Nothing
inst/shiny/app.R
In mmaqshiny: Explore Air-Quality Mobile-Monitoring Data
library(shiny)
library(Cairo)
library(DT)
library(plotly)
library(data.table)
library(stringr)
library(dplyr)
library(ggplot2)
library(leaflet)
library(XML)
library(htmltools)
library(shinyjs)
library(lubridate)
library(zoo)
library(caTools)
library(xts)
ui <- fluidPage(
h1("Explore Mobile Monitoring Air Pollution Data"),
tags$head(
tags$style(HTML(".sidebar {
height : 10vh; overflow-y : auto; font-size : 14px;
}" ,
".shiny-output-error-validation {
color : red; font-size : 14px;
}"
))),
sidebarLayout(position = "left",
sidebarPanel(width = 3,
conditionalPanel(condition = "input.tabs1 == 6",
tags$hr(),
h4("Alarms! Check for any malfunction."),
tags$hr()),
conditionalPanel(condition = "input.tabs1 == 4",
tags$hr(),
selectInput("palleInp", "Map this pollutant",
"Select"),
tags$hr()),
conditionalPanel(condition = "input.tabs1 == 3",
tags$hr(),
h4("Summary Statistics"),
tags$hr()),
conditionalPanel(condition = "input.tabs1 == 2",
tags$hr(),
h4("Time series plots"),
tags$hr()
),
conditionalPanel(condition = "input.tabs1 == 1",
tags$hr(),
helpText("Choose mobile monitoring files."),
tags$hr(),
useShinyjs(),
test <- a("Input Timezone* (link to supported
timezones)",
href = "https://en.wikipedia.org/wiki/List_of_tz_database_time_zones",
style = "font-size:14px; ",
target = "_blank"),
selectInput("timezone",
label = "",
choices = OlsonNames(),
selected = "Asia/Kolkata"),
tags$hr(),
fileInput("file1",
"GPSMAP 64s - location files",
multiple = TRUE,
accept = c('.gpx')),
tags$hr(),
fileInput("file2", "AE51 - BC files",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,
text/plain",
".csv")),
tags$hr(),
fileInput("file3", "DT8530 - PM2.5 files",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,
text/plain",
".csv")),
h5("Reference Correction for DT8530 - PM2.5"),
numericInput("Slope",
"Slope",
value = 1.0),
numericInput("Intercept",
"Intercept",
value = 0),
tags$hr(),
fileInput("file5", "RH files",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,
text/plain",
".csv")),
tags$hr(),
fileInput("file4", "CPC3007 -
Particle Concentration files",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,
text/plain",
".csv")),
numericInput("DF",
"Dilution factor for
CPC (1 for no diluter)",
value = 1),
tags$hr(),
fileInput("file6", "LI-COR - CO2 files",
multiple = TRUE,
accept = c("text/csv",
".txt",
"text/comma-separated-values,
text/plain",
".csv")),
helpText("*mandatory"),
tags$hr(),
actionButton("join_button", "JOIN"),
tags$hr(),
downloadButton('download',"Download as csv"),
tags$hr())),
mainPanel(
tags$a(img(src = 'logo.png', align = "right", height = 70,
width = 120),
href = "https://www.ilklabs.com/", target = "_blank"),
tags$head(
tags$style(type = 'text/css',
".nav-tabs {
font-size: 18px
} ")),
tabsetPanel(id = "tabs1",
tabPanel(value = 1,
title = "Joined File",
dataTableOutput("table1")),
tabPanel(
value = 3,
title = "Summary",
dataTableOutput("table")
),
tabPanel(
value = 2,
title = "Plots",
plotlyOutput("plot5", width = 800),
plotlyOutput("plot6", width = 800),
plotlyOutput("plot2", width = 800),
plotlyOutput("plot", width = 800),
plotlyOutput("plot4", width = 800),
plotlyOutput("plot3", width = 800),
plotlyOutput("plot7", width = 800)
),
tabPanel(
value = 4,
title = "Map",
leafletOutput("map", width = "100%",
height = 800)
),
tabPanel(value = 6,
title = "Alarms and Settings",
h5("AE51 Status: "),
dataTableOutput("table5"),
dataTableOutput("table2"),
h5("DT8530 Notes/Alarms: "),
dataTableOutput("table4"),
h5("CPC3007 Notes/Alarms: "),
dataTableOutput("table3")
)))
))
server <- function(input, output, session) {
options(shiny.maxRequestSize = 30*1024^2, shiny.launch.browser = TRUE)
## date matching
CPC_f_date <- reactive({
inFile <- input$file4
if (is.null(input$file4)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = FALSE, sep = ",",
row.names = NULL, skip = 4)
JSON_csv_date <- as.character(JSON_csv[1, 2])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%m/%d/%y")
if (is.na(JSON_csv_date)) {
JSON_csv <- read.delim(y, header = FALSE, sep = ",",
row.names = NULL, skip = 4)
JSON_csv_date <- as.character(JSON_csv[1, 2])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%m-%d-%Y")
}
as.Date(JSON_csv_date)
}
)}
CPC_f2_date <- df_list[[1]]
return(CPC_f2_date)
})
GPS_f_date <- reactive({
trial <- data.frame()
inFile <- input$file1
if (is.null(inFile)) {
return(NULL)
}
req(input$file1)
files3 <- lapply(inFile$datapath, function(y) {
pfile2 <- htmlTreeParse(y, error = function (...) {}, useInternalNodes = T)
elevations <- as.numeric(xpathSApply(pfile2, path = "//trkpt/ele",
xmlValue))
times <- xpathSApply(pfile2, path = "//trkpt/time", xmlValue)
coords <- xpathSApply(pfile2, path = "//trkpt", xmlAttrs)
lats <- as.numeric(coords["lat", ])
lons <- as.numeric(coords["lon", ])
geodf2 <- data.frame(latitude = lats, longitude = lons,
ele = elevations, time = times)
})
trial <- do.call(rbind, files3)
GPS_f <- trial
GPS_f <- mutate(GPS_f, date = with_tz(ymd_hms(time), input$timezone))
GPS_f <- GPS_f[!duplicated(as.POSIXct(GPS_f$date)), ]
GPS_f <- GPS_f %>%
dplyr::select(date, latitude, longitude)
names(GPS_f) <- c("date", "latitude", "longitude")
GPS_f_date <- as.Date(GPS_f[1, 1])
GPS_f_date <- as.Date(GPS_f_date, format="%Y-%m-%d") #"%m/%d/%Y"
return(GPS_f_date)
})
BC_f_date <- reactive({
inFile <- input$file2
if (is.null(input$file2)) {
return(NULL)
}else {
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv_header <- read.delim(y, header = FALSE, sep = ",",
skip = 15, row.names = NULL,
stringsAsFactors = FALSE)
JSON_csv_header <- JSON_csv_header[1, ]
JSON_csv <- read.delim(y, skip = 17, header = FALSE, sep = ",")
colnames(JSON_csv) <- unlist(JSON_csv_header)
JSON_csv <- completeFun(JSON_csv, c("BC"))
JSON_csv$date1 <- with(JSON_csv,
as.POSIXct(paste(
as.Date(Date, format = "%Y/%m/%d"), Time),
tz = input$timezone))
if (is.null(JSON_csv$date1)) {
JSON_csv$date1 <- with(JSON_csv,
as.POSIXct(paste(
as.Date(Date, format = "%d-%m-%Y"), Time),
tz = input$timezone))
}
JSON_csv
})
data <- do.call(rbind, df_list)
BC_f <- data
BC_f_date1 <- as.character(BC_f[1, 1])
BC_f_date <- as.Date(BC_f_date1, format = "%d-%m-%Y")
if (is.na(BC_f_date)) {
BC_f_date <- as.Date(BC_f_date1, format = "%Y/%m/%d") #"%d-%m-%Y"; "%Y/%m/%d"
}
return(BC_f_date)
}
})
DT_f_date <- reactive({
inFile <- input$file3
if (is.null(input$file3)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = TRUE, sep = ",",
row.names = NULL, skip = 29)
JSON_csv_date <- as.character(JSON_csv[1, 1])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%d-%m-%Y")
if (is.na(JSON_csv_date)) {
JSON_csv <- read.delim(y, header = TRUE, sep = ",",
row.names = NULL, skip = 29)
JSON_csv_date <- as.character(JSON_csv[1, 1])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%m/%d/%Y")
}
as.Date(JSON_csv_date)
})
DT_f_date <- df_list[[1]]
return(DT_f_date)
}
})
CO2_f_date <- reactive({
inFile <- input$file6
if (is.null(input$file6)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = TRUE, sep = ";",
row.names = NULL, skip = 2)
JSON_csv_date <- as.character(JSON_csv[1, 1])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%d-%m-%Y")
if (is.na(JSON_csv_date)) {
JSON_csv <- read.delim(y, header = TRUE, sep = ";",
row.names = NULL, skip = 29)
JSON_csv_date <- as.character(JSON_csv[1, 1])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%Y-%m-%d")
}
as.Date(JSON_csv_date)
})
CO2_f_date <- df_list[[1]]
return(CO2_f_date)
}
})
## GPS and pollutant file joining
CO2_f <- reactive({
inFile <- input$file6
if (is.null(input$file6)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, skip = 1, sep = ",",
header = TRUE, row.names = NULL,
stringsAsFactors = FALSE)
JSON_csv <- JSON_csv[ ,1:3]
names(JSON_csv) <- c("Date", "Time", "CO2")
JSON_csv
})
data <- do.call(rbind, df_list)
CO2_f <- data
CO2_f$date <- with(CO2_f,
as.POSIXct(paste(
as.Date(Date, format = "%d-%m-%Y"), Time),
tz = input$timezone)) #"%Y-%m-%d""%d-%m-%Y"
CO2_f <- CO2_f %>%
dplyr::select(date, CO2)
names(CO2_f) <- c("date", "CO2")
attributes(CO2_f$date)$tzone <- input$timezone
CO2_f <- data.table(CO2_f)
setkey(CO2_f, date)
return(CO2_f)
}
})
GPS_f <- reactive({
trial <- data.frame()
inFile <- input$file1
if (is.null(inFile)) {
return(NULL)
}
req(input$file1)
files3 <- lapply(inFile$datapath, function(y) {
pfile2 <- htmlTreeParse(y, error = function (...) {}, useInternalNodes = T)
elevations <- as.numeric(xpathSApply(pfile2, path = "//trkpt/ele",
xmlValue))
times <- xpathSApply(pfile2, path = "//trkpt/time", xmlValue)
coords <- xpathSApply(pfile2, path = "//trkpt", xmlAttrs)
lats <- as.numeric(coords["lat", ])
lons <- as.numeric(coords["lon", ])
geodf2 <- data.frame(latitude = lats, longitude = lons,
ele = elevations, time = times)
})
trial <- do.call(rbind, files3)
GPS_f <- trial
GPS_f <- mutate(GPS_f, date = with_tz(ymd_hms(time), input$timezone))
GPS_f <- GPS_f[!duplicated(as.POSIXct(GPS_f$date)), ]
GPS_f <- GPS_f %>%
dplyr::select(date, latitude, longitude)
names(GPS_f) <- c("date", "latitude", "longitude")
GPS_f <- data.table(GPS_f)
setkey(GPS_f, date)
return(GPS_f)
})
BC_f <- reactive({
inFile <- input$file2
if (is.null(input$file2)) {
return(NULL)
}else {
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
df_list <- lapply(inFile$datapath, function(y){
JSON_csv_header <- read.delim(y, header = FALSE, sep = ",", skip = 15,
row.names = NULL,
stringsAsFactors = FALSE)
JSON_csv_header <- JSON_csv_header[1, ]
JSON_csv <- read.delim(y, skip = 17, header = FALSE, sep = ",")
colnames(JSON_csv) <- unlist(JSON_csv_header)
JSON_csv <- completeFun(JSON_csv, c("BC"))
JSON_csv$date1 <- with(JSON_csv,
as.POSIXct(paste(
as.Date(
Date, format = "%Y/%m/%d"), Time),
tz = input$timezone))
if (is.null(JSON_csv$date1)) {
JSON_csv$date1 <- with(JSON_csv,
as.POSIXct(paste(
as.Date(Date, format = "%d-%m-%Y"), Time),
tz = input$timezone))
}
JSON_csv
})
data <- do.call(rbind, df_list)
BC_f <- data
BC_f$Date <- BC_f$date1 #"%Y/%m/%d", "%d-%m-%Y"
ef_file <- data.frame(BC_f)
ef_file <- ef_file[ef_file$Status == 0, ]
ef_file <- dplyr::select(ef_file, Date, ATN, BC)
ATN <- ef_file[1, 2]
# ef_file$ATN <- ef_file$ATN-(ATN) # for new filter
names(ef_file) <- c("Date", "ATN", "BC")
ef_file$BC1 <- (ef_file$BC / 1000)
BC_Final <- ef_file
ef_file$LD <- ef_file$BC1 - rollapply(ef_file$BC1, FUN = mean,
width = 30, align = "center",
partial = TRUE)
ef_file$LD25 <- runquantile(ef_file$LD, 300, 0.25, type = 2,
endrule = c("NA"))
ef_file$LD75 <- runquantile(ef_file$LD, 300, 0.75, type = 2,
endrule = c("NA"))
ef_file$BC2 <- ef_file$BC1
ef_file$BC3 <- ef_file$BC1
ef_file$BC2[ef_file$BC2 >= 0] <- 0
ef_file$BC2[ef_file$BC2 < 0] <- 1
ef_file$BC2 <- rollapply(ef_file$BC2, FUN = mean, width = 5,
align = "center", partial = TRUE)
Lower_LD25 <- ((ef_file$LD) > 5 * (ef_file$LD75))
Higher_LD75 <- ((ef_file$LD) < 5 * (ef_file$LD25))
ef_file$cev1 <- ifelse(Lower_LD25|Higher_LD75, ef_file$BC1, NA)
CEV <- data.frame(ef_file$Date, ef_file$cev1)
CEV$ef_file.cev1[!is.na(CEV$ef_file.cev1)] <- 1
CEV <- data.frame(CEV)
date_file <- data.frame(ef_file$Date, ef_file$BC2, ef_file$BC3)
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
CEV <- completeFun(CEV, c("ef_file.cev1"))
setDT(CEV)
setDT(date_file)
cev_file <- date_file[CEV, on = c('ef_file.Date')]
cev_file <- cev_file[!(cev_file$ef_file.BC2 == 0), ]
cev_file <- xts(cev_file, order.by = cev_file$ef_file.Date)
ef_file <- data.frame(ef_file)
CE <- data.frame(index(CEV))
i <- index(cev_file)
i_old <- index(cev_file)
i <- index(cev_file) + 1
j <- index(cev_file) + 2
k <- index(cev_file) - 2
l <- index(cev_file) - 1
i <- cbind(as.character(i), as.character(i_old), as.character(j),
as.character(k), as.character(l))
Date_cev <- data.frame(i)
remove_cev <- data.frame(Date = unlist(Date_cev, use.names = FALSE))
Date_Table <- unique(remove_cev[c("Date")])
e <- nrow(Date_Table)
if (e == 0) {
BC <- ef_file
BC$BC_Factor <- 1
}else {
Date_Table$BC_Factor <- 0
Date_Table$Date <- as.POSIXct(Date_Table$Date)
setDT(Date_Table)
setDT(ef_file)
BC <- Date_Table[ef_file, on = c('Date')]
BC$BC_Factor[is.na(BC$BC_Factor)] <- 1
}
BC$BC_Fi <- BC$BC_Factor * BC$BC1
BC$BC_Fi[BC$BC_Fi == 0] <- NA
BC$Tr <- exp(- BC$ATN / 100)
BC$CF <- 1 / (0.88 * BC$Tr + 0.12)
BC$BC_Final <- BC$BC_Fi * BC$CF
BC$BC_Fi[BC$BC_Fi < 0] <- NA
BC$BC_Fi[is.na(BC$BC_Fi)] <- " "
BC$BC_Final[BC$BC_Final < 0] <- NA
BC$BC_Final[is.na(BC$BC_Final)] <- " "
BC_Final <- dplyr::select(BC, Date, BC3, BC_Fi, BC_Final)
names(BC_Final) <- c("date", "BC", "BC_NR", "BC_NR_LC")
BC_Final$BC_NR_LC <- as.numeric(as.character(BC_Final$BC_NR_LC))
BC_Final$BC_NR <- as.numeric(as.character(BC_Final$BC_NR))
BC_Final$BC <- as.numeric(as.character(BC_Final$BC))
BC_Final <- data.table(BC_Final)
attributes(BC_Final$date)$tzone <- input$timezone
setkey(BC_Final, date)
return(BC_Final)
}
})
DT_f <- reactive({
inFile <- input$file3
if (is.null(input$file3)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = TRUE, sep = ",", row.names = NULL,
skip = 28)
names(JSON_csv) <- c("Date", "Time", "PM2.5")
