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app.R
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app.R
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#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
# R Packages --------------------------------------------------------------
library(shinydashboard)
library(vroom)
library(shiny)
library(shinyLP)
library(readxl)
library(gt)
library(reactable)
library(metathis)
library(highcharter)
library(tidyverse)
library(magrittr)
library(leaflet)
library(leaflet.extras)
library(lubridate)
library(janitor)
library(waiter)
library(sever)
library(sf)
library(zoo)
library(scales)
library(shinyalert)
# Helper Functions --------------------------------------------------------
blank <- "https://api.mapbox.com/styles/v1/datatx2036/ckc2gkem302hd1io7aaw54a09/tiles/256/{z}/{x}/{y}?access_token=pk.eyJ1IjoiZGF0YXR4MjAzNiIsImEiOiJja2J4cmJkMWUwYWh1MnNwamQ1a3NxMDlnIn0.pz06mEDLOJOhh4EyGar6Lg"
map_attr <- "<a href='https://www.mapbox.com/map-feedback/'>© MAPBOX</a> | <a href='http://texas2036.org'> MAP © TEXAS 2036</a>"
thumbnail_label <- function (title, label, content, button_link, button_label) {
div(class = "row", div(class = "col-sm-14 col-md-12",
div(class = "thumbnail",
HTML(title),
h3(label),
p(content),
actionButton(inputId=button_link,
label=button_label)
)))
}
tx2036_hc <- hc_theme_merge(
hc_theme_smpl(),
hc_theme(chart = list(backgroundColor = "#3A4A9F",
style = list(fontFamily = "Montserrat", fontSize = "28px",
color="#fff",fontWeight="500", textTransform="uppercase")),
title = list(style = list(fontFamily = "Montserrat",
fontWeight = "bold",
color="white"),
align = "left"),
subtitle = list(style = list(fontFamily = "Montserrat",
color="#fff",
textTransform="initial",
fontWeight="400",
fontSize = "14px"),
align = "left"),
legend = list(align = "right",
style = list(fontFamily = "Montserrat", color="white"),
itemStyle = list(fontFamily = 'Montserrat', color = 'white'),
itemHoverStyle = list(color = 'gray'),
verticalAlign = "top"),
credits = list(style = list(color = "#fff")),
xAxis = list(labels =list(style = list(fontFamily = "Montserrat", color="#fff")),
title = list(style = list(color = "#fff", fontSize = "12px",
color="#fff",fontWeight="500")),
gridLineWidth = 0,
gridLineColor = "#F3F3F3",
lineColor = 'rgba(255,255,255,0.7)',
minorGridLineColor = 'rgba(243,243,243,0.7)',
tickColor = "#F3F3F3",
tickWidth = 1),
yAxis = list(labels =list(style = list(fontFamily = "Montserrat", color="#fff")),
title = list(style = list(color = "#fff", fontSize = "12px",
color="#fff",fontWeight="500")),
gridLineWidth = .5,
gridLineColor = 'rgba(243,243,243,0.15)',
lineColor = 'rgba(255,255,255,0.15)',
minorGridLineColor = 'rgba(243,243,243,0.15)',
tickColor = "#F3F3F3",
tickWidth = 2)))
sever_default <- function (title = "Whoops!", subtitle = "You have been disconnected",
button = "Reload", button_class = "default")
{
tagList(tags$a(href='http://www.texas2036.org',
HTML('<svg viewBox="0 0 227.4 83.5" style="height:7vh;padding-bottom:1.1vh;margin-top:7px"><path fill="#fff" d="M192.5 66.2c2.2 0 3.9.6 3.9 2.6v4.1c0 2-1.7 3.6-3.9 3.6-2.1 0-3.8-1.6-3.8-3.6v-5.1h-7.8v5.1c0 5.9 5.2 10.6 11.6 10.6 6.4 0 11.5-4.6 11.7-10.4.6 5.4 5.6 10.4 11.6 10.4 6.4 0 11.6-4.8 11.6-10.6v-7.4c0-5.8-5.2-10.6-11.6-10.6-1.4 0-2.7.2-3.9.6v-4.1c0-1.9 1.8-3.5 3.9-3.5 2.1 0 3.8 1.6 3.8 3.5v2.2h7.8v-2.2c0-4-2.5-7.5-6.1-9.3 3.6-1.8 6.1-5.3 6.1-9.3V10.5c0-5.8-5.2-10.5-11.6-10.5-6.1 0-11.1 4.3-11.6 9.8-.4-5.5-5.5-9.8-11.7-9.8-6.4 0-11.6 4.7-11.6 10.6v2.6h7.8v-2.6c0-1.9 1.7-3.5 3.8-3.5 2.2 0 3.9 1.6 3.9 3.5v.8l-.1.1-13 15.6c-2.3 2.8-2.4 3-2.4 5.9v10.5h4.1c-2.5 1.9-4.1 4.8-4.1 8v2.2h7.8v-2.2c0-1.9 1.7-3.5 3.8-3.5 2.2 0 3.9 1.6 3.9 3.5v4.1c0 2-1.7 3.6-3.9 3.6h-2.4v7.1h2.4zm19.4-55.6c0-1.9 1.7-3.5 3.8-3.5 2.1 0 3.8 1.6 3.8 3.5v20.7c0 2-1.7 3.6-3.8 3.6-2.1 0-3.8-1.6-3.8-3.6V10.6zm-7.8 57c-.3-1.9-1.3-3.3-2.9-5 1.6-1.6 2.6-3.7 2.9-5.9v10.9zm-15.4-32.8v-2.6l13.1-15.8c1.6-1.9 2.2-2.6 2.3-3.8v20.3c0 .5 0 .9.1 1.3l2.1 6.4h6.8l-5.5 4 2.1 6.5-5.5-4-5.5 4 2.1-6.5-5.5-4h6.8l1.9-5.9h-15.3zm30.9 38.1c0 2-1.7 3.6-3.8 3.6-2.2 0-3.9-1.6-3.9-3.6v-7.4c0-1.9 1.8-3.5 3.9-3.5 2.1 0 3.8 1.6 3.8 3.5v7.4zM8.4 82.7V8H0V0h24.8v8h-8.4v74.8h-8zm45.4 0H33V0h20.8v8H41v29.5h12.8v8H41v29.4h12.8v7.8zm70.2 0V45.3h-12.8v37.4h-8V14.4c0-8 6.5-14.4 14.4-14.4 7.8 0 14.3 6.5 14.3 14.4v68.3H124zm0-68.3c0-3.6-2.9-6.5-6.3-6.5-3.6 0-6.5 2.9-6.5 6.5v22.9H124V14.4zm37.6 6.1v-6.2c0-3.5-2.9-6.3-6.3-6.3-3.5 0-6.3 2.9-6.3 6.3v6.3c0 1.5 0 1.5.4 2.1l17.9 31.6c2.4 4.2 2.4 4.2 2.4 7.8v6.2c0 8-6.5 14.3-14.3 14.3S141 76.4 141 68.4v-6.2h8v6.2c0 3.6 2.9 6.5 6.3 6.5 3.5 0 6.3-2.9 6.3-6.5v-6.2c0-1.4 0-1.4-.4-2l-17.9-31.6c-2.4-4.2-2.4-4.2-2.4-8v-6.3C141 6.5 147.5 0 155.3 0s14.3 6.5 14.3 14.3v6.2h-8zM95.9 0h-8.2l-9.2 28.7L69.3 0h-8.2l13.3 41.6L61.1 83h8.3l9.1-28.5L87.6 83h8.3L82.6 41.6z"></path><svg>'),
tags$title('Texas COVID-19 Resource Kit')),
tags$h1(title), tags$p(subtitle), reload_button(button, class = button_class))
}
bar_chart <- function(label, width = "100%", height = "14px", fill = "#00bfc4", background = NULL) {
