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aq_dataset_cpd_changed_time_period.R
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library(openair)
library(dplyr)
library(rmweather)
library(zoo)
require(RcppRoll)
library(ggplot2)
library(plotly)
library(tidyr)
#Processing code----------------------------------------------
london_kcl_meta = importMeta(source = "kcl") %>%
drop_na()
london_urb_sites <- filter(
kcl_noaa_nearest,
site_type %in% c("Urban Background", "Roadside"))
directory_met_data = "D:/cpdav/UK_met_data/noaa_UK_met_data_"
met_london_df_all = map2_dfr(.x = london_urb_sites$code,
.y = london_urb_sites$met_code,
.f = ~read_met_sites_london(site_code = .x, metcode = .y,
"2016-01-01", "2020-12-31"))
met_aq_london_urban_background_no2=met_aq_prepared_rm_vs_de(met_london_df_all,
"no2", "Urban Background", TRUE)
London_code_no2_urban_background = unique(as.character(met_aq_london_urban_background_no2$code))
#Note, ULEZ was initially implemented 8th April 2019, ran code a month prior to implementation
BAU_urban_background_no2_london_2 = map(.x = London_code_no2_urban_background,
.f = ~rmweather_BAU_observed_no_normal(df = met_aq_london_urban_background_no2,
site = .x, 300, "2016-01-01",
"2019-01-15", "2020-07-31",
0.85))
london_urban_background_no2_reformat_2 = urban_reformat_data_mean_sd_no_normal(BAU_urban_background_no2_london_2,
London_code_no2_urban_background)
london_urban_background_no2_wd_ws_2 = urban_reformat_data_delta_wd_ws(BAU_urban_background_no2_london_2,
met_aq_london_urban_background_no2,
London_code_no2_urban_background)
london_urban_background_no2_BAU_observed_2 = urban_reformat_data_BAU_output(BAU_urban_background_no2_london_2,
London_code_no2_urban_background)
london_urban_background_no2_observed_2 = urban_reformat_observed(BAU_urban_background_no2_london_2,
London_code_no2_urban_background)
london_urban_statistics_2 = urban_model_statistics(BAU_urban_background_no2_london_2,
London_code_no2_urban_background)
avgs_london_urban_background_no2 = london_urban_statistics %>%
group_by(stat, Data) %>%
summarise(avg = mean(value, na.rm = T), sd = sd(value, na.rm = T)) %>%
ungroup()%>%
mutate(lower_sd = avg-sd, upper_sd = avg+sd,
lower_2_sd = avg-2*sd, upper_2_sd = avg+2*sd)
#graphical outputs - visualisation
stats_plot = london_urban_statistics %>%
ggplot(aes(y = sites, x = value)) +
geom_point(aes(color = Data), size = 4) +
geom_vline(data = avgs_london_urban_background_no2, aes(xintercept = avg, color = Data), lty = 2,
lwd = 1.2) +
geom_rect(data = avgs_london_urban_background_no2, aes(xmin = lower_sd, xmax = upper_sd, ymin = -Inf,
ymax = Inf, fill = Data),
alpha = 0.2, inherit.aes = FALSE) +
labs(x= "Various Units", y = "Sites")+
theme_bw(base_size = 15) +
theme(legend.position = "top")+
facet_grid(~stat, scales = "free_x") + theme(panel.spacing = unit(2, "lines"))+
ggtitle("Model statistics: 68% confidence level (London Urban Background)")
urban_london_rm_mean = london_urban_background_no2_reformat %>%
select(date,d7_rollavg_rm_normal_mean)%>%
rename(value=d7_rollavg_rm_normal_mean)%>%
drop_na()
urban_london_delta_mean = london_urban_background_no2_reformat %>%
select(date, d7_rollavg_delta_BAU_predict_mean)%>%
