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IsoFishR.R
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IsoFishR.R
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#Clear the environment
rm(list=ls())
####Installing all required packages####
install_load <- function (package1,...) {
# convert arguments to vector
packages <- c(package1,...)
# start loop to determine if each package is installed
for(package in packages){
# if package is installed locally, load
if(package %in% rownames(installed.packages())){
do.call('library', list(package))}
# if package is not installed locally, download, then load
else {
install.packages(package)
do.call("library", list(package))
}
}
}
install_load('shiny', 'mgcv', 'shinydashboard', 'tidyverse','broom', 'DT', 'zoo', 'changepoint','colourpicker', 'shinyWidgets')
library(shiny)
#Turn off warnings
options(warn=-1)
#Uncomment to turn warnings back on
#options(warn=0)
####Creating the dashboard and ui outline####
ui <- dashboardPage(skin="black", title="IsoFishR",
dashboardHeader(title = img(src = 'logo.png',
title = "IsoFishR", height = "48px")),
dashboardSidebar(
sidebarMenu(
p(""),
menuItem("About", tabName="About", icon=icon("info")),
menuItem("Projects", tabName="Projects", icon=icon("folder")),
menuItem("Data Reduction ", tabName="Data_reduction",icon=icon("cogs")),
menuItem("Data Analysis", tabName="Data_analysis",icon=icon("area-chart")),
menuItem("Data Reporting", tabName="Data_reporting", icon=icon("table")),
hr(),
htmlOutput("ProcessHTML"),
htmlOutput("AnalysesHTML")
)),
####About####
dashboardBody(
tabItems(
tabItem(tabName = "About",
fluidRow(
box(width = 9, status = "primary",
h2("Welcome to IsoFishR"),
hr(),
h5("IsoFishR aims to provide a fast, reproducible, and transparent data reduction application for laser-ablation strontium isotope analysis"),
h5(a(target="_blank", href = 'https://github.com/MalteWillmes/IsoFishR/blob/master/README.md',"Tutorial")),
h5(a(target="_blank", href = 'https://github.com/MalteWillmes/IsoFishR',"Code on GitHub")),
h5(a(target="_blank", href = 'https://github.com/MalteWillmes/IsoFishR/blob/master/LICENSE',"MIT license")),
tags$br(),
h5("Funding sources"),
h5("This program has been developed in the Biogeochemistry and Fish Ecology Lab at UC Davis, California."),
tags$br(),
h5("Acknowledgements"),
h5("We would like to thank Julie Griffin, Mackenzie Gilliam, James Chhor, and Brian Healey for testing the application and providing helpful comments.")
),
box(
title = "Version history", width = 3, status="warning",
"0.1 - initial release (12/2016)",
br(),
"0.5 - moving forward (4/2017)",
br(),
"0.6 - updated projects management (8/2017)",
br(),
"0.9 - GitHub commit (11/2017)",
br(),
"1.0 - GitHub release (01/2018)"
),
box(
title = "Contact the Authors", width = 3, status="danger",
h5( "Malte Willmes ([email protected])"),
h5("Katherine Ransom ([email protected])"),
h5( "James Hobbs ([email protected])")
))
),
####Projects####
tabItem(tabName = "Projects",
fluidRow(
column(width = 4,
box(
title = "Project management", width = NULL, status = "primary",
selectInput(inputId = 'project.name',label = "Select Project",choices = list.files(path = file.path(".","Projects")),selected="Default"),
textInput(inputId="new.project",label="Create a new project ",value="Project"),
actionButton("save","Save new project")
),
box(
title = "Update project settings", width = NULL, solidHeader = TRUE, status = "warning",
actionButton("updatesettings","Update")
),
box(
title = "Project comments", width = NULL, status = "primary",
textAreaInput(inputId="project.comment",label=NULL,value="Add comment here"),
actionButton("comment","Save Comment"),DT::dataTableOutput("comments")
)
),
column(width = 4,
box(
title = "Default analysis parameters", width = NULL, status = "primary",
textInput("username","User name"),
textInput("sampletype",label="Sample type",value="Otolith"),
radioButtons("analysistype","Analysis type", choices=list ("Line"="Line", "Spot"="Spot")),
textInput("sampledirection",label="Analysis direction",value=""),
numericInput("spotsize",label= "Spot size (um)",value=40),
numericInput("speed", "Run speed (um/s)", value=10, step = 1),
radioButtons("smoother","Smoothing type for analysis",choices = list("MA"="MA","spline"="Spline", "Data Points (no smoothing)"="Data_points"),inline=TRUE),
numericInput("average_num","Moving average window",value=10,step=1,min=1,max=NA),
numericInput("spline_k","Spline k value",value=10,step=1,min=0.01,max=1000),
radioButtons("SD_outlier","Standard deviation for outlier detection",choices = list("3"=3,"2"=2,"1"=1, "No outlier rejection"="NA"), inline=TRUE),
numericInput("outlier_num","Outlier moving window",value=20,step=1,min=30),
numericInput("fluency","Fluency (J/cm2)",value=1.85),
numericInput("energy","Laser energy (%)",value=55)
),
box(
title = "Default manual filter names", width = NULL, status = "primary",
textInput("defrange1","Range 1 label", value="Range 1"),
textInput("defrange2","Range 2 label", value="Range 2"),
textInput("defrange3","Range 3 label", value="Range 3"),
textInput("defrange4","Range 4 label", value="Range 4"),
textInput("defrange5","Range 5 label", value="Range 5"),
textInput("defrange6","Range 6 label", value="Range 6"),
textInput("defrange7","Range 7 label", value="Range 7"),
textInput("defrange8","Range 8 label", value="Range 8")
)
),
column(width = 4,
box(
title = "Data reduction settings", width = NULL, status = "danger",