bar <- div(style = list(background = fill, width = width, height = height))
chart <- div(style = list(flexGrow = 1, marginLeft = "6px", background = background), bar)
div(style = list(display = "flex", alignItems = "center"), label, chart)
}
rating_cols <- c("pct_of_all")
rating_column <- function(maxWidth = 55, ...) {
colDef(maxWidth = maxWidth, align = "center", class = "cell number", ...)
}
make_color_pal <- function(colors, bias = 1) {
get_color <- colorRamp(colors, bias = bias)
function(x) rgb(get_color(x), maxColorValue = 255)
}
# off_rating_color <- make_color_pal(c("#ff2700", "#f8fcf8", "#44ab43"), bias = 1.3)
def_rating_color <- make_color_pal(c("#AAB9D0", "#5572A2", "#002D74", "#001E4D", "#000F26"), bias = 1)
# knockout_pct_color <- make_color_pal(c("#ffffff", "#f2fbd2", "#c9ecb4", "#93d3ab", "#35b0ab"), bias = 2)
# Waiting Screen ----------------------------------------------------------
disconnected <- sever_default(
# logo = "www/logo_short_w.png",
title = "Howdy!",
subtitle = "There's a lot of data here, so this app has been resting while you were away.",
button = "Push to Wake",
button_class = "info"
)
# DATA PREP CODE ----------------------------------------------------------
# **Coronavirus Data -----------------------------------------------------------
# ~~Key Events Data -------------------------------------------------------
key_events <- vroom("https://docs.google.com/spreadsheets/d/e/2PACX-1vQ2q_c5RpywszCamM3VINgAwZ51OJoPfBFflEvXpuAqAZrw9SDovcGnfDOlF7uwzCnZf5XMkEluhlUb/pub?output=csv") %>%
clean_names() %>%
mutate(date=as_date(date))
# ~~JHU Data --------------------------------------------------------------
jhu_cases_state_us <- vroom("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_state.csv") %>%
janitor::clean_names() %>%
filter(country_region=="US",
!str_detect(province_state, "Diamond Princess|Grand Princess|Guam|Puerto Rico|Virgin Islands|Northern Mariana Islands|American Samoa")) %>%
group_by(last_update) %>%
mutate(cases_rank=dense_rank(desc(confirmed)),
deaths_rank=dense_rank(desc(deaths)),
recovered_rank=dense_rank(desc(recovered)),
active_rank=dense_rank(desc(active)),
tested_rank=dense_rank(desc(people_tested)),
hospitalized_rank=dense_rank(desc(people_hospitalized)),
incident_rank=dense_rank(desc(incident_rate)),
testrate_rank=dense_rank(desc(testing_rate)),
mortality_rank=dense_rank(desc(mortality_rate)),
hosprate_rank=dense_rank(desc(hospitalization_rate))) %>%
ungroup()
jhu_cases_state <- jhu_cases_state_us %>%
filter(province_state=="Texas") %>%
mutate(fips=as.character(fips))
r_naught <- vroom("https://d14wlfuexuxgcm.cloudfront.net/covid/rt.csv") %>%
filter(region=="TX")
county_lat_long <- vroom("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases.csv") %>%
janitor::clean_names() %>%
filter(province_state=="Texas") %>%
select(fips,county=admin2,last_update,lat,long) %>%
separate(last_update, into=c("date","time"), sep=" ") %>%
mutate(fips=as.character(fips)) %>%
select(-time)
# ~~NYT Data --------------------------------------------------------------
nyt_county_cases <- vroom("clean_data/dshs/testing/county_table.csv") %>%
rename(cases=total_confirmed_cases,
deaths=total_fatalities) %>%
mutate(date=gsub("2020-07-30"," 2020-06-30",x=date)) %>%
mutate(min = min(cases),
max = max(cases),
incident_rate=round((cases/population)*100000, digits=2),
mort_rate=round(deaths/cases, digits=2),
tests_per_100k=round((total_tests/population)*100000, digits=2)) %>%
group_by(county) %>%
mutate(prev_day_cases = lag(cases,1),
prev_week_cases = lag(cases,7),
prev_day_deaths = lag(deaths,1),
prev_week_deaths = lag(deaths,7),
new_cases_1day = cases-prev_day_cases,
new_cases_7day= cases-prev_week_cases,
new_deaths_1day = deaths-prev_day_deaths,
new_deaths_7day= deaths-prev_week_deaths) %>%
ungroup() %>%
mutate(date=as_date(date))
date1 <- nyt_county_cases %>%
filter(date==max(date))
date_filter <- date1$date - days(5)
if (is.na(date1$active_cases)) {
nyt_county_cases_today <- nyt_county_cases %>%
filter(date>=date_filter) %>%
group_by(county) %>%
fill(active_cases) %>%
ungroup() %>%
filter(date==max(date))
} else {
nyt_county_cases_today <- date1
}
dshs_hosp_rate_ts <- vroom("clean_data/dshs/hospitals/hosp_rate_ts.csv") %>%
rename(hospitalizations=hospitalized)
dshs_state_trends <- vroom("clean_data/dshs/cases/state_test_trends.csv")
# nyt_state_cases_tx <- vroom("https://texas-2036.github.io/covid-data/state.csv") #%>%
nyt_state_cases_tx <- vroom("clean_data/dshs/cases/state_cases_trends.csv") %>%
left_join(dshs_hosp_rate_ts,by=c("date","state","fips")) %>%
left_join(dshs_state_trends, by=c("date","state","fips")) %>%
# select(-x1) %>%
rename(cases=cumulative_cases,
# daily_new_fatalities=fatalities_by_date_of_death,
deaths=cumulative_fatalities) %>%
mutate(deaths=replace(deaths,date=="2020-08-03",7261)) %>% #Adjusts for Aug 3 Absence in Reporting
mutate(population=27885195,
min = min(cases),
max = max(cases),
# total_tests=total_public_lab_tests+total_private_lab_tests,
tests_per_100k=round((total_tests_reported/population)*100000, digits=2),
