---
title: "Coronavirus in India"
author: "VC"
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
---
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
library("devtools")
#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
##devtools::install_github("RamiKrispin/coronavirus")
library(coronavirus)
#coronavirus::update_datasets(silence = TRUE)
#View(coronavirus)
# max(coronavirus$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "mediumvioletred"
active_color <- "black"
recovered_color <- "darkseagreen"
death_color <- "darkorange"
doubling_color <- "darkblue"
purple<-"purple"
#------------------ Data ------------------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "India") %>%
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 == "India") %>%
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))
```
```{r }
library(dplyr)
df_ind <- coronavirus %>%
# lubridate::ymd(date) %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "India" ) %>%
dplyr::filter(cases!=0) %>%
dplyr::filter(date >"2020-03-04") %>%
arrange(date)
df_ind<-df_ind %>% dplyr::mutate(day=1:nrow(df_ind)) %>%
tidyr::pivot_wider(names_from=type,values_from=cases)#|
# #select only confirmed cases
df_ind_conf<-df_ind %>%
dplyr::filter(!is.na(confirmed)) %>% select(day,date,confirmed) %>% mutate(total_cases= cumsum(confirmed))
# country == "China" |
# country == "Italy" |
# country == "Thailand"|country == "US" ) %>%
# 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))
###growth rate##################
#Estimate the ratio method , this just calculated pop at t+1/pop at t
#taking only last 15 days doubling time
df_ind_conf<-df_ind %>%
dplyr::filter(!is.na(confirmed)) %>% dplyr::select(day,date,confirmed)%>%
dplyr:: mutate(total_cases= cumsum(confirmed))
#dplyr::filter(row_number() >= (n() - 15)) #last 15 days
df_ind_conf.last15<- df_ind_conf %>% tail(15)
lambda.l15 = df_ind_conf.last15$total_cases[-1]/ df_ind_conf.last15$total_cases[-length(df_ind_conf.last15$total_cases)] #The #"-
r.last15 = mean(log(lambda.l15), na.rm = T)
lambda = df_ind_conf$total_cases[-1]/ df_ind_conf$total_cases[-length(df_ind_conf$total_cases)] #The #"-" notation excludes specified entry of a vector
#lambda
#mean lambda
#mean(lambda, na.rm = T)
#sd(lambda, na.rm = T)
#Mean and sd of continuous time disease growth rate
r = mean(log(lambda), na.rm = T)
sd_r = sd(log(lambda), na.rm = T)
#generate a function that takes a value of r and runs one stochastic simulation
#so we can use both means and variance of r
exp.stoch = function(r_sim) {
for (i in 1:time) {
N_sim[i + 1] = N_sim[i] * exp(r_sim[i]) #this is the exponential growth formula
#N at t+1 = N at t*exp(r*t)
}
return(N_sim)
}
```
```{r}
#Moving average
# df_daily.7 <- coronavirus %>%
# dplyr::filter(country == "India") %>%
# dplyr::filter(cases!=0) %>%
# dplyr::group_by(date, type) %>%
# dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
# tidyr::pivot_wider(
# names_from = type,
# values_from = total
# ) %>%
# dplyr::arrange(date) %>%
# dplyr::ungroup() %>%
#
# dplyr::filter(date >"2020-03-04") %>%
# dplyr::arrange(date)
#
# df_ind.daily.7<- df_daily.7 %>% dplyr::mutate(day=1:nrow(df_daily.7 )) %>%
# dplyr::filter(!is.na(confirmed)) %>% dplyr::select(day,date,confirmed) %>% dplyr::mutate(total_cases= cumsum(confirmed))
#
#
# df7.1 <- df_ind.daily.7 %>%dplyr::select(total_cases)
# #df7.2<- cumsum(df7.1, list(0:3),sum,fill=1:3)
# #df7.2<-colSums(matrix(df7.1$total_cases, nrow=6))
# df7.2<-as.data.frame(rollapply(df7.1, width=4, FUN=sum, by=4, partial=T, align="right"))
# df7.2$days<-seq(1,4*nrow(df7.2),4)
# lambda.df7.2 = df7.2$total_cases[-1]/ df7.2$total_cases[-length(df7.2$total_cases)] #The #"-
# r.last.7day = mean(log(lambda.df7.2), na.rm = T)
#
```
```{r}
