Lab5extra-R_BA.Rmd

---
title: "Interactive Graphs and Animations from the COVID-19 reporting data"
author: Berent Aldikacti
date: "9/28/2020"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = FALSE,message = FALSE)
```

```{r include=FALSE}
library(tidyverse)
library(lubridate)
library(gganimate)
library(transformr)
library(gifski)
library(plotly)
```

```{r}
time_series_confirmed_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
  rename(Province_State = "Province/State", Country_Region = "Country/Region")  %>% 
               pivot_longer(-c(Province_State, Country_Region, Lat, Long),
                             names_to = "Date", values_to = "Confirmed") 
# Let's get the times series data for deaths
time_series_deaths_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")) %>%
  rename(Province_State = "Province/State", Country_Region = "Country/Region")  %>% 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long),
               names_to = "Date", values_to = "Deaths")
# Create Keys 
time_series_confirmed_long <- time_series_confirmed_long %>% 
  unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
time_series_deaths_long <- time_series_deaths_long %>% 
  unite(Key, Province_State, Country_Region, Date, sep = ".") %>% 
  select(Key, Deaths)
# Join tables
time_series_long_joined <- full_join(time_series_confirmed_long,
    time_series_deaths_long, by = c("Key")) %>% 
    select(-Key)
# Reformat the data
time_series_long_joined$Date <- mdy(time_series_long_joined$Date)
# Create Report table with counts
time_series_long_joined_counts <- time_series_long_joined %>% 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
               names_to = "Report_Type", values_to = "Counts")
```


```{r}
data_time <- time_series_long_joined %>% 
    group_by(Country_Region,Date) %>% 
    summarise_at(c("Confirmed", "Deaths"), sum) %>% 
    filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US")) 
p <- ggplot(data_time, aes(x = Date,  y = Confirmed, color = Country_Region)) + 
      geom_point() +
      geom_line() +
      ggtitle("Confirmed COVID-19 Cases") +
      geom_point(aes(group = seq_along(Date))) +
      transition_reveal(Date) 
# Some people needed to use this line instead
animate(p,renderer = gifski_renderer(), end_pause = 15)
#animate(p, end_pause = 15)
```

## Challenges

1. Print a graph (different from the one above) to a png file using 3*ppi for the height and width and display the png file in the report using the above R Markdown format.
```{r fig.height=6, fig.width=8}
p2 <- time_series_long_joined_counts %>% 
  group_by(Country_Region,Date,Report_Type) %>% 
  summarise(Counts=sum(Counts)) %>% 
  filter (Country_Region == "Turkey") %>% 
    ggplot(aes(x = Date,  y = log2(Counts), color=Report_Type)) + 
    geom_line(size=2) +
    theme_bw()
    ggtitle("Turkey COVID-19 Deaths")
p2
ppi <- 300
png("images/time_series_example_plot.png", width=6*ppi, height=6*ppi, res=ppi)
p2
dev.off()
```

2. Turn one of the exercises from Lab 5 into an interactive graph with plotyly
```{r message=FALSE}
Top10 <- time_series_long_joined_counts %>% filter(Report_Type=="Deaths") %>% group_by(Country_Region) %>% summarise(Counts=sum(Counts)) %>% top_n(10)
pl <- time_series_long_joined_counts %>% filter(Report_Type=="Deaths" & Country_Region%in%Top10$Country_Region) %>%
  group_by(Country_Region,Date,Report_Type) %>%
  summarize(Counts = sum(Counts)) %>%
  ggplot(aes(x=Date,y=Counts,color=Country_Region)) + 
    geom_line(size=1) +
    theme_bw() +
    facet_wrap(~Country_Region, ncol=3) +
    labs(title = "COVID19 Deaths of Top10 Highest",
        x = "Date",
        y = "Death") +
    theme(axis.text.x = element_text(size = 12),
          axis.text.y = element_text(size = 12),
          text=element_text(size = 16))

ggplotly(pl, height = 800, width = 1000, res=300)
```

3. Create an animated graph of your choosing using the time series data to display an aspect (e.g. states or countries) of the data that is important to you.
```{r gganimate = list(fig.height=8, fig.width=12)}
p3 <- time_series_long_joined_counts %>% filter(Report_Type=="Confirmed" & Country_Region=="United Kingdom") %>%
  group_by(Province_State,Date) %>%
  summarize(Counts = sum(Counts)) %>%
  ggplot(aes(x=Date,y=Counts,color=Province_State)) + 
    geom_line(size=1) +
    theme_linedraw() +
    facet_wrap(~Province_State, ncol=3, scales = "free_y") +
    labs(title = "COVID19 Deaths of UK Territories",
        x = "Date",
        y = "Death") +
    theme(axis.text.x = element_text(size = 12),
          axis.text.y = element_text(size = 12),
          text=element_text(size = 16)) + 
    transition_reveal(Date)

animate(p3,renderer = gifski_renderer(), end_pause = 15, height=800, width=1200)
anim_save("Time-series-UK-COVID-data.gif", p3)
```