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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## last_plot
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## filter
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## layout
library(dplyr)
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## filter, lag
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## intersect, setdiff, setequal, union
library(mgcv)
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## Attaching package: 'nlme'
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## collapse
## This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
library(shiny)
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library(shinydashboard)
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## box
library(mgcv)
library(shinyWidgets)
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library(ggrepel)
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library(forcats)
library(AER)
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## Loading required package: car
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## recode
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## as.Date, as.Date.numeric
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library(devtools)
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library(rsconnect)
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## serverInfo
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=cov$dateRep,y=(cov$cases),
colour=factor(countriesAndTerritories),
label=factor(countriesAndTerritories)))+
geom_point(stat = "identity",size = 15)+geom_segment(aes(x= dateRep, y=100,
yend= cases,
xend= dateRep))+
geom_text(colour="black",size = 3)
#ggplotly(plot)
plot+transition_time((dateRep))
## Warning: Use of `cov$dateRep` is discouraged. Use `dateRep` instead.
## Warning: Use of `cov$cases` is discouraged. Use `cases` instead.
## Warning: Use of `cov$dateRep` is discouraged. Use `dateRep` instead.
## Warning: Use of `cov$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}")