Visualizing_Animation.Rmd

---
title: "Comparing new_deaths in USA & UK"
author: "Md Easin Hasan"
date: "4/17/2021"
output: word_document
---
```{r}
data<-read.csv(file="owid-covid-data417.csv",header=TRUE)
dim(data)
data1 <- data[data$iso_code == "USA", ] 
data2 <- data[data$iso_code == "GBR", ] 
data3<-rbind(data1,data2)
country <- ifelse(data3$iso_code == "USA",0,1)
data4 <- cbind(country,data3)
data4$country<-as.factor(data4$country)
data4$country<-factor(data4$country,levels=c(0,1),labels=c("USA", "UK"))
data5 <- data4[,-c(2)]
dat2 <-data5[,c(1,4,9)]
colnames(dat2)

```
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.



```{r, warning=FALSE}
library(ggplot2)
library(gganimate)
library(viridis)
library(scales)
theme_set(theme_bw())
plot <- ggplot(dat2, aes(x = as.Date(date), y = new_deaths, color = country)) +
  geom_line()+
  geom_point(alpha = 0.3)+ 
  scale_x_date(labels = date_format("%m-%d-%Y"))+
  labs(x = "date")+
  labs(y = "Number of new_deaths")+
  ggtitle("Plot of new deaths in USA & UK \n due to COVID-19")
plot




```

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.


# Data Visualization improvement:

```{r, warning=FALSE}
library(magick)
plot <- ggplot(dat2, aes(x = as.Date(date), y = new_deaths, color = country)) +
  geom_line()+
  geom_point(alpha = 0.7)+
  transition_reveal(as.Date(date))+ 
  scale_x_date(labels = date_format("%m-%d-%Y"))+
  labs(x = "date")+
  labs(y = "Number of new_deaths")+
  ggtitle("Plot of new deaths in USA & UK \n due to COVID-19")
animation <- animate(plot, nframes=100, renderer=magick_renderer())  
animation
image_write_gif(animation, 'animation.gif')

```