inst/extdata/aq2020-pval_plots_old.R
In mlysy/aq2020: Statistical Analysis of Air Quality Data
#' ---
#' title: "P-value Plots for AQ2020 Paper"
#' params:
#' crop: FALSE
#' output:
#' rmarkdown::pdf_document:
#' keep_tex: yes
#' rmarkdown::html_document:
#' theme: readable
#' highlight: tango
#' toc: yes
#' toc_float: true
#' ---
#+ r setup, include = FALSE, message = FALSE
knitr::opts_chunk$set(echo = FALSE, fig.align = "center")
knitr::knit_hooks$set(crop = knitr::hook_pdfcrop)
require(aq2020)
require(dplyr) # for basic things like %>%
require(tidyr) # for pivot_longer
## require(readr) # read_csv
require(lubridate) # dates
## require(fs) # opening files
require(ggplot2) # for plotting
## require(gridExtra) # for complex layout
## require(grid)
require(ggpubr) # for complex layout
## # helper functions
## get_fname <- function(path, ...) {
## fname <- paste0(list(...), collapse = "_")
## file.path(path, paste0(fname, ".csv"))
## }
# station/pollutant data
## no_wknd <- TRUE
## data_period <- "jan_2017_jun_2020"
## wknd_suffix <- ifelse(no_wknd, "no_wknd", "all_days")
## data_path <- path("data", "concentration", paste0("2017-2020_", wknd_suffix))
## pollutant_pval <- read_csv(get_fname("data", "conc-pval",
## data_period, wknd_suffix))
## # temperature data
## temperature_pval <- read_csv(get_fname("data", "temp-pval",
## data_period, wknd_suffix))
## # irradiance data
## irradiance_pval <- read_csv(get_fname("data", "irr-pval",
## data_period, wknd_suffix))
#` Format data for plotting.
#`
#` Converts the pairs [pollutant_data]/[pollutant_pval], [temperature_data]/[temperature_pval], or [irradiance_data]/[irradiance_pval] into the tibble format required for [pval_boxplot()].
#`
#` @param data Data frame of which a subset of columns is `Date`, `Station`, and the string stored in the parameter `value`. See [pollutant_data], [temperature_data], and [irradiance_data].
#` @param pval Data frame of which a subset of columns is `Station`, `Period`, `Month`, and `Pval`. See [pollutant_pval], [temperature_pval], and [irradiance_pval].
#` @param value String denoting the variable in `data` for which p-values are calculated. This is `Concentration` for [pollutant_data], `Mean_Temp` for [temperature_data], and `GHI` for [irradiance_data].
#` @param station_names Named character vector of which the names are the elements of the `Station` variable to filter, and the elements are the display names for the plots.
#` @param month Numeric vector of months to filter.
#` @param pval_y Scalar or vector of the same length as `station_names` indicating the y-value in the plot at which to display the p-values.
#`
#` @return A list with elements:
#` \describe{
#` \item{`data`}{A tibble with columns: `Station`, `Period`, `Year`, `Month`, `Day`, `Value`.}
#` \item{`pval`}{A tibble with columns: `Station`, `Pollutant`, `Period`, `Month`, `Ndays`, `Median`, `Pval`, `Value`, `Yvalue`.}
#` }
format_data <- function(data, pval, value, station_names, month, pval_y) {
no_wknd <- TRUE # p-values have weekends excluded
## # FIXME: save p-values with numeric month to avoid this
## month_labels <- c("January", "February", "March", "April", "May", "June")
data <- data %>%
filter(Station %in% names(station_names)) %>%
filter(lubridate::month(Date) %in% month) %>%
filter(!wday(Date, label = TRUE) %in% c("Sat", "Sun") | !no_wknd) %>%
mutate(Year = year(Date),
Month = month(Date, label = TRUE, abbr = TRUE),
Day = day(Date),
Station = factor(station_names[Station],
levels = station_names)) %>%
select(Year, Month, Day, Station, Value = !!value) %>%
mutate(Year = factor(Year),
Period = ifelse(Year == 2020, "2020", "2017-2019")) %>%
select(Station, Period, Year, Month, Day, Value)
pval <- pval %>%
filter(Station %in% names(station_names)) %>%
mutate(Value = Pval,
## Month = month(as.numeric(factor(Month, levels = month_labels)),
## label = TRUE, abbr = TRUE),
Month = lubridate::month(as.Date(paste0(Month, "0101"),
format = "%b%d%y")),
Station = factor(station_names[Station], levels = station_names)) %>%
filter(Month %in% month) %>%
mutate(Month = lubridate::month(Month, label = TRUE, abbr = TRUE))
if(length(pval_y) == 1) {
pval <- pval %>%
mutate(Yvalue = pval_y)
} else {
pval <- pval %>%
mutate(Yvalue = setNames(pval_y, station_names)[Station])
}
list(data = data, pval = pval)
}
#` Generate p-value boxplots.
