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Prepare input data for WESPR calculations

prepare-rawdata.Rmd

1) Prepare Raw data imports

The first step in calculating wespr scores for a wetland is to standardise and quality check the input data compiled by the user.

As of November 2025, data collection is comprised of two parts:

  • Field data collected using a survey123 form (WESP_FIELDV1.csv)
  • Desktop data collected using a survey123 form (WESP_DESKTOPV1.csv)

Note for advanced users the desktop analysis can be conducted using the R packageWESP_OF github repo. This is beyond the scope of this tutorial.

Field data

Field data is collected on site using the survey123 form. This is intended to control data inputs and standardize the data collection process. These comprise all the field and stressor questions.

The data is exported from survey123 as a .csv file. This will be the input file type for wespr.

An example data file is included within this package for testing. Note using system.file() can create some difficulties on an OS operating system.

# load library
library(wespr)

# Define the location of the survey123 Field data 
field_data <- system.file(file.path('extdata','WESP_FIELDV1.csv'), package = "wespr")

Desktop data

Desktop data is also collected using a survey123 form. A helper function import_spatial_data() can be used to import a stanadard set of spatial dataset required for the Desktop Analysis. A vingette which details this process can be found vignette("prepare_spatial_data").

Once the survey123 form has been completed, the data is exported as a .csv file. An example file is found within the package.

# Define the location of the survey123 Desktop data 
desktop_data <- system.file(file.path('extdata','WESP_DESKTOPV1.csv'), package = "wespr")

Once both of these files are downloaded you are ready to use the WESPR preparation steps.

2) Compile and format raw data in wespr

Once the two csv files are prepared, we use the format_rawdata function. This function standardizes field names and data types and then combines field and desktop data into a single table ready to be used to calculate WESPR values.

The user needs to define the following parameters:

  • field_data = the full filepath defining the location of the field data. Note this needs to be in R readable format and is expecting a .csv file. Example “WESP_FILEDV1.csv”

  • desktop_data = the full filepath defining the location of the desktop analysis data. Note this needs to be in R readable format and is expecting a .csv file. Example” WESP_DESKTOPV1.csv”

  • write_subfiles = TRUE or FALSE. This parameter determines if you want to produce individual files for each of the data components(field, desktop and stressor data components). This allows for an intermediate manual review process. The default values is FALSE.

  • out_dir = a filepath to the location in which your subfiles will be saved. This expects an R readable format and will only be used if write_subfiles = TRUE.

  • overwrite = TRUE or FALSE. If subfiles already exist, do you want to overwrite them?

An example of how to use this function is as follows:


# point to the location where you want the outputs to be saved
output_dir = "outputs"

ww <- format_rawinputs(
  field_data <- system.file(file.path('extdata','WESP_FIELDV1.csv'), package = "wespr"),
  desktop_data <- system.file(file.path('extdata','WESP_DESKTOPV1.csv'), package = "wespr"),
  write_subfiles = FALSE,
  out_dir = output_dir,
  overwrite = TRUE
)

# write out the file which will combine both the field and desktop answers so you can review
write.csv(ww, fs::path(output_dir,"wesp_input_20251125.csv"), row.names=FALSE)

The final output

The combined data should now be in the following format:

Question,1
F1_1,1
F1_2,4
F1_3,0
F1_4,2
F1_5,0
F1_6,1
F2_A1,1
F2_A2,0
F2_B1,0
F2_B2,0

Each row is a unique question and each column is a unique site.



3) Quality checking the data

We can then check QA the data using the check_indata function. This function checks the data for missing values, and ensures the data is in the correct format.

indata <- fs::path(output_dir,"wesp_input_20251125.csv")

check_indata(indata)

Note this will show where there are missing values.

In the example above we show the process for a single site.

If you have data for multiple sites, i.e. multiple rows of data within your survey123 csv files, the final compiled data, with the first 10 questions (rows) and 6 sites (columns) will look like the following:

Question,1,2,3,4,5,6
F1_1,1,0,1,1,1,2
F1_2,0,3,3,2,0,2
F1_3,1,0,3,1,1,1
F1_4,1,4,1,1,1,3
F1_5,1,0,1,1,1,0
F1_6,1,1,1,1,1,4
F2_A1,0,0,0,0,0,1
F2_A2,0,0,0,0,0,0
F2_B1,0,1,0,0,0,0
F2_B2,1,0,1,1,1,0

Congratulations, you have now prepared your raw data into a format which can be used to calcualte wespr scores.

The next step in this process is to calculate wespr scores. See here for a detailed step.