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Extract values around the sudden gain

Source: R/extract_values.R
extract_values.Rd

Extract values of measures around the time of a sudden gain.

Usage

extract_values(
  data,
  id_var_name,
  extract_var_list,
  sg_session_n_var_name = "sg_session_n",
  extract_measure_name = "x",
  start_numbering = 1,
  add_to_data = TRUE
)

Arguments

data

A bysg or byperson data set in wide format with the variable sg_session_n and all variables specified in extract_var_list.

id_var_name

String, specifying the name of the ID variable.

extract_var_list

List or vector, specifying the variable names of session to session scores to extract from. If this is a list, the name of each element will be used when creating new variables. Note that each element of this list must have the same number of repeated measures as specified in sg_var_list when creating the sudden gains data set.

sg_session_n_var_name

String, specifying variable name that contains information about the pregain session number. If the sudden gains data set was created using the suddengains R package, the default argument "sg_session_n" should be used.

extract_measure_name

String, if extract_var_list is a vector, this string will be used as the when creating new variables of the extracted measures.

start_numbering

Numeric, set to by default 1. Change to 0 if a pre-treatment (e.g. baseline assessment) measurement point is included in extract_var_list.

add_to_data

Logical, if set to TRUE, the extracted values are added as new variables to the input dataset. If set to false, only the ID variable and all extracted values will be returned.

Value

A wide dataset with values for extract_measure_name around the sudden gain.

Examples

# Create bysg dataset
bysg <- create_bysg(data = sgdata,
                    sg_crit1_cutoff = 7,
                    id_var_name = "id",
                    tx_start_var_name = "bdi_s1",
                    tx_end_var_name = "bdi_s12",
                    sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                    "bdi_s4", "bdi_s5", "bdi_s6",
                                    "bdi_s7", "bdi_s8", "bdi_s9",
                                    "bdi_s10", "bdi_s11", "bdi_s12"),
                    sg_measure_name = "bdi")
#> First, second, and third sudden gains criteria were applied.
#> The critical value for the third criterion was adjusted for missingness.

# For bysg dataset select "id" and "rq" variables first
sgdata_rq <- sgdata %>%
  dplyr::select(id, rq_s0:rq_s12)

# Join them with the sudden gains data set, here "bysg"
bysg_rq <- bysg %>%
  dplyr::left_join(sgdata_rq, by = "id")

# Extract "rq" scores around sudden gains on "bdi" in the bysg dataset
bysg_rq <- extract_values(data = bysg_rq,
                          id_var_name = "id_sg",
                          extract_var_list = c("rq_s1", "rq_s2", "rq_s3", "rq_s4",
                                               "rq_s5", "rq_s6", "rq_s7", "rq_s8",
                                               "rq_s9", "rq_s10", "rq_s11", "rq_s12"),
                          extract_measure_name = "rq",
                          add_to_data = TRUE)
#> Note: The measure specified in 'extract_var_list' must have the same number of repeated time points as the measure used to identify sudden gains.