JSON_csv
})
data <- do.call(rbind, df_list)
DT_f <- data
Date1 <- DT_f[1, 1]
Date1 <- as.Date(Date1, format = "%d-%m-%Y" )
if (is.na(Date1)) {
Date1 <- DT_f[1, 1]
Date1 <- as.Date(Date1, format = "%m/%d/%Y" )
}
DT_f$date <- strptime(paste(Date1, DT_f$Time),
format = "%Y-%m-%d %H:%M:%S")
DT_f$date <- as.POSIXct(DT_f$date, format = '%Y-%m-%d %H:%M:%S',
tz = input$timezone)
attributes(DT_f$date)$tzone <- input$timezone
DT_f$PM2.5 <- as.numeric(as.character(DT_f$PM2.5))
DT_f$PM2.5 <- DT_f$PM2.5 * 1000
DT_f <- dplyr::select(DT_f, date, PM2.5 )
DT_f <- data.table(DT_f)
setkey(DT_f, date)
return(DT_f)
}
})
## file name matching
file_name_CPC <- reactive({
inFile <- input$file4
if (is.null(inFile)) {
return(NULL)
}else {
z <- sub(".csv$", "", basename(input$file4$name))
name_CPC <- substr(z, 1, 10)
return(name_CPC)
}
})
CPC_f <- reactive({
DF <- input$DF
inFile <- input$file4
name_CPC <- file_name_CPC()
if (is.null(input$file4)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = TRUE, sep = ",", row.names = NULL,
skip = 17,
stringsAsFactors = FALSE, fileEncoding = "latin1")
JSON_csv
})
data <- do.call(rbind, df_list)
CPC_f <- data
u <- stringr::str_extract(name_CPC, "[0-9]{4}\\_[0-9]{2}\\_[0-9]{2}")
w <- str_replace_all(u, "_", "-")
CPC_f <- CPC_f[, 1:2]
names(CPC_f) <- c("Time", "Particle_conc")
CPC_f$Particle_conc <- CPC_f$Particle_conc * DF
CPC_f <- mutate(CPC_f, date = ymd_hms(paste(w, Time),
tz = input$timezone))
CPC_f <- dplyr::select(CPC_f, date, Particle_conc )
CPC_f <- data.table(CPC_f)
setkey(CPC_f, date)
return(CPC_f)
}
})
RH_f<- reactive({
if (is.null(input$file5)) {
return(NULL)
}else {
RH_f <- data.frame(read.delim(input$file5$datapath, header = TRUE,
sep = ",", skip = 6, row.names = NULL))
RH_f_Date <- RH_f[ ,2]
RH_f_Time <- RH_f[ ,3]
RH <- RH_f[ , grepl( "RH", names(RH_f ) ) ]
RH_f <- data.frame(RH_f_Date, RH_f_Time, RH)
names(RH_f) <- c("LogDate", "LogTime", "RH")
RH_f$LogTime <- gsub(".", ":", RH_f$LogTime, fixed = TRUE)
RH_f$LogTime <- gsub("AM", "", RH_f$LogTime, fixed = TRUE)
RH_f$LogTime <- gsub("PM", "", RH_f$LogTime, fixed = TRUE)
RH_f$date <- as.POSIXct(paste(RH_f$LogDate, RH_f$LogTime),
format = '%d-%m-%Y %H:%M:%S', tz = input$timezone)
RH_f <- RH_f%>%
dplyr::select(date, RH)
names(RH_f) <- c("date", "RH")
RH_f$RH <- as.numeric(as.character(RH_f$RH))
RH_f$date <- as.POSIXct(RH_f$date, format = '%Y-%m-%d %H:%M:%S',
tz = input$timezone)
RH_f <- data.table(RH_f)
setkey(RH_f, date)
return(RH_f)
}
})
file_name_CO2 <- reactive({
inFile <- input$file6
if (is.null(inFile)) {
return(NULL)
}else {
y <- sub(".csv$", "", basename(input$file6$name))
name_CO2 <- substr(y, 1, 10)
return(name_CO2)
}
})
file_name_RH <- reactive({
inFile <- input$file5
if (is.null(inFile)) {
return(NULL)
}else {
x <- sub(".csv$", "", basename(input$file5$name))
name_RH <- substr(x, 1, 10)
return(name_RH)
}
})
file_name_GPS <- reactive({
inFile <- input$file1
if (is.null(inFile)) {
return(NULL)
}else {
x <- sub(".csv$", "", basename(input$file1$name))
name_GPS <- substr(x, 1, 10)
return(name_GPS)
}
})
file_name_BC <- reactive({
inFile <- input$file2
if (is.null(inFile)) {
return(NULL)
}else {
y <- sub(".csv$", "", basename(input$file2$name))
name_BC <- substr(y, 1, 10)
return(name_BC)
}
})
file_name_DT <- reactive({
inFile <- input$file3
if (is.null(inFile)) {
return(NULL)
}else {
w <- sub(".csv$", "", basename(input$file3$name))
name_DT <- substr(w, 1, 10)
return(name_DT)
}
})
## preloaded table
data_blank <- reactive({
if (is.null(input$file1) & is.null(input$file2) & is.null(input$file3) &
is.null(input$file4) & is.null(input$file5) & is.null(input$file6)) {
pfile2 <- htmlTreeParse("data/GPSMAP64s/2019_09_25_h091000_KAN_Garmin_3.gpx",
error = function (...) {}, useInternalNodes = T)
elevations <- as.numeric(xpathSApply(pfile2, path = "//trkpt/ele",
xmlValue))
times <- xpathSApply(pfile2, path = "//trkpt/time", xmlValue)
coords <- xpathSApply(pfile2, path = "//trkpt", xmlAttrs)
lats <- as.numeric(coords["lat", ])
lons <- as.numeric(coords["lon", ])
GPS_f <- data.frame(latitude = lats, longitude = lons, ele = elevations,
time = times)
GPS_f <- mutate(GPS_f, date = with_tz(ymd_hms(time), "Asia/Kolkata"))
GPS_f <- GPS_f[!duplicated(as.POSIXct(GPS_f$date)), ]
GPS_f <- GPS_f %>%
dplyr::select(date, latitude, longitude)
names(GPS_f) <- c("date", "latitude", "longitude")
GPS_f <- data.table(GPS_f)
setkey(GPS_f, date)
CO2_f <- read.delim("data/LI-COR/2019_09_25_h091000_KAN_LI_COR.csv",
skip = 1, sep = ",", header = TRUE, row.names = NULL,
stringsAsFactors = FALSE)
CO2_f <- CO2_f[ ,1:3]
names(CO2_f) <- c("Date", "Time", "CO2")
CO2_f$date <- with(CO2_f,
as.POSIXct(paste(as.Date(Date, format = "%d-%m-%Y"),
Time), tz = "Asia/Kolkata"))
CO2_f <- CO2_f %>%
dplyr::select(date, CO2)
names(CO2_f) <- c("date", "CO2")
attributes(CO2_f$date)$tzone <- "Asia/Kolkata"
CO2_f <- data.table(CO2_f)
setkey(CO2_f, date)
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
BC_f_header <- read.delim("data/AE51/2019_09_25_h091000_KAN_AE51.csv",
header = FALSE,
sep = ",", skip = 15, row.names = NULL,
stringsAsFactors = FALSE)
BC_f_header <- BC_f_header[1, ]
BC_f <- read.delim("data/AE51/2019_09_25_h091000_KAN_AE51.csv", skip = 17,
header = FALSE, sep = ",")
colnames(BC_f) <- unlist(BC_f_header)
BC_f <-completeFun(BC_f, c("BC"))
BC_f$date1 <- with(BC_f,
as.POSIXct(paste(as.Date(Date, format="%Y/%m/%d"),
Time), tz="Asia/Kolkata"))
BC_f$Date <- BC_f$date1
ef_file <- data.frame(BC_f)
ef_file <- ef_file[ef_file$Status == 0, ]
ef_file <- dplyr::select(ef_file, Date, ATN, BC)
ATN <- ef_file[1, 2]
names(ef_file) <- c("Date", "ATN", "BC")
ef_file$BC1 <- (ef_file$BC / 1000)
BC_Final <- ef_file
ef_file$LD <- ef_file$BC1 - rollapply(ef_file$BC1, FUN = mean,
width = 30, align = "center",
partial = TRUE)
ef_file$LD25 <- runquantile(ef_file$LD, 300, 0.25, type = 2,
endrule = c("NA"))
ef_file$LD75 <- runquantile(ef_file$LD, 300, 0.75, type = 2,
endrule = c("NA"))
ef_file$BC2 <- ef_file$BC1
ef_file$BC3 <- ef_file$BC1
ef_file$BC2[ef_file$BC2 >= 0] <- 0
ef_file$BC2[ef_file$BC2 < 0] <- 1
ef_file$BC2 <- rollapply(ef_file$BC2, FUN = mean, width = 5,
align = "center",
partial = TRUE)
Lower_LD25 <- ((ef_file$LD) > 5 * (ef_file$LD75))
Higher_LD75 <- ((ef_file$LD) < 5 * (ef_file$LD25))
ef_file$cev1 <- ifelse(Lower_LD25|Higher_LD75, ef_file$BC1, NA)
CEV <- data.frame(ef_file$Date, ef_file$cev1)
CEV$ef_file.cev1[!is.na(CEV$ef_file.cev1)] <- 1
CEV <- data.frame(CEV)
date_file <- data.frame(ef_file$Date, ef_file$BC2, ef_file$BC3)
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
CEV <- completeFun(CEV, c("ef_file.cev1"))
setDT(CEV)
setDT(date_file)
cev_file <- date_file[CEV, on = c('ef_file.Date')]
cev_file <- cev_file[!(cev_file$ef_file.BC2 == 0),]
cev_file <- xts(cev_file, order.by = cev_file$ef_file.Date)
ef_file <- data.frame(ef_file)
CE <- data.frame(index(CEV))
i <- index(cev_file)
i_old <- index(cev_file)
i <- index(cev_file) + 1
j <- index(cev_file) + 2
k <- index(cev_file) - 2
l <- index(cev_file) - 1
i <- cbind(as.character(i), as.character(i_old), as.character(j),
as.character(k), as.character(l))
Date_cev <- data.frame(i)
remove_cev <- data.frame(Date = unlist(Date_cev, use.names = FALSE))
Date_Table <- unique(remove_cev[c("Date")])
e <- nrow(Date_Table)
if (e == 0) {
BC <- ef_file
BC$BC_Factor <- 1
}else {
Date_Table$BC_Factor <- 0
Date_Table$Date <- as.POSIXct(Date_Table$Date)
setDT(Date_Table)
setDT(ef_file)
BC <- Date_Table[ef_file, on = c('Date')]
BC$BC_Factor[is.na(BC$BC_Factor)] <- 1
}
BC$BC_Fi <- BC$BC_Factor * BC$BC1
BC$BC_Fi[BC$BC_Fi == 0] <- NA
BC$Tr <- exp(- BC$ATN / 100)
BC$CF <- 1 / (0.88 * BC$Tr + 0.12)
BC$BC_Final <- BC$BC_Fi * BC$CF
BC$BC_Fi[BC$BC_Fi < 0] <- NA
BC$BC_Fi[is.na(BC$BC_Fi)] <- " "
BC$BC_Final[BC$BC_Final < 0] <- NA
BC$BC_Final[is.na(BC$BC_Final)] <- " "
BC_Final <- dplyr::select(BC, Date, BC3, BC_Fi, BC_Final)
names(BC_Final) <- c("date", "BC", "BC_NR", "BC_NR_LC")
BC_Final$BC_NR_LC <- as.numeric(as.character(BC_Final$BC_NR_LC))
BC_Final$BC_NR <- as.numeric(as.character(BC_Final$BC_NR))
BC_Final$BC <- as.numeric(as.character(BC_Final$BC))
BC_Final <- data.table(BC_Final)
attributes(BC_Final$date)$tzone <- "Asia/Kolkata"
setkey(BC_Final, date)
DT_f <- read.delim("data/DT8530/2019_09_25_h091000_KAN_DT8530.csv",
header = TRUE,
sep = ",", row.names = NULL, skip = 28)
names(DT_f) <- c("Date", "Time", "PM2.5")
Date1 <- DT_f[1, 1]
Date1 <- as.Date(Date1, format = "%d-%m-%Y" )
if (is.na(Date1)) {
Date1 <- DT_f[1, 1]
Date1 <- as.Date(Date1, format = "%m/%d/%Y" )
}
DT_f$date <- strptime(paste(Date1, DT_f$Time),
format = "%Y-%m-%d %H:%M:%S")
DT_f$date <- as.POSIXct(DT_f$date, format = '%Y-%m-%d %H:%M:%S',
tz = "Asia/Kolkata")
attributes(DT_f$date )$tzone <- "Asia/Kolkata"
DT_f$PM2.5 <- as.numeric(as.character(DT_f$PM2.5))
DT_f$PM2.5 <- DT_f$PM2.5 * 1000
DT_f <- dplyr::select(DT_f, date, PM2.5)
DT_f <- data.table(DT_f)
setkey(DT_f, date)
CPC_f <- read.delim("data/CPC3007/2019_09_25_h091000_KAN_CPC3007.csv",
header = TRUE,
sep = ",", row.names = NULL, skip = 17,
stringsAsFactors = FALSE,
fileEncoding = "latin1")
CPC_f <- CPC_f[ ,1:2]
names(CPC_f) <- c("Time", "Particle_conc")
CPC_f$Particle_conc <- CPC_f$Particle_conc * 5.5
w <- as.Date("2019-09-25")
CPC_f <- mutate(CPC_f, date = ymd_hms(paste(w, Time),
tz = "Asia/Kolkata"))
CPC_f <- dplyr::select(CPC_f,date, Particle_conc )
CPC_f <- data.table(CPC_f)
setkey(CPC_f, date)
RH_f <- data.frame(read.delim("data/RH/2019_09_25_h091000_KAN_RH.csv",
header = TRUE, sep = ",", skip = 6,
row.names = NULL))
RH_f_Date <- RH_f[ ,2]
RH_f_Time <- RH_f[ ,3]
RH <- RH_f[ , grepl("RH" , names(RH_f) ) ]
RH_f <- data.frame(RH_f_Date, RH_f_Time, RH)
names(RH_f) <- c("LogDate", "LogTime", "RH")
RH_f$LogTime <- gsub(".", ":", RH_f$LogTime, fixed = TRUE)
RH_f$LogTime <- gsub("AM", "", RH_f$LogTime, fixed = TRUE)
RH_f$LogTime <- gsub("PM", "", RH_f$LogTime, fixed = TRUE)
RH_f$date <- as.POSIXct(paste(RH_f$LogDate, RH_f$LogTime),
format = '%d-%m-%Y %H:%M:%S', tz = "Asia/Kolkata")
RH_f <- RH_f%>%
dplyr::select(date, RH)#X2.P..RH, X1.P..RH
names(RH_f) <- c("date", "RH")
RH_f$RH <- as.numeric(as.character(RH_f$RH))
RH_f$date <- as.POSIXct(RH_f$date, format = '%Y-%m-%d %H:%M:%S',
tz = "Asia/Kolkata")
RH_f <- data.table(RH_f)
setkey(RH_f, date)
VecFunc <- function(x) {
if (x > 0.6) {
return (1 + (0.25 * (((x) * (x)) / (1 - x))))
} else
{
return (1)
}
}
joined_GPS_CO2 <- left_join(GPS_f, CO2_f, by = "date")
joined_GPS_CO2 <- dplyr::select(joined_GPS_CO2, date, CO2, latitude,
longitude )
joined_GPS_CO2 <- data.table(joined_GPS_CO2)
setkey(joined_GPS_CO2, date)
joined_GPS_DT <- left_join(GPS_f, DT_f, by = "date")
joined_GPS_DT$PM2.5_RHC <- NA
joined_GPS_DT <- dplyr::select(joined_GPS_DT, date, PM2.5, PM2.5_RHC,
latitude, longitude )
joined_GPS_DT <- data.table(joined_GPS_DT)
setkey(joined_GPS_DT, date)
DT_f <- RH_f[joined_GPS_DT, roll = "nearest"]
DT_f$RH <- DT_f$RH / 100
DT_f <- dplyr::select(DT_f, date, latitude, longitude, PM2.5, RH )
names(DT_f) <- c("date", "latitude", "longitude", "PM2.5", "RH")
DT_f$CF <- sapply(DT_f$RH, FUN = VecFunc)
DT_f$PM2.5_RHC <- DT_f$PM2.5 / DT_f$CF
DT_f$PM2.5_Ref <- (DT_f$PM2.5 * 1 ) + 0
DT_f$PM2.5_RHC_Ref <- (DT_f$PM2.5_RHC * 1) + 0
DT_f$date <- as.POSIXct(DT_f$date, format = '%Y-%m-%d %H:%M:%S')
attributes(DT_f$date )$tzone <- "Asia/Kolkata"
DT_f <- data.table(DT_f)
setkey(DT_f, date)
joined_GPS_BC <- left_join(GPS_f, BC_Final, by = "date")
joined_GPS_BC <- dplyr::select(joined_GPS_BC, date, BC, BC_NR, BC_NR_LC,
latitude, longitude )
joined_GPS_BC <- data.table(joined_GPS_BC)
setkey(joined_GPS_BC, date)
joined_GPS_CPC <- left_join(GPS_f, CPC_f, by = "date")
joined_GPS_CPC <- dplyr::select(joined_GPS_CPC, date, Particle_conc,
latitude, longitude )
joined_GPS_CPC <- data.table(joined_GPS_CPC)
setkey(joined_GPS_CPC, date)
joined2 <- left_join(joined_GPS_CPC, joined_GPS_CO2, by = "date")
setDT(joined2)
setkey(joined2, date)
joined_1 <- left_join(DT_f, joined2, by = "date")
setDT(joined_1)
setkey(joined_1, date)
joined <- joined_1[joined_GPS_BC, roll = "nearest"]
joined <- joined %>%
dplyr::select(date, latitude, longitude, BC, BC_NR, BC_NR_LC, PM2.5,
PM2.5_RHC, PM2.5_RHC_Ref, PM2.5_Ref, RH, Particle_conc,
CO2)
names(joined) <- c("date", "Latitude", "Longitude", "BC", "BC_NR",
"BC_NR_LC", "PM2.5", "PM2.5_RHC", "PM2.5_RHC_Ref",
"PM2.5_Ref", "RH", "Particle_conc", "CO2")
attributes(joined$date )$tzone <- "Asia/Kolkata"
joined <- joined[!duplicated(joined$date), ]
joined
}
})
joined_GPS_CO2 <- reactive({
name_GPS <- file_name_GPS()
name_CO2 <- file_name_CO2()
GPS_f <- GPS_f()
CO2_f <- CO2_f()
if (is.null(CO2_f)) {
joined_GPS_CO2 <- dplyr::select(GPS_f, date, latitude, longitude)
joined_GPS_CO2$CO2 <- NA
joined_GPS_CO2$CO2 <- as.numeric(as.character(joined_GPS_CO2$CO2))
}
if (!is.null(CO2_f)) {
validate(
need(try(file_name_GPS() == file_name_CO2()), "GPS File mandatory! File
names of GPS and LI-COR do not match, Please select the correct files")
)
validate(
need(try(GPS_f_date() == CO2_f_date()), "The files have different
date entries in GPS and LI-COR! Please check once again.")
