dataviz_hw6.Rmd

---
title: "Visualizing Amounts Improvement"
author: "Md Easin Hasan"
date: "3/9/2021"
output: word_document
---

# Import the dataset:
```{r}
data<-read.csv(file="owid-covid-data.csv",header=TRUE)
dim(data)
```

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}
data1 <- data[data$iso_code == "USA", ] 
data3 <- data[69885:69962,]
data3 <-data3[,c(1,4,35,36,37,38,39,40,41,42,43)]
colnames(data3)
```

Since we will visualize the number of people vaccinated in USA, here we kept the observations only on USA. The covid-19 vaccinations starts from Dec 20, 2020 so we considered the observations from Dec 20, 2020 to Mar 07, 2021 in USA. 

```{r}
miss.info<- function(dat, filename=NULL){  
  
  vnames <- colnames(dat); vnames 
  
  n <- nrow(dat)  
  
  out <- NULL  
  
  for (j in 1: ncol(dat)){  
    
    vname <- colnames(dat)[j]  
    
    x <- as.vector(dat[,j]) 
    
    n1 <- sum(is.na(x), na.rm=T)  
    
    n2 <- sum(x=="NA", na.rm=T)  
    
    n3 <- sum(x=="", na.rm=T)  
    
    nmiss <- n1 + n2 + n3  
    
    ncomplete <- n-nmiss 
    
    out <- rbind(out, c(col.number=j, vname=vname, mode=mode(x), n.levels=length(unique(x)), ncomplete=ncomplete, miss.perc=nmiss/n)) } 
  
  out <- as.data.frame(out)  
  
  row.names(out) <- NULL  
  
  return(out) 
  
}  

miss.info(data3) 

```

Here we checked the missing value in the selected observations. Since we would like to see in which date there is no vaccination reported, we didn't remove the missing values.

# people vaccinated
```{r}
library(ggplot2)
library(reshape2)
library(scales)

ggplot(data3, aes(x = as.Date(date), y = people_vaccinated)) +
    xlab("date") + ylab("People Vaccinated")+
    geom_bar(stat="identity")+
    scale_x_date(labels = date_format("%m-%d-%Y"))
```

From this graph we can see that number of people vaccinated is increasing gradually.

# people fully vaccinated
```{r}

ggplot(data3, aes(x = as.Date(date), y = people_fully_vaccinated)) +
  xlab("date") + ylab("People fully Vaccinated")+
  geom_bar(stat="identity")+
  scale_x_date(labels = date_format("%m-%d-%Y"))
```

The number of people fully vaccinated also increasing gradually.

# Visualizing Amounts Improvement
```{r}
dfm <- melt(data3[,c('date','people_vaccinated','people_fully_vaccinated')],id.vars = 1)

ggplot(dfm,aes(x = as.Date(date),y = value)) + 
  geom_bar(aes(fill = variable),stat = "identity") + 
  scale_x_date(labels = date_format("%m-%d-%Y"))+
  labs(x = "date")+
  labs(y = "Number of people vaccinated vs fully vaccinated")
```

From this visualization we can very easily compare the number of people vaccinated vs number of people fully vaccinated each day.