calculate_wespr_single_site
calculate_wespr_single_site.Rmd1) Import data in wesp site object
Once the wespr package has been installed, and the raw data processed using the steps outlined in the previous vignette, we can calculate the wespr scores and compare the scores to the ecoprovince calibration data.
An example file in the package, which we can use for demonstration purposes:
# point to example file
wesp_file <- system.file("input_data/reference_singlesite.csv", package = "wespr")
# load the wesp data
wesp_data <- load_wesp_data(wesp_file)
# we can take a look at this file to see the format
head(wesp_data)q_no, response_no,site_1
F1,1,1
F1,2,0
F1,3,1
F1,4,1
F1,5,1
F1,6,1
F2,A1,0
F2,A2,0
F2,B1,0
F2,B2,1We will also generate a key for the site names which we can use later after processing the site information.
# generate a key for site names
wespkey <- wespr::generate_ids(wesp_data)Next we convert the data into a special wesp_site
object. This process validates the input data, and will give informative
errors if a question is not answered correctly. It also calculates many
“derived values” from the questions, which are common inputs into
different indicator calculations.
If there is more than one site in the input data, you can select which site you would like to use — if no site is selected it will default to choosing the first site in the file. See here for a step by step guide to processing multisite datasets.
site <- as.wesp_site(wesp_data, site = 1)You should not normally need to work with the internals of the
wesp_site object as it is quite complex, however we can get
an overview of what is in it by just typing the name of the object:
site2) Explore in wesp site object
We can see the structure of the wesp_site object includes a site name, any warnings or flags for missing data and a list of derived values.You can see the indicators have not yet been calculated at this stage.
A wesp_site object
Site: site_1
Incomplete Questions: F59
* Please ensure that it is valid to leave these questions unanswered.
Derived values:
* AllWater = 0
* NeverWater = 0
* NoSeasonal = 0
* NoPersis = 0
* TempWet = 0
* AllPermW = 0
* HiFlucW = 0
* TooShallow = 0
* NoPond = 0
* NoDeepPonded = 0
* NoOW = 0
* NoOutletX = 0
* NoOutlet = 0
* Inflow = 1
* Disturb = 1
* FishFound = 0
* Moose = 0
* Beaver = 1
* Muskrat = 0
* Bear = 1
* Caribou = 0
* NoCA = 0
* Fishless = 0
* GDeco = 0
* CMeco = 0
* SIMeco = 1
* BPeco = 0
* TPeco = 0
* OutMap = 1
* S1_sum = 4
* S1_subscore = 0.33
* S2_sum = 1
* S2_subscore = 0.11
* S3_sum = 1
* S3_subscore = 0.11
* S4_sum = 4
* S4_subscore = 0.33
* S5_sum = 0
* S5_subscore = 0
* S6_sum = 2
* S6_subscore = 0.33
Indicators:
All indicators are NULL. Run `calc_indicators()` to calculate them.
3) Calculate indicator raw scores
Now, we can calculate the indicator scores with the
calc_indicators() function.
You must assign the output of this function back to the original
wesp_site object, as it updates the indicators
with the calculated values:
site <- calc_indicators(site)If we view the site object again, we can see the calculated indicator scores.
siteA wesp_site object
Site: site_1
Incomplete Questions: F59
* Please ensure that it is valid to leave these questions unanswered.
Derived values:
* AllWater = 0
* NeverWater = 0
* NoSeasonal = 0
* NoPersis = 0
* TempWet = 0
* AllPermW = 0
* HiFlucW = 0
* TooShallow = 0
* NoPond = 0
* NoDeepPonded = 0
* NoOW = 0
* NoOutletX = 0
* NoOutlet = 0
* Inflow = 1
* Disturb = 1
* FishFound = 0
* Moose = 0
* Beaver = 1
* Muskrat = 0
* Bear = 1
* Caribou = 0
* NoCA = 0
* Fishless = 0
* GDeco = 0
* CMeco = 0
* SIMeco = 1
* BPeco = 0
* TPeco = 0
* OutMap = 1
* S1_sum = 4
* S1_subscore = 0.33
* S2_sum = 1
* S2_subscore = 0.11
* S3_sum = 1
* S3_subscore = 0.11
* S4_sum = 4
* S4_subscore = 0.33
* S5_sum = 0
* S5_subscore = 0
* S6_sum = 2
* S6_subscore = 0.33
Indicators:
* WS:
- fun: 2.97
- ben: 3.3
* SR:
- fun: 0
- ben: 4.09
* PR:
- fun: 4.77
- ben: 3.03
* CP:
- fun: 7.06
* FR:
- fun: 4.93
- ben: 1.06
* SENS:
- fun: 7.02
* STR:
- fun: 6.61
* NR:
- fun: 5.27
- ben: 3.57
* APP:
- fun: 3.82
- ben: 4.39
* PD:
- fun: 5.59
- ben: 4.19
* KMH:
- fun: 6.41
- ben: 3.93
* WB:
- fun: 5.62
- ben: 10
* POL:
- fun: 5.95
- ben: 2.25
* RSB:
- fun: 5.05
- ben: 6.11
* OE:
- fun: 4.1
* AM:
- fun: 3.81
- ben: 8.1
* FH:
- fun: 3.08
- ben: 3.72
* SFTS:
- fun: 3.46
- ben: 3.63
* CRI:
- ben: 4.93
* Retrieve indicator scores with `get_indicator_scores()`We probably want to get the indicator scores out as a usable object.