numericInput("integration","Integration time (data points)",value=5),
checkboxInput("blanksubcheck", "Enable blank correction", value = TRUE, width = NULL),
numericInput("blanktime","Blank time (s)",value=30),
numericInput("raw88lowerthresh", "Min 88Sr (V)", value=0.5, step = .1),
numericInput("raw88upperthresh", "Max 88Sr (V)", value=9.8, step = .1),
numericInput("Sr8688ratio", "86Sr/88Sr ratio", value=0.1194, step = .001),
numericInput("Rb8587ratio", "85Rb/87Rb", value=2.59712, step = .001)
),
box(
title = "Runfile import settings", width = NULL, status = "danger",
numericInput("vskip"," Skip Header length",value=46),
numericInput("fskip"," Skip Footer length",value=0),
numericInput("raw88","Raw 88 column number",value=1),
numericInput("raw87","Raw 87 column number",value=2),
numericInput("raw86","Raw 86 column number",value=3),
numericInput("raw85","Raw 85 column number",value=4),
numericInput("raw84","Raw 84 column number",value=5),
numericInput("raw83","Raw 83 column number",value=6),
numericInput("cyclesec","Cycle seconds column number",value=8),
radioButtons("sep","Separator type",choices = list(Comma=",",Tab="\t",Decimal=".", Semicolon=";")),
checkboxInput("header","Data has column headings")
)
)
)
),
####Data reduction####
tabItem(tabName = "Data_reduction",
fluidRow(
column(width = 3,
box(
width = NULL,style='padding:2px;',
fileInput("file","Select one or multiple runfiles", multiple = TRUE),
uiOutput("Sample_selector"),
actionButton("save_data","Save reduced data"),
checkboxInput("appending_processed", "Append data?",value=TRUE),
uiOutput('narem'),
uiOutput('rollfill'))
),
column(width = 9,
box(
title = " total Sr (V)", collapsible = TRUE, width = 4, status = "danger",style='padding:2px;',
plotOutput("TotalSr_plot", height=200)),
box(
title = "83Kr (V)",collapsible = TRUE, width = 4, status = "success",style='padding:2px;',
plotOutput("Kr_plot", height=200)),
box(
title = "85Rb/88Sr", collapsible = TRUE, width = 4, status = "warning",style='padding:2px;',
plotOutput("Rb_plot", height=200))),
column(width = 9,
box(
width = NULL, style='padding:2px;', height=480,
div(style="width:40px",dropdownButton(
checkboxInput("main_points",label="Points", value=TRUE),
checkboxInput("main_outlier_points",label="Outlier", value=TRUE),
checkboxInput("main_ma",label="Moving average", value=TRUE),
checkboxInput("main_ci",label="95% CI", value=TRUE),
checkboxInput("main_sd",label="SD", value=FALSE),
checkboxInput("main_spline",label="Spline fit", value=FALSE),
checkboxInput("main_ocean",label="Mean ocean", value=FALSE),
checkboxInput("reduced_custom",label="Custom line", value=FALSE),
numericInput("reduced_custom_input",label="Custom line 87Sr/86Sr",value=0.705),
colourInput("linecol", "Line", "black", allowTransparent = TRUE,showColour = "background"),
colourInput("shadecol", "Shading", "gray95", allowTransparent = TRUE, showColour = "background"),
downloadButton('download_main_plot',''),
circle = TRUE, status = "warning", size = "sm",
icon = icon("gear"), label = NULL, tooltip = "Graph Settings", right = FALSE,
up = FALSE)),
plotOutput("mainplot", height=420)
)))),
tabItem(tabName = "Data_analysis",
fluidRow(
column(width = 3,
box(
width = NULL, title="Step 1: Select data to be analyzed", status = "primary", style='padding:2px;',
fileInput("processed_file","Select .csv file",
accept=c("txt/csv", "text/comma-separated-values,text/plain", ".csv"))
),
box(
width = NULL, title="Step 2: Select sample to edit", status = "primary", style='padding:2px;',height=305,
uiOutput("Analyzed_sample_selector"),
uiOutput("Sample_ID"),
uiOutput("Sample_comment"),
uiOutput("flag_review")
),
box(
title = "Step 3: Trim data", width = NULL, solidHeader = TRUE, status = "primary", style='padding:2px;',height=280,
uiOutput("trim_left"),
uiOutput("trim_right"),
uiOutput("recalculate_distance"),
uiOutput("trim_status_of_profile")
),
box(
title = "Step 4: Reverse the profile?", width = NULL, solidHeader = TRUE, status = "primary", style='padding:2px;',height=150,
uiOutput("profile_direction_selector"),
uiOutput("status_of_profile")
)
),
column(width = 9,
box(
title = " total Sr (V)", collapsible = TRUE, width = 4, status = "danger",style='padding:2px;',
plotOutput("TotalSr_plot_analyzed", height=200)),
box(
title = "83Kr (V)",collapsible = TRUE, width = 4, status = "success",style='padding:2px;',
plotOutput("Kr_plot_analyzed", height=200)),
box(
title = "85Rb/88Sr", collapsible = TRUE, width = 4, status = "warning",style='padding:2px;',
plotOutput("Rb_plot_analyzed", height=200)
)),
column(width = 9,
box(
width = NULL, height=480,style='padding:2px;',
div(style="width:40px",dropdownButton(
checkboxInput("analyze_points",label="Points", value=TRUE),
checkboxInput("analyze_outlier_points",label="Outlier", value=TRUE),
checkboxInput("analyze_ma",label="Moving average", value=TRUE),
checkboxInput("analyze_ci",label="95% CI", value=TRUE),
checkboxInput("analyze_sd",label="SD", value=FALSE),
checkboxInput("analyze_spline",label="Spline fit", value=FALSE),
checkboxInput("analyze_ocean",label="Mean ocean", value=FALSE),
checkboxInput("analyze_custom",label="Custom line", value=FALSE),
numericInput("analyze_custom_input",label="Custom line 87Sr/86Sr",value=0.705),
colourInput("analyze_linecol", "Line", "black", allowTransparent = TRUE,showColour = "background"),
colourInput("analyze_shadecol", "Shading", "gray", allowTransparent = TRUE, showColour = "background"),
downloadButton('download_analysis_plot',''),
circle = TRUE, status = "warning", size = "sm",