#`
#` @param data Element `data` from list as output by [format_data()].
#` @param pval Element `pval` from list as output by [format_data()].
#` @param ylim Optional range of values at which to clip the plot. Default is no clipping.
#` @param same_scale Whether to use the same scale for all plots. This corresponds to the `fixed` vs `free_y` argument to [ggplot2::facet_wrap()].
#` @param nrow Number of rows when there are multiple stations to plot. This corresponds to the `nrow` argument to [ggplot2::facet_wrap()].
#` @param pv_size Size of p-value labels.
#` @param pt_size Size of data points.
#` @param lab_size Size of axis labels. Sets the arguments `axis.title` and `axis.text` in [ggplot2::theme()].
#` @param leg_size Size of legend text. Sets the arguments `legend.title` and `legend.text` in [ggplot2::theme()].
#` @param main_size Size of station name in plot.
#` @param main_pos String for where to put the station name: "left" or "right".
#`
#` @return Boxplots with p-value labels of `Value` by `Period` within `Month`, tiled by `Station`.
pval_boxplot2 <- function(data, pval, ylim, same_scale = TRUE, nrow = NULL,
pv_size, pt_size, lab_size, leg_size, main_size,
main_pos = c("right", "left")) {
if(missing(ylim)) ylim <- c(-Inf, Inf)
main_pos <- match.arg(main_pos)
## main_pos <- ifelse("right", 1, -1)
data %>%
ggplot(aes(x = Month, y = pmin(Value, ylim[2]))) +
## geom_jitter(aes(fill = Year, group = Period, shape = Year),
## position = position_jitterdodge(.5)) +
geom_jitter(mapping = aes(fill = Year, group = Period, shape = Year),
## data = data %>% filter(Value < ylim[2]),
position = position_jitterdodge(.5, seed = 1),
size = pt_size, color = "transparent") +
## geom_jitter(mapping = aes(fill = Year, group = Period,
## shape = Year, y = ylim[2]),
## data = data %>% filter(Value >= ylim[2]),
## position = position_jitterdodge(.5, seed = 1),
## size = pt_size, color = "transparent") +
geom_text(data = data %>% filter(Value >= ylim[2]),
mapping = aes(label = round(Value, 2), group = Period),
check_overlap = TRUE, size = pv_size, vjust = 1.5,
position = position_jitterdodge(.5, seed = 1)) +
geom_boxplot(aes(color = Period), outlier.shape = NA, alpha = 0) +
geom_text(data = pval,
mapping = aes(x = Month, y = Yvalue, group = Period,
label = signif(round(Value, 2), 1)),
position = position_dodge(.8),
size = pv_size) +
geom_text(data=pval,
mapping = aes(label = Station, y = Inf,
x = ifelse(main_pos == "right", Inf, -Inf)),
size = main_size,
## hjust = 1.1,
hjust = ifelse(main_pos == "right", 1.1, -0.1),
vjust = 2) +
facet_wrap(~ Station, scales = ifelse(same_scale, "fixed", "free_y"),
nrow = nrow) +
scale_fill_manual(values = c("red", "blue", "orange", "darkgreen")) +
## scale_fill_manual(values = cb_palette) +
## scale_color_manual(values = c("black", "blue")) +
scale_shape_manual(values = 21:25) +
guides(fill = guide_legend(override.aes = list(size=2.5))) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text.x = element_blank(),
## legend.position = c(0,1),
legend.position = "top",
legend.direction = "horizontal",
## legend.justification = c(-.02,1.1),
legend.justification = c(0,.8),
axis.title = element_text(size = lab_size),
axis.text = element_text(size = lab_size),
## plot.margin = margin(t=30),
## plot.title = element_text(size = main_size,
## hjust = .97, vjust = -7),
legend.text = element_text(size = leg_size),
legend.title = element_text(size = leg_size),
legend.spacing = unit(.2, "cm"),
legend.margin = margin(2, 2, 2, 2),
legend.key.size = unit(.5, "cm"),
legend.background = element_rect(color = "black", size = .2),
panel.border = element_rect(fill = NA, size = 1.5),
axis.ticks = element_line(size = 1)
)
}
# common plotting information
poll_label <- c(O3 = "O[3]*' Concentration '*(ppb)",
PM25 = "PM[2.5]*' Concentration '*(mu*g/m^3)",
NO2 = "NO[2]*' Concentration '*(ppb)",
SO2 = "SO[2]*' Concentration '*(ppb)",
CO = "CO*' Concentration '*(ppm)")
pv_size <- 2.7
pt_size <- 1.2
lab_size <- 10
leg_size <- 8
main_size <- 3
#' # CO Concentrations: January-June
#+ S8_concentration_co_jj, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 6.5, crop = params$crop
pollutant <- "CO"
station_names <- pollutant_info %>%
filter(pollutant == !!pollutant & has_poll) %>%
pull(station)
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
pval_y <- .08
months <- 1:6
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
# plot
pval_boxplot2(data = poll_data$data %>% filter(Value < .6),
pval = poll_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size) +
