2 ToxCast, Water Sample Data
2.1 ToxCast, Water Samples: Detection Levels
As an initial exploration of limitations of using ToxCast with the water sample data, ToxCast endpoints were compared with detection levels for each analyzed compound. The exposure:activity ratio (EAR) for a concentration at the detection level for the endpoint with the minimum AC50 for that chemical is presented in this table. Only compounds with detection levels great than 10% of the lowest AC50 are included.
- - + +2.2 ToxCast, Water Samples: Chemical Summary Tables
This table focuses on the individual compounds and the prevalence of potential adverse biological effects (57 total sites). The table can be sorted by the fraction of sites with hits, maximum EAR, and number of endpoints with hits to evaluate results based on different priorities. “Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
- - + +2.2.1 ToxCast, Water Samples: Individual EndPoints
This table focuses on the individual endpoint/compound combinations and the prevalence of potential adverse biological effects (57 total sites). The table can be sorted by the fraction of sites with hits, maximum EAR, endpoints with hits, and several variables that describe the ToxCast endpoints to evaluate results based on different priorities. “Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
- - - + + +2.3 ToxCast, Water Samples: Site Summaries
This table focuses on the individual sites and the prevalence of potential adverse biological effects (39 total chemicals included in ToxCast). The table can be sorted by the fraction of samples with hits, number of chemicals with hits, number of endpoints with hits, maximum EAR, and land cover attributes to evaluate results based on different priorities. “Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
- - + +2.4 ToxCast, Water Sample: Boxplots by Site
@@ -274,28 +274,26 @@3 ToxCast, Passive Data
3.1 ToxCast, Passive: Detection Levels
As an initial exploration of limitations of using ToxCast with the passive data, ToxCast endpoints were compared with detection levels (MQL) for each analyzed compound. The exposure:activity ratio (EAR) for a concentration at the detection level is presented in this table. Only compounds with detection levels great than 10% of the lowest AC50 are included.
- - + +3.2 ToxCast, Passive: Chemical Summary Tables
This table focuses on the individual compounds, and the prevalence of potential adverse biological effects (57 total sites). The table can be sorted by the fraction of sites with hits, maximum exposure:activity ratio (EAR), and number of endpoints with hits to evaluate results based on different priorities. “Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
- - + +3.2.1 ToxCast, Passive: Individual EndPoints
This table focuses on the individual endpoint/compound combinations, and the prevalence of potential adverse biological effects (57 total sites). The table can be sorted by the fraction of sites with hits, maximum exposure:activity ratio (EAR), endpoints with hits, and several variables that describe the ToxCast endpoints to evaluate results based on different priorities. “Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
-## Warning in left_join_impl(x, y, by$x, by$y): joining character vector and
-## factor, coercing into character vector3.3 ToxCast, Passive: Site Summaries
This table focuses on the individual sites, and the prevalence of potential adverse biological effects (51 total chemicals included in ToxCast). The table can be sorted by number of chemicals with hits, number of endpoints with hits, maximum exposure:activity ratio (EAR), and land cover attributes to evaluate results based on different priorities. “Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
- - + +3.4 ToxCast, Passive: Boxplots by Sites
@@ -319,20 +317,20 @@4 Water Quality Guidelines, Water
4.1 Water Quality Guidelines, Water Samples: Detection Levels
As an initial exploration of limitations of using water quality guidelines with the water sample data, water quality guidelines endpoints were compared with detection levels for each analyzed compound. The exposure:activity ratio (EAR) for a concentration at the detection level is presented in this table. Only compounds with detection levels great than 10% of the lowest AC50 are included.