icon = icon("gear"), label = NULL, tooltip = "Graph Settings", right = FALSE,
up = FALSE)),
uiOutput("analysisplotter")
),
tabBox(title="Step 5: Filters",height=280, width = 9, selected="About",
tabPanel("About", "Filters can be used to split the data into different regions"),
tabPanel("Manual Filter",
div(style="float:left;width: 80px",
div(style="height:20px",textInput("range_one_label", label=NULL)),
div(style="height:35px",numericInput("range_one_min","",value=-1)),
div(style="height:55px",numericInput("range_one_max","",value=-1)),
div(style= "height:35px",actionButton("readregion1", "Read",style="background-color:#6b8acd"))),
div(style="float:left;width: 80px",
div(style="height:20px",textInput("range_two_label", label=NULL)),
div(style="height:35px",numericInput("range_two_min","",value=-1)),
div(style="height:55px", numericInput("range_two_max","",value=-1)),
div(style= "height:35px",actionButton("readregion2", "Read",style="background-color:#4cb28d"))),
div(style="float:left;width: 80px",
div(style="height:20px",textInput("range_three_label",label=NULL)),
div(style="height:35px",numericInput("range_three_min","",value=-1)),
div(style="height:55px",numericInput("range_three_max","",value=-1)),
div(style= "height:35px",actionButton("readregion3", "Read",style="background-color:#72853c"))),
div(style="float:left;width: 80px",
div(style="height:20px",textInput("range_four_label",label=NULL)),
div(style="height:35px",numericInput("range_four_min","",value=-1)),
div(style="height:55px", numericInput("range_four_max","",value=-1)),
div(style= "height:35px",actionButton("readregion4", "Read",style="background-color:#c78a40"))),
div(style="float:left;width: 80px",
div(style="height:20px",textInput("range_five_label", label=NULL)),
div(style="height:35px",numericInput("range_five_min","",value=-1)),
div(style="height:55px",numericInput("range_five_max","",value=-1)),
div(style= "height:35px",actionButton("readregion5", "Read",style="background-color:#c75d9c"))),
div(style="float:left;width: 80px",
div(style="height:20px",textInput("range_six_label", label=NULL)),
div(style="height:35px",numericInput("range_six_min","",value=-1)),
div(style="height:55px",numericInput("range_six_max","",value=-1)),
div(style= "height:35px",actionButton("readregion6", "Read",style="background-color:#cb554f"))),
div(style="float:left;width: 80px",
div(style="height:20px",textInput("range_seven_label", label=NULL)),
div(style="height:35px",numericInput("range_seven_min","",value=-1)),
div(style="height:55px",numericInput("range_seven_max","",value=-1)),
div(style= "height:35px",actionButton("readregion7", "Read",style="background-color:#9061c9"))),
div(style="float:left;width: 80px",
div(style="height:20px",textInput("range_eight_label", label=NULL)),
div(style="height:35px",numericInput("range_eight_min","",value=-1)),
div(style="height:55px",numericInput("range_eight_max","",value=-1)),
div(style= "height:35px",actionButton("readregion8", "Read",style="background-color:#83b844"))),
div(style="float:right; width:100px",actionButton("reset_ranges", "Reset ranges")),
div(style="float:right; width:100px","Range selection",verbatimTextOutput("brush_info")),
div(style="float:right; width:100px","Hover position", verbatimTextOutput("plot_hoverinfo")),
div(style="float:left; width:100px",checkboxInput("zoomselect",label="Enable manual filter selection", value=FALSE))
),
tabPanel("Changepoints",
div(style="float:left;width:120px;",uiOutput('change_points_check')),
div(style="float:left;width:100px", uiOutput('change_method')),
div(style="float:left;width:100px", uiOutput('change_penalty')),
div(style="float:left;width:100px",uiOutput('changepoint_plotting_check')),
div(style="float:right;width:120px;", colourInput("changepoint_linecol", "Line", "red", allowTransparent = TRUE,showColour = "background"))
)
),
box(
title = "Step 6: Saving data", width = 3, background = "orange", style='padding:2px;',
actionButton("save_file_edits","Save edits to profile")
),
box(
title = "Step 7: Export data", width = 3, background = "black", style='padding:2px;',
actionButton("save_edits","Export analyzed data"),
checkboxInput("appending_analyzed", "Append data?",value=FALSE)
)
)
)),
tabItem(tabName = "Data_reporting",
fluidRow(
tabBox(width = 12,
# The id lets us use input$tabset1 on the server to find the current tab
id = "tabset_datatables",
tabPanel("Analyzed data summary",
div(style = 'overflow-x: scroll',DT::dataTableOutput('analyzed_data_table_summary'))
),
tabPanel("Analyzed data all ",
div(style = 'overflow-x: scroll',DT::dataTableOutput('analyzed_data_table_all')
)),
tabPanel("Reduced data all",
div(style = 'overflow-x: scroll',DT::dataTableOutput('reduced_data_table_all'))
))))
)))
####Server side####
server <- shinyServer(function(input, output, session) {
#Set maximum filesize
options(shiny.maxRequestSize=3000*1024^2)
output$ProcessHTML <- renderText({
paste("Selected project: ", input$project.name)
})
output$AnalysesHTML <- renderText({
paste("Analysis type: ", input$analysistype)
})
####Read runfiles####
raw_data_all <- reactive({
infile <- input$file
raw_data_all<-""
if(is.null(infile)){return()}
for(i in 1:length(input$file[,1])){
raw_data <- read.table(c(input$file[[i, 'datapath']]), skip=input$vskip, sep=input$sep,header=ifelse(input$header==TRUE,TRUE,FALSE),stringsAsFactors =FALSE )
raw_data <- raw_data[1:(nrow(raw_data)-input$fskip),]
raw_data <- cbind(raw_data, input$file[i,],row.names = NULL)
raw_data_all <- rbind(raw_data_all, raw_data)
