Below are the solutions to these openair exercises
############### # # # Exercise 1 # # # ############### my1data <- importAURN(site = 'MY1', year = 2016, met = TRUE) ############### # # # Exercise 2 # # # ############### summary(my1data)
############### # # # Exercise 3 # # # ############### 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) ############### # # # Exercise 4 # # # ############### 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) ############### # # # Exercise 5 # # # ###############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)
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