ylab(ylab)
#' \newpage
#'
#' # CO Concentrations: July-November
#+ S8_concentration_co_jn, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 6.5, crop = params$crop
pollutant <- "CO"
station_names <- pollutant_info %>%
filter(pollutant == !!pollutant & has_poll) %>%
pull(station)
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
pval_y <- .08
months <- 7:11
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
# plot
pval_boxplot2(data = poll_data$data %>% filter(Value < .6),
pval = poll_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size) +
ylab(ylab)
#' \newpage
#'
#' # NO2 Concentrations: January-June
#+ S7_concentration_no2_jj, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 10, crop = params$crop
pollutant <- "NO2"
station_names <- c("Grand_Bend", "Kitchener", "Toronto_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- c(-1, -1, 2)
## ylim <- c(-Inf, Inf)
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
months <- 1:6
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
# plot
pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = FALSE, nrow = 3) + ylab(ylab)
#' \newpage
#'
#' # NO2 Concentrations: July-November
#+ S7_concentration_no2_jn, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 10, crop = params$crop
pollutant <- "NO2"
station_names <- c("Grand_Bend", "Kitchener", "Toronto_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- c(-1, -1, 2)
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
months <- 7:11
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
# plot
pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = FALSE, nrow = 3) + ylab(ylab)
#' \newpage
#'
#' # O3 and PM2.5 Concentrations: January-June
#+ S9_concentration_o3_pm25_jj, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 9.75, crop = params$crop
pollutant <- "O3"
station_names <- c("Sarnia", "Toronto_West", "Windsor_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- 0
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
months <- 1:6
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
plt_O3 <- pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = TRUE, nrow = 3) +
ylab(ylab) + ggtitle(expression(O[3]*" "*Concentration))
pollutant <- "PM25"
station_names <- c("Hamilton_West", "Ottawa_Downtown", "Windsor_Downtown")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
plt_PM25 <- pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = TRUE, nrow = 3) +
ylab(ylab) + ggtitle(expression(PM[2.5]*" "*Concentration))
ggarrange(plt_O3, plt_PM25, ncol = 2, common.legend = TRUE)
#' \newpage
#'
#' # O3 and PM2.5 Concentrations: July-November
#+ S9_concentration_o3_pm25_jn, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 9.75, crop = params$crop
pollutant <- "O3"
station_names <- c("Sarnia", "Toronto_West", "Windsor_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- 0
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
months <- 7:11
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
plt_O3 <- pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = TRUE, nrow = 3) +
ylab(ylab) + ggtitle(expression(O[3]*" "*Concentration))
pollutant <- "PM25"
station_names <- c("Hamilton_West", "Ottawa_Downtown", "Windsor_Downtown")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
plt_PM25 <- pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = TRUE, nrow = 3) +
ylab(ylab) + ggtitle(expression(PM[2.5]*" "*Concentration))
ggarrange(plt_O3, plt_PM25, ncol = 2, common.legend = TRUE)
#' \newpage
#'
#' # Temperature: January-June
#+ S1_temperature_jj, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 10, crop = params$crop, eval = FALSE
station_names <- c("Grand_Bend_Goderich", "Kitchener_Waterloo",
"Toronto_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
station_names[1:2] <- c("Grand Bend", "Kitchener")
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- -22
## ylim <- c(-Inf, Inf)
ylab <- parse(text = paste0("'Daily Mean Temperature '*(degree*C)",
"*' -- Weekdays Only'"))
months <- 1:6
# prepare data
temp_data <- format_data(data = temperature_data,
pval = temperature_pval,
value = "Mean_Temp",
station_names = station_names,
month = months,
pval_y = pval_y)
plt <- pval_boxplot2(data = temp_data$data, pval = temp_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size, main_pos = "left",
same_scale = FALSE, nrow = 3) + ylab(ylab)
## ggarrange(plt,
## ggplot() + theme_void(),
## ncol = 2, common.legend = TRUE)
plt
#' \newpage
#'
#' # Temperature: July-November