}
##Drop extra columns and rename and order the raw data
raw_data_all <- raw_data_all %>% filter(name!="")%>%dplyr::select (-size, -type,-datapath)
names(raw_data_all)[input$raw88] <- c("Raw88")
names(raw_data_all)[input$raw87] <- c("Raw87")
names(raw_data_all)[input$raw86] <- c("Raw86")
names(raw_data_all)[input$raw85] <- c("Raw85")
names(raw_data_all)[input$raw84] <- c("Raw84")
names(raw_data_all)[input$raw83] <- c("Raw83")
names(raw_data_all)[input$cyclesec] <- c("CycleSecs")
raw_data_all <- raw_data_all[,c("Raw88", "Raw87","Raw86", "Raw85", "Raw84", "Raw83", "CycleSecs","name")]
raw_data_all <- raw_data_all %>% filter (Raw88!= "")
return(raw_data_all)})
####Process data####
processed_data <- reactive({
raw <- raw_data_all()
if(is.null(raw_data_all())){return()}
#Change to numeric
raw <- raw %>% group_by(name)%>% mutate(across(.fns=as.numeric))
#Calculate average based on integration time
raw <- raw %>% group_by(name) %>% replace(1:6, rollapply(raw[1:6],width=input$integration, by=input$integration, FUN=mean, partial=TRUE,fill=NA)) %>%
drop_na()
if(input$blanksubcheck){
#Split data to background and sample based on background time
processed <- raw %>% group_by(name) %>%
filter(CycleSecs>input$blanktime)
background <- raw %>% group_by(name) %>%
filter(CycleSecs<=input$blanktime)
#Clean background data: Remove any values above the detection threshold and summarize
background <- background %>% group_by(name) %>%
filter(Raw88<input$raw88lowerthresh)%>%
dplyr::select (-CycleSecs)%>%
rename(Blk88=Raw88, Blk87=Raw87, Blk86=Raw86, Blk85=Raw85, Blk84=Raw84, Blk83=Raw83)%>%
summarise_all(funs(mean))
#Clean processed data: Remove any values below or above the detection threshold
processed <- processed %>% group_by(name) %>%
filter(Raw88>=input$raw88lowerthresh)%>%
filter(Raw88<=input$raw88upperthresh)
#Perform a background subtraction on the processed data
processed <- processed %>% group_by(name) %>%
left_join(background, processed, by = "name")%>%
mutate(Raw88=Raw88-Blk88)%>%
mutate(Raw87=Raw87-Blk87)%>%
mutate(Raw86=Raw86-Blk86)%>%
mutate(Raw85=Raw85-Blk85)%>%
mutate(Raw84=Raw84-Blk84)%>%
mutate(Raw83=Raw83-Blk83)
}
#If no background subtraction just filter by detector cutoffs
else{
processed <- raw %>% group_by(name)%>%
filter(Raw88>=input$raw88lowerthresh)%>%
filter(Raw88<=input$raw88upperthresh)
}
#Calculate distance based on user input of laser scan speed
processed <- processed %>% group_by(name) %>%
mutate(Distance=(CycleSecs-min(CycleSecs))*input$speed)
#Calculate raw ratios and Mass Bias
processed <- processed %>% group_by(name) %>%
mutate(Sr8688=Raw86/Raw88)%>%
mutate(Sr8488=Raw84/Raw88)%>%
mutate(Sr8486=Raw84/Raw86)%>%
mutate(Sr8786_raw=Raw87/Raw86)%>%
mutate(Rb85Sr88=Raw85/Raw88)%>%
mutate(MbFactor=log(input$Sr8688ratio/Sr8688)/log(85.9092607/87.9056123))
#Rubidium correction and apply Mass Bias
processed <- processed %>% group_by(name) %>%
mutate(Rb87=(Raw85/input$Rb8587ratio)*(84.9117897/86.9091805)^MbFactor)%>%
mutate(Sr87=Raw87-Rb87)%>%
mutate(Sr87Sr86_rbcor=Sr87/Raw86)%>%
mutate(Sr87Sr86_rbmbcor=(Sr87/Raw86)*(86.9088775/85.9092607)^MbFactor)
#Calculate total Sr voltage and rename 87Sr/86Sr column
processed <- processed %>% group_by(name) %>%
mutate(Sr87Sr86=Sr87Sr86_rbmbcor)%>%
mutate(totalSr=Raw88+Raw86+Raw84+Sr87)
#Outlier rejection
#Outlier rejection
SDin <- as.numeric(input$SD_outlier) # user input confidence interval
if (is.na(SDin)) {
processed <- processed %>% group_by(name) %>%
mutate(means=rollapply(Sr87Sr86,input$outlier_num, median, partial=TRUE, fill="extend"))%>%
mutate(sds=rollapply(Sr87Sr86,input$outlier_num, sd, partial=TRUE, fill="extend"))%>%
mutate(Sr87Sr86_outlier=NA)%>%
mutate(Sr87Sr86=Sr87Sr86)
}else {
processed <- processed %>% group_by(name) %>%
mutate(means=rollapply(Sr87Sr86,input$outlier_num, median,fill="extend"))%>%
mutate(sds=rollapply(Sr87Sr86,input$outlier_num, sd,fill="extend"))%>%
mutate(Sr87Sr86_outlier=ifelse(Sr87Sr86 > (means+SDin*sds) | Sr87Sr86 < (means-SDin*sds), Sr87Sr86, NA))%>%
mutate(Sr87Sr86=ifelse(Sr87Sr86 > means+SDin*sds | Sr87Sr86 < (means-SDin*sds), NA,Sr87Sr86 ))
}
#Apply smoothing function: Moving average
processed <- processed %>% group_by(name) %>%
mutate(Sr87Sr86_MA=rollapply(Sr87Sr86, input$average_num, FUN= mean, partial = TRUE,fill=input$rollfill, na.rm=input$narem, align = "center"))%>%
mutate(Sr87Sr86_MA_sds=rollapply(Sr87Sr86,input$average_num,FUN=sd, partial = TRUE, fill=input$rollfill, na.rm=input$narem, align = "center"))%>%
mutate(Sr87Sr86_MA_ses= (Sr87Sr86_MA_sds/sqrt(input$average_num)))%>%
mutate(Sr87Sr86_MA_CI95up=Sr87Sr86_MA+1.96*Sr87Sr86_MA_ses)%>%
mutate(Sr87Sr86_MA_CI95low=Sr87Sr86_MA-1.96*Sr87Sr86_MA_ses)
#Apply Spline fit
processed_spline <- processed %>%
group_by(name)%>%
nest()%>%
mutate(Sr87Sr86_spline = map(data, ~mgcv::gam(Sr87Sr86 ~ s(Distance, k=input$spline_k, bs="tp"), data=.x))) %>%
mutate(processed_spline_aug = map2(Sr87Sr86_spline, data, ~broom::augment(.x, newdata = .y, se_fit = TRUE))) %>%
unnest(processed_spline_aug)%>%
dplyr::select(name, Distance,.fitted,.se.fit)
#Join data together based on name and Distance
processed <- left_join(processed, processed_spline, by = c("name","Distance"))
#Estimate NA values
processed <- processed %>% mutate (.fitted=na.approx(.fitted, na.rm = FALSE))%>% mutate (.se.fit=na.approx(.se.fit, na.rm = FALSE))
#Add extra columns to hold default values (remember to add these also into the data_processsed clean dataframe in the next step "Create data subsets")