-
-
+
+
4.2 Water Quality Guidelines, Water Samples: Chemical Summary Tables
This table focuses on the individual compounds, and the prevalence of potential adverse biological effects (57 total sites). The table can be sorted by the fraction of sites with hits, maximum EAR, and number of endpoints with hits to evaluate results based on different priorities.“Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
-
-
+
+
4.3 Water Quality Guidelines, Water Samples: Site Summaries
This table focuses on the individual endpoint/compound combinations, and the prevalence of potential adverse biological effects (57 total sites). The table can be sorted by the fraction of sites with hits, maximum EAR, endpoints with hits, and several variables that describe the water quality benchmark endpoints to evaluate results based on different priorities.“Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
-
-
+
+
4.4 Water Quality Guidelines, Water Samples: Boxplots by Site
@@ -535,22 +533,22 @@ 6 Maps
6.1 ToxCast, Water Samples: Map
This map summarizes the water sample ToxCast results by showing maximum exposure:activity ratio (EAR) for each site as the color scale, and number of chemicals with hits as the symbol size. Additional information is available by clicking on the points.
Color: Maximum EAR
Size: Number of chemicals with hits (0-19)
-
-
+
+
6.2 ToxCast, Passive: Map
This map summarizes the passive ToxCast results by showing maximum exposure:activity ratio (EAR) for each site as the color scale, and number of chemicals with hits as the symbol size. Additional information is available by clicking on the points.
Color: Maximum EAR
Size: Number of chemicals with hits (0-3)
-
-
+
+
6.3 Water Quality Guidelines, Water Samples: Map
This map summarizes the passive water quality guideline results by showing maximum exposure:activity ratio (EAR) for each site as the color scale, and number of chemicals with hits as the symbol size. Additional information is available by clicking on the points.
Color: Maximum EAR
Size: Number of chemicals with hits (0-10)
-
-
+
+
4.1 Water Quality Guidelines, Water Samples: Detection Levels
As an initial exploration of limitations of using water quality guidelines with the water sample data, water quality guidelines endpoints were compared with detection levels for each analyzed compound. The exposure:activity ratio (EAR) for a concentration at the detection level is presented in this table. Only compounds with detection levels great than 10% of the lowest AC50 are included.
- - + +4.2 Water Quality Guidelines, Water Samples: Chemical Summary Tables
This table focuses on the individual compounds, and the prevalence of potential adverse biological effects (57 total sites). The table can be sorted by the fraction of sites with hits, maximum EAR, and number of endpoints with hits to evaluate results based on different priorities.“Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
- - + +4.3 Water Quality Guidelines, Water Samples: Site Summaries
This table focuses on the individual endpoint/compound combinations, and the prevalence of potential adverse biological effects (57 total sites). The table can be sorted by the fraction of sites with hits, maximum EAR, endpoints with hits, and several variables that describe the water quality benchmark endpoints to evaluate results based on different priorities.“Hits” are defined as EAR > 0.1, indicating that the measured concentration is 10% of the AC50 or greater.
- - + +4.4 Water Quality Guidelines, Water Samples: Boxplots by Site
@@ -535,22 +533,22 @@6 Maps
6.1 ToxCast, Water Samples: Map
This map summarizes the water sample ToxCast results by showing maximum exposure:activity ratio (EAR) for each site as the color scale, and number of chemicals with hits as the symbol size. Additional information is available by clicking on the points.
Color: Maximum EAR
Size: Number of chemicals with hits (0-19)
6.2 ToxCast, Passive: Map
This map summarizes the passive ToxCast results by showing maximum exposure:activity ratio (EAR) for each site as the color scale, and number of chemicals with hits as the symbol size. Additional information is available by clicking on the points.
Color: Maximum EAR
Size: Number of chemicals with hits (0-3)
6.3 Water Quality Guidelines, Water Samples: Map
This map summarizes the passive water quality guideline results by showing maximum exposure:activity ratio (EAR) for each site as the color scale, and number of chemicals with hits as the symbol size. Additional information is available by clicking on the points.
