Numerous studies show that asthma is a leading cause of students missing school. Air pollution has been identified as a cause of some asthma symptoms. Currently there is only one national data set for absentee students from K-12 school, from the 2013-2014 school year. This chart is part of a future blog post on looking at daily Air Quality Index (AQI) values and seeing if a relationship can be seen between air quality, which in the US measures ozone, sulfer dioxide, carbon monoxide, PM 2.5 and nitrogen dioxide.
A first step in understanding daily AQI values is in the chart below. The US AQI also has color values associated with different AQI levels. Green indicates “Good” air quality, yellow indicates “Moderate” air quality and “orange” indicates “Unhealthy for Sensitive Groups” which includes children. The exact colors in this chart were chosen to be color-blind friendly.
#2013 AQI data by county
ALdailyAqi2013 <- filter(dailyAqi2013, State.Name == "Alabama") %>% group_by(county.Name)
ALdailyAqi2013$Date <- mdy(ALdailyAqi2013$Date)
plot13AL <- ggplot(data = ALdailyAqi2013, aes(x = Date, y = AQI), na.rm = TRUE) +
geom_line() +
facet_wrap(~county.Name, ncol = 3) +
geom_ribbon(aes(ymin = 0, ymax = 50), fill = "#009E73", alpha = .3) +
geom_ribbon(aes(ymin = 51, ymax = 100), fill = "#F0E442", alpha = .3) +
geom_ribbon(aes(ymin = 101, ymax = 150), fill = "#D55E00", alpha = .3) +
labs(title= "2013 Alabama Daily Air Quality Index (AQI) by County")
ggplotly(plot13AL, width = 800)The next steps involve calculating county chronic absenteeism rates and matching that with the county, across all states and possibly doing regression analysis to see if a relationship can be seen with this data.