lab 2

Question:1

Step 1

Covid Url (Read In The Url)

library(tidyverse)
covid=read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv")

Step 2

q1 = covid %>% 
  filter(state == "California")%>%
  group_by(county) %>% 
  mutate(newCases = c(cases[1], diff(cases))) %>% 
  ungroup() %>% 
  filter(date >= max(date)-13)

Making A Subset That Filters The Data To California And Added A New Column (Mutate) With The Daily New Cases Using Either diff or lag.

Step 3

most_all_time = q1 %>% 
  filter(date == max(date)) %>% 
  slice_max(cases, n = 5) %>% 
  select(county, cases)

knitr::kable(most_all_time, caption = "Most Cumulative Cases / Most Cases Of All Time", col.names = c("Counties", "Max Cases To Date"))

Most Cumulative Cases / Most Cases Of All Time

Counties	Max Cases To Date
Los Angeles	253985
Riverside	55073
Orange	52121
San Bernardino	50699
San Diego	42742

Five Counties with the Most Cumulative Case

most_new_data_from_yesterday = q1 %>% 
  filter(date == max(date)) %>% 
  slice_max(newCases, n = 5)

knitr::kable(most_all_time, caption = "Five Counties With The Most New Data (From Yesterday)", col.names = c("Counties", "New Cases"))

Five Counties With The Most New Data (From Yesterday)

Counties	New Cases
Los Angeles	253985
Riverside	55073
Orange	52121
San Bernardino	50699
San Diego	42742

Step 4

Please download the data and store it in the data directory of your project. = Done!

Step 5

Loaded the population data with the “dataset importer” (it was found in the file in my data directory via the file explorer –> click on it –> “Import Dataset”. I copied the code preview (ignored the View(…)) and inserted it in this Rmarkdown. This will allow the data to be referenced every time the file is run! = Done!

Step 6

library(readxl)

PopulationEstimates=read_excel("../data/PopulationEstimates.xls", 
  skip = 2) %>% 
  select(fips = FIPStxt, pop2019 = "POP_ESTIMATE_2019", Area_Name)

Step 7

j1 = left_join(covid, PopulationEstimates, by = "fips")

Join the population data to the California COVID data.

Step 9

Number Of Cases In The Last 14 Days Per 100,000

last14 = j1 %>% 
  filter(state ==c("California")) %>% 
  group_by(county, date) %>% 
  summarize(totalcases = sum(cases, na.rm = TRUE),
            pop2019    = sum(pop2019, na.rm = TRUE)) %>%
  ungroup() %>% 
  filter(date >= max(date) - 13) %>% 
  group_by(county, pop2019) %>% 
  mutate(newcases = totalcases-lag(totalcases)) %>% 
  summarize(totNewCases = sum(newcases, na.rm = TRUE)) %>% 
  mutate(per100 = totNewCases / (pop2019/100000)) %>% 
  filter(per100 <= 100)

Question 2

In this question, we looked at the story of 4 states and the impact scale it can have on data interpretation. The states include: New York, California, Louisiana, and Florida. My task was to make a faceted bar plot showing the number of daily, new cases at the state level.

Step 1

fourStates=covid %>% 
  group_by(state,date) %>% 
  summarize(cases=sum(cases)) %>%
  ungroup() %>% 
  filter(state %in% c("California", "New York", "Florida", "Louisiana")) %>%
  group_by(state) %>% 
  mutate(newCases=cases-lag(cases)) %>%
  mutate(roll7=zoo::rollmean(newCases,7,fill=NA,
                      align="right"))

Step 2

ggplot(data = fourStates, aes(x = date)) + 
  geom_col(aes(y = newCases), col = "pink") + 
  geom_line(aes(y= roll7), col = "darkred")+
  facet_wrap(~state) +
  labs(title = "Daily New Cases",  
       x = "Date",  
       y = "New Cases",  
       caption = "Based On NY Times Covid Data",  
       subtitle = 'Data Source: NY Times') +
       theme_bw()

new = fourStates %>% left_join(PopulationEstimates, by = c("state" = "Area_Name") ) %>% 
  mutate(perCapNew = newCases / pop2019, 
         perCap7 = zoo::rollmean(perCapNew, 7, fill = NA, align = "center" ))


ggplot(data = new, aes(x = date)) + 
  geom_col(aes(y = perCapNew), col = "pink") + 
  geom_line(aes(y= perCap7), col = "darkred")+
  facet_wrap(~state) +
  labs(title = "Daily New Cases",  
       x = "Date",  
       y = "New Cases",  
       caption = "Based On NY Times Covid Data",  
       subtitle = 'Data Source: NY Times') +
       theme_bw()