)
})
output$latest <- renderText({
paste0("Current to ",
as.character(max(cases$Date)),
" (Country), ",
as.character(max(cases_state$Date)),
" (State & County).")})
newhost <- a("data-viz.it.wisc.edu/pandemic",
href="https://data-viz.it.wisc.edu/pandemic/")
output$newhost <- renderUI({
tagList("New URL:", newhost)
})
# Fit line for reported cases.
output$fitcase <- renderTable({
req(input$states, input$casetypes)
# Get estimate doubling time
if(length(units_reactive() > 1)) {
req(units_reactive())
if(length(units_reactive()) == 1) {
form <- formula(Count ~ Date)
} else {
switch(
req(input$states),
Counties = {
form <- formula(Count ~ Date * County)
},
States = {
form <- formula(Count ~ Date * State)
},
Continents = {
form <- formula(Count ~ Date * Continent)
},
Regions = {
form <- formula(Count ~ Date * Region)
},
Countries = {
form <- formula(Count ~ Date * Country)
})
}
fit <- glm(form, cases_reactive() %>% mutate(Count = as.integer(round(Count))),
weight = Weight, family = "poisson")
coefs <- coef(fit)
coefs <- coefs[str_detect(names(coefs), "Date")]
coefs[-1] <- coefs[-1] + coefs[1]
names(coefs) <- units_reactive()
} else {
form <- formula(Count ~ Date)
fit <- glm(form, cases_reactive() %>% mutate(Count = as.integer(round(Count))),
weight = Weight, family = "poisson")
coefs <- coef(fit)
coefs <- coefs[str_detect(names(coefs), "Date")]
names(coefs) <- units_reactive()
}
# Time cannot be negative, but could be really small.
doubling <- round(log(2) / pmax(0, coefs) / 86400, 1)
dat <- cases_reactive()
if(isTruthy(input$rate)){
dat <- dat %>%
mutate(Count = Count * 100000 / Population)
}
# Get data from last date.
out <- switch(input$states,
States = {
dat %>%
group_by(Type, State) %>%
summarize(Count = max_na(Count)) %>%
ungroup %>%
select(Type, State, Count)
},
Countries = {
dat %>%
group_by(Type, Country) %>%
summarize(Count = max_na(Count)) %>%
ungroup %>%
select(Type, Country, Count)
},
Counties = {
dat %>%
group_by(Type, State, County) %>%
summarize(Count = max_na(Count)) %>%
ungroup
},
Regions = {
dat %>%
group_by(Type, Region) %>%
summarize(Count = max_na(Count)) %>%
ungroup
},
Continents = {
dat %>%
# filter(Type == input$casetypes)
group_by(Type, Continent) %>%
summarize(Count = max_na(Count)) %>%
ungroup
})
out %>%
mutate(Count = suppressWarnings(as.integer(round(Count)))) %>%