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postanalysis.R
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postanalysis.R
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library(tidyverse)
library(tidyquant)
#################
#--Data Import--#
#################
back <- read_csv("GIS/jongno_rep.csv") %>%
filter(type == "Back") %>%
slice(tail(row_number(), 13008)) %>%
group_by(date, hour) %>%
summarise(no2_mean = mean(no2_mean),
no2_sd = mean(no2_sd))
road <- read_csv("GIS/jongno_rep.csv") %>%
filter(type == "Road") %>%
slice(tail(row_number(), 13008)) %>%
group_by(date, hour) %>%
summarise(no2_mean = mean(no2_mean),
no2_sd = mean(no2_sd))
datetime <- read_csv("GIS/jongno_rep.csv") %>%
filter(type == "Back") %>%
select(date, hour) %>%
slice(tail(row_number(), 13008))
###############
#--NO2 level--#
###############
##--Scenario: NO
no_no2_1 <- read_csv("Result/No_no2_1.csv") %>%
cbind(datetime) %>%
group_by(date, hour) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
select(-ticks) %>%
mutate(scenario = "No")
no_no2_2 <- read_csv("Result/No_no2_2.csv") %>%
cbind(datetime) %>%
group_by(date, hour) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
select(-ticks) %>%
mutate(scenario = "No")
##--Scenario: YES
yes_no2_1 <- read_csv("Result/Yes_no2_1.csv") %>%
cbind(datetime) %>%
group_by(date, hour) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
select(-ticks) %>%
mutate(scenario = "Yes")
yes_no2_2 <- read_csv("Result/Yes_no2_2.csv") %>%
cbind(datetime) %>%
group_by(date, hour) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
select(-ticks) %>%
mutate(scenario = "Yes")
no2 <- bind_rows(yes_no2_1, yes_no2_2, no_no2_1, no_no2_2)
##############
#--Traffic--##
##############
##--Scenario: NO
no_traffic_1 <- read_csv("Result/No_Traffic_1.csv") %>%
cbind(datetime) %>%
group_by(date, hour) %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
select(-ticks) %>%
mutate(scenario = "No")
no_traffic_2 <- read_csv("Result/No_Traffic_1.csv") %>%
cbind(datetime) %>%
group_by(date, hour) %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
select(-ticks) %>%
mutate(scenario = "No")
##--Scenario: YES
yes_traffic_1 <- read_csv("Result/Yes_Traffic_1.csv") %>%
cbind(datetime) %>%
group_by(date, hour) %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
select(-ticks) %>%
mutate(scenario = "Yes")
yes_traffic_2 <- read_csv("Result/Yes_Traffic_1.csv") %>%
cbind(datetime) %>%
group_by(date, hour) %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
select(-ticks) %>%
mutate(scenario = "Yes")
traffic <- bind_rows(no_traffic_1, no_traffic_2, yes_traffic_1, yes_traffic_2)
##########
#--Plot--#
##########
## NO2
no2_melt <- no2 %>% reshape2::melt(id=c("date", "hour", "scenario"), variable.name = "road", value.name = "no2")
no2.labs <- c("사직로", "율곡로", "종로4가", "퇴계로", "세종대로", "삼일대로", "도로평균")
names(no2.labs) <- c("sajikro", "yulgokno", "jongno", "twegero", "sejongdaero", "samildaero", "roadmean")
no2_melt %>%
mutate(dh = paste(date, hour, sep = " ")) %>%
ggplot(aes(dh, no2, group = scenario, colour = scenario)) +
geom_line() +
facet_wrap(~ road + scenario, labeller = labeller(road = no2.labs)) +
theme_tq() +
theme(axis.title.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_text(size = 13),
strip.text.x = element_text(size = 13,
margin = margin(.1,0,.1,0, "cm")),
legend.position = "none",
legend.title=element_text(size=13),
legend.text=element_text(size=13)
)-> no2line_gg
ggsave("result_no2_line.png", no2line_gg, width = 10, height = 6, dpi = 300)
no2_melt %>%
group_by(date, road, scenario ) %>%
summarise(meanno2 = mean(no2)) %>%
reshape2::dcast(date + road ~ scenario) %>%
mutate(minus = No - Yes) %>%
arrange(desc(No), desc(Yes)) %>%
#filter(date == "2018-03-27") %>%
View()
no2_melt %>%
ggplot(aes(factor(hour), no2, fill = scenario)) +
geom_boxplot() +
ylim(0,160) +
facet_wrap(~ road, ncol = 2, labeller = labeller(road = no2.labs)) +
theme_tq() +
theme(axis.title.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.x=element_text(size = 9),
axis.text.y=element_text(size = 13),
strip.text.x = element_text(size = 13,
margin = margin(.1,0,.1,0, "cm")),
legend.position = "bottom",
legend.title=element_text(size=13),
legend.text=element_text(size=13)
) -> no2box_gg
ggsave("result_no2_box.png", no2box_gg, width = 9, height = 9, dpi = 300)
## Traffic
traffic %>%
select(-hour) %>%
group_by(date, scenario) %>%
summarise_if(is.numeric, funs(sum), na.rm=TRUE) %>%
reshape2::melt(id=c("date", "scenario"), variable.name = "road", value.name = "count") -> traffic_melt
traff.labs <- c("사직로", "율곡로", "종로4가", "퇴계로", "삼일대로", "세종대로")
names(traff.labs) <- c("cars_sajik", "cars_yulgok", "cars_jongno", "cars_twege", "cars_samil", "cars_sejong")
traffic_melt %>%
group_by(date, scenario, road) %>%
summarise(nn = sum(count)) %>%
ggplot(aes(date, nn, colour = scenario)) +
geom_line() +
facet_wrap(~ road, scales = "free", labeller = labeller(road = traff.labs)) +
theme_tq() +
theme(axis.title.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.x=element_text(size = 13),
axis.text.y=element_text(size = 13),
strip.text.x = element_text(size = 13,
margin = margin(.1,0,.1,0, "cm")),
legend.position = "right",
legend.title=element_text(size=13),
legend.text=element_text(size=13)
) -> traffic_gg
ggsave("result_traffic.png", traffic_gg, width = 12, height = 6, dpi = 600)