#+ S1_temperature_jn, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 10, crop = params$crop, eval = FALSE
station_names <- c("Grand_Bend_Goderich", "Kitchener_Waterloo",
"Toronto_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
station_names[1:2] <- c("Grand Bend", "Kitchener")
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- -22
## ylim <- c(-Inf, Inf)
ylab <- parse(text = paste0("'Daily Mean Temperature '*(degree*C)",
"*' -- Weekdays Only'"))
months <- 7:11
# prepare data
temp_data <- format_data(data = temperature_data,
pval = temperature_pval,
value = "Mean_Temp",
station_names = station_names,
month = months,
pval_y = pval_y)
plt <- pval_boxplot2(data = temp_data$data, pval = temp_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size, main_pos = "left",
same_scale = FALSE, nrow = 3) + ylab(ylab)
## ggarrange(plt,
## ggplot() + theme_void(),
## ncol = 2, common.legend = TRUE)
plt
#' \newpage
#'
#' # Solar Irradiance
#+ S2_irradiance, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 7.5, crop = params$crop, eval = FALSE
station_names <- c("Ottawa", "Delhi")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- -22
## ylim <- c(-Inf, Inf)
ylab <- parse(text = paste0("'Daily Global Horizontal Irradiance '*(Wm^{-2}*day^{-1})",
"*' -- Weekdays Only'"))
# prepare data
irr_data <- format_data(data = irradiance_data,
pval = irradiance_pval,
value = "GHI",
station_names = station_names, pval_y = pval_y)
pval_boxplot2(data = irr_data$data, pval = irr_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size, main_pos = "left",
same_scale = FALSE, nrow = 3) + ylab(ylab)
#--- scratch -------------------------------------------------------------------
## format_poll <- function(pollutant, station_names, pval_y) {
## poll_data <- pollutant_data %>%
## filter(Pollutant == pollutant & Station %in% names(station_names)) %>%
## filter(month(Date) %in% 1:6) %>%
## filter(!wday(Date, label = TRUE) %in% c("Sat", "Sun") | !no_wknd) %>%
## mutate(Year = year(Date),
## Month = month(Date, label = TRUE, abbr = TRUE),
## Day = day(Date),
## Station = station_names[Station]) %>%
## select(Year, Month, Day, Station, Value = Concentration) %>%
## mutate(Year = factor(Year),
## Period = ifelse(Year == 2020, "2020", "2017-2019"))
## pval_data <- pollutant_pval %>%
## filter(Pollutant == pollutant & Station %in% names(station_names)) %>%
## mutate(Value = Pval_med,
## Month = month(as.numeric(ordered(Month)),
## label = TRUE, abbr = TRUE),
## Station = station_names[Station])
## if(length(pval_y) == 1) {
## pval_data <- pval_data %>%
## mutate(Yvalue = pval_y)
## } else {
## pval_data <- pval_data %>%
## mutate(Yvalue = setNames(pval_y, station_names)[Station])
## }
## list(data = poll_data, pval = pval_data)
## }
## format_temp <- function(station_names, pval_y) {
## temp_data <- temperature_data %>%
## filter(Station %in% names(station_names)) %>%
## filter(month(Date) %in% 1:6) %>%
## filter(!wday(Date, label = TRUE) %in% c("Sat", "Sun") | !no_wknd) %>%
## mutate(Year = year(Date),
## Month = month(Date, label = TRUE, abbr = TRUE),
## Day = day(Date),
## Station = station_names[Station]) %>%
## select(Year, Month, Day, Station, Value = Mean_Temp) %>%
## mutate(Year = factor(Year),
## Period = ifelse(Year == 2020, "2020", "2017-2019"))
## pval_data <- temperature_pval %>%
## filter(Station %in% names(station_names)) %>%
## mutate(Value = Pval_med,
## Month = month(as.numeric(ordered(Month)),
## label = TRUE, abbr = TRUE),
## Station = station_names[Station])
## if(length(pval_y) == 1) {
## pval_data <- pval_data %>%
## mutate(Yvalue = pval_y)
## } else {
## pval_data <- pval_data %>%
## mutate(Yvalue = setNames(pval_y, station_names)[Station])
## }
## list(data = temp_data, pval = pval_data)
## }
## get_legend <- function(plt) {
## tmp <- ggplot_gtable(ggplot_build(plt))
## leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
## legend <- tmp$grobs[[leg]]
## return(legend)
## }
## # same arguments as grid.arrange
## grid_arrange_shared_legend <- function(..., ylab) {
## plots <- list(...)
## g <- ggplotGrob(plots[[1]] + theme(legend.position="bottom"))$grobs
## legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
## lheight <- sum(legend$height)
## grid.arrange(
## legend,
## do.call(arrangeGrob, lapply(plots, function(x)
## x + theme(legend.position="none"))),
## ncol = 1,
## heights = unit.c(lheight, unit(1, "npc") - lheight),
## left = ylab)
## }
mlysy/aq2020 documentation built on April 14, 2022, 12:43 a.m.
