ss_count: Compute the number or count of matching conditions

In ABCDscores: Summary Scores of the Adolescent Brain Cognitive Development (ABCD) Study

Compute the number or count of matching conditions

Description

Computes the number of conditions (provided as a character vectorcond), involving the input variables vars, that were found to be TRUE. Options available to exclude certain values from the input variables (provided as a character vector exclude).

Usage

ss_count(
  data,
  name,
  vars,
  vars_temp = NULL,
  exclude = NULL,
  combine = FALSE,
  allow_missingness = TRUE,
  cond
)

Arguments

data	tbl. Data frame containing the columns to be summarized.
name	character. The name of the summary score.
vars	character vector. The name(s) of the column(s) to be summarized.
vars_temp	character vector. The name(s) of temporary column(s) used to compute the summary score. Note, these columns are not checked for missingness. See allow_missingness.
exclude	character (vector). The value(s) to be excluded (Default: NULL; all values are used).
combine	logical. Whether to combine the summary score column with the input data frame (Default: FALSE).
allow_missingness	logical. Default set to TRUE. If TRUE, summary score is set to NA only when ALL the in-going fields have missingness. If FALSE, summary score is set to NA when ANY of the in-going fields have missingness. NOTE: exclude operation is performed prior to checking for missingness.
cond	character vector. Each specified condition, involving the values of specific input fields, gets tested for 1 (TRUE) or 0 (FALSE). If a condition is specified as "field_name", the numeric value in the field is counted and could be greater than 1. Whereas other conditions when met can get a value of 1 or 0. The summary score is a sum over all the values obtained from testing each condition specified in cond.

Value

tbl. The input data frame with the summary score appended as a new column.

Examples

dat <- tibble::tibble(
  id   = c("1", "2", "3", "4", "5", "6", "7", "8"),
  a_1  = c(1, 1, NA, 1, 1, 1, 1, 1),
  a_2  = c(1, NA, NA, 1, 1, NA, 1, 1),
  b_1  = c(1, 1, NA, NA, 1, 1, 1, 1),
  b_2  = c(1, 1, NA, 1, 1, NA, 1, 1),
  c    = c(NA, 1, NA, 1, 777, 0, 1, 0)
)

# define conditions to assess
conditions <- c(
  "a_1 == 1 & a_2 == 1",
  "b_1 == 1 & b_2 == 1",
  "c"
)

# count number of matched conditions
ss_count(
  data    = dat,
  name    = "ss",
  vars    = c("a_1", "a_2", "b_1", "b_2", "c"),
  cond    = conditions,
  combine = TRUE
)

ss_count(
  data    = dat,
  name    = "ss",
  vars    = c("a_1", "a_2", "b_1", "b_2", "c"),
  cond    = conditions,
  exclude = c("777"),
  combine = TRUE
)

conditions <- paste(
  c(
    "a_1 == 1 & a_2 == 1",
    "b_1 == 1 & b_2 == 1",
    "c >= 1"
  ),
  collapse = "&"
)

ss_count(
  data    = dat,
  name    = "ss",
  vars    = c("a_1", "a_2", "b_1", "b_2", "c"),
  cond    = conditions,
  exclude = c("777"),
  combine = TRUE
)

ss_count(
  data = dat,
  name = "ss",
  vars = c("a_1", "a_2", "b_1", "b_2", "c"),
  cond = conditions,
  exclude = c("777"),
  allow_missingness = FALSE,
  combine = TRUE
)

ABCDscores documentation built on Sept. 11, 2025, 5:08 p.m.