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2_EDA.R
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2_EDA.R
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###############################################################
#### Causal Inference for Time Series (Heat and Mortality) ####
###############################################################
### Script 2 ###
# Input: Processed data from script 1
# Output: EDA figures
#------------------------------------------------------------------------#
#--------- load libraries ---------#
#------------------------------------------------------------------------#
library(ggplot2)
library(tidyverse)
library(ggpubr)
#------------------------------------------------------------------------#
#--------- set file paths ---------#
#------------------------------------------------------------------------#
# path for loading processed data
processed_data_path <- "data/intermediate/"
# path for EDA figures
EDA_path <- "results/figures/EDA/"
#------------------------------------------------------------------------#
#--------- import data ---------#
#------------------------------------------------------------------------#
# load processed data from script 1
processed_data <- read.csv(file = paste0(processed_data_path, "all_processed.csv"))
#------------------------------------------------------------------------#
# ----------- EDA ----------- #
#------------------------------------------------------------------------#
#----- summer trends in pollutants -----#
gathered <- processed_data %>%
gather(key = "poll_name", value = "poll_value", c(12,16:21))
# fix city factor levels
gathered$city <- factor(gathered$city,
levels = c("pitt", "seat", "chic", "la", "ny"))
levels(gathered$city) <- c("Pittsburgh", "Seattle", "Chicago", "LA", "NY")
# fix pollutant factor levels
gathered$poll_name <- factor(gathered$poll_name,
levels = c("tmax", "o3", "no2", "so2", "co", "pm25", "pm10"))
levels(gathered$poll_name) <- c("Temp.[max.]", "O[3]", "NO[2]", "SO[2]", "CO", "PM[2.5]", "PM[10]")
# fix month factor levels
gathered$month <- factor(gathered$month,
levels = c("Jun", "Jul", "Aug", "Sep"))
pdf(file = paste0(EDA_path, "summer_trends.pdf"), width = 5, height = 6)
# # plotted by week
# gathered %>%
# group_by(week, city, poll_name) %>%
# summarise(mean_poll = mean(poll_value, na.rm = TRUE)) %>%
# ggplot() +
# geom_line(aes(x = week, y = mean_poll, color = poll_name), lwd = 1.3) +
# scale_color_brewer(palette = "Dark2") +
# facet_grid(poll_name ~ city,
# scales = "free",
# labeller = label_parsed) +
# labs(x = "Week of the Year",
# y = "Mean Pollutant Level") +
# theme_bw() +
# theme(legend.position = "none",
# panel.border = element_rect(colour = "gray", fill = NA, size = 0.5),
# strip.background = element_rect(fill = "white", color = "gray"),
# panel.grid.major = element_blank(), panel.grid.minor = element_blank())
# plotted by month
gathered %>%
group_by(month, city, poll_name) %>%
summarise(mean_poll = mean(poll_value, na.rm = TRUE)) %>%
ggplot() +
geom_line(aes(x = month, y = mean_poll, color = poll_name, group = poll_name), lwd = 1.3) +
scale_color_brewer(palette = "Dark2") +
facet_grid(poll_name ~ city,
scales = "free",
labeller = label_parsed) +
labs(x = "Week of the Year",
y = "Mean Pollutant Level") +
theme_bw() +
theme(legend.position = "none",
panel.border = element_rect(colour = "gray", fill = NA, size = 0.5),
strip.background = element_rect(fill = "white", color = "gray"),
panel.grid.major = element_blank(), panel.grid.minor = element_blank())
