First Processing Steps

Now that I’ve got the data into R, lets see what it looks like.

First, lets look at which sites have been up the longest:

107  NC_Asheville_13_S           35.42    -82.56  11
108   NC_Asheville_8_SSW       35.49    -82.61   11
88   KY_Versailles_3_NNW      38.09    -84.75  10
105  MT_Wolf_Point_29_ENE    48.31  -105.10  10
106  MT_Wolf_Point_34_NE     48.49   -105.21  10
117  NH_Durham_2_N             43.17      -70.93   10
118   NH_Durham_2_SSW       43.11     -70.95  10
137  NV_Mercury_3_SSW         36.62   -116.02  10
150  RI_Kingston_1_NW          41.49     -71.54   10
151  RI_Kingston_1_W            41.48     -71.54    10

The “hump” in the data would be what we would expect over the seasons we need to “anomalize” the data to remove the seasonal effect.

This is done by applying the as.anomaly function:

as.anomaly<-function(index, values){

averages = tapply(values, INDEX = index,FUN= mean, na.rm = "T")
anom <-mapply(function(x, y) y - averages[as.numeric(x)] ,index, values)
}

Which give us this “bow tie” plot

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