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Visualizing_Animation.Rmd

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---
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title: "Comparing new_deaths in USA & UK"
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author: "Md Easin Hasan"
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date: "4/17/2021"
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output: word_document
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---
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```{r}
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data<-read.csv(file="owid-covid-data417.csv",header=TRUE)
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dim(data)
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data1 <- data[data$iso_code == "USA", ]
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data2 <- data[data$iso_code == "GBR", ]
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data3<-rbind(data1,data2)
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country <- ifelse(data3$iso_code == "USA",0,1)
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data4 <- cbind(country,data3)
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data4$country<-as.factor(data4$country)
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data4$country<-factor(data4$country,levels=c(0,1),labels=c("USA", "UK"))
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data5 <- data4[,-c(2)]
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dat2 <-data5[,c(1,4,9)]
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colnames(dat2)
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```
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We have obtained the data-set on COVID-19 (coronavirus) by Our World in Data. They have up-to-date data on confirmed cases, deaths, hospitalizations, testing, and vaccinations, throughout the duration of the COVID-19 pandemic.
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```{r, warning=FALSE}
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library(ggplot2)
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library(gganimate)
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library(viridis)
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library(scales)
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theme_set(theme_bw())
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plot <- ggplot(dat2, aes(x = as.Date(date), y = new_deaths, color = country)) +
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geom_line()+
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geom_point(alpha = 0.3)+
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scale_x_date(labels = date_format("%m-%d-%Y"))+
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labs(x = "date")+
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labs(y = "Number of new_deaths")+
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ggtitle("Plot of new deaths in USA & UK \n due to COVID-19")
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plot
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```
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From this visualization, we can compare the new_deaths in USA & UK due to COVID-19. In addition, we can figure out the number of new_deaths.
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# Data Visualization improvement:
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```{r, warning=FALSE}
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library(magick)
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plot <- ggplot(dat2, aes(x = as.Date(date), y = new_deaths, color = country)) +
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geom_line()+
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geom_point(alpha = 0.7)+
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transition_reveal(as.Date(date))+
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scale_x_date(labels = date_format("%m-%d-%Y"))+
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labs(x = "date")+
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labs(y = "Number of new_deaths")+
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ggtitle("Plot of new deaths in USA & UK \n due to COVID-19")
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animation <- animate(plot, nframes=100, renderer=magick_renderer())
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animation
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image_write_gif(animation, 'animation.gif')
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```

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