processed <- processed %>% mutate(Sample_ID=name)%>%
mutate(Date=strftime(Sys.time(), format="%Y-%m-%d_%H:%M:%S"))%>%
mutate(Run_ID=name)%>%
mutate(Sr84Sr86=Sr8486) %>%
mutate(Sr87Sr86=Sr87Sr86) %>%
mutate(Sr87Sr86_spline=.fitted) %>%
mutate(Sr87Sr86_spline_ses=.se.fit)%>%
mutate(profile_direction="Profile is normal")%>%
mutate(trim_left=0)%>%
mutate(trim_right=0)%>%
mutate(recalc_distance=TRUE)%>%
mutate(comment="")%>%
mutate(flag_review=FALSE) %>%
dplyr::select( -.fitted, -.se.fit) %>%
ungroup()%>%
mutate(name=paste0(Run_ID,"_",strftime(Sys.time(), format="%Y%m%d%H%M%S")))%>%
group_by(name)%>%
mutate (changepoints=FALSE)%>%
mutate (manual_pen=0.00005)%>%
mutate (change_method="")%>%
mutate (changepoint_number= 0) %>%
mutate (changepoint_mean=0)%>%
mutate (changepoint_plotting=FALSE)%>%
mutate (region_number=0)%>%
mutate (region_name="")%>%
mutate (region_mean=0)%>%
mutate (region_sd=0)%>%
mutate (region_mindistance=0)%>%
mutate (region_maxdistance=0)
return(processed)})
####Create data subsets####
#background data#
background_data <- reactive({
raw <- raw_data_all()
if(is.null(raw_data_all())){return()}
background <- raw %>% group_by(name) %>%
filter(CycleSecs<=input$blanktime)
#Background data subset: Remove any values above the detection threshold and summarize
background <- background %>% group_by(name) %>%
filter(Raw88<input$raw88lowerthresh)%>%
rename(Blk88=Raw88, Blk87=Raw87, Blk86=Raw86, Blk85=Raw85, Blk84=Raw84, Blk83=Raw83)
return(background)})
#processed data subset#
processed_data_cleaned <- reactive({
processed_data_cleaned <- processed_data()
if(is.null(processed_data())){return()}
#Add the columns to keep for the next step in data processed (remove blanks, raw ratios, and ratio calculations)
processed_data_cleaned <- processed_data_cleaned %>% group_by(name) %>% dplyr::select (name, Run_ID, Sample_ID, Date, CycleSecs, Distance, totalSr, Raw83, Rb85Sr88, Sr84Sr86, Sr87Sr86, Sr87Sr86_outlier, Sr87Sr86_MA, Sr87Sr86_MA_sds, Sr87Sr86_MA_ses, Sr87Sr86_MA_CI95up, Sr87Sr86_MA_CI95low, Sr87Sr86_spline,
Sr87Sr86_spline_ses, profile_direction, trim_right, trim_left, recalc_distance, comment, flag_review, changepoints, manual_pen, change_method, changepoint_number,changepoint_mean, changepoint_plotting,
region_number, region_name, region_mean, region_sd, region_mindistance, region_maxdistance) %>%
mutate(Distance=round(Distance, digits=0))%>%
mutate(totalSr=round(totalSr, digits=4))%>%
mutate(Raw83=round(Raw83, digits=8))%>%
mutate(Rb85Sr88=round(Rb85Sr88, digits=8))%>%
mutate(Sr84Sr86=round(Sr84Sr86, digits=8))%>%
mutate(Sr87Sr86=round(Sr87Sr86, digits=8))%>%
mutate(Sr87Sr86_outlier=round(Sr87Sr86_outlier, digits=8))%>%
mutate(Sr87Sr86_MA=round(Sr87Sr86_MA, digits=8))%>%
mutate(Sr87Sr86_MA_sds=round(Sr87Sr86_MA_sds, digits=8))%>%
mutate(Sr87Sr86_MA_ses=round(Sr87Sr86_MA_ses, digits=8)) %>%
mutate(Sr87Sr86_MA_CI95up=round(Sr87Sr86_MA_CI95up,digits=8))%>%
mutate(Sr87Sr86_MA_CI95low=round(Sr87Sr86_MA_CI95low,digits=8))%>%
mutate(Sr87Sr86_spline=round(Sr87Sr86_spline, digits=8)) %>%
mutate(Sr87Sr86_spline_ses=round(Sr87Sr86_spline_ses, digits=8))%>%
group_by(name)%>%
mutate (row_num=row_number())%>%
ungroup()
return(processed_data_cleaned )})
####Saving data####
observeEvent(input$save_data, {
t <- try(
if(input$appending_processed){
write.table(raw_data_all(),file=file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_01_raw_data.csv")),col.names=!file.exists(file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_01_raw_data.csv"))),row.names=FALSE,sep="," ,append = TRUE)
write.table(background_data(),file=file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_02_background_data.csv")),col.names=!file.exists(file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_02_background_data.csv"))),row.names=FALSE,sep=",",append = TRUE)
write.table(processed_data_cleaned(),file=file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_03_reduced_data.csv")),col.names=!file.exists(file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_03_reduced_data.csv"))),row.names=FALSE,sep=",",append = TRUE)
}
else{
write.table(raw_data_all(),file=file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_01_raw_data.csv")),row.names=FALSE,col.names= TRUE, sep=",")
write.table(background_data(),file=file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_02_background_data.csv")),col.names= TRUE, row.names=FALSE,sep=",")
write.table(processed_data_cleaned(),file=file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_03_reduced_data.csv")),col.names= TRUE, row.names=FALSE,sep=",")
},
silent=TRUE)
# if there is no error print a success message otherwise print an error message
if ("try-error" %in% class(t)){
save_data_failed_notification <- showNotification(paste("File access denied: Saving failed. Is the file open?"), duration = 8, type="error")
} else {
save_data_notification <- showNotification(paste("Reduced data saved"), duration = 8, type="message")
}
})
observeEvent(input$save_edits, {
t <- try(
if(input$appending_analyzed){
write.table(analyzer_overwatch$analyzed,file=file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_04_analyzed_data.csv")),col.names=!file.exists(file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_04_analyzed_data.csv"))), row.names=FALSE,sep=",", append = TRUE)
}
else{
write.table(analyzer_overwatch$analyzed,file=file.path("Projects",input$project.name,"/Data",paste0(input$project.name,"_04_analyzed_data.csv")),row.names=FALSE, col.names= TRUE, sep=",")
