logger_plotter.r

#Set working directory
#dir_SM <- "F:/Kaitlyn_Toledo_Data/RMP_Project/SMData"
dir_SM <- "/home/nak/kate-R/try1"

library(reshape2)
library(ggplot2)

# We an use the recursive option to get all the CSV files
csv_filenames <- list.files(dir_SM,
                            recursive=TRUE,
                            pattern="*.csv",
                            full.names=TRUE)

# Initialize empty list to store dataframes from each logger
# After the for..in loop runs, access each logger with all_data[["Logger1"]]
all_data = list()

# This loops over all the CSV files, 25 of them
for (i in 1:length(csv_filenames)) {
  data <- read.csv(csv_filenames[i],
                   skip=3,
                   colClasses=c("character", rep("numeric",5)),
                   na.string=c("#N/A","#DIV/0!"),
                   header=FALSE)
  
  # Find the logger name from file path
  logger_regex <- regexpr("Logger[[:digit:]]", csv_filenames[i])
  logger_name <- regmatches(csv_filenames[i], logger_regex)
  
  # V1 is the first column, V2 is 2nd, etc
  # Re-name them to match reality
  port_names <- c("V2" = "Port1",
                  "V3" = "Port2",
                  "V4" = "Port3",
                  "V5" = "Port4",
                  "V6" = "Port5")
  
  # Re-name columns
  names(data) <- c("V1" = "DateTime", port_names)
  
  # Try the first Date format, if we get a warning try the 2nd format
  tryCatch({
    data$DateTime <- as.POSIXct(data$DateTime, format="%m/%d/%Y %I:%M %p")
  }, warning = function(w) {
    data$DateTime <- as.POSIXct(data$DateTime, format="%m/%d/%Y %H:%M")   
  })

  # Only get a subset of dates
  data <- subset(data,
                 DateTime > "2015-04-01" &
                 DateTime < "2015-08-01")
  
  # Bind the rows of this file with previous files loaded into the list
  all_data[[logger_name]] <- rbind(all_data[[logger_name]], data)
}

# Now that we've loaded all the CSV data into a list of data frames (all_data)
# we can loop over each logger data frame object and then plot it
for(logger_name in names(all_data)) {
  data <- all_data[[logger_name]]
  
  # Remove columns that are completely NA (thanks StackOverflow)
  data <- data[, colSums(is.na(data)) != nrow(data)]
  
  # Melt takes a wide data format and makes it into a column instead
  # This is needed for ggplot2
  data <- melt(data,
               
               # Here I'm just getting all the names of the ports, avoiding
               # The DateTime column
               measure.vars = names(data)[c(2:length(names(data)))],
               na.rm = TRUE)
  
  # Remove data points less than zero
  data <- subset(data, value > 0)
  
  # Print is needed to show ggplots in a loop, that's it!
  print(ggplot(data, aes(x = DateTime, y = value)) +
    geom_line() +
    facet_grid(variable~.))
}