View source: R/weather2chillR.R
weather2chillR | R Documentation |
Convert downloaded weather data into a data frame that makes running other chillR functions easy.
weather2chillR(downloaded_weather, database = "GSOD", drop_most = TRUE)
downloaded_weather |
weather file downloaded with the get_weather function. This can be a data.frame or a list with elements database and weather as produced by get_weather |
database |
weather database that the file was downloaded from. Can only be "GSOD" at this point. |
drop_most |
boolean variable indicating if most columns should be dropped from the file. If set to TRUE (default), only essential columns for running chillR functions are retained. |
weather databases, from which chillR can download data: NOAA NCDC Global Summary of the Day - "GSOD" (https://data.noaa.gov/dataset/global-surface-summary-of-the-day-gsod)
California Irrigation Management Information System (CIMIS) - "CIMIS" (http://www.cimis.water.ca.gov/)
University of California Integrated Pest Management (UCIPM) - "UCIPM" (http://ipm.ucdavis.edu/WEATHER/)
data should first be downloaded with get_weather. Then the database name is passed to the function and can be skipped in the call. If only a data.frame is provided, then the database name must be specified.
Processing the data with this function will make the data work well with the remainder of this package.
a data.frame with weather data, according to the downloaded file provided as input. If drop_most is FALSE, all columns from the original dataset are preserved, although some column names are adjusted to chillR's preferences ("Year","Month","Day","Tmin","Tmax","Tmean","Prec"). If drop_most is TRUE, only columns likely to be of interest to chillR users are retained. If a list with elements database and weather is passed to this function, this structure will be retained in the output.
Many databases have data quality flags, which may sometimes indicate that data aren't reliable. These are not considered by this function!
Eike Luedeling
The chillR package:
Luedeling E, Kunz A and Blanke M, 2013. Identification of chilling and heat requirements of cherry trees - a statistical approach. International Journal of Biometeorology 57,679-689.
# All examples are disabled, because the database is sometimes unavailable. This then generates
# an error when R runs its package functionality checks. To run the examples, remove the # mark,
# before running the code.
#stat_list<-get_weather(location=c(lat=40,lon=-120,ele=150),time_interval=c(2015,2016),
#database="UCIPM")
#chillRcode<-stat_list[which(stat_list$Perc_interval_covered==
#max(stat_list$Perc_interval_covered)),"chillR_code"][1]
#chillRcode should equal "DOYLE.C" now.
#gw<-get_weather(location="DOYLE.C",time_interval=c(2002,2002),database="UCIPM")
#weather<-weather2chillR(gw$weather,"GSOD")
#weather<-weather2chillR(gw)
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