)
joined_GPS_CO2 <- left_join(GPS_f, CO2_f, by = "date")
joined_GPS_CO2 <- dplyr::select(joined_GPS_CO2, date, CO2,
latitude, longitude )
}
joined_GPS_CO2 <- data.table(joined_GPS_CO2)
setkey(joined_GPS_CO2, date)
return(joined_GPS_CO2)
})
joined_GPS_DT <- reactive({
name_GPS <- file_name_GPS()
name_DT <- file_name_DT()
GPS_f <- GPS_f()
DT_f <- DT_f()
if (is.null(DT_f)) {
joined_GPS_DT <- dplyr::select(GPS_f, date, latitude, longitude)
joined_GPS_DT$PM2.5 <- NA
joined_GPS_DT$PM2.5_RHC <- NA
joined_GPS_DT$PM2.5 <- as.numeric(joined_GPS_DT$PM2.5)
}
if (!is.null(DT_f)) {
validate(
need(try(file_name_GPS() == file_name_DT()), "GPS File mandatory!
File names of GPS and DustTrak-8530 do not match,
Please select the correct files")
)
validate(
need(try(GPS_f_date() == DT_f_date()), "The files have different date
entries in GPS and DustTrak-8530! Please check once again.")
)
joined_GPS_DT <- left_join(GPS_f, DT_f, by = "date")
joined_GPS_DT$PM2.5_RHC <- NA
joined_GPS_DT <- dplyr::select(joined_GPS_DT, date, PM2.5, PM2.5_RHC,
latitude, longitude )
}
joined_GPS_DT <- data.table(joined_GPS_DT)
setkey(joined_GPS_DT, date)
return(joined_GPS_DT)
})
DT_RH <- reactive({
name_RH <- file_name_RH()
name_DT <- file_name_DT()
DT_fi <- DT_f()
RH_f <- RH_f()
joined_GPS_DT <- joined_GPS_DT()
Slope <- input$Slope
Intercept <- input$Intercept
VecFunc <- function(x) {
if (x > 0.6) {
return (1 + (0.25 * (((x) * (x)) / (1 - x))))
} else
{
return (1)
}
}
if (is.null(RH_f()) | is.null(DT_f())) {
DT_f <- joined_GPS_DT
DT_f$RH <- NA
DT_f$PM2.5_RHC <- NA
DT_f$PM2.5_Ref <- (DT_f$PM2.5 * Slope) + Intercept
DT_f$PM2.5_RHC_Ref <- NA
DT_f$RH <- as.numeric(DT_f$RH)
}
if (!is.null(RH_f()) & !is.null(DT_f())) {
validate(
need(try(file_name_DT() == file_name_RH()), "File names of DustTrak
8530 and RH do not match, Please select the correct files")
)
setkey(RH_f, date)
DT_f <- RH_f[joined_GPS_DT, roll = "nearest"]
DT_f$RH <- DT_f$RH / 100
DT_f <- dplyr::select(DT_f, date, latitude, longitude, PM2.5, RH )
names(DT_f) <- c("date", "latitude", "longitude", "PM2.5", "RH")
DT_f$CF <- sapply(DT_f$RH, FUN = VecFunc)
DT_f$PM2.5_RHC <- DT_f$PM2.5 / DT_f$CF
DT_f$PM2.5_Ref <- (DT_f$PM2.5 * Slope) + Intercept
DT_f$PM2.5_RHC_Ref <- (DT_f$PM2.5_RHC * Slope) + Intercept
}
DT_f$date <- as.POSIXct(DT_f$date, format = '%Y-%m-%d %H:%M:%S')
attributes(DT_f$date )$tzone <- input$timezone
DT_f <- data.table(DT_f)
setkey(DT_f, date)
return(DT_f)
})
joined_GPS_BC <- reactive({
name_GPS <- file_name_GPS()
name_BC <- file_name_BC()
GPS_f <- GPS_f()
BC_Final <- BC_f()
if (is.null(BC_f())) {
joined_GPS_BC <- dplyr::select(GPS_f, date, latitude, longitude)
joined_GPS_BC$BC <- NA
joined_GPS_BC$BC_NR <- NA
joined_GPS_BC$BC_NR_LC <- NA
joined_GPS_BC$BC <- as.numeric(as.character(joined_GPS_BC$BC))
joined_GPS_BC$BC_NR <- as.numeric(as.character(joined_GPS_BC$BC_NR))
joined_GPS_BC$BC_NR_LC <- as.numeric(as.character(joined_GPS_BC$BC_NR_LC))
}
if (!is.null(BC_f())) {
validate(
need(try(file_name_GPS() == file_name_BC()), "GPS File mandatory!
File names of GPS and AE51 do not match, Please select the correct
files")
)
validate(
need(try(GPS_f_date() == BC_f_date()), "The files have different
date entries in GPS and AE51! Please check once again.")
)
joined_GPS_BC <- left_join(GPS_f, BC_Final, by = "date")
joined_GPS_BC <- dplyr::select(joined_GPS_BC, date, BC, BC_NR, BC_NR_LC,
latitude, longitude )
}
joined_GPS_BC <- data.table(joined_GPS_BC)
setkey(joined_GPS_BC, date)
return(joined_GPS_BC)
})
joined_GPS_CPC <- reactive({
name_GPS <- file_name_GPS()
name_CPC <- file_name_CPC()
GPS_f <- GPS_f()
CPC_f <- CPC_f()
if (is.null(CPC_f)) {
joined_GPS_CPC <- dplyr::select(GPS_f, date, latitude, longitude)
joined_GPS_CPC$Particle_conc <- NA
joined_GPS_CPC$Particle_conc <- as.numeric(
as.character(joined_GPS_CPC$Particle_conc))
}
if (!is.null(CPC_f)) {
validate(
need(try(file_name_GPS() == file_name_CPC()), "GPS File mandatory!
File names of GPS and CPC-3007 do not match,
Please select the correct files")
)
validate(
need(try(GPS_f_date() == CPC_f_date()), "The files have different
date entries in GPS and CPC-3007! Please check once again.")
)
joined_GPS_CPC <- left_join(GPS_f, CPC_f, by = "date")
joined_GPS_CPC <- dplyr::select(joined_GPS_CPC, date, Particle_conc,
latitude, longitude )
}
joined_GPS_CPC <- data.table(joined_GPS_CPC)
setkey(joined_GPS_CPC, date)
return(joined_GPS_CPC)
})
observeEvent(input$timezone,{
toggleState(id = "join_button",
condition = (input$timezone != "" |
is.null(input$timezone) | is.na(input$timezone)))
})
data_joined <- eventReactive(input$join_button,{
joined_GPS_CPC <- joined_GPS_CPC()
joined_GPS_CO2 <- joined_GPS_CO2()
joined_GPS_BC <- joined_GPS_BC()
DT_RH <- DT_RH()
joined_GPS_DT <- joined_GPS_DT()
name_GPS <- file_name_GPS()
name_BC <- file_name_BC()
name_CPC <- file_name_CPC()
name_DT <- file_name_DT()
name_RH <- file_name_RH()
name_CO2 <- file_name_CO2()
GPS_f <- GPS_f()
CPC_f <- CPC_f()
BC_Final <- BC_f()
DT_f <- DT_f()
RH_f <- RH_f()
CO2_f <- CO2_f()
joined2 <- left_join(joined_GPS_CPC, joined_GPS_CO2, by = "date")
setDT(joined2)
setkey(joined2, date)
joined_1 <- left_join(DT_RH, joined2, by = "date")
setDT(joined_1)
setkey(joined_1, date)
joined <- joined_1[joined_GPS_BC, roll = "nearest"]
joined <- joined %>%
dplyr::select(date, latitude, longitude, BC, BC_NR, BC_NR_LC, PM2.5,
PM2.5_RHC, PM2.5_RHC_Ref, PM2.5_Ref, RH, Particle_conc, CO2)
names(joined) <- c("date", "Latitude", "Longitude", "BC", "BC_NR",
"BC_NR_LC", "PM2.5", "PM2.5_RHC", "PM2.5_RHC_Ref",
"PM2.5_Ref", "RH", "Particle_conc", "CO2")
attributes(joined$date )$tzone <- input$timezone
joined <- joined[!duplicated(joined$date), ]
return(joined)
})
## Final corrected, joined table
output$table1 <- DT::renderDataTable({
if (is.null(input$file1) & is.null(input$file2) & is.null(input$file3) &
is.null(input$file4) & is.null(input$file5) & is.null(input$file6)) {
data_joined <- data_blank()
}else{
data_joined <- data_joined()
}
data_joined$BC <- round(as.numeric(data_joined$BC),
digits = 2)
data_joined$BC_NR <- round(as.numeric(data_joined$BC_NR),
digits = 2)
data_joined$BC_NR_LC <- round(as.numeric(data_joined$BC_NR_LC),
digits = 2)
data_joined$PM2.5 <- round(as.numeric(data_joined$PM2.5),
digits = 2)
data_joined$PM2.5_RHC <- round(as.numeric(data_joined$PM2.5_RHC),
digits = 2)
data_joined$PM2.5_Ref <- round(as.numeric(data_joined$PM2.5_Ref),
digits = 2)
data_joined$PM2.5_RHC_Ref <- round(as.numeric(data_joined$PM2.5_RHC_Ref),
digits = 2)
data_joined$Longitude <- round(as.numeric(data_joined$Longitude),
digits = 4)
data_joined$Latitude <- round(as.numeric(data_joined$Latitude),
digits = 4)
data_joined$Particle_conc <- round(as.numeric(data_joined$Particle_conc),
digits = 2)
data_joined$CO2 <- round(as.numeric(data_joined$CO2),
digits = 2)
data_joined$RH <- data_joined$RH * 100
data_joined$RH <- round(as.numeric(data_joined$RH),
digits = 2)
data_joined <- data_joined %>%
dplyr::select(date, Latitude, Longitude, BC, BC_NR, BC_NR_LC, PM2.5,
PM2.5_RHC, PM2.5_RHC_Ref, PM2.5_Ref, RH,
Particle_conc, CO2)
names(data_joined) <- c("date", "Latitude", "Longitude", "AE51_BC (ug/m3)",
"AE51_BC_NR (ug/m3)", "AE51_BC_NR_LC (ug/m3)",
"DT8530_PM2.5 (ug/m3)", "DT8530_PM2.5_RHC (ug/m3)",
"DT8530_PM2.5_RHC_Ref (ug/m3)",
"DT8530_PM2.5_Ref (ug/m3)", "RH(%)",
"CPC3007_Particle Concentration (#/cm3)",
"LI-COR_CO2 (ppm)")
datatable(data_joined, options = list("pageLength" = 25))
})
## Download the csv generated
output$download <- downloadHandler(
filename <- function() {"joined_file.csv"},
content <- function(fname) {
if (is.null(input$file1) & is.null(input$file2) & is.null(input$file3) &
is.null(input$file4) & is.null(input$file5) & is.null(input$file6)) {
data_joined <- data_blank()
}else {
data_joined <- data_joined()
}
data_joined <- data_joined %>%
dplyr::select(date, Latitude, Longitude, BC, BC_NR, BC_NR_LC, PM2.5,
PM2.5_RHC, PM2.5_RHC_Ref, PM2.5_Ref, RH,
Particle_conc, CO2)
names(data_joined) <- c("date(LT)", "Latitude", "Longitude",
"AE51_BC (ug/m3)",
"AE51_BC_NR (ug/m3)", "AE51_BC_NR_LC (ug/m3)",
"DT8530_PM2.5 (ug/m3)", "DT8530_PM2.5_RHC (ug/m3)",
"DT8530_PM2.5_RHC_Ref (ug/m3)",
"DT8530_PM2.5_Ref (ug/m3)",
"RH(%)", "CPC3007_Particle Concentration (#/cm3)",
"LI-COR_CO2 (ppm)")
write.csv(data_joined, fname)
})
## Summary Statistics
output$table <- DT::renderDataTable({
if (is.null(input$file1) & is.null(input$file2) & is.null(input$file3) &
is.null(input$file4) & is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
}else {
data <- data_joined()
}
data <- dplyr::select(data, BC, BC_NR, BC_NR_LC, PM2.5, PM2.5_RHC,
PM2.5_RHC_Ref, PM2.5_Ref, RH, Particle_conc, CO2)
data[["BC"]] <- as.numeric(as.character(data[["BC"]]))
data[["BC_NR"]] <- as.numeric(as.character(data[["BC_NR"]]))
data[["BC_NR_LC"]] <- as.numeric(as.character(data[["BC_NR_LC"]]))
data$RH <- data$RH * 100
names(data) <- c("AE51_BC (ug/m3)", "AE51_BC_NR (ug/m3)",
"AE51_BC_NR_LC (ug/m3)",
"DT8530_PM2.5 (ug/m3)", "DT8530_PM2.5_RHC (ug/m3)",
"DT8530_PM2.5_RHC_Ref (ug/m3)",
"DT8530_PM2.5_Ref (ug/m3)", "RH(%)",
"CPC3007_Particle Concentration (#/cm3)",
"LI-COR_CO2 (ppm)")
columns <- c("AE51_BC (ug/m3)", "AE51_BC_NR (ug/m3)", "AE51_BC_NR_LC (ug/m3)",
"DT8530_PM2.5 (ug/m3)", "DT8530_PM2.5_RHC (ug/m3)",
"DT8530_PM2.5_RHC_Ref (ug/m3)", "DT8530_PM2.5_Ref (ug/m3)",
"RH(%)", "CPC3007_Particle Concentration (#/cm3)",
"LI-COR_CO2 (ppm)")
data[ , columns] <- lapply(columns,
function(x) as.numeric(as.character(data[[x]])))
tmp1 <- do.call(data.frame,
list(Mean = apply(data, 2, mean, na.rm = TRUE),
SD = apply(data, 2, sd, na.rm = TRUE),
Median = apply(data, 2, median, na.rm = TRUE),
IQR = apply(data, 2, IQR, na.rm = TRUE),
Min = apply(data, 2, min, na.rm = TRUE),
Max = apply(data, 2, max, na.rm = TRUE),
p1 = apply(data, 2, quantile, probs = c(0.01),
na.rm = TRUE),
p10 = apply(data, 2, quantile, probs = c(0.1),
na.rm = TRUE),
p25 = apply(data, 2, quantile, probs = c(0.25),
na.rm = TRUE),
p75 = apply(data, 2, quantile, probs = c(0.75),
na.rm = TRUE),
p90 = apply(data, 2, quantile, probs = c(0.9),
na.rm = TRUE),
p99 = apply(data, 2, quantile, probs = c(0.99),
na.rm = TRUE),
Total_non_NA = apply(data, 2,
function(x)
{length(which(!is.na(x)))})))
tmp <- data.frame(tmp1)
tmp$Mean <- round(as.numeric(as.character(tmp$Mean)), digits = 2)
tmp$IQR <- round(as.numeric(as.character(tmp$IQR)), digits = 2)
tmp$Median <- round(as.numeric(as.character(tmp$Median)), digits = 2)
tmp$Min <- round(as.numeric(as.character(tmp$Min)), digits = 2)
tmp$Max <- round(as.numeric(as.character(tmp$Max)), digits = 2)
tmp$p10 <- round(as.numeric(as.character(tmp$p10)), digits = 2)
tmp$SD <- round(as.numeric(as.character(tmp$SD)), digits = 2)
tmp$p90 <- round(as.numeric(as.character(tmp$p90)), digits = 2)
tmp$p75 <- round(as.numeric(as.character(tmp$p75)), digits = 2)
tmp$p99 <- round(as.numeric(as.character(tmp$p99)), digits = 2)
tmp$p1 <- round(as.numeric(as.character(tmp$p1)), digits = 2)
tmp$p25 <- round(as.numeric(as.character(tmp$p25)), digits = 2)
tmp
tmp <- t(tmp)
datatable(tmp, options = list("pageLength" = 13))
})
## Alarms and settings
output$table4 <- DT::renderDataTable({
inFile <- input$file3
if (is.null(GPS_f()) & is.null(BC_f()) & is.null(CPC_f()) & is.null(DT_f()) &
is.null(RH_f()) & is.null(CO2_f())) {
DT_f <- read.delim("data/DT8530/2019_09_25_h091000_KAN_DT8530.csv",
header = FALSE, sep = ",", row.names = NULL, skip = 2)
DT_f <- DT_f[1:11, ]
DT_f <- DT_f[ ,1:2]
datatable(DT_f, options = list("pageLength" = 11))
}
else if (is.null(DT_f())) {}
else if (!is.null(DT_f())) {
files3 <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = FALSE, sep = ",", row.names = NULL,
skip = 2)
names(JSON_csv) <- c("Setting", "Value")
JSON_csv <- JSON_csv[1:11, ]
JSON_csv <- JSON_csv[ ,1:2]
JSON_csv
})
data <- do.call(rbind, files3)
DT_f <- data
datatable(DT_f, options = list("pageLength" = 11))
}
})
output$table3 <- DT::renderDataTable({
inFile <- input$file4
if (is.null(GPS_f()) & is.null(BC_f()) & is.null(CPC_f()) & is.null(DT_f()) &
