Last data update: 2014.03.03

 R: NIOZ monitoring data of Westerschelde estuary.
NIOZ Westerschelde monitoringR Documentation

NIOZ monitoring data of Westerschelde estuary.

Description

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: http://www.nioz.nl/monitoring-data-downloads

Usage

data(WSnioz)
data(WSnioz.table)

Format

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.

Author(s)

Karline Soetaert <karline.soetaert@nioz.nl>

References

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

See Also

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.

Examples

# save plotting parameters
 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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(OceanView)
Loading required package: plot3D
Loading required package: plot3Drgl
Loading required package: rgl
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/OceanView/WSnioz.Rd_%03d_medium.png", width=480, height=480)
> ### Name: NIOZ Westerschelde monitoring
> ### Title: NIOZ monitoring data of Westerschelde estuary.
> ### Aliases: WSnioz WSnioz.table
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # save plotting parameters
>  pm <- par("mfrow")
>  mar <- par("mar")
>    
> ## =============================================================================
> ## Show stations and measured variables
> ## =============================================================================  
>  unique(WSnioz[,c("Station", "Latitude", "Longitude")])
     Station Latitude Longitude
3798       1 51.41265   3.56628
3823       2 51.43517   3.66932
3773       3 51.41720   3.69982
3848       4 51.34888   3.82437
3873       5 51.41733   3.92140
67         6 51.43597   4.01965
94         7 51.42615   4.03527
121        8 51.40118   4.03735
22         9 51.37145   4.08503
175       10 51.39227   4.20527
1         11 51.35030   4.24122
229       12 51.29790   4.28232
43        13 51.25148   4.32050
283       14 51.22493   4.39357
310       15 51.17525   4.32642
337       16 51.12912   4.31897
364       17 51.11913   4.22827
>  unique(WSnioz[,c("VariableName", "VariableDesc")])
     VariableName                                VariableDesc
3798      SPMCHLA         Chlorophyll-a in SPM (HPLC-FLU/DAD)
3808      WCSALIN                  Salinity measured with CTD
3809       WCTEMP               Temperature measured with CTD
3811         WNH4        Ammonia concentration in water (SFA)
3812         WNO2        Nitrite concentration in water (SFA)
3813         WNO3        Nitrate concentration in water (SFA)
3814         WPO4 Free phosphate concentration in water (SFA)
3816          WSi       Silicate concentration in water (SFA)
8489          WO2                            Dissolved Oxygen
>                                   
> ## =============================================================================
> ## 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)
[1] "1"  "17"
>   
>  Mplot(dat2, lty = 1)
> 
> ## =============================================================================
> ## tabular format of the same data
> ## =============================================================================
>  head(WSnioz.table)
     SamplingDateTimeREAL Station   SPMCHLA WCSALIN WCTEMP      WNH4     WNO2
[1,]             34822.02      11  4.163144     4.3  13.83  61.07143 14.28571
[2,]             34822.47       9 10.078139     9.6  12.88  28.21429  8.50000
[3,]             34822.56      13 17.964073     0.9  13.95 201.35714 15.00000
[4,]             34841.34       6 31.722332    17.0  13.08   0.00000  1.34900
[5,]             34841.34       7 38.931877    16.9  13.11   0.00000  3.70500
[6,]             34841.35       8 42.878340    17.0  13.11   0.00000  3.99900
             WNO3 WO2     WPO4      WSi
[1,]   0.42857143  NA 4.516129 181.1032
[2,]   0.07142857  NA 3.709677 122.2064
[3,]   0.57142857  NA 5.322581 210.7117
[4,] 238.54700000  NA 4.145000  40.2700
[5,] 226.15400000  NA 4.102000  33.0670
[6,] 225.70300000  NA 4.131000  34.0960
> 
> # 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)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


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