},
silent=TRUE)
# if there is no error print a success message otherwise print an error message
if ("try-error" %in% class(t)){
save_edits_failed_notification <- showNotification(paste("File access denied: Saving failed. Is the file open?"), duration = 8, type="error")
} else {
save_edits_notification <- showNotification(paste("Analyzed data saved"), duration = 8, type="message")
}
})
####Plot code####
fmt_dcimals <- function(decimals=0){
function(x) format(x,nsmall = decimals,scientific = FALSE)
}
#mainplot
mainplot <- function(){
req(input$Sample_selector)
if(is.null(processed_data_cleaned())){return()}
processed_plot <-processed_data_cleaned()
processed_plot <-processed_plot %>% filter(name==input$Sample_selector)
p <- ggplot(processed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none")
p <- p + labs(y=expression(paste(""^"87"*"Sr/"^"86"*"Sr")), x=expression(paste("Distance (",mu,"m)")), title=paste0("Runfile:"," ",unlist(strsplit(as.character(input$Sample_selector), split='.', fixed=TRUE))[1]))
p <- p + theme(axis.text.x = element_text(size=12), axis.text.y = element_text(size=12), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15))
p <- p + scale_y_continuous(labels = fmt_dcimals(5), breaks = scales::pretty_breaks(n = 10)) + scale_x_continuous(expand=c(0,0), breaks = scales::pretty_breaks(n = 10))
if(input$main_sd){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_MA-Sr87Sr86_MA_sds, ymax=Sr87Sr86_MA+Sr87Sr86_MA_sds), fill=input$shadecol)
}
if(input$main_ci){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_MA_CI95low, ymax=Sr87Sr86_MA_CI95up), fill=input$shadecol)
}
if(input$main_ma){
p <- p +geom_line(aes(x=Distance,y=Sr87Sr86_MA), color=input$linecol)}
if(input$main_spline){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_spline-Sr87Sr86_spline_ses, ymax=Sr87Sr86_spline+Sr87Sr86_spline_ses), fill=input$shadecol)
p <- p +geom_line(aes(x=Distance,y=Sr87Sr86_spline), color=input$linecol)}
if(input$main_points){
p <- p + geom_point(aes(x=Distance,y=Sr87Sr86), shape=1)}
if(input$main_outlier_points){
p <- p + geom_point(aes(x=Distance,y=Sr87Sr86_outlier), color="red", shape=1)}
if(input$main_ocean){
p <- p + geom_line(aes(x=Distance,y=0.70918), color="blue")}
if(input$reduced_custom){
p <- p + geom_line(aes(x=Distance,y=input$reduced_custom_input), color="orange")}
return(p)}
output$mainplot <- renderPlot({mainplot()})
#Total Sr plot
TotalSr_plot <- function(){
req(input$Sample_selector)
if(is.null(processed_data_cleaned())){return()}
processed_plot <-processed_data_cleaned()
processed_plot <-processed_plot %>% filter(name==input$Sample_selector)
p <- ggplot(processed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=("Sr (V)"), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(2), breaks = scales::pretty_breaks(n = 8)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 8))
p <- p + geom_point(aes(x=Distance,y=totalSr), size=1, color="red", shape=1, na.rm=TRUE)
return(p)}
output$TotalSr_plot <- renderPlot({TotalSr_plot()})
#Kr plot
Kr_plot <- function(){
req(input$Sample_selector)
if(is.null(processed_data_cleaned())){return()}
processed_plot <-processed_data_cleaned()
processed_plot <-processed_plot %>% filter(name==input$Sample_selector)
p <- ggplot(processed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=expression(paste(""^"83"*"Kr (V)")), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(5), breaks = scales::pretty_breaks(n = 8)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 8))
p <- p + geom_point(aes(x=Distance,y=Raw83), size=1, color="lightgreen", shape=1 , na.rm=TRUE)
return(p)}
output$Kr_plot <- renderPlot({Kr_plot()})
#Rb plot
Rb_plot <- function(){
req(input$Sample_selector)
if(is.null(processed_data_cleaned())){return()}
processed_plot <-processed_data_cleaned()
processed_plot <-processed_plot %>% filter(name==input$Sample_selector)
p <- ggplot(processed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=expression(paste(""^"85"*"Rb/"^"88"*"Sr")), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(5), breaks = scales::pretty_breaks(n = 8)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 8))
p <- p + geom_point(aes(x=Distance,y=Rb85Sr88),size=1, color="orange", shape=1, na.rm=TRUE)
return(p)}
output$Rb_plot <- renderPlot({Rb_plot()})
###Action to save all plots from processing tab #####
observeEvent(input$save_data, {
names <- as.character(unique(processed_data_cleaned()$name))
t <- try(
for (i in names){
#mainplot
mainplot <- function(){
if(is.null(processed_data_cleaned())){return()}
processed_plot <-processed_data_cleaned()
processed_plot <-processed_plot %>% filter(name==i)
p <- ggplot(processed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none")
p <- p + labs(y=expression(paste(""^"87"*"Sr/"^"86"*"Sr")), x=expression(paste("Distance (",mu,"m)")), title=processed_plot$name)
p <- p + theme(axis.text.x = element_text(size=12), axis.text.y = element_text(size=12), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15))
p <- p + scale_y_continuous(labels = fmt_dcimals(4), breaks = scales::pretty_breaks(n = 10)) + scale_x_continuous(expand=c(0,0), breaks = scales::pretty_breaks(n = 10))
if(input$main_sd){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_MA-Sr87Sr86_MA_sds, ymax=Sr87Sr86_MA+Sr87Sr86_MA_sds), fill=input$shadecol)
}
if(input$main_ci){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_MA_CI95low, ymax=Sr87Sr86_MA_CI95up), fill=input$shadecol)
}
if(input$main_ma){
