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Data_Read.R
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Data_Read.R
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#Program that complies lab generated reference CO to measure CO. Complication generated from #inconsistent file format and file name. Also account for time difference between Ghana and Boulder, #CO.
setwd("~LocalDir")
weird_file_format_flag = 1
time_diff_flag = 6
myfiles = list.files(pattern = '.txt')
myfiles = myfiles[substr(myfiles, nchar(myfiles)-2, nchar(myfiles)) == 'txt']
myfiles = myfiles[substr(myfiles, 1, 6) == 'Lascar']
lascar_number = substr(myfiles,10,11)
file_names = paste("dataset",lascar_number,sep = '')
name_dataset = "dataset"
for (file in myfiles)
{
for (i in 1:length(file_names))
{
if (exists(file_names[i]) && substr(file,10,11) == substr(file_names[i],8,9) )
{
if (weird_file_format_flag == 0)
{
temp_dataset = read.table(file, header = TRUE,sep=',')
}
if(weird_file_format_flag == 1)
{
header = read.table(file, nrows = 1, header = FALSE, sep =',', stringsAsFactors = FALSE)
header$V1 = substr(file,1,11)
header$V4 = NULL
header$V5 = NULL
header$V6 = NULL
header$V7 = NULL
temp_dataset <- read.table(file, skip = 3, header = FALSE, sep =',')
colnames( temp_dataset ) <- unlist(header)
}
colnames(temp_dataset)[1] = substr(file,1,11)
colnames(temp_dataset)[3] = "CO.ppm"
if(ncol(temp_dataset) > 3){
temp_dataset[,4:ncol(temp_dataset)] = NULL
}
temp_dataset$seconds = 0
temp = rbind(eval(parse(text = file_names[i])),temp_dataset)
assign(file_names[i],temp)
}
if (!exists(file_names[i]) && substr(file,10,11) == substr(file_names[i],8,9))
{
if (weird_file_format_flag == 0)
{
temp = read.table(file, header = TRUE,sep=',')
}
if(weird_file_format_flag == 1)
{
header = read.table(file, nrows = 1, header = FALSE, sep =',', stringsAsFactors = FALSE)
header$V4 = NULL
header$V5 = NULL
header$V6 = NULL
header$V7 = NULL
temp <- read.table(file, skip = 3, header = FALSE, sep =',')
colnames( temp ) <- unlist(header)
}
colnames(temp)[1] = substr(file,1,11)
colnames(temp)[3] = "CO.ppm"
if(ncol(temp) > 3){
temp[,4:ncol(temp)] = NULL
}
temp$seconds = 0
assign(file_names[i],temp)
}
}
}
data_names = ls()
data_names = data_names[substr(data_names,1,7)=="dataset"]
for (i in 1:length(file_names)){
x = unique(eval(parse(text = file_names[i])))
x$seconds = as.numeric(as.POSIXct(x$Time))
assign(file_names[i],x)
}
rm(temp,temp_dataset,x,header)
csv_files = list.files(pattern="*.csv")
csv_files = csv_files[substr(csv_files, nchar(csv_files)-2, nchar(csv_files)) == 'csv']
if(!is.null(csv_files))
{
for (file in csv_files)
{
if (exists("ref_data"))
{
temp_data <-read.table(file, header=TRUE, sep=",")
if (ncol(temp_data)>2)
{
temp_data[,3:ncol(temp_data)] = NULL
}
temp_data$seconds = as.numeric(as.POSIXct(strptime(temp_data$Datetime,format = "%m/%d/%Y %H:%M")))
ref_data<-rbind(ref_data, temp_data)
}
# if the merged dataset doesn't exist, create it
if (!exists("ref_data"))
{
ref_data <- read.table(file, header=TRUE, sep=",")
if (ncol(ref_data)>2)
{
ref_data[,3:ncol(ref_data)] = list(NULL)
}
ref_data$seconds = as.numeric(as.POSIXct(strptime(ref_data$Date,format = "%m/%d/%Y %H:%M")))
}
# if the merged dataset does exist, append to it
}
}
ref_data = unique(ref_data)
row.names(ref_data) <- 1:nrow(ref_data)
rm(temp_data)
ref_data = ref_data[!is.na(ref_data$CO_ppm),]
row.names(ref_data) <- 1:nrow(ref_data)
bad.time.index = which(is.na(ref_data$seconds))
ref_data$seconds[bad.time.index] = as.numeric(as.POSIXct(strptime(ref_data$Datetime[bad.time.index],format = "%m-%d-%y %H:%M")))
#as.POSIXct(1403552033,origin = "1970-01-01") ## Converts seconds to time again
for(i in 1:length(data_names))
{
d = eval(parse(text = data_names[i]))
bad.time.d = which(d$seconds<0)
d$seconds[bad.time.d] = as.numeric(as.POSIXct(strptime(d$Time,format = "%d/%m/%Y %H:%M")))
d$seconds = d$seconds - time_diff_flag*60*60 #
assign(data_names[i],d)
}
df <- data.frame(lascar_number=integer(),
COLascar=integer(),
lascartime=double(),
refCO=integer(),
reftime=double(),
time.diff = integer(),
CO.diff = integer()
)
min_val_ref = min(ref_data$seconds,na.rm = TRUE)
max_val_ref = max(ref_data$seconds,na.rm = TRUE)
l = 1
for (i in 1:length(data_names))
{
d = eval(parse(text = data_names[i]))
min_val_d = min(d$seconds,na.rm = TRUE)
max_val_d = max(d$seconds,na.rm = TRUE)
min_val = max(c(min_val_d, min_val_ref))
max_val = min(c(max_val_d,max_val_ref))
new_val_d = d[which(d$seconds <= max_val & d$seconds>=min_val),]
new_val_ref = ref_data[which(ref_data$seconds <= max_val & ref_data$seconds>=min_val),]
row.names(new_val_d) = 1:nrow(new_val_d)
row.names(new_val_ref) = 1:nrow(new_val_ref)
for (j in 1:nrow(new_val_d))
{
diffs = abs(new_val_d$seconds[j]-new_val_ref$seconds)
index = which.min(diffs)
if(diffs[index] <= 60){
df[l,1] = substr(data_names[i],8,9)
df[l,2] = new_val_d$CO.ppm[j]
df[l,3] = new_val_d$seconds[j]
df[l,4] = mean(new_val_ref$CO_ppm[new_val_ref$seconds[index]-30 <= new_val_ref$seconds & new_val_ref$seconds <= new_val_ref$seconds[index]+30])
df[l,5] = new_val_ref$seconds[index]
df[l,6] = (df[l,3]-df[l,5])
df[l,7] = (df[l,2] - df[l,4])
l=l+1
}
}
}
df = unique(df)
df$DateTime = as.POSIXct(df$lascartime,origin = "1970-01-01")
if (!exists("compiled_data"))
{
compiled_data = data.frame(lascar_number=integer(),
COLascar=integer(),
refCO=integer(),
reftime=double(),
lascartime=double(),
lascarDate = double(),
refDate = double()
)
compiled_data = rbind(compiled_data,df)
} else
{
compiled_data = rbind(compiled_data,df)
}
#compiled_data$DateTime = as.POSIXct(compiled_data$lascartime,origin = "1970-01-01")
rm(list=setdiff(ls(), "compiled_data"))
split_data = split(compiled_data,compiled_data$lascar_number)
lapply(names(split_data), function(x){write.table(split_data[[x]], file = paste("output", x,".txt" ,sep = " "))})
write.csv(compiled_data, file = "Compiled_data.csv")