Extract data from a dexter database

get_responses(
dataSrc,
predicate = NULL,
columns = c("person_id", "item_id", "item_score")
)

## Arguments

dataSrc

a connection to a dexter database, a matrix, or a data.frame with columns: person_id, item_id, item_score

predicate

an expression to select data on

columns

the columns you wish to select, can include any column in the project, see: get_variables

## Value

a data.frame of responses

## Details

Many functions in Dexter accept a data source and a predicate. Predicates are extremely flexible but they have a few limitations because they work on the individual response level. It is therefore not possible for example, to remove complete person cases from an analysis based on responses to a single item by using just a predicate expression.

For such cases, Dexter supports selecting the data and manipulating it before passing it back to a Dexter function or possibly doing something else with it. The following example will hopefully clarify this.

## Examples

# \donttest{
if (FALSE) {
# goal: fit the extended nominal response model using only persons
# without any missing responses
library(dplyr)

# the following would not work since it will omit only the missing
# responses, not the persons; which is not what we want in this case
wrong = fit_enorm(db, response != 'NA')

# to select on an aggregate level, we need to gather the data and
# manipulate it ourselves
data = get_responses(db,
columns=c('person_id','item_id','item_score','response')) %>%
group_by(person_id) %>%
mutate(any_missing = any(response=='NA')) %>%
filter(!any_missing)

correct = fit_enorm(data)

}# }