| Title: | Rainfall event processing |
|---|---|
| Description: | Rainmaker is a collection of functions used to process instantaneous rainfall data to define event rainfall depths, rainfall intensities, rainfall erosivity and antecedent rainfall values. Event definition can be determined solely within rainmaker using the instantaneous rainfall data or can be focused directly on dates and times of interest (for example, using specific water quality sampling periods). |
| Authors: | Steve Corsi [aut, cre], Rebecca Carvin [aut] |
| Maintainer: | Steve Corsi <[email protected]> |
| License: | file LICENSE |
| Version: | 1.0.2 |
| Built: | 2024-11-15 02:50:05 UTC |
| Source: | https://github.com/USGS-R/Rainmaker |
Rainmaker is a collection of functions used to process instantaneous rainfall data to define event rainfall depths, rainfall intensities and antecedent rainfall values. Event definition can be determined solely within rainmaker using the instantaneous rainfall data or can be focused directly on dates and times of interest (for example, using specific water quality sampling periods).
| Package: | Rainmaker |
| Type: | Package |
| Version: | 1.0.0 |
| Date: | 2014-01-10 |
| License: | Unlimited for this package, dependencies have more restrictive licensing. |
| Copyright: | This software is in the public domain because it contains materials that originally came from the United States Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at http://www.usgs.gov/visual-id/credit_usgs.html#copyright |
| LazyLoad: | yes |
Collection of functions..
Steve Corsi [email protected]
Instantaneous values of flow from Cedar Creek
Steve Corsi [email protected]
Instantaneous values of rainfall from Cedar Creek
Steve Corsi [email protected]
Beginning and ending dates and times for composite samples collected at Cedar Creek. This information can be used to determine rainfall for specific time periods using RMeventsSamples.
Steve Corsi [email protected]
Discharge event determination - calculates event start and end times based on discharge record.
discharge_events( df, ieHr = 6, qthresh, discharge = "Value", time = "pdate", ieHr_check = TRUE )discharge_events( df, ieHr = 6, qthresh, discharge = "Value", time = "pdate", ieHr_check = TRUE )
df |
dataframe that contains discharge and timestamps |
ieHr |
numeric, Interevent period in hours, defaults to 6. The amount of time between discharge measurements above threshold required to be considered a new event. |
qthresh |
numeric, Discharge threshold value, over which an event is considered to be occuring. |
discharge |
string, Column name where discharge values are stored. Defaults to "Value". |
time |
string, column name where dates are stored as POSIXct values, defaults to "pdate". |
ieHr_check |
logical, whether to check the calculated events data frame for events that are closer together than ieHr. This can happen due to the start and end time corrections for the "tails" of the event. If TRUE, events closer than ieHr apart will be combined. If FALSE, events will be left as-is. See the details section below for more information. |
When a discharge measurement is above qthresh, the algorthim decides whether it belongs to a
"new" event by looking backwards at the last measurement above qthresh. If the difference in time
is greater than ieHr, then a new event begins. The start and end times of each event,
however, are adjusted to the previous or next timestamp, respectively, to account for the "tails" of
the event. The consequence of adjusting the start and end times, however, is that depending on the frequency
of discharge observations, two observations above qthresh that are greater than ieHr apart (and therefore in seperate events)
can have start and end times that are closer together than ieHr. If ieHr_check = TRUE (default), the algorithm makes a
final pass through the events dataset and checks for any events that are closer in time than ieHr. If so,
it will combine those events. If ieHr_check = FALSE, the algorithm will leave the events as-is, and depending
on discharge measurement frequency and ieHr, you may see events that are closer together than ieHr. In most use-cases,
ieHr_check = TRUE is appropriate. However, if discharge measurements are infrequent, and the adjustment to the start
and end times are significant, the user may not want the secondary filter on the events.
dataframe of all discharge events based on ieHr and qthresh criteria. Includes start and end times for each event.
