air quality index
and ozone
are with no relationships with asthma emergency
visit times; CO
, NO
, SO2
and
PM2.5
are with positive relationships with asthma emergency
visit times. Thus, we can use those predictors for our modeling.asthma_air_graph1 =
brfss_air2_df %>%
ggplot(aes(x = mean_aqi_month, y = asthma_emergency)) +
geom_point()+
geom_smooth()
asthma_air_graph1
asthma_air_graph2 =
brfss_air2_df %>%
ggplot(aes(x = mean_ozone_month, y = asthma_emergency)) +
geom_point()+
geom_smooth()
asthma_air_graph2
asthma_air_graph3 =
brfss_air2_df %>%
ggplot(aes(x = mean_co_month, y = asthma_emergency)) +
geom_point()+
geom_smooth()
asthma_air_graph3
asthma_air_graph4 =
brfss_air2_df %>%
ggplot(aes(x = mean_no2_month, y = asthma_emergency)) +
geom_point()+
geom_smooth()
asthma_air_graph4
asthma_air_graph5 =
brfss_air2_df %>%
ggplot(aes(x = mean_so2_month, y = asthma_emergency)) +
geom_point()+
geom_smooth()
asthma_air_graph5
asthma_air_graph6 =
brfss_air2_df %>%
ggplot(aes(x = mean_pm2_5_month, y = asthma_emergency)) +
geom_point()+
geom_smooth()
asthma_air_graph6
asthma_air_graph1 =
brfss_air2_df %>%
ggplot(aes(x = mean_aqi_month, y = asthma_visit)) +
geom_point()+
geom_smooth()
asthma_air_graph1
asthma_air_graph2 =
brfss_air2_df %>%
ggplot(aes(x = mean_ozone_month, y = asthma_visit)) +
geom_point()+
geom_smooth()
asthma_air_graph2
asthma_air_graph3 =
brfss_air2_df %>%
ggplot(aes(x = mean_co_month, y = asthma_visit)) +
geom_point()+
geom_smooth()
asthma_air_graph3
asthma_air_graph4 =
brfss_air2_df %>%
ggplot(aes(x = mean_no2_month, y = asthma_visit)) +
geom_point()+
geom_smooth()
asthma_air_graph4
asthma_air_graph5 =
brfss_air2_df %>%
ggplot(aes(x = mean_so2_month, y = asthma_visit)) +
geom_point()+
geom_smooth()
asthma_air_graph5
asthma_air_graph6 =
brfss_air2_df %>%
ggplot(aes(x = mean_pm2_5_month, y = asthma_visit)) +
geom_point()+
geom_smooth()
asthma_air_graph6