rtremeaud.github.io/Untitled.Rmd at master · rtremeaud/rtremeaud.github.io · GitHub

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---
title: "COVID19 figures new cases, testing, death"
subtitle: "Sweden, Italy, France, Switzerland, Germany update 2020-12-04 "
output:
  html_document:
    keep_md: true
---


```{r setup, message = FALSE, echo = FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(utility)
library(plotly)
library(hrbrthemes)
date<-Sys.Date()
data <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM")

data2 <- read.csv("https://covid.ourworldindata.org/data/owid-covid-data.csv", na.strings = "", fileEncoding = "UTF-8-BOM")

Gruppen<-data[ data$countriesAndTerritories=="Switzerland" | data$countriesAndTerritories=="Sweden" |  data$countriesAndTerritories=="France" | data$countriesAndTerritories=="Germany" | data$countriesAndTerritories=="Italy" ,]
Gruppen$dateRep <- as.Date.factor(Gruppen$dateRep, format= "%d/%m/%Y")
colnames(Gruppen)[1]<-"Date"
colnames(Gruppen)[7]<- "Countries"
colnames(Gruppen)[12]<-"Cumul_14_days_cases_per_100000"
dd<-Gruppen[(Gruppen$Date > "2020-06-01" & Gruppen$Date < "2020-09-12"),]
########<><><><><><><><>####

 p <- ggplot(Gruppen,aes(x = Date,y = Cumul_14_days_cases_per_100000,color=Countries))+geom_line() + theme_ipsum() + guides( color = FALSE)
p1 <- ggplot(Gruppen,aes(x = Date,y = cases,color=Countries))+geom_line() + theme_ipsum() + guides( color = FALSE)
p2 <- ggplot(Gruppen,aes(x = Date,y = deaths,color=Countries))+geom_line() + theme_ipsum() + guides( color = FALSE)
p3 <- ggplot(Gruppen,aes(x = Date,y = (Cumul_14_days_cases_per_100000)/2,color=Countries))+geom_line() + theme_ipsum() + guides( color = FALSE)

p<- ggplotly(p, dynamicTicks = TRUE) %>%
  rangeslider()
p1<- ggplotly(p1, dynamicTicks = TRUE) %>%
  rangeslider()
p2<- ggplotly(p2, dynamicTicks = TRUE) %>%
  rangeslider()


```
***

##### New confirmed cases of COVID-19 (7-day smoothed) per million
***
```{r, message = FALSE, echo = FALSE, warning=FALSE,out.width="100%"}
Gruppen2<-data2[ data2$location=="Switzerland" | data2$location=="France" |data2$location=="Sweden" | data2$location=="Germany" | data2$location=="Italy" ,]
Gruppen2$date <- as.Date.factor(Gruppen2$date, format= "%Y-%m-%d")
dd<-Gruppen2[(Gruppen2$date > "2020-06-01"),]

p3 <- ggplot(dd,aes(x = date,y=new_cases_smoothed_per_million,color=location)) + geom_line () + theme_ipsum()
ggplotly(p3, dynamicTicks = TRUE) %>%
rangeslider()
```

##### Total_cases_per_million of COVID-19
***
```{r , message = FALSE, echo = FALSE, warning=FALSE,out.width="100%"}
Gruppen2<-data2[ data2$location=="Switzerland" | data2$location=="France" |data2$location=="Sweden" | data2$location=="Germany" | data2$location=="Italy" ,]
Gruppen2$date <- as.Date.factor(Gruppen2$date, format= "%Y-%m-%d")
dd<-subset(Gruppen2,date > "2020-05-01")

p3 <- ggplot(dd,aes(x = date,y=total_cases_per_million,color=location)) + geom_line () + theme_ipsum()
ggplotly(p3, dynamicTicks = TRUE) %>%
rangeslider()
```

##### Death COVID-19 (new_deaths_per_million)
***
```{r, message = FALSE, echo = FALSE, warning=FALSE,out.width="100%"}
Gruppen2<-data2[ data2$location=="Switzerland" | data2$location=="France" |data2$location=="Sweden" | data2$location=="Germany" | data2$location=="Italy",]
Gruppen2$date <- as.Date.factor(Gruppen2$date, format= "%Y-%m-%d")
dd<-subset(Gruppen2,date > "2020-05-01")

p3 <- ggplot(dd,aes(x = date,y=new_deaths_per_million,color=location)) + geom_line () +
  theme_ipsum()
ggplotly(p3, dynamicTicks = TRUE) %>%
rangeslider()
```


##### New deaths attributed to COVID-19 (7-day smoothed)
***
New tests for COVID-19 (7-day smoothed).
For countries that don't report testing data on a daily basis, we assume that testing changed equally on a daily basis over any periods in which no data was reported.
This produces a complete series of daily figures, which is then averaged over a rolling 7-day window
***


```{r, message = FALSE, echo = FALSE, warning=FALSE,out.width="100%"}
Gruppen2<-data2[ data2$location=="Switzerland" | data2$location=="France" |data2$location=="Sweden" | data2$location=="Germany" | data2$location=="Italy" ,]
Gruppen2$date <- as.Date.factor(Gruppen2$date, format= "%Y-%m-%d")
dd<-subset(Gruppen2,date > "2020-05-01")

p3 <- ggplot(dd,aes(x = date,y=new_deaths_per_million,color=location)) + theme_ipsum() +geom_smooth()
ggplotly(p3, dynamicTicks = TRUE) %>%
rangeslider()
```
##### new_tests_smoothed_per_thousand COVID-19 (7-day smoothed).

***
```{r, message = FALSE, echo = FALSE, warning=FALSE,out.width="100%"}
Gruppen2<-data2[ data2$location=="Switzerland" | data2$location=="France" |data2$location=="Sweden" | data2$location=="Germany" | data2$location=="Italy" ,]
Gruppen2$date<-as.Date(Gruppen2$date)
Gruppen2<-subset(Gruppen2,date > "2020-05-01")

p5 <- ggplot(Gruppen2,aes(x = date,y=new_tests_smoothed_per_thousand		,color=location)) + geom_line () + theme_ipsum()
ggplotly(p5, dynamicTicks = TRUE)
```


##### Tests conducted per new confirmed case of COVID-19

given as a rolling 7-day average (this is the inverse of positive_rate)

***

```{r, message = FALSE, echo = FALSE, warning=FALSE,out.width="100%"}
Gruppen2$date<-as.Date(Gruppen2$date)
dafortest<-subset(Gruppen2,date > "2020-05-01")

p5 <- ggplot(dafortest,aes(x = date,y=tests_per_case,color=location)) + geom_line () + theme_ipsum()
ggplotly(p5, dynamicTicks = TRUE)
```


Sources:

***

[opendata ecdc](https://opendata.ecdc.europa.eu/covid19/casedistribution/csv)

[ourworldindata](https://covid.ourworldindata.org/data/owid-covid-data.csv)

[Code book information](https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-codebook.csv)

***