incident_rate=round((cases/population)*100000, digits=2),
mort_rate=round(deaths/cases, digits=4),
# recovered=round(recovered, digits=0),
test_rate=round((cases/population)*100000, digits=2),
hosp_rate=round((hospitalizations/active)*100, digits=2),
date = ymd(date),
prev_day_deaths = lag(deaths,1),
new_deaths_1day = deaths-prev_day_deaths,
prev_day_cases = lag(cases,1),
prev_week_cases = lag(cases,7),
new_cases_1day = cases-prev_day_cases,
new_cases_7day= cases-prev_week_cases) %>%
ungroup()
date2 <- nyt_state_cases_tx %>%
filter(date==max(date))
date_filter <- date2$date - days(60)
hosp_rate_hchart <- nyt_state_cases_tx %>%
arrange(date) %>%
select(date, hospitalizations,active) %>%
mutate(state="TX") %>%
group_by(state) %>%
mutate(hosp_rate_7day_avg = rollmean(hospitalizations, 7,
fill=0, align = "right")) %>%
filter(date >= as.Date(date_filter)) %>%
# mutate(min_new = min(hosp_rate, na.rm = TRUE),
# max_new = max(hosp_rate, na.rm = TRUE)) %>%
ungroup()
hospt_rate_hchart <- nyt_state_cases_tx %>%
arrange(date) %>%
select(date,hosp_rate) %>%
mutate(hosp_rate_7day_avg = rollmean(hosp_rate, 7,
fill=0, align = "right")) %>%
filter(date >= as.Date(date_filter),
!is.na(hosp_rate)) %>%
mutate(min_new = min(hosp_rate, na.rm = TRUE),
max_new = max(hosp_rate, na.rm = TRUE))
nyt_state_cases_text <- nyt_state_cases_tx %>%
fill(c(active,recovered, mort_rate), .direction="down") %>%
filter(date==max(date)) %>%
select(cases,mort_rate,active,recovered) %>%
mutate_at(vars(cases,active,recovered), scales::comma) %>%
mutate_at(vars(mort_rate), scales::percent_format(scale=100,accuracy=.01))
tx_counties <- read_rds("clean_data/population/county_pop.rds") %>%
mutate(NAME=gsub(" County, Texas","",x=NAME)) %>%
left_join(county_lat_long, by=c("GEOID"="fips", "NAME"="county")) %>%
select(-date)
county_list <- nyt_county_cases_today %>%
as_tibble() %>%
select(`County Names`=county) %>%
distinct() %>%
arrange(`County Names`) %>%
as.list()
tx_county_sf <- tx_counties %>%
left_join(nyt_county_cases_today, by=c("NAME"="county")) %>%
st_as_sf() %>%
st_transform(crs="+init=epsg:4326") %>%
fill(c("date"),.direction="downup") %>%
# select(-state, -country_region, -time, -combined_key, -iso3) %>%
mutate(cases_label=case_when(
is.na(cases) ~ "No Reported",
is.numeric(cases) ~ scales::comma(cases)
),
active_label=case_when(
is.na(active_cases) ~ "No Reported",
is.numeric(active_cases) ~ scales::comma(active_cases)
)) %>%
rename(county=NAME,
active=active_cases)
# ~~COVID Tracking Data --------------------------------------------------------------
test_daily <- vroom("https://api.covidtracking.com/v1/states/daily.csv") %>%
filter(state=="TX") %>%
mutate(date=as.character(date)) %>%
mutate(date=str_replace(date,"(\\d{4})(\\d{2})(\\d{2})$","\\1-\\2-\\3")) %>%
mutate(date = ymd(date))
test_positive <- test_daily %>%
arrange(date) %>%
select(date, state, positive, totalTestResults, positiveIncrease, negativeIncrease,totalTestResultsIncrease) %>%
mutate(daily_test_pos_rate = round((positiveIncrease/totalTestResultsIncrease), digits=4),
daily_test_pos_rate_7day_avg = rollmean(daily_test_pos_rate, 5,
fill=0, align = "right"),
test_pos_label = daily_test_pos_rate,
test_pos_7day_label = daily_test_pos_rate_7day_avg) %>%
mutate_at(vars(test_pos_label,test_pos_7day_label),scales::percent_format(accuracy = .11, scale=100)) %>%
gather(increase_type,increase,5:6)
tex_today_tests <- test_daily %>%
arrange(date) %>%
select(date, state, positive, totalTestResults, positiveIncrease, negativeIncrease,totalTestResultsIncrease) %>%
mutate(per_capita = round((totalTestResults/27885195)*100000, digits=2),
per_capita_per_day = round((totalTestResultsIncrease/27885195)*100000, digits=2),
daily_test_pos_rate = round(positiveIncrease/totalTestResultsIncrease, digits=4),
daily_test_pos_rate_7day_avg = rollmean(daily_test_pos_rate, 7,
fill=0, align = "right"),
test_pos_label = daily_test_pos_rate,
test_pos_7day_label = daily_test_pos_rate_7day_avg) %>%
mutate_at(vars(totalTestResults), scales::comma_format(accuracy=1)) %>%
mutate_at(vars(per_capita), scales::comma_format(accuracy = .1)) %>%
mutate_at(vars(test_pos_label,test_pos_7day_label),scales::percent_format(accuracy = .01, scale=100)) %>%
filter(totalTestResultsIncrease!=0) %>%
filter(date==max(date))
# COVID Relief Data -------------------------------------------------------
covid_relief <- vroom("clean_data/covid_relief/crf_data.csv")
# ~~DSHS Data ----
dshs_state_demographics <- vroom("clean_data/dshs/cases/time_series_demographics.csv") %>%
mutate(fips=as.character(fips))
dshs_case_trends <- vroom("clean_data/dshs/cases/state_cases_trends.csv")
dshs_case_fatality_totals <- dshs_case_trends %>%
select(date,cases=cumulative_cases, fatalities=cumulative_fatalities) %>%
gather(report_type,totals, 2:3)
dshs_completed_investigation <- dshs_state_demographics %>%
group_by(date, report_type) %>%
summarise(completed_investigations=sum(count)) %>%
ungroup() %>%
left_join(dshs_case_fatality_totals, by=c("date","report_type")) %>%
mutate(completed_investigations=completed_investigations/3,
diff=totals-completed_investigations) %>%