p <- p +geom_line(aes(x=Distance,y=Sr87Sr86_MA), color=input$linecol)}
if(input$main_spline){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_spline-Sr87Sr86_spline_ses, ymax=Sr87Sr86_spline+Sr87Sr86_spline_ses), fill=input$shadecol, na.rm=TRUE)
p <- p +geom_line(aes(x=Distance,y=Sr87Sr86_spline), na.rm=TRUE, color=input$linecol)}
if(input$main_points){
p <- p + geom_point(aes(x=Distance,y=Sr87Sr86), shape=1, na.rm=TRUE)}
if(input$main_outlier_points){
p <- p + geom_point(aes(x=Distance,y=Sr87Sr86_outlier), color="red", shape=1, na.rm=TRUE)}
if(input$main_ocean){
p <- p + geom_line(aes(x=Distance,y=0.70918), color="blue")}
if(input$reduced_custom){
p <- p + geom_line(aes(x=Distance,y=input$reduced_custom_input), color="orange")}
return(p)}
#Total Sr plot
TotalSr_plot <- function(){
if(is.null(processed_data_cleaned())){return()}
processed_plot <-processed_data_cleaned()
processed_plot <-processed_plot %>% filter(name==i)
p <- ggplot(processed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=("Sr (V)"), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(2), breaks = scales::pretty_breaks(n = 10)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 10))
p <- p + geom_point(aes(x=Distance,y=totalSr), size=1, color="red", shape=1, na.rm=TRUE)
return(p)}
#Kr plot
Kr_plot <- function(){
if(is.null(processed_data_cleaned())){return()}
processed_plot <-processed_data_cleaned()
processed_plot <-processed_plot %>% filter(name==i)
p <- ggplot(processed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=expression(paste(""^"83"*"Kr (V)")), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(5), breaks = scales::pretty_breaks(n = 10)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 10))
p <- p + geom_point(aes(x=Distance,y=Raw83), size=1, color="lightgreen", shape=1 , na.rm=TRUE)
return(p)}
#Rb plot
Rb_plot <- function(){
if(is.null(processed_data_cleaned())){return()}
processed_plot <-processed_data_cleaned()
processed_plot <-processed_plot %>% filter(name==i)
p <- ggplot(processed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=expression(paste(""^"85"*"Rb/"^"88"*"Sr")), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(5), breaks = scales::pretty_breaks(n = 10)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 10))
p <- p + geom_point(aes(x=Distance,y=Rb85Sr88),size=1, color="orange", shape=1, na.rm=TRUE)
return(p)}
ggsave(filename=file.path("Projects",input$project.name,"Plots/87Sr86Sr_reduced",paste0(i,"_87Sr86Sr_reduced.pdf")), plot = mainplot(), device = "pdf",width=8,height=4,scale=1.5)
ggsave(filename=file.path("Projects",input$project.name,"Plots/totalSr",paste0(i,"_totalSr_volts.pdf")), plot = TotalSr_plot(), device = "pdf",width=8,height=4,scale=1.5)
ggsave(filename=file.path("Projects",input$project.name,"Plots/85Rb88Sr",paste0(i,"_85Rb88Sr.pdf")), plot = Rb_plot(), device = "pdf",width=8,height=4,scale=1.5)
ggsave(filename=file.path("Projects",input$project.name,"Plots/83Kr",paste0(i,"_83Kr_volts.pdf")), plot = Kr_plot(), device = "pdf",width=8,height=4,scale=1.5)
},silent=TRUE)
# if there is no error print a success message otherwise print an error message
if ("try-error" %in% class(t)){
save_process_plots_failed_notification <- showNotification(paste("Plot file access denied: Saving failed. Is the file open?"), duration = 12, type="error")
} else {
save_process_plots_notification <- showNotification(paste("Reduced data plots saved"), duration = 8, type="message")
}
})
####Analyzed plot ####
analyzed_plot <- function(){
#Check if available
req(input$Analyzed_sample_selector)
analyzed_plot <- analyzer_overwatch$analyzed
analyzed_plot <- analyzed_plot %>% filter(name==input$Analyzed_sample_selector)
p <- ggplot(analyzed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none")
p <- p + labs(y=expression(paste(""^"87"*"Sr/"^"86"*"Sr")), x=expression(paste("Distance (",mu,"m)")), title=paste0("Runfile:"," ",unlist(strsplit(as.character(input$Analyzed_sample_selector), split='.', fixed=TRUE))[1]," ","Sample ID:"," ",input$Sample_ID))
p <- p + theme(axis.text.x = element_text(size=12), axis.text.y = element_text(size=12), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15))
p <- p + scale_y_continuous(labels = fmt_dcimals(5), breaks = scales::pretty_breaks(n = 10)) + scale_x_continuous(expand=c(0,0), breaks = scales::pretty_breaks(n = 10))
p <- p +coord_cartesian(xlim = ranges_zoom$x, ylim = ranges_zoom$y, expand = FALSE)
if(input$analyze_sd){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_MA-Sr87Sr86_MA_sds, ymax=Sr87Sr86_MA+Sr87Sr86_MA_sds), fill=input$analyze_shadecol, na.rm=FALSE)
}
if(input$analyze_ci){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_MA_CI95low, ymax=Sr87Sr86_MA_CI95up), fill=input$analyze_shadecol, na.rm=FALSE)
}
if(input$analyze_ma){
p <- p +geom_line(aes(x=Distance,y=Sr87Sr86_MA), na.rm=TRUE, color=input$analyze_linecol)}
if(input$analyze_spline){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_spline-Sr87Sr86_spline_ses, ymax=Sr87Sr86_spline+Sr87Sr86_spline_ses), fill=input$shadecol, na.rm=TRUE)
p <- p +geom_line(aes(x=Distance,y=Sr87Sr86_spline), na.rm=TRUE, color=input$linecol)}
if(input$analyze_points){
p <- p + geom_point(aes(x=Distance,y=Sr87Sr86), shape=1, na.rm=TRUE)}
if(input$analyze_outlier_points){
p <- p + geom_point(aes(x=Distance,y=Sr87Sr86_outlier), color="red", shape=1, na.rm=TRUE)}
if(input$analyze_ocean){
p <- p + geom_line(aes(x=Distance,y=0.70918), color="blue")}
if(input$analyze_custom){