rename(value=d7_rollavg_delta_BAU_predict_mean) %>%
drop_na()
urban_london_rm_cp = df_cp_detection(urban_london_rm_mean, 14, 14, TRUE)
urban_london_delta_cp = df_cp_detection(urban_london_delta_mean, 14, 14, TRUE)
london_cp_df = rbind(
urban_london_rm_cp %>% mutate(delta = "rmweather"),
urban_london_delta_cp %>% mutate(delta = "BAU - observed (delta)")
)
delta_comparison_london_no2 = london_urban_background_no2_reformat %>%
select(date, d7_rollavg_delta_BAU_predict_mean, d7_rollavg_rm_normal_mean) %>%
drop_na()%>%
rename("BAU - observed (delta)"=d7_rollavg_delta_BAU_predict_mean,
"rmweather"=d7_rollavg_rm_normal_mean) %>%
pivot_longer(-date, names_to = "delta")
test_plot = delta_comparison_london_no2 %>%
filter(date >= as.Date("2019-01-01") & date <= as.Date("2019-06-30")) %>%
ggplot(aes(x = date, y = value))+
annotate("rect", xmin = as.POSIXct(as.Date("2019-04-08")),
xmax = as.POSIXct(as.Date("2019-06-30")), ymin = -Inf, ymax = Inf,
alpha = .2)+
geom_vline(data = filter(london_cp_df, flag,date >= as.Date("2019-03-15")
& date <= as.Date("2019-06-30")),
aes(xintercept = date, colour = delta)) +
geom_line(aes(colour = delta), lwd = 1.5) +
facet_grid(delta~., scales = "free_y")+
labs(x= "Date", y = "Various Units", colour = "Human impact comparison")+
geom_vline(xintercept = as.POSIXct(as.Date("2019-03-15")),
color = "black",
lwd = 1,
linetype = "dashed")+
theme_bw(base_size = 20)
london_urban_background_no2_BAU_observed %>%
filter(date >= as.Date("2019-01-01") & date <= as.Date("2019-07-31")) %>%
ggplot(aes(x = date, y = value))+
geom_line(aes(colour = variables), lwd = 1.5)+
labs(x= "Date", y = "Various Units", colour = "Variables")+
geom_vline(xintercept = as.POSIXct(as.Date("2019-03-15")),
color = "black",
lwd = 1,
linetype = "dashed")+
theme_bw(base_size = 20)+
ggtitle("Training stops 15th March")
london_urban_background_no2_BAU_observed_2 %>%
filter(date >= as.Date("2019-01-01") & date <= as.Date("2019-07-31")) %>%
ggplot(aes(x = date, y = value))+
geom_line(aes(colour = variables), lwd = 1.5)+
labs(x= "Date", y = "Various Units", colour = "Variables")+
geom_vline(xintercept = as.POSIXct(as.Date("2019-01-15")),
color = "black",
lwd = 1,
linetype = "dashed")+
theme_bw(base_size = 20)+
ggtitle("Training stops 15th Jan")
london_urban_background_no2_wd_ws %>%
filter(date >= as.Date("2019-01-01") & date <= as.Date("2019-06-30")) %>%
ggplot(aes(x = date, y = value))+
geom_line(aes(colour = variables), lwd = 1.5) +
facet_grid(variables~., scales = "free_y")+
labs(x= "Date", y = "Various Units", colour = "Variables")+
geom_vline(xintercept = as.POSIXct(as.Date("2019-03-15")),
color = "black",
lwd = 1,
linetype = "dashed")+
theme_bw(base_size = 20)
london_urban_background_no2_observed %>%
filter(date >= as.Date("2019-01-01") & date <= as.Date("2019-06-30")) %>%
ggplot(aes(x = date, y = d7_rollavg_mean)) +
geom_ribbon(aes(y = d7_rollavg_mean,
ymin = d7_rollavg_mean - d7_rollavg_sd,
ymax = d7_rollavg_mean + d7_rollavg_sd), alpha = .3) +
geom_line(colour = "red", lwd = 1.5)+
geom_vline(xintercept = as.POSIXct(as.Date("2019-03-15")),
color = "black",
lwd = 1,
linetype = "dashed") +
labs(x= "Date", y = "Observed")