We can do that with get_indicator_scores(), which gives
them to us as a data.frame:
ind_scores <- get_indicator_scores(site) site indicator fun ben
<chr> <chr> <dbl> <dbl>
1 site_1 WS 3.61 6.98
2 site_1 SR 0.778 4.28
3 site_1 PR 6.20 3.06
4 site_1 CP 6.78 NA
5 site_1 FR 1.67 7.51
6 site_1 SENS 4.86 NA
7 site_1 STR 9.58 NA
8 site_1 NR 5.52 3.06
9 site_1 APP 0 2.99
10 site_1 PD 0 5.51
11 site_1 KMH 2.99 5.60
12 site_1 WB 0 10
13 site_1 POL 6.30 1.67
14 site_1 RSB 6.94 4.83
15 site_1 OE 4.54 NA
16 site_1 AM 5.03 3.10
17 site_1 FH 0 3.33
18 site_1 SFTS 2.94 3.27
19 site_1 CRI NA 6.58
An example format of the ind_score output is shown above, with the service type (rows) for each site with a column for function (fun) and benefit (ben). Note not every service has both a function and benefit so you will see NA values.
# add site specific names
ind_scores <- dplyr::left_join(wespkey, ind_scores, by = "site")
# lets see what it looks like now
ind_scoresThere are a number of helper functions to extract information from
the wespr_site object. For example we can gather just the responses with
get_responses():
get_responses(site)no question response_no value
F1 Vegetation Height & Form Diversity F1_1 3.00
F1 Vegetation Height & Form Diversity F1_2 0.00
F1 Vegetation Height & Form Diversity F1_3 2.00
F1 Vegetation Height & Form Diversity F1_4 2.00
F1 Vegetation Height & Form Diversity F1_5 1.00
F1 Vegetation Height & Form Diversity F1_6 3.00
F10 Dense Moss Extent F10_1 0.00
F10 Dense Moss Extent F10_2 1.00
F10 Dense Moss Extent F10_3 0.00
F10 Dense Moss Extent F10_4 0.00 We can also get out a data.frame of derived values, those values which are calculated from the responses, and used as inputs into many indicators.
get_derived_values(site)name value
AllWater 0.0000000
NeverWater 0.0000000
NoSeasonal 0.0000000
NoPersis 0.0000000
TempWet 0.0000000
AllPermW 0.0000000
HiFlucW 0.0000000
TooShallow 0.0000000
NoPond 0.0000000
NoDeepPonded 0.0000000 4) Assign Jenks scores
Once we have calculated our scores we may wish to compare to see how our particular site compares with other wetlands in the same Ecoprovince. Wespr protocol is based on wetland assessment in eight Ecoprovinces (Georgia Depression (GD), Southern Interior (SI), Central Interior (CI), Southern Interior Mountains (SIM), Sub-Boreal Interior (SBI), Boreal and Taiga Plains (BTP), Northern Boreal Mountains (NBM), and Coast and Mountains (CM)).
Using our data from above we see the ind_score object is
a data table with a row for each indicator and columns for the raw
values for functions (fun) and benefits (ben). An example format of the
ind_score output is shown, with the service type (rows) for each site
with a column for function (fun) and benefit (ben). Note not every
service has both a function and benefit so you will see NA values.
ind_scoressite wetland_id indicator fun ben
site_1 SIM_90009 WS 2.968247 3.304924
site_1 SIM_90009 SR 0.000000 4.094300
site_1 SIM_90009 PR 4.772264 3.031987
site_1 SIM_90009 CP 7.055459 NA
site_1 SIM_90009 FR 4.930556 1.056566
site_1 SIM_90009 SENS 7.015852 NA
site_1 SIM_90009 STR 6.607143 NA
We use the assign_jenks_score() function to assign a
catergory of Low/Medium/High to each of the values, based on where our
site sits in relation to 100 or more calibration sites for the given
ecoprovince.
The calibration data is stored within the package and can be updated by package maintainers. This is an on-going project and data will be added as comes available. Steps to update the calibration data are outline here. Note this is an advanced/admin level action and not required to calculate values. Please contact the package dev team for assistance.
Threshold values to define the low , medium or high class are determined by normalising all the reference data for each value and then looking for natural breask in the distribution (i.e. Jenks Breaks). These thresholds are then used as benchmarks and compared to our site of interest to determine which category it falls within.
Note in some cases, out site value may fall below or above data gathers for the calibration sites. In this case, a warning message is provided to the user.
To run this function we require:
- the indicator score data (i.e. ind_scores in this workflow),
- calibration data (this is stored as a file within the wespr package)
- name of Ecoprovince in which the site occurs.
- if a report is to be generated (T/F)
- if a report = TRUE, an output directory where the report is to be saved.
If the ecoprovince does not yet have calibration data loaded, users should select the next best ecoprovince available.
out <- assign_jenks_score(
ind_scores,
calibration_scores,
EcoP = "GD",
report = FALSE,
output_dir = "temp")The output scores will include a column with a calibration_score (L,M,H)
site indicator value service_type calibrated_score
<chr> <chr> <dbl> <chr> <chr>
1 site_1 WS 3.61 f L
2 site_1 SR 0.778 f M
3 site_1 PR 6.20 f M
4 site_1 CP 6.78 f L
5 site_1 FR 1.67 f L
6 site_1 SENS 4.86 f L
7 site_1 STR 9.58 f H
8 site_1 NR 5.52 f M
9 site_1 APP 0 f L
10 site_1 PD 0 f L
Now you have a data table with all indicators, service_type (function or benefit), raw scores and calibrated score (L/M/H). This can be saved or exported as a .csv to be further explored.