Score scale calculates scale scores.

Usage

score_scale(
  data,
  filter,
  sum = FALSE,
  min_valid = 1,
  max_na = NA,
  label = NULL,
  fun = NULL,
  var_weight = NULL,
  var_recoding = "recodes"
)

Arguments

data: A data frame
filter: A logical expression for any dic attribute (e.g. scale == "ITRF" & subscale == "Int")
sum: If FALSE, a weighted mean function is applied for building the scores. If TRUE, a weighted sum function is applied. When argument fun is set, sum is ignored.
min_valid: Minimal number of valid values that is required for calculating the mean. A value between 0 and 1 indicates a proportion of values (e.g., 0.5 = 50 percent of values have to be valid).
max_na: Maximum number of NAs that are allowed before returning NA. A value between 0 and 1 indicates a proportion of values (e.g., 0.5 = 50 percent NAs are allowed).
label: A character string with a label for the resulting score variable. Automatically generated if label is not set.
fun: A function for calculating the score (e.g., weighted.median). See details.
var_weight: Name of the dic attribute that is applied to derive weights. Defaults to weight.
var_recoding: Name if the dic attribute that may contain recoding information. Defaults to scores.

Value

A data frame

Details

If you provide your own function, the first argument of that function must take the vector of values and the second argument the weights.

Examples

dat <- apply_dic(ex_scaledic_data, ex_scaledic_dic)
#> Found the following invalid values:
#> 
#> 'rel_2' is 66 at row 3
#> 'rel_4' is 11 at row 9
#> 'rel_5' is 66 at row 11
#> 'sui_1' is 55 at row 10
#> 'sui_4' is 66 at row 17
#> 'age' is 13, 13 at rows 4, 16
#> 
#> Cautions:
#> 1: Invalid values replaced with NA
#> 2: Missing values replaced with NA
#> 3: Scales scored
# apply the default weighted mean function
score_scale(dat, scale == "rel", label = "Religious beliefs")
#> Religious beliefs
#> (weighted mean of items: scale == "rel") 
#> ║Data type is numeric
#> ║Valid values: From 1 to 5.4
#> ║Value labels:
#> ║  1 = min
#> ║  5.4 = max
#> ║Length: 20 (0 missing)
#> ║ [1] 2.20 1.60 2.50 2.60 3.80 3.60 2.40 3.40 2.75 4.80 2.75 3.40 3.80 2.25 2.60
#> ║[16] 3.80 3.80 4.50 4.20 3.00
# apply the weighted sum function
score_scale(dat, scale == "rel", label = "Religious beliefs", sum = TRUE)
#> Religious beliefs
#> (weighted sum of items: scale == "rel") 
#> ║Data type is numeric
#> ║Valid values: From 5 to 27
#> ║Value labels:
#> ║  5 = min
#> ║  27 = max
#> ║Length: 20 (0 missing)
#> ║ [1] 11  8 10 13 19 18 12 17 11 24 11 17 19  9 13 19 19 18 21 15
# provide an external function (here the weighted median function from the spatstat package)
#score_scale(dat, scale == "rel", label = "Religious beliefs", fun = spatstat.geom::weighted.median)