is.null(RH_f()) & is.null(CO2_f())) {
CPC_f <- read.delim("data/CPC3007/2019_09_25_h091000_KAN_CPC3007.csv",
header = FALSE, sep = ",", row.names = NULL, skip = 1)
names(CPC_f) <- c("Setting", "Value")
CPC_f <- CPC_f[1:13, ]
CPC_f <- CPC_f[ ,1:2]
datatable(CPC_f, options = list("pageLength" = 13))
}
else if (is.null(CPC_f())) {}
else if (!is.null(CPC_f())){
files3 <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = FALSE, sep = ",", row.names = NULL,
skip = 1)
names(JSON_csv) <- c("Setting", "Value")
JSON_csv <- JSON_csv[1:13, ]
JSON_csv <- JSON_csv[ ,1:2]
JSON_csv
})
data <- do.call(rbind, files3)
CPC_f <- data
datatable(CPC_f, options = list("pageLength" = 13))
}
})
output$table2 <- DT::renderDataTable({
inFile <- input$file2
if (is.null(BC_f())) {}
else if (!is.null(BC_f())) {
files3 <- lapply(inFile$datapath, function(y) {
JSON_csv_header <- read.delim(y, header = FALSE, sep=",", skip = 15,
row.names = NULL, stringsAsFactors = FALSE)
JSON_csv_header <- JSON_csv_header[1, ]
JSON_csv <- read.delim(y, skip = 17, header = FALSE, sep = ",")
colnames(JSON_csv) <- unlist(JSON_csv_header)
JSON_csv
})
data <- do.call(rbind, files3)
BC_f <- data
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
BC_f <- completeFun(BC_f, c("BC"))
BC_f <- BC_f[BC_f$Status != 0, ]
BC_f
}
})
output$table5 <- DT::renderDataTable({
inFile <- input$file2
if (is.null(GPS_f()) & is.null(BC_f()) & is.null(CPC_f()) & is.null(DT_f()) &
is.null(RH_f()) & is.null(CO2_f())) {
BC_f <- read.delim("data/AE51/2019_09_25_h091000_KAN_AE51.csv",
header = FALSE,
sep = " ", skip = 1, row.names = NULL)
BC_f <- BC_f[1:14, ]
names(BC_f) <- c("Setting")
datatable(BC_f, options = list("pageLength" = 14))
}else if (is.null(BC_f())) {
"No AE51 files available"
}else if (!is.null(BC_f())){
files3 <- lapply(inFile$datapath, function(y){
JSON_csv <- read.delim(y, header = FALSE, sep = " ",
skip = 1, row.names = NULL)
JSON_csv <- JSON_csv[1:14, ]
JSON_csv
})
data <- do.call(rbind, files3)
BC_f <- data
names(BC_f) <- c("Setting")
datatable(BC_f, options = list("pageLength" = 14))
}
})
## Raw pollutants/GPS plot
output$plot5 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(Latitude))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Latitude (degree)",
y = "",
x = "") +
scale_x_datetime( date_labels = "%H:%M") +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(GPS_f())) {}
else if (!is.null(GPS_f())) {
data <- data_joined()
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(Latitude))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Latitude (degree)",
y = "",
x = "") +
scale_x_datetime( date_labels = "%H:%M") +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot6 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)){
data <- data_blank()
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(Longitude))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Longitude (degree)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(GPS_f())) {}
else if (!is.null(GPS_f())) {
data <- data_joined()
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(Longitude))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Longitude (degree)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
data$PM2.5 <- as.numeric(as.character(data$PM2.5))
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(PM2.5))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "DT8530_PM2.5 (ug/m3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(DT_f())) {}
else if (!is.null(DT_f())) {
data <- data_joined()
data$PM2.5 <- as.numeric(as.character(data$PM2.5))
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(PM2.5))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "DT8530_PM2.5 (ug/m3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot4<- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
data$RH <- data$RH * 100
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(RH))) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "Relative Humidity (%)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous(limits = c(20,100)) +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(RH_f())) {}
else if (!is.null(RH_f())) {
data <- data_joined()
data$RH <- data$RH * 100
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(RH))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Relative Humidity (%)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous(limits = c(20,100)) +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot2 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
data$BC <- as.numeric(as.character(data$BC))
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(BC))) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "AE51_BC (ug/m3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(BC_f())) {}
else if (!is.null(BC_f())) {
data <- data_joined()
data$BC <- as.numeric(as.character(data$BC))
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(BC))) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "AE51_BC (ug/m3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot3 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
data$Particle_conc <- as.numeric(as.character(data$Particle_conc))
ggplotly(ggplot(data, aes(as.POSIXct(date), Particle_conc)) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "CPC3007_Particle Concentration (#/cm3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(CPC_f())) {}
else if (!is.null(CPC_f())) {
data <- data_joined()
data$Particle_conc <- as.numeric(as.character(data$Particle_conc))
ggplotly(ggplot(data, aes(as.POSIXct(date), Particle_conc)) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "CPC3007_Particle Concentration (#/cm3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot7 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
ggplotly(ggplot(data, aes(as.POSIXct(date), CO2)) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "LI-COR_CO2 (ppm)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(CO2_f())) {}
else if (!is.null(CO2_f())) {
data <- data_joined()
ggplotly(ggplot(data, aes(as.POSIXct(date), CO2)) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "LI-COR_CO2 (ppm)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
observe({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data_joined <- data_blank()
data_joined$date <- NULL
data_joined$Latitude <- NULL
data_joined$Longitude <- NULL
data_joined$PM2.5_RHC <- NULL
data_joined$PM2.5_RHC_Ref <- NULL
}else {
data_joined <- data_joined()
data_joined$date <- NULL
data_joined$Latitude <- NULL
data_joined$Longitude <- NULL
if (is.null(CPC_f())) {
data_joined$Particle_conc <- NULL
}
if (!is.null(CPC_f())) {
data_joined$Particle_conc <- data_joined$Particle_conc
}
if (is.null(RH_f())) {
data_joined$RH <- NULL
}
if (!is.null(RH_f())) {
data_joined$RH <- data_joined$RH
}
if (is.null(DT_f())) {
data_joined$PM2.5 <- NULL
data_joined$PM2.5_RHC <- NULL
data_joined$PM2.5_RHC_Ref <- NULL
data_joined$PM2.5_Ref <- NULL
}
if (!is.null(DT_f())) {
data_joined$PM2.5 <- data_joined$PM2.5
data_joined$PM2.5_RHC <- data_joined$PM2.5_RHC
data_joined$PM2.5_RHC_Ref <- data_joined$PM2.5_RHC_Ref
data_joined$PM2.5_Ref <- data_joined$PM2.5_Ref
}
if (is.null(BC_f())) {
data_joined$BC <- NULL
data_joined$BC_NR <- NULL
data_joined$BC_NR_LC <- NULL
}
if (!is.null(BC_f())) {
data_joined$BC <- data_joined$BC
data_joined$BC_NR <- data_joined$BC_NR
data_joined$BC_NR_LC <- data_joined$BC_NR_LC
}
if (is.null(CO2_f())) {
data_joined$CO2 <- NULL
}
if (!is.null(CO2_f())) {
data_joined$CO2 <- data_joined$CO2
}
}
updateSelectInput(session, "palleInp", choices = names(data_joined))
})
## Mapping pollutant
output$map <- renderLeaflet({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
}else {
data <- data_joined()
}
data$RH <- data$RH * 100
if (input$palleInp == "BC_NR" | input$palleInp == "BC_NR_LC") {
risk.bins <- c(0, 0.5, 2, 5, 10, 20, 40, 100, 500, 2000, 10000)
pal <- colorBin( "Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
} else if (input$palleInp == "BC") {
risk.bins <- c(-100, -40, -20, 0, 5, 10, 20, 40, 100, 500, 2000)
pal <- colorBin("Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
} else if (input$palleInp == "PM2.5") {
risk.bins <- c(0, 10, 25, 50, 100, 500, 2000, 5000, 10000)
pal <- colorBin("Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
} else if (input$palleInp == "Particle_conc") {
risk.bins <- c(0, 5000, 10000, 20000, 40000, 70000, 100000, 150000,
200000, 500000)
pal <- colorBin("Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
}else if (input$palleInp == "RH") {
pal <- colorNumeric("RdYlGn", domain = data$RH, na.color = "#808080",
reverse = TRUE)
}else if (input$palleInp == "CO2") {
pal <- colorNumeric("RdYlGn", domain = data$CO2, na.color = "#808080",
reverse = TRUE)
}else if (input$palleInp == "Latitude") {
pal <- colorNumeric("RdYlGn", domain = data$Latitude, na.color = "#808080",
reverse = TRUE)
}else {
risk.bins <- c(0, 10, 25, 50, 100, 500, 2000, 5000, 10000)
pal <- colorBin("Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
}
leaflet(data) %>%
addProviderTiles(providers$Stamen.TonerLite) %>%
addCircles(data = data,
lng = ~Longitude,
lat = ~Latitude,
popup = paste("Date:", data$date, "<br>",
"AE51_BC (ug/m3):",
round(as.numeric(data$BC), digits = 2), "<br>",
"DT8530_PM2.5 (ug/m3):",
round(as.numeric(data$PM2.5), digits = 2), "<br>",
"RH(%):",
round(as.numeric(data$RH), digits = 2), "<br>",
"CPC3007_Particle Concentration (#/cm3):",
round(as.numeric(data$Particle_conc), digits = 2),
"<br>",
"CO2:", round(as.numeric(data$CO2),digits = 2)),
weight = 3, radius = 8,
col = ~pal(data[[input$palleInp]]), stroke = TRUE,
fillOpacity = 0.8) %>%
leaflet::addLegend("bottomright", pal = pal,
values = ~data[[input$palleInp]],
title = paste(input$palleInp))
})
}
## Run app
shinyApp(ui, server)
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library(shiny)
library(Cairo)
library(DT)
library(plotly)
library(data.table)
library(stringr)
library(dplyr)
library(ggplot2)
library(leaflet)
library(XML)
library(htmltools)
library(shinyjs)
library(lubridate)
library(zoo)
library(caTools)
library(xts)
ui <- fluidPage(
h1("Explore Mobile Monitoring Air Pollution Data"),
tags$head(
tags$style(HTML(".sidebar {
height : 10vh; overflow-y : auto; font-size : 14px;
}" ,
".shiny-output-error-validation {
color : red; font-size : 14px;
}"
))),
sidebarLayout(position = "left",
sidebarPanel(width = 3,
conditionalPanel(condition = "input.tabs1 == 6",
tags$hr(),
h4("Alarms! Check for any malfunction."),
tags$hr()),
conditionalPanel(condition = "input.tabs1 == 4",
tags$hr(),
selectInput("palleInp", "Map this pollutant",
"Select"),
tags$hr()),
conditionalPanel(condition = "input.tabs1 == 3",
tags$hr(),
h4("Summary Statistics"),
tags$hr()),
conditionalPanel(condition = "input.tabs1 == 2",
tags$hr(),
h4("Time series plots"),
tags$hr()
),
conditionalPanel(condition = "input.tabs1 == 1",
tags$hr(),
helpText("Choose mobile monitoring files."),
tags$hr(),
useShinyjs(),
test <- a("Input Timezone* (link to supported
timezones)",
href = "https://en.wikipedia.org/wiki/List_of_tz_database_time_zones",
style = "font-size:14px; ",
target = "_blank"),
selectInput("timezone",
label = "",
choices = OlsonNames(),
selected = "Asia/Kolkata"),
tags$hr(),
fileInput("file1",
"GPSMAP 64s - location files",
multiple = TRUE,
accept = c('.gpx')),
tags$hr(),
fileInput("file2", "AE51 - BC files",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,
text/plain",
".csv")),
tags$hr(),
fileInput("file3", "DT8530 - PM2.5 files",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,
text/plain",
".csv")),
h5("Reference Correction for DT8530 - PM2.5"),
numericInput("Slope",
"Slope",
value = 1.0),
numericInput("Intercept",
"Intercept",
value = 0),
tags$hr(),
fileInput("file5", "RH files",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,
text/plain",
".csv")),
tags$hr(),
fileInput("file4", "CPC3007 -
Particle Concentration files",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,
