General note: the below vignette contains frozen output of 16 Sep 2019. This makes it possible to build the package with vignettes without access to the Watina database.
XG3 is an umbrella for the three geohydrological variables LG3, HG3
and VG3, which are yearly statistics which can be calculated from series
of water level measurements (on condition that enough data are
available). More explanation on these variables is given by the
documentation of get_xg3().
Let’s suppose that we want to select locations (within an area,
i.e. after having used get_locs()) for which:
min_dur and max_gap arguments.We can store specific conditions in a dataframe; the available
statistics are explained by the documentation of
eval_xg3_avail() and eval_xg3_series():
conditions_df <-
tribble(
~xg3_variable, ~statistic, ~criterion, ~direction,
"combined", "ser_lastyear", 2015, "min"
)
conditions_df %>%
kable| xg3_variable | statistic | criterion | direction |
|---|---|---|---|
| combined | ser_lastyear | 2015 | min |
The other aspects (which XG3 types?, max_gap,
min_dur) are directly specified in the
selectlocs_xg3() function:
watina <- connect_watina()
get_locs(watina,
area_codes = "TOR",
loc_type = c("P", "S")) %>%
get_xg3(watina, 1900) %>%
selectlocs_xg3(xg3_type = c("L", "H"),
max_gap = 1,
min_dur = 5,
conditions = conditions_df)
#> Dropped 19 locations in series evaluation because no XG3 series are available at those locations, for the requested XG3 variables: TORP012, TORP013, TORP019, TORP023, TORP025, TORP026, TORP027, TORP030, TORP031, TORS001, TORS002, TORS003, TORS004, TORS005, TORS006, TORS007, TORS008, TORS010, TORS012
#> # A tibble: 16 x 1
#> loc_code
#> <chr>
#> 1 TORP001
#> 2 TORP002
#> 3 TORP004
#> 4 TORP005
#> 5 TORP010
#> 6 TORP011
#> 7 TORP014
#> 8 TORP015
#> 9 TORP016
#> 10 TORP018
#> 11 TORP020
#> 12 TORP022
#> 13 TORP024
#> 14 TORP028
#> 15 TORP029
#> 16 TORS009With the argument list = TRUE you can also obtain
intermediate test results. For further information, see the
documentation of the selectlocs_xg3() function.
The get_xg3() function in the above example retrieved
XG3 values, from given locations, as a lazy object (unless
collect = TRUE). A timeframe (in hydrological years) is
used to filter XG3 values in Watina (arguments startyear
and endyear). By default, the endyear argument
is set to the previous hydrological year. Here we only provide the
startyear:
mylocs <- get_locs(watina, area_codes = "KAL")
mylocs %>% get_xg3(watina, 2010)
#> # Source: lazy query [?? x 5]
#> # Database: Microsoft SQL Server
#> # Ordered by: area_code, loc_code, loc_code, hydroyear
#> loc_code hydroyear lg3_lcl hg3_lcl vg3_lcl
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 KALP022 2014 -0.948 -0.135 -0.473
#> 2 KALP022 2015 -1.05 -0.112 -0.340
#> 3 KALP022 2016 -1.07 -0.124 -0.269
#> 4 KALP022 2017 NA NA -0.326
#> 5 KALP079 2010 NA NA -0.873
#> 6 KALP079 2011 NA NA -0.747
#> 7 KALP079 2012 NA NA -0.773
#> 8 KALP079 2013 NA NA -0.64
#> 9 KALP081 2010 NA NA -0.557
#> 10 KALP081 2013 NA NA -0.413
#> # … with more rowsRetrieving the data locally:
mylocs %>% get_xg3(watina, 2010, collect = TRUE)
#> # A tibble: 46 x 5
#> loc_code hydroyear lg3_lcl hg3_lcl vg3_lcl
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 KALP022 2014 -0.948 -0.135 -0.473
#> 2 KALP022 2015 -1.05 -0.112 -0.340
#> 3 KALP022 2016 -1.07 -0.124 -0.269
#> 4 KALP022 2017 NA NA -0.326
#> 5 KALP079 2010 NA NA -0.873
#> 6 KALP079 2011 NA NA -0.747
#> 7 KALP079 2012 NA NA -0.773
#> 8 KALP079 2013 NA NA -0.64
#> 9 KALP081 2010 NA NA -0.557
#> 10 KALP081 2013 NA NA -0.413
#> # … with 36 more rowsBy default, the “local” vertical coordinate reference system (CRS) is
used to express water levels, but also the CRS Ostend height (EPSG 5710, also known as ‘TAW’ or ‘DNG’) is
available from the database by setting
vert_crs = "ostend".
mylocs %>% get_xg3(watina, 2010, vert_crs = "ostend")
#> # Source: lazy query [?? x 5]
#> # Database: Microsoft SQL Server
#> # Ordered by: area_code, loc_code, loc_code, hydroyear
#> loc_code hydroyear lg3_ost hg3_ost vg3_ost
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 KALP022 2014 22.5 23.4 23.0
#> 2 KALP022 2015 22.4 23.4 23.1
#> 3 KALP022 2016 22.4 23.4 23.2
#> 4 KALP022 2017 NA NA 23.2
#> 5 KALP079 2010 NA NA 19.6
#> 6 KALP079 2011 NA NA 19.7
#> 7 KALP079 2012 NA NA 19.7
#> 8 KALP079 2013 NA NA 19.8
#> 9 KALP081 2010 NA NA 20.9
#> 10 KALP081 2013 NA NA 21.0
#> # … with more rowsJoining results to mylocs:
mylocs %>%
get_xg3(watina, 2010) %>%
left_join(mylocs %>%
select(-loc_wid),
.) %>%
collect
#> Joining, by = "loc_code"
#> # A tibble: 109 x 13
#> loc_code area_code area_name x y loc_validitycode loc_validity
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 KALP030 KAL Kalmthou… 152990 238600 VLD Gevalideerd
#> 2 KALP035 KAL Kalmthou… 153000 236850 VLD Gevalideerd
#> 3 KALP039 KAL Kalmthou… 160940 235060 VLD Gevalideerd
#> 4 KALP052 KAL Kalmthou… 153822 233761 VLD Gevalideerd
#> 5 KALP053 KAL Kalmthou… 153781 233736 VLD Gevalideerd
#> 6 KALP059 KAL Kalmthou… 154754 232459 VLD Gevalideerd
#> 7 KALP060 KAL Kalmthou… 154702 232476 VLD Gevalideerd
#> 8 KALP061 KAL Kalmthou… 154559 230477 VLD Gevalideerd
#> 9 KALP062 KAL Kalmthou… 154553 230423 VLD Gevalideerd
#> 10 KALP063 KAL Kalmthou… 154669 230340 VLD Gevalideerd
#> # … with 99 more rows, and 6 more variables: loc_typecode <chr>,
#> # loc_typename <chr>, hydroyear <int>, lg3_lcl <dbl>, hg3_lcl <dbl>,
#> # vg3_lcl <dbl>You can characterize the locations of a dataset of XG3 values, using
the eval_xg3_avail() and eval_xg3_series()
functions. You are invited to read their documentation and try their
examples!
The selectlocs_xg3() function, of which we saw a
demonstration above, sits on top of these. It calls
eval_xg3_avail() and eval_xg3_series() by
itself, but alternatively the user can provide the result of those
functions as input to selectlocs_xg3().