Precip sample data from site 05408480
Steve Corsi [email protected]
This function computes antecedent rainfall for Rainmaker event files or any file with a list of specified dates.
Input files must have a as.POSIXctformatted date/time column. This format can be achieved by using the RMprep function The name of the rainfall column can also be changed as desired using the RMprep funtion
Subset the data by antecedent time period (can also assign to a df if you like) then define ARF values for the subset. Do this for all date periods in the sample file.
RMarf( df, date = "date", rain = "rain", df.events, sdate = "StartDate", days = c(0.5, 1, 2, 3, 5, 10, 15, 20), varnameout = "ARF" )RMarf( df, date = "date", rain = "rain", df.events, sdate = "StartDate", days = c(0.5, 1, 2, 3, 5, 10, 15, 20), varnameout = "ARF" )
df |
dataframe Unit values rain file |
date |
string Date column name in df as POSIX |
rain |
string Column in df with instantaneous rain values |
df.events |
dataframe with dates/times for events |
sdate |
string Name of start date column in df.events rain file as POSIX |
days |
vector of times in days for summing antecedent rainfall |
varnameout |
string prefix for resulting antecedent rainfall variable names |
df.events dataframe
RDB <- CedarRRain RDB2 <- RMprep(RDB,prep.type=1,date.type=1,dates.in="CST.Time",tz="CST6CDT") RDB3 <- subset(RDB2, upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.>-1) event.list <- RMevents(df=RDB3,ieHr=6, rainthresh=0.2, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2 intensities <- RMintensity(RDB3,date="pdate", rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", events.0.2,sdate="StartDate",edate="EndDate",depth="rain",xmin=c(5,15,30)) ARFrain <- RMarf(df = RDB3, date = "pdate", rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", df.events=intensities,sdate="StartDate",days=c(1,3,5),varnameout="ARF")RDB <- CedarRRain RDB2 <- RMprep(RDB,prep.type=1,date.type=1,dates.in="CST.Time",tz="CST6CDT") RDB3 <- subset(RDB2, upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.>-1) event.list <- RMevents(df=RDB3,ieHr=6, rainthresh=0.2, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2 intensities <- RMintensity(RDB3,date="pdate", rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", events.0.2,sdate="StartDate",edate="EndDate",depth="rain",xmin=c(5,15,30)) ARFrain <- RMarf(df = RDB3, date = "pdate", rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", df.events=intensities,sdate="StartDate",days=c(1,3,5),varnameout="ARF")
This function computes the erosive power of the rainfall
RMerosivity(df, ieHr, method, rain = "rain", StormSummary = StormSummary)RMerosivity(df, ieHr, method, rain = "rain", StormSummary = StormSummary)
df |
dataframe with instantaneous rainfall and events calculated. This is the 'tipsbystorm' output from RMevents, or the third list component from the output of RMevents. This dataframe must include colums for rain values, dates, 'dif_time' or the time elapsed from one tip (row) to the next, and 'event' which numbers each row or tip by which event to which it belongs (starting at 1). |
ieHr |
time between events in hours |
method |
choose which energy equation to use (see below for more details) |
rain |
string, column name of rainfall unit values, defaults to "rain" |
StormSummary |
dataframe output by RMIntense method=1: McGregor (1995) Supercedes Brown and Foster equation (1987), which superceded Agriculture Handbook 537 (1979). method=2: Wischmeier, Agriculture Handbook 537 (1979, 1981), correct computation of formula 2 found in AH537 method=3: Original Rainmaker (1997) USGS Wisconsin Water Science Center, based on equation in Agriculture Handbook 537. Storms with I30>2.5 are incorrectly computed. |
McGregor, K. C., R. L. Binger, A. J. Bowie, and G. R. Foster. 1995. Erosivity index values for northern Mississippi. Trans. Amer. Soc. Agric. Eng. 38:1039-1047;
Wischmeier, W. H. and D. D. Smith. 1978. Predicting rainfall erosion losses-A guide to conservation planning. U.S. Department of Agriculture, Agriculture Handbook 537, 58 pp.