select(-totals)
dshs_new_hosp_data <- vroom("clean_data/dshs/hospitals/new_hosp_data_dshs.csv") %>%
clean_names() %>%
filter(date >= as_date("2020-07-25"))
dshs_tsa_hosp_data <- read_rds("clean_data/dshs/hospitals/texas_hosp_bed_ts_data.rds") %>%
st_transform(crs="+init=epsg:4326")
dshs_tsa_24hr_data <- vroom("clean_data/dshs/hospitals/texas_hosp_bed_ts_24hr_data.csv") %>%
ungroup() %>%
mutate(date=ymd(date),
date=as.Date(date))
dshs_tsa_avail_latest <- dshs_tsa_24hr_data %>%
filter(!is.na(icu_avail_rate)) %>%
filter(date==max(date)) %>%
as_tibble() %>%
distinct(date)
dshs_tsa_num_latest <- dshs_tsa_24hr_data %>%
filter(!is.na(total_susp_covid_in_hosp_at_time_of_report)) %>%
filter(date==max(date)) %>%
as_tibble() %>%
distinct(date)
dshs_test_pos <- vroom("clean_data/dshs/testing/test_pos_tx_ts.csv")
dshs_syndromic_tx <- vroom("clean_data/dshs/syndromic_tx.csv") %>%
rename(influenza_like_illness=2,covid_like_illness=3)
# ** Population Data ---------------------------------------------------------
state_pop <- read_rds("clean_data/population/state_pop.rds")
tsa_shps <- dshs_tsa_hosp_data %>%
filter(date==max(date)) %>%
filter(!str_detect(tsa,"Total|total")) %>%
select(tsa,tsa_counties,geometry) %>%
group_by(tsa) %>%
summarise(tsa_counties = toString(tsa_counties)) %>%
ungroup()
dshs_cases_text <- dshs_case_trends %>%
filter(date==max(date)) %>%
select(cumulative_cases) %>%
mutate_at(vars(cumulative_cases), scales::comma)
# ** NPI Data ----------------------------------------------------------------
# ~~Google Mobility -------------------------------------------------------
google_mobil_tx <- vroom("https://texas-2036.github.io/covid-data/google_mobility_tx_state.csv")
google_mobil_tx_cnties <- vroom("https://texas-2036.github.io/covid-data/google_mobility_tx_cnty.csv")
# ** Economic Data -----------------------------------------------------------
# ~~Affinity Spending Data --------------------------------------------------
dd <- vroom("clean_data/opportunity_tracker/affinity_tables.csv")
# ~~Homebase Data ---------------------------------------------------------
hb_state_only <- vroom("https://texas-2036.github.io/covid-data/homebase_state_summary.csv", na = " ") %>%
filter(!is.na(prev_day)) %>%
mutate(change=as.numeric(change),
change=round(change,digits=1)) %>%
mutate(prev_day=as_date(prev_day))
# hb_state_industry <- vroom("https://texas-2036.github.io/covid-data/homebase_state_industry_breakdown.csv")
hb_hours_worked_all <- hb_state_only %>%
filter(variable=="hours_worked")
hb_businesses_open_all <- hb_state_only %>%
filter(variable=="businesses_open")
hb_employees_working_all <- hb_state_only %>%
filter(variable=="employees_working")
hb_hours_worked <- hb_state_only %>%
filter(variable=="hours_worked",
date == max(date))
hb_businesses_open <- hb_state_only %>%
filter(variable=="businesses_open",
date == max(date))
hb_employees_working <- hb_state_only %>%
filter(variable=="employees_working",
date == max(date))
# ~~TWC County UI Claims Data ------------------------------------------------------
twc_ui_by_county <- vroom("https://raw.githubusercontent.com/texas-2036/covid_tracker/master/clean_data/twc/county_claims_by_industry_detail.csv") %>%
filter(rank <= 5) %>%
rename(county=1) %>%
select(county,rank,naics_title,pct_label)
twc_claims_cnty_raw <- vroom("https://raw.githubusercontent.com/texas-2036/covid_tracker/master/clean_data/twc/county_claims_data.csv")
twc_claims_cnty <- twc_claims_cnty_raw %>%
filter(str_detect(date, "^2020")) %>%
mutate(date=ymd(date)) %>%
mutate(date=as.Date(date))
twc_claims_date <- twc_claims_cnty_raw %>%
filter(str_detect(date, "^2020")) %>%
mutate(date=ymd(date)) %>%
select(date) %>%
filter(date==max(date)) %>%
distinct()
twc_claims_cnty_summ <- twc_claims_cnty_raw %>%
mutate(date=as.Date(date)) %>%
mutate(date=ymd(date)) %>%
filter(date >= as.Date("2020-03-21")) %>%
group_by(county) %>%
summarise(all_claims=sum(value)) %>%
mutate_at(vars(all_claims), scales::comma_format(accuracy=1))
twc_ui_top_25_tbl <- vroom("https://lmci.state.tx.us/shared/dashboarddata/UI_BY_NAICS_TOP25.csv") %>%
clean_names() %>%
mutate(pct_of_all = round(naics_count/texas_total_ui_count, digits = 4),
index = 1:25) %>%
select(index, 1,2,4)
# ~~FREDR Data -----------------------------------------------------
tx_series_all <- vroom("clean_data/fredr/ui_claims_ts.csv")
tx_cont_series <- vroom("clean_data/fredr/cont_claims_ts.csv", delim=",") %>%
mutate_at(vars(value),scales::comma) %>%
filter(date==max(date))
tx_series <- vroom("clean_data/fredr/tx_series_summ.csv", delim=",") %>%
mutate_at(vars(value),scales::comma)
tx_urn <- vroom("clean_data/fredr/unemploy_rate.csv")
# DERIVED METRICS -----------------------------------
# ~~Testing Metrics ----------------------------------------------------------
# total_population <- state_pop %>%
# clean_names() %>%
# rename(state=name, total_population=estimate, fips=geoid) %>%
# filter(state=="Texas")
#
# total_tests <- dshs_state_tests %>%
# group_by(state,fips) %>%
# summarise(total_tests = sum(tests)) %>%
# ungroup() %>%
# left_join(total_population, by=c("state","fips")) %>%
# left_join(dshs_state_hospitalizations, by=c("state","fips")) %>%
# left_join(jhu_cases_state, by=c("state"="province_state","fips")) %>%
# mutate(tests_per_100k = round((total_tests/total_population)*100000,digits=4))
active_cases <- jhu_cases_state %>%