p <- p + geom_line(aes(x=Distance,y=input$analyze_custom_input), color="orange")}
#Manual regions
p <- p + annotate("rect", xmin = reac$range_one_min, xmax = reac$range_one_max, ymin = -Inf, ymax = +Inf,
alpha = .8, fill="#6b8acd") +
annotate("rect", xmin = reac$range_two_min, xmax = reac$range_two_max, ymin = -Inf, ymax = +Inf,
alpha = .8, fill="#4cb28d") +
annotate("rect", xmin = reac$range_three_min, xmax = reac$range_three_max, ymin = -Inf, ymax = +Inf,
alpha = .8, fill="#72853c") +
annotate("rect", xmin = reac$range_four_min, xmax = reac$range_four_max, ymin = -Inf, ymax = +Inf,
alpha = .8, fill="#c78a40") +
annotate("rect", xmin = reac$range_five_min, xmax = reac$range_five_max, ymin = -Inf, ymax = +Inf,
alpha = .8, fill="#c75d9c") +
annotate("rect", xmin = reac$range_six_min, xmax = reac$range_six_max, ymin = -Inf, ymax = +Inf,
alpha = .8, fill="#cb554f") +
annotate("rect", xmin = reac$range_seven_min, xmax = reac$range_seven_max, ymin = -Inf, ymax = +Inf,
alpha = .8, fill="#9061c9") +
annotate("rect", xmin = reac$range_eight_min, xmax = reac$range_eight_max, ymin = -Inf, ymax = +Inf,
alpha = .8, fill="#83b844")
#Changepoints
if(analyzed_plot[1,"changepoint_plotting"]==TRUE){
p <- p + geom_line(aes(x=Distance,y=changepoint_mean), color=input$changepoint_linecol)}
return(p)}
output$analyzed_plot <- renderPlot({analyzed_plot()})
# Create the brush output for analyzed_plot
output$brush_info <- renderPrint({
str(input$analyzed_plot_brush$xmin)
str(input$analyzed_plot_brush$xmax)
})
output$plot_hoverinfo <- renderPrint({
str(input$plot_hover$x,give.head = FALSE)
str(input$plot_hover$y,give.head = FALSE, digits=5)
})
#Total Sr plot
TotalSr_plot_analyzed <- function(){
req(input$Analyzed_sample_selector)
analyzed_plot <- analyzer_overwatch$analyzed
analyzed_plot <- analyzed_plot %>% filter(name==input$Analyzed_sample_selector)
p <- ggplot(analyzed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=("Sr (V)"), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(2), breaks = scales::pretty_breaks(n = 8)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 8))
p <- p + geom_point(aes(x=Distance,y=totalSr), size=1, color="red", shape=1, na.rm=TRUE)
return(p)}
output$TotalSr_plot_analyzed <- renderPlot({TotalSr_plot_analyzed()})
#Kr plot
Kr_plot_analyzed <- function(){
req(input$Analyzed_sample_selector)
analyzed_plot <- analyzer_overwatch$analyzed
analyzed_plot <- analyzed_plot %>% filter(name==input$Analyzed_sample_selector)
p <- ggplot(analyzed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=expression(paste(""^"83"*"Kr (V)")), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(5), breaks = scales::pretty_breaks(n = 8)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 8))
p <- p + geom_point(aes(x=Distance,y=Raw83), size=1, color="lightgreen", shape=1 , na.rm=TRUE)
return(p)}
output$Kr_plot_analyzed <- renderPlot({Kr_plot_analyzed()})
#Rb plot
Rb_plot_analyzed <- function(){
req(input$Analyzed_sample_selector)
analyzed_plot <- analyzer_overwatch$analyzed
analyzed_plot <- analyzed_plot %>% filter(name==input$Analyzed_sample_selector)
p <- ggplot(analyzed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none") + labs(y=expression(paste(""^"85"*"Rb/"^"88"*"Sr")), x=expression(paste("Distance (",mu,"m)")))
p <- p + scale_y_continuous(labels = fmt_dcimals(5), breaks = scales::pretty_breaks(n = 8)) + scale_x_continuous(expand=c(.01,0.01), breaks = scales::pretty_breaks(n = 8))
p <- p + geom_point(aes(x=Distance,y=Rb85Sr88),size=1, color="orange", shape=1, na.rm=TRUE)
return(p)}
output$Rb_plot_analyzed <- renderPlot({Rb_plot_analyzed()})
####Action to save all plots from analysis tab ####
observeEvent(input$save_edits, {
names <- as.character(unique(analyzed_data()$name))
t <- try(
for (i in names){
analyzed_plot <- function(){
if(is.null(analyzed_data())){return()}
analyzed_plot <- analyzer_overwatch$analyzed
analyzed_plot <- analyzed_plot %>% filter(name==i)
manual_region_plotter <- analyzed_plot %>% group_by(region_number) %>% dplyr::select(region_number, Distance)%>%
mutate (min_distance =min(Distance))%>%
mutate (max_distance=max(Distance))%>% dplyr::select(-Distance)%>%distinct()
region_colors <- c("NA", "#6b8acd", "#4cb28d", "#72853c", "#c78a40", "#c75d9c", "#cb554f", "#9061c9", "#83b844")
region_alpha <- c(0,0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8)
region_colors <-data.frame(region_colors, region_alpha) %>%
mutate(region_number=row_number()-1)
#Join data with colors
manual_region_plotter<-left_join(manual_region_plotter, region_colors, by="region_number")
p <- ggplot(analyzed_plot)
p <- p + theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(legend.position="none")
p <- p + labs(y=expression(paste(""^"87"*"Sr/"^"86"*"Sr")), x=expression(paste("Distance (",mu,"m)")), title= paste0(analyzed_plot$name, " ","Sample ID:"," ",analyzed_plot$Sample_ID))
p <- p + theme(axis.text.x = element_text(size=12), axis.text.y = element_text(size=12), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15))
p <- p + scale_y_continuous(labels = fmt_dcimals(4), breaks = scales::pretty_breaks(n = 10)) + scale_x_continuous(expand=c(0,0), breaks = scales::pretty_breaks(n = 10))
if(input$analyze_sd){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_MA-Sr87Sr86_MA_sds, ymax=Sr87Sr86_MA+Sr87Sr86_MA_sds), fill=input$analyze_shadecol, na.rm=FALSE)
}
if(input$analyze_ci){
p <- p +geom_ribbon(aes(x=Distance,ymin=Sr87Sr86_MA_CI95low, ymax=Sr87Sr86_MA_CI95up), fill=input$analyze_shadecol, na.rm=FALSE)