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1.5 Import and Format Datasets

  1. Exercise 1.5 - Download and extract zip folder into your preferred location
  2. Set working directory to the extracted folder in R under File - Change dir...
  3. First we need to load the packages needed for the exercise
    library(chron)
    library(rgdal)
    library(RAtmosphere)

  4. 4. Now open the script "TimeLagScript.R" and run code directly from the script

  5. Determine the name of your file ("temp" in our case here)
    We can then enter the path with this name to bring our dataset into R



    temp <-read.csv("Y2005_UTM_date.csv", header=T)
  6. It is often necessary to determine the time lag between successive locations within your dataset

    # modify time to include seconds
    temp$time <- paste(as.character(temp$LMT_TIME),"00",sep=":")
    # convert to chron date
    temp$date_time <- chron(as.character(temp$LMT_DATE),
    temp$time,format=c(dates="m/d/y",times="h:m:s"))
    # calc diff in minutes
    timediff <- diff(temp$date_time)*24*60
    # remove first entry without any difference
    temp <- temp[-1,]
    # assign timediff column
    temp$timediff <- as.numeric(timediff)

    The above code will result in a dataset that includes "timediff" that is the time between successive GPS points
    Locationdate_timedifference
    1    
    2 (07/13/05 10:00:00) 7
    3 (07/13/05 10:30:00) 30
    4 (07/13/05 11:00:00) 30
    5 (07/13/05 11:30:00) 30
    6 (07/13/05 12:00:00) 30
    7 (07/13/05 12:30:00) 30
    8 (07/13/05 13:00:00) 30
    9 (07/13/05 13:30:00) 30
    10 (07/13/05 14:00:00) 30
    11 (07/13/05 15:00:00) 60
    12 (07/13/05 15:30:00) 30
    13 (07/13/05 16:00:00) 30
    14 (07/13/05 16:30:00) 31
    15 (07/13/05 17:00:00) 30
    16 (07/13/05 17:30:00) 29
  7. We can then either export this file as an excel file for use in other programs or use it in R in subsequent analysis

    write.table(temp,"TimeDiffdata.csv", row.names=TRUE, sep=" ", col.names=TRUE, quote=TRUE, na = "NA")
  8. Next we can add code below to include night and day into datasets and to also to
    account for daylight savings. Package RAtmosphere will eliminate the need for the
    chunk of code below if working on an earlier version of R (i.e., code not available for R
    3.2.1 plus). Thanks to Duane Diefenbach, PA Coop Unit Leader, for compiling all this
    from online sources.
    ###################################################################
    #We first need to create a SPDF and transform to Lat Long then return to a
    #Data Frame. You only need this section of code if you need to acquire Lat #Long coordinates for your dataset
    ###################################################################
    #utm.crs <-CRS("+proj=utm +zone=12 +datum=WGS84")
    #dataSPDF<-data.frame(x = temp$UTMe, y = temp$UTMn)
    #utm.spdf <- SpatialPointsDataFrame(coords = dataSPDF, data = temp,
    proj4string = utm.crs)
    #ll.crs <- CRS("+proj=longlat +ellps=WGS84")
    #datall <-spTransform(utm.spdf, CRS=ll.crs)
    #str(datall)

    #temp <- as.data.frame(datall)
    #str(temp)

  9. Separate times into categories "Day" and "Night" based on sunrise-sunset table by running function below or simply using the RAtmosphere package if available

  10. First run line of code below with d being the day of year, Lat is latitude in decimal
    degrees, and Long is longitude in decimal degrees (negative == West) available at
    suncalc
    suncalc <- function(d,Lat=39.14133,Long=-106.7722){
    ##This method is copied from:
    ##Teets, D.A. 2003. Predicting sunrise and sunset times.
    ## The College Mathematics Journal 34(4):317-321.
    ## At the default location the estimates of sunrise and sunset are within
    ## seven minutes of the correct times
    ##(http://aa.usno.navy.mil/data/docs/RS_OneYear.php)
    ## with a mean of 2.4 minutes error.
    ## Function to convert degrees to radians
    rad <- function(x)pi*x/180
    ##Radius of the earth (km)
    R=6378
    ##Radians between the xy-plane and the ecliptic plane
    epsilon=rad(23.45)
    ##Convert observer’s latitude to radians
    L=rad(Lat)
    ## Calculate offset of sunrise based on longitude (min)
    ## If Long is negative, then the mod represents degrees West of
    ## a standard time meridian, so timing of sunrise and sunset should
    ## be made later.
    ##NOTE: If working with UTC times use timezone = -4*(abs(Long)%%15)*sign(Long)
    timezone = -6*(abs(Long)%%15)*sign(Long)
    ## The earth’s mean distance from the sun (km)
    r = 149598000
    theta = 2*pi/365.25*(d-80)
    z.s = r*sin(theta)*sin(epsilon)
    r.p = sqrt(r^2-z.s^2)
    t0 = 1440/(2*pi)*acos((R-z.s*sin(L))/(r.p*cos(L)))
    ##a kludge adjustment for the radius of the sun
    that = t0+5

    ## Adjust "noon" for the fact that the earth’s orbit is not circular:
    n = 720-10*sin(4*pi*(d-80)/365.25)+8*sin(2*pi*d/365.25)
    ## now sunrise and after sunset are:
    sunrise = (n-that+timezone)/60
    sunset = (n+that+timezone)/60
    suntime <- cbind(sunrise,sunset)
    return(suntime)
    }

  11. Read in location data and retain lat, lon, and date
    temp$Date <- paste((temp$Year),substr(temp$date_time, 2,3),
    substr(temp$date_time, 5,6),sep="-")
    str(temp)
    #calculate calendar day and center of locations
    calday <- as.numeric(as.Date(temp$Date)-as.Date("2005-01-01"), units="days")
    dat1 <- cbind(temp,calday)
    moda <- format(as.Date(temp$Date),"%d-%b")
    str(dat1)
    dat1 <- cbind(dat1, suncalc(dat1$calday, Lat=dat1$LATITUDE, Long=dat1$LONGITUDE),
    moda)
    hrchar <- as.character(substr(dat1$time,1,2))
    hr <- as.numeric(as.character(substr(dat1$time,1,2)))
    minchar <- as.character(substr(dat1$time,4,5))
    min <- as.numeric(minchar)
    localhr <- hr+min/60
    dat1 <- cbind(dat1,hr,hrchar,minchar,localhr)
    Diel <- ifelse(localhr<dat1$sunrise | localhr>dat1$sunset, ’Night’, ’Day’)
    dat1 <- cbind(dat1,Diel)
    str(dat1)
    dat1[1:50,]