0. Install and load libraries

library(data.table)
library(tidyverse)

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library(plotly)

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library(knitr)
library(widgetframe)

## Loading required package: htmlwidgets

1. Read in the data

• Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv”
• Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv””
• Merge datasets

cv_states_readin <- 
  data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")


state_pops <- data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv")

state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

cv_states <- merge(cv_states_readin, state_pops, by="state")

2. Look at the data

• Inspect the dimensions, head, and tail of the data
• Inspect the structure of each variables. Are they in the correct format?

dim(cv_states)
head(cv_states)
tail(cv_states)
str(cv_states)

3. Format the data

• Make date into a date variable
• Make state into a factor variable
• Order the data first by state, second by date
• Confirm the variables are now correctly formatted
• Inspect the range values for each variable. What is the date range? The range of cases and deaths?

cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")


state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)


cv_states = cv_states[order(cv_states$state, cv_states$date),]

str(cv_states)
head(cv_states)
tail(cv_states)


head(cv_states)
summary(cv_states)
min(cv_states$date)
max(cv_states$date)

4. Add new_cases and new_deaths and correct outliers

• Add variables for new cases, new_cases, and new deaths, new_deaths:

  • Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2

• Filter to dates after October 1, 2022

• Use ggplotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?

• Correct outliers: Set negative values for new_cases or new_deaths to 0

• Inspect data again interactively

5. Add additional variables

• Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:

  • per100k = cases per 100,000 population
  • newper100k= new cases per 100,000
  • deathsper100k = deaths per 100,000
  • newdeathsper100k = new deaths per 100,000

• Add a “naive CFR” variable representing deaths / cases on each date for each state

• Create a dataframe representing values on the most recent date, cv_states_today

6. Explore scatterplots using plot_ly()

• Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)

  • Color points by state and size points by state population
  • Use hover to identify any outliers.

• Remove those outliers and replot.

• Choose one plot. For this plot:

• Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes

• Add layout information to title the chart and the axes

• Enable hovermode = "compare"

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

• For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
• Explore the pattern between \(x\) and \(y\)
• Explain what you see. Do you think pop_density correlates with newdeathsper100k?

8. Multiple line chart

• Create a line chart of the naive_CFR for all states over time using plot_ly()

  • Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?

• Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()

  • Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?

9. Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than January 1st, 2023 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out?

• Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks.

#create heatmap

10. Map

• Create a map to visualize the naive_CFR by state on March 15, 2023

pick.date = "2023-03-15"

# Create the map

• Compare with a map visualizing the naive_CFR by state on most recent date

# Map for today's date