Wischmeier, W. H. and D. D. Smith. 1981. Supplement and Errata for "Predicting rainfall erosion losses-A guide to conservation planning". U.S. Department of Agriculture, Agriculture Handbook 537, 58 pp.
Renard, K. G., G. R. Foster, G. A. Weesies, D. K. McCool, and D. C. Yoder. 1997. Predicting soil erosion by water: A guide to conservation planning with the Revised Soil Loss Equation (RUSLE). U.S. Department of Agriculture, Agriculture Handbook 703, 404 pp.
Compute rainfall event variables based on time series of rain data with only one rain gage or one mean radar rain column.
RMevents(df, ieHr = 6, rainthresh = 5.1, rain = "rain", time = "pdate")RMevents(df, ieHr = 6, rainthresh = 5.1, rain = "rain", time = "pdate")
df |
dataframe with rainfall |
ieHr |
numeric Interevent period in hours, defaults to 6, |
rainthresh |
numeric Minimum event depth in units of the rain column, default is given as 5.1 assuming millimeters (0.2") |
rain |
string Column name of rainfall unit values, defaults to "rain" |
time |
string column with as.POSIXctdate, defaults to "pdate" |
Rainfall event determination
list of all rain events that surpass rainthresh (storms2) and all rain events (storms). Also returns all a data frame of all rain observations > 0 with the associated date/time and assigned event number (tipsbystorm) and the minimum time difference between observations (timeInterval)
RDB <- CedarRRain RDB2 <- RMprep(RDB, prep.type = 1, date.type = 1, dates.in = "CST.Time", tz = "CST6CDT") event.list <- RMevents(df = RDB2, ieHr = 6, rainthresh = 0.2, rain = "upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2RDB <- CedarRRain RDB2 <- RMprep(RDB, prep.type = 1, date.type = 1, dates.in = "CST.Time", tz = "CST6CDT") event.list <- RMevents(df = RDB2, ieHr = 6, rainthresh = 0.2, rain = "upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2
Compute rainfall event variables based on time series of rain data with only one rain gage or one mean radar rain column. The function does not calculate events based on the rain data itself (such as in RMevents), but rather calculates event variables based on an input of sample/event start and end times.
RMevents_sample( dfrain, ieHr = 6, rain = "rain", time = "pdate", dfsamples, bdate = "bpdate", edate = "epdate" )RMevents_sample( dfrain, ieHr = 6, rain = "rain", time = "pdate", dfsamples, bdate = "bpdate", edate = "epdate" )
dfrain |
dataframe with rainfall |
ieHr |
numeric Interevent period in hours, defaults to 6, |
rain |
string Column name of rainfall unit values, defaults to "rain" |
time |
string column with as.POSIXctdate, defaults to "pdate" |
dfsamples |
dataframe with the beginning and ending dates and times of sampling periods in POSIXct format |
bdate |
character column name in dfsamples for the beginning of the sampling period |
edate |
character column name in dfsamples for the ending of the sampling period |
list of storms and storms2
RDB <- CedarRRain cedarSamples <- cedarSamples names(RDB)[2] <- "UVRain" RDB2 <- RMprep(RDB,prep.type=1,date.type=1, dates.in="CST.Time",tz="CST6CDT") eventListSamples <- RMevents_sample(df=RDB2,ieHr=6, rain="UVRain", time="pdate", dfsamples=cedarSamples, bdate="pSstart",edate="pSend")RDB <- CedarRRain cedarSamples <- cedarSamples names(RDB)[2] <- "UVRain" RDB2 <- RMprep(RDB,prep.type=1,date.type=1, dates.in="CST.Time",tz="CST6CDT") eventListSamples <- RMevents_sample(df=RDB2,ieHr=6, rain="UVRain", time="pdate", dfsamples=cedarSamples, bdate="pSstart",edate="pSend")