select(state=province_state, active, active_rank)
test_pos_today <- dshs_state_trends %>%
filter(!is.na(covid_19_positivity_rate)) %>%
filter(date==max(date)) %>%
mutate(test_pos_label=covid_19_positivity_rate) %>%
select(date,test_pos_label) %>%
mutate_at(vars(test_pos_label),scales::percent_format(accuracy=.01, scale=100))
# **STATE EXPLORER METRICS ------------------------------------------------------
# ~~Current Case Data -----------------------------------------------------
tx_cases <- jhu_cases_state %>%
select(cases_rank) %>%
mutate_at(vars(cases_rank), scales::label_ordinal())
tx_mort <- jhu_cases_state %>%
select( mortality_rank) %>%
mutate_at(vars(mortality_rank), scales::label_ordinal())
tx_active <- active_cases %>%
mutate_at(vars(active_rank), scales::label_ordinal())
tx_recover <- jhu_cases_state %>%
select(recovered_rank) %>%
mutate_at(vars(recovered_rank), scales::label_ordinal())
date1 <- dshs_case_trends %>%
filter(date==max(date))
date_filter <- date1$date - days(30)
state_case_growth <- dshs_case_trends %>%
select(date,cumulative_cases) %>%
arrange(date) %>%
mutate(daily_growth_rate = round(((cumulative_cases/lag(cumulative_cases))-1)*100, digits=1)) %>%
mutate(daily_growth_rate_7day_avg = round(rollmean(daily_growth_rate, 7,
fill=0, align = "right"), digits=1)) %>%
mutate(min_new = min(daily_growth_rate, na.rm = TRUE),
max_new = max(daily_growth_rate, na.rm = TRUE)) %>%
mutate(min_new = as.numeric(min_new),
max_new = as.numeric(max_new)) %>%
filter(date >= as.Date(date_filter)) %>%
mutate(date=as_date(date)) %>%
ungroup()
# HEADER CODE ------------------------------------------------------------
header <- dashboardHeader(disable = FALSE,
title = tags$a(href='http://www.texas2036.org',
HTML('<svg viewBox="0 0 227.4 83.5" style="height:4.5vh;padding-bottom:1.1vh;margin-top:7px"><path fill="#3a4a9f" d="M192.5 66.2c2.2 0 3.9.6 3.9 2.6v4.1c0 2-1.7 3.6-3.9 3.6-2.1 0-3.8-1.6-3.8-3.6v-5.1h-7.8v5.1c0 5.9 5.2 10.6 11.6 10.6 6.4 0 11.5-4.6 11.7-10.4.6 5.4 5.6 10.4 11.6 10.4 6.4 0 11.6-4.8 11.6-10.6v-7.4c0-5.8-5.2-10.6-11.6-10.6-1.4 0-2.7.2-3.9.6v-4.1c0-1.9 1.8-3.5 3.9-3.5 2.1 0 3.8 1.6 3.8 3.5v2.2h7.8v-2.2c0-4-2.5-7.5-6.1-9.3 3.6-1.8 6.1-5.3 6.1-9.3V10.5c0-5.8-5.2-10.5-11.6-10.5-6.1 0-11.1 4.3-11.6 9.8-.4-5.5-5.5-9.8-11.7-9.8-6.4 0-11.6 4.7-11.6 10.6v2.6h7.8v-2.6c0-1.9 1.7-3.5 3.8-3.5 2.2 0 3.9 1.6 3.9 3.5v.8l-.1.1-13 15.6c-2.3 2.8-2.4 3-2.4 5.9v10.5h4.1c-2.5 1.9-4.1 4.8-4.1 8v2.2h7.8v-2.2c0-1.9 1.7-3.5 3.8-3.5 2.2 0 3.9 1.6 3.9 3.5v4.1c0 2-1.7 3.6-3.9 3.6h-2.4v7.1h2.4zm19.4-55.6c0-1.9 1.7-3.5 3.8-3.5 2.1 0 3.8 1.6 3.8 3.5v20.7c0 2-1.7 3.6-3.8 3.6-2.1 0-3.8-1.6-3.8-3.6V10.6zm-7.8 57c-.3-1.9-1.3-3.3-2.9-5 1.6-1.6 2.6-3.7 2.9-5.9v10.9zm-15.4-32.8v-2.6l13.1-15.8c1.6-1.9 2.2-2.6 2.3-3.8v20.3c0 .5 0 .9.1 1.3l2.1 6.4h6.8l-5.5 4 2.1 6.5-5.5-4-5.5 4 2.1-6.5-5.5-4h6.8l1.9-5.9h-15.3zm30.9 38.1c0 2-1.7 3.6-3.8 3.6-2.2 0-3.9-1.6-3.9-3.6v-7.4c0-1.9 1.8-3.5 3.9-3.5 2.1 0 3.8 1.6 3.8 3.5v7.4zM8.4 82.7V8H0V0h24.8v8h-8.4v74.8h-8zm45.4 0H33V0h20.8v8H41v29.5h12.8v8H41v29.4h12.8v7.8zm70.2 0V45.3h-12.8v37.4h-8V14.4c0-8 6.5-14.4 14.4-14.4 7.8 0 14.3 6.5 14.3 14.4v68.3H124zm0-68.3c0-3.6-2.9-6.5-6.3-6.5-3.6 0-6.5 2.9-6.5 6.5v22.9H124V14.4zm37.6 6.1v-6.2c0-3.5-2.9-6.3-6.3-6.3-3.5 0-6.3 2.9-6.3 6.3v6.3c0 1.5 0 1.5.4 2.1l17.9 31.6c2.4 4.2 2.4 4.2 2.4 7.8v6.2c0 8-6.5 14.3-14.3 14.3S141 76.4 141 68.4v-6.2h8v6.2c0 3.6 2.9 6.5 6.3 6.5 3.5 0 6.3-2.9 6.3-6.5v-6.2c0-1.4 0-1.4-.4-2l-17.9-31.6c-2.4-4.2-2.4-4.2-2.4-8v-6.3C141 6.5 147.5 0 155.3 0s14.3 6.5 14.3 14.3v6.2h-8zM95.9 0h-8.2l-9.2 28.7L69.3 0h-8.2l13.3 41.6L61.1 83h8.3l9.1-28.5L87.6 83h8.3L82.6 41.6z"></path><svg>'),
tags$title('Texas COVID-19 Resource Kit')))
# SIDEBAR CODE-----------------------------------------------------------
sidebar <- dashboardSidebar(disable = FALSE,
collapsed = FALSE,
sidebarMenu(
id = "tabs",
menuItem("Introduction",
tabName = "intro",
icon = icon("square", class="fad fa-square")),
menuItem("Reopening",
tabName = "reopening",
icon = icon("door-open", class="fad fa-door-open")),
menuItem("State Explorer",
tabName = "state_profiles",
icon = icon("landmark", class="fad fa-landmark")),
menuItem("County Explorer",
tabName = "county_profiles",
icon = icon("city", class="fad fa-city")),
menuItem("Credits",
tabName = "credits",
icon = icon("circle", class="fad fa-circle")),
menuItem("Data",
tabName = "data",
icon = icon("circle", class="fad fa-circle")),
actionButton("about", "About"),
actionButton("learn", "Learn More")
# actionButton("show", "Learn More", icon = icon("info-circle", class = "fa-pull-left"), style="color: #152934"),
)
)
# BODY CODE -----------------------------------------------------
body <- dashboardBody(
tags$head(
tags$script(src="https://kit.fontawesome.com/5272d94c6c.js", crossorigin="anonymous"),
