Description Usage Format Author(s) References See Also Examples
Part of the long-term monitoring data of the Westerschelde estuary, from 1996 till 2004.
A total of 17 stations were monitored on a monthly basis.
The dataset WSnioz is in long format and contains the following variables:
oxygen, temperature, salinity, nitrate, ammonium, nitrite, phosphate,
silicate and chlorophyll.
The dataset WSnioz.table is in tabular format.
The full dataset can be downloaded from:
https://www.nioz.nl/monitoring-data-downloads
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WSnioz is a data.frame with the following columns:
SamplingDateTime, a string with the date and time of sampling.
SamplingDateTimeREAL, a numeric value with day as per 1900.
Station, the station number.
Latitude, Longitude, the station position.
VariableName, the variable acronym.
VariableDesc, description of the variable.
VariableUnits, units of measurement.
DataValue, the actual measurement.
Karline Soetaert <karline.soetaert@nioz.nl>
Soetaert, K., Middelburg, JJ, Heip, C, Meire, P., Van Damme, S., Maris, T., 2006. Long-term change in dissolved inorganic nutrients in the heterotrophic Scheldt estuary (Belgium, the Netherlands). Limnology and Oceanography 51: 409-423. DOI: 10.4319/lo.2006.51.1_part_2.0409
http://aslo.org/lo/toc/vol_51/issue_1_part_2/0409.pdf
image2D for plotting images, package plot3D.
ImageOcean for an image of the ocean's bathymetry, package plot3D.
scatter2D for making scatterplots, package plot3D.
Oxsat for a 3-D data set, package plot3D.
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pm <- par("mfrow")
mar <- par("mar")
## =============================================================================
## Show stations and measured variables
## =============================================================================
unique(WSnioz[,c("Station", "Latitude", "Longitude")])
unique(WSnioz[,c("VariableName", "VariableDesc")])
## =============================================================================
## An image for Nitrate:
## =============================================================================
# 1. use db2cross to make a cross table of the nitrate data
# assume that samples that were taken within 5 days belong to the same
# monitoring campaign (df.row).
NO3 <- db2cross(WSnioz, row = "SamplingDateTimeREAL",
col = "Station", val = "DataValue",
subset = (VariableName == "WNO3"), df.row = 5)
# 2. plot the list using image2D; increase resolution
image2D(NO3, resfac = 3)
## =============================================================================
## All timeseries for one station
## =============================================================================
st1 <- db2cross(WSnioz, row = "SamplingDateTimeREAL",
col = "VariableName", val = "DataValue",
subset = (WSnioz$Station == 1), df.row = 5)
Mplot(cbind(st1$x/365+1900,st1$z))
## =============================================================================
## All timeseries for multiple stations
## =============================================================================
dat <- NULL
for (st in 1:17) {
dd <- db2cross(WSnioz, row = "SamplingDateTimeREAL",
col = "VariableName", val = "DataValue",
subset = (WSnioz$Station == st), df.row = 5)
dat <- rbind(dat, cbind(st, time = dd$x/365+1900, dd$z))
}
# select data for station 1, 17
dat2 <- Msplit(dat, split = "st", subset = st %in% c(1, 17))
names(dat2)
Mplot(dat2, lty = 1)
## =============================================================================
## tabular format of the same data
## =============================================================================
head(WSnioz.table)
# plot all data from station 1:
Mplot(WSnioz.table, select = 3:11, subset = Station == 1, legend = FALSE)
Mplot(Msplit(WSnioz.table, "Station", subset = Station %in% c(1, 13)) ,
select = c("WNO3", "WNO2", "WNH4", "WO2"), lty = 1, lwd = 2,
xlab = "Daynr", log = c("y", "y", "y", ""),
legend = list(x = "left", title = "Station"))
# reset plotting parameters
par(mar = mar)
par(mfrow = pm)
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