text/plain",
".csv")),
numericInput("DF",
"Dilution factor for
CPC (1 for no diluter)",
value = 1),
tags$hr(),
fileInput("file6", "LI-COR - CO2 files",
multiple = TRUE,
accept = c("text/csv",
".txt",
"text/comma-separated-values,
text/plain",
".csv")),
helpText("*mandatory"),
tags$hr(),
actionButton("join_button", "JOIN"),
tags$hr(),
downloadButton('download',"Download as csv"),
tags$hr())),
mainPanel(
tags$a(img(src = 'logo.png', align = "right", height = 70,
width = 120),
href = "https://www.ilklabs.com/", target = "_blank"),
tags$head(
tags$style(type = 'text/css',
".nav-tabs {
font-size: 18px
} ")),
tabsetPanel(id = "tabs1",
tabPanel(value = 1,
title = "Joined File",
dataTableOutput("table1")),
tabPanel(
value = 3,
title = "Summary",
dataTableOutput("table")
),
tabPanel(
value = 2,
title = "Plots",
plotlyOutput("plot5", width = 800),
plotlyOutput("plot6", width = 800),
plotlyOutput("plot2", width = 800),
plotlyOutput("plot", width = 800),
plotlyOutput("plot4", width = 800),
plotlyOutput("plot3", width = 800),
plotlyOutput("plot7", width = 800)
),
tabPanel(
value = 4,
title = "Map",
leafletOutput("map", width = "100%",
height = 800)
),
tabPanel(value = 6,
title = "Alarms and Settings",
h5("AE51 Status: "),
dataTableOutput("table5"),
dataTableOutput("table2"),
h5("DT8530 Notes/Alarms: "),
dataTableOutput("table4"),
h5("CPC3007 Notes/Alarms: "),
dataTableOutput("table3")
)))
))
server <- function(input, output, session) {
options(shiny.maxRequestSize = 30*1024^2, shiny.launch.browser = TRUE)
## date matching
CPC_f_date <- reactive({
inFile <- input$file4
if (is.null(input$file4)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = FALSE, sep = ",",
row.names = NULL, skip = 4)
JSON_csv_date <- as.character(JSON_csv[1, 2])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%m/%d/%y")
if (is.na(JSON_csv_date)) {
JSON_csv <- read.delim(y, header = FALSE, sep = ",",
row.names = NULL, skip = 4)
JSON_csv_date <- as.character(JSON_csv[1, 2])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%m-%d-%Y")
}
as.Date(JSON_csv_date)
}
)}
CPC_f2_date <- df_list[[1]]
return(CPC_f2_date)
})
GPS_f_date <- reactive({
trial <- data.frame()
inFile <- input$file1
if (is.null(inFile)) {
return(NULL)
}
req(input$file1)
files3 <- lapply(inFile$datapath, function(y) {
pfile2 <- htmlTreeParse(y, error = function (...) {}, useInternalNodes = T)
elevations <- as.numeric(xpathSApply(pfile2, path = "//trkpt/ele",
xmlValue))
times <- xpathSApply(pfile2, path = "//trkpt/time", xmlValue)
coords <- xpathSApply(pfile2, path = "//trkpt", xmlAttrs)
lats <- as.numeric(coords["lat", ])
lons <- as.numeric(coords["lon", ])
geodf2 <- data.frame(latitude = lats, longitude = lons,
ele = elevations, time = times)
})
trial <- do.call(rbind, files3)
GPS_f <- trial
GPS_f <- mutate(GPS_f, date = with_tz(ymd_hms(time), input$timezone))
GPS_f <- GPS_f[!duplicated(as.POSIXct(GPS_f$date)), ]
GPS_f <- GPS_f %>%
dplyr::select(date, latitude, longitude)
names(GPS_f) <- c("date", "latitude", "longitude")
GPS_f_date <- as.Date(GPS_f[1, 1])
GPS_f_date <- as.Date(GPS_f_date, format="%Y-%m-%d") #"%m/%d/%Y"
return(GPS_f_date)
})
BC_f_date <- reactive({
inFile <- input$file2
if (is.null(input$file2)) {
return(NULL)
}else {
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv_header <- read.delim(y, header = FALSE, sep = ",",
skip = 15, row.names = NULL,
stringsAsFactors = FALSE)
JSON_csv_header <- JSON_csv_header[1, ]
JSON_csv <- read.delim(y, skip = 17, header = FALSE, sep = ",")
colnames(JSON_csv) <- unlist(JSON_csv_header)
JSON_csv <- completeFun(JSON_csv, c("BC"))
JSON_csv$date1 <- with(JSON_csv,
as.POSIXct(paste(
as.Date(Date, format = "%Y/%m/%d"), Time),
tz = input$timezone))
if (is.null(JSON_csv$date1)) {
JSON_csv$date1 <- with(JSON_csv,
as.POSIXct(paste(
as.Date(Date, format = "%d-%m-%Y"), Time),
tz = input$timezone))
}
JSON_csv
})
data <- do.call(rbind, df_list)
BC_f <- data
BC_f_date1 <- as.character(BC_f[1, 1])
BC_f_date <- as.Date(BC_f_date1, format = "%d-%m-%Y")
if (is.na(BC_f_date)) {
BC_f_date <- as.Date(BC_f_date1, format = "%Y/%m/%d") #"%d-%m-%Y"; "%Y/%m/%d"
}
return(BC_f_date)
}
})
DT_f_date <- reactive({
inFile <- input$file3
if (is.null(input$file3)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = TRUE, sep = ",",
row.names = NULL, skip = 29)
JSON_csv_date <- as.character(JSON_csv[1, 1])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%d-%m-%Y")
if (is.na(JSON_csv_date)) {
JSON_csv <- read.delim(y, header = TRUE, sep = ",",
row.names = NULL, skip = 29)
JSON_csv_date <- as.character(JSON_csv[1, 1])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%m/%d/%Y")
}
as.Date(JSON_csv_date)
})
DT_f_date <- df_list[[1]]
return(DT_f_date)
}
})
CO2_f_date <- reactive({
inFile <- input$file6
if (is.null(input$file6)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = TRUE, sep = ";",
row.names = NULL, skip = 2)
JSON_csv_date <- as.character(JSON_csv[1, 1])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%d-%m-%Y")
if (is.na(JSON_csv_date)) {
JSON_csv <- read.delim(y, header = TRUE, sep = ";",
row.names = NULL, skip = 29)
JSON_csv_date <- as.character(JSON_csv[1, 1])
JSON_csv_date <- as.Date(JSON_csv_date, format = "%Y-%m-%d")
}
as.Date(JSON_csv_date)
})
CO2_f_date <- df_list[[1]]
return(CO2_f_date)
}
})
## GPS and pollutant file joining
CO2_f <- reactive({
inFile <- input$file6
if (is.null(input$file6)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, skip = 1, sep = ",",
header = TRUE, row.names = NULL,
stringsAsFactors = FALSE)
JSON_csv <- JSON_csv[ ,1:3]
names(JSON_csv) <- c("Date", "Time", "CO2")
JSON_csv
})
data <- do.call(rbind, df_list)
CO2_f <- data
CO2_f$date <- with(CO2_f,
as.POSIXct(paste(
as.Date(Date, format = "%d-%m-%Y"), Time),
tz = input$timezone)) #"%Y-%m-%d""%d-%m-%Y"
CO2_f <- CO2_f %>%
dplyr::select(date, CO2)
names(CO2_f) <- c("date", "CO2")
attributes(CO2_f$date)$tzone <- input$timezone
CO2_f <- data.table(CO2_f)
setkey(CO2_f, date)
return(CO2_f)
}
})
GPS_f <- reactive({
trial <- data.frame()
inFile <- input$file1
if (is.null(inFile)) {
return(NULL)
}
req(input$file1)
files3 <- lapply(inFile$datapath, function(y) {
pfile2 <- htmlTreeParse(y, error = function (...) {}, useInternalNodes = T)
elevations <- as.numeric(xpathSApply(pfile2, path = "//trkpt/ele",
xmlValue))
times <- xpathSApply(pfile2, path = "//trkpt/time", xmlValue)
coords <- xpathSApply(pfile2, path = "//trkpt", xmlAttrs)
lats <- as.numeric(coords["lat", ])
lons <- as.numeric(coords["lon", ])
geodf2 <- data.frame(latitude = lats, longitude = lons,
ele = elevations, time = times)
})
trial <- do.call(rbind, files3)
GPS_f <- trial
GPS_f <- mutate(GPS_f, date = with_tz(ymd_hms(time), input$timezone))
GPS_f <- GPS_f[!duplicated(as.POSIXct(GPS_f$date)), ]
GPS_f <- GPS_f %>%
dplyr::select(date, latitude, longitude)
names(GPS_f) <- c("date", "latitude", "longitude")
GPS_f <- data.table(GPS_f)
setkey(GPS_f, date)
return(GPS_f)
})
BC_f <- reactive({
inFile <- input$file2
if (is.null(input$file2)) {
return(NULL)
}else {
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
df_list <- lapply(inFile$datapath, function(y){
JSON_csv_header <- read.delim(y, header = FALSE, sep = ",", skip = 15,
row.names = NULL,
stringsAsFactors = FALSE)
JSON_csv_header <- JSON_csv_header[1, ]
JSON_csv <- read.delim(y, skip = 17, header = FALSE, sep = ",")
colnames(JSON_csv) <- unlist(JSON_csv_header)
JSON_csv <- completeFun(JSON_csv, c("BC"))
JSON_csv$date1 <- with(JSON_csv,
as.POSIXct(paste(
as.Date(
Date, format = "%Y/%m/%d"), Time),
tz = input$timezone))
if (is.null(JSON_csv$date1)) {
JSON_csv$date1 <- with(JSON_csv,
as.POSIXct(paste(
as.Date(Date, format = "%d-%m-%Y"), Time),
tz = input$timezone))
}
JSON_csv
})
data <- do.call(rbind, df_list)
BC_f <- data
BC_f$Date <- BC_f$date1 #"%Y/%m/%d", "%d-%m-%Y"
ef_file <- data.frame(BC_f)
ef_file <- ef_file[ef_file$Status == 0, ]
ef_file <- dplyr::select(ef_file, Date, ATN, BC)
ATN <- ef_file[1, 2]
# ef_file$ATN <- ef_file$ATN-(ATN) # for new filter
names(ef_file) <- c("Date", "ATN", "BC")
ef_file$BC1 <- (ef_file$BC / 1000)
BC_Final <- ef_file
ef_file$LD <- ef_file$BC1 - rollapply(ef_file$BC1, FUN = mean,
width = 30, align = "center",
partial = TRUE)
ef_file$LD25 <- runquantile(ef_file$LD, 300, 0.25, type = 2,
endrule = c("NA"))
ef_file$LD75 <- runquantile(ef_file$LD, 300, 0.75, type = 2,
endrule = c("NA"))
ef_file$BC2 <- ef_file$BC1
ef_file$BC3 <- ef_file$BC1
ef_file$BC2[ef_file$BC2 >= 0] <- 0
ef_file$BC2[ef_file$BC2 < 0] <- 1
ef_file$BC2 <- rollapply(ef_file$BC2, FUN = mean, width = 5,
align = "center", partial = TRUE)
Lower_LD25 <- ((ef_file$LD) > 5 * (ef_file$LD75))
Higher_LD75 <- ((ef_file$LD) < 5 * (ef_file$LD25))
ef_file$cev1 <- ifelse(Lower_LD25|Higher_LD75, ef_file$BC1, NA)
CEV <- data.frame(ef_file$Date, ef_file$cev1)
CEV$ef_file.cev1[!is.na(CEV$ef_file.cev1)] <- 1
CEV <- data.frame(CEV)
date_file <- data.frame(ef_file$Date, ef_file$BC2, ef_file$BC3)
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
CEV <- completeFun(CEV, c("ef_file.cev1"))
setDT(CEV)
setDT(date_file)
cev_file <- date_file[CEV, on = c('ef_file.Date')]
cev_file <- cev_file[!(cev_file$ef_file.BC2 == 0), ]
cev_file <- xts(cev_file, order.by = cev_file$ef_file.Date)
ef_file <- data.frame(ef_file)
CE <- data.frame(index(CEV))
i <- index(cev_file)
i_old <- index(cev_file)
i <- index(cev_file) + 1
j <- index(cev_file) + 2
k <- index(cev_file) - 2
l <- index(cev_file) - 1
i <- cbind(as.character(i), as.character(i_old), as.character(j),
as.character(k), as.character(l))
Date_cev <- data.frame(i)
remove_cev <- data.frame(Date = unlist(Date_cev, use.names = FALSE))
Date_Table <- unique(remove_cev[c("Date")])
e <- nrow(Date_Table)
if (e == 0) {
BC <- ef_file
BC$BC_Factor <- 1
}else {
Date_Table$BC_Factor <- 0
Date_Table$Date <- as.POSIXct(Date_Table$Date)
setDT(Date_Table)
setDT(ef_file)
BC <- Date_Table[ef_file, on = c('Date')]
BC$BC_Factor[is.na(BC$BC_Factor)] <- 1
}
BC$BC_Fi <- BC$BC_Factor * BC$BC1
BC$BC_Fi[BC$BC_Fi == 0] <- NA
BC$Tr <- exp(- BC$ATN / 100)
BC$CF <- 1 / (0.88 * BC$Tr + 0.12)
BC$BC_Final <- BC$BC_Fi * BC$CF
BC$BC_Fi[BC$BC_Fi < 0] <- NA
BC$BC_Fi[is.na(BC$BC_Fi)] <- " "
BC$BC_Final[BC$BC_Final < 0] <- NA
BC$BC_Final[is.na(BC$BC_Final)] <- " "
BC_Final <- dplyr::select(BC, Date, BC3, BC_Fi, BC_Final)
names(BC_Final) <- c("date", "BC", "BC_NR", "BC_NR_LC")
BC_Final$BC_NR_LC <- as.numeric(as.character(BC_Final$BC_NR_LC))
BC_Final$BC_NR <- as.numeric(as.character(BC_Final$BC_NR))
BC_Final$BC <- as.numeric(as.character(BC_Final$BC))
BC_Final <- data.table(BC_Final)
attributes(BC_Final$date)$tzone <- input$timezone
setkey(BC_Final, date)
return(BC_Final)
}
})
DT_f <- reactive({
inFile <- input$file3
if (is.null(input$file3)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = TRUE, sep = ",", row.names = NULL,
skip = 28)
names(JSON_csv) <- c("Date", "Time", "PM2.5")
JSON_csv
})
data <- do.call(rbind, df_list)
DT_f <- data
Date1 <- DT_f[1, 1]
Date1 <- as.Date(Date1, format = "%d-%m-%Y" )
if (is.na(Date1)) {
Date1 <- DT_f[1, 1]
Date1 <- as.Date(Date1, format = "%m/%d/%Y" )
}
DT_f$date <- strptime(paste(Date1, DT_f$Time),
format = "%Y-%m-%d %H:%M:%S")
DT_f$date <- as.POSIXct(DT_f$date, format = '%Y-%m-%d %H:%M:%S',
tz = input$timezone)
attributes(DT_f$date)$tzone <- input$timezone
DT_f$PM2.5 <- as.numeric(as.character(DT_f$PM2.5))
DT_f$PM2.5 <- DT_f$PM2.5 * 1000
DT_f <- dplyr::select(DT_f, date, PM2.5 )
DT_f <- data.table(DT_f)
setkey(DT_f, date)
return(DT_f)
}
})
## file name matching
file_name_CPC <- reactive({
inFile <- input$file4
if (is.null(inFile)) {
return(NULL)
}else {
z <- sub(".csv$", "", basename(input$file4$name))
name_CPC <- substr(z, 1, 10)
return(name_CPC)
}
})
CPC_f <- reactive({
DF <- input$DF
inFile <- input$file4
name_CPC <- file_name_CPC()
if (is.null(input$file4)) {
return(NULL)
}else {
df_list <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = TRUE, sep = ",", row.names = NULL,
skip = 17,
stringsAsFactors = FALSE, fileEncoding = "latin1")
