Greetings,
I am using your codes modified by Antoine Soetewey to create a New Dashboard for Haiti as this country lacks a good dashboard
https://statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/
When knitting to flexdashboard I am getting the following error:
Quitting from lines 156-185 (Haiti-Coronavirus-Dashboard.Rmd)
Error in validateCssUnit(sizeInfo$width) :
CSS units must be a single-element numeric or character vector
Calls: ... -> need_screenshot -> toHTML -> validateCssUnit
In addition: Warning message:
package 'flexdashboard' was built under R version 4.0.3
Execution halted
I am using RStudio Version 1.3.1093. Do you have an idea why I am getting this error message and how to fix it. I would be very grateful to you.
The codes are below:
Best,
Patrick
title: "Coronavirus in Haiti"
author: "Patrick Stephenson"
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
# View(coronavirus)
# max(coronavirus$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Haiti") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
dplyr::filter(country == "Haiti") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
#dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(active = confirmed - death) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
# recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
df1 <- coronavirus %>% dplyr::filter(date == max(date))
Summary
Row {data-width=400}
confirmed {.value-box}
valueBox(
value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = confirmed_color
)
death {.value-box}
valueBox(
value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate)",
icon = "fas fa-heart-broken",
color = death_color
)
Row
Daily cumulative cases by type (Haiti only)
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
# plotly::add_annotations(
# x = as.Date("2020-02-04"),
# y = 1,
# text = paste("First case"),
# xref = "x",
# yref = "y",
# arrowhead = 5,
# arrowhead = 3,
# arrowsize = 1,
# showarrow = TRUE,
# ax = -10,
# ay = -90
# ) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
Comparison
Column {data-width=400}
Daily new confirmed cases
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::mutate(country = country) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~Haiti,
type = "scatter",
mode = "lines+markers",
name = "Haiti"
) %>%
# plotly::add_trace(
# x = ~date,
# y = ~France,
# type = "scatter",
# mode = "lines+markers",
# name = "France"
# ) %>%
# plotly::add_trace(
# x = ~date,
# y = ~Spain,
# type = "scatter",
# mode = "lines+markers",
# name = "Spain"
# ) %>%
plotly::add_trace(
x = ~date,
y = ~`Dominican Republic`,
type = "scatter",
mode = "lines+markers",
name = "Dominican Republic"
) %>%
plotly::add_trace(
x = ~date,
y = ~Jamaica,
type = "scatter",
mode = "lines+markers",
name = "Jamaica"
) %>%
plotly::add_trace(
x = ~date,
y = ~Cuba,
type = "scatter",
mode = "lines+markers",
name = "Cuba"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.7, y = 0.9),
yaxis = list(title = "New confirmed cases"),
xaxis = list(title = "Date"),
# paper_bgcolor = "black",
# plot_bgcolor = "black",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
Cases distribution by type
df_EU <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Haiti" |
country == "Dominican Republic " |
country == "Jamaica" |
country == "Cuba") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_EU,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Confirmed",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)
) %>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
Map
World map of cases (use + and - icons to zoom in/out)
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
# dplyr::filter(country == "Haiti") %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(country, province, lat, long, type) %>%
dplyr::summarise(cases = sum(cases)) %>%
dplyr::mutate(log_cases = 2 * log(cases)) %>%
dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk(function(df) {
map_object <<- map_object %>%
addCircleMarkers(
data = cv_data_for_plot.split[[df]],
lng = ~long, lat = ~lat,
# label=~as.character(cases),
color = ~ pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("type", "cases", "country", "province")
),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(
noHide = F,
direction = "auto"
)
)
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
About
The Coronavirus Dashboard: the case of Haiti
This Coronavirus dashboard: the case of Haiti provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Haiti. This dashboard is built with R using the R Makrdown framework and was adapted from this dashboard{target="_blank"} by Rami Krispin.
Code
The code behind this dashboard is available on GitHub{target="_blank"}.
Data
The input data for this dashboard is the dataset available from the {coronavirus}{target="_blank"} R package. Make sure to download the development version of the package to have the latest data:
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository{target="_blank"}.
Information
More information about this dashboard (and how to replicate it for your own country) can be found in this article.
Update
The data is as of r format(max(coronavirus$date), "%A %B %d, %Y") and the dashboard has been updated on r format(Sys.time(), "%A %B %d, %Y").
Go back to statsandr.com (blog) or antoinesoetewey.com (personal website).
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