1) Table 1.Interactive Table displaying flu and vaccination data across all counties in California for different levels of race/ethnicity by quarter.
Interpretation: We have chosen to display the flu and vaccination data across all counties in California for the different levels of race/ethnicity by quarter. We have flagged the groups of people that less than 70% of the population has been vaccinated against COVID 19 in yellow, and sorted by the risk of flu per 100,000 population. We have also included a column specifying if the infection risk for that race/ethnicity category in that given quarter is above the average infection risk for that race/ethnicity across all quarters of data. We note that Hispanic (any race) and Multiracial categories consistently have lower than 70% of the population vaccinated and have among the top flu risks per 100,000 in any quarter of data.
2) Plot 1.Flu infection rate per 100,000 across all counties grouped by race and quarter.
Interpretation: This bar graph demonstrates that while some race/ethnicities including Alaska Native, White (Non-Hispanic) and Hispanic (any race) have proportionally higher infection rates across all quarters of data, it is clear that the quarter starting on 1/1/2023 (demonstrated by the medium blue part of each bar) consists of the highest number of infections across all races/ethnicities.
`summarise()` has grouped output by 'race_ethnicity'. You can override using
the `.groups` argument.
ggplot(re_joined_bar,aes(x=race_ethnicity, y=infection_rate, fill=quarter)) +geom_bar(stat ="identity") +labs(x ="Race/Ethnicity", y ="% infected",title ="Percent of population infected by ehtnicty and quarter") +theme(axis.text.x =element_text(size =10, angle =45, hjust =1, vjust =0.9))
3) Plot 2.Flu rate by county for Hispanic (any race) in the quarter starting on 1/1/2023.
Interpretation:Given some of the patterns we have observed in Table 1 and that flu rate seems to be consistent across race but highest in the quarter starting in January 2023 from plot 1, we have chosen to focus on the Hispanic (any race) population in additional visualizations to determine if there is any correlation between COVID vaccination rates and flu within a given race in the quarter with the highest infection rates. From this plot, we see that the proportion of the population with flu ranges from a low of 32.37K per 100,000 in San Mateo county to a high of 34.92K per 100,000 in Calaveras county for Hispanic (any race) populations.
re_plot_hisp <- re_joined %>%filter(quarter=="2023-01-01", race_ethnicity =="Hispanic (any race)") %>%select(county, quarter, flu_rate, vax_rate, race_ethnicity, pop, estimated_pop, fully_vaccinated, new_infections, flu_risk) plot_ly( re_plot_hisp,x=~county,y=~flu_risk,name="Flu Rate per 100,000",type="scatter",mode="markers") %>%layout(xaxis=list(title="Flu Rate by county for Hispanic (any race) in the quarter starting 2023-01-01"),yaxis=list(title="Flu Rate per 100,000"))
4) Plot 3.Flu rate by county for Hispanic (any race) in the quarter starting on 1/1/2023 vs. proportion of the population that is fully vaccinated for COVID 19.
Interpretation: In order to see if there is any correlation between the rate of flu and the proportion of the population that is vaccinated, we plot the proportion of the population that is vaccinated vs. the rate of flu. We see that within Hispanic populations in the quarter starting on 1/1/23, there may be pattern demonstrating potential lower rates of flu infection per 100,000 among populations with higher proportions who are fully vaccinated against COVID-19, as demonstrated by the blue descending line of best fit.
ggplot(re_plot_hisp, aes(x = vax_rate, y = flu_risk)) +geom_point() +geom_label_repel(aes(label=county, size =NULL), nudge_y =0.5) +geom_smooth(fullrange=TRUE, method ="lm", se =FALSE) +labs(x ="Proportion of Population that is Vaccinated, Hispanic (any race)", y ="Flu rate per 100,000 population",title ="Flu Rate Compared to % of Vaccinated Hispanic Population",subtitle ="Data by county in quarter starting 1/1/2023")
