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Run analysis on SWAN raw values to return t-scores

Source: R/swan.R
score_swan.Rd

score_swan() returns gendered and non-gendered t-scores for the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale (SWAN) assessment

[Experimental]

Usage

score_swan(df = NULL, file = FALSE, output_folder = NULL, ignore_check = FALSE)

Arguments

df

If you already have the SWAN data in your R environment, pass the dataframe to this parameter

file

If you prefer scoring a spreadsheet...

  1. Change to TRUE to pop-up a finder to allow you select a file. Alternatively, leave df and file empty to pop-up a finder.

  2. Or specify a pathway

output_folder

Optional, output file pathway. Defauts to NULL. Specify a pathway to output a csv file.

ignore_check

Data are validated to look for missing or improperly formatted values before scoring. Errors are thrown when data aren't valid; however, this can cause issues in real data sets where data vary for good reasons. To skip the validation process, set ignore_check to TRUE. NAs will be returned where data are invalid

Value

table with t-scores attached to raw swan values

Examples

# Read in the file of scores
# This is an example file
csv <- system.file("extdata", "sample_swan.csv", package = "sfsScorer")

# Score via the file parameter
scores_csv <- score_swan(file = csv)
#> ✔ The model scored 5 observations.

# Score via the df paramter
df <- rio::import(csv)
scores_csv <- score_swan(df = df)
#> ✔ The model scored 5 observations.

# Data will be validated
df_mod <- df |>
  dplyr::mutate(swan1 = 6)
try(scores_csv <- score_swan(df = df_mod))
#> There are 5 impossible values in the file.
#> The only valid values are -3, -2, -1, 0, 1, 2, 3, and NA.
#> 
#> Error in clean_file(df, test = "swan", ignore_check = ignore_check) : 
#>   Please correct or remove these rows - "Row 1: swan1 - 6", "Row 2: swan1
#> - 6", "Row 3: swan1 - 6", "Row 4: swan1 - 6", and "Row 5: swan1 - 6"

# To ignore the validation errors and introduce `NA`, set `ignore_check = TRUE`
scores_csv <- score_swan(df = df_mod, ignore_check = TRUE)
#> ! 5 impossible values were changed to NA. This could impact scores. 
#> The only valid values are -3, -2, -1, 0, 1, 2, 3, and NA. To correct, review the following rows before running - "Row 1: swan1 - 6", "Row 2: swan1 - 6", "Row 3: swan1 - 6", "Row 4: swan1 - 6", and "Row 5: swan1 - 6"
#> ✔ The model scored 5 observations.