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#' ---
#' title: "P-value Plots for AQ2020 Paper"
#' params:
#' crop: FALSE
#' output:
#' rmarkdown::pdf_document:
#' keep_tex: yes
#' rmarkdown::html_document:
#' theme: readable
#' highlight: tango
#' toc: yes
#' toc_float: true
#' ---
#+ r setup, include = FALSE, message = FALSE
knitr::opts_chunk$set(echo = FALSE, fig.align = "center")
knitr::knit_hooks$set(crop = knitr::hook_pdfcrop)
require(aq2020)
require(dplyr) # for basic things like %>%
require(tidyr) # for pivot_longer
## require(readr) # read_csv
require(lubridate) # dates
## require(fs) # opening files
require(ggplot2) # for plotting
## require(gridExtra) # for complex layout
## require(grid)
require(ggpubr) # for complex layout
## # helper functions
## get_fname <- function(path, ...) {
## fname <- paste0(list(...), collapse = "_")
## file.path(path, paste0(fname, ".csv"))
## }
# station/pollutant data
## no_wknd <- TRUE
## data_period <- "jan_2017_jun_2020"
## wknd_suffix <- ifelse(no_wknd, "no_wknd", "all_days")
## data_path <- path("data", "concentration", paste0("2017-2020_", wknd_suffix))
## pollutant_pval <- read_csv(get_fname("data", "conc-pval",
## data_period, wknd_suffix))
## # temperature data
## temperature_pval <- read_csv(get_fname("data", "temp-pval",
## data_period, wknd_suffix))
## # irradiance data
## irradiance_pval <- read_csv(get_fname("data", "irr-pval",
## data_period, wknd_suffix))
#` Format data for plotting.
#`
#` Converts the pairs [pollutant_data]/[pollutant_pval], [temperature_data]/[temperature_pval], or [irradiance_data]/[irradiance_pval] into the tibble format required for [pval_boxplot()].
#`
#` @param data Data frame of which a subset of columns is `Date`, `Station`, and the string stored in the parameter `value`. See [pollutant_data], [temperature_data], and [irradiance_data].
#` @param pval Data frame of which a subset of columns is `Station`, `Period`, `Month`, and `Pval`. See [pollutant_pval], [temperature_pval], and [irradiance_pval].
#` @param value String denoting the variable in `data` for which p-values are calculated. This is `Concentration` for [pollutant_data], `Mean_Temp` for [temperature_data], and `GHI` for [irradiance_data].
#` @param station_names Named character vector of which the names are the elements of the `Station` variable to filter, and the elements are the display names for the plots.
#` @param month Numeric vector of months to filter.
#` @param pval_y Scalar or vector of the same length as `station_names` indicating the y-value in the plot at which to display the p-values.
#`
#` @return A list with elements:
#` \describe{
#` \item{`data`}{A tibble with columns: `Station`, `Period`, `Year`, `Month`, `Day`, `Value`.}
#` \item{`pval`}{A tibble with columns: `Station`, `Pollutant`, `Period`, `Month`, `Ndays`, `Median`, `Pval`, `Value`, `Yvalue`.}
#` }
format_data <- function(data, pval, value, station_names, month, pval_y) {
no_wknd <- TRUE # p-values have weekends excluded
## # FIXME: save p-values with numeric month to avoid this
## month_labels <- c("January", "February", "March", "April", "May", "June")
data <- data %>%
filter(Station %in% names(station_names)) %>%
filter(lubridate::month(Date) %in% month) %>%
filter(!wday(Date, label = TRUE) %in% c("Sat", "Sun") | !no_wknd) %>%
mutate(Year = year(Date),
Month = month(Date, label = TRUE, abbr = TRUE),
Day = day(Date),
Station = factor(station_names[Station],
levels = station_names)) %>%
select(Year, Month, Day, Station, Value = !!value) %>%
mutate(Year = factor(Year),
Period = ifelse(Year == 2020, "2020", "2017-2019")) %>%
select(Station, Period, Year, Month, Day, Value)
pval <- pval %>%
filter(Station %in% names(station_names)) %>%
mutate(Value = Pval,
## Month = month(as.numeric(factor(Month, levels = month_labels)),
## label = TRUE, abbr = TRUE),
Month = lubridate::month(as.Date(paste0(Month, "0101"),
format = "%b%d%y")),
Station = factor(station_names[Station], levels = station_names)) %>%
filter(Month %in% month) %>%
mutate(Month = lubridate::month(Month, label = TRUE, abbr = TRUE))
if(length(pval_y) == 1) {
pval <- pval %>%
mutate(Yvalue = pval_y)
} else {
pval <- pval %>%
mutate(Yvalue = setNames(pval_y, station_names)[Station])
}
list(data = data, pval = pval)
}
#` Generate p-value boxplots.
#`
#` @param data Element `data` from list as output by [format_data()].
#` @param pval Element `pval` from list as output by [format_data()].
#` @param ylim Optional range of values at which to clip the plot. Default is no clipping.
#` @param same_scale Whether to use the same scale for all plots. This corresponds to the `fixed` vs `free_y` argument to [ggplot2::facet_wrap()].