tags$link(rel="shortcut icon", href="favicon.png"),
tags$link(rel = "stylesheet", type = "text/css", href = "custom2.css"),
tags$link(rel = "stylesheet", type = "text/css", href = "tbl_css.css"),
# includeHTML("social_tags.html"),
includeHTML(("google_analytics.html")),
tags$script(HTML("$('body').addClass('fixed');")),
tags$style(type = "text/css", "div.info.legend.leaflet-control br {clear: both;}"),
tags$link(rel="stylesheet", href="https://fonts.googleapis.com/css2?family=Montserrat:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,400&display=swap")),
# **Landing Page ----------------------------------------------------------
use_sever(),
useShinyalert(),
meta() %>%
meta_social(
title = "Texas 2036 | Texas COVID-19 Data Resource",
description = "A comprehensive look at Texas COVID-19 Health and Economic Data",
url = "http://covid19.texas2036.org",
image = "https://texas-2036.github.io/covid-pages/images/trends_cover.png",
image_alt = "Texas 2036 | Texas COVID-19 Data Resource",
twitter_creator = "@mrworthington",
twitter_card_type = "summary",
twitter_site = "@texas2036"
),
tabItems(
tabItem(tabName = "intro",
jumbotron("Texas COVID-19 Data Resource",
"A Comprehensive Look At Our Current Moment",
button = FALSE),
# hr(style="border-top: 48px solid #fff;"),
# HTML("<i style='color:#F26852;display: block;text-align: center;margin-top:-82px;margin-bottom: 20px;font-size: 112px;}' class='fas fa-2x fa-hands-helping'></i>"),
hr(),
br(),
fluidRow(
column(4, thumbnail_label(title="<i class='fad fa-door-open fa-3x'></i>",
label = 'Reopening Analysis',
content = includeMarkdown("markdown/intro/reopening.md"),
button_link ='explore_reopen',
button_label = 'Explore')),
column(4, thumbnail_label(title="<i class='fad fa-landmark fa-3x'></i>",
label = 'State Explorer',
content = includeMarkdown("markdown/intro/state.md"),
button_link ='explore_state',
button_label = 'Explore')),
column(4, thumbnail_label(title="<i class='fad fa-city fa-3x'></i>",
label = 'County Explorer',
content = includeMarkdown("markdown/intro/county.md"),
button_link = 'explore_county',
button_label = 'Explore'))
)),
tabItem(tabName = "reopening",
fluidRow(
HTML("<iframe width='100%' height=1200vh' style='border-top-width: 0px;border-right-width: 0px;border-bottom-width: 0px;border-left-width: 0px;' src='https://staging.convex.design/texas-2036/texas-covid-live-report/?currentCounty=Anderson'></iframe>"))),
tabItem(tabName = "state_profiles",
use_waiter(),
use_hostess(),
waiter_show_on_load(html = tagList(spin_flower(),h4("Thanks for being patient while we get everything set up.")),
color = "#3A4A9F",
logo = "logo_short_w.png"),
# waiter_show_on_load(
# color = "#f7fff7",
# hostess_loader(
# "loader",
# preset = "circle",
# text_color = "black",
# class = "label-center",
# center_page = TRUE
# )
# ),
# **STATEWIDE PROFILE UI ----------------------------------------------------
h1(style="font-weight:800;", "STATEWIDE PROFILE", span="id='statewide-profile'"),
# ~~Current Cases Data ---------------------------------------------------------
h3(class="covid-topic", "Current Case Data"),
fluidRow(
infoBoxOutput("tx_cases", width=3),
infoBoxOutput("tx_mort", width=3),
infoBoxOutput("tx_recover", width=3),
infoBoxOutput("tx_active", width=3)
),
# fluidRow(
# br(),
# column(width = 12,
# tags$div(class="source-notes",
# br(),
# h4(style="font-weight:700;display:inline;","Source Notes:"),
# h4(style="font-weight:400;display:inline;","Before July, Texas 2036 sourced figures for confirmed cases, deaths as a percent of all cases, estimated recoveries, and estimated active cases from Johns Hopkins University's COVID-19 Data Repository. As of July 2014, we have switched to sourcing figures from the Texas Department of State Health Services' 'COVID-19 Accessible Dashboard Data'. In addiiton, rankings are still being produced by Texas 2036 using the Johns Hopkins University dataset (which records data from other territorities states), but rankings have been updated to only reflect comparisons between Texas, other US States, and DC only. Prior to July, comparisons included other US Territories."),
# ),
# hr()
# )
# ),
# ~~Trends Over Time --------------------------------------------------------
h3(class="covid-topic", "Trends Over Time", span="id='statewide_tot"),
fluidRow(
column(width=6,
highchartOutput("state_new_cases_hchart", height = 375)),
column(width=6,
highchartOutput("infection_chart", height = 375)),
),
fluidRow(
column(width = 6,
highchartOutput("state_curves_hchart", height = 350)),
column(width = 6,
highchartOutput("state_growth_rate_hchart", height = 350))
),
fluidRow(
column(width = 6,
highchartOutput("state_new_deaths_hchart", height = 350)),
column(width = 6,
highchartOutput("state_new_tests_hchart", height = 350))
),
# ~~Population Health ---------------------------------------------------------
h3(class="covid-topic", "Population Health", span="id='pop_health"),
column(width = 12,
tags$div(class="tsa-paragraph",
br(),
h4(style="font-weight:700;display:inline;","NOTE:"),