Color: Maximum EAR
Size: Number of chemicals with hits (0-10)
USEPA: Anthony Schroeder, Dan Villeneuve, Brett Blackwell, Gerald Ankley" +date: "`r format(Sys.time(), '%d %B, %Y')`" +output: + rmarkdown::html_vignette: + toc: true + number_sections: true + fig_caption: yes +vignette: > + %\VignetteEngine{knitr::rmarkdown} + %\VignetteIndexEntry{ToxEval Summary} + \usepackage[utf8]{inputenc} +--- + +```{r message=FALSE, echo=TRUE, results='asis'} +library(toxEval) +library(DT) +library(dplyr) +library(tidyr) + +wData <- wData +pCodeInfo <- pCodeInfo + +packagePath <- system.file("extdata", package="toxEval") +filePath <- file.path(packagePath, "stationINFO.RData") +load(file=filePath) + +endPoint <- endPointToxCreate(pCodeInfo) +chemicalSummary <- chemSummBasic(wData,pCodeInfo,endPoint) +endPointInfo <- endPointInfo +# epSumm <- endPointSumm(chemicalSummary, endPointInfo=endPointInfo) + +siteKey <- setNames(stationINFO$shortName, stationINFO$fullSiteID) +summary <- siteSumm(chemicalSummary,siteKey) + +``` + + +```{r } +group <- "transcription factor" +groupCol <- "intended_target_type_sub" +chemicalSummary.site <- "site" +chemicalSummary.Key <- "endPoint" +chemicalSummary.Date <- "date" +funcToSummerise <- "sumEAR" + +endPointInfoSub <- select_(endPointInfo, "assay_component_endpoint_name", groupCol) %>% + unique() + +chemicalSummary <- chemicalSummary %>% + filter(endPoint %in% endPointInfoSub$assay_component_endpoint_name ) %>% + left_join(endPointInfoSub, + by=c("endPoint"="assay_component_endpoint_name")) + +groupSumm <- chemicalSummary %>% + group_by(site) %>% + summarise(nChem = length(unique(chnm)), + nEndPoints = length(unique(endPoint))) %>% + select(nChem,nEndPoints) %>% + mutate(nChem = median(nChem), + nEndPoints = median(nEndPoints)) %>% + unique() + +endpointSummary <- chemicalSummary %>% + filter_(paste0(groupCol," == '", group, "'")) %>% + select_("hits","EAR","endPoint", + chemicalSummary.site,chemicalSummary.Date,groupCol) %>% + group_by_(chemicalSummary.site, chemicalSummary.Date) %>% + summarise(sumEAR = sum(EAR), + nHits = sum(hits)) + +statsOfSum <- endpointSummary%>% + group_by_(chemicalSummary.site) %>% + summarise_(meanEAR = paste0("mean(",funcToSummerise,")"), + # medianEAR = paste0("median(",funcToSummerise,")"), + maxEAR = paste0("max(",funcToSummerise,")"), + sumHits = "sum(nHits)", + nSamples = "n()")%>% + data.frame()%>% + mutate(site = siteKey[site]) + + + + + +datatable(statsOfSum, rownames = FALSE, + caption=paste("Summary of", funcToSummerise, "of",group)) %>% + formatRound(c("meanEAR","maxEAR","sumHits","nSamples"), + digits = 1) + + +``` + + +```{r fig.width=7, fig.height=7,} +library(ggplot2) + +siteLimits <- stationINFO %>% + mutate(lakeCat = factor(Lake, + levels=c("Lake Superior","Lake Michigan", + "Lake Huron", "St. Lawrence River", + "Detroit River and Lake St. Clair","Lake Erie","Lake Ontario"))) %>% + arrange(lakeCat) %>% + mutate(lakeColor = c("red","black","green","brown","brown","brown","blue")[as.numeric(lakeCat)] ) %>% + filter(fullSiteID %in% unique(endpointSummary$site)) + + +endPointSummBP <- endpointSummary %>% + data.frame()%>% + mutate(site = siteKey[site]) %>% + mutate(site = factor(site, levels=siteLimits$Station.shortname)) + +sToxWS <- ggplot(endPointSummBP, aes(x=site, y=sumEAR)) + + geom_boxplot() + + theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.25, colour=siteLimits$lakeColor), + legend.position = "none")+ + scale_x_discrete(limits=siteLimits$Station.shortname) + + # scale_y_log10(limits=c(0.03,5000)) + + # labs(x = "Site") + + geom_text(data=data.frame(), aes(x=c(5, 18,31,45,56), + y=-.5, label=c("Superior","Michigan","Huron","Erie","Ontario")), + colour=factor(c("red","black","green","brown","blue"), + levels=c("red","black","green","brown","blue")), size=3) + +sToxWS +``` diff --git a/inst/doc/endPointExploration.html b/inst/doc/endPointExploration.html new file mode 100644 index 00000000..7e8c0eee --- /dev/null +++ b/inst/doc/endPointExploration.html @@ -0,0 +1,185 @@ + + + + + + + + + + + + + +
+USGS: Steve Corsi, Laura DeCicco, Austin Baldwin, David Alvarez, Peter Lenaker
+
+
+
+EndpointExploration
+USGS: Steve Corsi, Laura DeCicco, Austin Baldwin, David Alvarez, Peter Lenaker
USEPA: Anthony Schroeder, Dan Villeneuve, Brett Blackwell, Gerald Ankley
+05 August, 2015
+library(toxEval)
+library(DT)
+library(dplyr)
+library(tidyr)
+
+wData <- wData
+pCodeInfo <- pCodeInfo
+
+packagePath <- system.file("extdata", package="toxEval")
+filePath <- file.path(packagePath, "stationINFO.RData")
+load(file=filePath)
+
+endPoint <- endPointToxCreate(pCodeInfo)
+chemicalSummary <- chemSummBasic(wData,pCodeInfo,endPoint)
+endPointInfo <- endPointInfo
+# epSumm <- endPointSumm(chemicalSummary, endPointInfo=endPointInfo)
+
+siteKey <- setNames(stationINFO$shortName, stationINFO$fullSiteID)
+summary <- siteSumm(chemicalSummary,siteKey)group <- "transcription factor"
+groupCol <- "intended_target_type_sub"
+chemicalSummary.site <- "site"
+chemicalSummary.Key <- "endPoint"
+chemicalSummary.Date <- "date"
+funcToSummerise <- "sumEAR"
+
+endPointInfoSub <- select_(endPointInfo, "assay_component_endpoint_name", groupCol) %>%
+ unique()
+
+chemicalSummary <- chemicalSummary %>%
+ filter(endPoint %in% endPointInfoSub$assay_component_endpoint_name ) %>%
+ left_join(endPointInfoSub,
+ by=c("endPoint"="assay_component_endpoint_name"))
+
+groupSumm <- chemicalSummary %>%
+ group_by(site) %>%
+ summarise(nChem = length(unique(chnm)),
+ nEndPoints = length(unique(endPoint))) %>%
+ select(nChem,nEndPoints) %>%
+ mutate(nChem = median(nChem),
+ nEndPoints = median(nEndPoints)) %>%
+ unique()
+
+endpointSummary <- chemicalSummary %>%
+ filter_(paste0(groupCol," == '", group, "'")) %>%
+ select_("hits","EAR","endPoint",
+ chemicalSummary.site,chemicalSummary.Date,groupCol) %>%
+ group_by_(chemicalSummary.site, chemicalSummary.Date) %>%
+ summarise(sumEAR = sum(EAR),
+ nHits = sum(hits))
+
+statsOfSum <- endpointSummary%>%
+ group_by_(chemicalSummary.site) %>%
+ summarise_(meanEAR = paste0("mean(",funcToSummerise,")"),
+ # medianEAR = paste0("median(",funcToSummerise,")"),
+ maxEAR = paste0("max(",funcToSummerise,")"),
+ sumHits = "sum(nHits)",
+ nSamples = "n()")%>%
+ data.frame()%>%
+ mutate(site = siteKey[site])
+
+
+
+
+
+datatable(statsOfSum, rownames = FALSE,
+ caption=paste("Summary of", funcToSummerise, "of",group)) %>%
