COVID-19.R

#################### STEP 1 ########################

# Load the readr, ggplot2, and dplyr packages
library(readr)
library(ggplot2)
library(dplyr)

# Read datasets/confirmed_cases_worldwide.csv into confirmed_cases_worldwide
confirmed_cases_worldwide <- read_csv("F:/datas/datasets/confirmed_cases_worldwide.csv")

# See the result
confirmed_cases_worldwide

#################### STEP 2 ########################

# Draw a line plot of cumulative cases vs. date
# Label the y-axis
ggplot(confirmed_cases_worldwide,aes(date,cum_cases)) +
  geom_line() +
  labs(y="Cumulative confirmed cases")

#################### STEP 3 ########################


# Read in datasets/confirmed_cases_china_vs_world.csv
confirmed_cases_china_vs_world <- read_csv("F:/datas/datasets/confirmed_cases_china_vs_world.csv")

# See the result
glimpse(confirmed_cases_china_vs_world)

# Draw a line plot of cumulative cases vs. date, grouped and colored by is_china
# Define aesthetics within the line geom
plt_cum_confirmed_cases_china_vs_world <- ggplot(confirmed_cases_china_vs_world) +
  geom_line(aes(x=date,y=cum_cases,color=is_china,group=is_china)) +
  ylab("Cumulative confirmed cases")

# See the plot
plt_cum_confirmed_cases_china_vs_world


#################### STEP 4 ########################

who_events <- tribble(
  ~ date, ~ event,
  "2020-01-30", "Global health\nemergency declared",
  "2020-03-11", "Pandemic\ndeclared",
  "2020-02-13", "China reporting\nchange"
) %>%
  mutate(date = as.Date(date))

# Using who_events, add vertical dashed lines with an xintercept at date
# and text at date, labeled by event, and at 100000 on the y-axis
plt_cum_confirmed_cases_china_vs_world +
 geom_vline(aes(xintercept = date),data=who_events,linetype="dashed") +
  geom_text(aes(label=event,x=date),data=who_events,y=1e5)


#################### STEP 5 ########################

# Filter for China, from Feb 15
china_after_feb15 <- confirmed_cases_china_vs_world %>%
filter(is_china=="China",date>="2020-02-15")

# Using china_after_feb15, draw a line plot cum_cases vs. date
# Add a smooth trend line using linear regression, no error bars
ggplot(china_after_feb15,aes(x=date,y=cum_cases))+
  geom_line() +
  geom_smooth(method="lm",se=FALSE) +
  ylab("Cumulative confirmed cases")


#################### STEP 6 ########################


# Filter confirmed_cases_china_vs_world for not China
not_china <- confirmed_cases_china_vs_world %>%
filter(is_china=="Not China")

# Using not_china, draw a line plot cum_cases vs. date
# Add a smooth trend line using linear regression, no error bars
plt_not_china_trend_lin <- ggplot(not_china,aes(date,cum_cases)) +
  geom_line() +
  geom_smooth(method="lm",se= FALSE) +
  ylab("Cumulative confirmed cases")

# See the result
plt_not_china_trend_lin 


#################### STEP 7 ########################


# Modify the plot to use a logarithmic scale on the y-axis
plt_not_china_trend_lin + 
  scale_y_log10()


#################### STEP 8 ########################


# Run this to get the data for each country
confirmed_cases_by_country <- read_csv("F:/datas/datasets/confirmed_cases_by_country.csv")
glimpse(confirmed_cases_by_country)

# Group by country, summarize to calculate total cases, find the top 7
top_countries_by_total_cases <- confirmed_cases_by_country %>%
  group_by(country) %>%
  summarize(total_cases=max(cum_cases)) %>%
  top_n(7)

# See the result
top_countries_by_total_cases
#################### STEP 9 ########################

# Run this to get the data for the top 7 countries
confirmed_cases_top7_outside_china=read_csv("F:/datas/datasets/confirmed_cases_top7_outside_china.csv")

# 
glimpse(confirmed_cases_top7_outside_china)

# Using confirmed_cases_top7_outside_china, draw a line plot of
# cum_cases vs. date, grouped and colored by country
ggplot(confirmed_cases_top7_outside_china,aes(date,cum_cases,color=country,group=country))+geom_line()+ylab("Cumulative confirmed cases")