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stacked_supplement_w_captions.Rmd
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stacked_supplement_w_captions.Rmd
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---
output:
pdf_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
message = FALSE,
warning = FALSE,
fig.width = 10,
fig.height = 9)
library(tidyverse)
library(toxEval)
library(ggpubr)
library(cowplot)
source(file = "read_chemicalSummary.R")
source(file = "R/analyze/get_sites_ready.R")
source(file = "R/report/stack_plots.R")
source(file = "R/report/combo_plot2.R")
source(file = "R/analyze/explore_detection_limits.R")
tox_list <- create_toxEval(file.path(Sys.getenv("PASSIVE_PATH"), "data","data_for_git_repo","clean",
"passive.xlsx"))
site_info <- prep_site_list(tox_list$chem_site)
cas_final <- readRDS(file.path(Sys.getenv("PASSIVE_PATH"),
"data", "data_for_git_repo",
"clean",
"cas_df.rds"))
graphData_tox_det <- readRDS(file.path(Sys.getenv("PASSIVE_PATH"),
"data", "data_for_git_repo",
"clean","graphData_tox_det.rds"))
color_map <- class_colors(tox_list)
font_size <- 8
ordered_class <- levels(graphData_tox_det$Class)
levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Food Additive/Plasticizer"] <- "Food Additive"
levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial disinfectant"] <- "Antimicrobial"
ordered_class[ordered_class == "Food Additive/Plasticizer"] <- "Food Additive"
ordered_class[ordered_class == "Antimicrobial disinfectant"] <- "Antimicrobial"
tox_list$chem_info$Class[tox_list$chem_info$Class == "Food Additive/Plasticizer"] <- "Food Additive"
tox_list$chem_info$Class[tox_list$chem_info$Class == "Antimicrobial disinfectant"] <- "Antimicrobial"
name_key <- chemicalSummary %>%
select(CAS, chnm) %>%
distinct() %>%
left_join(select(cas_final, CAS, chnm_new = chnm), by = "CAS") %>%
arrange(chnm)
name_key$chnm_new[name_key$chnm_new == "Tris(1,3-dichloro-2-propyl)phosphate (TDCPP)"] <- "Tris(1,3-dichloro-2-propyl)phosphate"
name_key$chnm_new[name_key$chnm_new == "Tris(2-butoxyethyl)phosphate (TBEP)"] <- "Tris(2-butoxyethyl)phosphate"
name_key$chnm_new[name_key$chnm_new == "Tris(1-chloro-2-propyl)phosphate (TCPP)"] <- "Tris(1-chloro-2-propyl)phosphate"
name_key$chnm_new[name_key$chnm_new == "Tris(2-ethylhexyl)phosphate (TEHP)"] <- "Tris(2-ethylhexyl)phosphate"
levels(chemicalSummary$chnm) <- name_key$chnm_new[which(name_key$chnm == levels(chemicalSummary$chnm))]
classes <- unique(tox_list$chem_info$Class)
num_chem_to_keep <- 5
class_plots <- list()
site_info <- site_info %>%
arrange(`Short Name`)
levels(site_info$`Short Name`) <- paste0(levels(site_info$`Short Name`),
" (",site_info$map_nm,")")
color_6 <- colorspace::rainbow_hcl(6,
start = -360, end = -55,
c = 100, l = 64)
for(i in ordered_class){
chem_i <- chemicalSummary %>%
dplyr::filter(Class == i,
EAR > 0)
if(nrow(chem_i) == 0){
next
}
orig_levels <- levels(chem_i$chnm)
chem_i$chnm <- droplevels(chem_i$chnm)
graphData <- graph_chem_data(chem_i)
orderChem_df <- graphData %>%
mutate(logEAR = log(meanEAR),
chnm = as.character(chnm)) %>%
group_by(chnm) %>%
summarise(max = max(logEAR[logEAR != 0], na.rm = TRUE)) %>%
ungroup()
orderChem_df$max[is.na(orderChem_df$max)] <- min(orderChem_df$max, na.rm = TRUE) - 1
orderChem_df <- arrange(orderChem_df, max)
new_levels <- orderChem_df$chnm %>%
rev()
chem_i$chnm <- factor(as.character(chem_i$chnm), levels = new_levels)
if(length(levels(chem_i$chnm)) > num_chem_to_keep+1){
just_right <- levels(chem_i$chnm)[1:num_chem_to_keep]
too_small <- levels(chem_i$chnm)[(num_chem_to_keep+1):length(levels(chem_i$chnm))]
too_small_text <- paste0("Other [",length(too_small),"]")
chem_i$chnm <- as.character(chem_i$chnm)
chem_i$chnm[chem_i$chnm %in% too_small] <- too_small_text
chem_i$chnm <- factor(chem_i$chnm, levels = c(just_right, too_small_text))
}
color_i <- color_6[1:length(levels(chem_i$chnm))]
names(color_i) <- levels(chem_i$chnm)
if(grepl("Other",names(color_i)[length(color_i)])){
color_i[length(color_i)] <- "grey77"
}
class_plots[[i]] <- whole_stack(chem_i, site_info, tox_list,
color_i, font_size,
category = "Chemical",
title = i)
}
rel_widths <- c(3.25,3,rep(c(1,3),2), 1, 4)
general_lp <- c(0.05,0.87)
```
```{r fig1, fig.height=13}
create_DL_plot(tox_list)
```
Figure SI 1: Exposure activity ratios (EARs) at the detection level for chemicals monitored with passive samplers in Great Lakes tributaries, 2010-2014.