JSON_csv
})
data <- do.call(rbind, df_list)
CPC_f <- data
u <- stringr::str_extract(name_CPC, "[0-9]{4}\\_[0-9]{2}\\_[0-9]{2}")
w <- str_replace_all(u, "_", "-")
CPC_f <- CPC_f[, 1:2]
names(CPC_f) <- c("Time", "Particle_conc")
CPC_f$Particle_conc <- CPC_f$Particle_conc * DF
CPC_f <- mutate(CPC_f, date = ymd_hms(paste(w, Time),
tz = input$timezone))
CPC_f <- dplyr::select(CPC_f, date, Particle_conc )
CPC_f <- data.table(CPC_f)
setkey(CPC_f, date)
return(CPC_f)
}
})
RH_f<- reactive({
if (is.null(input$file5)) {
return(NULL)
}else {
RH_f <- data.frame(read.delim(input$file5$datapath, header = TRUE,
sep = ",", skip = 6, row.names = NULL))
RH_f_Date <- RH_f[ ,2]
RH_f_Time <- RH_f[ ,3]
RH <- RH_f[ , grepl( "RH", names(RH_f ) ) ]
RH_f <- data.frame(RH_f_Date, RH_f_Time, RH)
names(RH_f) <- c("LogDate", "LogTime", "RH")
RH_f$LogTime <- gsub(".", ":", RH_f$LogTime, fixed = TRUE)
RH_f$LogTime <- gsub("AM", "", RH_f$LogTime, fixed = TRUE)
RH_f$LogTime <- gsub("PM", "", RH_f$LogTime, fixed = TRUE)
RH_f$date <- as.POSIXct(paste(RH_f$LogDate, RH_f$LogTime),
format = '%d-%m-%Y %H:%M:%S', tz = input$timezone)
RH_f <- RH_f%>%
dplyr::select(date, RH)
names(RH_f) <- c("date", "RH")
RH_f$RH <- as.numeric(as.character(RH_f$RH))
RH_f$date <- as.POSIXct(RH_f$date, format = '%Y-%m-%d %H:%M:%S',
tz = input$timezone)
RH_f <- data.table(RH_f)
setkey(RH_f, date)
return(RH_f)
}
})
file_name_CO2 <- reactive({
inFile <- input$file6
if (is.null(inFile)) {
return(NULL)
}else {
y <- sub(".csv$", "", basename(input$file6$name))
name_CO2 <- substr(y, 1, 10)
return(name_CO2)
}
})
file_name_RH <- reactive({
inFile <- input$file5
if (is.null(inFile)) {
return(NULL)
}else {
x <- sub(".csv$", "", basename(input$file5$name))
name_RH <- substr(x, 1, 10)
return(name_RH)
}
})
file_name_GPS <- reactive({
inFile <- input$file1
if (is.null(inFile)) {
return(NULL)
}else {
x <- sub(".csv$", "", basename(input$file1$name))
name_GPS <- substr(x, 1, 10)
return(name_GPS)
}
})
file_name_BC <- reactive({
inFile <- input$file2
if (is.null(inFile)) {
return(NULL)
}else {
y <- sub(".csv$", "", basename(input$file2$name))
name_BC <- substr(y, 1, 10)
return(name_BC)
}
})
file_name_DT <- reactive({
inFile <- input$file3
if (is.null(inFile)) {
return(NULL)
}else {
w <- sub(".csv$", "", basename(input$file3$name))
name_DT <- substr(w, 1, 10)
return(name_DT)
}
})
## preloaded table
data_blank <- reactive({
if (is.null(input$file1) & is.null(input$file2) & is.null(input$file3) &
is.null(input$file4) & is.null(input$file5) & is.null(input$file6)) {
pfile2 <- htmlTreeParse("data/GPSMAP64s/2019_09_25_h091000_KAN_Garmin_3.gpx",
error = function (...) {}, useInternalNodes = T)
elevations <- as.numeric(xpathSApply(pfile2, path = "//trkpt/ele",
xmlValue))
times <- xpathSApply(pfile2, path = "//trkpt/time", xmlValue)
coords <- xpathSApply(pfile2, path = "//trkpt", xmlAttrs)
lats <- as.numeric(coords["lat", ])
lons <- as.numeric(coords["lon", ])
GPS_f <- data.frame(latitude = lats, longitude = lons, ele = elevations,
time = times)
GPS_f <- mutate(GPS_f, date = with_tz(ymd_hms(time), "Asia/Kolkata"))
GPS_f <- GPS_f[!duplicated(as.POSIXct(GPS_f$date)), ]
GPS_f <- GPS_f %>%
dplyr::select(date, latitude, longitude)
names(GPS_f) <- c("date", "latitude", "longitude")
GPS_f <- data.table(GPS_f)
setkey(GPS_f, date)
CO2_f <- read.delim("data/LI-COR/2019_09_25_h091000_KAN_LI_COR.csv",
skip = 1, sep = ",", header = TRUE, row.names = NULL,
stringsAsFactors = FALSE)
CO2_f <- CO2_f[ ,1:3]
names(CO2_f) <- c("Date", "Time", "CO2")
CO2_f$date <- with(CO2_f,
as.POSIXct(paste(as.Date(Date, format = "%d-%m-%Y"),
Time), tz = "Asia/Kolkata"))
CO2_f <- CO2_f %>%
dplyr::select(date, CO2)
names(CO2_f) <- c("date", "CO2")
attributes(CO2_f$date)$tzone <- "Asia/Kolkata"
CO2_f <- data.table(CO2_f)
setkey(CO2_f, date)
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
BC_f_header <- read.delim("data/AE51/2019_09_25_h091000_KAN_AE51.csv",
header = FALSE,
sep = ",", skip = 15, row.names = NULL,
stringsAsFactors = FALSE)
BC_f_header <- BC_f_header[1, ]
BC_f <- read.delim("data/AE51/2019_09_25_h091000_KAN_AE51.csv", skip = 17,
header = FALSE, sep = ",")
colnames(BC_f) <- unlist(BC_f_header)
BC_f <-completeFun(BC_f, c("BC"))
BC_f$date1 <- with(BC_f,
as.POSIXct(paste(as.Date(Date, format="%Y/%m/%d"),
Time), tz="Asia/Kolkata"))
BC_f$Date <- BC_f$date1
ef_file <- data.frame(BC_f)
ef_file <- ef_file[ef_file$Status == 0, ]
ef_file <- dplyr::select(ef_file, Date, ATN, BC)
ATN <- ef_file[1, 2]
names(ef_file) <- c("Date", "ATN", "BC")
ef_file$BC1 <- (ef_file$BC / 1000)
BC_Final <- ef_file
ef_file$LD <- ef_file$BC1 - rollapply(ef_file$BC1, FUN = mean,
width = 30, align = "center",
partial = TRUE)
ef_file$LD25 <- runquantile(ef_file$LD, 300, 0.25, type = 2,
endrule = c("NA"))
ef_file$LD75 <- runquantile(ef_file$LD, 300, 0.75, type = 2,
endrule = c("NA"))
ef_file$BC2 <- ef_file$BC1
ef_file$BC3 <- ef_file$BC1
ef_file$BC2[ef_file$BC2 >= 0] <- 0
ef_file$BC2[ef_file$BC2 < 0] <- 1
ef_file$BC2 <- rollapply(ef_file$BC2, FUN = mean, width = 5,
align = "center",
partial = TRUE)
Lower_LD25 <- ((ef_file$LD) > 5 * (ef_file$LD75))
Higher_LD75 <- ((ef_file$LD) < 5 * (ef_file$LD25))
ef_file$cev1 <- ifelse(Lower_LD25|Higher_LD75, ef_file$BC1, NA)
CEV <- data.frame(ef_file$Date, ef_file$cev1)
CEV$ef_file.cev1[!is.na(CEV$ef_file.cev1)] <- 1
CEV <- data.frame(CEV)
date_file <- data.frame(ef_file$Date, ef_file$BC2, ef_file$BC3)
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
CEV <- completeFun(CEV, c("ef_file.cev1"))
setDT(CEV)
setDT(date_file)
cev_file <- date_file[CEV, on = c('ef_file.Date')]
cev_file <- cev_file[!(cev_file$ef_file.BC2 == 0),]
cev_file <- xts(cev_file, order.by = cev_file$ef_file.Date)
ef_file <- data.frame(ef_file)
CE <- data.frame(index(CEV))
i <- index(cev_file)
i_old <- index(cev_file)
i <- index(cev_file) + 1
j <- index(cev_file) + 2
k <- index(cev_file) - 2
l <- index(cev_file) - 1
i <- cbind(as.character(i), as.character(i_old), as.character(j),
as.character(k), as.character(l))
Date_cev <- data.frame(i)
remove_cev <- data.frame(Date = unlist(Date_cev, use.names = FALSE))
Date_Table <- unique(remove_cev[c("Date")])
e <- nrow(Date_Table)
if (e == 0) {
BC <- ef_file
BC$BC_Factor <- 1
}else {
Date_Table$BC_Factor <- 0
Date_Table$Date <- as.POSIXct(Date_Table$Date)
setDT(Date_Table)
setDT(ef_file)
BC <- Date_Table[ef_file, on = c('Date')]
BC$BC_Factor[is.na(BC$BC_Factor)] <- 1
}
BC$BC_Fi <- BC$BC_Factor * BC$BC1
BC$BC_Fi[BC$BC_Fi == 0] <- NA
BC$Tr <- exp(- BC$ATN / 100)
BC$CF <- 1 / (0.88 * BC$Tr + 0.12)
BC$BC_Final <- BC$BC_Fi * BC$CF
BC$BC_Fi[BC$BC_Fi < 0] <- NA
BC$BC_Fi[is.na(BC$BC_Fi)] <- " "
BC$BC_Final[BC$BC_Final < 0] <- NA
BC$BC_Final[is.na(BC$BC_Final)] <- " "
BC_Final <- dplyr::select(BC, Date, BC3, BC_Fi, BC_Final)
names(BC_Final) <- c("date", "BC", "BC_NR", "BC_NR_LC")
BC_Final$BC_NR_LC <- as.numeric(as.character(BC_Final$BC_NR_LC))
BC_Final$BC_NR <- as.numeric(as.character(BC_Final$BC_NR))
BC_Final$BC <- as.numeric(as.character(BC_Final$BC))
BC_Final <- data.table(BC_Final)
attributes(BC_Final$date)$tzone <- "Asia/Kolkata"
setkey(BC_Final, date)
DT_f <- read.delim("data/DT8530/2019_09_25_h091000_KAN_DT8530.csv",
header = TRUE,
sep = ",", row.names = NULL, skip = 28)
names(DT_f) <- c("Date", "Time", "PM2.5")
Date1 <- DT_f[1, 1]
Date1 <- as.Date(Date1, format = "%d-%m-%Y" )
if (is.na(Date1)) {
Date1 <- DT_f[1, 1]
Date1 <- as.Date(Date1, format = "%m/%d/%Y" )
}
DT_f$date <- strptime(paste(Date1, DT_f$Time),
format = "%Y-%m-%d %H:%M:%S")
DT_f$date <- as.POSIXct(DT_f$date, format = '%Y-%m-%d %H:%M:%S',
tz = "Asia/Kolkata")
attributes(DT_f$date )$tzone <- "Asia/Kolkata"
DT_f$PM2.5 <- as.numeric(as.character(DT_f$PM2.5))
DT_f$PM2.5 <- DT_f$PM2.5 * 1000
DT_f <- dplyr::select(DT_f, date, PM2.5)
DT_f <- data.table(DT_f)
setkey(DT_f, date)
CPC_f <- read.delim("data/CPC3007/2019_09_25_h091000_KAN_CPC3007.csv",
header = TRUE,
sep = ",", row.names = NULL, skip = 17,
stringsAsFactors = FALSE,
fileEncoding = "latin1")
CPC_f <- CPC_f[ ,1:2]
names(CPC_f) <- c("Time", "Particle_conc")
CPC_f$Particle_conc <- CPC_f$Particle_conc * 5.5
w <- as.Date("2019-09-25")
CPC_f <- mutate(CPC_f, date = ymd_hms(paste(w, Time),
tz = "Asia/Kolkata"))
CPC_f <- dplyr::select(CPC_f,date, Particle_conc )
CPC_f <- data.table(CPC_f)
setkey(CPC_f, date)
RH_f <- data.frame(read.delim("data/RH/2019_09_25_h091000_KAN_RH.csv",
header = TRUE, sep = ",", skip = 6,
row.names = NULL))
RH_f_Date <- RH_f[ ,2]
RH_f_Time <- RH_f[ ,3]
RH <- RH_f[ , grepl("RH" , names(RH_f) ) ]
RH_f <- data.frame(RH_f_Date, RH_f_Time, RH)
names(RH_f) <- c("LogDate", "LogTime", "RH")
RH_f$LogTime <- gsub(".", ":", RH_f$LogTime, fixed = TRUE)
RH_f$LogTime <- gsub("AM", "", RH_f$LogTime, fixed = TRUE)
RH_f$LogTime <- gsub("PM", "", RH_f$LogTime, fixed = TRUE)
RH_f$date <- as.POSIXct(paste(RH_f$LogDate, RH_f$LogTime),
format = '%d-%m-%Y %H:%M:%S', tz = "Asia/Kolkata")
RH_f <- RH_f%>%
dplyr::select(date, RH)#X2.P..RH, X1.P..RH
names(RH_f) <- c("date", "RH")
RH_f$RH <- as.numeric(as.character(RH_f$RH))
RH_f$date <- as.POSIXct(RH_f$date, format = '%Y-%m-%d %H:%M:%S',
tz = "Asia/Kolkata")
RH_f <- data.table(RH_f)
setkey(RH_f, date)
VecFunc <- function(x) {
if (x > 0.6) {
return (1 + (0.25 * (((x) * (x)) / (1 - x))))
} else
{
return (1)
}
}
joined_GPS_CO2 <- left_join(GPS_f, CO2_f, by = "date")
joined_GPS_CO2 <- dplyr::select(joined_GPS_CO2, date, CO2, latitude,
longitude )
joined_GPS_CO2 <- data.table(joined_GPS_CO2)
setkey(joined_GPS_CO2, date)
joined_GPS_DT <- left_join(GPS_f, DT_f, by = "date")
joined_GPS_DT$PM2.5_RHC <- NA
joined_GPS_DT <- dplyr::select(joined_GPS_DT, date, PM2.5, PM2.5_RHC,
latitude, longitude )
joined_GPS_DT <- data.table(joined_GPS_DT)
setkey(joined_GPS_DT, date)
DT_f <- RH_f[joined_GPS_DT, roll = "nearest"]
DT_f$RH <- DT_f$RH / 100
DT_f <- dplyr::select(DT_f, date, latitude, longitude, PM2.5, RH )
names(DT_f) <- c("date", "latitude", "longitude", "PM2.5", "RH")
DT_f$CF <- sapply(DT_f$RH, FUN = VecFunc)
DT_f$PM2.5_RHC <- DT_f$PM2.5 / DT_f$CF
DT_f$PM2.5_Ref <- (DT_f$PM2.5 * 1 ) + 0
DT_f$PM2.5_RHC_Ref <- (DT_f$PM2.5_RHC * 1) + 0
DT_f$date <- as.POSIXct(DT_f$date, format = '%Y-%m-%d %H:%M:%S')
attributes(DT_f$date )$tzone <- "Asia/Kolkata"
DT_f <- data.table(DT_f)
setkey(DT_f, date)
joined_GPS_BC <- left_join(GPS_f, BC_Final, by = "date")
joined_GPS_BC <- dplyr::select(joined_GPS_BC, date, BC, BC_NR, BC_NR_LC,
latitude, longitude )
joined_GPS_BC <- data.table(joined_GPS_BC)
setkey(joined_GPS_BC, date)
joined_GPS_CPC <- left_join(GPS_f, CPC_f, by = "date")
joined_GPS_CPC <- dplyr::select(joined_GPS_CPC, date, Particle_conc,
latitude, longitude )
joined_GPS_CPC <- data.table(joined_GPS_CPC)
setkey(joined_GPS_CPC, date)
joined2 <- left_join(joined_GPS_CPC, joined_GPS_CO2, by = "date")
setDT(joined2)
setkey(joined2, date)
joined_1 <- left_join(DT_f, joined2, by = "date")
setDT(joined_1)
setkey(joined_1, date)
joined <- joined_1[joined_GPS_BC, roll = "nearest"]
joined <- joined %>%
dplyr::select(date, latitude, longitude, BC, BC_NR, BC_NR_LC, PM2.5,
PM2.5_RHC, PM2.5_RHC_Ref, PM2.5_Ref, RH, Particle_conc,
CO2)
names(joined) <- c("date", "Latitude", "Longitude", "BC", "BC_NR",
"BC_NR_LC", "PM2.5", "PM2.5_RHC", "PM2.5_RHC_Ref",
"PM2.5_Ref", "RH", "Particle_conc", "CO2")
attributes(joined$date )$tzone <- "Asia/Kolkata"
joined <- joined[!duplicated(joined$date), ]
joined
}
})
joined_GPS_CO2 <- reactive({
name_GPS <- file_name_GPS()
name_CO2 <- file_name_CO2()
GPS_f <- GPS_f()
CO2_f <- CO2_f()
if (is.null(CO2_f)) {
joined_GPS_CO2 <- dplyr::select(GPS_f, date, latitude, longitude)
joined_GPS_CO2$CO2 <- NA
joined_GPS_CO2$CO2 <- as.numeric(as.character(joined_GPS_CO2$CO2))
}
if (!is.null(CO2_f)) {
validate(
need(try(file_name_GPS() == file_name_CO2()), "GPS File mandatory! File
names of GPS and LI-COR do not match, Please select the correct files")
)
validate(
need(try(GPS_f_date() == CO2_f_date()), "The files have different
date entries in GPS and LI-COR! Please check once again.")
