normalize_morpho_measurements

normalizeCharacter <-
  function(data,
           character,
           # character = "width_cm",
           normalize_by,
           # normalize_by = "length_cm",
           # a_start = 0.5,
           # b_start = 1,
           new_col_name = str_c(character,
                                "_normalized")){
    
    # estimate a and b values
    # https://www.statology.org/power-regression-in-r/
    # nls was throwing errors and this uses lm + transforms to solve
    # answers are slightly diff between lm and nls, so lm produces starting vals for nls
    allometric_formula_lm <- 
      as.formula(str_c("log(",
                       character,
                       ") ~ log(",
                       normalize_by,
                       ")"))
    model_lm <-
      lm(allometric_formula_lm,
         data = data)
    
    a_start <- exp(model_lm$coefficients[1])
    b_start <- model_lm$coefficients[2]
    
    # need to use variables to dynamically set formula 
    # https://stackoverflow.com/questions/55877110/pass-dynamically-variable-names-in-lm-formula-inside-a-function
    allometric_formula_nls <-
      as.formula(str_c(character,
                       " ~ a * ",
                       normalize_by,
                       "^b"))
    
    
    # # use non linear model to estimate coefficient b
    model_nls <-
      nls(allometric_formula_nls,
          start = list(a = a_start,
                       b = b_start),
          data = data)
    
    b <- summary(model_nls)$parameters["b", "Estimate"]
    
    # add column with normalized measure
    # can set column name from variable as long as it's a quosure and we use :=   
    # https://stackoverflow.com/questions/26003574/use-dynamic-name-for-new-column-variable-in-dplyr
    # use sym() to coerce a character variable to a quosure 
    # https://github.com/r-lib/rlang/issues/116
    data %>%
      dplyr::mutate(
        !!sym(new_col_name) := 
          !!sym(character) * 
          (mean(!!sym(normalize_by),
                na.rm = TRUE) / 
             !!sym(normalize_by)) ^
          b,
        
      ) #%>%
    #for trouleshooting
    # pull(!!sym(new_col_name))
  }