Function to graph rainfall for a given x-day window around specified event periods
RMevents.plot( df, date = "pdate", rain = "rain", df.events, sdate = "StartDate", edate = "EndDate", depth = "depth", plot.buffer = 3, site.name = "" )RMevents.plot( df, date = "pdate", rain = "rain", df.events, sdate = "StartDate", edate = "EndDate", depth = "depth", plot.buffer = 3, site.name = "" )
df |
dataframe with unit value rainfall data |
date |
string Date column in df as POSIX |
rain |
string Column in df with instantaneous rain values |
df.events |
dateframe with start and end dates/times for events |
sdate |
string Start date column in df.events rain file as POSIX |
edate |
string End date column in df.events rain file as POSIX |
depth |
string column in df.events with event rain depth |
plot.buffer |
numeric Used to define plotting window in days. Graphs will include data Time period preceding beginning of event for including in the graphs |
site.name |
string |
RDB <- CedarRRain RDB2 <- RMprep(RDB, prep.type = 1, date.type = 1, dates.in = "CST.Time", tz = "CST6CDT") RDB3 <- subset(RDB2, upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2. > -1) event.list <- RMevents(df=RDB3, ieHr=6, rainthresh=0.2, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2 # pdf("events.pdf") RMevents.plot(RDB3,date="pdate", rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", df.events=events.0.2,sdate="StartDate","EndDate",depth= "rain",plot.buffer=2, site.name="Example Site") # dev.off()RDB <- CedarRRain RDB2 <- RMprep(RDB, prep.type = 1, date.type = 1, dates.in = "CST.Time", tz = "CST6CDT") RDB3 <- subset(RDB2, upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2. > -1) event.list <- RMevents(df=RDB3, ieHr=6, rainthresh=0.2, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2 # pdf("events.pdf") RMevents.plot(RDB3,date="pdate", rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", df.events=events.0.2,sdate="StartDate","EndDate",depth= "rain",plot.buffer=2, site.name="Example Site") # dev.off()
Function to graph rainfall and flow for a given x-day window around specified event periods
RMevents.plotQ( df, dfQ, date = "pdate", Qdate = "pdate", rain = "rain", Q = "Q", df.events, sdate = "StartDate", edate = "EndDate", erain = "depth", plot.buffer = 3, logy = "", site.name = "", SampleInfo, sampbdate = "", sampedate = "" )RMevents.plotQ( df, dfQ, date = "pdate", Qdate = "pdate", rain = "rain", Q = "Q", df.events, sdate = "StartDate", edate = "EndDate", erain = "depth", plot.buffer = 3, logy = "", site.name = "", SampleInfo, sampbdate = "", sampedate = "" )
df |
dataframe with unit value rainfall data |
dfQ |
dataframe with unit value Q data |
date |
string Date column in df as POSIXct |
Qdate |
string Date column in dfQ as POSIXct |
rain |
string Column in df with instantaneous rain values |
Q |
string Column in dfQ with instantaneous Q values |
df.events |
dataframe with start and end dates/times for events |
sdate |
string Start date column in df.events rain file as POSIXct |
edate |
string End date column in df.events as POSIXct |
erain |
string Event rainfall depth column in df.events |
plot.buffer |
numeric Used to define plotting window in days. Graphs will include |
logy |
string "y" if log y-axis for Q or "" if linear axis. Will default to "". |
site.name |
site name as data type character |
SampleInfo |
if TRUE then sample start and end dates/times are plotted on the hydrograph; if FALSE then sample start and end dates/times are not plotted on the hydrograph. |