+ formatRound(c("meanEAR","maxEAR","sumHits","nSamples"),
+ digits = 1)library(ggplot2)
+
+siteLimits <- stationINFO %>%
+ mutate(lakeCat = factor(Lake,
+ levels=c("Lake Superior","Lake Michigan",
+ "Lake Huron", "St. Lawrence River",
+ "Detroit River and Lake St. Clair","Lake Erie","Lake Ontario"))) %>%
+ arrange(lakeCat) %>%
+ mutate(lakeColor = c("red","black","green","brown","brown","brown","blue")[as.numeric(lakeCat)] ) %>%
+ filter(fullSiteID %in% unique(endpointSummary$site))
+
+
+endPointSummBP <- endpointSummary %>%
+ data.frame()%>%
+ mutate(site = siteKey[site]) %>%
+ mutate(site = factor(site, levels=siteLimits$Station.shortname))
+
+sToxWS <- ggplot(endPointSummBP, aes(x=site, y=sumEAR)) +
+ geom_boxplot() +
+ theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.25, colour=siteLimits$lakeColor),
+ legend.position = "none")+
+ scale_x_discrete(limits=siteLimits$Station.shortname) +
+ # scale_y_log10(limits=c(0.03,5000)) +
+ # labs(x = "Site") +
+ geom_text(data=data.frame(), aes(x=c(5, 18,31,45,56),
+ y=-.5, label=c("Superior","Michigan","Huron","Erie","Ontario")),
+ colour=factor(c("red","black","green","brown","blue"),
+ levels=c("red","black","green","brown","blue")), size=3)
+
+sToxWS
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/siteTables.html b/inst/doc/siteTables.html
index d95eaf9a..0a0548d8 100644
--- a/inst/doc/siteTables.html
+++ b/inst/doc/siteTables.html
@@ -27,7 +27,7 @@
+
+Site Tables
USGS: Steve Corsi, Laura DeCicco, Austin Baldwin, David Alvarez, Peter Lenaker
USEPA: Anthony Schroeder, Dan Villeneuve, Brett Blackwell, Gerald Ankley
-26 June, 2015
+05 August, 2015
diff --git a/inst/shiny/server.R b/inst/shiny/server.R
index 8f4f7210..09e9bb92 100644
--- a/inst/shiny/server.R
+++ b/inst/shiny/server.R
@@ -72,12 +72,16 @@ shinyServer(function(input, output) {
chemicalSummary <- rbind(chemicalSummaryWS, chemicalSummaryPS)
}
+ chemicalSummary$grouping <- chemicalSummary$chnm
+
chemicalSummary
})
chemicalSummaryFiltered <- reactive({
+ chemicalSummary <- chemicalSummary()
+
if(is.null(input$groupCol)){
groupCol <- names(endPointInfo)[20]
} else {
@@ -87,7 +91,7 @@ shinyServer(function(input, output) {
endPointInfoSub <- select_(endPointInfo, "assay_component_endpoint_name", groupCol) %>%
distinct()
- chemicalSummaryFiltered <- chemicalSummary() %>%
+ chemicalSummaryFiltered <- chemicalSummary %>%
rename(assay_component_endpoint_name=endPoint) %>%
filter(assay_component_endpoint_name %in% endPointInfoSub$assay_component_endpoint_name ) %>%
data.table()%>%
@@ -135,11 +139,25 @@ shinyServer(function(input, output) {
siteToFind <- input$sites
}
+ if(is.null(input$radio)){
+ radio <- 1
+ } else {
+ radio <- input$radio
+ }
+
+ if(radio == 2){
+ chemicalSummaryFiltered <- chemicalSummaryFiltered %>%
+ mutate(grouping=class)
+ } else {
+ chemicalSummaryFiltered <- chemicalSummaryFiltered %>%
+ mutate(grouping=chnm)
+ }
+
chemSiteSumm <- chemicalSummaryFiltered %>%
filter_(paste0(groupCol," == '", group, "'")) %>%
mutate(site = siteKey[site]) %>%
filter(site %in% siteToFind) %>%
- group_by(chnm, date) %>%
+ group_by(grouping, date) %>%
summarise(sumEAR = sum(EAR),
nHits = sum(hits))
@@ -168,11 +186,25 @@ shinyServer(function(input, output) {
