Working with air quality and meteorological data exercises part 1: Solutions

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# Exercise 1  #
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my1data <- importAURN(site = 'MY1', year = 2016, met = TRUE)

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# Exercise 2  #
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summary(my1data)

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# Exercise 3  #
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monthly_pm10 <- aggregate(my1data["pm10"], format(my1data["date"],"%Y-%m"), mean, na.rm = TRUE)
monthly_pm2.5 <- aggregate(my1data["pm2.5"], format(my1data["date"],"%Y-%m"), mean, na.rm = TRUE)
monthly_nox <- aggregate(my1data["nox"], format(my1data["date"],"%Y-%m"), mean, na.rm = TRUE)
monthly_no <- aggregate(my1data["no"], format(my1data["date"],"%Y-%m"), mean, na.rm = TRUE)
monthly_o3 <- aggregate(my1data["o3"], format(my1data["date"],"%Y-%m"), mean, na.rm = TRUE)

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# Exercise 4  #
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daily_pm10 <- aggregate(my1data["pm10"], format(my1data["date"],"%Y-%j"), mean, na.rm = TRUE)
daily_pm2.5 <- aggregate(my1data["pm2.5"], format(my1data["date"],"%Y-%j"), mean, na.rm = TRUE)
daily_nox <- aggregate(my1data["nox"], format(my1data["date"],"%Y-%j"), mean, na.rm = TRUE)
daily_no <- aggregate(my1data["no"], format(my1data["date"],"%Y-%j"), mean, na.rm = TRUE)
daily_o3 <- aggregate(my1data["o3"], format(my1data["date"],"%Y-%j"), mean, na.rm = TRUE)

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# Exercise 5  #
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daily_max_nox <- aggregate(my1data["nox"], format(my1data["date"],"%Y-%j"), max, na.rm = TRUE)
daily_max_no <- aggregate(my1data["no"], format(my1data["date"],"%Y-%j"), max, na.rm = TRUE)