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Compute the number or count of matching conditions

Source: R/utils.R
ss_count.Rd

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.

Details

After the exclusion step, the input columns are coerced to numeric. Values that cannot be interpreted as numbers (e.g., "abc") are set to NA with an informative warning, and non-finite values (Inf, -Inf, NaN) – including character values such as "Inf" that only become non-finite through the coercion – are converted to NA silently. Both are then treated like any other missing value (e.g., they count toward the allowed number of missing items).

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
)
#> # A tibble: 8 × 7
#>   id      a_1   a_2   b_1   b_2     c    ss
#>   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1         1     1     1     1    NA     2
#> 2 2         1    NA     1     1     1     2
#> 3 3        NA    NA    NA    NA    NA    NA
#> 4 4         1     1    NA     1     1     2
#> 5 5         1     1     1     1   777   779
#> 6 6         1    NA     1    NA     0     0
#> 7 7         1     1     1     1     1     3
#> 8 8         1     1     1     1     0     2

ss_count(
  data    = dat,
  name    = "ss",
  vars    = c("a_1", "a_2", "b_1", "b_2", "c"),
  cond    = conditions,
  exclude = c("777"),
  combine = TRUE
)
#> # A tibble: 8 × 7
#>   id      a_1   a_2   b_1   b_2     c    ss
#>   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1         1     1     1     1    NA     2
#> 2 2         1    NA     1     1     1     2
#> 3 3        NA    NA    NA    NA    NA    NA
#> 4 4         1     1    NA     1     1     2
#> 5 5         1     1     1     1   777     2
#> 6 6         1    NA     1    NA     0     0
#> 7 7         1     1     1     1     1     3
#> 8 8         1     1     1     1     0     2

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
)
#> # A tibble: 8 × 7
#>   id      a_1   a_2   b_1   b_2     c    ss
#>   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1         1     1     1     1    NA     0
#> 2 2         1    NA     1     1     1     0
#> 3 3        NA    NA    NA    NA    NA    NA
#> 4 4         1     1    NA     1     1     0
#> 5 5         1     1     1     1   777     0
#> 6 6         1    NA     1    NA     0     0
#> 7 7         1     1     1     1     1     1
#> 8 8         1     1     1     1     0     0

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
)
#> # A tibble: 8 × 7
#>   id      a_1   a_2   b_1   b_2     c    ss
#>   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1         1     1     1     1    NA    NA
#> 2 2         1    NA     1     1     1    NA
#> 3 3        NA    NA    NA    NA    NA    NA
#> 4 4         1     1    NA     1     1    NA
#> 5 5         1     1     1     1   777    NA
#> 6 6         1    NA     1    NA     0    NA
#> 7 7         1     1     1     1     1     1
#> 8 8         1     1     1     1     0     0