h4(style="font-weight:400;display:inline;","These charts were developed by recording each day's value from the DSHS daily report published on their 'Accessible Dashboard Data' report posted on their site every day, saving a copy of that information in a timestamped file on our github, and then organizing each of the individual timestamped files into a single time-series dataset that allows us to track trends in population health as the completes more cases and fatality investigations. As of July 27th, fatality demographics have shifted from being processed using data from completed epidemiological fatality investigations to being processed using data from death certificates."),
),
br(),
hr()
),
fluidRow(
column(width = 6,
highchartOutput("state_cases_report_rate", height = 225)),
column(width = 6,
highchartOutput("state_fatalities_report_rate", height = 225))
),
fluidRow(
column(width = 6,
highchartOutput("state_cases_race", height = 350)),
column(width = 6,
highchartOutput("state_fatalities_race", height = 350))
),
fluidRow(
column(width = 6,
highchartOutput("state_cases_age", height = 350)),
column(width = 6,
highchartOutput("state_fatalities_age", height = 350))
),
fluidRow(
column(width = 6,
highchartOutput("state_cases_gender", height = 350)),
column(width = 6,
highchartOutput("state_fatalities_gender", height = 350))
),
# ~~Testing ---------------------------------------------------------
h3(class="covid-topic", "Current Testing Data"),
fluidRow(
column(width = 3, class="economic-grid",
h2(class="economic-tile",
p(class="tile-header", "Total Tests"),
hr(),
p(style = "text-align:center;font-size:1em;font-weight:800", paste0(tex_today_tests$totalTestResults)),
p(style = "text-align:center;font-size:.6em;font-weight:600;color:#DBDCDD;",
paste0("Since: Mar 4, 2020")),
p(style = "text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://github.com/COVID19Tracking/covid-tracking-data","Source: COVID-Tracking Project")))),
column(width = 3, class="economic-grid",
h2(class="economic-tile",
p(class="tile-header", "Total Tests Per Capita"),
hr(),
p(style = "text-align:center;font-size:1em;font-weight:800", paste0(tex_today_tests$per_capita)),
p(style = "text-align:center;font-size:.6em;font-weight:600;color:#DBDCDD;",
paste0("Per 100,000 People")),
p(style = "text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://github.com/COVID19Tracking/covid-tracking-data","Source: COVID-Tracking Project")))),
column(width = 3, class="economic-grid",
h2(class="economic-tile",
p(class="tile-header", "Daily Tests Per Capita"),
hr(),
p(style = "text-align:center;font-size:1em;font-weight:800", paste0(tex_today_tests$per_capita_per_day)),
p(style = "text-align:center;font-size:.6em;font-weight:600;color:#DBDCDD;",
paste0("Per 100,000 People")),
p(style = "text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://github.com/COVID19Tracking/covid-tracking-data","Source: COVID-Tracking Project")))),
column(width = 3, class="economic-grid",
h2(class="economic-tile",
p(class="tile-header", "Test Positive Rate"),
hr(),
p(style = "text-align:center;font-size:1em;font-weight:800", paste0(test_pos_today$test_pos_label)),
p(style = "text-align:center;font-size:.6em;font-weight:600;color:#DBDCDD;",
paste0("Last 7 Days From ", format(test_pos_today$date, format="%b %d"))),
p(style = "text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://www.dshs.state.tx.us/coronavirus/additionaldata/","Source: Texas Department of State Health Services")))),
fluidRow(
column(width = 12,
highchartOutput("test_pos_chart", height = 350))
),
),
# ~~Syndromic Data ---------------------------------------------------------
h3(class="covid-topic", "Current Syndromic Data"),
fluidRow(
fluidRow(
column(width = 6,
highchartOutput("cli_hchart", height = 350)),
column(width = 6,
highchartOutput("ili_hchart", height = 350))
),
),
# ~~Hospital Data -----------------------------------------------------------
h3(class="covid-topic", "Current Hospital + Trends Data"),
fluidRow(
infoBoxOutput("hospitalized_rate", width=3),
infoBoxOutput("tx_beds", width=3),
infoBoxOutput("tx_icu_beds", width=3),
infoBoxOutput("tx_vents", width=3)
),
hr(),
fluidRow(
column(width = 6,
highchartOutput("state_hosp_chart", height = 375)),
column(width = 6,
highchartOutput("state_hosp_rate_chart", height = 375))
),
fluidRow(
column(width = 12,
highchartOutput("state_hosp_resource_hchart", height = 400))
),
fluidRow(
column(width = 6,
highchartOutput("total_conf_covid_gen_hchart", height = 375)),
column(width = 6,
highchartOutput("total_conf_covid_icu_hchart", height = 375))
),
fluidRow(
column(width = 6,
highchartOutput("daily_covid_gen_admits_hchart", height = 375)),
column(width = 6,
highchartOutput("daily_covid_icu_admits_hchart", height = 375))
),
fluidRow(
column(width = 6,
highchartOutput("daily_covid_ers_hchart", height = 375)),
column(width = 6,
highchartOutput("daily_covid_of_total_er_hchart", height = 375))
),