)
joined_GPS_CO2 <- left_join(GPS_f, CO2_f, by = "date")
joined_GPS_CO2 <- dplyr::select(joined_GPS_CO2, date, CO2,
latitude, longitude )
}
joined_GPS_CO2 <- data.table(joined_GPS_CO2)
setkey(joined_GPS_CO2, date)
return(joined_GPS_CO2)
})
joined_GPS_DT <- reactive({
name_GPS <- file_name_GPS()
name_DT <- file_name_DT()
GPS_f <- GPS_f()
DT_f <- DT_f()
if (is.null(DT_f)) {
joined_GPS_DT <- dplyr::select(GPS_f, date, latitude, longitude)
joined_GPS_DT$PM2.5 <- NA
joined_GPS_DT$PM2.5_RHC <- NA
joined_GPS_DT$PM2.5 <- as.numeric(joined_GPS_DT$PM2.5)
}
if (!is.null(DT_f)) {
validate(
need(try(file_name_GPS() == file_name_DT()), "GPS File mandatory!
File names of GPS and DustTrak-8530 do not match,
Please select the correct files")
)
validate(
need(try(GPS_f_date() == DT_f_date()), "The files have different date
entries in GPS and DustTrak-8530! Please check once again.")
)
joined_GPS_DT <- left_join(GPS_f, DT_f, by = "date")
joined_GPS_DT$PM2.5_RHC <- NA
joined_GPS_DT <- dplyr::select(joined_GPS_DT, date, PM2.5, PM2.5_RHC,
latitude, longitude )
}
joined_GPS_DT <- data.table(joined_GPS_DT)
setkey(joined_GPS_DT, date)
return(joined_GPS_DT)
})
DT_RH <- reactive({
name_RH <- file_name_RH()
name_DT <- file_name_DT()
DT_fi <- DT_f()
RH_f <- RH_f()
joined_GPS_DT <- joined_GPS_DT()
Slope <- input$Slope
Intercept <- input$Intercept
VecFunc <- function(x) {
if (x > 0.6) {
return (1 + (0.25 * (((x) * (x)) / (1 - x))))
} else
{
return (1)
}
}
if (is.null(RH_f()) | is.null(DT_f())) {
DT_f <- joined_GPS_DT
DT_f$RH <- NA
DT_f$PM2.5_RHC <- NA
DT_f$PM2.5_Ref <- (DT_f$PM2.5 * Slope) + Intercept
DT_f$PM2.5_RHC_Ref <- NA
DT_f$RH <- as.numeric(DT_f$RH)
}
if (!is.null(RH_f()) & !is.null(DT_f())) {
validate(
need(try(file_name_DT() == file_name_RH()), "File names of DustTrak
8530 and RH do not match, Please select the correct files")
)
setkey(RH_f, date)
DT_f <- RH_f[joined_GPS_DT, roll = "nearest"]
DT_f$RH <- DT_f$RH / 100
DT_f <- dplyr::select(DT_f, date, latitude, longitude, PM2.5, RH )
names(DT_f) <- c("date", "latitude", "longitude", "PM2.5", "RH")
DT_f$CF <- sapply(DT_f$RH, FUN = VecFunc)
DT_f$PM2.5_RHC <- DT_f$PM2.5 / DT_f$CF
DT_f$PM2.5_Ref <- (DT_f$PM2.5 * Slope) + Intercept
DT_f$PM2.5_RHC_Ref <- (DT_f$PM2.5_RHC * Slope) + Intercept
}
DT_f$date <- as.POSIXct(DT_f$date, format = '%Y-%m-%d %H:%M:%S')
attributes(DT_f$date )$tzone <- input$timezone
DT_f <- data.table(DT_f)
setkey(DT_f, date)
return(DT_f)
})
joined_GPS_BC <- reactive({
name_GPS <- file_name_GPS()
name_BC <- file_name_BC()
GPS_f <- GPS_f()
BC_Final <- BC_f()
if (is.null(BC_f())) {
joined_GPS_BC <- dplyr::select(GPS_f, date, latitude, longitude)
joined_GPS_BC$BC <- NA
joined_GPS_BC$BC_NR <- NA
joined_GPS_BC$BC_NR_LC <- NA
joined_GPS_BC$BC <- as.numeric(as.character(joined_GPS_BC$BC))
joined_GPS_BC$BC_NR <- as.numeric(as.character(joined_GPS_BC$BC_NR))
joined_GPS_BC$BC_NR_LC <- as.numeric(as.character(joined_GPS_BC$BC_NR_LC))
}
if (!is.null(BC_f())) {
validate(
need(try(file_name_GPS() == file_name_BC()), "GPS File mandatory!
File names of GPS and AE51 do not match, Please select the correct
files")
)
validate(
need(try(GPS_f_date() == BC_f_date()), "The files have different
date entries in GPS and AE51! Please check once again.")
)
joined_GPS_BC <- left_join(GPS_f, BC_Final, by = "date")
joined_GPS_BC <- dplyr::select(joined_GPS_BC, date, BC, BC_NR, BC_NR_LC,
latitude, longitude )
}
joined_GPS_BC <- data.table(joined_GPS_BC)
setkey(joined_GPS_BC, date)
return(joined_GPS_BC)
})
joined_GPS_CPC <- reactive({
name_GPS <- file_name_GPS()
name_CPC <- file_name_CPC()
GPS_f <- GPS_f()
CPC_f <- CPC_f()
if (is.null(CPC_f)) {
joined_GPS_CPC <- dplyr::select(GPS_f, date, latitude, longitude)
joined_GPS_CPC$Particle_conc <- NA
joined_GPS_CPC$Particle_conc <- as.numeric(
as.character(joined_GPS_CPC$Particle_conc))
}
if (!is.null(CPC_f)) {
validate(
need(try(file_name_GPS() == file_name_CPC()), "GPS File mandatory!
File names of GPS and CPC-3007 do not match,
Please select the correct files")
)
validate(
need(try(GPS_f_date() == CPC_f_date()), "The files have different
date entries in GPS and CPC-3007! Please check once again.")
)
joined_GPS_CPC <- left_join(GPS_f, CPC_f, by = "date")
joined_GPS_CPC <- dplyr::select(joined_GPS_CPC, date, Particle_conc,
latitude, longitude )
}
joined_GPS_CPC <- data.table(joined_GPS_CPC)
setkey(joined_GPS_CPC, date)
return(joined_GPS_CPC)
})
observeEvent(input$timezone,{
toggleState(id = "join_button",
condition = (input$timezone != "" |
is.null(input$timezone) | is.na(input$timezone)))
})
data_joined <- eventReactive(input$join_button,{
joined_GPS_CPC <- joined_GPS_CPC()
joined_GPS_CO2 <- joined_GPS_CO2()
joined_GPS_BC <- joined_GPS_BC()
DT_RH <- DT_RH()
joined_GPS_DT <- joined_GPS_DT()
name_GPS <- file_name_GPS()
name_BC <- file_name_BC()
name_CPC <- file_name_CPC()
name_DT <- file_name_DT()
name_RH <- file_name_RH()
name_CO2 <- file_name_CO2()
GPS_f <- GPS_f()
CPC_f <- CPC_f()
BC_Final <- BC_f()
DT_f <- DT_f()
RH_f <- RH_f()
CO2_f <- CO2_f()
joined2 <- left_join(joined_GPS_CPC, joined_GPS_CO2, by = "date")
setDT(joined2)
setkey(joined2, date)
joined_1 <- left_join(DT_RH, joined2, by = "date")
setDT(joined_1)
setkey(joined_1, date)
joined <- joined_1[joined_GPS_BC, roll = "nearest"]
joined <- joined %>%
dplyr::select(date, latitude, longitude, BC, BC_NR, BC_NR_LC, PM2.5,
PM2.5_RHC, PM2.5_RHC_Ref, PM2.5_Ref, RH, Particle_conc, CO2)
names(joined) <- c("date", "Latitude", "Longitude", "BC", "BC_NR",
"BC_NR_LC", "PM2.5", "PM2.5_RHC", "PM2.5_RHC_Ref",
"PM2.5_Ref", "RH", "Particle_conc", "CO2")
attributes(joined$date )$tzone <- input$timezone
joined <- joined[!duplicated(joined$date), ]
return(joined)
})
## Final corrected, joined table
output$table1 <- DT::renderDataTable({
if (is.null(input$file1) & is.null(input$file2) & is.null(input$file3) &
is.null(input$file4) & is.null(input$file5) & is.null(input$file6)) {
data_joined <- data_blank()
}else{
data_joined <- data_joined()
}
data_joined$BC <- round(as.numeric(data_joined$BC),
digits = 2)
data_joined$BC_NR <- round(as.numeric(data_joined$BC_NR),
digits = 2)
data_joined$BC_NR_LC <- round(as.numeric(data_joined$BC_NR_LC),
digits = 2)
data_joined$PM2.5 <- round(as.numeric(data_joined$PM2.5),
digits = 2)
data_joined$PM2.5_RHC <- round(as.numeric(data_joined$PM2.5_RHC),
digits = 2)
data_joined$PM2.5_Ref <- round(as.numeric(data_joined$PM2.5_Ref),
digits = 2)
data_joined$PM2.5_RHC_Ref <- round(as.numeric(data_joined$PM2.5_RHC_Ref),
digits = 2)
data_joined$Longitude <- round(as.numeric(data_joined$Longitude),
digits = 4)
data_joined$Latitude <- round(as.numeric(data_joined$Latitude),
digits = 4)
data_joined$Particle_conc <- round(as.numeric(data_joined$Particle_conc),
digits = 2)
data_joined$CO2 <- round(as.numeric(data_joined$CO2),
digits = 2)
data_joined$RH <- data_joined$RH * 100
data_joined$RH <- round(as.numeric(data_joined$RH),
digits = 2)
data_joined <- data_joined %>%
dplyr::select(date, Latitude, Longitude, BC, BC_NR, BC_NR_LC, PM2.5,
PM2.5_RHC, PM2.5_RHC_Ref, PM2.5_Ref, RH,
Particle_conc, CO2)
names(data_joined) <- c("date", "Latitude", "Longitude", "AE51_BC (ug/m3)",
"AE51_BC_NR (ug/m3)", "AE51_BC_NR_LC (ug/m3)",
"DT8530_PM2.5 (ug/m3)", "DT8530_PM2.5_RHC (ug/m3)",
"DT8530_PM2.5_RHC_Ref (ug/m3)",
"DT8530_PM2.5_Ref (ug/m3)", "RH(%)",
"CPC3007_Particle Concentration (#/cm3)",
"LI-COR_CO2 (ppm)")
datatable(data_joined, options = list("pageLength" = 25))
})
## Download the csv generated
output$download <- downloadHandler(
filename <- function() {"joined_file.csv"},
content <- function(fname) {
if (is.null(input$file1) & is.null(input$file2) & is.null(input$file3) &
is.null(input$file4) & is.null(input$file5) & is.null(input$file6)) {
data_joined <- data_blank()
}else {
data_joined <- data_joined()
}
data_joined <- data_joined %>%
dplyr::select(date, Latitude, Longitude, BC, BC_NR, BC_NR_LC, PM2.5,
PM2.5_RHC, PM2.5_RHC_Ref, PM2.5_Ref, RH,
Particle_conc, CO2)
names(data_joined) <- c("date(LT)", "Latitude", "Longitude",
"AE51_BC (ug/m3)",
"AE51_BC_NR (ug/m3)", "AE51_BC_NR_LC (ug/m3)",
"DT8530_PM2.5 (ug/m3)", "DT8530_PM2.5_RHC (ug/m3)",
"DT8530_PM2.5_RHC_Ref (ug/m3)",
"DT8530_PM2.5_Ref (ug/m3)",
"RH(%)", "CPC3007_Particle Concentration (#/cm3)",
"LI-COR_CO2 (ppm)")
write.csv(data_joined, fname)
})
## Summary Statistics
output$table <- DT::renderDataTable({
if (is.null(input$file1) & is.null(input$file2) & is.null(input$file3) &
is.null(input$file4) & is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
}else {
data <- data_joined()
}
data <- dplyr::select(data, BC, BC_NR, BC_NR_LC, PM2.5, PM2.5_RHC,
PM2.5_RHC_Ref, PM2.5_Ref, RH, Particle_conc, CO2)
data[["BC"]] <- as.numeric(as.character(data[["BC"]]))
data[["BC_NR"]] <- as.numeric(as.character(data[["BC_NR"]]))
data[["BC_NR_LC"]] <- as.numeric(as.character(data[["BC_NR_LC"]]))
data$RH <- data$RH * 100
names(data) <- c("AE51_BC (ug/m3)", "AE51_BC_NR (ug/m3)",
"AE51_BC_NR_LC (ug/m3)",
"DT8530_PM2.5 (ug/m3)", "DT8530_PM2.5_RHC (ug/m3)",
"DT8530_PM2.5_RHC_Ref (ug/m3)",
"DT8530_PM2.5_Ref (ug/m3)", "RH(%)",
"CPC3007_Particle Concentration (#/cm3)",
"LI-COR_CO2 (ppm)")
columns <- c("AE51_BC (ug/m3)", "AE51_BC_NR (ug/m3)", "AE51_BC_NR_LC (ug/m3)",
"DT8530_PM2.5 (ug/m3)", "DT8530_PM2.5_RHC (ug/m3)",
"DT8530_PM2.5_RHC_Ref (ug/m3)", "DT8530_PM2.5_Ref (ug/m3)",
"RH(%)", "CPC3007_Particle Concentration (#/cm3)",
"LI-COR_CO2 (ppm)")
data[ , columns] <- lapply(columns,
function(x) as.numeric(as.character(data[[x]])))
tmp1 <- do.call(data.frame,
list(Mean = apply(data, 2, mean, na.rm = TRUE),
SD = apply(data, 2, sd, na.rm = TRUE),
Median = apply(data, 2, median, na.rm = TRUE),
IQR = apply(data, 2, IQR, na.rm = TRUE),
Min = apply(data, 2, min, na.rm = TRUE),
Max = apply(data, 2, max, na.rm = TRUE),
p1 = apply(data, 2, quantile, probs = c(0.01),
na.rm = TRUE),
p10 = apply(data, 2, quantile, probs = c(0.1),
na.rm = TRUE),
p25 = apply(data, 2, quantile, probs = c(0.25),
na.rm = TRUE),
p75 = apply(data, 2, quantile, probs = c(0.75),
na.rm = TRUE),
p90 = apply(data, 2, quantile, probs = c(0.9),
na.rm = TRUE),
p99 = apply(data, 2, quantile, probs = c(0.99),
na.rm = TRUE),
Total_non_NA = apply(data, 2,
function(x)
{length(which(!is.na(x)))})))
tmp <- data.frame(tmp1)
tmp$Mean <- round(as.numeric(as.character(tmp$Mean)), digits = 2)
tmp$IQR <- round(as.numeric(as.character(tmp$IQR)), digits = 2)
tmp$Median <- round(as.numeric(as.character(tmp$Median)), digits = 2)
tmp$Min <- round(as.numeric(as.character(tmp$Min)), digits = 2)
tmp$Max <- round(as.numeric(as.character(tmp$Max)), digits = 2)
tmp$p10 <- round(as.numeric(as.character(tmp$p10)), digits = 2)
tmp$SD <- round(as.numeric(as.character(tmp$SD)), digits = 2)
tmp$p90 <- round(as.numeric(as.character(tmp$p90)), digits = 2)
tmp$p75 <- round(as.numeric(as.character(tmp$p75)), digits = 2)
tmp$p99 <- round(as.numeric(as.character(tmp$p99)), digits = 2)
tmp$p1 <- round(as.numeric(as.character(tmp$p1)), digits = 2)
tmp$p25 <- round(as.numeric(as.character(tmp$p25)), digits = 2)
tmp
tmp <- t(tmp)
datatable(tmp, options = list("pageLength" = 13))
})
## Alarms and settings
output$table4 <- DT::renderDataTable({
inFile <- input$file3
