Ginko-Rfun/get_indicatorNe_data.R at main · CCGenetics/Ginko-Rfun · GitHub

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###
### This R function takes as input the output of the Kobo form "International Genetic Indicator testing"
### and reformat its in order to have the data in a dataframe useful for estimating
### Genetic Diversity Indicator 1 (the proportion of populations within species with
###                                a genetically effective size, Ne, greater than 500.)
###

### If you use this script, please check https://ccgenetics.github.io/guidelines-genetic-diversity-indicators/docs/Contact_cite/Contact_cite.html
### for citation guidelines

get_indicatorNe_data<-function(kobo_output=kobo_output){
  ###
  ### Arguments:
  ###

  # kobo_output = a data frame object read into R from the `.csv` file
  # resulting from exporting the Kobotoolbox data from the form
  # "International Genetic Indicator testing" wit the settings explaiend at
  # https://ccgenetics.github.io/guidelines-genetic-diversity-indicators/docs/5_Data_collection/Kobo_toolbox_help.html
  # you can use the raw data, or clean it before.

  ### Needed libraries:

  #  library(tidyr)
  #  library(dplyr)
  #  library(utile.tools)
  #  library(stringr)

  ###
  ### Function
  ###

  ### Get data
  kobo_output<-kobo_output

  ### Separate data

    # create a variable with the full taxon name if this variable doesn't exist already
    # (raw kobo output doesn't include it, but it may exists in a "clean" version of the
    # output if ran through the quality check pipeline)

    if("taxon" %in% colnames(kobo_output)){
       print("the data already contained a taxon column, that was used instead of creating a new one")

    }else {
    kobo_output<-kobo_output %>%
      mutate(taxon=(utile.tools::paste(genus, species, subspecies_variety, na.rm=TRUE))) %>%
        # remove white space at the end of the name
        mutate(taxon=str_trim(taxon, "right"))
    }

   ## Add a variable to the metadata stating if the taxon was assessed multiple times or only a single time

  # object with duplicated taxa within a single country
  # duplicated() is run twice, the second time with  fromLast = TRUE so that
  # the first occurrence is also accounted for, i.e. we can subset all records with the same taxon for a given country
  kobo_output_duplicates <- kobo_output[which(duplicated(kobo_output[c('taxon', 'country_assessment')]) | duplicated(kobo_output[c('taxon', 'country_assessment')], fromLast = TRUE)), ]

  # if it is a duplicate then tag it as multi_assessment, if it is not duplicated within the country then single
  kobo_output <- kobo_output %>%
    mutate(multiassessment= if_else(
      X_uuid %in% kobo_output_duplicates$X_uuid, "multiassessment", "single_assessment"))


   ### Process data already including taxon column and multiassessment

    indicatorNe_data <- kobo_output %>%

    # create variable with year in which assessment was done (based on date the form was completed)
    mutate(year_assesment=substr(end,1,4)) %>%

    # make sure some variables that seem numbers are actually character,
    # because there may be character and integer values depending on how data was written)
    # for example in IntroductionYear, NeYear and NcYear...
    mutate(across(starts_with("IntroductionYear"), as.character)) %>%
    mutate(across(starts_with("NeYear"), as.character)) %>%
    mutate(across(starts_with("NcYear"), as.character)) %>%
    mutate(across(starts_with("NcRangeDetails"), as.character)) %>%

    ## select relevant columns
    # taxon and assessment info
    dplyr::select(country_assessment, taxonomic_group, taxon, scientific_authority,
                  genus, taxon, year_assesment, name_assessor, email_assessor, kobo_tabular,

                  # method to define populations
                  defined_populations,

                  # indicator 1 data
                  time_populations, Name_pop1:Comments_pop25,

                  # kobo validation status
                  X_validation_status,

                  # uuid, this is a unique id generated by kobo for each unique record. It will be used to differentiate different records even if the same species is evaluated twice
                  X_uuid,

                  # to know if the taxon was assessed multiple times or only a single time
                  multiassessment) %>%

  ### Get population data as single variables
    pivot_longer(cols = matches("_pop[0-9]"),
                 names_to=c(".value", "population"),
                 names_sep = "_",
                 values_drop_na = TRUE) %>%

  # omit populations w/data (empty because they were not filled, this is ok)
  filter(Origin!="")  %>% #origin is a mandatory question, so it should be answered, if not, the pop doesn't exist

  # change all "" (empty) cells to NA. Important: preserve original types (e.g. numeric stays numeric).

      mutate(across(everything(), ~{
        if (is.character(.) || is.factor(.)) {
          na_if(as.character(.), "")
        } else {
          .
        }
      })) %>%

  # change -999 to Na
  mutate(Ne=na_if(Ne, -999),
         NeLower=na_if(NeLower, -999),
         NeUpper=na_if(NeUpper, -999),
         NcPoint=na_if(NcPoint, -999),
         NcLower=na_if(NcLower, -999),
         NcUpper=na_if(NcUpper, -999)) %>%

  # change "" in kobo_tabular to "kobo" ("" means that question was not answered because the taxon had less populations that the min to trigger tabular)
  mutate(kobo_tabular=ifelse(is.na(kobo_tabular), "kobo", kobo_tabular))


  # End of function
}