Lab6-R_BA.Rmd

---
title: "Data Maps and Interactive Graphs from the COVID-19 reporting data"
author: Berent Aldikacti
date: "09/28/2020"
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(maps)
library(mapdata)
library(lubridate)
library(viridis)
library(wesanderson)
library(plotly)
```

```{r message=FALSE}
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
  rename(Long = "Long_") 
```

```{r warning=FALSE}
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
    borders("world", colour = NA, fill = "grey90") +
    theme_bw() +
    geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
    labs(title = 'World COVID-19 Confirmed cases',x = '', y = '',
        size="Cases (x1000))") +
    theme(legend.position = "right") +
    coord_fixed(ratio=1.5)
```
```{r warning=FALSE}
library(RColorBrewer)
# To display only colorblind-friendly brewer palettes, specify the option colorblindFriendly = TRUE as follow:
# display.brewer.all(colorblindFriendly = TRUE)
# Get and format the covid report data
report_03_27_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  unite(Key, Admin2, Province_State, sep = ".") %>% 
  group_by(Key) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Key = tolower(Key))
# dim(report_03_27_2020)
# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>% 
  unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, report_03_27_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  # Add data layer
  borders("state", colour = "black") +
  geom_polygon(data = state_join, aes(fill = Confirmed)) +
  scale_fill_gradientn(colors = brewer.pal(n = 5, name = "PuRd"),
                       breaks = c(1, 10, 100, 1000, 10000, 100000),
                       trans = "log10", na.value = "White") +
  ggtitle("Number of Confirmed Cases by US County") +
  theme_bw() 
```

## Exercises

1. For the above graph “World COVID-19 Confirmed case” summarize the counts for each Country on the graph and update the graph to 9/26/2020. You may need to adjust the size of the points.
```{r message=FALSE}
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-27-2020.csv")) %>%
  rename(Long = "Long_") 
```

```{r warning=TRUE, fig.height=8, fig.width=12}
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
    borders("world", colour = NA, fill = "grey90") +
    theme_bw() +
    geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5, size=0.5) +
    labs(title = 'World COVID-19 Confirmed cases',x = '', y = '', size="Cases (x1000)") + 
    theme(legend.position = "right") + 
    coord_fixed(ratio=1.5)
```


2. Update Anisa Dhana’s graph layout of the US to 9/26/2020. You may need to adjust the size of the points.
```{r message=FALSE ,warning=FALSE}
daily_report <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-27-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Country_Region == "US") %>% 
  filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
                  "Puerto Rico","Northern Mariana Islands", 
                  "Virgin Islands", "Recovered", "Guam", "Grand Princess",
                  "District of Columbia", "Diamond Princess")) %>% filter(Lat > 0)
```

```{r warning=FALSE, fig.height=8, fig.width=12}
mybreaks <- c(1, 100, 1000, 10000, 100000)
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed)) +
    borders("state", colour = "white", fill = "grey90") +
    geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
    scale_size_continuous(name="Cases", trans="log", range=c(1,7), 
                        breaks=mybreaks, labels = c("1-99",
                        "100-999", "1,000-9,999", "10,000-99,999", "100,000+")) +
    scale_color_viridis_c(option="viridis",name="Cases",
                        trans="log", breaks=mybreaks, labels = c("1-99",
                        "100-999", "1,000-9,999", "10,000-99,999", "100,000+"))  +
# Cleaning up the graph
  
  theme_void() + 
    guides( colour = guide_legend()) +
    labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US'") +
    theme(
      legend.position = "bottom",
      text = element_text(color = "#22211d",size = 16),
      plot.background = element_rect(fill = "#ffffff", color = NA), 
      panel.background = element_rect(fill = "#ffffff", color = NA), 
      legend.background = element_rect(fill = "#ffffff", color = NA)
    ) +
    coord_fixed(ratio=1.5)
```

3. Update the above graph “Number of Confirmed Cases by US County” to 9/26/2020 and use a different color scheme or theme
```{r message=FALSE, warning=FALSE, fig.height=8, fig.width=12}
library(RColorBrewer)
# To display only colorblind-friendly brewer palettes, specify the option colorblindFriendly = TRUE as follow:
# display.brewer.all(colorblindFriendly = TRUE)
# Get and format the covid report data
report_09_27_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-27-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  unite(Key, Admin2, Province_State, sep = ".") %>% 
  group_by(Key) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Key = tolower(Key))
# dim(report_03_27_2020)
# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>% 
  unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, report_09_27_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  # Add data layer
  borders("state", colour = "black") +
  geom_polygon(data = state_join, aes(fill = Confirmed)) +
  scale_fill_distiller(palette = "RdBu",
                       breaks = c(1, 10, 100, 1000, 10000, 100000),
                       trans = "log10", na.value = "Gray", direction = -1) +
  ggtitle("Number of Confirmed Cases by US County") +
  theme_bw() +
  theme(text = element_text(size = 16))
```

4. Make an interactive plot using a state of your chosing using a theme different from used in the above exammples.
```{r message=FALSE}
daily_report <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-27-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Province_State == "Ohio") %>% 
  group_by(Admin2) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Admin2 = tolower(Admin2))
us <- map_data("state")
oh_us <- subset(us, region == "ohio")
counties <- map_data("county")
oh_county <- subset(counties, region == "ohio")
state_join <- left_join(oh_county, daily_report, by = c("subregion" = "Admin2")) 
```

```{r message=FALSE, warning=FALSE}
p <- ggplot(data = oh_county, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = state_join, aes(fill = Confirmed, label=subregion), color = "black") +
    scale_fill_distiller(palette = "RdBu",
                       trans = "log10", na.value = "Gray", 
                       direction = -1) +
  ggtitle("COVID-19 Cases in Ohio") +
# Cleaning up the graph
  labs(x=NULL, y=NULL) +
  theme(panel.border = element_blank()) +
  theme(panel.background = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(axis.text = element_blank()) +
  theme(text = element_text(size = 16))

ggplotly(p)
```

5. Create a report with static maps and interactive graphs that is meant to be read by others (e.g. your friends and family). Hide warnings, messages and even the code you used so that it is readable. Included references. Link to the Lab 10 report from your Github site. Submit the link to Moodle. animations that is meant to be read by others (e.g. your friends and family). Hide warnings, messages and even the code you used so that it is readable. Included references. Link to the Lab 6 report from your Github site. Submit the link to Moodle.