#` @param nrow Number of rows when there are multiple stations to plot. This corresponds to the `nrow` argument to [ggplot2::facet_wrap()].
#` @param pv_size Size of p-value labels.
#` @param pt_size Size of data points.
#` @param lab_size Size of axis labels. Sets the arguments `axis.title` and `axis.text` in [ggplot2::theme()].
#` @param leg_size Size of legend text. Sets the arguments `legend.title` and `legend.text` in [ggplot2::theme()].
#` @param main_size Size of station name in plot.
#` @param main_pos String for where to put the station name: "left" or "right".
#`
#` @return Boxplots with p-value labels of `Value` by `Period` within `Month`, tiled by `Station`.
pval_boxplot2 <- function(data, pval, ylim, same_scale = TRUE, nrow = NULL,
pv_size, pt_size, lab_size, leg_size, main_size,
main_pos = c("right", "left")) {
if(missing(ylim)) ylim <- c(-Inf, Inf)
main_pos <- match.arg(main_pos)
## main_pos <- ifelse("right", 1, -1)
data %>%
ggplot(aes(x = Month, y = pmin(Value, ylim[2]))) +
## geom_jitter(aes(fill = Year, group = Period, shape = Year),
## position = position_jitterdodge(.5)) +
geom_jitter(mapping = aes(fill = Year, group = Period, shape = Year),
## data = data %>% filter(Value < ylim[2]),
position = position_jitterdodge(.5, seed = 1),
size = pt_size, color = "transparent") +
## geom_jitter(mapping = aes(fill = Year, group = Period,
## shape = Year, y = ylim[2]),
## data = data %>% filter(Value >= ylim[2]),
## position = position_jitterdodge(.5, seed = 1),
## size = pt_size, color = "transparent") +
geom_text(data = data %>% filter(Value >= ylim[2]),
mapping = aes(label = round(Value, 2), group = Period),
check_overlap = TRUE, size = pv_size, vjust = 1.5,
position = position_jitterdodge(.5, seed = 1)) +
geom_boxplot(aes(color = Period), outlier.shape = NA, alpha = 0) +
geom_text(data = pval,
mapping = aes(x = Month, y = Yvalue, group = Period,
label = signif(round(Value, 2), 1)),
position = position_dodge(.8),
size = pv_size) +
geom_text(data=pval,
mapping = aes(label = Station, y = Inf,
x = ifelse(main_pos == "right", Inf, -Inf)),
size = main_size,
## hjust = 1.1,
hjust = ifelse(main_pos == "right", 1.1, -0.1),
vjust = 2) +
facet_wrap(~ Station, scales = ifelse(same_scale, "fixed", "free_y"),
nrow = nrow) +
scale_fill_manual(values = c("red", "blue", "orange", "darkgreen")) +
## scale_fill_manual(values = cb_palette) +
## scale_color_manual(values = c("black", "blue")) +
scale_shape_manual(values = 21:25) +
guides(fill = guide_legend(override.aes = list(size=2.5))) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text.x = element_blank(),
## legend.position = c(0,1),
legend.position = "top",
legend.direction = "horizontal",
## legend.justification = c(-.02,1.1),
legend.justification = c(0,.8),
axis.title = element_text(size = lab_size),
axis.text = element_text(size = lab_size),
## plot.margin = margin(t=30),
## plot.title = element_text(size = main_size,
## hjust = .97, vjust = -7),
legend.text = element_text(size = leg_size),
legend.title = element_text(size = leg_size),
legend.spacing = unit(.2, "cm"),
legend.margin = margin(2, 2, 2, 2),
legend.key.size = unit(.5, "cm"),
legend.background = element_rect(color = "black", size = .2),
panel.border = element_rect(fill = NA, size = 1.5),
axis.ticks = element_line(size = 1)
)
}
# common plotting information
poll_label <- c(O3 = "O[3]*' Concentration '*(ppb)",
PM25 = "PM[2.5]*' Concentration '*(mu*g/m^3)",
NO2 = "NO[2]*' Concentration '*(ppb)",
SO2 = "SO[2]*' Concentration '*(ppb)",
CO = "CO*' Concentration '*(ppm)")
pv_size <- 2.7
pt_size <- 1.2
lab_size <- 10
leg_size <- 8
main_size <- 3
#' # CO Concentrations: January-June
#+ S8_concentration_co_jj, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 6.5, crop = params$crop
pollutant <- "CO"
station_names <- pollutant_info %>%
filter(pollutant == !!pollutant & has_poll) %>%
pull(station)
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
pval_y <- .08
months <- 1:6
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
# plot
pval_boxplot2(data = poll_data$data %>% filter(Value < .6),
pval = poll_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size) +
ylab(ylab)
#' \newpage
#'
#' # CO Concentrations: July-November
#+ S8_concentration_co_jn, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 6.5, crop = params$crop
pollutant <- "CO"
station_names <- pollutant_info %>%
filter(pollutant == !!pollutant & has_poll) %>%
pull(station)
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
pval_y <- .08
months <- 7:11
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