if (is.null(GPS_f()) & is.null(BC_f()) & is.null(CPC_f()) & is.null(DT_f()) &
is.null(RH_f()) & is.null(CO2_f())) {
DT_f <- read.delim("data/DT8530/2019_09_25_h091000_KAN_DT8530.csv",
header = FALSE, sep = ",", row.names = NULL, skip = 2)
DT_f <- DT_f[1:11, ]
DT_f <- DT_f[ ,1:2]
datatable(DT_f, options = list("pageLength" = 11))
}
else if (is.null(DT_f())) {}
else if (!is.null(DT_f())) {
files3 <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = FALSE, sep = ",", row.names = NULL,
skip = 2)
names(JSON_csv) <- c("Setting", "Value")
JSON_csv <- JSON_csv[1:11, ]
JSON_csv <- JSON_csv[ ,1:2]
JSON_csv
})
data <- do.call(rbind, files3)
DT_f <- data
datatable(DT_f, options = list("pageLength" = 11))
}
})
output$table3 <- DT::renderDataTable({
inFile <- input$file4
if (is.null(GPS_f()) & is.null(BC_f()) & is.null(CPC_f()) & is.null(DT_f()) &
is.null(RH_f()) & is.null(CO2_f())) {
CPC_f <- read.delim("data/CPC3007/2019_09_25_h091000_KAN_CPC3007.csv",
header = FALSE, sep = ",", row.names = NULL, skip = 1)
names(CPC_f) <- c("Setting", "Value")
CPC_f <- CPC_f[1:13, ]
CPC_f <- CPC_f[ ,1:2]
datatable(CPC_f, options = list("pageLength" = 13))
}
else if (is.null(CPC_f())) {}
else if (!is.null(CPC_f())){
files3 <- lapply(inFile$datapath, function(y) {
JSON_csv <- read.delim(y, header = FALSE, sep = ",", row.names = NULL,
skip = 1)
names(JSON_csv) <- c("Setting", "Value")
JSON_csv <- JSON_csv[1:13, ]
JSON_csv <- JSON_csv[ ,1:2]
JSON_csv
})
data <- do.call(rbind, files3)
CPC_f <- data
datatable(CPC_f, options = list("pageLength" = 13))
}
})
output$table2 <- DT::renderDataTable({
inFile <- input$file2
if (is.null(BC_f())) {}
else if (!is.null(BC_f())) {
files3 <- lapply(inFile$datapath, function(y) {
JSON_csv_header <- read.delim(y, header = FALSE, sep=",", skip = 15,
row.names = NULL, stringsAsFactors = FALSE)
JSON_csv_header <- JSON_csv_header[1, ]
JSON_csv <- read.delim(y, skip = 17, header = FALSE, sep = ",")
colnames(JSON_csv) <- unlist(JSON_csv_header)
JSON_csv
})
data <- do.call(rbind, files3)
BC_f <- data
completeFun <- function(data, desiredColumns) {
completeVec <- complete.cases(data[ , desiredColumns])
return(data[completeVec, ])
}
BC_f <- completeFun(BC_f, c("BC"))
BC_f <- BC_f[BC_f$Status != 0, ]
BC_f
}
})
output$table5 <- DT::renderDataTable({
inFile <- input$file2
if (is.null(GPS_f()) & is.null(BC_f()) & is.null(CPC_f()) & is.null(DT_f()) &
is.null(RH_f()) & is.null(CO2_f())) {
BC_f <- read.delim("data/AE51/2019_09_25_h091000_KAN_AE51.csv",
header = FALSE,
sep = " ", skip = 1, row.names = NULL)
BC_f <- BC_f[1:14, ]
names(BC_f) <- c("Setting")
datatable(BC_f, options = list("pageLength" = 14))
}else if (is.null(BC_f())) {
"No AE51 files available"
}else if (!is.null(BC_f())){
files3 <- lapply(inFile$datapath, function(y){
JSON_csv <- read.delim(y, header = FALSE, sep = " ",
skip = 1, row.names = NULL)
JSON_csv <- JSON_csv[1:14, ]
JSON_csv
})
data <- do.call(rbind, files3)
BC_f <- data
names(BC_f) <- c("Setting")
datatable(BC_f, options = list("pageLength" = 14))
}
})
## Raw pollutants/GPS plot
output$plot5 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(Latitude))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Latitude (degree)",
y = "",
x = "") +
scale_x_datetime( date_labels = "%H:%M") +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(GPS_f())) {}
else if (!is.null(GPS_f())) {
data <- data_joined()
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(Latitude))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Latitude (degree)",
y = "",
x = "") +
scale_x_datetime( date_labels = "%H:%M") +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot6 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)){
data <- data_blank()
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(Longitude))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Longitude (degree)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(GPS_f())) {}
else if (!is.null(GPS_f())) {
data <- data_joined()
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(Longitude))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Longitude (degree)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
data$PM2.5 <- as.numeric(as.character(data$PM2.5))
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(PM2.5))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "DT8530_PM2.5 (ug/m3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(DT_f())) {}
else if (!is.null(DT_f())) {
data <- data_joined()
data$PM2.5 <- as.numeric(as.character(data$PM2.5))
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(PM2.5))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "DT8530_PM2.5 (ug/m3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot4<- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
data$RH <- data$RH * 100
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(RH))) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "Relative Humidity (%)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous(limits = c(20,100)) +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(RH_f())) {}
else if (!is.null(RH_f())) {
data <- data_joined()
data$RH <- data$RH * 100
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(RH))) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "Relative Humidity (%)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous(limits = c(20,100)) +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot2 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
data$BC <- as.numeric(as.character(data$BC))
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(BC))) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "AE51_BC (ug/m3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(BC_f())) {}
else if (!is.null(BC_f())) {
data <- data_joined()
data$BC <- as.numeric(as.character(data$BC))
ggplotly(ggplot(data, aes(as.POSIXct(date), as.numeric(BC))) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "AE51_BC (ug/m3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot3 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
data$Particle_conc <- as.numeric(as.character(data$Particle_conc))
ggplotly(ggplot(data, aes(as.POSIXct(date), Particle_conc)) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "CPC3007_Particle Concentration (#/cm3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(CPC_f())) {}
else if (!is.null(CPC_f())) {
data <- data_joined()
data$Particle_conc <- as.numeric(as.character(data$Particle_conc))
ggplotly(ggplot(data, aes(as.POSIXct(date), Particle_conc)) +
geom_line(size = 0.6, color = "dodgerblue2")+
labs(title = "CPC3007_Particle Concentration (#/cm3)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
output$plot7 <- renderPlotly({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
ggplotly(ggplot(data, aes(as.POSIXct(date), CO2)) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "LI-COR_CO2 (ppm)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
else if (is.null(CO2_f())) {}
else if (!is.null(CO2_f())) {
data <- data_joined()
ggplotly(ggplot(data, aes(as.POSIXct(date), CO2)) +
geom_line(size = 0.6, color = "dodgerblue2") +
labs(title = "LI-COR_CO2 (ppm)",
y = "",
x = "") +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous() +
theme_minimal() +
theme(legend.text = element_text(size = 18),
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(colour = "black",
fill = NA, size = 1.2))
)}
})
observe({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data_joined <- data_blank()
data_joined$date <- NULL
data_joined$Latitude <- NULL
data_joined$Longitude <- NULL
data_joined$PM2.5_RHC <- NULL
data_joined$PM2.5_RHC_Ref <- NULL
}else {
data_joined <- data_joined()
data_joined$date <- NULL
data_joined$Latitude <- NULL
data_joined$Longitude <- NULL
if (is.null(CPC_f())) {
data_joined$Particle_conc <- NULL
}
if (!is.null(CPC_f())) {
data_joined$Particle_conc <- data_joined$Particle_conc
}
if (is.null(RH_f())) {
data_joined$RH <- NULL
}
if (!is.null(RH_f())) {
data_joined$RH <- data_joined$RH
}
if (is.null(DT_f())) {
data_joined$PM2.5 <- NULL
data_joined$PM2.5_RHC <- NULL
data_joined$PM2.5_RHC_Ref <- NULL
data_joined$PM2.5_Ref <- NULL
}
if (!is.null(DT_f())) {
data_joined$PM2.5 <- data_joined$PM2.5
data_joined$PM2.5_RHC <- data_joined$PM2.5_RHC
data_joined$PM2.5_RHC_Ref <- data_joined$PM2.5_RHC_Ref
data_joined$PM2.5_Ref <- data_joined$PM2.5_Ref
}
if (is.null(BC_f())) {
data_joined$BC <- NULL
data_joined$BC_NR <- NULL
data_joined$BC_NR_LC <- NULL
}
if (!is.null(BC_f())) {
data_joined$BC <- data_joined$BC
data_joined$BC_NR <- data_joined$BC_NR
data_joined$BC_NR_LC <- data_joined$BC_NR_LC
}
if (is.null(CO2_f())) {
data_joined$CO2 <- NULL
}
if (!is.null(CO2_f())) {
data_joined$CO2 <- data_joined$CO2
}
}
updateSelectInput(session, "palleInp", choices = names(data_joined))
})
## Mapping pollutant
output$map <- renderLeaflet({
if (is.null(input$file1) & is.null(input$file2) &
is.null(input$file3) & is.null(input$file4)
& is.null(input$file5) & is.null(input$file6)) {
data <- data_blank()
}else {
data <- data_joined()
}
data$RH <- data$RH * 100
if (input$palleInp == "BC_NR" | input$palleInp == "BC_NR_LC") {
risk.bins <- c(0, 0.5, 2, 5, 10, 20, 40, 100, 500, 2000, 10000)
pal <- colorBin( "Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
} else if (input$palleInp == "BC") {
risk.bins <- c(-100, -40, -20, 0, 5, 10, 20, 40, 100, 500, 2000)
pal <- colorBin("Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
} else if (input$palleInp == "PM2.5") {
risk.bins <- c(0, 10, 25, 50, 100, 500, 2000, 5000, 10000)
pal <- colorBin("Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
} else if (input$palleInp == "Particle_conc") {
risk.bins <- c(0, 5000, 10000, 20000, 40000, 70000, 100000, 150000,
200000, 500000)
pal <- colorBin("Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
}else if (input$palleInp == "RH") {
pal <- colorNumeric("RdYlGn", domain = data$RH, na.color = "#808080",
reverse = TRUE)
}else if (input$palleInp == "CO2") {
pal <- colorNumeric("RdYlGn", domain = data$CO2, na.color = "#808080",
reverse = TRUE)
}else if (input$palleInp == "Latitude") {
pal <- colorNumeric("RdYlGn", domain = data$Latitude, na.color = "#808080",
reverse = TRUE)
}else {
risk.bins <- c(0, 10, 25, 50, 100, 500, 2000, 5000, 10000)
pal <- colorBin("Spectral", bins = risk.bins, na.color = "#808080",
reverse = TRUE)
}
leaflet(data) %>%
addProviderTiles(providers$Stamen.TonerLite) %>%
addCircles(data = data,
lng = ~Longitude,
lat = ~Latitude,
popup = paste("Date:", data$date, "<br>",
"AE51_BC (ug/m3):",
round(as.numeric(data$BC), digits = 2), "<br>",
"DT8530_PM2.5 (ug/m3):",
round(as.numeric(data$PM2.5), digits = 2), "<br>",
"RH(%):",
round(as.numeric(data$RH), digits = 2), "<br>",
"CPC3007_Particle Concentration (#/cm3):",
round(as.numeric(data$Particle_conc), digits = 2),
"<br>",
"CO2:", round(as.numeric(data$CO2),digits = 2)),
weight = 3, radius = 8,
col = ~pal(data[[input$palleInp]]), stroke = TRUE,
fillOpacity = 0.8) %>%
leaflet::addLegend("bottomright", pal = pal,
values = ~data[[input$palleInp]],
title = paste(input$palleInp))
})
}
## Run app
shinyApp(ui, server)
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