Standard Time Series Analysis and Visualization
Simplified analysis and display of standardizedtime series modeled after examples found here
It's easy to install using Hadley Wickham's devtools.
library(devtools)
install_github('btupper/stsaav')
library(stsaav)
str(airport_wx)
# 'data.frame': 6498 obs. of 24 variables:
# $ Date : POSIXct, format: "1996-06-30" "1996-07-01" "1996-07-02" "1996-07-03" ...
# $ Max.TemperatureF : int 41 60 64 62 62 64 51 59 55 53 ...
# $ Mean.TemperatureF : int 41 48 55 52 52 53 48 50 50 48 ...
# $ Min.TemperatureF : int 39 37 46 42 42 42 44 42 46 44 ...
# $ Max.Dew.PointF : int 36 37 39 41 43 43 46 46 48 46 ...
# $ MeanDew.PointF : int 35 33 24 38 39 39 41 39 37 44 ...
# $ Min.DewpointF : int 34 27 16 34 34 32 37 34 23 41 ...
# $ Max.Humidity : int 81 100 71 81 81 93 100 100 100 100 ...
# $ Mean.Humidity : int 80 65 32 60 61 62 82 65 61 83 ...
# $ Min.Humidity : int 76 27 18 34 34 32 62 39 29 67 ...
# $ Max.Sea.Level.PressureIn : num 29.8 29.8 29.9 29.9 29.9 ...
# $ Mean.Sea.Level.PressureIn: num 29.8 29.8 29.8 29.9 29.9 ...
# $ Min.Sea.Level.PressureIn : num 29.8 29.8 29.8 29.9 29.9 ...
# $ Max.VisibilityMiles : int 6 6 6 6 6 6 6 6 6 6 ...
# $ Mean.VisibilityMiles : int 6 6 6 6 6 6 6 6 6 6 ...
# $ Min.VisibilityMiles : int 6 6 6 6 6 6 6 6 6 6 ...
# $ Max.Wind.SpeedMPH : int 4 12 18 16 13 15 16 15 23 15 ...
# $ Mean.Wind.SpeedMPH : int 2 4 10 5 5 6 5 6 11 7 ...
# $ Max.Gust.SpeedMPH : int NA NA NA NA NA NA NA NA NA NA ...
# $ PrecipitationIn : num 0 0 0 0 0 0 0 0 0 0 ...
# $ CloudCover : int 3 2 1 3 1 2 7 5 6 6 ...
# $ Events : chr "" "" "" "" ...
# $ WindDirDegrees : int 128 258 254 245 290 264 259 245 79 237 ...
# $ Fog : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
x <- stsaav(airport_wx, tcol = 'Date', vcol = 'Max.TemperatureF')
plot(x, main = 'Maximum Daily Temp in Narsarsuaq, Greenland')
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