# ~~COVID Relief Funds Data ---------------------------------------------------------
# h3(class="covid-topic", "COVID-19 Relief Funds Data"),
# fluidRow(
# column(width=12, class="economic-grid",
# gt_output(outputId = "state_related_crf"))
# ),
# ~~Mobility Data --------------------------------------------------------
h3(class="covid-topic", "Mobility Trends", span="id='statewide_mob"),
fluidRow(
column(width = 6,
highchartOutput("state_grocery_hchart", height = 350)),
column(width = 6,
highchartOutput("state_parks_hchart", height = 350))
),
fluidRow(
column(width = 6,
highchartOutput("state_transit_hchart", height = 350)),
column(width = 6,
highchartOutput("state_retail_rec_hchart", height = 350))
),
fluidRow(
column(width = 6,
highchartOutput("state_workplace_hchart", height = 350)),
column(width = 6,
highchartOutput("state_residential_hchart", height = 350))
),
# ~~Economic Data -----------------------------------------------------------
h3(class="covid-topic", "Economy + Society"),
fluidRow(
column(width = 2, class="economic-grid",
h2(class="economic-tile",
p(style="text-align:center;font-size:1em;font-weight:800",
paste0(tx_series$value)),
p(style="text-align:center;font-size:.6em;font-weight:600;color:#00A9C5;",
paste0("Since: Mar 21")),
p(style="text-align:center;font-size:.45em;font-weight:300","Jobless Claims"),
p(style="text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://fred.stlouisfed.org/series/TXICLAIMS","Source: BLS via FREDr")))),
column(width = 2, class="economic-grid",
h2(class="economic-tile",
p(style="text-align:center;font-size:1em;font-weight:800",
paste0(tx_cont_series$value)),
p(style="text-align:center;font-size:.6em;font-weight:600;color:#00A9C5;",
paste0("As of: ", format(tx_cont_series$date, format="%b %d"))),
p(style="text-align:center;font-size:.45em;font-weight:300","Continued Claims"),
p(style="text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://fred.stlouisfed.org/series/TXCCLAIMS","Source: BLS via FREDr")))),
column(width = 2, class="economic-grid",
h2(class="economic-tile",
p(style="text-align:center;font-size:1em;font-weight:800",paste0(tx_urn$value,"%")),
p(style="text-align:center;font-size:.6em;font-weight:600;color:#00A9C5;",
paste0("As of: ", format(tx_urn$date, format="%b %d"))),
p(style="text-align:center;font-size:.45em;font-weight:300","Unemployment Rate"),
p(style="text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://fred.stlouisfed.org/series/TXURN","Source: BLS via FREDr")))),
column(width = 2, class="economic-grid",
h2(class="economic-tile",
p(style="text-align:center;font-size:1em;font-weight:800",paste0(hb_businesses_open$pct,"%")),
p(style=paste0("text-align:center;font-size:.6em;font-weight:600;color:",hb_businesses_open$color,";"),
paste0(hb_businesses_open$change_lbl, hb_businesses_open$change, "% From ", format(hb_businesses_open$prev_day, format="%b %d"))),
p(style="text-align:center;font-size:.43em;font-weight:500","Est. Local Businesses Open"),
p(style="text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://joinhomebase.com/data/","Source: Homebase")))),
column(width = 2, class="economic-grid",
h2(class="economic-tile",
p(style="text-align:center;font-size:1em;font-weight:800",paste0(hb_hours_worked$pct,"%")),
p(style=paste0("text-align:center;font-size:.6em;font-weight:600;color:",hb_hours_worked$color,";"),
paste0(hb_hours_worked$change_lbl, hb_hours_worked$change, "% From ", format(hb_hours_worked$prev_day, format="%b %d"))),
p(style="text-align:center;font-size:.43em;font-weight:500","Est. Reduction In Hours Worked"),
p(style="text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://joinhomebase.com/data/","Source: Homebase")))),
column(width = 2, class="economic-grid",
h2(class="economic-tile",
p(style="text-align:center;font-size:1em;font-weight:800",paste0(hb_employees_working$pct,"%")),
p(style=paste0("text-align:center;font-size:.6em;font-weight:600;color:",hb_employees_working$color,";"),
paste0(hb_employees_working$change_lbl, hb_employees_working$change, "% From ", format(hb_employees_working$prev_day, format="%b %d"))),
p(style="text-align:center;font-size:.43em;font-weight:500","Est. Hourly Employees Working"),
p(style="text-align:center;font-size:.4em;font-weight:400",
tags$a(href="https://joinhomebase.com/data/","Source: Homebase"))))
),
fluidRow(
column(width = 6,
highchartOutput("state_claims_hchart", height = 350)),
column(width = 6,
highchartOutput("state_businesses_hchart", height = 350))
),
fluidRow(
column(width = 6,
highchartOutput("state_hours_hchart", height = 350)),
column(width = 6,
highchartOutput("state_employees_hchart", height = 350))
),
fluidRow(
column(width = 6,
h3("Top 25 Unemployment Claims, by Industry", style="text-align:left;font-size:2em;font-weight:700"),
h3(style="text-align:left;font-size:1em;font-weight:500", includeMarkdown("markdown/charts/unemployment_top25.md")),
reactableOutput("top25_ui_claims", height = 775)),
column(width = 6,
h3("Consumer Spending Trends, by Industry", style="text-align:left;font-size:2em;font-weight:700"),
h3(style="text-align:left;font-size:1em;font-weight:500", includeMarkdown("markdown/charts/spending_trends.md")),
tabBox(
title = NULL,
# The id lets us use input$tabset1 on the server to find the current tab