```{r fig2A}
plot_grid(
class_plots[[ordered_class[1]]]$chem_count,
class_plots[[ordered_class[1]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[2]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[2]]]$no_axis +
theme(legend.position = general_lp ,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[3]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[3]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[4]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[4]]]$no_axis +
theme(legend.position = c(0.015, 0.84),
strip.text.y =
element_text(size = 1.25*font_size),
axis.title.x = element_blank()),
rel_widths = rel_widths,
nrow = 1, labels = c("A")
)
```
\begin{center}
Sum of Maximum EAR\textsubscript{chem} Values
\end{center}
Figure SI 2-A: Maximum exposure-activity ratios (EAR) for `r paste0(tolower(ordered_class[1:4]), collapse = ", ")` computed from passive sampler chemistry data from 69 Great Lakes tributaries collected in 2010 and 2014 for chemicals included in the ToxCast database. [Site names are followed parenthetically by the map names from Figure 1; # Chems, number of chemicals with computed EAR values; EAR~chem~, exposure activity ratio]")
```{r fig2B}
plot_grid(
class_plots[[ordered_class[5]]]$chem_count ,
class_plots[[ordered_class[5]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[6]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[6]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[7]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[7]]]$no_axis +
theme(legend.position = general_lp ,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[8]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[8]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_text(size = 1.25*font_size),
axis.title.x = element_blank()),
rel_widths = rel_widths,
nrow = 1, labels = c("B")
)
```
\begin{center}
Sum of Maximum EAR\textsubscript{chem} Values
\end{center}
Figure SI 2-B: Maximum exposure-activity ratios (EAR) for `r paste0(tolower(ordered_class[5:8]), collapse = ", ")` computed from passive sampler chemistry data from 69 Great Lakes tributaries collected in 2010 and 2014 for chemicals included in the ToxCast database. [Site names are followed parenthetically by the map names from Figure 1; # Chems, number of chemicals with computed EAR values; EAR~chem~, exposure activity ratio]")
```{r fig2C}
plot_grid(
class_plots[[ordered_class[9]]]$chem_count ,
class_plots[[ordered_class[9]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[10]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[10]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[11]]]$chem_count +
theme(axis.text.y = element_blank()),
class_plots[[ordered_class[11]]]$no_axis +
theme(legend.position = c(0.025, 0.92) ,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[12]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[12]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_text(size = 1.25*font_size),
axis.title.x = element_blank()),
rel_widths = rel_widths,
nrow = 1, labels = c("C")
)
```
\begin{center}
Sum of Maximum EAR\textsubscript{chem} Values
\end{center}
Figure SI 2-C: Maximum exposure-activity ratios (EAR) for `r paste0(tolower(ordered_class[9:12]), collapse = ", ")` computed from passive sampler chemistry data from 69 Great Lakes tributaries collected in 2010 and 2014 for chemicals included in the ToxCast database. [Site names are followed parenthetically by the map names from Figure 1; # Chems, number of chemicals with computed EAR values; EAR~chem~, exposure activity ratio]")
```{r fig2D}
plot_grid(
class_plots[[ordered_class[13]]]$chem_count ,
class_plots[[ordered_class[13]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[14]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[14]]]$no_axis +
theme(legend.position = general_lp,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[15]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[15]]]$no_axis +
theme(legend.position = c(0.05, 0.92) ,
strip.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[16]]]$chem_count +
theme(axis.text.y = element_blank(),
axis.title.x = element_blank()),
class_plots[[ordered_class[16]]]$no_axis +
theme(legend.position = c(0.05, 0.90),
strip.text.y = element_text(size = 1.25*font_size),
axis.title.x = element_blank()),
rel_widths = rel_widths,
nrow = 1, labels = c("D")
)
```
\begin{center}
Sum of Maximum EAR\textsubscript{chem} Values
\end{center}
Figure SI 2-D: Maximum exposure-activity ratios (EAR) for `r paste0(tolower(ordered_class[12:16]), collapse = ", ")` computed from passive sampler chemistry data from 69 Great Lakes tributaries collected in 2010 and 2014 for chemicals included in the ToxCast database. [Site names are followed parenthetically by the map names from Figure 1; # Chems, number of chemicals with computed EAR values; EAR~chem~, exposure activity ratio]")