# plot
pval_boxplot2(data = poll_data$data %>% filter(Value < .6),
pval = poll_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size) +
ylab(ylab)
#' \newpage
#'
#' # NO2 Concentrations: January-June
#+ S7_concentration_no2_jj, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 10, crop = params$crop
pollutant <- "NO2"
station_names <- c("Grand_Bend", "Kitchener", "Toronto_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- c(-1, -1, 2)
## ylim <- c(-Inf, Inf)
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
months <- 1:6
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
# plot
pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = FALSE, nrow = 3) + ylab(ylab)
#' \newpage
#'
#' # NO2 Concentrations: July-November
#+ S7_concentration_no2_jn, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 10, crop = params$crop
pollutant <- "NO2"
station_names <- c("Grand_Bend", "Kitchener", "Toronto_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- c(-1, -1, 2)
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
months <- 7:11
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
# plot
pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = FALSE, nrow = 3) + ylab(ylab)
#' \newpage
#'
#' # O3 and PM2.5 Concentrations: January-June
#+ S9_concentration_o3_pm25_jj, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 9.75, crop = params$crop
pollutant <- "O3"
station_names <- c("Sarnia", "Toronto_West", "Windsor_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- 0
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
months <- 1:6
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
plt_O3 <- pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = TRUE, nrow = 3) +
ylab(ylab) + ggtitle(expression(O[3]*" "*Concentration))
pollutant <- "PM25"
station_names <- c("Hamilton_West", "Ottawa_Downtown", "Windsor_Downtown")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
plt_PM25 <- pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = TRUE, nrow = 3) +
ylab(ylab) + ggtitle(expression(PM[2.5]*" "*Concentration))
ggarrange(plt_O3, plt_PM25, ncol = 2, common.legend = TRUE)
#' \newpage
#'
#' # O3 and PM2.5 Concentrations: July-November
#+ S9_concentration_o3_pm25_jn, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 9.75, crop = params$crop
pollutant <- "O3"
station_names <- c("Sarnia", "Toronto_West", "Windsor_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- 0
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
months <- 7:11
# prepare data
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
plt_O3 <- pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = TRUE, nrow = 3) +
ylab(ylab) + ggtitle(expression(O[3]*" "*Concentration))
pollutant <- "PM25"
station_names <- c("Hamilton_West", "Ottawa_Downtown", "Windsor_Downtown")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
ylab <- parse(text = paste0(poll_label[pollutant],
"*' -- Weekdays Only'"))
poll_data <- format_data(data = pollutant_data %>%
filter(Pollutant == pollutant),
pval = pollutant_pval %>%
filter(Pollutant == pollutant),
value = "Concentration",
station_names = station_names,
month = months,
pval_y = pval_y)
plt_PM25 <- pval_boxplot2(data = poll_data$data, pval = poll_data$pval,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size,
same_scale = TRUE, nrow = 3) +
ylab(ylab) + ggtitle(expression(PM[2.5]*" "*Concentration))
ggarrange(plt_O3, plt_PM25, ncol = 2, common.legend = TRUE)
#' \newpage
#'
#' # Temperature: January-June
#+ S1_temperature_jj, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 10, crop = params$crop, eval = FALSE
station_names <- c("Grand_Bend_Goderich", "Kitchener_Waterloo",
"Toronto_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
station_names[1:2] <- c("Grand Bend", "Kitchener")
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- -22
## ylim <- c(-Inf, Inf)
ylab <- parse(text = paste0("'Daily Mean Temperature '*(degree*C)",
"*' -- Weekdays Only'"))
months <- 1:6
# prepare data
temp_data <- format_data(data = temperature_data,
pval = temperature_pval,
value = "Mean_Temp",
station_names = station_names,
month = months,
pval_y = pval_y)
plt <- pval_boxplot2(data = temp_data$data, pval = temp_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size, main_pos = "left",
same_scale = FALSE, nrow = 3) + ylab(ylab)
## ggarrange(plt,
## ggplot() + theme_void(),
## ncol = 2, common.legend = TRUE)
plt
#' \newpage
#'
#' # Temperature: July-November
#+ S1_temperature_jn, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 10, crop = params$crop, eval = FALSE