# ##############################################
# # STEP 1: DEFINE ODEs
#
# #############################################
# SIR = function(t, y, params) {
# with(as.list(c(y, params)), {
# incid = Beta * S * I#Just for convenience
# dS.dt = -incid/N
# dE.dt= incid/N - alpha*E
# dI.dt = alpha*E - gamma*I
# # dI.dt = incid - gamma * I
# dR.dt = gamma * I
# return(list(c(dS.dt, dE.dt,dI.dt, dR.dt)))
# })
# }
#
# #################################################
# # STEP 2: SUPPLY INITIAL CONDITIONS
# #################################################
#
# # Initial conditions: starting values of state variables
# # (S, I and R)
# ########################################################
# logN = log(1700000000 ) #12447000
# N = exp(logN)
# #N= 5000000
# #Total N
#
#
#
# I = 10#24530/N #
# R = 0#6278/N
# E = 1#1/N
# S = N-I-R-E #N/N -E- I-R
# R0=2.2
# #N = S + I + R #Total pop size is the sum of the S, I and R
#
# # Need *named* initial conditions. That is, put initial values of
# # S, I and R and give it a name. Here, we are calling the vector
# # of initial values "y"
#
# y = c(S = S, I = I,E=E, R = R)
#
# ###########################################################
# # STEP 3: SUPPLY PARAMETER VALUES
# #
# #Parameters: values of Beta and gamma
# ##########################################################
# # β and γ represented the probability of a susceptible-infected contact resulting in a new infection
# #and the probability of an infected case recovering and moving into the resistant phase, respectively.
#
# gamma = 1/3# 1/ time for healing 0.125#rate of recovery=8 days for milf 1/3.38#1/5 #0.08
# social_distancing=1
# Beta= 1.9/3#social_distancing*1.9/3
# alpha= 1/5.2 #incubation#beta= Rt/T infection
#
# #beta is the parameter to change for social distancing
# #reduce Rt and beta would be lower
# #gamma depends on disease, cant change unless cure appears
# #beta depends on social distancing
# #
# params = list(Beta = Beta, gamma = gamma, alpha=alpha)
#
# #time over which to solve the odes
# t = seq(0,100, by =1)
# #Initial population vector (initial conditions)
#
# ########################################################
# # STEP 4a: SOLVE THE ODEs AND PLOT RESULTS
# ########################################################
#
#
# library(deSolve)
# results0 = ode(
# y = y,
# times = t,
# func = SIR,
# parms = params
# )
#
#
# #Change the data structure to data.frame so it can be plotted
# results = data.frame(results0)
# results
# ########################################################
# # STEP 4b: EXAMINE AND PLOT THE RESULTS
# ########################################################
#
# head(results)
# tail(results)
# #785836.2*0.02
#
# matplot(
# t,
# results[, -c(1,2,4)],
# type = 'l',
# lty = 1:3,
# col = 1:3,
# xlab = "Time",
# ylab = "S, I or R",
# cex = 1.5,
# cex.main = 1.5,
# cex.lab = 1.5,
# lwd = 2,
# cex.axis = 1.5,
# ylim=c(0,10e+2)
# )
# legend(
# 'right',
# c( 'I','R'),
# lty = 1:3,
# col = 1:3,
# bty = 'n',
# lwd = 2,
# cex = 1.5
# )
```
Summary
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases today",
#icon = "fas fa-user-md",
color = confirmed_color
)
```
### death {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate) today",
# icon = "fas fa-heart-broken",
color = death_color
)
```
### Growth rate {.value-box}
```{r }
r = mean(log(lambda), na.rm = T)
valueBox(
value = paste(format(round(r,2)) ),
caption = "Growth rate today",
icon = "fas fa-heart-broken",
color = doubling_color
)
```
### Doubling time for last 15 days data {.value-box}
```{r }
valueBox(
value = paste(
round(log(2)/r.last15, 3),
"days" ),
caption = "Doubling period (last 15 days)",
icon = "fas fa-heart-broken",
color = purple
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type** (India only)
```{r}
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_trace(
x = ~date,
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::add_annotations(
x = as.Date("2020-01-30"),
y = 1,
text = paste("First case"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-12"),
y = 3,
text = paste("First death"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -90,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-24"),
y = 14,
text = paste(
"New containment",
"",
"measures"
),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-04-14"),
y = 14,
text = paste(
"Cont. ",
"",
"ext1 "
),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-05-04"),
y = 14,
text = paste(
"Cont. ",
"",
"ext2."