# # plotted by date
# # class character (not a great solution)
# gathered$month_day <- format(as.Date(gathered$date), "%m-%d")
# gathered %>%
# group_by(month_day, city, poll_name) %>%
# summarise(mean_poll = mean(poll_value, na.rm = TRUE)) %>%
# ggplot() +
# geom_line(aes(x = month_day, y = mean_poll, color = poll_name, group = poll_name), lwd = 1.3) +
# scale_color_brewer(palette = "Dark2") +
# facet_grid(poll_name ~ city,
# scales = "free",
# labeller = label_parsed) +
# labs(x = "Week of the Year",
# y = "Mean Pollutant Level") +
# theme_bw() +
# theme(legend.position = "none",
# panel.border = element_rect(colour = "gray", fill = NA, size = 0.5),
# strip.background = element_rect(fill = "white", color = "gray"),
# panel.grid.major = element_blank(), panel.grid.minor = element_blank())
dev.off()
#----- correlation between pollutants and tmax during summer months -----#
cities <- c("pitt", "seat", "chic", "la", "ny")
corr_plots <- list()
for(i in 1:length(cities)){
corr_plots[[i]] <- processed_data %>%
gather(key = "poll_name", value = "poll_value", 16:21) %>%
filter(poll_name == names(processed_data)[i+15]) %>%
ggplot(aes(x = poll_value, y = tmax)) +
geom_point(alpha = 0.2, color = "dodgerblue") +
geom_smooth() +
labs(x = names(processed_data)[i+15]) +
facet_grid(~ city, scales = "free")
}
pdf(file = paste0(EDA_path, "tmax_correlations.pdf"), width = 5, height = 7)
ggarrange(corr_plots[[1]], corr_plots[[2]], corr_plots[[3]],
corr_plots[[4]], corr_plots[[5]], nrow = 5)
dev.off()
#----- daily mortality density -----#
processed_data %>%
ggplot() +
geom_density(aes(death, fill = city), col = "white", alpha = 0.7) +
ggtitle("Distribution of Death Counts") +
xlab("Deaths over 3 Day Experiment") +
theme_classic()
#----- daily mortality histograms -----#
processed_data %>%
ggplot() +
geom_histogram(aes(death), bins = 20, col = "white") +
facet_wrap(~city, scales= "free") +
xlab("Deaths over 3 Day Experiment") +
theme_bw()
#----- missing pollutant measurements -----#
pdf(file = paste0(EDA_path, "missing_pollutants.pdf"), width = 6, height = 2)
processed_data |>
group_by(city, year) |>
summarize(pct_measured = (sum(!is.na(o3)) / n())*100) |>
ggplot() +
geom_bar(aes(x = year, y = pct_measured), stat = "identity") +
labs(title = "o3", x = "Year", y = "% of days w/ data") +
facet_wrap(~city, nrow = 1)
processed_data |>
group_by(city, year) |>
summarize(pct_measured = (sum(!is.na(no2)) / n())*100) |>
ggplot() +
geom_bar(aes(x = year, y = pct_measured), stat = "identity") +
labs(title = "no2", x = "Year", y = "% of days w/ data") +
facet_wrap(~city, nrow = 1)
processed_data |>
group_by(city, year) |>
summarize(pct_measured = (sum(!is.na(co)) / n())*100) |>
ggplot() +
geom_bar(aes(x = year, y = pct_measured), stat = "identity") +
labs(title = "co", x = "Year", y = "% of w/ data") +
facet_wrap(~city, nrow = 1)
processed_data |>
group_by(city, year) |>
summarize(pct_measured = (sum(!is.na(so2)) / n())*100) |>
ggplot() +
geom_bar(aes(x = year, y = pct_measured), stat = "identity") +
labs(title = "so2", x = "", y = "% of days w/ data") +
facet_wrap(~city, nrow = 1)
processed_data |>
group_by(city, year) |>
summarize(pct_measured = (sum(!is.na(pm25)) / n())*100) |>
ggplot() +
geom_bar(aes(x = year, y = pct_measured), stat = "identity") +
labs(title = "pm25", x = "", y = "% of days w/ data") +
facet_wrap(~city, nrow = 1)
processed_data |>
group_by(city, year) |>
summarize(pct_measured = (sum(!is.na(pm10)) / n())*100) |>
ggplot() +
geom_bar(aes(x = year, y = pct_measured), stat = "identity") +
labs(title = "pm10", x = "", y = "% of days w/ data") +
facet_wrap(~city, nrow = 1)
processed_data |>
group_by(city, year) |>
summarize(pct_measured = (sum(!is.na(rhum)) / n())*100) |>
ggplot() +
geom_bar(aes(x = year, y = pct_measured), stat = "identity") +
labs(title = "rhum", x = "", y = "% of days w/ data") +
facet_wrap(~city, nrow = 1)
dev.off()