siteToFind <- input$sites
}
+ if(is.null(input$radio)){
+ radio <- 1
+ } else {
+ radio <- input$radio
+ }
+
+ if(radio == 2){
+ chemicalSummaryFiltered <- chemicalSummaryFiltered %>%
+ mutate(grouping=class)
+ } else {
+ chemicalSummaryFiltered <- chemicalSummaryFiltered %>%
+ mutate(grouping=chnm)
+ }
+
groupSiteSumm <- chemicalSummaryFiltered %>%
filter_(paste0(groupCol," == '", group, "'")) %>%
mutate(site = siteKey[site]) %>%
filter(site %in% siteToFind) %>%
- group_by(chnm, date) %>%
+ group_by(grouping, date) %>%
summarise(sumEAR = sum(EAR),
nHits = sum(hits))
@@ -223,9 +255,9 @@ shinyServer(function(input, output) {
statsOfChem <- reactive({
chemSiteSumm <- chemSiteSumm()
-
+
statsOfChem <- chemSiteSumm %>%
- group_by(chnm) %>%
+ group_by(grouping) %>%
summarise(meanEAR = mean(sumEAR),
maxEAR = max(sumEAR),
sumHits = sum(nHits),
@@ -252,6 +284,12 @@ shinyServer(function(input, output) {
groupCol <- input$groupCol
}
+ if(is.null(input$radio)){
+ radio <- 1
+ } else {
+ radio <- input$radio
+ }
+
statsOfColumn <- chemicalSummary %>%
rename(assay_component_endpoint_name=endPoint) %>%
filter(assay_component_endpoint_name %in% endPointInfo$assay_component_endpoint_name ) %>%
@@ -264,11 +302,15 @@ shinyServer(function(input, output) {
if(length(siteToFind) == 1){
# statsOfColumn <- filter_(statsOfColumn, paste0("site == '", siteToFind, "'"))
- statsOfColumn <- filter(statsOfColumn, site %in% siteToFind) %>%
- mutate(site = chnm)
+ statsOfColumn <- filter(statsOfColumn, site %in% siteToFind)
+ if(radio == 2){
+ statsOfColumn <- mutate(statsOfColumn, site = class)
+ } else {
+ statsOfColumn <- mutate(statsOfColumn, site = chnm)
+ }
}
-
+
statsOfColumn <- statsOfColumn %>%
group_by(site, date,choices) %>%
summarise(sumEAR = sum(EAR),
@@ -294,6 +336,12 @@ shinyServer(function(input, output) {
groupCol <- input$groupCol
}
+ if(is.null(input$radio)){
+ radio <- 1
+ } else {
+ radio <- input$radio
+ }
+
chemGroup <- chemicalSummary %>%
rename(assay_component_endpoint_name=endPoint) %>%
filter(assay_component_endpoint_name %in% endPointInfo$assay_component_endpoint_name ) %>%
@@ -303,6 +351,15 @@ shinyServer(function(input, output) {
mutate(site = siteKey[site]) %>%
rename(endPoint=assay_component_endpoint_name) %>%
select_("hits","EAR","chnm","class","site","date","choices"=groupCol)
+
+ if(radio == 2){
+ chemGroup <- chemGroup %>%
+ mutate(grouping=class)
+ } else {
+ chemGroup <- chemGroup %>%
+ mutate(grouping=chnm)
+ }
+
})
statsOfGroup <- reactive({
@@ -324,12 +381,12 @@ shinyServer(function(input, output) {
stats2 <- chemGroup %>%
filter(site %in% siteToFind) %>%
group_by(site,choices) %>%
- summarise(nChem = length(unique(chnm[EAR > 0.1])))
+ summarise(nChem = length(unique(grouping[EAR > 0.1])))
statsOfGroup <- chemGroup %>%
filter(site %in% siteToFind) %>%
group_by(site, date,choices) %>%
- summarise(nChemWithHits = length(unique(chnm[EAR > 0.1]))) %>%
+ summarise(nChemWithHits = length(unique(grouping[EAR > 0.1]))) %>%
group_by(site,choices) %>%
summarise(maxChem = max(nChemWithHits),
meanChem = mean(nChemWithHits)) %>%
@@ -352,23 +409,33 @@ shinyServer(function(input, output) {
})
output$TableHeader <- renderUI({
- HTML(paste("