),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-05-18"),
y = 14,
text = paste(
"Cont. ",
"",
"ext3."
),
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**
```{r}
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::filter(date >= "2020-02-03") %>%
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 = ~India,
type = "scatter",
mode = "lines+markers",
name = "India"
) %>%
plotly::add_trace(
x = ~date,
y = ~US,
type = "scatter",
mode = "lines+markers",
name = "U.S.A"
) %>%
# plotly::add_trace(
# x = ~date,
# y = ~Spain,
# type = "scatter",
# mode = "lines+markers",
# name = "Spain"
# ) %>%
plotly::add_trace(
x = ~date,
y = ~Italy,
type = "scatter",
mode = "lines+markers",
name = "Italy"
) %>%
# plotly::add_trace(
# x = ~date,
# y = ~ "Korea, South" ,
# type = "scatter",
# mode = "lines+markers",
# name = "South Korea"
# ) %>%
plotly::add_trace(
x = ~date,
y = ~Thailand,
type = "scatter",
mode = "lines+markers",
name = "Thailand"
) %>%
plotly::add_trace(
x = ~date,
y = ~China,
type = "scatter",
mode = "lines+markers",
name = "China"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of 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**
```{r daily_summary}
df_EU <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "India" |
country == "China" |
country == "Italy" |
country == "Thailand"|country == "US" ) %>%
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 = "cyan")
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = "purple")
) %>%
plotly::layout(
barmode = "dodge",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
Projections
=======================================================================
```{r}
library(dplyr)
df_ind <- coronavirus %>%
# lubridate::ymd(date) %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "India" ) %>%
dplyr::filter(cases!=0) %>%
dplyr::filter(date >"2020-03-04") %>%
dplyr::arrange(date)
df_ind<-df_ind %>% dplyr::mutate(day=1:nrow(df_ind)) %>%
tidyr::pivot_wider(names_from=type,values_from=cases)#|
#select only confirmed cases
df_ind_conf<-df_ind %>%
dplyr::filter(!is.na(confirmed)) %>% dplyr::select(day,date,confirmed) %>% dplyr::mutate(total_cases= cumsum(confirmed))
# country == "China" |
# country == "Italy" |
# country == "Thailand"|country == "US" ) %>%
# 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))
###growth rate##################
#Estimate the ratio method , this just calculated pop at t+1/pop at t
lambda = df_ind_conf$total_cases[-1]/ df_ind_conf$total_cases[-length(df_ind_conf$total_cases)] #The #"-" notation excludes specified entry of a vector
#lambda
#mean lambda
#mean(lambda, na.rm = T)
#sd(lambda, na.rm = T)
#Mean and sd of continuous time disease growth rate
r = mean(log(lambda), na.rm = T)
sd_r = sd(log(lambda), na.rm = T)
#generate a function that takes a value of r and runs one stochastic simulation
#so we can use both means and variance of r
exp.stoch = function(r_sim) {
for (i in 1:time) {
N_sim[i + 1] = N_sim[i] * exp(r_sim[i]) #this is the exponential growth formula
#N at t+1 = N at t*exp(r*t)
}
return(N_sim)
}
time = length(lambda)#how many time steps of data is availble
#sample a value of r for each time step
r_sim = rnorm(time, r, sd_r)
#Initialize the population vector, and use N0 as the starting population size
N_sim = rep(NA, time)
len = length(df_ind_conf$total_cases)-1
#Initialize the population vector, and use N0 as the starting population size
N0 = df_ind_conf$total_cases[1] #To check the model
N_sim[1] = N0
#number of simulations to run
n_sims = 1000 # change it to any number you want...
