Tidy metaconfoundr data layouts — mc_detect

mc_longer() and mc_wider() are helper functions to put metaconfoundr() for long and wide data sets, respectively. results into a tidy format. mc_detect_layout() chooses between the two automatically based on the number of variables in the data frame. mc_study_values() helps standardize evaluations of control quality.

mc_detect_layout(...)

mc_longer(
  study = contains("construct"),
  construct = contains("construct"),
  variable = matches("variable|factor"),
  control_quality = contains("control_quality"),
  is_confounder = contains("confounder"),
  study_values = mc_study_values()
)

mc_study_values(inadequate = 0, some_concerns = 1, adequate = 2)

mc_wider(
  construct = contains("construct"),
  variable = matches("variable|factor"),
  is_confounder = contains("confounder"),
  study = everything(),
  study_values = mc_study_values()
)

Arguments

...: Additional arguments passed to mc_wider() or mc_longer()
study: The column with the name of the studies
construct: The domain or construct column
variable: The column that describes the confounding variables
control_quality: The column that describes the confounding control quality
is_confounder: The column that describes if a variable is a confounder
study_values: What are the levels of control_quality? Use mc_study_values() to set up.
inadequate: Which value signifies inadequate control?
some_concerns: Which value signifies control with some concerns?
adequate: Which value signifies adequate control?

Value

a function that tidies the data