HOBO Data cleanup

## HOBO Data Clean Up Procedures

library(ggplot2)
library(dplyr)
library(tidyr)
library(gtools)
library(ggbiplot)
library(dismo)
library(rjson)  
library(RCurl) 
library(raster)
library(sp)

setwd("C:/Users/maxge/Downloads/Grad School Papers/Data-Analysis")

# Acquired HOBO Data collects readings every 30 minutes 
## This means there's lots of data to comb through and make averages for 
### So this will act as a way to create averages for each month
#### Before starting you will need to change the date and time columns into only month names for this to work
### Making averages for the day will specify into the just the date which can be a bit tedious to do by hand

MMA <- read.csv("Revised Data/MM Air Master.csv")
MMW <- read.csv("Revised Data/MM Water Master.csv")
TONA <- read.csv("Revised Data/TON Air Master.csv")
TONW <- read.csv("Revised Data/TON Water Master.csv")
UIA <- read.csv("Revised Data/UI Air Master.csv")
UIW <- read.csv("Revised Data/UI Water.csv")

Ultima <- list(MMA, MMW, TONA, TONW, UIA, UIW)

#This will tell us how many data points we have per month 
# Now with the data point count we can begin averaging everything 

avglist <- c()
files <- c("MMA", "MMW", "TONA", "TONW", "UIA", "UIW")
folder <- "Revised Data/"

#This will take all the data and average it then write individual files
## this can be changed to make it into a list or data frame if desired
## I prefer separate files for making figures down the line

for (i in 1:6){
  flare <- Ultima[[i]]
  months <- unique(flare$Date.Time..CST.)
  for (i in 1:7){
  month <- months[[i]]
  data <- flare %>%
    filter(Date.Time..CST. == month)
  avg <- mean(data[, 3])
  avglist[i] <- avg
  fileA <- files[i]
  fileB <- ".csv"
  finalfile <- paste(folder, fileA, fileB, sep = "")
  write.csv(cbind.data.frame(avglist), finalfile)
}
  }