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library(readxl)
require(caTools)

## Loading required package: caTools

## Warning: package 'caTools' was built under R version 3.6.3

library(ggplot2)

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library(plotly)

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library(dplyr)

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library(mgcv)

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## Attaching package: 'nlme'

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## This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.

library(shiny)

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library(shinydashboard)

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library(mgcv)
library(shinyWidgets)

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library(ggrepel)

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library(forcats)
library(AER)

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library(devtools)

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library(rsconnect)

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COVID <- read_excel("COVID-19-geographic-disbtribution-worldwide.xlsx")


x <- format(as.Date(COVID$dateRep), "%Y-%m")

COVID$data <- x
ggplot(data=COVID,aes(x=COVID$dateRep,y=COVID$cases))+geom_jitter()

## Warning: Use of `COVID$dateRep` is discouraged. Use `dateRep` instead.

## Warning: Use of `COVID$cases` is discouraged. Use `cases` instead.

coutry<- 
  as.data.frame(aggregate(COVID$cases~as.factor(COVID$countriesAndTerritories), data=COVID, FUN=sum) )

x<- coutry[order(-coutry$`COVID$cases`) , ]
x[1:10,1]

##  [1] United_States_of_America Spain                    Italy                   
##  [4] Germany                  China                    France                  
##  [7] Iran                     United_Kingdom           Turkey                  
## [10] Switzerland             
## 204 Levels: Afghanistan Albania Algeria Andorra Angola ... Zimbabwe

z<- x[x$`as.factor(COVID$countriesAndTerritories)`=="Italy"|
        x$`as.factor(COVID$countriesAndTerritories)`=="Spain",]

# COVID %>%
#   filter(COVID$countriesAndTerritories=="Italy"|
#            countriesAndTerritories=="United_Kingdom"|
#            countriesAndTerritories=="France"|
#            countriesAndTerritories=="Italy")

library(dplyr)
library(gapminder)

## Warning: package 'gapminder' was built under R version 3.6.3

head(gapminder)

## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

library(datasauRus)

## Warning: package 'datasauRus' was built under R version 3.6.3

library(ggplot2)
library(gganimate)

## Warning: package 'gganimate' was built under R version 3.6.3

cov<- COVID%>%
  filter(COVID$countriesAndTerritories %in% x[1:10,1])

plot<- ggplot(data=cov,aes(x=countriesAndTerritories,y=(cov$cases),
                    colour=factor(countriesAndTerritories),
                                  label=factor(countriesAndTerritories)))+
  geom_point(stat = "identity",size = 15)+geom_segment(aes(x= countriesAndTerritories, y=0,
                                                         yend= cases,
                                                           xend= countriesAndTerritories))+
  geom_text(colour="black",size = 3)+
  coord_flip()

#ggplotly(plot)

p<- plot+transition_time((dateRep))+ease_aes("linear")
animate(p,fps=3)

## Warning: Use of `cov$cases` is discouraged. Use `cases` instead.

## Warning: Use of `cov$cases` is discouraged. Use `cases` instead.

# 
# p <- ggplot(
#   gapminder, 
#   aes(x = gdpPercap, y=lifeExp, size = pop, colour = country)
# ) +
#   geom_point(show.legend = FALSE, alpha = 0.7) +
#   scale_color_viridis_d() +
#   scale_size(range = c(2, 12)) +
#   scale_x_log10() +
#   labs(x = "GDP per capita", y = "Life expectancy")
# p
# 
## file_renderer(dir = ".", prefix = "gganim_plot", overwrite = FALSE)
# 
# 
# p + transition_time(year) +
#   labs(title = "Year: {frame_time}")