sampbdate |
character column name in df.events for the beginning of the sampling period |
sampedate |
character column name in df.events for the ending of the sampling period |
plots of rainfall events and discharge
#Example 1 - Rainfall/Q plots without sample start/end arrows RDB <- CedarRRain dfQ <- cedarq dfQ <- RMprep(dfQ,prep.type=1,date.type=3,tz="CST6CDT") RDB2 <- RMprep(RDB,prep.type=1,date.type=1,dates.in="CST.Time",tz="CST6CDT") RDB3 <- subset(RDB2, upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.>-1) event.list <- RMevents(df=RDB3,ieHr=6,rainthresh=0.2, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2 site.name <- "Example Site" SampleInfo <- FALSE RMevents.plotQ(RDB3,dfQ, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", df.events=events.0.2,erain="rain", site.name=site.name,SampleInfo=SampleInfo) #Example 2- Rainfall/Q plots with sample start/end arrows RDB <- CedarRRain cedarSamples <- cedarSamples names(RDB)[2] <- "UVRain" RDB2 <- RMprep(RDB, prep.type=1, date.type=1, dates.in="CST.Time", tz="CST6CDT") eventListSamples <- RMevents_sample(df=RDB2, ieHr=6, rain="UVRain", time="pdate", dfsamples=cedarSamples, bdate="pSstart",edate="pSend") dfQ <- cedarq dfQ <- RMprep(dfQ,prep.type=1,date.type=3,tz="CST6CDT") site.name <- "Example Site" SampleInfo <- TRUE sampbdate <- "pSstart" sampedate <- "pSend" #RMevents.plotQ(RDB2, # dfQ, # rain="UVRain", # df.events=eventListSamples, # sdate="Braindate", # edate="Eraindate", # erain="depth",logy="",site.name=site.name, # sampbdate="pSstart",sampedate="pSend")#Example 1 - Rainfall/Q plots without sample start/end arrows RDB <- CedarRRain dfQ <- cedarq dfQ <- RMprep(dfQ,prep.type=1,date.type=3,tz="CST6CDT") RDB2 <- RMprep(RDB,prep.type=1,date.type=1,dates.in="CST.Time",tz="CST6CDT") RDB3 <- subset(RDB2, upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.>-1) event.list <- RMevents(df=RDB3,ieHr=6,rainthresh=0.2, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2 site.name <- "Example Site" SampleInfo <- FALSE RMevents.plotQ(RDB3,dfQ, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", df.events=events.0.2,erain="rain", site.name=site.name,SampleInfo=SampleInfo) #Example 2- Rainfall/Q plots with sample start/end arrows RDB <- CedarRRain cedarSamples <- cedarSamples names(RDB)[2] <- "UVRain" RDB2 <- RMprep(RDB, prep.type=1, date.type=1, dates.in="CST.Time", tz="CST6CDT") eventListSamples <- RMevents_sample(df=RDB2, ieHr=6, rain="UVRain", time="pdate", dfsamples=cedarSamples, bdate="pSstart",edate="pSend") dfQ <- cedarq dfQ <- RMprep(dfQ,prep.type=1,date.type=3,tz="CST6CDT") site.name <- "Example Site" SampleInfo <- TRUE sampbdate <- "pSstart" sampedate <- "pSend" #RMevents.plotQ(RDB2, # dfQ, # rain="UVRain", # df.events=eventListSamples, # sdate="Braindate", # edate="Eraindate", # erain="depth",logy="",site.name=site.name, # sampbdate="pSstart",sampedate="pSend")
Function to compute maximum x-minute rainfall intensities in units of depth/hr
RMintensity( df, date = "r.date", rain = "rain", df.events, sdate = "StartDate", edate = "EndDate", depth = "depth", xmin = c(60, 180, 360) )RMintensity( df, date = "r.date", rain = "rain", df.events, sdate = "StartDate", edate = "EndDate", depth = "depth", xmin = c(60, 180, 360) )
df |
dataframe |
date |
string Date column name in df as POSIX |
rain |
string Column name in df with instantaneous rain values |
df.events |
dateframe with start and end dates/times for events |
sdate |
