Daily Exercise 08

ESS 330 - Quantitative Reasoning

Author

Zachary Cramton 

# 20 Feburary 2025
# This is the R script for assignment 08 where the COVID-19 data from 
# assignment 06 will be visualized expanding on the visualization from
# assignment 07.

# Prepare packages
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
library(datasets)
library(scales)
Warning: package 'scales' was built under R version 4.4.3
#Read in and store NY-Times Data
covid <- readr::read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv")
Rows: 2502832 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): county, state, fips
dbl  (2): cases, deaths
date (1): date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Create state-region data frame
df <- data.frame(region = state.region,
                 abbr = state.abb,
                 state = state.name)

# Check data frame
head(df)
  region abbr      state
1  South   AL    Alabama
2   West   AK     Alaska
3   West   AZ    Arizona
4  South   AR   Arkansas
5   West   CA California
6   West   CO   Colorado
# Join df to covid data
covid_joined <- inner_join(df,covid, by = "state")

# Aggregate (Split-apply) COVID-19 data based on region.
covid_summary <- covid_joined %>% 
  group_by(region, date) %>% #Split by region and date
  summarize(
    Cases = sum(cases/1000, na.rm = TRUE), #Apply sum function
    Deaths = sum(deaths/1000, na.rm = TRUE)
  )
`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.
# Pivot data from to long format
covid_long <- covid_summary %>% 
  pivot_longer(cols = c(Cases, Deaths), 
              names_to = "metric", 
              values_to = "count")

# Plot the long data
us_regional_covid_trends = ggplot(covid_long, aes(x = date, y = count, color = region)) +
  geom_line() +
  facet_grid(metric~region, scales = "free_y",) +
  labs(title = "COVID-19 Cases and Deaths by Region",
       subtitle = "Since 2020 (per 1,000 people)",
       caption = "Based on NY-Times COVID-19 Data.",
       x = "Date",
       y = "Cumulative Cases",
       color = "Regions") +
  theme_bw() + 
  theme(legend.position = "none",
        axis.text.y = element_text(angle = 45, hjust = 1)) + 
  scale_x_date(date_breaks = "8 month", date_labels = "%b %y") +
  scale_y_continuous(labels = label_number())
  
# Save plot as an image
#ggsave(us_regional_covid_trends,
     # file = "img/us_regional_covid_trends.png",
     # width = 10,
     # height = 6)