station_names <- c("Grand_Bend_Goderich", "Kitchener_Waterloo",
"Toronto_West")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
station_names[1:2] <- c("Grand Bend", "Kitchener")
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- -22
## ylim <- c(-Inf, Inf)
ylab <- parse(text = paste0("'Daily Mean Temperature '*(degree*C)",
"*' -- Weekdays Only'"))
months <- 7:11
# prepare data
temp_data <- format_data(data = temperature_data,
pval = temperature_pval,
value = "Mean_Temp",
station_names = station_names,
month = months,
pval_y = pval_y)
plt <- pval_boxplot2(data = temp_data$data, pval = temp_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size, main_pos = "left",
same_scale = FALSE, nrow = 3) + ylab(ylab)
## ggarrange(plt,
## ggplot() + theme_void(),
## ncol = 2, common.legend = TRUE)
plt
#' \newpage
#'
#' # Solar Irradiance
#+ S2_irradiance, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 7.5, crop = params$crop, eval = FALSE
station_names <- c("Ottawa", "Delhi")
station_names <- setNames(gsub("_", replacement = " ", station_names),
nm = station_names)
## pv_size <- 2.7
## pt_size <- 1.2
## lab_size <- 10
## leg_size <- 8
## main_size <- 3
pval_y <- -22
## ylim <- c(-Inf, Inf)
ylab <- parse(text = paste0("'Daily Global Horizontal Irradiance '*(Wm^{-2}*day^{-1})",
"*' -- Weekdays Only'"))
# prepare data
irr_data <- format_data(data = irradiance_data,
pval = irradiance_pval,
value = "GHI",
station_names = station_names, pval_y = pval_y)
pval_boxplot2(data = irr_data$data, pval = irr_data$pval,
## ylim = ylim,
pv_size = pv_size, pt_size = pt_size,
lab_size = lab_size, leg_size = leg_size,
main_size = main_size, main_pos = "left",
same_scale = FALSE, nrow = 3) + ylab(ylab)
#--- scratch -------------------------------------------------------------------
## format_poll <- function(pollutant, station_names, pval_y) {
## poll_data <- pollutant_data %>%
## filter(Pollutant == pollutant & Station %in% names(station_names)) %>%
## filter(month(Date) %in% 1:6) %>%
## filter(!wday(Date, label = TRUE) %in% c("Sat", "Sun") | !no_wknd) %>%
## mutate(Year = year(Date),
## Month = month(Date, label = TRUE, abbr = TRUE),
## Day = day(Date),
## Station = station_names[Station]) %>%
## select(Year, Month, Day, Station, Value = Concentration) %>%
## mutate(Year = factor(Year),
## Period = ifelse(Year == 2020, "2020", "2017-2019"))
## pval_data <- pollutant_pval %>%
## filter(Pollutant == pollutant & Station %in% names(station_names)) %>%
## mutate(Value = Pval_med,
## Month = month(as.numeric(ordered(Month)),
## label = TRUE, abbr = TRUE),
## Station = station_names[Station])
## if(length(pval_y) == 1) {
## pval_data <- pval_data %>%
## mutate(Yvalue = pval_y)
## } else {
## pval_data <- pval_data %>%
## mutate(Yvalue = setNames(pval_y, station_names)[Station])
## }
## list(data = poll_data, pval = pval_data)
## }
## format_temp <- function(station_names, pval_y) {
## temp_data <- temperature_data %>%
## filter(Station %in% names(station_names)) %>%
## filter(month(Date) %in% 1:6) %>%
## filter(!wday(Date, label = TRUE) %in% c("Sat", "Sun") | !no_wknd) %>%
## mutate(Year = year(Date),
## Month = month(Date, label = TRUE, abbr = TRUE),
## Day = day(Date),
## Station = station_names[Station]) %>%
## select(Year, Month, Day, Station, Value = Mean_Temp) %>%
## mutate(Year = factor(Year),
## Period = ifelse(Year == 2020, "2020", "2017-2019"))
## pval_data <- temperature_pval %>%
## filter(Station %in% names(station_names)) %>%
## mutate(Value = Pval_med,
## Month = month(as.numeric(ordered(Month)),
## label = TRUE, abbr = TRUE),
## Station = station_names[Station])
## if(length(pval_y) == 1) {
## pval_data <- pval_data %>%
## mutate(Yvalue = pval_y)
## } else {
## pval_data <- pval_data %>%
## mutate(Yvalue = setNames(pval_y, station_names)[Station])
## }
## list(data = temp_data, pval = pval_data)
## }
## get_legend <- function(plt) {
## tmp <- ggplot_gtable(ggplot_build(plt))
## leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
## legend <- tmp$grobs[[leg]]
## return(legend)
## }
## # same arguments as grid.arrange
## grid_arrange_shared_legend <- function(..., ylab) {
## plots <- list(...)
## g <- ggplotGrob(plots[[1]] + theme(legend.position="bottom"))$grobs
## legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
## lheight <- sum(legend$height)
## grid.arrange(
## legend,
## do.call(arrangeGrob, lapply(plots, function(x)
## x + theme(legend.position="none"))),
## ncol = 1,
## heights = unit.c(lheight, unit(1, "npc") - lheight),
## left = ylab)
## }
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