#Create an empty matrix to hold results
stoch.out = matrix(, nrow = time + 1, ncol = n_sims)
for (s in 1:n_sims) {
r_sim = rnorm(time, r, sd_r)
N = exp.stoch(r_sim)
stoch.out[, s] = N
}
Projected_Cases = rowMeans(stoch.out)
q95 = apply(stoch.out, 1, quantile, probs = c(.025, 0.975))
lower = (q95[1, ])
upper = (q95[2, ])
stoch.out<-as.data.frame(cbind(Projected_Cases,lower,upper,df_ind_conf$day, df_ind_conf$total_cases))
#####################
plotly::plot_ly(
data = stoch.out,
x = ~V4,
# y = ~unrecovered,
y = ~ Projected_Cases,
# text = ~days ,
# textposition = 'auto',
type = "scatter",
mode='lines',
name = "Average projected cases"
#marker = list(color = "cyan")
) %>%
plotly::add_trace(
y = ~upper,
# text = ~ death,
# textposition = 'auto',
type = "scatter",
mode='lines',
name = "Upper CI"
#xlim=c(0,100000),
#marker = list(color = "purple")
) %>%
plotly::add_trace(
y = ~lower,
# text = ~ death,
# textposition = 'auto',
name = "Lower CI",
type = "scatter",
mode='lines'
# marker = list(color = "purple")
) %>%
plotly::add_trace(
y = ~V5,
type = "scatter",
mode= "points",
# text = ~ death,
# textposition = 'auto',
name = "Observed cases"
#marker = list(color = "purple")
)
```
SIR
====================================================================
```{r}
# india
r0= 5*r+1
Infected <- df_ind_conf$confirmed
Day <- 1:(nrow(df_ind_conf))
N <- 1378131607#66000000 # pupulation of the UK
# old <- par(mfrow = c(1, 2))
# plot(Day, Infected, type ="b",col='red')
# plot(Day, Infected, log = "y",col='purple')
# abline(lm(log10(Infected) ~ Day))
# title("Confirmed infections COVID-19 in the India", outer = TRUE, line = -2)
#
SIR <- function(time, state, parameters) {
par <- as.list(c(state, parameters))
with(par, {
dS <- -beta/N * I * S
dI <- beta/N * I * S - gamma * I
dR <- gamma * I
list(c(dS, dI, dR))
})
}
library(deSolve)
init <- c(S = N-Infected[1], I = Infected[1], R = 0)
sir <- function(parameters) {
names(parameters) <- c("beta", "gamma")
out <- ode(y = init, times = Day, func = SIR, parms = parameters)
fit <- out[ , 3]
sum((Infected - fit)^2)
}
Opt <- optim(c(0.5, 0.5), sir, method = "L-BFGS-B", lower = c(0, 0), upper = c(1, 1)) # optimize with some sensible conditions
#Opt$message
## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
Opt_par <- setNames(Opt$par, c("beta", "gamma"))
print(Opt_par)
## beta gamma
## 0.6746089 0.3253912
R0= Opt$par[1]/Opt$par[2]
print(R0)
t <- 1:100 # time in days
fit <- data.frame(ode(y = init, times = t, func = SIR, parms = Opt_par))
#col <- 1:3 # colour
# print(fit)
#
# plotly::plot_ly(
# data = fit,
# x = ~t,
# # y = ~unrecovered,
# y = ~ log(fit[,3]),
# # text = ~days ,
# # textposition = 'auto',
# type = "scatter",
# mode='lines',
# name = "Infected")
# # yaxis = list(type = "log")) %>%
# #marker = list(color = "cyan")
#
# plotly::add_trace(
# y = ~ fit[,4],
# # text = ~ death,
# # textposition = 'auto',
# type = "scatter",
# yaxis = list(type = "log"),
# mode='lines',
# name = "Recovered"
# #xlim=c(0,100000),
# #marker = list(color = "purple")
# )
matplot(fit$time, fit[ , 3], type = "l", xlab = "Day", ylab = "Number of cases", lwd = 2, lty = 1, col = c("orange")) #log = "y")
points(Day, Infected,col='green')
legend("topright", c( "Infected"), lty = 1, lwd = 2, col =c("orange"), inset = 0.05)
title("Predicted Cases India (worst case)", outer = TRUE, line = -2)
```
Map
=======================================================================
### **World map of cases** (*use + and - icons to zoom in/out*)
```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
# dplyr::filter(country == "India") %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(country, province, lat, long, type) %>%
dplyr::summarise(cases = sum(cases)) %>%
dplyr::mutate(log_cases = 2 * log(cases)) %>%
dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("green", "orange", "pink"), domain = c("confirmed", "death", "recovered"))
map_object<- leaflet() %>% addProviderTiles(providers$OpenStreetMap)
#map_object <- leaflet() %>% addProviderTiles(providers$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 = 0.4,
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 India**
This Coronavirus dashboard: the case of India provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for India. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.
Code also modified from
https://www.antoinesoetewey.com/files/coronavirus-dashboard.html
**Code**
**Data**
The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/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 data and dashboard are refreshed on a daily basis.
The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.