string Start date column in df.events rain file as POSIX |
edate |
string End date column in df.events rain file as POSIX |
depth |
string rain depth in event file, defaults to "depth", |
xmin |
vector vector of values representing X-minute max rainfall requested |
df.events dataframe, X-hour maximum rainfall intensities
RDB <- CedarRRain RDB2 <- RMprep(RDB, prep.type=1, date.type=1, dates.in="CST.Time",tz="CST6CDT") RDB3 <- subset(RDB2, upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.>-1) event.list <- RMevents(df=RDB3, ieHr=6, rainthresh=0.2, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2 intensities <- RMintensity(RDB3,date="pdate", rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", events.0.2,sdate="StartDate",edate="EndDate",depth="rain",xmin=c(5,15,30))RDB <- CedarRRain RDB2 <- RMprep(RDB, prep.type=1, date.type=1, dates.in="CST.Time",tz="CST6CDT") RDB3 <- subset(RDB2, upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.>-1) event.list <- RMevents(df=RDB3, ieHr=6, rainthresh=0.2, rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.") events.0.2 <- event.list$storms2 intensities <- RMintensity(RDB3,date="pdate", rain="upload.ph3_site_basin_cedar_creek.Id.0....Geographical.Mean.kg.m.2.", events.0.2,sdate="StartDate",edate="EndDate",depth="rain",xmin=c(5,15,30))
This function is used to prepare data files for Rainmaker functions. Dates are transformed to as.POSIXctdates using the as.POSIXct function Multiple common date formats are included as options for tranformation The original date column is transformed to a character variable. Column header names are changed to desired names
RMprep( df, prep.type = 1, date.type = 1, dates.in = "default", dates.out = "pdate", cnames.in = "", cnames.new = "rain", tz = "" )RMprep( df, prep.type = 1, date.type = 1, dates.in = "default", dates.out = "pdate", cnames.in = "", cnames.new = "rain", tz = "" )
df |
dataframe |
prep.type |
numeric 1=date to as.POSIXct 2=name change, 3=both |
date.type |
numeric 1=mm/dd/YYYY hh:mm, 2=YYYY-mm-ddTHH:MM, 3=RDB_example1: 2 columns, Date and Time Date=m/d/Y; time=h:mm, 4=RDB_example2: 4 columns, Year, Month, Day and Minute Date=m/d/Y; time=h:mm, 5=Lake level from Great Lakes "Tides and Currents", Date=YYYYMMDD; Time = H:MM |
dates.in |
string Vector of column names for date/time definition. Defaults are as follows for different date.type options date.type=1: One column name -> "GMT.Time", date.type=2: One column name -> "GMT.Time", date.type=3: two column names -> c("DATE","TIME"), date.type=4: four column names > c("YEAR","MONTH","DAY","MINUTE"), date.type=5: two column names -> c("Date","Time"), If no value is given, the defaults given above are used. Enter value as c("name1","name2",...) |
dates.out |
string Column name of output dates, defaults to 'pdate' which is used elsewhere in Rainmaker. |
cnames.in |
string Column names of the input data which should be changed. |
cnames.new |
string New column names for the columns specified in cnames.in. |
tz |
string time zone, CST6CDT for central time. For other times use values in the TZ* column here: http://en.wikipedia.org/wiki/List_of_zoneinfo_time_zones |
df dataframe
RDB <- cedarq RDB2 <- RMprep(RDB,prep.type=1,date.type=3,tz="CST6CDT") RDB <- CedarRRain RDB2 <- RMprep(RDB,prep.type=1,date.type=1,dates.in="CST.Time",tz="CST6CDT")RDB <- cedarq RDB2 <- RMprep(RDB,prep.type=1,date.type=3,tz="CST6CDT") RDB <- CedarRRain RDB2 <- RMprep(RDB,prep.type=1,date.type=1,dates.in="CST.Time",tz="CST6CDT")